Technology and Engineering International Journal of Recent

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International Journal of Recent Technology and Engineering

ISSN : 2277 - 3878 Website: www.ijrte.org Volume-9 Issue-2, JULY 2020 Published by: Blue Eyes Intelligence Engineering and Sciences Publication

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www.ijrte.org Exploring Innovation Editor-In-Chief & CEO Dr. Shiv Kumar Ph.D. (CSE), M.Tech. (IT, Honors), B.Tech. (IT) Senior Member of IEEE, Member of the Elsevier Advisory Panel CEO, Blue Eyes Intelligence Engineering and Sciences Publication, Bhopal (MP), India

Associate Editor-In-Chief Prof. Dr. Takialddin Al Smadi PhD. (ECE) M.Sc. (ECE), B.Sc (EME), Member of the Elsevier Professor, Department of Communication and Electronics, Jerash Universtiy, Jerash, Jordan.

Dr. Vo Quang Minh PhD. (Agronomy), MSc. (Agronomy), BSc. (Agronomy) Senior Lecturer and Head, Department of Land Resources, College of Environment and Natural Resources (CENRes), Can Tho City, Vietnam.

Dr. Stamatis Papadakis PhD. (Philosophy), M.Sc. (Preschool Education), BSc. (Informatics) Member of IEEE, ACM, Elsevier, Springer, PubMed Lecturer, Department of Preschool Education, University of Crete, Greece

Dr. Ali OTHMAN Al Janaby Ph.D. (LTE), MSc. (ECE), BSc (EE) Lecturer, Department of Communications Engineering, College of Electronics Engineering University of Ninevah, Iraq.

Dr. Hakimjon Zaynidinov PhD. (Signal Processing) Professor and Head, Department of Computer Science, Tashkent University of Information Technologies, Uzbekistan.

Dr. Anil Singh Yadav Ph.D(ME), ME(ME), BE(ME) Professor, Department of Mechanical Engineering, LNCT Group of Colleges, Bhopal (M.P.), India.

Associate Editor-In-Chief Members Dr. Ahmed Daabo PhD. (ME), MSc. (ME), BSc. (ME) Member of Elsevier Lecturer and Researcher, Department of Mining Engineering, University of Mosul, Iraq.

Prof. MPS Chawla ME (Power Electronics), BE (Electrical) Member of IEEE, Elsevier, Springer Ex-Chairman, IEEE MP Sub-Section, India, Professor-Incharge (head)-Library, Associate Professor, Department of Electrical Engineering, G.S. Institute of Technology and Science Indore, Madhya Pradesh, India.

Dr. Morteza Pakdaman Ph.D. (Mathematics), MSc. (Applied Mathematics), BSc. (Applied Mathematics) Member of Springer Assistant Professor, Department of CRI, Climatology of Atmospheric Disasters Research Group, Climatological Research Institute (CRI), Mashhad, Iran.

Technical Program Committee Members Dr. Hany Elazab PhD. (Chem. Eng.), MSc. (Chem. Eng.), BSc. (Chem. Eng.) Assistant Professor and Program Director, Faculty of Engineering, Department of Chemical Engineering, British University, Egypt.

Members of Reviewer Chair Dr. Hao Yi Ph.D. (ME), MS (ME), BS (ME) Member of ASME, ACM, CMES, CSAA. Assistant Professor, Department of Mechanical Engineering, Chongqing University, China.

Dr. Omid A. Yamini Ph.D. (Ocean Engineering and Hydraulics Structures), M.Sc.(Hydraulics Structures), B.Sc.(Civil Engineering) Professor, Department of Civil Engineering, K. N. Toosi University of Technology, Vali Asr Street, Mirdamad Intersection, Tehran, Iran. Dr. Lakshmi Narayana Thalluri Ph.D,(ECE) M.Tech.(VLSI), B.Tech(ECE) Assistant Professor, Department of Electrical Communication Engineering, Andhra Loyola Institute of Engineering and Technology, Vijayawada (A.P), India.

Dr. Ramu Nagarajapillai Ph.D(Banking), M.F.M(Financial Management), M.Phil (Banking), M.Com(Banking & Cooperation) Professor, Department of Commerce, Annamalai University Chidambaram (Tamil Nadu), India.

Dr. Karthikeyan Parthasarathy Ph.D (Management), M.Phil. (Management), MBA, M.Sc (Applied Psychology) Assistant Professor, School of Management Studies, Kongu Engineering College Erode (Tamil Nadu), India.

Dr. Ramani Kannan Ph.D.(Power Electronics), M.E(Power Electronics), B.E(Electronics & Communication) Member of IEEE Power Electronics Society, Malaysia. Senior Lecturer, Department of Electrical and Electronics Engineering, Center for Smart Grid Energy Research, Institute of Autonomous system. Universiti Teknologi PETRONAS (UTP), Malaysia.

Dr. Sabyasachi Pramanik PhD. (CSE), M.Tech.(CSE), B.Tech. (ETE) Assistant Professor, Department of Computer Science and Engineering, Haldia Institute of Technology, Haldia (West Bengal), India.

Dr. M.L. Pavan Kishore PhD.(ME), M.Tech.(ME), B.Tech.(ME) Senior Assistant Professor, Department of Mechanical Engineering, The ICFAI Foundation for Higher Education, Hyderabad (Telangana), India.

Dr. H S Prasantha PhD.(ECE), M.Tech. (EEE), B.Tech. (ECE) Senior Member of IEEE Professor, Department of Electronics & Communication Engineering, Nitte Meenakshi Institute of Technology, Bangalore (Karnataka), India.

Dr. S. Gomathi PhD (Data Mining), M.Phil, MBA, MCA, B.Sc. Member of IEEE Assistant Professor, Department of Information Technology, Bharathiar University (Tamil Nadu), India.

Dr. Diwakar Ramanuj Tripathi Ph.D.(CS), MCA (CA), MBA Member of IEEE, ACM Department of Information Technology, Drtcomptech Group, Nagpur (Maharashtra), India.

Dr. Rubini P Ph.D. (CSE), M.E. (CSE), B.E. (CSE) Member of Elsevier Associate Professor, Department of Computer Science and Engineering, CMR University Main Campus, Bangalore (Karnataka), India.

Dr. Bhavana Narain Ph.D.(CS), LLB, M.Phil. (CS) Associate Professor, Department of Information Technology, MATS University Raipur (Chhattisgarh), India.

Dr. P. Periasamy Ph.D.(Management), M.Phil. (Management), MBA (Marketing & Finance) Member of Elsevier Associate Professor, Department of MBA Finance, CMS B School, Jain Deemed to be University, Bengaluru (Karnataka), India.

Dr. Mohan Seelam Ph.D (Organic Chemistry), M.Phil (Organic Chemistry), M.Sc. (Organic Chemistry) Assistant Professor, Department of Chemistry, Bapatla Engineering College, Bapatla, (Andhra Pradesh), India.

Dr. M. Azhagiri Ph.D (CSE), ME (CSE), BE (IT) Member of Elsevier, Springer, PubMed Assistant Professor, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai (Tamil Nadu), India. Dr. Bhagya R Ph.D (TE), M.Tech. (TE), B.E (CSE) Member of IEEE Associate Professor, Department of Telecommunication Engineering, RV College of Engineering, Bengaluru (Karnataka), India.

Dr. Bhuvana Suganthi D Ph.D (EC), ME (EC), B.E (EC) Associate Professor, Department of Electronics and Communication Engineering, BNM Institute of Technology, Bengaluru (Karnataka), India.

Dr. D. Sai Chaitanya Kishore PhD.(Mechanical), M.Tech (Mechanical), B.Tech (Mechanical) Associate Professor & HOD, Department of Mechanical Engineering, Srinivasa Ramanujan Institute of Technology, Chedulla (Andhra Pradesh), India.

Dr. Owais Yousuf Ph.D. (Food Engineering), M.Tech(Food Engineering), B.Tech (Food Technology) Assistant Professor, Department of Bio-Engineering, Integral University, Lucknow (Uttar Pradesh), India.

Dr. TR. Kalai Lakshmi Ph.D.(Management), M.Phil.(Management), MBA (Management), M.A.(Public Administration) Associate Professor, Department of Management Studies, Sathyabama institute of science and Technology, Chennai (Tamil Nadu), India.

Dr. Ketan D Parikh Ph.D. (Physics), M.Phil. (Physics), M.Sc.(Physics), B.Sc. (Physics) Member of Elsevier, Springer Associate Professor, Department of Physics, M. P. Shah Arts and Science College, Surendra Nagar (Gujarat), India.

Dr. Dileep Kumar Singh PhD. (Accordance), MBA, M.com Member of Elsevier Assistant Professor, Department of MBA, G H Raisoni College of Engineering, Nagpur (Maharastra), India.

Dr. K. Gurushankar Ph.D. (Physics), M.Phil. (Physics), M.Sc.(Physics), B.Sc. (Physics) Assistant Professor, Department of Physics, Kalasalingam University, Virudhunagar (Tamil Nadu), India.

Dr. K.V.N. Kavitha PhD. (Wireless Communication), M.Tech. (Applied Electronics), MBA(HRM), B.Tech(ECE) Member of IEEE, Elsevier Associate Professor, Department of Communication Engineering, VIT University, Vellore (Tamil Nadu), India.

Prof. Dr. Deependra Sharma Ph.D., MBA, M.A (Economics), B.Sc. Member of Elsevier Professor, Department of Amity Business School, Amity University, Gurgaon (Haryana), India.

Dr. S. Mohan Ph.D.(English), M.Phil(English), PG(English) Associate Professor, Department of English, Kalasalingam Academy of Research and Education, Srivilliputhur (Tamil Nadu), India.

Dr. Beemkumar N PhD. (Energy Engineering), M.Tech.(Energy Engineering), BE.(Energy Engineering) Member of IEEE, Elsevier, Springer Associate Professor, Department of Mechanical Engineering, Jain (Deemed-to-be University), Bengaluru (Karnataka), India.

Dr. D. Elil Raja Ph.D.(Mechanical), M.E(CAD/CAM), B.E(Mechanical) Member of Elsevier, Springer Associate Professor, Department of Mechanical Engineering, St. Joseph’s Institute of Technology, OMR, Chennai (Tamil Nadu), India.

Dr. Pankaj Kumar Tyagi Ph.D.(Bioscience), M.Sc.(Life Science), B.Sc.(Biology) Member of Elsevier, Springer Professor and Dean Research, Department of Biotechnology, Noida Institute of Engineering and Technology, Noida (Uttar Pradesh), India. Volume-9 Issue-2, July 2020, ISSN: 2277-3878 (Online) S. No Published By: Blue Eyes Intelligence Engineering and Sciences Publication Page No.

Authors: Atchara S, Bhuvaneswari M, Jayavani V, Ramalakshmi K

Paper Title: Smart Agro- Supply Chain Management Abstract: In this digital era, technological importance has been excellent support for making decisions in agriculture. The development of agriculture has been under development for the past few years due to a lack of technology usage and environmental changes. The aim of this paper is to reach farmers marketing through technology. The study used a statistical survey design technique to collect data from farmers for their awareness of e-Commerce. E-Agriculture is a platform to support the marketing of agricultural products. The main objective of our project is to establish a bridge between a farmer and a customer. The price will be fixed by the farmers, and there is no intermediate between farmers and the customers. So the customers also get their products (vegetables/fruits/grains, etc…) at the actual price, and also, the farmers get the right estimate from a customer.

Keywords: Agriculture, Farmers, Organic products, Profit, Middle man.

References:

1. http://ijarcsse.com/Before_August_2017/docs/papers/Volume_5/1_January2015/V5I1-0292.pdf 2. https://www.researchgate.net/publication/311822033_E_Agriculture_and_rural_development 3. https://www.agripartner.com/software-development-agriculture/ 1. 4. https://www.researchgate.net/publication/251954470_A_Web_ Based_Project_Management_System_for_Agricultural_Scientific_Research 5. https://www.academia.edu/36964413/E_Agricultural_Concepts_for_Improving_Productivity_A_Review 1-5 6. https://en.wikipedia.org/wiki/Information_and_communications_technology_in_agriculture 7. https://www.academia.edu/36964413/E_Agricultural_Concepts_for_Improving_Productivity_A_Review 8. https://www.intel.in/content/dam/www/public/us/en/documents/corporate-information/eagriculture_program_cs.pdf 9. http://www.academia.edu/Documents/in/Agricultural_marketing 10. https://www.irjet.net/archives/V6/i4/IRJET-V6I476.pdf 11. http://ap.fftc.agnet.org/ap_db.php?id=1000 12. http://www.businessworld.in/article/E-Commerce-In-Agriculture-Marketing-A-New-Frontier/04-10-2017-127543/ 13. http://www.businessworld.in/article/E-Commerce-In-Agriculture-Marketing-A-New-Frontier/04-10-2017-127543/ 14. sciencedirect.com/science/article/pii/S2212567115005730 15. https://www.sciencedirect.com/science/article/pii/S2212567115005730 16. https://spore.cta.int/en/trends/article/new-opportunities-for-agribusiness-in-e-commerce-sid0573c5461-b118-4fc2-b6c5- 17fbabe14860 17. https://www.gsma.com/mobilefordevelopment/resources/e-commerce-in-agriculture-new-business-models-for-smallholders- inclusion-into-the-formal-economy/ 18. https://link.springer.com/chapter/10.1007/978-1-4757-5226-7_13 19. https://journals.sagepub.com/doi/abs/10.5367/000000003101294235?journalCode=oaga 20. https://scialert.net/fulltext/?doi=itj.2006.230.234 21. http://ijasrm.com/wp-content/uploads/2018/02/IJASRM_V3S1_440_99_104.pdf 22. http://www.choicesmagazine.org/UserFiles/file/cmsarticle_337.pdf 23. http://www.jocpr.com/articles/development-model-of-agricultural-ecommerce-in-the-context-of-social-commerce.pdf 24. http://ijasrm.com/wp-content/uploads/2018/02/IJASRM_V3S1_440_99_104.pdf 25. https://www.researchgate.net/publication/314783177_Research_on_the_Development_of_E- commerce_Model_of_Agricultural_Products Authors: Sumanta Kuila, Sayandeep Maity, Suman Kumar Mal, Subhankar Joardar

Paper Title: Machine Learning Classification and Feature Extraction of Arrhythmic ECG Data Abstract: Electrocardiogram (ECG) is the analysis of the electrical movement of the heart over a period of time. The detailed information about the condition of the heart is measured by analyzing the ECG signal. Wavelet transform, fast Fourier transform are the different methods to disorganize cardiac disease. The paper elaborates the survey on ECG signal analysis and related study on arrhythmic and non arrhythmic data. Here we discuss the efficient feature extraction process for electrocardiogram, where based on position and priority six best P-QRS-T fragments are studied. This survey examines the the outcome of the system by using various Machine learning classification algorithms for feature extraction and analysis of ECG Signals. Support Vector Machine (SVM), K- 2. Nearest Neighbor (KNN), Artificial Neural Network (ANN) are the most important algorithms used here for this purpose. There are several publicly available data sets which are used for arrhythmia analysis and among them MIT-BIH ECG-ID database is mostly used. The drawbacks and limitations are also discussed here and from 6-12 there future challenges and concluding remarks can be done.

Keywords: Electrocardiogram, Machine learning , Classification , Arrhythmia Database ,Physionet.

References:

1. Qiao Li, Cadathur Rajagopalan,Gari D. Clifford, “Ventricular Fibrillation and Tachycardia Classification Using a Machine Learning Approach” IEEE Transactions on Biomedical Engineering, Vol. 61, No. 6, pp 1607-1613, June 2014. 2. B Pyakillya, N Kazachenko and Nikhailovsky, “Deep Learning for ECG Classification” , IOP Conf. Series: Journal of Physics: Conf. Series 913 (2017) 012004, doi :10.1088/1742- 6596/913/1/012004 , pp 2-5. 3. C. Saritha, V. Sukanya, Y. Narasimha Murthy, “ECG Signal Analysis Using Wavelet Transforms” , Bulg. J. Phys, PACS number: 87.85.J; 02.30.Nw, 16 February 2008 , pp 68-77. 4. Rodrigo V. Andreão, Bernadette Dorizzi, Jérôme Boudy, “ECG Signal Analysis Through Hidden Markov Models” , IEEE Transactions on Biomedical engineering, Vol 53, No. 8, August 2006, pp 1541-1549. 4. Priyarani S. Jagatap and Rupali R. Jagtap, “Electrocardiogram (ECG) Signal Analysis and Feature Extraction: A Survey”, International Journal of Computer Sciences and Engineering, Vol.-2(5), May 2014, pp 1-3. 5. P. S. Hamilton, W. J. Tompkins, “Quantitative Investigation of QRS Detection Rules Using MIT/BIH Arrhythmia Database”, IEEE Transactions on Biomedical Engineering, Vol. 31, No.3, March 2007, pp. 1157-1165. 6. V.K.Srivastava, Dr. Devendra Prasad, “Dwt - Based Feature Extraction from ecg Signal “, American Journal of Engineering Research (AJER), Volume-02, Issue-03,2013, pp 44-50. 7. Abhinav Vishwa, Mohit K. Lal, Sharad Dixit, Pritish Vardwaj, “Clasification Of Arrhythmic ECG Data Using Machine Learning”, International Journal of Artificial Intelligence and Interactive Multimedia, Vol 1, No. 4, DOI: 10.9781/ijimai.2011.1411, 2011,pp 68-71. 8. A. Selcuk Adabag, Barry J. Maron,Evan Appelbaum, Caitlin J. Harrigan, “Occurrence and Frequency of Arrhythmias in Hypertrophic Cardiomyopathy in Relation to Delayed Enhancement on Cardiovascular Magnetic Resonance”, Journal of the American College of Cardiology, Vol. 51, No. 14, 2008, pp 1369-1374. 9. P.Chazal, M. O’Dwyer, and R. B. Reilly, “ Auto-matic classification of heartbeats using ECG morphology and heartbeat interval features”, IEEE Trans. Biomedical Engineering, 2004, pp 1196–1206. 10. R.G. Mark, P.S. Schluter, G.B. Moody, “An annotated ECG database for evaluating arrhythmia Detectors”, 4th Annual Conf. IEEE EMBS. Long Beach, Frontiers of Engineering in Health Care–1982, pp. 205-210. 11. V.Mahesh, A. Kandaswamy, C. Vimal, B. Sathish, “ECG arrhythmia classification based on logistic model tree”, J. Biomedical Science and Engineering 2”, Vol.2, No.6, 2009,pp 405-41. 12. American National Standard for Testing and Reporting Performance Results of Cardiac Rhythm and ST Segment Measurement Algorithms. AMI/ANSI Standard EC57: 2012. 18 December 2012. 13. Jinkwon Kim, Se Dong Min and Myoungho Lee, “An arrhythmia classification algorithm using a dedicated wavelet adapted to different subjects”, BioMedical Engineering OnLine, 2011 , http://www.biomedical-engineering- online.com/content/10/1/56. 14. George B.Moody and Roger G.Mark, “ The MIT-BIH Arrhythmia database on CD-ROM AND Software for use with-it”, IEEE conference, 0276-6574/91/00000/0185$01.00 , 1991, pp 185-188. 15. K.L Ripley and G.C.Oliver, “Development of an ECG database for arrhythmia detector evaluation” Computers in cardiology,1997. pp 203-209. 16. Kiran Kumar Patro, P.Rajesh Kumar, “Machine Learning Classification Approaches for Biometric Recognition System using ECG Signals”, Journal of Engineering Science and Technology Review, doi:10.25103/jestr.106.01 , December 7 , 2017, pp 1-8. 17. M Murugappan, Reena Thirumani, Mohd Iqbal Omar,Subbulakshmi Murugappan, “Development of Cost Effective ECG Data Acquisition System for Clinical Applications using LabVIEW”, IEEE 10th International Colloquium on Signal Processing & its Applications, March 2014, pp 100-105. 18. V. Mondéjar-Guerraa,J. Novoa, J. Roucoa, M.G. Penedoa, M. Ortegaa, “Heartbeat classification fusing temporal and morphological information of ECGs via ensemble of classifiers”, Biomedical Signal Processing and Control, 1746-8094/© 2018 Published by Elsevier Ltd. , pp 41-48. 19. G.B. Moody, R.G. Mark, “The impact of the MIT-BIH arrhythmia database”, IEEE Engineering, Medical ,Biol. Mag. 20 (3) http://dx.doi.org/10.1109/51.932724 , 2001, pp 45–50. 20. E.J. da S. Luz, W.R. Schwartz, G. Cmara-Chvez, D. Menotti, “ECG-basedheartbeat classification for arrhythmia detection: a survey”, Computer Methods and Programs in Biomedicine, 2016 , pp 144-164, ://dx.doi.org/10.1016/j.cmpb.2015.12.008. 21. Kiran Kumar Patro, Dr.P.Rajesh Kumar , "De-Noising of ECG raw Signal by Cascaded Window based Digital filters Configuration", IEEE Power, Communication and Information Technology Conference, October 2015. 22. D. Jeyarani, T. J. Singh, “Analysis of Noise Reduction Techniques on QRS ECG Waveform - by Applying Different Filters”, IEEE conference on Recent Advances in Space Technology Services and Climate Change (RSTSCC), Chennai, 2010. 23. R. de F. Dalvi, G. T. Zago, R. V. Andreão, “Heartbeat classification system based on neural networks and dimensionality reduction”, Research on Biomedical Engineering, Vol. 32, No.4 Rio de Janeiro Oct./Dec. 2016 Epub Jan 12, 2017. 24. Wen Zhu, Nancy Zeng, Ning Wang, “Sensitivity, Specificity, Accuracy, Associated Confidence Interval and ROC Analysis with Practical” , Health care Life Sciences, NESUG 2010, pp 1-9. 25. S. Thulasi Prasad, S. Varadarajan, “ECG Signal Analysis: Different Approaches” , International Journal of Engineering Trends and Technology , Vol.7, No. 5, Jan 2014, pp 212-216. 26. G. Doquire, G. de Lannoy, D. Franc¸ois, M. Verleysen, “Feature selection for inter patient supervised heart beat classification”, Computational Intelligence and Neuroscienc, 2011, pp 1–9. 27. C Alexakis, HO Nyongesa, R Saatchi, ND Harris, C Davies, C Emery, RH Ireland, SR Heller,“Feature Extraction and Classification of Electrocardiogram (ECG) Signals Related to Hypoglycaemia” , Computers in Cardiology, 2003, pp537−540. 28. L. Kanaan, D. Merheb, M. Kallas, C. Francis, H. Amoud, P.Honeine, “PCA and KPCA of ECG signals with binary SVM classification”, IEEE Workshop on Signal Processing Systems, 2011, pp. 344–348. 29. E.E.M.Bolumu, “Feature Selection for ECG Beat Classification using Genetic Algorithms with A Multi-objective Approach”, 26th Signal Processing and Communications Applications Conference, 2-5 May 2018. 30. A.Elsayyad, M.Al-Dhaifallah,A.M.Nassef," Feature Selection for Arrhythmia Diagnosis using Relief-F Algorithm and Support Vector Machine", 14th International Multi-Conference on Systems, Signals & Devices (SSD), 28-31 March, 2017, pp 462-468. 31. 35.Y.H.Hu, W.J Tompkins,Q. Xue, “Artificial Neural Network for ECG Arrhythmia Monitoring”, 0-7803-0559-0 /92 33.00 8, IEEE 1992, pp 987-992. 32. 36. P. de Chazal, R. B. Reilly, “Automatic Classification of ECG Beats using Waveform Shape and Heart Beat Interval Features”, International Conference on Acoustics, Speech and Signal Processing, 2003, pp 269-272. 33. The CSE Working Party, “Recommendations for measurement standards in quantitative Electrocardiography”, European Heart Journal, 1985 , pp 815-825. 34. 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Pereira, “Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data”, WhizBang! Labs–Research, 4616 Henry Street, Pittsburgh, PA 15213 USA , June 2001. 43. Rashmi Agrawal, ”Extensions of k-Nearest Neighbor Algorithm” ,Research Journal of Applied Sciences, Engineering and Technology , July 2016, pp 24-29. Authors: Rishabh Rastogi, Yogesh, Nikhil Kumar Pankaj, B. R. G. Robert

Paper Title: Use of Poly Vinyl Chloride (PVC) Tape to Increase the Strength of Bamboo Abstract: Poly vinyl chloride is suggested to use as confining material around bamboo to increase its strength. Investigation is done on PVC confined bamboo by performing tensile and compressive strength test. Then we compared the mechanical performance of unconfined bamboo and PVC confined bamboo. In this research bamboo is also tested for water absorption with and without PVC tape reinforcement. Later, on these grounds we also derived adhesive value of PVC tape, which can be used to find out length of tape required for any percent of increment in strength of bamboo. Preliminary results of tensile tests, compressive tests and water absorption test on bamboo (scientific name Bambusa vulgaris native name chadao bans or jad vala bans) are presented in this research. As bamboo confined in PVC is 27.25% more strong in tension and 77.19% more strong in compression than unconfined bamboo which depicts that bamboo confined with PVC tape can be a future alternative for steel in reinforced concrete.

3. Keywords: Poly Vinyl Chloride (PVC) tape, Bamboo splint, Bamboo culm, Splitting buckling, End bearing buckling, Tape adhesion. 13-18 References: 1. Bhalla, S., Gupta, S., Puttaguna, S. and Suresh, R. (2009), “Bamboo as Green Alternative To Concrete and Steel for Modern Structures”, Journal of Environmental Research and Development. 2. Fei Ye1 and Wenxi Fu, Ph.D., “Physical and Mechanical Characterization of Fresh Bamboo for Infrastructure Projects” American Society of Civil Engineers. 3. A. W. Mekonnen and J. N. Mandal, “Model Studies on Bamboo-Geogrid Reinforced Fly Ash Walls under Uniformly Distributed Load” 2017 American Society of Civil Engineers. 4. Wenqing Wu, M.ASCE, “Experimental Analysis of Bending Resistance of Bamboo Composite I-Shaped Beam” 2013 American Society of Civil Engineers. 5. Vishal Puri and Pradipta Chakrabortty, “Policy Issues in Affordable Housing Made with Bamboo Reinforced Structural Component” 2013 American Society of Civil Engineers. 6. Nathan Schneider, Weichiang Pang, Mengzhe Gu, “Application of Bamboo for Flexural and Shear Reinforcement in Concrete Beams” Structures Congress 2014 © ASCE 2014. 7. Guiqiu Huang, Zhen Huang, Xueyuan Deng, Jing Jiang, “Experimental Study on Carbon Fiber Polymer Reinforced Bamboos” Civil Engineering and Urban Planning 2012 (CEUP 2012) ©ASCE 2012. Authors: Prithi S, Roxanna Samuel, Ponmani S, J.Jinu Sophia

Paper Title: Dynamic Load Balancer for Traffic Management in Cloud Environment Abstract: The Cloud traffic is the major issue faced by users’ every day. Users are not able to access the files at the desired time due to the delay, caused by traffic. Traffic is induced when many users access the same network at a given point of time. This paper aims to reduce the cloud traffic by allowing the user to enter the datacenter for quick access of files with maximum capacity left instead of entering the datacenter in sequential order as in existing systems. The foremost key and challenging problem for handling big data centers in clouds are to balance the Load while flow scheduling since a huge amount of data are transferred at regular intervals throughout a thousand of customers and clients. With a rapid growth in applications, capability of utilizing the data centers has become a challenging task to cloud service, particularly during the peak time usage of data centers and when the requests of user’s are unbalanced and when there are amount of demands need to be handled. In this project, Software Defined Network (SDN) controller is applied to improvise the utilization of bandwidth of Dynamic Circuit Network (DCN) as well as reduce the delay that occurs for end users. This project presents the Genetic load balancing algorithm to provide the provider with a high utilization of 4. bandwidth and to low the end-users dealy.

Keywords: Cloud traffic, Load balancer, Datacenter, Load balancing algorithm. 19-22

References:

1. Amazon web services [Online]. Available: http://aws.amazon. com, 2014. 2. V. K. Adhikari, Y. Guo, F. Hao, M. Varvello, V. Hilt, M. Steiner, and Z.-L. Zhang, “Unreeling netflix: Understanding and rising multi-CDN moving picture delivery,” in Proc. IEEE Conf. Comput. Commun., 2012, pp. 1620–1628. 3. A. Cockcroft. (2011). Netflix in the cloud [Online]. Available: http://velocityconf.com/ velocity 2011/public/schedule/detail/ 17785 4. B. Wong and E. G. Sirer, “Closestnode.com: associate degree open access, scalable, shared geocast service for distributed systems,” operative Syst. Rev., vol. 40, no. 1, pp. 62–64, 2006. 5. H. Ballani, P. Costa, T. Karagiannis, and A. Rowstron, “Towards predictable datacenter networks,” in Proc. ACM SIGCOMM Conf., Toronto, ON, Canada, 2011, pp. 242–253. 6. N. Laoutaris, M. Sirivianos, X. Yang, and P.Rodriguez, “Interdatacenter bulk transfers with netstitcher,” in Proc. ACM SIGCOMM Conf., Toronto, ON, Canada, 2011, pp. 74–85. 7. A. Singh, M. Korupolu, and D. Mohapatra, “Server-storage virtualization: Integration and cargo equalisation in information centers,” in Proc. ACM/IEEE Conf. Supercomput., 2008, p. 53. 8. R. Buyya, R. Ranjan, and R. N. Calheiros, “Intercloud: Utilityoriented federation of cloud computing environments for scaling of application services,” in Proc. 10th Int. Conf. Algorithms Archit. Parallel Process., 2010, pp. 13–31. 9. A. Qureshi, R. Weber, H. Balakrishnan, J. V. Guttag, and B. M. Maggs, “Cutting the electrical bill for internet-scale systems,” in Proc. ACM SIGCOMM Conf., 2009, pp. 123–134. 10. J. Jinu Sophia, R .Angelin Hannah, J. Daisy, K. Jebima Jessy “Reducing The Duplication Data For Secure Authorization In Hybrid Cloud” in International Journal Of Advanced Research Trends In Engineering And Technology, Volume 3, Special Issue 19, April 2016. Authors: S. Ahmad Saidulu, R.Sai Vineeth, Y.Tanmayee, B.Meenakshi

Paper Title: Performance Measures of Different Gate Oxide Materials in Gate All Around Fet Abstract: The classical planar Metal Oxide Semiconductor Field Effect Transistors (MOSFET) is fabricated by oxidation of a semiconductor namely Silicon. In this generation, an advanced technique called 3D system architecture FETs, are introduced for high performance and low power quality of devices. Based on the limitations of Short Channel Effect (SCE), Silicon (Si) FET cannot be scaled under 10nm. Hence various performing measures like methods, principles, and geometrics are done to upscale the semiconductor. CMOS using alternate channel materials like GE and III-Vs on substrates is a highly anticipated technique for developing nanowire structures. By considering these issues, in this paper, we developed a simulation model that provides accurate results basing on Gate layout and multi-gate NW FET's so that the scaling can be increased few nanometers long and performance limits gradually increases. The model developed is SILVACO that tests 5. the action of FET with different gate oxide materials. Keywords: Gaafet’s; Gate Materials; Short Channel Effect (Sce); Sensitivity. 23-25 References:

1. R. V. T. da Nobrega, Y. M. Fonseca, R. A. Costa and U. R. Duarte “Comparative Study on the Performance of Silicon and III-V Nanowire Gate-AllAround Field-Effect Transistors for Different Gate Oxides” 2. P. Anil Kumar, L. Hema Harshitha, K.Hemanth, G.Chandrika, L.Sai Spandana, "Multi-Face Detection and Recognition", Journal of Applied Science and Computations, Volume VI, Issue IV, April/2019, ISSN NO: 1076-5131, PP: 1594-1601. 3. B. Yang, et al. Vertical Silicon-Nanowire Formation and Gate-All-Around MOSFET, IEEE Electron Device Letters, vol. 29, no. 7, pp 791-794, Jul. 2008. 4. Shashi K. Dargar and Viranjay M. Srivastava “Performance Analysis of High-k Dielectric Based Silicon Nanowire Gate-All- Around Tunneling FET” International Journal of Electrical and Electronic Engineering & Telecommunications Vol. 8, No. 6, November 2019. 5. S. Bangsaruntip, G. M. Cohen, A. Majumdar, et al., “High performance and highly uniform gate-all-around silicon nanowire MOSFETs with wire size dependent scaling,” in Proc. IEEE Int. Electron Devices Meeting, Baltimore, MD, 2009, pp. 1-4. Authors: K.Ravi Teja, D.V.N. Ananth, G. Joga Rao Modeling and Design of Cascaded h-bridge type multi-level Inverters up to Thirty-one level for the Paper Title: Reduction and Performance Improvement Abstract: Multilevel inverter (MLI) becomes more popular in high voltage DC (HVDC) applications, power electronic converters and drives. This paper describes the simulation of single phase multilevel cascaded H- bridge inverter. Simulation of three level, five level, thirteen level, fifteen, twenty-one, thirty-one level inverters are done in MATLAB/ Simulink. The switching schemes, and topologies are discussed in detail here up to thirty-one levels. This paper discusses the voltage level to achieve sinusoidal waveform & compare different voltage level by increasing the level through simulation. The closed loop space-vector based pulse width modulation technique is adopted for effective controlling and lower harmonic voltage conversion. The comparative results are presented for multilevel inverter up-to thirty-one level which shows the total harmonic distortion (THD) is decreased as the number of voltage level rises.

Keywords: Multi-level Inverters, Sinusoidal pulse width modulation, cascaded H-bridge inverter, total harmonic distortion, selective harmonic reduction

6. References:

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IEEE Transactions on Power Electronics 34, no. 10 (2019): 9479-9502. 5. El-Hosainy, Asmaa, Hany A. Hamed, Haitham Z. Azazi, and E. E. El-Kholy. "A review of multilevel inverter topologies, control techniques, and applications." In 2017 Nineteenth International Middle East Power Systems Conference (MEPCON), pp. 1265- 1275. IEEE, 2017. 6. Chattopadhyay, Sumit K., and Chandan Chakraborty. "Full-Bridge Converter With Naturally Balanced Modular Cascaded H- Bridge Waveshapers for Offshore HVDC Transmission." IEEE Transactions on Sustainable Energy 11, no. 1 (2019): 271-281. 7. Adam, Grain Philip, Islam Azmy Gowaid, Stephen Jon Finney, Derrick Holliday, and Barry W. Williams. "Review of dc–dc converters for multi-terminal HVDC transmission networks." IET Power Electronics 9, no. 2 (2016): 281-296. 8. Yadav, Apurv Kumar, K. Gopakumar, Loganathan Umanand, Kouki Matsuse, and Hisao Kubota. "Instantaneous balancing of neutral-point voltages for stacked DC-link capacitors of a multilevel inverter for dual-inverter-fed induction motor drives." IEEE Transactions on Power Electronics 34, no. 3 (2018): 2505-2514. 9. Kirankumar, B., YV Siva Reddy, and M. Vijayakumar. "Multilevel inverter with space vector modulation: intelligence direct torque control of induction motor." IET Power Electronics 10, no. 10 (2017): 1129-1137. 10. Khanal, Saroj, and Vahid R. Disfani. "Reduced Switching-Frequency Modulation Design for Model Predictive Control Based Modular Multilevel Converters." arXiv preprint arXiv:1912.08433 (2019). 11. Shanono, Ibrahim Haruna, Nor Rul Hasma Abdullah, and Aisha Muhammad. "A survey of multilevel voltage source inverter topologies, controls, and applications." International Journal of Power Electronics and Drive Systems 9, no. 3 (2018): 1186. 12. Rojas, Christian A., Samir Kouro, Marcelo A. Perez, and Javier Echeverria. "DC–DC MMC for HVdc grid interface of utility- scale photovoltaic conversion systems." IEEE Transactions on Industrial Electronics 65, no. 1 (2017): 352-362. 13. Chattopadhyay, Sumit K., and Chandan Chakraborty. "A new asymmetric multilevel inverter topology suitable for solar PV applications with varying irradiance." IEEE Transactions on Sustainable Energy 8, no. 4 (2017): 1496-1506. 14. Wang, Tianzhen, Jie Qi, Hao Xu, Yide Wang, Lei Liu, and Diju Gao. "Fault diagnosis method based on FFT-RPCA-SVM for cascaded-multilevel inverter." ISA transactions 60 (2016): 156-163. 15. Ananth D. V, Kumar Y. N, Tilak B. B. G, Raghunath P. S.: Multi-level inverters and its application of statcom using svpwm and spwm techniques, IOSR Journal of Electrical and Electronics Engineering (IOSRJEEE), (2012), vol. 2 no 5, pp. 30-38. 16. Prabaharan, Natarajan, and Kaliannan Palanisamy. "A comprehensive review on reduced switch multilevel inverter topologies, modulation techniques and applications." Renewable and Sustainable Energy Reviews 76 (2017): 1248-1282. 17. Farias, João Victor Matos, Allan Fagner Cupertino, Victor De Nazareth Ferreira, Heverton Augusto Pereira, Seleme Isaac Seleme Junior, and Remus Teodorescu. "Reliability-Oriented Design of Modular Multilevel Converters for Medium-Voltage STATCOM." IEEE Transactions on Industrial Electronics (2019). 18. Yuan, Yubo, Peng Li, Xiangping Kong, Jiankun Liu, Qun Li, and Ye Wang. "Harmonic influence analysis of unified power flow controller based on modular multilevel converter." Journal of Modern Power Systems and Clean Energy 4, no. 1 (2016): 10-18. 19. Yadav, Apurv Kumar, K. Gopakumar, Loganathan Umanand, Kouki Matsuse, and Hisao Kubota. "Instantaneous balancing of neutral-point voltages for stacked DC-link capacitors of a multilevel inverter for dual-inverter-fed induction motor drives." IEEE Transactions on Power Electronics 34, no. 3 (2018): 2505-2514. 20. Lee, Sze Sing, and Kyo-Beum Lee. "Dual-T-type seven-level boost active-neutral-point-clamped inverter." IEEE Transactions on Power Electronics 34, no. 7 (2019): 6031-6035. 21. Lei, Yutian, Christopher Barth, Shibin Qin, Wen-Chuen Liu, Intae Moon, Andrew Stillwell, Derek Chou et al. "A 2-kW single- phase seven-level flying capacitor multilevel inverter with an active energy buffer." IEEE Transactions on Power Electronics 32, no. 11 (2017): 8570-8581. 22. Barth, Christopher B., Pourya Assem, Thomas Foulkes, Won Ho Chung, Tomas Modeer, Yutian Lei, and Robert CN Pilawa- Podgurski. "Design and control of a GaN-based, 13-level, flying capacitor multilevel inverter." IEEE Journal of Emerging and Selected Topics in Power Electronics (2019). 23. Vemuganti, Hari Priya, Dharmavarapu Sreenivasarao, and Ganjikunta Siva Kumar. "Zero-sequence voltage injected fault tolerant scheme for multiple open circuit faults in reduced switch count-based MLDCL inverter." IET Power Electronics 11, no. 8 (2018): 1351-1364. 24. Garapati, Durga Prasad, V. Jegathesan, and Moorthy Veerasamy. "Minimization of power loss in newfangled cascaded H-bridge multilevel inverter using in-phase disposition PWM and wavelet transform based fault diagnosis." Ain Shams Engineering Journal 9, no. 4 (2018): 1381-1396. 25. Narendran, A., and R. Sureshkuma. "Hysteresis-Controlled-Landsman Converter Based Multilevel Inverter Fed Induction-Motor System Using PIC." Microprocessors and Microsystems (2020): 103099. 26. Yang, Wenbo, Qiang Song, Shukai Xu, Hong Rao, and Wenhua Liu. "An MMC topology based on unidirectional current H- bridge submodule with active circulating current injection." IEEE Transactions on Power Electronics 33, no. 5 (2017): 3870- 3883. 27. Marzoughi, Alinaghi, Rolando Burgos, Dushan Boroyevich, and Yaosuo Xue. "Design and comparison of cascaded H-bridge, modular multilevel converter, and 5-L active neutral point clamped topologies for motor drive applications." IEEE Transactions on Industry Applications 54, no. 2 (2017): 1404-1413. 28. Liu, Ying, Rui Fan, Xiaoning Zhang, Zhentao Tu, and Jun Zhang. "Bipolar high voltage pulse generator without H-bridge based on cascade of positive and negative Marx generators." IEEE Transactions on Dielectrics and Electrical Insulation 26, no. 2 (2019): 476-483. 29. Meenalochani, K., B. Shanthi, and C. R. Balamurugan. "Performance analysis on bipolar PWM strategies for single phase quasi- Z-source fed seven level modified cascaded H-bridge inverter with asymmetric switched-inductor cell." International Journal of Engineering & Technology 7, no. 2.8 (2018): 583-587. 30. Venkataramanaiah, J., Y. Suresh, and Anup Kumar Panda. "A review on symmetric, asymmetric, hybrid and single DC sources based multilevel inverter topologies." Renewable and Sustainable Energy Reviews 76 (2017): 788-812. 31. Hajizadeh, Mehdi, and Seyed Hamid Fathi. "Fundamental frequency switching strategy for grid-connected cascaded H-bridge multilevel inverter to mitigate voltage harmonics at the point of common coupling." IET Power Electronics 9, no. 12 (2016): 2387-2393. 32. Masaoud, Ammar, Saad Mekhilef, Hew Wooi Ping, and Kiing Ing Wong. "A simplified structure for three-phase 4-level inverter employing fundamental frequency switching technique." IET Power Electronics 10, no. 14 (2017): 1870-1877. 33. Raman, S. Raghu, Yat Chi Fong, Yuanmao Ye, and Ka Wai Eric Cheng. "Family of Multiport Switched-Capacitor Multilevel Inverters for High-Frequency AC Power Distribution." IEEE Transactions on Power Electronics 34, no. 5 (2018): 4407-4422. 34. Sathik, M. Jagabar, Kaustubh Bhatnagar, Yam P. Siwakoti, Hussain M. Bassi, Muhyaddin Rawa, N. Sandeep, Yongheng Yang, and Frede Blaabjerg. "Switched-capacitor multilevel inverter with self-voltage-balancing for high-frequency power distribution system." IET Power Electronics (2020). 35. Barbie, Eli, Raul Rabinovici, and Alon Kuperman. "Analytic formulation and optimization of weighted total harmonic distortion in single-phase staircase modulated multilevel inverters." Energy (2020): 117470. 36. Kamani, Piyush L., and Mahmadasraf A. Mulla. "Middle-level SHE pulse-amplitude modulation for cascaded multilevel inverters." IEEE Transactions on Industrial Electronics 65, no. 3 (2017): 2828-2833. 37. Devi, B. Gayathri, and B. K. Keshavan. "A novel hybrid Phase Shifted-Modified Synchronous Optimal Pulse Width Modulation based 27-level inverter for grid-connected PV system." Energy 178 (2019): 309-317. 38. Chokkalingham, Bharatiraja, Sanjeevikumar Padmanaban, and Frede Blaabjerg. "Investigation and comparative analysis of advanced PWM techniques for three-phase three-level NPC-MLI drives." Electric Power Components and Systems 46, no. 3 (2018): 258-269. 39. Mukherjee, Sarbojit, Sayan De, Santomit Sanyal, Suman Das, and Sumit Saha. "A 15-level asymmetric H-bridge multilevel inverter using d-SPACE with PDPWM technique." International Journal of Engineering, Science and Technology 11, no. 1 (2019): 22-32. 40. Irusapparajan, G., Periyaazhagar, D., Prabaharan, N. and Jerin, A.R.A., 2019. Experimental verification of trinary DC source cascaded H-bridge multilevel inverter using unipolar pulse width modulation. Automatika, 60(1), pp.19-27. 41. Chokkalingam, Bharatiraja, Mahajan Sagar Bhaskar, Sanjeevikumar Padmanaban, Vigna K. Ramachandaramurthy, and Atif Iqbal. "Investigations of multi-carrier pulse width modulation schemes for diode free neutral point clamped multilevel inverters." Journal of Power Electronics 19, no. 3 (2019): 702-713. 42. Jayabalan, Maalmarugan, Baskaran Jeevarathinam, and Thamizharasan Sandirasegarane. "Reduced switch count pulse width modulated multilevel inverter." IET Power Electronics 10, no. 1 (2017): 10-17. 43. Shuvo, Shuvangkar, Eklas Hossain, Tanveerul Islam, Abir Akib, Sanjeevikumar Padmanaban, and Md Ziaur Rahman Khan. "Design and hardware implementation considerations of modified multilevel cascaded h-bridge inverter for photovoltaic system." Ieee Access 7 (2019): 16504-16524. Authors: Ankush Yadav, Aman Singh, Aniket Sharma, Ankur Sindhu, Umang Rastogi

Paper Title: Desktop Voice Assistant for Visually Impaired Abstract: A personal voice assistant is the software that can perform task and provide different services to the individual as per the individual’s dictated commands. This is done through a synchronous process involving recognition of speech patterns and then, responding via synthetic speech. Through these assistants a user can automate tasks ranging from but not limited to mailing, tasks management and media playback. As the technology is developing day by day people are becoming more dependent on it, one of the mostly used platform is computer. We all want to make the use of these computers more comfortable, traditional way to give a command to the computer is through keyboard but a more convenient way is to input the command through voice. Giving input through voice is not only beneficial for the normal people but also for those who are visually impaired who are not able to give the input by using a keyboard. For this purpose, there is a need of a voice assistant which can not only take command through voice but also execute the desired instructions and give output either in the form of voice or any other means.

Keywords: Python script, speech recognition, voice assistant Abbreviation: API (Application program interface), NLP (natural language processing), TTS (Text-To-Speech).

References:

1. Sutar Shekhar, Pophali Sameer, Kamad Neha, Deokate Laxman, intelligent voice assistant using Android platform. International Journal of Advance research in computer Science and Management Studies. Volume 3, Issue 3, march 2015 7. 2. Mhamunkar, M. p. v., Bansode, M. k. S., & Naik, L.S. (2013). Android application to get word meaning through voice. International journal of Advance Research in Computer Engineering & Technology (IJARCET). 2(2), pp-572. 3. Apte, T. V., Ghosalkar, S., pandey, S., & Padhra, S. (2014). Android app for blind using speech technology. International Journal 36-39 of Research in Computer and Communication Technology (IJRCCT), #(3), 391-394. 4. Anwani, R., Santuramani, U., Raina, D., & RL, P. Vmail: voice Based Email Application. International Journal of Computer Science and Information Technologies, Vol. 6(3), 2015 5. Deepak shende, Ria Umahiya, Monika Raghorte, Aishwarya Bhisikar, Anup Bhange.AI based voice assistant using python. JETIR, feburary 2019, vol 6, issue 2. 6. Thakur, N., Hiwrale, A., Selote, S., Shinde, A. and Mahakalkar, N., Artificially Intelligent Chatbot 7. Yu, T. L., Gande, S., & Yu, R. (2015, January). An open -Source Based Speech Recognition Android Application for Helping Handicapped Students Writing Programs. In Proceedings of the International Conference on Wireless Networks (ICWN) (p. 71). The Steering Committee of The World Congress in Computer Science, Computer Engineering And Applied Computing (WorldComp). 8. Yannawar, P. (2010). Santosh K. Gaikwad Bharti W. Gawali Pravin Yannawar. A Review on Speech Recognition Technique. International journal of Computer Application 9. Brandon Ballinger, Cyril Allauzen, Alexander Gruenstein, Johan Schalkwyk, On-Demand Language Model Interpolation for Mobile Speech Input INTERSPEECH 2010, 26-30 September 2010, Makuhari, Chiba, Japan, pp 1812-1815. 10. IOSR Journal of Engineering Mar. 2012, Vol. 2(3) pp: 420-423 ISSN: 2250-3021 www.iosrjen.org 420 | “Android Speech to Text Converter for SMS Application” Ms. Anuja Jadhav* Prof. Arvind Patil**. 11. Michael Stinson, Sandy Eisenberg, Christy Horn, Judy Larson, Harry Levitt, and Ross Stuckless” REAL-TIME SPEECH-TO- TEXT SERVICES.” 12. International Journal of Information and Communication Engineering 6:1 2010 “The Main Principles of Text-to-Speech Synthesis System”,K.R. Aida–Zade, C. Ardil and A.M. Sharifova. 13. Review of text-to-speech conversion for English, Dennis H. Klatt, Room3 6-523, Massachuset Institute of Technology Cambridge Massachusetts. 14. DOUGLAS O’SHAUGHNESSY, SENIOR MEMBER, IEEE, “Interacting With Computers by Voice: Automatic Speech Recognition and Synthesis” proceedings of THE IEEE, VOL. 91, NO. 9, SEPTEMBER 2003. 15. Shibwabo, B. K., & Omyonga, K. (2015). The application of real-time voice recognition to control critical mobile device operations. Authors: Ayah Alsarayreh, Fatma Susilawati Mohamad Constrained Local Models (CLM) For Facial Feature Extraction using CLNF and SVR as Patch Paper Title: Experts Abstract: Methods for detection of facial characteristics have again developed greatly in recent times. However, they also argue in the presence of poor lighting conditions for amazing pose or occlusions. A well- established group of strategies for facial feature extraction is the Constrained Local Model (CLM). Recently, they are bringing cascaded regression-built methodologies out of favor. This is because the failure of presenting nearby CLM detectors to model the highly complex special signature look affected to a small degree by voice, illumination, facial hair and make-up. This paper keeps tabs on execution to collect facial features for the Constrained Local Model (CLM). CLM model relies on patch model to collect facial image demand features. In 8. this paper patch model built using Support Vector Regression (SVR) and Constrained Local Neural Field (CLNF). We show that the CLNF model exceeds SVR by a large margin on the LFPW database to identify 40-43 facial landmarks.

Keywords: Features Extraction, CLNF, SVR, CLM.

References: 1. Cristinacce, D. and T.F. Cootes. Feature detection and tracking with constrained local models. in Bmvc. 2006. Citeseer. 2. Saragih, J.M., S. Lucey, and J.F. Cohn, Deformable model fitting by regularized landmark mean-shift. International Journal of Computer Vision, 2011. 91(2): p. 200-215. 3. Baltrusaitis, T., P. Robinson, and L.-P. Morency. Constrained local neural fields for robust facial landmark detection in the wild. in Proceedings of the IEEE International Conference on Computer Vision Workshops. 2013. 4. Asthana, A., et al. Robust discriminative response map fitting with constrained local models. in Proceedings of the IEEE conference on computer vision and pattern recognition. 2013. 5. Ramanan, D. and X. Zhu. Face detection, pose estimation, and landmark localization in the wild. in Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2012. Citeseer. 6. Tzimiropoulos, G. and M. Pantic. Gauss-newton deformable part models for face alignment in-the-wild. in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2014. 7. Zhu, X., et al. Face alignment across large poses: A 3d solution. in Proceedings of the IEEE conference on computer vision and pattern recognition. 2016. 8. Cao, X., et al., Face alignment by explicit shape regression. International Journal of Computer Vision, 2014. 107(2): p. 177-190. 9. Sun, Y., X. Wang, and X. Tang. Deep convolutional network cascade for facial point detection. in Proceedings of the IEEE conference on computer vision and pattern recognition. 2013. 10. Trigeorgis, G., et al. Mnemonic descent method: A recurrent process applied for end-to-end face alignment. in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016. 11. Baltrušaitis, T., P. Robinson, and L.-P. Morency. 3D constrained local model for rigid and non-rigid facial tracking. in 2012 IEEE Conference on Computer Vision and Pattern Recognition. 2012. IEEE. 12. Smola, A.J. and B. Schölkopf, A tutorial on support vector regression. Statistics and computing, 2004. 14(3): p. 199-222. Authors: Jyoti Snehi, Abhinav Bhandari, Vidhu Baggan, Manish Snehi, Ritu

Paper Title: Diverse Methods for Signature based Intrusion Detection Schemes Adopted Abstract: Intrusion Detection Systems (IDS) is used as a tool to detect intrusions on IT networks, providing support in network monitoring to identify and avoid possible attacks. Most such approaches adopt Signature- based methods for detecting attacks which include matching the input event to predefined database signatures. Signature based intrusion detection acts as an adaptable device security safeguard technology. This paper discusses various Signature-based Intrusion Detection Systems and their advantages; given a set of signatures and basic patterns that estimate the relative importance of each intrusion detection system feature, system administrators may help identify cyber-attacks and threats to the network and Computer system. Eighty percent of incidents can be easily and promptly detected using signature-based detection methods if used as a precautionary phase for vulnerability detection and twenty percent rest by anomaly-based intrusion detection system that involves comparing definitions of normal activity or event behavior with observed events in identifying the significant deviations and deciding the traffic to flag.

Keywords: Intrusion detection system (IDS), Signature Based IDS, Anomaly Based IDS.

References:

1. A. K. Saxena, S. Sinha, and P. Shukla, “General study of intrusion detection system and survey of agent based intrusion detection system,” Proceeding - IEEE International Conference on Computing, Communication and Automation, ICCCA 2017, vol. 2017- Janua, pp. 417–421, 2017, doi: 10.1109/CCAA.2017.8229866. 2. N. Agarwal and S. Z. Hussain, “A Closer Look at Intrusion Detection System for Web Applications,” Security and Communication Networks, vol. 2018, 2018, doi: 10.1155/2018/9601357. 3. A. Bhandari, A. L. Sangal, and K. Kumar, “Characterizing flash events and DDoS attacks - An Empirical Investigation,” International Journal of Applied Engineering Research, vol. 9, no. 22. pp. 5968–5974, 2014, doi: 1. 10.1002/sec. 4. W. Yassin, N. I. Udzir, Z. Muda, A. Abdullah, and M. T. Abdullah, “A Cloud-based Intrusion Detection Service framework,” 9. Proceedings 2012 International Conference on Cyber Security, Cyber Warfare and Digital Forensic, CyberSec 2012, pp. 213– 218, 2012, doi: 10.1109/CyberSec.2012.6246098. 5. C. Day, “Intrusion prevention and detection systems,” Managing Information Security: Second Edition, pp. 119–142, 2013, doi: 44-49 10.1016/B978-0-12-416688-2.00005-2. 6. S. M. Othman, N. T. Alsohybe, F. M. Ba-alwi, and A. T. Zahary, “Survey on Intrusion Detection System Types,” no. December, 2018. 7. M. H. Bhuyan, D. K. Bhattacharyya, and J. K. Kalita, “Network anomaly detection: Methods, systems and tools,” IEEE Communications Surveys and Tutorials, vol. 16, no. 1, pp. 303–336, 2014, doi: 10.1109/SURV.2013.052213.00046. 8. A. Khraisat, I. Gondal, P. Vamplew, and J. Kamruzzaman, “Survey of intrusion detection systems: techniques, datasets and challenges,” Cybersecurity, vol. 2, no. 1, 2019, doi: 10.1186/s42400-019-0038-7. 9. M. Aldwairi, A. M. Abu-Dalo, and M. Jarrah, “Pattern matching of signature-based IDS using Myers algorithm under MapReduce framework,” EURASIP Journal on Information Security, vol. 2017, no. 1, 2017, doi: 10.1186/s13635-017-0062-7. 10. V. B. Salve, V. Savalkar, and S. Mhatre, “Efficient pattern matching algorithms in IDS,” Proceedings of the 2nd International Conference on Inventive Systems and Control, ICISC 2018, no. Icisc, pp. 1083–1089, 2018, doi: 10.1109/ICISC.2018.8398971. 11. W. Lee, “An Overview of Intrusion Detection Techniques,” 2004, doi: 10.1201/9780203507223.ch48. 12. M. S. Islam Mamun and S. K. A.F.M, “Hierarchical Design Based Intrusion Detection System For Wireless Ad Hoc Sensor Network,” International Journal of Network Security & Its Applications, vol. 2, no. 3, pp. 102–117, 2010, doi: 10.5121/ijnsa.2010.2307. 13. S. G. Kene and D. P. Theng, “A review on intrusion detection techniques for cloud computing and security challenges,” 2nd International Conference on Electronics and Communication Systems, ICECS 2015, no. May 2016, pp. 227–232, 2015, doi: 10.1109/ECS.2015.7124898. 14. M. R. Deshmukh, M. R. Deshmukh, and P. M. Sharma, “Rule-Based and Cluster-Based Intrusion Detection Technique for Wireless Sensor Network,” vol. 2, no. June, pp. 200–208, 2013. 15. F. Zhang and D. Wang, “An effective feature selection approach for network intrusion detection,” Proceedings - 2013 IEEE 8th International Conference on Networking, Architecture and Storage, NAS 2013, pp. 307–311, 2013, doi: 10.1109/NAS.2013.49. 16. K. Kumar, G. Kumar, and . Kumar , “Feature Selection Approach for Intrusion Detection System ,” International Journal of Advanced Trends in Computer Science and Engineering (IJATCSE), vol. 2, no. 5, pp. 47–53, 2013, [Online]. Available: http://warse.org/pdfs/2013/icceitsp09.pdf. 17. A. Ids and A. Ids, “Application and Signature - Based Ids.” 18. M. Tiwari, “Intrusion Detection System,” no. April, pp. 39–57, 2011, doi: 10.1142/9781848164482_0004. 19. K. S. Desale, C. N. Kumathekar, and A. P. Chavan, “Efficient intrusion detection system using stream data mining classification technique,” Proceedings - 1st International Conference on Computing, Communication, Control and Automation, ICCUBEA 2015, pp. 469–473, 2015, doi: 10.1109/ICCUBEA.2015.98. 20. J. Ng, D. Joshi, and S. M. Banik, “Applying data mining techniques to intrusion detection,” Proceedings - 12th International Conference on Information Technology: New Generations, ITNG 2015, pp. 800–801, 2015, doi: 10.1109/ITNG.2015.146. 21. G. A. Isaza, A. G. Castillo, and N. D. Duque, “An intrusion detection and prevention model based on intelligent multi-agent systems, signatures and reaction rules ontologies,” Advances in Intelligent and Soft Computing, vol. 55, pp. 237–245, 2009, doi: 10.1007/978-3-642-00487-2_25. 22. J. Yu, P. Tian, H. Feng, and Y. Xiao, “Research and Design of Subway BAS Intrusion Detection Expert System,” Proceedings of 2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2018, no. Iaeac, pp. 152–156, 2018, doi: 10.1109/IAEAC.2018.8577262. 23. H. ong and Z. X. Feng, “Expert system based intrusion detection system,” Proceedings - 3rd International Conference on Information Management, Innovation Management and Industrial Engineering, ICIII 2010, vol. 4, pp. 404–407, 2010, doi: 10.1109/ICIII.2010.578. 24. I. Annals, “The March of IDES: The Advent and Early History of Intrusion Detection Expert Systems Jeffrey R. Yost, Charles Babbage Institute, University of Minnesota,” pp. 1–19, 2015. 25. Z. P. Jia, Z. L. ao, and S. F. Liu, “An expert system for preventing and auditing intrusion,” Proceedings of the 9th International Conference on Computer Supported Cooperative Work in Design, vol. 2, pp. 852–855, 2005, doi: 10.1109/cscwd.2005.194297. 26. K. Rai, M. S. Devi, and A. Guleria, “Decision Tree Based Algorithm for Intrusion Detection,” International Journal of Advanced Networking and Applications, vol. 07, no. 04, pp. 2828–2834, 2016, [Online]. Available: https://www.researchgate.net/publication/298175900. 27. M. Kumar, M. Hanumanthappa, and T. V. S. Kumar, “Intrusion Detection System using decision tree algorithm,” International Conference on Communication Technology Proceedings, ICCT, pp. 629–634, 2012, doi: 10.1109/ICCT.2012.6511281. Authors: Akiladevi R, Nandhini Devi B, Nivesh Karthick V, Nivetha P

Paper Title: Prediction and Analysis of Pollutant using Supervised Machine Learning Abstract: Air is the most essential natural resource for the survival of humans, animals, and plants on the planet. Air is polluted due to the burning of fuels, exhaust gases from factories and industries, and mining operations. Now, air pollution becomes the most dangerous pollution that humanity ever faced. This causes many health effects on humans like respiratory, lung, and skin diseases, which also causes effects on plants, and animals to survive. Hence, air quality prediction and evaluation as becoming an important research area. In this paper, a machine learning-based prediction model is constructed for air quality forecasting. This model will help us to find the major pollutant present in the location along with the causes and sources of that particular pollutant. Air Quality Index value for India is used to predict air quality. The data is collected from various places throughout India so that the collected data is preprocessed to recover from null values, missing values, and duplicate values. The dataset is trained and tested with various machine learning algorithms like Logistic Regression, Naïve Bayes Classification, Random Forest, Support Vector Machine, K Nearest Neighbor, and Decision Tree algorithm in order to find the performance measurement of the above-mentioned algorithms. From this, the prediction model is constructed using the Decision Tree algorithm to predict the air quality, because it provides the best and highest accuracy of 100%. The machine learning-based air quality prediction model helps India meteorological department in predicting the future of air quality, and its status and depends on that they can take action.

Keywords: Prediction, Decision Tree algorithm, Air Quality Index, Air Pollution.

References:

1. Aly Akhtar., Sarfaraz Masood., Chaitanya Gupta., and Adil Masood, “Prediction and Analysis of Pollution Levels in Delhi Using Multilayer Perceptron,” 2018. 10. 2. Atakan Kurt and Ayse Betul Oktay, “Forecasting air pollutant indicator levels with geographic models 3 days in advance using neural networks,” in Expert Systems with Applications 37 (2010) 7986-7992, 2010. 3. Chao Zhang., Junchi Yan., Yunting Li., Feng Sun., Jinghai Yan., Dawei Zhang., Xiaoguang Rui., and Rongfang Bie, “Early Air 50-54 Pollution Forecasting as a Service: an Ensemble Learning Approach,” in 2017 IEEE 24th International Conference on Web Services, 2017. 4. Dixian Zhu., Changjie Cai., Tianbao Yang., and Xun Zhou, “A Machine Learning Approach for Air Quality Prediction: Model Regularization and Optimization,” in Big data and cognitive computing, 2018. 5. Dongming qin., Jian Yu., Guojinian Zou., Ruihan Yong., Qin Zhao., and Bo Zhang, “A Novel Combine Prediction Scheme Based on CNN and LSTM for Urban PM2.5 Concentration,” in Digital Object Identifier 10.1109/ACCESS.2019.DOI, 2019. 6. Ebrahim Sahafizadeh and Esmail Ahmadi, “Prediction of Air Pollution of Boushehr City Using Data Mining,” in 2009 Second International Conference on Environmental and Computer Science, 2009. 7. Elias Kalapanidas and Nikolaos Avouris, “Short-term air quality prediction using a case-based classifier,” in Environmental Modelling and Software 16 (2001) 263-272, 2000. 8. Emanuel Lacic., Dominik Kowald., and Elisabeth Lex, “High Enough? Explaining and Predicting Traveler Satisfaction Using Airline Reviews,” 2016. 9. Fang Mingjian., Zhu Guocheng., Zheng Xuxu., and Yin Zhongyi, “Study on air fine particles pollution prediction of main traffic route using artificial neural network,” in 2011 International Conference on Computer Distributed Control and Intelligent Environmental Monitoring, 2011. 10. Gaganjot Kaur Kang., Jerry Zeyu Gao., Sen Chiao., Shengqiang Lu., and Gang Xie, “Air Quality Prediction: Big Data and Machine Learning Approaches,” in International Journal of Environment Science and Development, 2018. 11. Guanghui Yue., Ke Gu., and Junfei Qiao, “Effective and Efficient Photo –Based PM2.5 Concentration Estimation,” 2019. 12. Ibrahim Yakut., Tugba Turkoglu., and Fijriye Yakut, “Understanding Customers Evaluations Through Mining Airline Reviews,” in International Journal of Data Mining and Knowledge Management Process (IJDKP), 2015. 13. Ishan Verma., Rahul Ahuja., Hardik Meisheri., and Lipika Dey, “Air Pollutant severity prediction using Bi-directional LSTM Network,” in IEEE/WIC/ACM International Conference on Web Intelligence (WI), 2018. 14. Kaixi Zhu., Mitchell P. Krawiee-Thayer., and Amir H. Assadi, “Stochastic Local-to-Global Methods for Air Quality Prediction,” 2017. 15. Ke Gu., Junfei Qiao., and Weisi Lin, “Recurrent Air Quality Predictor Based on Meterology- and Pollution-Related Factors,” 2017. 16. Khaled Bashir Shaban., Abdullah Kadri., and Eman Rezk, “Urban Air Pollution Monitoring System With Forecasting Models,” in IEEE Sensors Journal, 2016. 17. Ling Wang., Xi-yuvan Xiao., and Jian-yao Meng, “Prediction of Air Pollution Based on FCM-HMM Multi-model,” in Proceedings of the 35th Chinese Control Conference, 2016. 18. Luke Curtis., William Rea., Patricia Smith-Willis., Ervin Fenyves., and Yaqin Pan, “Adverse health effects of outdoor air pollutants,” in Environment International 32 (2006) 815-830, 2006. 19. MinHan Kim., YongSu Kim., SuWhan Sung., and ChangKyoo Yoo, “Data-Driven Prediction Model of Indoor Air Quality by the Preprocessed Recurrent Neural Networks,” in ICROS-SICE International Joint Conference 2009, 2009. 20. Nitin Sadashiv Desai and John Sahaya Rani Alex, “IoT based air pollution monitoring and predictor system on Beagle Bone Black,” 2017. 21. Praveen Kumar Sharma., Tanmay De., and Sujoy Saha, “IoT based Indoor Environment Data Modelling and Prediction,” 2018. 22. Saba Ameer., Munam Ali Shah., Abid Khan., Houbing Song., Carsten Maple., Saif ul Islam., and Muhammad Nabeel Asghar, “Comparative analysis of machine learning techniques for predicting air quality in smart cities,” in Digital Object Identifier 10.1109/ACCESS.2017.Doi Number, 2017. 23. Shajulin Benedict, “Revenue Oriented Air Quality Prediction MicroServices for Smart Cities,” 2017. 24. Shuting Li., Shujun Song., and Xin Fei, “Spatial Characteristics of Air Pollution in the Main City Area of Chengdu, China,” 2011. 25. Shweta Taneja., Dr. Nidhi Sharma., Kettun Oberoi., and Yash Navoria, “Prediction Trends in Air Pollution in Delhi using Data Mining,” 2016. 26. Temesegan Walelign Ayele and Rutvik Mehta, “Air pollution monitoring and prediction using IoT,” in Proceedings of the 2nd Internaional Conference on Inventive Communication and Computational Technologies (ICICCT 2018) IEEE Xplore Compliant, 2018. 27. Tsang-Chu Yu., Chung-Chin Lin., Ren-Gury Lee., Chao-Heng Tseng., and Shi-Ping Liu, “Wireless Sensing System for Prediction Indoor Air Quality,” 2012. 28. Xia Xi., Zhao Wei., Rui Xiaoguang., Wang Yijie., Bai Xinxin., Yin Wenjun., and don Jin, “A Comprehensive Evaluation of Air Pollution Prediction Improvement by a Machine Learning Method,” in 2015 IEEE International Conference on Service Operations and Logistics, And Informatics (SOLI), 2015. 29. Xinlong Tao., Jianqiang Yi., Zhiqiang Pu., and Tianyi Xiong, “State-Estimator-Integrated Robust Adaptive Tracking Control for Flexible Air-Breathing Hypersonic Vehicle With Noisy Measurements,” 2019. 30. Yue Shan Chang , Kuan-Ming Lin , Yi-Ting Tsai , Yu-Ren Zeng and Cheng-Xiang Hung, “Big data platform for air quality analysis and prediction” in the 27th Wireless and Optical Communications Conference (WOCC2018) in 2018. 31. Zhiwen Hu., Zixuan Bai., and Kaigui Bian, “Real-Time Fine-Grained Air Quality Sensing Networks in Smart City: Design, Implementation and Optimization,” in IEEE Internet of Things Journal, 2019. 32. Ziyue Guan and Richard O. Sinnott, “Prediction of Air Pollution through Machine Learning Approaches on the Cloud,” in 2018 IEEE/ACM 5th International Conference on Big Data Computing Applications and Technologies (BDCAT), 2018. Authors: Mohammad Majid Masroor, Kushagra Mishra, E. Sasikala

Paper Title: Providing Security in Multiserver Authentication Scheme using Efficient Three Factor Encryption Abstract: For last couple of months, Using Two Factor Authentication was not so secure to protect the data inheriting from Multiserver Authentication. Providing Three Factor Authentication helped a lot in these fields to enhance the security while decrypting the encrypted files but failed in many aspects as there were no Multiserver Authentication was used. Here, the approach is used to provide security in Multiserver Platform Using Three Factor Encryption efficiently so as to remove 99.9% chances of failure in security from decryption of the files without going through Three Factor Security present at different Servers to verify at same time and providing permission to access. For the duration between Feb. 2019 and Jan 2020, There were millions of cases reporting loss of data even having two factor authentication, It happens in a way to get through the first factor on first server taking approval, and the packet can be dropped in-between to use that authentication to get through the second factor on second server to access the data. With the improvement of spread choosing development to the degree unfaltering quality and point of confinement and boundless affiliation have been set as parameters to improve. So, we have implemented these changes in our model to reduce the complexity by this effect providing more security using different layers and format to keep the file secure and going through 3-layer security and authentication before getting access.

Keywords: Authentication, Factor, Multiserver. Two, Three.

11. References:

1. L. Lamport, "Secret phrase confirmation with shaky correspondence," Communications of The ACM, vol. 24, no. 11, pp. 770– 55-59 772, 1981. 2. X. Huang, Y. Xiang, E. Bertino, J. Zhou, and L. Xu, "Powerful multifaceted confirmation for delicate correspondences," IEEE Transactions on Dependable and Secure Computing, vol. 11, no. 6, pp. 568–581, 2014. 3. D. He, S. Zeadally, N. Kumar, and J. Lee, "Unknown confirmation for remote body region systems with provable security," IEEE Systems Journal, pp. 1–12, 2016. 4. L. Li, L. Lin, and M. Hwang, "A remote secret word confirmation conspire for multiserver design utilizing neural systems," IEEE Transactions on Neural Networks, vol. 12, no. 6, pp. 1498–1504, 2001. 5. W. Juang, "Proficient multi-server secret word validated key understanding utilizing savvy cards," IEEE Transactions on Consumer Electronics, vol. 50, no. 1, pp. 251–255, 2004. 6. C. C. Chang and J. S. Lee, "A proficient and secure multi-server secret word validation conspire utilizing brilliant cards," in International Conference on Cyberworlds, 2004, pp. 417–422. 7. J.- L. Tsai, "Productive multi-server confirmation conspire dependent on single direction hash work without check table," Computers and Security, vol. 27, no. 3C4, pp. 115–121, 2008. 8. W. Tsaur, J. Li, and W. Lee, "A proficient and secure multi-server validation conspire with key understanding," Journal of Systems and Software, vol. 85, no. 4, pp. 876–882, 2012. 9. Y. Liao and C. Hsiao, "A tale multi-server remote client verification conspire utilizing self- guaranteed open keys for portable customers," Future Generation Computer Systems, vol. 29, no. 3, pp. 886–900, 2013. 10. T. S. Messerges, E. A. Dabbish, and R. H. Sloan, "Inspecting smartcard security under the danger of intensity examination assaults," IEEE Transactions on Computers, vol. 51, no. 5, pp. 541–552, 2002. 11. D. Wang and P. Wang, Offline Dictionary Attack on Password Authentication Schemes Using Smart Cards. Springer International Publishing, 2015. 12. J. K. Lee, S. R. Ryu, and K. Y. Yoo, "Unique mark based remote client verification plot utilizing shrewd cards," Electronics Letters, vol. 38, no. 12, pp. 554– 555, 2002. 13. C. Lin and Y. Lai, "An adaptable biometrics remote client confirmation conspire," Computer Standards and Interfaces, vol. 27, no. 1, pp. 19–23, 2004. 14. C. Chang and I. Lin, "Comments on unique finger impression based remote client verification conspire utilizing shrewd cards," Operating Systems Review, vol. 38, no. 4, pp. 91–96, 2004. 15. H. Kim, S. Lee, and K. Yoo, "Id-based secret key confirmation plot utilizing brilliant cards and fingerprints," Operating Systems Review, vol. 37, no. 4, pp. 32–41, 2003. 16. M. Scott, “Cryptanalysis of an id-based password authentication scheme using smart cards and fingerprints,” Operating Systems Review, vol. 38, no. 2, pp. 73–75, 2004. Authors: Manjya Naik R, G A Bidkar

Paper Title: Energy Efficient Optimized LEACH and SEP Routing Protocol for WSN Abstract: We all know how Wireless Sensor Network (WSN) is making its way in the modern world and how its application is growing effectively. It has been useful technology which helps to transmit and receive the data. In WSN, all information of physical parameter is sensed and processed by the sensor nodes. Along with the growth of technology in WSN, growth of sensor node is also in progress, it means that size of sensor node is getting reduced. Due to this dimension of the battery of the sensor node is also decreases. Hence Power storage of the battery is also reduced which is a demerit in WSN. But in sensor network replacement of battery is not possible. So we can increase the energy efficiency of sensor node by using LEACH protocol. This protocol helps us to increase life span of network. For heterogeneous network, LEACH produces greater unstable region. To the network stable the paper proposes another protocol is Stable Election protocol (SEP). In this paper LEACH 12. and SEP protocol are tested with MATLAB simulation and comparison of both has done.

Keywords: LEACH, LEACH-C protocol, SEP, WSN, Sensor node, cluster member and Cluster head. 60-65

References:

1. Technology, Protocols and application for WSN by Minoli and shoraby. 2. Rosenberg, “Homogeneous vs. heterogeneous clustered sensor networks: A comparative study,” in Proceedings of 2004 IEEE International Conference on Communications (ICC 2004), June 2004. 3. Technology and Research (IJNTR), April 2018, ISSN:2454-4115, Volume-4, Issue-4, pages 86-90combination 4. Kumar Singh, M Singh, Dharmendra Kumar Singh, “EnergyEfficient Homogenous Clustering Algorithm for WSN”, International JournalMobile Networks ( IJWMN ),2011 5. Georgios,Ibrahimmatta “SEP in wireless sensor networks” NSF grants ITR ANI-0205294 6. AgarwalMurugananda, “LEACH routing Protocol: Simulation and Analysis using MATLAB”, 2019 International Conference,GalgotiasUniversity,UP, India. Sep 30-31, 2019. Authors: A. Ajay Reddy, V. Punnarao

Paper Title: Maximizing the Impact Spread in Communal Web an Agent Based Proposal Abstract: Communal Impact replicating and maximization has more control over e-business and marketing. Present studies focus mainly on optimization of positive communal impact so as to market products adoptions supported stable web photos. Present procedures just raise impact in communal web briefly term, yet can’t produce sustainable or future results. Inside our proposed system we concentrate on maintaining future impact in communal web also suggest an agent based impact preservation replica which may choose predominant junction supports present reputation within operative communal webs in various measure. Constant impact is achieved more using several pulse pip choices than that of one pulse pip choices. The proposed system automatically keep enduring impact in order to get impact preservation . It is applicable to achieve durable trade aim.

Keywords: Impact scattering, Impact conservation, agent-based replicating, long-lasting impact.

References:

13. 1. Alex David Kempe, Jon Kleinberg, and ´Eva Tardos. Maximizing the impact spread through a communal web. In Proceedings of the ninth ACM SIGKDD international conference on Knowledge discover and data mining, pages 137–146, Washington, DC, USA, 2003. ACM. 66-70 2. Weihua Li, Quan Bai, Tung Doan Nguyen, and Minjie Zhang. Agent-based impact conservation in communal web. In Proceedings of the 16th Conference on Autonomous Agents and Multi Agent Systems (pages 1592–1594). International Foundation for Autonomous Agents and Multi agent Systems, 2017. 3. Manuel Gomez Rodriguez and Bernhard Sch¨olkopf. Impact maximization in continuous pulse scattering web. arXiv preprint arXiv:1205.1682, 2012. 4. Yanhao Wang, Qi Fan, Yuchen Li, and Kian-Lee Tan. Real-pulse impact maximization on dynamic communal streams. Proceedings of the VLDB Endowment, 10(7):805–816, 2017. 5. Weihua Li, Quan Bai, and Minjie Zhang. Agent-based impact propagation in communal web. In Proceedings of IEEE International Conference on Agents. Springer, 2016. 6. Peter-Paul van Maanen and Bob van der Vecht. An agent-based approach to replicating online communal impact. In Proceedings of the 2013 IEEE/ACM International Conference on Advances in Communal Web Analysis and Mining, pages 600–607. ACM, 2013. 7. Wei Chen, Yajun Wang, and Siyu Yang. Efficient impact maximization in communal web. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 199–208, Paris, France, 2009. ACM 8. Amit Goyal, Francesco Bonchi, and Laks V.S. Lakshmanan. Learning impact probabilities in communal web. In Proceedings of the Third ACM International Conference on Web Search and Data Mining, WSDM ’10, pages 241–250, New York, NY, USA, 2010. ACM. 9. Jie Tang, Jimeng Sun, Chi Wang, and Zi Yang. Communal impact analysis in large-scale web. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 807–816. ACM, 2009. 10. John C Turner. Social influence. Thomson Brooks/Cole Publishing Co, 1991. 11. Valencia. Short-term vs. long-term online marketing, August 2013. [Online; posted 22-August-2013]. Authors: Vidya. M, P. Pramila, A. M. Nagaraj

Paper Title: DSTATCOM Performance for Voltage Sag/swell Mitigation Abstract: The Indian economy has been growing at a fast pace since the beginning of this millennium. Due to constraints in the availability of fuel and environmental concerns, the power generation sector has not kept pace with other industrial sectors. One way of increasing the power availability is by reducing the high losses in the existing power transmission and distribution systems. The current increases in the motor windings when the voltages in the three phases are unbalanced. Compensation for reactive power and unbalance in the power distribution system are key factors in improving the power quality to the end user. A Distributed Static Compensator [DSTATCOM] is a custom power device, which is connected in shunt with the load in the distribution system to compensate the reactive power due unbalanced loads. The performance of the DSTATCOM is based on the control technique used for finding the voltage referred and current components to be considered. Voltage compensation is defined as the error in voltage in the grid and that the value of voltage that has to be induced in the grid. This is analyzed by using DSTATCOM for voltage compensation with series converter controller block. This paper gives the simulation of voltage compensation to rectify the issue of voltage swell/sag in order to improve the power quality in the distribution system.

Keywords: DSTATCOM, voltage compensation, series converter.

References:

14. 1. GeraldThomasHeydt,Life,FellowIEEE,RajapandianAyyanar,SeniorM emberIEEEKory.W.Hedman,Member,IEEE,andVijayVittal,FellowIE EE, “Electric Power and Energy Engineering: The First Century” 71-74 2. Alexis Polycarpou Frederick University, Cyprus, “Power Quality and Voltage Sag Indices in Electrical Power Systems” 3. TheCalifornia EnergyCommission, “Power Quality Solutions for Industrial Customers- A Guidebook 4. SinghR;Vadodra InstofEngg,”Simulation of DSTATCOM forVoltageFluctuation”SecondIEEEInternational Conference on, Advanced Computing & Communication Technologies (ACCT), (2012) 5. Andrzej Bachry,“Power Quality Studies in Distribution SystemsInvolvingSpectralDecomposition”,Otto-von-Guericke-Univer sitätMagdeburg 2004. 6. T. Naveen,Assistant professor,Department Of Electrical &ElectronicsEngineering,vignanabharathiinstituteoftechnologyColleg e,Ghatkesar,Rangareddy; A.P, India, “Improvement of Power Quality Using D-Statcom Based PV Distribution System with Various Load Conditions”, International Journal of Engineering Research & Technology (IJERT) Vol. 2 Issue 9, September – 2013, ISSN: 2278-0181. 7. Brijesh Parmar, Prof. Shivani Johri, Chetan Chodavadiya, ElectricalEngineeringDepartment,SBTC,Jaipur,India,AssistantProfess or,ElectricalEngineeringDepartment,SBTC,Jaipur, India,AssistantProfessor, Electrical Engineering Department, MGITER, Navsari, India, “Improvement of Voltage Profile usingDSTATCOM–Simulationundersagandswellcondition”,Internatio nal Journal of innovative research in electrical, electronics, instrumentation and control engineering, vol. 2, issue7,July 2014. 8. PedroRoncero-Sanchez and Enrique Acha School of IndustrialEngineering,UniversityofCastilla-LaMancha, CampusUniversitari 13071Ciudad Real,Spain,Department of Electrical Engineering, Tampere University of Technology, Korkeakoulunkatu 10,FI- 33720 Tampere, Finland; E-Mail: [email protected]“DesignofControlSchemeforDistributionStaticSyn chronousCompensatorswithPower-QualityImprovement Capability” ISSN 1996-1073 9. Wei-NengChang,Member,IEEE,andKuan-DihYeh,DepartmentofElect ricalEngineering,ChangGungUniversity,Taiwan, ROC,“DesignandImplementationofDSTATCOMwith SymmetricalComponentsMethodforFast Load Compensation of Unbalanced Distribution Systems”. 10. ArchanaM.Kadam,SatyenDhamdhere,D.S.Bankar,Application of DSTATCOM for Improvement of Power Quality usingMATLABSimulation,International Journal of Science andModernEngineeringISSN:2319-6386,Volume-1, Issue-1, December 2012. Authors: K Jayashree, Monika K A, Preetha R, Piraisoodan S P

Paper Title: The Smart Health Care Prediction using Chatbot Abstract: Our healthcare is very much important to lead a peaceful and honest life. If any health issue occurs, we need to go to the hospital and consult the doctor for the very minor problems. our healthcare chatbot is developed to help the people to predict their health issue early at home before they visit the doctor or hospital for the mi nor problems. For the minor issues we are spending lots of costs. The healthcare chatbot is design to reduce such costs and also its improves the efficiency of the medical healthcare. There were many chatbots available they act as a reference for the patient to know more about their health issue. The healthcare chatbot is something different from the other chatbots which predicts the diseases by using symptoms and gives the doctor 15. details to consult the doctor. The healthcare chatbot is developed by using AI in the text to text conversation mode. The user who knows only to write and read can use this chatbot for their minor issue. In this healthcare 75-78 chatbot, the system predicts the diseases based on the symptom given by the user using the pattern concept in AIML algorithm. The system also predicts the prescription and also give the doctor details to the user based on the diseases predicted for their symptom given. By using this healthcare chat bot people will know the minor diseases at early stage with no costs. Whenever the patient or user gets the time they will consult doctor for their health issue. This will make people to know more about their health issue anywhere at any time.

Keywords: AIML, Disease, Pattern, Symptoms, Chatbot.

References:

1. R Babu , K Jayashree, “A Survey on the Role of IOT and Cloud in Health Care,” in International journeal of scientific Engineering and techonology research, chennai, 2015. 2. Divya S, Indumathi V, Ishwarya S, Priyasankari M and Kalpana Devi S, , “Survey on Medical Self-Diagnosis Chatbot for Accurate Analysis Using Artifical Intelligence,” in International journal of trend in research and development , chennai, 2018. 3. Vivek Katariya, VItthal s Gutte, “Intelligent Healthbot for transforming healthcare,” in Proceding of National Conferences on Machine Learning, Pune, 2019. 4. Rashmi Dharwadkar, Neeta A Deshpande, “A Medical Chatbot,” in International Journal of Computer Trends and Techonology, Pune, 2018. 5. Krishnendu Rarhi, Abhishek Bhattacharya, Abhishek Mishra,Krishnasis Mandal, “Automated Medical Chatbot,” in SSRN Electronic Journal, 2018. 6. Divya S, Indumathi V, Ishwarya S, Priyasankari M and Kalpana Devi S, , “A Self-Diagonosis Medical Chatbot Using Artifical Intelligence,” in MAT journal, chennai, 2018. 7. Tobias Kowatsch, Marcia Nißen, Chen-Hsuan Iris Shih, Dominik Rüegger, Dirk , “Text-based Healthcare Chatbots supporting Patient and Helth Professional,” switzerland, 2017. 8. Kavitha B R, Chethana R Murthy, “Chatbot for healthcare system using Artifical Intelligence,” in International Journey of Advance Research ,Ideas And Innovations In Technology, Karnataka, 2019. 9. Flora Amato, Stefano Marrone, Vincenzo Moscato, Gabriele Piantadosi, Antonio Picariello, and Carlo Sansone, “Chatbots meet eHealth: automatizing healthcare,” Italy, 2017. 10. Nourchene Ouerhani, Ahmed Maalel , and Henda Ben Ghezela, “Towards a chatbot based smart pervasive healthcare medical emergency cases,” Tunisia, 2019. 11. Ahmed Fadhil, Gianluca Schiavo, “Designing for Health Chatbots,” Italy, 2018. Authors: Anees Fatima Khan, Bhavya P, R. Ravinder Reddy

Paper Title: Land Classification using Convolutional Neural Networks Abstract: Identifying the physical aspect of the earth’s surface (Land cover) and also how we exploit the land (Land use) is a challenging problem in environment monitoring and much of other subdomains. One of the most efficient ways to do this is through Remote Sensing (analyzing satellite images). For such classification using satellite images, there exist many algorithms and methods, but they have several problems associated with them, such as improper feature extraction, poor efficiency, etc. Problems associated with established land-use classification methods can be solved by using various optimization techniques with the Convolutional neural networks(CNN). The structure of the Convolutional neural network model is modified to improve the classification performance, and the overfitting phenomenon that may occur during training is avoided by optimizing the training algorithm. This work mainly focuses on classifying land types such as forest lands, bare lands, residential buildings, Rivers, Highways, cultivated lands, etc. The outcome of this work can be further processed for monitoring in various domains.

Keywords : Convolution Neural Networks(CNN), Deep Learning, Land Classification

References:

1. Baoxuan Jin., Peng Ye., Xueying Zhang., Weiwei Song., Shihua Li(2019).. Object-Oriented Method Combined with Deep Convolutional Neural Networks for Land-Use-Type Classification of Remote Sensing Images. 2. Zhang, Ce. (2018). Deep Learning for Land Cover and Land Use Classification. 10.17635/lancaster/thesis/428. 3. Jin, Y. T., Yang, X. F., Gao, T., Guo, H. M., & Liu, S. M. (2018). The typical object extraction method based on object-oriented 16. and deep learning. Remote Sensing for Land and Resources, 30(1), 22–29. 4. Al Khawlani, M. M., Elmogy, M., & Elbakry, H. M. (2015). Content-based image retrieval using local features descriptors and bag-of-visual words. International Journal of Advanced Computer Science & Applications, 6(9), 212–219. 79-83 5. Hu, F., Xia, G. S., Hu, J. W., & Zhang, L. P. (2015). Transferring deep convolutional neural networks for the scene classification of high-resolution remote sensing imagery. Remote Sensing, 7(11), 14680–14707. 6. Zheng, Y., Wu, F. D., & Liu, Y. F. (2010). A feature analysis approach for object-oriented classification. Geography and Geo- Information Science, 26(2), 19–22. 7. Tzeng, Y. C. (2006). Remote sensing images classification/data fusion using distance weighted multiple classifiers systems. Journal of Chinese Institute of Engineers, 31(4), 639–647. 8. Meng, Q., Cieszewski, C. J., Madden, M., & Borders, B. E. (2007). K nearest neighbor method for forest inventory using remote sensing data. GI Science & Remote Sensing, 44(2), 149–165. 9. Bruzzone, L., & Prieto, D. F. (2001). Unsupervised retraining of a maximum likelihood classifier for the analysis of multitemporal remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 39(2), 456–460. 10. Lee, S. (2005). Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data. International Journal of Remote Sensing, 26(7), 1477–1491. 11. Zhang, L., Zhang, L., & Du, B. (2016). Deep learning for remote sensing data: A technical tutorial on the state of the art. IEEE Geoscience and Remote Sensing Magazine, 4(2), 22–40. 12. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84–90. 13. Hu, F., Xia, G. S., Hu, J. W., & Zhang, L. P. (2015). Transferring deep convolutional neural networks for the scene classification of high-resolution remote sensing imagery. Remote Sensing, 7(11), 14680–14707. 14. Bosch, A., Muñoz, X., & Martí, R. (2007). Which is the best way to organize/classify images by content? Image and Vision Computing, 25(6), 778–791. 15. Sands, R. D., & Leimbach, M. (2003). Modeling agriculture and land use in an integrated assessment framework. Climatic Change, 56(1–2), 185–210. 16. Song, X., Duan, Z., & Jiang, X. (2012). Comparison of artificial neural networks and support vector machine classifiers for land cover classification in Northern China using a SPOT-5 HRG image. International Journal of Remote Sensing, 33(10), 3301–3320. 17. Chen, Y. S., Jiang, H. L., Li, C. Y., Jia, X. P., & Chamisi, P. (2016). Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE Transactions on Geoscience and Remote Sensing, 54(10), 6232–6251.

17. Authors: Suspend Paper Title: Abstract:

Keywords : 84-88

References: Authors: Anjali Harit, Anurag Sharma, S.R. Singh

Paper Title: An Inventory Model based on Imperfect Production with Shortage Backorder Abstract: In this paper, we discussed about the imperfect items. In practice items may get damaged due to production or transportation conditions. Each lot receives some imperfect items. This model also considers the effects of business strategies such as optimal order size of raw materials, production rates and unit production costs, and idle time in different areas on the cooperation of marketing systems. The model can be used in industries such as textiles and footwear, chemicals, food. We develop an inventory model based on imperfect products and shortages. We consider demand is constant and continuous. Purpose of this study is not only to find the retailer`s optimal replenishment policies but also to minimize the total average cost. Finally, a numerical example is presented to illustrate the proposed model and sensitivity analysis of the optimal solution concerning parameters is carried out using the Mathematica 10.0 software.

Keywords: Demand, Deterioration, Imperfect items, Inventory, Shortage Backordering.

References:

1. P. N. Ghare and G. F. Schrader, “A model for exponentially decaying inventories.,” The Journal of Industrial Engineering, vol. 15, 1963, pp. 238–243. 2. G. F. Lindsay and A. B. Bishop, “Allocation of Screening Inspection Effort—A Dynamic-Programming Approach,” Manag. Sci., vol. 10, no. 2, 1964, pp. 342–352. 3. W. Shih, “Optimal inventory policies when stockouts result from defective products,” Int. J. Prod. Res., vol. 18, no. 6, 1980, pp. 677–686, 18. 4. R. H. Hollier and K. L. Mak, “Inventory replenishment policies for deteriorating items in a declining market,” Int. J. Prod. Res., vol. 21, no. 6, 1983, pp. 813–836. 5. R. S. Sachan, “On (T, Si) Policy Inventory Model for Deteriorating Items with Time Proportional Demand,” J. Oper. Res. Soc., 89-92 vol. 35, no. 11, 1984, pp. 1013–1019. 6. S. K. Goyal, “Economic Order Quantity under Conditions of Permissible Delay in Payments,” J. Oper. Res. Soc., vol. 36, no. 4, 1985, pp. 335-339. 7. H. L. Lee and M. J. Rosenblatt, “Optimal Inspection and Ordering Policies for Products with Imperfect Quality,” IIE Trans., vol. 17, no. 3, 1985, pp. 284–289. 8. M. J. Rosenblatt and H. L. Lee, “Economic Production Cycles with Imperfect Production Processes,” IIE Trans., vol. 18, no. 1, 1986, pp. 48–55. 9. X. Zhang and Y. Gerchak, “Joint Lot Sizing and Inspection Policy in an EOQ Model with Random Yield,” IIE Trans., vol. 22, no. 1, 1990, pp. 41–47. 10. M. K. Salameh and M. Y. Jaber, “Economic production quantity model for items with imperfect quality,” Int. J. Prod. Econ., vol. 64, no. 1, 2000, pp. 59–64. 11. L.-Y. Ouyang, T.-P. Hsieh, C.-Y. Dye, and H.-C. Chang, “an inventory model for deteriorating items with stock-dependent demand under the conditions of inflation and time-value of money,” Eng. Econ., vol. 48, no. 1, 2003, pp. 52–68 12. K.-J. Chung, C.-C. Her, and S.-D. Lin, “A two-warehouse inventory model with imperfect quality production processes,” Comput. Ind. Eng., vol. 56, no. 1, 2009, pp. 193–197 13. C. K. Jaggi and P. Verma, “An optimal replenishment policy for non-instantaneous deteriorating items with two storage facilities,” Int. J. Serv. Oper. Inform., vol. 5, no. 3, 2010 pp. 209-216. 14. S. Jain, S. Tiwari, L. E. Cárdenas-Barrón, A. A. Shaikh, and S. R. Singh, “A fuzzy imperfect production and repair inventory model with time dependent demand, production and repair rates under inflationary conditions,” RAIRO - Oper. Res., vol. 52, no. 1, 2018, pp. 217–239. 15. H.-L. Yang, “An Inventory Model for Ramp-Type Demand with Two-Level Trade Credit Financing Linked to Order Quantity,” Open Journal of Business and Management, vol. 7, 2019, pp. 427–446. 16. S. Panja and S. K. Mondal, “Analyzing a four-layer green supply chain imperfect production inventory model for green products under type-2 fuzzy credit period,” Comput. Ind. Eng., vol. 129, 2019, pp. 435–453 17. R. V. Azevedo, M. das C. Moura, I. D. Lins, and E. L. Droguett, “a multi-objective approach for solving a replacement policy problem for equipment subject to imperfect repairs,” Appl. Math. Model., vol. 86, 2020, pp. 1-19 Authors: Eknath V R

Paper Title: Factors That Affect Finance in Pre-Owned Two-Wheeler Market Abstract: This paper focus in identifying the important factors that affects finance in pre-owned two wheeler market. The study shows that there is so much of enquiry for finance in the pre-owned two wheeler segment. This paper also shows the market condition in the used two wheeler segment at Thrissur and Ernakulam districts 19. of Kerala. The customer profiling, customer’s preference in vehicles, market potentiality and so on. For this purpose primary data is collected from the dealers of the used two wheelers from the Thrissur and Ernakulam districts of Kerala. The used two wheeler segment is not organized and hence the data is procured from each 93-97 dealer from their respective location in the mode of interview. Even though the customers can get a brand new vehicle on finance, most of them do not prefer the new vehicle on finance and look for used vehicle on finance as per the dealers. It’s seen that sales for the dealers with finance availability for customers is more than the dealers who do not have finance facility. But in the market there are only few financers and the present financers charge too much of rates, which hit the sales for the dealers. Most of the financers in this segment have withdrawn from the market as the legal requirements and terms and conditions in providing finance to the customer changed. Thus, the market availability for the financers are high and in through this paper we will identify the factors that affect finance in pre-owned two wheeler market.

Keywords: Finance factors, pre-owned, two-wheeler market, used two-wheeler.

References:

1. Akerlof, G. A. (1978). The market for “lemons”: Quality uncertainty and the market mechanism. In Uncertainty in economics (pp. 235-251). Academic Press. 2. Mundu, R., Trivedi, H., & Kurade, Y. (2011). Analysis of factors influencing two wheeler purchases by women. Beacon management review, 11-18. 3. Devaki, V., & Balakrishnan, H. (2013). THE FUTURE OF HERO MOTO CORP: A STUDY ON THE CUSTOMER PREFERENCE TOWARDS HERO TWO WHEELER AFTER THE TERMINATION OF HERO HONDA. CLEAR International Journal of Research in Commerce & Management, 4(11). 4. Kathiravanaa, C., Panchanathamaa, N., & Anushan, S. (2010). The competitive implications of consumer evaluation of brand image, product attributes, and perceived quality in competitive two-wheeler markets of India. Serbian Journal of Management, 5(1), 21-38. 5. John, B., & Pragadeeswaran, S. (2013). A study of small car consumer preference in Pune city. TRANS Asian Journal of Marketing & Management Research (TAJMMR), 2(3and4), 1-14. Authors: Shruti Pandey, Siva Shanmugam G

Paper Title: A Novel Interpolation Perspective for Handwritten Digit Recognition using Neural Network Abstract: In this work, we present an innovative technique for manually written character recognition that is disconnected, using deep neural networks. Since of the accessibility of enormous knowledge calculation and numerous algorithmic advances that are emerging, it has become easier in this day and age to train deep neural systems. And seeks to classify the numerical digits so that digits can be translated into pixels. Today, the computing force measure required to prepare a neural system has increased owing to the proliferation of GPUs and other cloud-based administrations like Google and Amazon offer tools to prepare a cloud-based neural system. We also developed a system for the recognition of character dependent on manually written image division. This project uses libraries such as NumPy, pandas, sklearn, seaborn to accomplish this either by linear and non-linear algorithm, to know its precision on confusion matrix and accuracy. This idea spins with RBF(radial basis function) which consists of two parameters as C and Gamma and classifying the pixel digits. To train those models, research work includes Convolutional Neural Network (CNN), Dynamic Neural Network(DNN), Recurrent Neural Network(RNN), and TensorFlow algorithms using Keras , which can be accurately used for the classification of the digits.

Keywords: Digit recognizer, Numerical Digits, Pixel Format, Handwritten Recognition.

20. References:

1. Elie Krevat, Elliot Cuzzillo. Improving Offline Handwritten Character Recognition with Hidden Markov Models. 98-102 2. Lisa Yan. Recognizing Handwritten Characters. CS231N Final Research Paper. 3. Oivind Due Trier, Anil K. Jain, Torfinn Taxt. Feature Extraction Methods for Character Recognition–A Survey Pattern Recognition. 1996 4. TesseractModel:https://github.com/tesseractocr/tesseract/wiki/TrainingTesseract-4.00 5. Fabian Tschopp. Efficient Convolutional Neural Networks for Pixelwise Classification on Heterogeneous Hardware Systems 6. T. Wang, D. Wu, A. Coates, A. Ng." End-to-End Text Recognition with Convolutional Neural Networks" ICPR 2012. 7. Thodore Bluche, Jrme Louradour, Ronaldo Messina. Scan, Attend and Read: End-to-End Handwritten Paragraph Recognition with MDLSTM Attention. 8. Siva Shanmugam Gopal., & N.Ch.Sriman Narayana Iyengar,. (2019). Improving Energy Efficient and Impairing Environmental Impacts on Cloud Centers by Transforming VM into Self-Adaptive Resource Container. Int. Arab J. Inf. Technol., 16(4), 30-37. 9. Vijayalaxmi R Rudraswamimath, Bhavanishankar K, Handwritten Digit Recognition using CNN, International Journal of Innovative Science and Research Technology. 10. S. M. Shamim, Md Badrul Alam Miah, Angona Sarker,Handwritten Digit Recognition using Machine Learning Algorithms, Research Gate march 2018. 11. Layers Fathma Siddique1, Shadman Sakib , Md. Abu Bakr Siddique ,Recognition of Handwritten Digit using Convolutional Neural Network in Python with Tensorflow and Comparison of Performance for Various Hidden 12. priya, rajendra singh, dr. soni changlani,Review on handwritten digit recognition, IJNRD Volume 2, Issue 4 April 2017. 13. Anushri Ravikumar, Ashitha Nayak ,Mamatha,Digit Recognizer, IARJSET Vol. 4, Special Issue 8, May 2017. 14. Meer Zohra, D.Rajeswara Rao,A Comprehensive Data Analysis on Handwritten Digit Recognition using Machine Learning Approach, International Journal of Innovative Technology and Exploring Engineering (IJITEE) Volume-8 Issue-6, April 2019 15. Joseph JamesS , C.Lakshmi, UdayKiran P, Parthiban,An Efficient Offline Hand Written Character Recognition using CNN and Xgboost, International Journal of Innovative Technology and Exploring Engineering (IJITEE), Volume-8 Issue-6, April 2019 Authors: Ananya Sridhar

Paper Title: 4D – Distracted Driver Detection Device

21. Abstract: This paper describes an inexpensive distracted driver detection device built using a Raspberry PI3, a video camera, and python code. Distracted and drowsy driving are two of the leading causes of automobile accidents in the United States, and this inexpensive standalone can help prevent those deaths. live video feed of 103-106 the driver’s face is read in and through the facial landmark detector within DLIB, the co-ordinates of the eyes and mouth are extracted in real time. The ratio of the distance between the eyelids in the vertical direction to the horizontal direction defined as Eye Open Ratio is used to evaluate if the eye is open or closed and a similar ratio on the mouth(Mouth Open Ratio) is used to identify if it is open or closed. By looking at the eye and mouth open ratio for several consecutive frames, it is determined with >95% accuracy whether the driver is drowsy, distracted, or yawning. If any of these behaviors are noted, the device will prompt an audible warning to encourage the driver to focus on the road. The prototype was tested under a variety of lighting conditions from dark to bright light and on different subjects with and without glasses. This test data was used to determine the threshold for when the eye or mouth is determines open or closed. Additionally, the prototype’s settings are customizable for a primary driver to further improve the accuracy. The device connects to a smart phone and sends information with the time stamp of the distracted driver incident. This device can be used to prevent distracted or drowsy driving-related deaths, and is an inexpensive attachment that can easily be fitted into a preexisting vehicle.

Keywords: Raspberry Pi, Python, Night vision camera. Distracted driving.

References:

1. Soukupová and Čech, “Real-Time Eye Blink Detection suing Facial Landmarks”; 21st Computer Vision Winter Workshop, 2016 2. V. Kazemi and J. Sullivan, "One Millisecond Face Alignment with an Ensemble of Regression Trees", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1867-1874, 2014. 3. P.Viola and M.Jones, “Rapid Object Detection using a Boosted Cascade of Simple Features”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2001. 4. Adrian Rosenbrock, tutorials on face recognition, https://www.pyimagesearch.com/ Authors: Authors: Ashwanth V, Manan Jain, P.Prabhu

Paper Title: Biometric Gloves for Health Monitoring Abstract: Global health, defined by the access to healthcare in every region, is symbolized by its demands to be economical and easily accessible. IOT based remote health monitoring system, advancing in the health care sector, is one of the most up-and-coming technological interventions. A serious concern world over, has been perilous work environment that pose health and safety hazards. With the advancements in technology, it is now possible to supervise individual health parameters and provide comprehensive information on health conditions. Users vital information can be monitored from anywhere with access to a specific control centre where the information is stored in real time. In this paper we present a remote health monitoring system that uses an IOT based smart edge technology, where wearable vital sensors transmit data to the cloud using a Wi-Fi module.

Keywords: Internet of Things; Wearable Sensors; Activity monitoring; Health sensing; Pervasive Healthcare.

References:

1. D. Metcalf, S. T. J. Milliard, M. Gomez and M. Schwartz, ”Wear-able’s and the Internet of Things for Health: Wearable, Intercon-nected Devices Promise More Efficient and Comprehensive Health Care,“ in IEEE Pulse, vol. 7, no. 5, pp. 35-39, Sept.- Oct. 2016. doi:10.1109/MPUL.2016.2592260 2. Shivayogi Hiremath ; Geng Yang ; Kunal Mankodiya,” Wearable Internet of Things: Concept, architectural components and promises for person-centered healthcare,“in IEEE Explore, vol. 7, no. 5, pp. 35-39, 3-5 Nov.2014. doi: 10.1109/MOBIHEALTH.2014.7015971 3. Ranjan, Y. Zhao, H. B. Sahu and P. Misra, “Opportunities and Challenges in Health Sensing for Extreme Industrial Environment: 22. Perspectives From Underground Mines,” in IEEE Access, vol. 7, pp. 139181-139195, 2019. doi: 10.1109/ACCESS.2019.2941436 4. F. Nikayin, M. Heikkila,¨ M. de Reuver, and S. Solaimani, “Workplace primary prevention programmes enabled by information 107-111 and commu-nication technology,” Technol. Forecasting Social Change, vol. 89, pp. 326-32,Nov. 2014. 5. Market Study Report, “Wearable Computing Devices, Like Apple’s iWatch, Will Exceed 485 Million Annual Shipments by 2018”, ABI Research. Feb. 21, 2013. 6. Gubbi, Jayavardhana, et al. ”Internet of Things (IoT): A vi-sion,architectural elements, and future directions.” Future Generation Computer Systems 29.7 (2013): 1645-1660. 7. Mankodiya, K., et al. ”Wearable ECG module for long-term recordings using a smartphone processor.” Proceedings of the 5th International Workshop on Ubiquitous Health and Wellness, Denmark. 2010. 8. M. Kritzler, M. Backman,¨ A. Tenfalt,¨ and F. Michahelles, “Wearable technology as a solution for workplace safety,” in Proc. 14th Int. Conf.Mobile Ubiquitous Multimedia, 2015, pp. 213-17. 9. M. F. Alam, S. Katsikas, and S. Hadjiefthymiades, “An advanced system architecture for the maintenance work in extreme environment,” in Proc. IEEE Int. Symp. Syst. Eng. (ISSE), Sep. 2015, pp. 406-11. 10. S. Chandra, R. Gupta, S. Ghosh, and S. Mondal, “An intelligent and power efficient biomedical sensor node for wireless cardiovascular health monitoring,” IETE J. Res., pp. 1-1, Mar. 2019. doi: 10.1080/ 03772063.2019.1611489. 11. Preventing Injuries in the Workplace Using Real-Time Safety Monitor-ing Through Wearables and the IoT. Accessed: Jan. 10, 2019. [Online]. Available: https://www.ibm.com/case-studies/nation-waste-inc 12. V. Adjiski, Z. Despodov, D. Mirakovski, and D. Sera-ovski, “System architecture to bring smart personal protective equipment wearables and sensors to transform safety at work in the underground mining industry,” Mining-Geol.-Petroleum Eng. Bull., vol. 34, no. 1, pp. 37-4, 2019. 13. A. Milenkovi¢, C. Otto, and E. Jovanov, “Wireless sensor networks for personal health monitoring: Issues and an implementation,” Comput. Com- mun., vol. 29, nos. 13-4, pp. 2521-2533, 2006. 14. V. Adjiski, Z. Despodov, D. Mirakovski, and D. Sera-movski, “System architecture to bring smart personal protective equipment wearables and sensors to transform safety at work in the underground mining industry,” Mining-Geol.-Petroleum Eng. Bull., vol. 34, no. 1, pp. 37-44, 2019. 15. P. S. Wilhelm and M. Reza, “Survey on a smart health monitoring system based on context awareness sensing,” Commun. CCISA, vol. 25, pp. 1-13,feb,2019. Authors: Suhail Khaliq, R.R Ghadge

Paper Title: Fatigue Behavior of Epoxy Bonded Composite Lap Joints. Abstract: Laminated composites are rapidly finding uses in different engineering applications like Aviation, Sports equipment, Marine technology, Aerospace and Electronics. This is due to their versatile engineering properties. Bolting and Bonding are the widely used joining techniques in composites. However bonding has added advantages over bolting in terms of weight reduction and strength .This paper explores the fatigue strength of single lap epoxy bonded joint between composite laminates. The experimental results are compared with FEA (Finite Element Analysis) results.

23. Keywords: Adhesive, Composite, Epoxy adhesive, Fatigue, FEA

References: 112-114

1. Ismail Sarac et al. Experimental determination of the static and fatigue strength of the adhesive joints bonded by epoxy adhesive including different particles, Composites Part B, 155 (2018) 92-103 2. Dong Quan et al. Mechanical and fracture properties of epoxy adhesives modified with graphene nanoplatelets, International Journal of Adhesion and Adhesives 81 (2018) 21–29 3. Zhemin Jia et al. Graphene Reinforced Epoxy Adhesive For Fracture Resistance, Composites Part B 155 (2018) 457–462 4. M.M. Shokrieh et al. Mechanical Properties of Graphene/Epoxy Nanocomposites under Static and Flexural Fatigue Loadings , Mechanics of Advanced Composite Structures 1 (2014) 1 – 7 5. Swetha Chandrasekaran et al. Fracture toughness and failure mechanism of graphene based epoxy composites, Composites Science and Technology 97 (2014) 90–99 Authors: Mark S Nonghuloo, Nagaraja Rao A

Paper Title: Analyses and Modeling of Neural Machine Translation for English-to-Khasi Abstract: Language barrier is a common issue faced by humans who move from one community or group to another. Statistical machine translation has enabled us to solve this issue to a certain extent, by formulating models to translate text from one language to another. Statistical machine translation has come a long way but they have their limitations in terms of translating words that belongs to an entirely different context that is not available in the training dataset. This has paved way for neural Machine Translation (NMT), a deep learning approach in solving sequence to sequence translation. Khasi is a language popularly spoken in Meghalaya, a north-east state in India. Its wide and unexplored. In this paper we will discuss about the modeling and analyzing of a NMT base model and a NMT model using Attention mechanism for English to Khasi.

Keywords: Deep Learning, Recurrent Neural Network, LSTM, Neural Machine Translation, Semi-Supervised Machine Learning.

References: 24. 1. Hwee Tou Ng and Hian Beng Lee. 1996. Integrating multiple knowledge sources to disambiguate word sense: An exemplar- based approach. In Proceedings of the 34th Annual Meeting of the Association for Computational Linguistics, pages 40–47 115-118 2. Rie Kubota Ando and Tong Zhang. 2005. A framework for learning predictive structures from multiple tasks and unlabeled data. Journal of Machine Learning Research, 6:1817–1853 3. Rie Kubota Ando. 2006. Applying alternating structure optimization to word sense disambiguation. In Proceedings of the Tenth Conference on Computational Natural Language Learning, pages 77–84. 4. Sonali. B. Maind, Priyanka Wankar,Research Paper on Basic of Artificial Neural Network, International Journal on Recent and Innovation Trends in Computing and Communication, 2014, Vol 2, Issue 1 5. Joseph Turian, Lev-Arie Ratinov, and Yoshua Bengio, Word representations: A simple and general method for semi-supervised learning. In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics,. 2010, pages 384–394 6. Martens, J. Deep learning via Hessian-free optimization. In Proceedings of the 27th International Conference on Machine Learning (ICML). ICML 2010, 2010. 7. R. Collobert and J. Weston. A Unified Architecture for Natural Language Processing: Deep Neural Networks with Multitask Learning. In International Conference on Machine Learning, ICML, 2008. 8. Andriy Mnih and Geoffrey E. Hinton, A scalable hierarchical distributed language model. In Advances in Neural Information Processing Systems 21, . 2010, pages 1081–1088. 9. Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. Efficient estimation of word representations in vector space. In Proceedings of Workshop at International Conference on Learning Representations, 2013a. 10. Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeffrey Dean, Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems 26,2013b, pages 3111–3119 11. Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation, 2016. Authors: Mohammed Inayathulla, PA Hima Kiran, M Chandana Sri, M Deepika

Paper Title: A Heuristic Model for Predicting Human Fall Detection using Machine Learning Techniques Abstract: It is very obvious that human fall due to unconsciousness is a very common health problem in every 25. human being. With the evolution of many smart health devices, we should contribute the technological advancement of machine learning into it. Different techniques are already used in order to detect human fall 119-121 detection in human beings. In this paper we have studied the patterns of falling of human through the fall detection dataset while this human was performing various motions. By understanding all these we have generated the prediction protocol which estimates the fall of a person using fall detection dataset. Machine Learning classifiers were used to predict the human fall and a comparative study of various algorithms used was developed to find out the best classifier.

Keywords: Classification, Fall Prediction, Machine Learning, Random Forest.

References:

1. G M Basavaraj, Ashok Kusagur “Vision Based Surveillance System for Detection of Human Fall”, 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT) 2017. 2. Zhen-Peng Bian, ,Junhui Hou, ,Lap-Pui Chau, Nadia Magnenat-Thalmann, “Fall Detection Based on Body Part Tracking Usinga Depth Camera”, IEEE Journal Of Biomedical And Health Informatics 2014. 3. Subhash Chand Agrawal, Rajesh Kumar Tripathi, Anand Singh Jalal, “Human-fall Detection from an Indoor Video Surveillance”, 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT) 2017. 4. Kun Wang, Guitao Cao, Dan Meng, Weiting Chen, Wenming Cao, “Automatic fall detection of human in video using combination of features”, IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 2016. 5. Pranesh Vallabh, Reza Malekian, Ning Ye, Dijana Capeska Bogatinoska, “Fall Detection Using Machine Learning Algorithms”, 24th International Conference on Software, Telecommunications and Computer Networks (SoftCOM) 2016. 6. Tran Tri, Hai Truong, Tran Khanh, “Automatic Fall Detection System of Unsupervised Elderly People Using Smartphone “,International Journal of Advanced Computer Science and Applications, Dec 2016. 7. Jia-Luen Chua, Yoong Choon Chang, Wee Keong Lim, :A simple vision-based fall detection technique for indoor video surveillance”, Springer-Verlag, Vol. 9, 2015. 8. Takumi Kaneko, Meifen Cao, “Human fall detection using CHLAC features with skeletal image sequences”, IEEE International Symposium on Robotics and Intelligent Sensors (IRIS) 2016. 9. Anahita Shojaei-Hashemi , Panos Nasiopoulos , James J. Little , Mahsa T. Pourazad, “Video-based Human Fall Detection in Smart Homes Using Deep Learning”, IEEE International Symposium on Circuits and Systems (ISCAS) 2018. 10. Weidong Min , Hao Cui , Hong Rao , Zhixun Li , Leiyue Yao , “Detection of Human Falls on Furniture Using Scene Analysis Based on Deep Learning and Activity Characteristics”, IEEE Access Recent Advantages of Computer Vision based on Chinese Conference on Computer Vision (CCCV) 2017. 11. https://www.kaggle.com/pitasr/falldata Authors: D.Naga Malleswari, Hemalatha Anumolu, Likitha Vampugadawala, Divya Sai Jonnalagadda

Paper Title: Realization of Reliability for Data and Software to Improve Quality Abstract: The goal is to look for code performance metrics. Reliability is an important aspect of any program that cannot be ignored and difficult to measure. "Program reliability is defined as the probability of running programs without disruption in a specific environment for a specified period of time." The reliability of the technology differs from the performance of the hardware. Program reliability is difficult because the complexity of the program is high. Different methods can be used to increase system performance, but it is difficult to balance development time, budget, and software quality. But the best way to ensure technology consistency is to build high-quality programs throughout the life cycle of the program. We will discuss software reliability metrics in this paper. Metrics used early on can help detect and correct defects of requirements that will prevent 26. program lifecycle errors later. It also provides consistency quality of the information system database with the help of RStudio, and we can also illustrate reliability based on the value of cyclomatic complexity and we can say whether the data or software is more reliable, less reliable or somewhat reliable. 122-125

Keywords: Reliability, Consistency Quality, Cyclomatic Complexity.

References:

1. Capucci, Federico, and Vijaygauri Gumaste.” Test Case Writing (Creation)” Test University. 2. UTest, 16 Oct. 2013. Web. 17 Mar. 2014. http://help.utest.com/testers/crash-courses/functional/test-casewriting-creation-101 Rosenberg, Linda H., PhD, Theodore F. Hammer, and Lenore L. Huffman. 3. Essential QA Metrics for Determining Solution Quality White paper. Web. 17 march. 2014 4. Reporting Defect Trends and Status. Tool Mentor. Rational Software, 1998. Web. 17 Mar.2014. 5. http://www.interface.ru/rational/rup51/toolment/clearquest/tm_repdd.htm Hoffman, Authors: Nanda M B, Madhura K, Chathurya K, Laxmi Tripathi

Paper Title: IoT-Based Air Quality and Sound Intensity Monitoring System using Raspberry Pi Abstract: In day to day life, the increase in Air and Sound pollution has become a distressing problem. It has now become a vital issue that is to be considered. To overcome this problem, an IoT based system to monitor the pollution levels constantly has been proposed. Nowadays Internet of things (IoT) is one of the most widely used and researched technology to monitor the environmental changes. It gives an innovative approach where various devices can be connected together with the use of the internet. By interconnecting different objects located at 27. different locations, we can collectively analyze the data at a single place. This feature is useful in data analytics. Raspberry Pi mini-computer is used to collect different data from different sensors and this data is monitored. In 126-130 our proposed system we are using four different modules namely Air Quality Monitoring System, Sound Intensity Monitoring System, Cloud based Monitoring System, Notification system. These modules are integrated together to monitor the environmental changes. This system can be implemented in remote areas where the bulky equipment cannot be placed. Industrial areas where the pollution levels are high can be constantly monitored and precautionary measures can be implemented if the pollution is more.

Keywords: IoT, Raspberry Pi, Air Quality Monitoring, Sound Intensity Monitoring, Cloud storage.

References:

1. Giovanni B. Fioccola, Raffaele Sommese, Imma Tufano, Roberto Canonico and Giorgio Ventre, “Polluino: An Efficient Cloud- based Management of IoT Devices for Air Quality Monitoring”, IEEE Conference paper, 2016. 2. Anwar Alshamsi, Younas Anwar, Maryam Almulla, Mouza Aldohoori, Nasser Hamad, Mohammed Awad, “Monitoring Pollution: Applying IoT to Create a Smart Environment”, IEEE Conference paper, 2017. 3. Arnab Kumar Saha, Sachet Sircar, Priyasha Chatterjee, Souvik Dutta, Anwesha Mitra, Aishwarya Chatterjee, Soummyo Priyo Chattopadhyay, Himadri Nath Saha, “A Raspberry Pi Controlled Cloud Based Air and Sound Pollution Monitoring System with Temperature and Humidity Sensing”, IEEE Conference paper, 2018. 4. Gagan Parmar, Sagar Lakhani, Manju K. Chattopadhyay, “An IoT Based Low Cost Air Pollution Monitoring System”, IEEE Conference paper, 2017. 5. MingHua yang, SiJie Shao, XiaoYan Wang, “The monitoring system design of harmful gas inside special vehicle”, IEEE Conference paper, 2012. 6. Kiruthika.R, A. Umamakeswari, “Low Cost Pollution Control and Air Quality Monitoring System using Raspberry Pi for Internet of Things”, IEEE Conference paper, 2017. 7. Yung-Chung Tsao1, Bo-Rui Su2, Chin-Tan Lee2 and Chia-Chun Wu, “An Implementation of a Distributed Sound Sensing System to Visualize the Noise Pollution”, IEEE Conference paper, 2017. 8. Xuan Zhao, Siming Zuo, Rami Ghannam, Qammer H. Abbasi and Hadi Heidari, “Design and Implementation of Portable Sensory System for Air Pollution Monitoring”, IEEE Conference paper, 2018. 9. V.Sandeep, K.Lalith Gopal, S.Naveen, A.Amudhan, L. S. Kumar, “Globally Accessible Machine Automation Using Raspberry Pi” , IEEE Conference paper, 2015. 10. Md.Shaedul Islam, “An Intelligent System on Environment Quality Remote Monitoring and Cloud Data Logging Using Internet of Things (IoT)”, IEEE Conference paper, 2018. Authors: S. Southamirajan, D. Dhavashankar, K. Anbarasi, P. Kanaka, R. Jegan

Paper Title: Scrutinization of Flexural Practices of Light Weight Concrete by using Sisal Fibres and Bamboo Abstract: Lightweight concrete is the way to reduce the weight as well as deflection in concrete members without affecting its properties. Many of the researches are in progress to find a substitute for this lightweight material. In this project, we would like to take the naturally available fibre named sisal fibre and bamboo as partial replacement material. The influence of sisal fibres on the strength of concrete is taken as the main objective of this experimental study. The addition of natural fibre to the lightweight concrete will enhance the various strength parameters like flexural strength, compressive strength, and increase the ductile behaviour. In the present work, it is aimed to investigate the mechanical properties of lightweight concrete with a replacement of sisal fibre for cement and bamboo as a replacement in coarse aggregate in different percentages. The compressive strength, flexural strength, deflection of the beam is studied with consideration of M25 concrete specimens. Totally 36 number of 500 x 100 x 100mm flexural member cast and tested. It is recommended up to 5% replacement of coarse aggregate with bamboo and 5% addition of sisal fibres with cement provide at M25 grade of concrete gives the optimum increases of strength values. The test results indicated that the sisal fibres were effective in improving the strength of lightweight concrete.

Keywords: Natural fibre, Sisal fibre, light weight concrete, mechanical properties, compressive strength and flexural strength.

28. References: 1. Venkateshwaran.S, Kalaiyarrasi. A. R. R(2018), "Sisal Fiber Reinforced Concrete "Journal of Emerging Technologies and 131-135 Innovative Research,volume 5, issue 6, ISSN No:2349-5162, Page No:65-69 2. Md. Azhar Hoda and Premit Kumar Patil (2018), ‘Performance Evaluation of Concrete by using Sisal Fibre and Bamboo Fibre’, International Journal of Engineering and Management Research, Volume-8, Issue-3, June 2018, Issn (Online): 2250-0758, Issn (Print): 2394-6962, Page Number: 177-180 3. M. Alajmi,and A. Alajmi(2017), "Insulation Characteristics of Sisal Fibre/Epoxy Composites",International Journal of Polymer Science, Volume 2017, Article ID 7312609, Pages :6 4. Sathish Kumar, R. (2012) carried out ‘Experimental study on the properties of concrete made with alternate construction materials’, International Journal of Modern Engineering Research (IJMER) Vol. 2, Issue 5, pp3006-3012 5. K.T. Radha Sumithra, ABS Dadapheer, “Experimental Investigation On The Propreties Of Sisal Fibre Reinforced Concrete”, International Research Journal of Engineering and Technology (IRJET), e-ISSN: 2395 -0056, Volume: 04, Issue: 04 , Apr - 2017,Pg No: 2774-2777. 6. Abdul Rahuman, SaikumarYeshika, (2015) ‘Study on Properties of Sisal Fibre Reinforced Concrete with Different Mix Proportions and Different Percentage of Fibre Addition’, IJRET eISSN: 2319-1163, PISSN: 2321-730 Volume: 04 Issue: 03. 7. Dhanasekar, K. Manikandan, R. Ancil, R. Venkat raman, R. and Selva surrender, P. ‘Strength and Durability Evaluation of Sisal Fibre Reinforced Concrete’ International Journal of Civil Engineering and Technology (IJCIET) (2017), Volume 8, Issue 9, pp. 741–748. 8. Vivek Kumar Shrivastava (2017) ‘Sisal Fibre Behavior as Reinforcement in Composites’ Journal of Basic and Applied Engineering Research’ Volume 4, Issue 2; pp. 172-175. 9. Gollapalle Priyankarani, Dr .P. Srichandana, Ph.D, “Experimental study on Effects of sisal fibre reidnforced concrete”International Journal & Magazine of Engineering, Technology, Management and Research (2015), ISSN No: 2348- 4845, Volume No:2 (2015), Issue no: 3(march) pg 388-392. 10. Yashwant S Munde, Ravindra B Ingle and et.al(2019), “Effect of sisal fiber loading on mechanical, morphological and thermal properties of extruded polypropylene composites” Volume 6, Number 8, Authors: Saravanan A, Kishore S, Gowtham A, Mathan S

29. Paper Title: Design and Fabrication of Raisin dryer using Solar Powered Air Blower Abstract: This project describes the drying of grapes using a solar powered air blower and a heating module 136-138 (peltier chip) .The preparation and maintenance of grapes has been considered to be a major complex issue for a long time. In order to achieve more product and higher marks and achieve customer satisfaction more attention is given to quality features. Quality factors including color, size, taste were very important as they would differ from the dehydration process. This project is used to reduce drying time by using solar energy. Solar power plants are important in the tropics, which face challenges in accessing electricity, which severely limits the refrigeration usage as storage of agricultural products is limited, and the need to make products competitive overseas. In this project, the solar energy used to dry food is described; it is thought that hot weather conditions are favorable during the summer season. A DC drive for suction fan operation is used to send atmospheric air into the system and the air temperature is increased using a peltier chip placed in the air. DC's utility system is powered by a battery charged with the help of solar panels and electricity supply. It works well that the design will be able to shorten the final product time rather than the traditional method.

Keywords: Grapes drying, Raisin drying, Solar drying.

References:

1. Adiletta G, Senadeera W, Liguori L, Crescitelli A, Albanese D, Russo P. (2015). The influence of abrasive pretreatment on hot air drying of grape. Food and Nutrition Sciences, 6(3), 355–364. 2. Ayensu A., Dehydration of Food Crops Using Solar Dryer with Convective Heat Flow, 2000, Research of Department of Physics, University of Cape Coast, Ghana. 3. Boyer, R. Huff, Karleigh. Using Dehydration to Preserve Fruits, Vegetables, and Meats. Virginia Tech, Virginia state University, pp 348-597.,April 2008. 4. Gabas, A.L.; Menegalli, F.C.; Telis-Romero, J. Effect of chemical pretreatment on the physical properties of dehydrated grapes. Drying Technology 1999, 17(6), 1215–1226. 5. Garg & Prakash, H. P. Garg, “Solar energy: fundamentals and applications”, Tata McGraw-Hill Education, 2000. ijstr@2019 6. Hosseinpour, S.; Rafiee, S.; Mohtasebi, S.S. Application of image processing to analyze shrinkage and shape changes of shrimp batchduring drying. Drying Technology 2011, 29(12), 1416–1438. 7. Jairaj KS, Singh SP, Srikant K. (2009). A review of solar dryers developed for grape drying. Solar Energy, 83(9), 1698–1712. 8. Olaleye D.O., The Design and Construction of a Solar Incubator, 2008, Project Report, submitted to Department of Mechanical Engineering, University of Agriculture, Abeokuta 9. Olayinka ADUNOLA, Design and construction of a Domestic Passive Solar Food Dryer,2014, department of mechanical, Nigeria 10. Ong K.S, Results of investigation into forced convection and natural solar heater and dryers.Reg J Energy Heat and Mass Transfer, 1982, 4 (1),pp. 29-45. 11. Patel, A; Shah, S.A; Bhargav, Hitesh. Review on Solar Dryer for Grains, Vegetables and Fruits. Birla Vishvakarma Mahavidyalaya Engineering College. International Journal of Engineering Research & Technology (IJERT), Vol. 2 Issue 1, January- 2013 12. Ravi Hosamani, Dr.Satish.R.Desai “Solar Based Temperature Controlled Fruit Drying System”, International Journal of Research in Instrumentation Engineering (IJRAIE), Vol. 1, Issue. 2, Sep. 2013. 13. Sharma K, A. Colangelo & G. Spagna , "Experimental Performance Of Indirect Type Solar Fruit and Vegetable Dryer", ENEA- C. R. E. Trisaia, Italy 14. Sharma VK, Sharma S, Ray RA, Garg HP. (1986). Design and performance studies of a solar dryer suitable for rural applications. Energy Conversion and Management, 26(1), 111–119. 15. Sukhatme S.P., Solar-Energy-Principles of Thermal Collection and Storage, Tata McGraw Hill Publishing Company Limited, 1996. 16. Thool V R , R.C.Thool , W.S.Khandlikar , A. G.More “Development of Microcontroller Based Grape Dryer” Iaald Afita Wcca2008 World Conference On Agricultural Information And IT . Authors: Pankaj J. Gandhi, Prasit G. Agnihotri The use of Metaheuristic Algorithms in Early Prediction and Forecasting of Flood – A Use of Paper Title: Cuckoo Search Algorithm based Optimization for Flood Controlling Abstract: Flood is one of the disasters which have multiple impacts on the society and industry. It has severe impacts on the urban economy and has forced the scholars to develop resiliency plans. Various types of flood forecasting techniques developed by the scholars and have certain limitations. There are various types of multiple modeling techniques which are being used for flood controlling and each has certain limitations. The optimization techniques along with the artificial intelligence algorithms can be helpful for monitoring and early prediction of flood. The neural network models promises better accuracy compared to convention models for prediction, but they face great difficulties in selection of appropriate model parameters. In the said context, here an effort has been made to explore the importance of Cuckoo theorem in flood management. The cuckoo search algorithm can be used for parameter tuning. The hybrid approach of using cuckoo search algorithm with neural networks has given far better accuracy compared to standalone algorithms. The use of such Cuckoo Search 30. Metaheuristic algorithm will help us to predict early warning system than any other method and helps us to align the flood controlling activities. The paper presents the used of variants of cuckoo search algorithm for early flood prediction. The paper unfolds major insights of flood scenarios along with the significance of flood control 139-143 and monitoring.

Keywords: Flood control, Cuckoo Algorithm, Modelling, Optimization.

References:

1. R. Brown, H. Chanson, Madhani Jai, And D. M. And, “Turbulent Velocity And Suspended Sediment Concentration Measurements in an Urban Environment of the Brisbane River Flood Plain at Gardens Point On 12-13 January 2011,” 2011. 2. A. Aggarwal, F. Rafique, E. Rajesh, and S. Ahmed, “Urban flood hazard mapping using change detection on wetness transformed images,” Hydrol. Sci. J., vol. 61, no. 5, pp. 816–825, 2016, doi: 10.1080/02626667.2014.952638. 3. A. Bhat, G. K.; Raghupathi, U.; Rajasekar, U.; Karanath, “Urbanization – Poverty – Climate Change. A synthesis Report – India,” Gurgaon, Haryana, India, 2013. 4. A. K. Biswas, U. and Saklani, and C. Tortajada, “Truth about urban flooding: Cities like Mumbai get inundated regularly due to administrative apathy, not climate change,” Times of India, Mumbai Edition, Aug. 31, 2017. 5. R. von Glasow et al., “Megacities and Large Urban Agglomerations in the Coastal Zone: Interactions Between Atmosphere, Land, and Marine Ecosystems,” Ambio, vol. 42, no. 1, pp. 13–28, 2013, doi: 10.1007/s13280-012-0343-9. 6. R. Dhiman, R. VishnuRadhan, T. I. Eldho, and A. Inamdar, “Flood risk and adaptation in Indian coastal cities: recent scenarios,” Appl. Water Sci., vol. 9, no. 1, pp. 1–16, 2019, doi: 10.1007/s13201-018-0881-9. 7. M. J. Hammond, A. S. Chen, S. Djordjević, D. Butler, and O. Mark, “Urban flood impact assessment: A state-of-the-art review,” Urban Water J., vol. 12, no. 1, pp. 14–29, 2015, doi: 10.1080/1573062X.2013.857421. 8. A. M. Dewan, M. M. Islam, T. Kumamoto, and M. Nishigaki, “Evaluating Flood Hazard for Land-Use Planning in Greater Dhaka of Bangladesh Using Remote Sensing and GIS Techniques,” Water Resour. Manag., vol. 21, no. 9, pp. 1601–1612, 2007, doi: 10.1007/s11269-006-9116-1. 9. R. K. Waghwala and P. G. Agnihotri, “Flood risk assessment and resilience strategies for flood risk management: A case study of Surat City,” Int. J. Disaster Risk Reduct., vol. 40, p. 101155, 2019, doi: https://doi.org/10.1016/j.ijdrr.2019.101155. 10. A. Sangomla, “India witnessed extreme weather events every month in 2018,” 2019. 11. P. P. Mujumdar, “Flood Wave Propagation - The Saint Venant Equations,” RESONANCE, 2001. 12. P. M and P. Rk, “Afully mass conservative variable parameter McCarthy-Muskingum method: Theory and verification,” J. Hydrol., pp. 89–102, 2013, doi: https:// www.sciencedirect.com/science/article/pii/S002216941300 601X. 13. P. M and B. Sahoo, “Applicability criteria of the variable parameter Muskingum stage and discharge routing methods,” Water Resoures, 2007, doi: 10.1029/ 2006WR00490. 14. D. D. Potphode, A. Gangadharan, and C. S. Sharma, “Carbon Soot for Electrochemical Energy Storage Applications,” Proc. Indian Natl. Sci. Acad., no. 4, pp. 705–722, 2019, doi: 10.16943/ptinsa/2019/49648. 15. Guru N and R Jha, “Flood Frequency Analysis of Tel Basin of Mahanadi River System, India using Annual Maximum and POT Flood Data,” Aquat. Procedia, pp. 427–434, 2015. 16. M. S. Kamal V et al., “Flood frequency analysis of Ganga river at Haridwar and Garhmukteshwar,” Appl Water Sci, pp. 1979– 1986, 2017, doi: 10.1007/s13201-016-0378-3. 17. R. Kumar, C. Chatterjee, S. Kumar, A. K. Lohani, and R. D. Singh, “Development of Regional Flood Frequency Relationships Using L-moments for Middle Ganga Plains Subzone 1(f) of India,” Water Resour. Manag., vol. 17, no. 4, pp. 243–257, 2003, doi: 10.1023/A:1024770124523. 18. B. Basu and V. V Srinivas, “Formulation of a mathematical approach to regional frequency analysis,” Water Resour. Res., vol. 49, no. 10, pp. 6810–6833, 2013, doi: 10.1002/wrcr.20540. 19. X. S. Yang and S. Deb, “Cuckoo search via Lévy flights,” 2009 World Congr. Nat. Biol. Inspired Comput. NABIC 2009 - Proc., pp. 210–214, 2009, doi: 10.1109/NABIC.2009.5393690. 20. V. G. Pentapalli, V. K. Varma, and P. Ravi, “Cuckoo Search Optimization and its Applications: A Review,” Int. J. Adv. Res. Comput. Commun. Eng. ISO, vol. 3297, no. 11, pp. 556–562, 2007, doi: 10.17148/IJARCCE.2016.511119. 21. A. B. Mohamad, A. M. Zain, and N. E. N. Bazin, “Cuckoo search algorithm for optimization problems - A literature review and its applications,” Appl. Artif. Intell., vol. 28, no. 5, pp. 419–448, 2014, doi: 10.1080/08839514.2014.904599. 22. S. Z. Abu Bakar, R. Ghazali, L. H. Ismail, T. Herawan, and A. Lasisi, “Implementation of Modified Cuckoo Search Algorithm on Functional Link Neural Network for Climate Change Prediction via Temperature and Ozone Data,” in Recent Advances on Soft Computing and Data Mining, 2014, pp. 239–247. 23. S. Walton, O. Hassan, K. Morgan, and M. R. Brown, “Modified cuckoo search: A new gradient free optimisation algorithm,” Chaos, Solitons and Fractals, vol. 44, no. 9, pp. 710–718, 2011, doi: 10.1016/j.chaos.2011.06.004. 24. S. Phitakwinai, S. Auephanwiriyakul, and N. Theera-Umpon, “Multilayer perceptron with Cuckoo search in water level prediction for flood forecasting,” Proc. Int. Jt. Conf. Neural Networks, vol. 2016-Octob, no. 1, pp. 519–524, 2016, doi: 10.1109/IJCNN.2016.7727243. 25. S. H. Wood and A. D. Ziegler, “Floodplain sediment from a 30-year-recurrence flood in 2005 of the Ping River in northern Thailand,” Hydrol. Earth Syst. Sci. Discuss., vol. 4, no. 5, pp. 3839–3868, 2007, doi: 10.5194/hessd-4-3839-2007. 26. C. Zhu and X. Ma, “Simulation of flood water level using PSO-based RBF neural network,” 3rd Int. Symp. Intell. Inf. Technol. Appl. IITA 2009, vol. 1, pp. 68–71, 2009, doi: 10.1109/IITA.2009.302. 27. F. S. Zhonghuan Tian, “Survey of Meta-Heuristic Algorithms for Deep Learning Training,” Intech, vol. 1, p. 13, 2016, doi: http://dx.doi.org/10.5772/63785. 28. 28. L. M. Rasdi Rere, M. I. Fanany, and A. M. Arymurthy, “Metaheuristic Algorithms for Convolution Neural Network,” Comput. Intell. Neurosci., vol. 2016, 2016, doi: 10.1155/2016/1537325. Authors: R.K. Behera, S.K.Dhal The Impact of ERP Systems on financial Performance of Central Public Sector Enterprises Working Paper Title: in Mineral and Metal Sector Abstract: The Central Public Sector Enterprises have been performing vital macroeconomic objectives of a country such as economic growth, development of infrastructure, and contribute to the positive market situation. ERP Systems implementation in CPSEs working in mineral and metal sector enhances the financial performance. Financial indicator like Return on Assets, Return on invested Capital, return on equity, and Return on sale have a significant impact on ERP Adopter when it compares with ERP non- adopter working in mineral and metal sector.

Keywords: ERP, ERP System, CPSE, Financial Performance.

31. References:

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Information Systems Management, 32(3), 252–264. 6. Ali, Irfan (2016) The impact of ERP implementation on the financial performance of the firm- Ph.D. Thesis. 7. Ali Mohammad Ghanbari, Leila Soleimani (2017), The Impact of ERP Implementation on Financial Processes: A Case Study, Petroleum Business Review Vol 1 (1) PP 40-48 8. Ali Parto, Saudah Sofian, Maisarah Mohamed Saat (2016), The Impact of Enterprise Resource Planning on Financial Performance in a Developing Country, International Review of Management and Business Research, Vol-5 (1) PP-177-187 9. Bhati, P. S., & Trivedi, M. C. (2016). Applicability and Impact of ERP: A Survey. 2016 Second International Conference on Computational Intelligence & Communication Technology (CICT). doi:10.1109/cict.2016.15 10. 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Empowering benefits of ERP systems implementation: an empirical study of industrial firms. Journal of Systems and Information Technology, 20(1), 54–72. doi:10.1108/jsit-05-2017-0038 34. Saleh, T., & Thoumy, M. (2018). The impact of ERP systems on organizational performance: In Lebanese wholesale engineering companies. 2018 7th International Conference on Industrial Technology and Management (ICITM). 35. Shanab, E. A. A., & Saleh, Z. (2014). Contributions of ERP systems in Jordan. International Journal of Business Information Systems, 15(2), 244. doi:10.1504/ijbis.2014.059255 36. Shari Shang & Peter B Seddon (2002) Assessing and managing the benefits of enterprise systems: the business manager’s perspective, Info Systems J 12, 271–299 37. Singha Chaveesuk, Sitthiros Hongsuwan (2017), A Structural Equation Model of ERP Implementation Success in Thailand, Review of Integrative Business and Economics Research, Vol. 6, Issue 3 Pp194-204 38. Tenhiälä, A., & Helkiö, P. (2015). Performance effects of using an ERP system for manufacturing planning and control under dynamic market requirements. Journal of Operations Management, 36, 147–164. doi:10.1016/j.jom.2014.05.001 39. Trinoverly, Y., Handayani, P. W., & Azzahro, F. (2018). Analyzing The Benefit of ERP Implementation in Developing Country: A State-Owned Company Case Study. 2018 International Conference on Information Management and Technology (ICIMTech). doi:10.1109/icimtech.2018.8528166 40. Velcu, O. (2007). Exploring the effects of ERP systems on organizational performance: Evidence from Finnish companies. Industrial Management & Data Systems, 107(9), 1316-1334. doi: doi:10.1108/02635570710833983 41. Voulgaris, F., Lemonakis, C., & Papoutsakis, M. (2015). The impact of ERP systems on firm performance: the case of Greek enterprises. Global Business and Economics Review, 17(1), 112. doi:10.1504/gber.2015.066536 Authors: Shrabanti Mandal, Girish Kumar Singh

Paper Title: LSA Based Text Summarization Abstract: In this study we propose an automatic single document text summarization technique using Latent Semantic Analysis (LSA) and diversity constraint in combination. The proposed technique uses the query based 32. sentence ranking. Here we are not considering the concept of IR (Information Retrieval) so we generate the query by using the TF-IDF(Term Frequency-Inverse Document Frequency). For producing the query vector, we 150-156 identify the terms having the high IDF. We know that LSA utilizes the vectorial semantics to analyze the relationships between documents in a corpus or between sentences within a document and key terms they carry by producing a list of ideas interconnected to the documents and terms. LSA helps to represent the latent structure of documents. For selecting the sentences from the document Latent Semantic Indexing (LSI) is used. LSI helps to arrange the sentences with its score. Traditionally the highest score sentences have been chosen for summary but here we calculate the diversity between chosen sentences and produce the final summary as a good summary should have maximum level of diversity. The proposed technique is evaluated on OpinosisDataset1.0.

Keywords: Text summarization, LSA, SVD,LSI and diversity constraint.

References:

1. A. Bellaachia, A. Mahajan, “Text Summary Using Latent Semantic Indexing and Information Retrieval Technique: Comparison of Four Strategies”, In EGC 2004, vol. RNTI-E-2, pp.453-464. 2. B. Baldwin and T.S. Morton. Dynamic coreference based summarization. In Proceedings of The Third Conference on Empirical Methods in Natural Language Processing (EMNLP3), Granada, Spain, June 1998. 3. C. Buckley and et al.. The smart/empire tipster ir system. In Proceedings of TIPSTER Phase III Workshop. 1999. 4. D. J. Gillick “The Elements of Automatic Summarization” Electrical Engineering and Computer Sciences, University of California, May 2011. 5. D. McDonald et al., “Using Sentence-Sentence Heuristics to Rank Text Segments in TXTRACTOR, ” MIS Dept., U. of Arizona, Tucson, AZ. 6. D. Oluwajana,” Single-Document summarization using Latent Semantic Analysis”, DOI: 10.13140/RG.2.1.4075.6320. 7. E. Hovy and C-Y. Lin, “automated text summarization and the SUMMARIST system” ,Information Sciences Institute of the University of Southern California, 1998. 8. E.Hovy and C. Lin. Automated text summarization in summarist. In Proceedings of the TIPSTER Workshop, Baltimore, MD, 1998. 9. G. Murray, S. Renals, J. Carletta “Extractive Summarization of Meeting Recordings”, Centre for Speech Technology Research, University of Edinburgh, Scotland, 2005. 10. G. Salton and C. Buckley” Term Weighting Approaches in automatic Text Retrieval”, Department of Computer Science, Cornell University, New York, November 1987. 11. G.W. Furnas, S.C. Deerwester, S.T , Dumais, T.K. Landauer, R.A. Harshman, L.A.Streeter, K.E. Lochbaum, Information retrieval using a singular value decomposition model of latent semantic structure. SIGIR Forum 51(2), 90–105 (2017). 12. H. Daume III and D. Marcu “A Tree-Position Kernel for Document Compression Proceedings of the Document Understanding Conference”, Boston, MA. May 6-7, 2004. 13. H. P. Edmundson “New Methods in Automatic Extracting” Journal of the Association for Computing Machinery, Vol. 16, No. 2, April 1969. 14. H.P. Luhn, The Automatic Creation of Literature Abstracts. in Maybury, M.T. ed. Advances in Automatic Text Summarization. The MIT Press, Cambridge, 1958, 15-22. 15. http://www.SRA.com. 16. J. Goldstain, M. Kantrowitz, V. Mittal and J. Carbonell. Summarizing text documents: Sentence selection and evaluation metrics. In Proceedings of ACM SIGIR ’ 99, Berkeley, CA, Aug 1999. 17. Jezek and Steinberger “Automatic Text Summarization” The State of Art 2008 and the challenges, Bratislava. 2008. 18. K. Knight and M. Daniel “Statistical-Based Summarization One step: Sentence compression”, Information sciences Institute and Department of Computer Sciences University of Southern California, 2002. 19. M. Gülçin Özsoy “Text Summarization Using Latent Semantic Analysis” Graduate School of Natural and Applied Sciences, Middle East Technical University, Ankara.2011. 20. R. Barzilay and M. Elhadad. Using lexical chains for text summarization”, in Proceedings of the Workshop on Intelligent Scalable Text Summarization, Madrid, Spain, Aug. 1997. 21. S. Mandal,G. K. Singh,A. Pal,” PSO Based Text Summarization Approach Using Sentiment Analysis”, Advances in Intelligent Systems and Computing ,Springer ,Vol 810,p.p.- 845-854, 2019, https://doi.org/10.1007/978-981-13-1513-8_86. 22. S. Mandal,G. K. Singh, A. Pal,” Text Summarization Technique by Sentiment Analysis and Cuckoo Search Algorithm”,Advances in Intelligent Systems and Computing, Springer , Vol 1025 ,p.p.- 357-366, 2020. 23. T. Firmin, and M.J. Chrzanowski, An Evaluation of Automatic ,1999. 24. Y. Gong and X. Liu. Generic Text Summarization Using Relevance Measure and latent Semantic Analysis. In Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 19 – 25, 2001. Authors: Gladiss Merline N.R, Vinoth Kumar. D, Hariharan. M, Aswath R.S

Paper Title: E -Patient Deidentified Record Integration using Cloud Computing Abstract: With the development of science and technology, the design of modern architecture is becoming more and more attractive. Now a days the medical fields become more wide development in machinery the same way the data storage also developed higher . The main reason for proposing the paper is to store the patient data into the cloud. The patient can access the data from anywhere at any time. . The delivering of public health solutions can lead to increased efficiency in health related data. Many nations across the globe have launched aggressive stimulus programs aimed at solving public health care problems in efficient way .This paper proposed for maintain the patient health record in cloud computing.

33. Keywords:Cloud computing ,Mobile Application, Server, Website, Mobile Phone, Patients, Doctors, Hospitals

References: 157-160

1. Adeel Akbar Memon, "A New Cloud Computing Solution For Government Hospitals To Better Access Patients Medical Information", American Journal Of System And Software, 2014,Vol.2,No.3,56-59. 2. R. Nithiavathy," Data Integrity And Data Dynamics With Secure Storage Service In Cloud", Department Of Computer Science And Engineering Coimbatore Institute Nad Engineering And Technology, Proceedings Of The 2013 International Conference On Pattern Recognition, Informatics And Mobile Engineering, February 21-22. 3. Yung-Li Hu, "Design of Event-Based Intrusion Detection System on Open Flow Network", Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan,2013 IEEE paper 4. G.J. Edward, “Cloud computing: innovating the business of health care”, Healthcare Financial Management, pp 130-131, May, 2011. 5. J. A. Bowen, “Cloud Computing: Issues in Data Privacy/Security and Commercial Consideration”, Computer & Internet Lawyer, vol. 28, no. 8, pp. 1-8, 2011. 6. Neha Dubey, Sangeeta Vishwakarma, “Cloud Computing in Healthcare”, International Journal of Current Trends in Engineering and Research, E-ISSN 245-1392, Volume2, Issue 5, May-2016, pp.211-216. 7. Ming Li, Shucheng Yu, Yao Zheng, Kui and Wenjing Lou, “Scalable and Secure Sharing of Personal Health Records in Cloud Computing using Attribute Based Encryption”, IEEE Transactions on Parallel and Distributed Systems, Vol:24, No:1 Year 2013. 8. Ning Cao, Cong Wang, Ming Li, Kui Ren, Wenjing Lou, “Privacy-Preserving Multi-keyword Ranked Search over Encrypted Cloud Data”, IEEE Transactions on Parallel and Distributed Systems, Vol:25 No:1, Year 2014. Authors: Manjula. C. Gudgeri , Varsha

Paper Title: Double Domination Number of Some Families of Graph Abstract: In a graph G = (V, E) each vertex is said to dominate every vertex in its closed neighborhood. In a graph G, if S is a subset of V then S is a double dominating set of G if every vertex in V is dominated by at least two vertices in S. The smallest cardinality of a double dominating set is called the double domination number γx2 (G). [4]. In this paper, we computed some relations between double domination number, domination number, number of vertices (n) and maximum degree (Δ) of Helm graph, Friendship graph, Ladder graph, Circular Ladder graph, Barbell graph, Gear graph and Firecracker graph.

Keywords: Dominating set, domination number, double dominating set, double domination number. We denote n, Δ respectively by number of vertices, maximum degree of a graph G. 34. References: 161-164 1. K. Ameenai Bibi and R.Selvakumar, “The Inverse Split and Non-split Domination in Graphs”, International Journal of Computer Applications ( 0975-8887) , Volume 8-No 7.October 2010. 2. Ashaq Ali , Hifza Iqbal , Waqas Nazeer , Shin Min Kang, “On Topological Indices for the line graph of firecracker graph”. International Journal of Pure and Applied Mathematics Volume 116 No. 4 2017, 1035-1042. 3. Ersin Aslan and Alpay Kirlangic, “Computing The Scattering Number and The Toughness for Gear Graphs”, Bulletin of Society of Mathematicans Banja Luka, ISSN 0354-5792, ISSN 1986-521X(o) Vol. 18(2011), 5-15. 4. F. Harary and T.W.Haynes, “Double domination in graphs”, Ars Combis. 55 April(2000) 201-213. 5. T.W.Haynes, S.Teresa. Hedetniemi and P.J.Slater, “Domination in Graphs”: Advanced Topics (Marcel Dekker, New york, (1998). 6. Mustapha. Chellali, Abdelkader Khelladi and Frederic Maffray,” EXACT DOUBLE DOMINATION IN GRAPHS”, Discussiones Mathematicae 291 Graph Theory 25 (2005 ) , Volume :25, Issue:3 , page 291–302, ISSN: 2083-5892 . 7. From Wikipedia, the free encyclopedia https://en.wikipedia.org/wiki/. 8. Wolfram Math World , https://mathworld.wolfram.com/. Authors: Chiranjeevi N, Aditya Shankar Hegde, Ravishankar Holla

Paper Title: Design of Multi-nodal Li-Fi systems Abstract: Now-a-days, the RF spectrum is used for most of the applications and automation. This is leading to the inadequacy of the RF spectrum for the human and machine requirements. As a solution to this crisis, we have proposed the use of Li-Fi (Light-Fidelity) as an alternate mode of communication. Wireless communication inside buildings and indoors is an important part of the next generation wireless communication system and these concepts can be applied to external wireless communication. Li-Fi provides high data rate (Up to 10 Gbps), improved security and high capacity to support more users. The spectrum bandwidth of light is very large, resulting in accommodation of more number users. A hybrid model of Wi-Fi and Li-Fi increases the advantages and applications, resulting in the best of both communication techniques. This paper describes the hardware implementation of Li-Fi based on IR transmitter and receiver and gives an overview of the signal conditioning for the implementation of a Li-Fi based system. This is followed by the simulation of a Multi nodal Li-Fi based system in MATLAB for determining the coverage, received power, SNR and output signal. With half angle as the variable, the coverage of an entire Multi nodal Li-Fi based system along with signal strength and other parameters can be determined. It also proposes a machine learning algorithm for selecting the best channel by considering factors like the received power, varying noise, etc. 35. Keywords: Li-Fi, Li-Fi hardware model, Signal Conditioning, Multi-nodal Li-Fi simulation, SNR, MATLAB, Channel selection, Machine Learning. 165-168

References:

1. H. Haas, L. Yin, Y. Wang and C. Chen, "What is LiFi?," in Journal of Lightwave Technology IEEE, vol. 34, no. 6, pp. 1533- 1544, 15 March 15, 2016 2. S. Kulkarni, A. Darekar and P. Joshi, "A survey on Li-Fi technology," 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET) IEEE, Chennai, 2016, pp. 1624-1625. 3. S. Dimitrov and H. Haas, Principles of LED Light Communications: Towards Networked Li-Fi. Cambridge, U.K.: Cambridge Univ. Press, Mar. 2015. 4. X. Wu, M. Safari and H. Haas, "Access Point Selection for Hybrid Li-Fi and Wi-Fi Networks," in IEEE Transactions on Communications, vol. 65, no. 12, pp. 5375-5385, Dec. 2017. 5. Yaseein Soubhi Hussein and Amresh Chetty Annan, “Li-Fi Technology: High data transmission securely”,IOP Conf. Series: Journal of Physics: Conf. Series 1228 (2019) 012069. 6. D. Neumann, T. Wiese and W. Utschick, "Learning the MMSE Channel Estimator," in IEEE Transactions on Signal Processing, vol. 66, no. 11, pp. 2905-2917, 1 June1, 2018, doi: 10.1109/TSP.2018.2799164 7. T. D. C. Little, P. Dib, K. Shah, N. Barraford, and B. Gallagher. “Using LED Lighting for Ubiquitous Indoor Wireless Networking”. IEEE International Conference on Wireless & Mobile Computing, Networking & Communication, pp. 373-378, 12-14 Oct 2008 8. T. Komine and M. Nakagawa. “Fundamental Analysis for Visible-Light Communication System using LED Lights”. IEEE Trans. on Consumer Electronics, vol. 50, no. 1, pp. 100-107, 2004 9. S. Rajagopal, R. Roberts, and S.-K. Lim, “IEEE 802.15.7 visible light communication: Modulation schemes and dimming support,” IEEE Commun. Mag., vol. 50, no. 3, pp. 72–82, Mar. 2012. 10. D. Tsonev, H. Chun, S. Rajbhandari, J. McKendry, S. Videv, E. Gu, M. Haji, S. Watson, A. Kelly, G. Faulkner, M. Dawson, H. Haas, and D. O’Brien, “A 3-Gb/s single-LED OFDM-based wireless VLC link using a gallium nitride μLED,” IEEE Photon. Technol. Lett., vol. 26, no. 7, pp. 637–640, Apr. 2014. 11. D. A. Basnayaka and H. Haas, “Hybrid RF and VLC systems: Improving user data rate performance of VLC Systems,” in Proc. IEEE 81st Veh. Technol. Conf. (VTC Spring), Glasgow, U.K., May 2015, pp. 1–5 12. Z. Chen, N. Serafimovski, and H. Haas, “Angle diversity for an indoor cellular visible light communication system,” presented at the Vehicular Technology Conf., Seoul, Korea, May 18–21, 2014 13. E. Sarbazi, M. Uysal, M. Abdallah, and K. Qaraqe, “Ray tracing-based channel modeling for visible light communications,” in Proc. 22nd Signal Process. Commun. Appl. Conf., Apr. 2014, pp. 702–705 14. Ahn, K.-I & Kwon, Jae Kyun. (2012). Color Intensity Modulation for Multicolored Visible Light Communications. Photonics Technology Letters, IEEE. 24. 2254-2257. 10.1109/LPT.2012.2226570 15. Y. Wang, X. Wu, and H. Haas, “Distributed load balancing for Internet of Things by using Li-Fi and RF hybrid network,” in Proc. IEEE 26th Annu. Int. Symp. Pers., Indoor, Mobile Radio Commun. (PIMRC), Hong Kong, Aug./Sep. 2015, pp. 1289–1294. 16. X. Li, R. Zhang, and L. Hanzo, “Cooperative load balancing in hybrid visible light communications and WiFi,” IEEE Trans. Commun., vol. 63, no. 4, pp. 1319–1329, Apr. 2015. 17. L. A. Zadeh, “Fuzzy sets,” Inf. Control, vol. 8, no. 3, pp. 338–353, Jun. 1965. [14] H. Burchardt, S. Sinanovic, Z. Bharucha, and H. Haas, “Distributed and autonomous resource and power allocation for wireless networks,” IEEE Trans. Commun., vol. 61, no. 7, pp. 2758–2771, Jul. 2013. 18. E. Perahia and R. Stacey, Next Generation Wireless LANs: 802.11n and 802.11ac. Cambridge, U.K.: Cambridge Univ. Press, 2013 19. Z. Chen, D. Tsonev, and H. Haas, “A novel double-source cell configuration for indoor optical attocell networks,” presented at the Global Telecommunication Conf., Austin, TX, USA, Dec. 8–12, 2014. 20. S. Rajbhandari, H. Chun, G. Faulkner, K. Cameron, A. V. N. Jalajakumari, R. Henderson, D. Tsonev, M. Ijaz, Z. Chen, H. Haas, E. Xie, J. J. D. McKendry, J. Herrnsdorf, E. Gu, M. D. Dawson, and D. O’Brien, IEEE Proof 12 JOURNAL OF LIGHTWAVE TECHNOLOGY “High-speed integrated visible light communication system: Device constraints and design considerations,” IEEE J. Sel. Areas Commun., vol. 33, no. 9, pp. 1750–1757, Sep. 2015. Authors: Samrin Fatema, Abhisek Chakrabarty Accident Hotspot Identification on the Midnapore Kharagpur Development Authority Planning Paper Title: Area. Abstract: Road accidents are a vital problem in our country for various reasons. According to WHO reports, approximately 1.25 million people died each year, and more than 50 million people injured in road accidents all over the world. Road accident is mostly human-made, and it's affecting your life negatively. Regarding, many studies or research has been performed to reduce road accident and identify the accident blackspot. This paper represents a methodology to find out the accident-prone zone, estimation of Kernel Density and black Site & black Spot identification of major roads Medinipur and Kharagpur development Authority (MKDA) planning area using of Geographical Information Systems (GIS). For this study, road accident data collected from Paschim Medinipur Kotwali Police station from 2016 to 2019. A kernel density estimation was created to identify black spots & black sites of the study area. Based on the result, suggestions are provided to improve the situation in the future.

Keywords: Hotspot, Black site, GIS, Accident Analysis, Kernel Density.

References:

1. Abd Kudus, and Zaini, 1998, Analisa Kecelakaan Lalu lintas Di Propinsi Riau (Studi Kasus Pada Ruas Jalan Rimbo Panjang – Bangkinang), Unpublished report, pp. 7-12 2. Anchan, S. S., Basavaraja, N. H. and Bhat, H. G. (2018) ‘Identification and Analysis of Accident Black Spots along the Selected 36. Stretches of NH-75 Using Remote Sensing & GIS Technology’, (October 2015). 3. Anderson, T.K. 2009. Kernel density estimation and K-means clustering to profile road accident hotspots, Accident Analysis & Prevention 41(3): 359-364. 4. Chainey, S.; Ratcliffe, J. 2013. GIS and crime mapping. John Wiley & Sons. USA. 442 p. 169-174 5. Edisantoni. 2012, Karakteristik Kecelakaan dan Audit Keselamatan Jalan pada Ruas Jalan Kaharudin Nasution Pekanbaru. Universitas Islam Riau, Unpublished report, pp. 22-32. 6. Fotheringham, A.S.; Brunsdon, C.; Charlton, M., 2000. Quantitative Geography: Perspectives on Spatial Data Analysis. SAGE Publications. Ireland. 267 p. 7. Gattis, J. L., Alguire, M. S. and Narla, S. R. K. (1996) ‘Guardrail end-types, vehicle weights, and accident severities’, Journal of Transportation Engineering, 122(3), pp. 210–214. doi: 10.1061/(ASCE)0733-947X(1996)122:3(210). 8. Ghosh, S. K., Parida, M. and Uraon, J. K. (2004) ‘Traffic Accident Analysis for Dehradun City Using GIS’, ITPI Journal, 1(3), pp. 40–54. Available at: http://itpi.org.in/pdfs/july2004/chapter6.pdf. 9. Homburger, Carter, E.C, 1978, Introduction to Transportation Engineering, Preston, Publishing Company Inc, Virginia, USA, pp. 13-35 10. K, D. J. and Ganeshkumar, B. (2010) ‘Identification of Accident Hot Spots : A GIS Based Implementation for Kannur District , Kerala’, 1(1), pp. 51–59. 11. Pusdiklat Perhubungan Darat, 1998. Perhubungan Darat dalam Angka, 2011, Land Transportation in Figure, 2011, Kementrian Perhubungan Direktorat Jendral Perhubungan Darat, Jakarta, pp. 10 -1 12. Raut, U. M., Nalawade, D. B. and Kale, K. V (2016) ‘Mapping and Analysis of Accident Black Spot in Aurangabad City using Geographic Information System’, International Journal of Advanced Research in Computer Science and Software Engineering, 6(1), pp. 511–518. 13. Sandhyavitri, A. and Wiyono, S. (2017) ‘ScienceDirect ScienceDirect Three Strategies Reducing Accident Rates at Black Spots and Black Three Strategies Reducing Accident Rates at Black Spots and Black Sites Road in Riau Province , Indonesia Sites Road in Riau Province , Indonesia’, Transportation Research Procedia. Elsevier B.V., 25, pp. 2153–2166. doi: 10.1016/j.trpro.2017.05.415. 14. Shad, R. and Rahimi, S. (2017) ‘Identification of Road Crash Black-Sites Using Geographical Information System’, International Journal for Traffic and Transport Engineering, 7(3), pp. 368–380.doi: 10.7708/ijtte.2017.7(3).07. 15. Sketch of Kernel Density Method which is Employed in GIS Processing for this Research Source:(Bailey and Gatrell, 1995) 16. Sun, Y.; Chang, H.; Miao, Z.; Zhong, D. 2012. Solution method of overtopping risk model for earth dams, Safety science 50(9): 1906-1912. 17. Transport Department Government of West Bengal (2016) Road Safety – SAFE DRIVE SAVE LIFE. Available at: http://transport.wb.gov.in/references/road-safety/. Authors: Debajit Datta, Dheeba J

Paper Title: Exploration of Various Attacks and Security Measures Related to the Internet of Things Abstract: In this era of technological advances, it will be impractical to think of a day without the usage of gadgets. Development and popularity of the Internet of Things have helped mankind a lot in several ways, but at the same time, there has also been an increase in attacks invading the underlying security. Advances in studies have resulted in the development of evolved algorithms that can be used in order to reduce the attacks and threats to the Internet of Things. With several advancements in studies and research works, the security measures on various Internet of Things based components and protocols are developing with time, but concurrently more advanced threats and attacks on these components are also evolving. These attacks are not only harmful to the components, but rather they also affect the users and applications that are associated with it, by breaching data, increase in inconsistency and inaccuracy, and many more. This work deals with the study of several attacks that are associated with the Internet of Things and also the approaches to secure the IoT systems. This work will also help in coming up with further up-gradation in the currently implemented security protocols and also in predicting the possible attacks that can be developed against the IoT protocols.

Keywords: Internet of Things, Security Issue, Cyber Attacks, Protocol, Privacy, Security Measure.

References:

1. Mosenia, Arsalan, and Niraj K. Jha. "A comprehensive study of security of internet-of-things." IEEE Transactions on Emerging Topics in Computing 5.4 (2016): 586-602. 2. Kumar, Sathish Alampalayam, Tyler Vealey, and Harshit Srivastava. "Security in internet of things: Challenges, solutions and future directions." 2016 49th Hawaii International Conference on System Sciences (HICSS). IEEE, 2016. 3. Oracevic, Alma, Selma Dilek, and Suat Ozdemir. "Security in internet of things: A survey." 2017 International Symposium on Networks, Computers and Communications (ISNCC). IEEE, 2017. 4. Nandy, Tarak, et al. "Review on Security of Internet of Things Authentication Mechanism." IEEE Access 7 (2019): 151054- 151089. 5. Adat, Vipindev, and B. B. Gupta. "Security in Internet of Things: issues, challenges, taxonomy, and architecture." Telecommunication Systems 67.3 (2018): 423-441. 6. Li, Fangyu, et al. "Enhanced cyber-physical security in internet of things through energy auditing." IEEE Internet of Things Journal 6.3 (2019): 5224-5231. 7. Zhang, Ning, et al. "RAV: Relay aided vectorized secure transmission in physical layer security for Internet of things under active 37. attacks." IEEE Internet of Things Journal 6.5 (2019): 8496-8506. 8. Harbi, Yasmine, et al. "A review of security in internet of things." Wireless Personal Communications 108.1 (2019): 325-344. 9. Tahsien, Syeda Manjia, Hadis Karimipour, and Petros Spachos. "Machine learning based solutions for security of Internet of 175-184 Things (IoT): A survey." Journal of Network and Computer Applications (2020): 102630. 10. Ghasemi, Mohsen, Mohammad Saadaat, and Omid Ghollasi. "Threats of social engineering attacks against security of Internet of Things (IoT)." Fundamental Research in Electrical Engineering. Springer, Singapore, 2019. 957-968. 11. Reddy, Vijender Busi, et al. "A Similarity based Trust Model to Mitigate Badmouthing Attacks in Internet of Things (IoT)." 2019 IEEE 5th World Forum on Internet of Things (WF-IoT). IEEE, 2019. 12. Valente, Junia, Matthew A. Wynn, and Alvaro A. Cardenas. "Stealing, spying, and abusing: Consequences of attacks on internet of things devices." IEEE Security & Privacy 17.5 (2019): 10-21. 13. Ali, Inayat, Sonia Sabir, and Zahid Ullah. "Internet of things security, device authentication and access control: a review." arXiv preprint arXiv:1901.07309 (2019). 14. Shim, Kyung-Ah. "Universal Forgery Attacks on Remote Authentication Schemes for Wireless Body Area Networks Based on Internet of Things." IEEE Internet of Things Journal 6.5 (2019): 9211-9212. 15. Doshi, Rohan, Noah Apthorpe, and Nick Feamster. "Machine learning ddos detection for consumer internet of things devices." 2018 IEEE Security and Privacy Workshops (SPW). IEEE, 2018. 16. Kumar, Sathish Alampalayam, Tyler Vealey, and Harshit Srivastava. "Security in internet of things: Challenges, solutions and future directions." 2016 49th Hawaii International Conference on System Sciences (HICSS). IEEE, 2016. 17. Yin, Da, Lianming Zhang, and Kun Yang. "A DDoS attack detection and mitigation with software-defined Internet of Things framework." IEEE Access 6 (2018): 24694-24705. 18. Chahid, Yassine, Mohamed Benabdellah, and Abdelmalek Azizi. "Internet of things security." 2017 International Conference on Wireless Technologies, Embedded and Intelligent Systems (WITS). IEEE, 2017. 19. Deogirikar, Jyoti, and Amarsinh Vidhate. "Security attacks in IoT: A survey." 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC). IEEE, 2017. 20. Ahmed, M. Ejaz, and Hyoungshick Kim. "DDoS attack mitigation in Internet of Things using software defined networking." 2017 IEEE third international conference on big data computing service and applications (BigDataService). IEEE, 2017. 21. Sha, Kewei, et al. "On security challenges and open issues in Internet of Things." Future Generation Computer Systems 83 (2018): 326-337. 22. Mishra, Alekha Kumar, et al. "Analytical model for sybil attack phases in internet of things." IEEE Internet of Things Journal 6.1 (2018): 379-387. 23. Shah, Sameena, Syed Suhail A. Simnani, and M. Tariq Banday. "A Study of Security Attacks on Internet of Things and Its Possible Solutions." 2018 International Conference on Automation and Computational Engineering (ICACE). IEEE, 2018. 24. Yan, Qiao, et al. "A multi-level DDoS mitigation framework for the industrial internet of things." IEEE Communications Magazine 56.2 (2018): 30-36. 25. Ronen, Eyal, and Adi Shamir. "Extended functionality attacks on IoT devices: The case of smart lights." 2016 IEEE European Symposium on Security and Privacy (EuroS&P). IEEE, 2016. 26. Parker, Donn B. Fighting computer crime. New York, NY: Scribner, 1983. 27. Lonzetta, Angela M., et al. "Security vulnerabilities in Bluetooth technology as used in IoT." Journal of Sensor and Actuator Networks 7.3 (2018): 28. 28. Wang, Yongkang, Chunxia Chen, and Qijie Jiang. "Security algorithm of internet of things based on ZigBee protocol." Cluster Computing 22.6 (2019): 14759-14766. 29. Noura, Mahda, Mohammed Atiquzzaman, and Martin Gaedke. "Interoperability in internet of things: Taxonomies and open challenges." Mobile Networks and Applications 24.3 (2019): 796-809. 30. Al-Kashoash, Hayder AA, et al. "Congestion control in wireless sensor and 6LoWPAN networks: toward the Internet of Things." Wireless Networks 25.8 (2019): 4493-4522. 31. Naik, Nitin. "Choice of effective messaging protocols for IoT systems: MQTT, CoAP, AMQP and HTTP." 2017 IEEE international systems engineering symposium (ISSE). IEEE, 2017. 32. Soni, Dipa, and Ashwin Makwana. "A survey on mqtt: a protocol of internet of things (iot)." International Conference On Telecommunication, Power Analysis And Computing Techniques (ICTPACT-2017). 2017. 33. Aijaz, Adnan, Hongjia Su, and Abdol-Hamid Aghvami. "CORPL: A routing protocol for cognitive radio enabled AMI networks." IEEE Transactions on Smart Grid 6.1 (2014): 477-485. 34. Basagni, Stefano, et al. "CARP: A channel-aware routing protocol for underwater acoustic wireless networks." Ad Hoc Networks 34 (2015): 92-104. Authors: Pratik S. Meshram, Dhanashree K. Parate, Chetan B. Jawale, Akshay V. Yewate, Netra Lokhande

Paper Title: A Machine Learning Access for Person Identification using Dental X-Ray Images Abstract: Dental radiographs do a great deal of work on the evidence of criminal classification. Science deontology is used in crimes that deal with the evidence of a person's separation related to dental exposure. Due to the advances in data design and the need to evaluate more cases by legal professionals, it is important to use a human evidence framework. Dental radiographs can be classified as biometric if there are no alternatives to body biometrics, for example, palm, finger, iris, face, leg print, and so on. The human body seen using dental radiographs is best under certain conditions when there are no biometric alternatives because the teeth and bones are treated like skin tissues and tissues found in the human body.

Keywords: CNN, Dental Radiographs, HoG, KNN.

References:

1. Hong Chen and A. K. Jain, "Dental biometrics: alignment and matching of dental radiographs", IEEE Deals on Pattern Analysis and Machine Intelligence, vol. 27, no. 8, August 2005 38. 2. Vijay kumari Pushparaj, Ulaganathan Gurunathan, and Banumath iArumuga, "Dental radiographs and photographs in human forensic identification", IET Biometrics 2013, Vol.2, Iss.2 pp56-63. 3. Eyad Haj Said, E.H. Nassar, D.E. Gamal Fahmy, and M. Hany H Ammar, "Teeth segmentation in digitized dental X-ray films using mathematical morphology", IEEE Transaction on Information and Forensic Security, 2006 178189 185-189 4. R. L. R. Tinoco, E. C. Martins, and E. Daruge, "Dental Anomalies And Their Value in Human Identification", an incident report from Piracicaba Dental School State University of Campinas, Department of Forensic Odontology Av. Limeira, 901 Caixa Postal 52. 5. Anny Yuniarti, Anindhita SigitNugroho, Bilqis Amaliah, and Agus Zainal Arifin, "Classification and Numbering of dental radiographs for an automated human identification system", TELKOMNIKA, Vol.10 No.1 March 2012 pp. 137 146 ISSN: 1693- 6930 6. Surendra Ramteke, Rahul Patil, and Nilima Patil, "A State of Art Automated Dental Identification System (ADIS)", Advances in computational research ISSN: 0975-3273 Vol.4 Issues 1, 2012 pp-95-98 7. Martin L. Tangel, Chastine Fatichah, Fei Yan, and Kaoru Hirota, "Dental Classification for Periapical Radiograph Based on Multiple Fuzzy Attribute", IEEE 978-1-4799-0348-1/13 2013 8. Anil K. Jain, Hong Chen, "Matching of dental X-ray images for human identification", ELSEVIER Pattern Recognition, 37(2004) 1519-1532 9. Rohit Thanki, Deven Trivedi, "Introduction of the novel tooth for human identification based on dental image matching", International Journal of 10. Emerging Technology and Advanced Engineering, ISSN 2250-2459, Volume 2, Issue 10, October 2012 11. Shubhangi Dighe, Revati Shriram, "Preprocessing, Segmentation, and Matching of dental radiographs used in dental biometrics" International Journal of Science and Applied Information Technology, Volume 1, No.2, May June 2012. 12. Wang Xinzui, Dong Ningning, and Li Huanli, “Features Extraction and Matching of Teeth Image Based on the SIFT Algorithm”, The 2nd International Conference on Computer Application and System Modelling, 2012. Authors: Vidhyavani.A, Pooja Gopi, Sushil Ram, Sujay Sukumar

Paper Title: Adaptive Prediction of User Interaction based on Deep Learning Abstract: This application starter work in the region of site page expectation is introduced. The structured and actualized model offers customized association by anticipating the client's conduct from past web perusing history. Those forecasts are a short time later used to streamline the client's future connections. We propose a Profile-based Interaction Prediction Framework (PIPF), which can illuminate the occasion activated connection expectation issue productively and adequately. In PIPF, we initially change the cooperation sign into a Sliding- window Evolving Graph (SEG) to decrease the information volume and steadily update SEG as the association 39. log develops. At that point, we construct profiles intended to introduce clients' conduct by separating the static and astounding highlights from SEG. The static (separately, astonishing) stress mirrors the normality of clients' 190-192 conduct (individually, the transient conduct). At the point when an occasion happens, we process the closeness between the event and every competitor connects. Keywords: Deep learning, gated recurrent unit (GRU), Navigation prediction, user interaction, web applications.

References:

1. Y. Zhang, H. Dai, C. Xu, J. Feng, T. Wang, J. Bian, B. Wang, and T.-Y. Liu, ''Sequential click prediction for sponsored search with recurrent neural networks,'' in Proc. AAAI, Quebec City, QC, Canada, 2014, pp. 1369–1375. 2. P. P. K. Chan, X. Hu, L. Zhao, D. S. Yeung, D. Liu, and L. Xiao, ''Convolutional neural networks based click-through rate prediction with multiple feature sequences,'' in Proc. IJCAI, Stockholm, Sweden, 2018, pp. 2007–2013. 3. G. Zhou, X. Zhu, C. Song, Y. Fan, H. Zhu, X. Ma, Y. Yan, J. Jin, H. Li, and K. Gai, ''Deep interest network for click-through rate prediction,'' in Proc. KDD, London, U.K., 2018, pp. 1059–1068. 4. M. Gan and K. Xiao, ''R-RNN: Extracting user recent behavior sequence for click-through rate prediction,'' IEEE Access, vol. 7, pp. 111767–111777, Jul. 2019. 5. W. Ouyang, X. Zhang, S. Ren, C. Qi, Z. Liu and Y. Du, ''Representation learning-assisted click-through rate prediction,'' in Proc. IJCAI, Macao, China, 2019, pp. 4561–4567. 6. W. Ouyang, X. Zhang, L. Li, H. Zou, X. Xing, Z. Liu and Y. Du, ''Deep Spatio-temporal neural networks for click-through rate prediction,'' in Proc. KDD, Anchorage, AK, USA, 2019, pp. 2078–2086. 7. Zhen Liao, Yang Song, Yalou Huang, Li-Wei He, and Qi He "Task Trail: An Effective Segmentation of User Search Behavior," IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 26, NO. 12, DECEMBER 2014. 8. George Gkotsis ·Karen Stepanyan ·Alexandra I. Christie · Mike Joy," Entropy-based automated wrapper generation for weblog data extraction," Received: 31 October 2012 / Revised: 24 October 2013 Accepted: 4 November 2013 / Published online: 21 November 2013 © Springer Science+Business Media New York 2013. 9. V. S. Dixit • ShvetaKundra Bhatia," Refinement and evaluation of web session cluster quality," Springer transaction received: 20 February 2014 / Revised: 2 May 2014. 10. Renuka Mahajan & J. S. Sodhi & Vishal Mahajan," Usage patterns discovery from a web log in an Indian e-learning site: A case study," Springer Science+Business Media New York 2014. 11. Muhammad Muzammal · Rajeev Raman," Mining sequential patterns from probabilistic databases," Received: 11 April 2013 / Revised: 11 May 2014 / Accepted: 3 July 2014 © Springer-Verlag London 2014. Authors: Nataraj Vijapur, R. Srinivasa Rao Kunte Efficient Machine Learning Techniques to Detect Glaucoma using Structure and Texture based Paper Title: Features Abstract: Survey of world health organization has revealed that retinal eye disease Glaucoma is the second leading cause for the blindness worldwide. It is the disease which will steal the vision of the patient without any warning or symptoms. About half of the world Glaucoma patients are estimated to be in Asia. Hence, for social and economic reasons, Glaucoma detection is necessary in preventing blindness and reducing the cost of surgical treatment of the disease. The objective of the paper is to predict and detect Glaucoma efficiently using image processing and machine learning based classification techniques. Segmentation techniques such as unique template approach, Gray Level Coherence Matrix based feature extraction approach and wavelet transform based approach are used to extract these structure and texture based features. Combination of structure based and texture based techniques along with machine learning techniques improves the efficiency of the system. Developed efficient Computer aided Glaucoma detection system classifies a fundus image as either Normal or Glaucomatous image based on the structural features of the fundus image such as Cup-to-Disc Ratio (CDR), Rim-to-Disc Ratio (RDR), Superior and Inferior neuro-retinal rim thicknesses, Vessel structure based features and Distribution of texture features in the fundus images.

Keywords: GLCM, Glaucoma, Cup-to-Disc Ratio (CDR), Rim-to-Disc Ratio (RDR), Image Processing, Machine Learning, Structure features and Texture features.

References:

1. Raychaudhuri A, Lahiri SK, Bandyopadhyay M, Foster PJ, Reeves BC, Johnson GJ. A Population Based Survey of the Prevalence and Types of Glaucoma in Rural West Bengal. The West Bengal Glaucoma Study. The British Journal of 40. Ophthalmology. 2005; 89:12. 1559–1564. 2. Xiaoyang Song,Keou Song, Yazhu Chen. A Computer-based Diagnosis System for Early Glaucoma Screening. Proc. IEEE Engineering in Medicine and Biology 27th Annual Conf. 2005; pp. 6608-6611. 193-201 3. Eye Anatomy. Available from http://www.optos.com/en-US/Patients/Healthy-sight /Eye-anatomy/ [Accessed: 2016-12-15] 4. Guyton, A. C. and J. E. Hall. Textbook of Medical Physiology. Elseveir Saunders. 9th edition. 1996. 5. Malaya Kumar Nath, Samarendra Dandapat. Techniques of Glaucoma Detection from Color Fundus Images: A Review. International Journal of Image, Graphics and Signal Processing. Vol. 4, 2012; 44-51. DOI: 10.5815. 6. M. Mishra, M. K. Nath, S. R. Nirmala, S. Dandapat. Image Processing Techniques for Glaucoma Detection. Communications in Computer and Information Science: Advances in Computing and Communications: Springer. Vol. 192. 2011; 365-373. 7. Jost D. Jonas, Xuan N.Nguyen, Gottfried O. H. Naumann. Parapapillary Retinal Vessel Diameter in Normal and Glaucoma Eyes: Morphometric data. Investigative Ophthalmology & Visual Science, Vol.30, No.7, 1989; 1599-1603. 8. Jennifer K. Hall, md, Anthony P. Andrews, md, Rebecca Walker, md, Jody r, Piltz-seymour, md. Association of Retinal Vessel Caliber and Visual Field Defects in Glaucoma. Am J Ophthalmol, Vol.132, No.6, 2001; 857-859. 9. S. Dua, U. R. Acharya, E. Y. K. Ng, Computational Analysis of the Human Eye With Applications. World Scientific Press. 2011. 10. Attila Budai, Jan Odstrcilik, High-Resolution Fundus (HRF) Image Database. https://www5.cs.fau.de/ research/data/fundus- images/. 11. Jan Odstrcilik, Radim Kolar, Attila Budai, Joachim Hornegger, Jiri Jan, Jiri Gazarek, Tomas Kubena, Pavel Cernosek, Ondrej Svoboda, Elli Angelopoulou. Retinal vessel segmentation by improved matched filtering: evaluation on a new high-resolution fundus image database. IET Image Processing, Volume 7, Issue 4, June 2013; 373-383, DOI: 10.1049/iet-ipr.2012.0455. 12. J. Nayak, Rajendra Acharya U, P. S. Bhat, N. Shetty, T.-C. Lim. Automated Diagnosis of Glaucoma Using Digital Fundus Images. Journal of Medical Systems. Springer. Vol. 33. 2009; 337-346. DOI: 10.1007/s10916-008-9195-z2009. 13. LászlóG.Nyúl. Retinal image analysis for automated Glaucoma risk evaluation. Medical Imaging, Parallel Processing of Images, and Optimization Techniques, edited by Jianguo Liu, KunioDoi, Aaron Fenster, S. C. Chan. Proc. of SPIE. Vol. 7497, 2009; 74971C-1 to 74971C-9. DOI: 10.1117/12.851179. 14. Y. Xu, D. Xu, S. Lin, J. Liu, J. Cheng, C. Y. Cheung, T. Aung, T. Y. Wong. Sliding Window and Regression Based Cup Detection in Digital Fundus Images for Glaucoma Diagnosis. Springer-Verlag. Part III. LNCS 6893. 2011; pp. 1-8. 15. S. Dua, U. R. Acharya, P. Chowriappa, S. V. Sree. Wavelet-Based Energy Features for Glaucomatous Image Classification. IEEE Transactions on Information Technology in Biomedicine. Vol. 16. 2012; 80-87. DOI: 10.1109/TITB.2011.2176540. 16. Levenberg, Kenneth. “A method for the solution of Certain Non-Linear Problems in Least Squares. Quarterly of Applied mathematics. Volume 2. 1944; 164-168. 17. Auer, Peter, Harald Burgsteiner, Wolfgang Maass. A learning rule for very simple universal approximators consisting of a single layer of perceptrons. Neural Networks. 21 (5). 2008; 786–795. 18. Nataraj Vijapur, Smita Chitins, R. Srinivasa Rao Kunte. Improved Efficiency of Glaucoma Detection by using Wavelet Filters, Prediction and Segmentation Method. International Journal of Electronics, Electrical and Computational System. Academic Science Publishers. Volume 3. Issue 8. Oct 2014; 1-13. 19. Lowell J, Hunter A, Steel, D. Basu, A, Ryder R, Fletcher, E. Kennedy, L. Optic nerve head segmentation. IEEE Transactions on Medical Imaging, vol. 23. no. 2. Feb 2004; 256-264. 20. Uhm KB, Lee DY, Kim JT, Hong C. Peripapillary atrophy in normal and primary open-angle glaucoma. Korean J Ophthalmol. Jun 1998; 37-50. 21. Real Statistics Using Excel: Correlation: Basic Concepts, Available: http://www.real-statistics.com/correlation/. [Accessed: 2015- 02-22] 22. Nataraj A Vijapur, R. Srinivasa Rao Kunte. Glaucoma Detection by Using Pearson-R Correlation Filter. Proc. 4th IEEE International Conference on Communication and Signal Processing (ICCSP’15). April 2015; 1194-1198. DOI: 10.1109/ICCSP.2015.7322695. 23. Vijapur, N.A. & Kunte, R.S.R. Sensitized Glaucoma Detection Using a Unique Template Based Correlation Filter and Undecimated Isotropic Wavelet Transform,. J. Med. Biol. Eng. (2017) 37: 365. https://doi.org/10.1007/s40846-017-0234-4 24. Patton N, Aslam TM, MacGillivray T, Deary IJ, Dhillon B, Eikelboom RH, Yogesan K, Constable I J. Retinal image analysis: concepts, applications and potential. ProgRetin Eye Res., Epub. 2005; 99-127. 25. Seoung-Block Lee, Ki Bang Uhmandand Chul Hong. Retinal Vessel Diameter In Normal and Primary Open Angle Glaucoma. Korean J. Opthalmol. Vol. 12. 1998; 51-59. 26. Jean-Luc Starck, Jalal Fadili, FionnMurtagh. The Undecimated Wavelet Decomposition and its Reconstruction. IEEE Transactions On Image Processing. Vol. 16. No. 2. Feb. 2007; 297-309. Authors: Chinmayanand Jagtap, Adesh Jadhav, Pratik Gaikwad, Mayur Patil, Abhimanyu Chandgude

Paper Title: Effect of Delamination in Carbon Fibre Reinforced Polymer during Abrasive Water Jet Machining Abstract: Delamination is a type of defect produced while machining composites or layered materials. Due to matrix crack, shear crack and bending crack delamination is caused. Delamination is usually a separation along a plane parallel to the surface as in the separation of a coating from a substrate or layers of coating from each other. The aim of this project is to explore the study of delamination in carbon Fibre/epoxy composites under abrasive water jet machining. An experimental investigation was held to study the effect of delamination due to abrasive water jet machining on carbon fibre reinforced polymer. Effect of various parameters such as transverse speed, standoff distance, abrasive mass flow rate and water pressure was analysed. Taguchi method was used for overall analysis of parameters. Effect of Kerf width in CFRP material and on fibre cut was analysed step by step. Further observation was done on scanning electron microscopes. It can affect the compression strength of composite laminate and it will slowly cause the composite to experience failure through the buckling. Here the composite of Carbon Fibre Reinforced Polymer is made using carbon fibers and epoxy resin. Further cutting of CFRP is done using Abrasive water jet machining and analysis of delamination at various phases of the material is done. Analysis is done at which parameters delamination is reduced to minimum.

Keywords: Abrasive Water Jet Machining, Carbon Fibre Reinforced Polymer, Delamination, Kerf Geometry.

References:

1. Ajit Dhanawade and Shailendra Kumar , An Experimental Study Of Surface Roughness In Abrasive Waterjet Machining Of Carbon Fiber Reinforced Polymer Using Orthogonal Array With Grey Relational Analysis , Journal of Manufacturing Engineering, March 2016, Vol. 11, Issue. 1, pp 001-006. 2. Mm IW, Azmi AI, Lee CC, Mansor AF. Kerf taper and delamination damage minimization of FRP hybrid composites under 41. abrasive water-jet machining. The International Journal of Advanced Manufacturing Technology. 2016. 3. S.T. Kumaran, T.J. Ko, M. Uthayakumar, M.M. Islam, Prediction of surface roughness in abrasive water jet machining of CFRP composites using regression analysis, Journal ofAlloys and Compounds (2017), doi: 10.1016/j.jallcom.2017.07.108. 202-208 4. Dhanawade Ajit and Kumar Shailendra, Abrasive Water Jet Machining Of Composites: A Review, Journal of Manufacturing Engineering, September 2014, Vol. 9, Issue. 3, pp 136-142. 5. Ajit Dhanawade & Shailendra Kumar , Multi-performance optimization of abrasive water jet machining of carbon epoxy composite material, Indian Journal of Engineering & Materials Sciences Vol. 25, August 2018, pp. 406-416. 6. Ajit Dhanawade and Shailendra Kumar , An Experimental Study Of Surface Roughness In Abrasive Waterjet Machining Of Carbon Fiber Reinforced Polymer Using Orthogonal Array With Grey Relational Analysis , Journal of Manufacturing Engineering, March 2016, Vol. 11, Issue. 1, pp 001-006. 7. Shweta Jagdale, Ajit Dhanawade and Shailendra Kumar , An Experimental Study Of Influence Of Process Parameters On Kerf Properties Of Abrasive Water Jet Machined Carbon Fibre Reinforced Polymer , Journal of the Association of Engineers, India Vol. 86, No. 1 & 2, 2016. 8. Ajit Dhanawade, Shailendra Kumar, and R.V. Kalmekar , Abrasive Water Jet Machining of Carbon Epoxy Composite , Defence Science Journal, Vol. 66, No. 5, September 2016, pp. 522-528, DOI : 10.14429/dsj.66.9501 , 2016, DESIDOC 9. R. Senthil Kumara, S. Gajendran, R. Kesavan , Estimation of Optimal Process Parameters for Abrasive Water Jet Machining Of Marble Using Multi Response Techniques , Proceedings 5 (2018) 11208–11218. 10. M.A. Azmir, A.K. Ahsan.Investigation on Glass/Epoxy composite surfaces machined by abrasive water jet machining, Journal of Materials Processing Technology 2008; 198:122-28. 11. Puneet Trivedi, Ajit Dhanawade & Shailendra Kumar (2016): An experimental investigation on cutting performance of abrasive water jet machining of austenite steel (AISI 316L), Advances in Materials and Processing Technologies, DOI: 10.1080/2374068X.2015.1128176. 12. Kamlesh Phapale, Ramesh Singh, Sandip Patil and RKP Singh , Delamination Characterization and Comparative Assessment of Delamination Control Techniques in Abrasive Water Jet Drilling of CFRP , Selection and peer-review under responsibility of the Scientific Programme Committee of NAMRI/SME ,The Authors. Published by Elsevier B.V, Volume 5, 2016, Pages 521–535. 13. Shanmugam DK, Nguyen T, Wang J. A study of delamination on graphite/epoxy composites in abrasive waterjet machining. Composites Part A. 2008;39(6):923-9. 14. Schwartzentruber, J., Papini, M., Spelt, J.K., Characterizing and Modelling Delamination of Carbon-Fiber Epoxy Laminates during Abrasive Waterjet Cutting, Composites: Part A (2018). 15. Badgujar P P, Rathi M G, Taguchi method implementation in abrasive water jet machining process optimization. International Journal of Engineering and Advanced Technology. 3(5) (2014) 66-70. Authors: Dhruv Deepak, P. R. Minde Exploration of Characteristics of Steel Fiber Reinforced Concrete and Its Influence on Regular Paper Title: Concrete Abstract: Concrete is the most widely used product in the construction sector mainly because of its properties and its capability to be moulded to any size. Plain concrete has low tensile strength and forms internal micro cracks. It has been proven that with the addition of natural fibers and synthetic fibers in concrete, it helps in the durability and functionality of structure. The steel fibers are added to the concrete in very low volume doses and it has been effective in decreasing the plastic shrinkage in cracking and also acting as a crack arrestor. In this journal, experimental analysis on steel fiber reinforced concrete is done on M30 and M50 mix with 0.5%, 1%, 1.5% and 2% volume fraction of steel fiber content and is compared with samples of 0% steel fiber content and these samples are investigated on their compressive, split tensile and flexural strengths.

Keywords: Compressive strength, Flexural Strength, Resistance, Steel fibers, Tensile strength

References: 42. 1. Ashfaque Ahmed Jhatial, Samiullah Sohu, Nadeem-ul-Karim Bhatti, Muhammad Tahir Lakhiar, “Effect of steel fibers on the compressive and flexural strength of concrete”, International Journal of Advanced and Applied Sciences, 2018, Vol 05(10) 209-212 2. Pramod Kawde, Abhijit Warudkar, “Steel fiber reinforced concrete: A review”, International journal of engineering sciences and research technology, 2017, Vol 06(01) 3. Virat Choudhary, “A research paper on the performance of synthetic fiber reinforced concrete”, International Research Journal of Engineering and Technology (IRJET), 2017, Vol 04, Issue 12 4. J Novák and A Kohoutková, “Fiber reinforced concrete when exposed to elevated temperature”, IOP Conf. Series Materials Science and Engineering, 2017 5. Nafisa Tabassum, Pranta Biswas, Dr. Md. Saiful Islam, “Study on the compressive and strength behavior of steel fiber reinforced concrete beam”, International Journal of Advanced Research, 2018, Vol 06(08) 6. A.M. Shende, A.M. Pande, M. Gulfam Pathan, “Experimental Study on steel fiber reinforced concrete for M40 grade”, International Refereed Journal of Engineering and Science (IRJES), 2012, Volume 1, Issue 1 7. Dr.S. Suriya, S. Sownmiya Sadhana, Suman Naaz Shaikh, “Study of modified steel fiber reinforced concrete, International Journal of Advanced Research in biology, Science and technology, 2015 8. E. Arunakanthi,J.D.Chaitanya Kumar, “Experimental Studies on fiber reinforced concrete”, International Journal of Civil Engineering and Technology (IJCIET), 2016, Volume 7, Issue 5 9. Rachit Silawat, Anil Kumar, “Studies on fiber reinforced concrete”, International Journal for scientific research and development,2018, Vol 4, Issue 7 10. Avinash Joshi, Pradeep Reddy, Punith Kumar, Pramod Hatkar, “Experimental work on steel fiber reinforced concrete”, International journal of scientific and engineering research, 2016, Vol 7, Issue 10 Authors: Chaitra H.K, Suneetha K.R

Paper Title: Data Pre-Processing on Web Server Access Logs of University for User Interaction Patterns Abstract: In the current digital Era, websites are developed and organized into multifaceted in nature. It is essential to distinguish user sessions/intent and browsing behavior from logs in order to recommend appropriate content for the web designers and administrators. This paper focuses on data preprocessing of the weblogs received from Kannada University Hampi, Vidyaranya Karnataka state are cleaned viably by applying various pre-processing methodologies. The work identifies the superior quality of data to discover user interactions, user sessions, the specific web pages, and the regularly visited Uri’s, most visited pages, most time spent on pages and incorrect webpages served to users. These pre-processed webserver access log files will be utilized to discover patterns, fine grained analysis and study. This paper also focuses on challenges of log file analysis.

Keywords: Data cleaning, User Analysis, Log files, Data Preprocessing.

References:

43. 1. R. Kosala, H. Blockeel, Web mining research: a survey, SIGKDD: SIGKDD explorations: newsletter of the special interest group (SIG) on knowledge discovery & data mining, ACM 2 (1), 1–15, 2000 2. Bamshad Mobasher et.al “Effective Personalization based on Association rule Discovery from Web usage data” WIDM01 3rd 213-220 ACM workshop on Web Information and data management, November 9 2001, Atlanta 2001. 3. R. Cooley, B. Mobasher, and J. Srivastava, Data Preparation for Mining World Wide Web Browsing Patterns. Journal of Knowledge and Information Systems, (1), 1999. 4. Mohd Helmy Abd Wahab, Mohd Norzali Haji Mohd, et. Al, “Data Preprocessing on Web Server Logs for Generalized Association Rules Mining Algorithm”, World Academy of Science, Engineering and Technology, 2008 5. Jaideep Srivastava, Robert Cooley, Mukund Deshpande, Pang Ning Tan “Web usage mining: Discovery and Applications of usage patterns from web data” SIGKDD Explorations- vol-1, issue-2 Jan 2000 pages 12-33. 6. Mobasher, B., Cooley, R., Srivastava J.: Automatic personalization based on web usage mining .ACM43(8),142-151(2000) 7. Radha.M, K. Santhi,” An Novel approach on Pre-Processing Techinique on web log mining” International Research journal of engineering and technology, volume 4, issue 5, May 2017. 8. Dr. R. Krishnamoorthi and K. R. Suneetha,” Identifying User Behavior by Analyzing Web Server Access Log File”, International Journal of Computer Science and Network Security, April 2009, vol 9, no.4, pp.327-332. 9. W.W.W. Consortium the Common Log File format http://www.w3.org/Daemon/User/Config/ Logging.html#common-logfile- format, 1995 10. Lizhen Liu, Junjie Chen, Hantao Song, “The Research of Web Mining”, Proceedings of the 4th World Congress on Intelligent Control and Automation, June 10-14, Shanghai/China, 2002 11. Ling Zheng, Hui GUI and Feng Li. 2010. Optimized Data Preprocessing Technology for Web Log Mining. IEEE International Conference on Computer Design and Applications (ICCDA), pp. 19-21. 12. JING Chang-bin and Chen Li. 2010. Web Log Data Preprocessing Based On Collaborative Filtering. IEEE 2nd International Workshop on Education Technology and Computer Science, pp. 11 13. R.M. Suresh, R. Padmajavalli. “An Overview of Data Preprocessing in Data and Web Usage Mining”. 2006 IEEE 14. V. Pushpa et al,” An Efficient Preprocessing Method to Detect User Access Patterns from Weblogs” International Journal of Computer Science and Mobile Computing, Vol.5 Issue.9, September- 2016, pg. 16-22 15. Zhang Huiying, Laing Wei “An Intelligent Algorithm of Data Pre-processing in Web Usage Mining” Proceedings of the 5th world Congress on Intelligent Control and Automation, June15-19, 2004 Hangzhou, P.R. China. 16. Tsuyoshi Murata and Kota Saito “Extracting Users Interests from Web Log Data” Proceedings of the 2006 IEEE/WIC/ACM International Conference of Web Intelligence (WI 2006 Main Conference Proceedings) (WI’06) 2006 IEEE. 17. Internet: Hypertext Transfer Protocol Overview, http://www.w3.org/Protocols/,http://www.w3.org/Protocols/rfc2616/rfc2616- sec1.html, 1995. 18. P.Sukumar,L.Robert,S,Yuvaraj “Review on Modern Data Preprocessing Techniques in web usage mining “,International conference on computational system and information systems for sustainable solutions.2016 Authors: Ritbik Kumar, Sourin Karmakar, Wamiq Asrar, Raju Ranjan

Paper Title: Modification in Braking Technology of Ships Abstract: This paper aims to draw people’s attention towards one of the major cause of accidents at sea, its impact and the methods we can adopt to reduce the chances of accidents considerably. The paper finds slow braking rate as the key cause of maximum accidents and suggests two systems designed to increase the deceleration rate of the ships. A detailed explanation about the working and the setting up of our proposed systems ECDS (Emergency Cargo Drop Stop) and EBP (Emergency Braking Propellers) has been provided in the course of the paper. It also draws a comparison between the pros and the cons of these systems. The study’s conclusion indicates the significance of these two systems and how it paves the way for further study on the 44. application and benefits of these systems.

221-224 Keywords: Braking system, Cargo, Collisions, Propellers.

References:

1. Marine Accident and Incident Investigation. Annual Report 2019. Japan Transport Safety Board. Tokyo, Japan, December 2019, pp 153-194. 2. Carlton, John. Marine Propellers and Propulsion: Resistance and Propulsion. Butterworth-Heinemann. Cambridge, UK, 2019, pp 313-365. 3. Kerwin, Justin E., Annual Review of Fluid Mechanics: Marine Propellers. Department of Ocean Engineering, MIT, Vol. 18, Cambridge, UK., 1986, pp 367-405. Authors: Tulika Bal, Sunil Kumar Dhal

Paper Title: Integrated Reporting: It‟s Impact on Value Creation Abstract: Corporate reporting provides the comprehensive picture of an organisation’s performance and position to the stakeholders. In the recent years, corporate reporting has seen a major changes and it has evolved from the financial reporting to the integrated reporting (IR). IR is a corporate reporting reform practised recently by many big companies all over the world. In a precise way, IR has combined the financial report and sustainability report, thus making it more integrated and transparent. Integrated report focuses on the six capitals in a broad way and their value creation for the company over the years. This article has examined many recent research articles to find out the research progress in the area of IR. Analysis of data of 12 companies in six sectors has been made to analyse the value creation of these companies in six capitals. It is observed that the score of reporting for human capital, social and relationship capital, and financial capital was better as compared to intellectual capital, manufacturing capital and natural capital.

Keywords: Corporate Reporting, Integrated Reporting, Sustainability reporting, Stakeholders.

References: 45. 1. Bernardi, C. and Stark, A.W. (2018) environmental, social and governance disclosure, integrated reporting and the accuracy of analyst forecasts. The British Accounting Review, 50, 16-31. 225-229 2. Bruke, J. and Clarke, C.E. (2016). The business case for integrated reporting: Insights from leading practitioner, regulators and academics. Business Horizons. 59, 273-283. 3. Girella, L., Rossi, P. and Zambon, S. (2019). Exploring the firm and country determinant of the voluntary adoption of integrated reporting. Business Strategy and the Environment, 1-18 4. Humphrey, C., O‟Dwyer, B. and Unerman, J. (2017). Re-theorizing the configuration of organisational fields: the IIRC and the pursuit of „Enlightened‟ corporate reporting. Accounting and Business Research, 47, 30-63. 5. Kilic, M. and Kuzey, C. (2018). Determinants of forward looking disclosures in integrated reporting. Managerial Auditing Journal, 33, 115-144. 6. Lee, K.W. and Yeo, G.H. (2016). The association between integrated reporting and firm valuation. Review of Quantitative Finance and Accounting, 47, 1221-1250 7. Pavlopoulos, A., Magnis, C. and Iatridis, G.E. (2019). Integrated reporting: An accounting disclosure tool for high quality financial reporting. Research in International business and finance, 49, 13-40. 8. Perego, P., Kennedy, S. and Whiteman, G. (2016). A lot of icing but little cake? Taking integrated reporting forward. Journal of Cleaner Production, 1-12. 9. Rupley, K.H., Brown, D. and Marshall, S. (2017). Evolution of corporate reporting: from stand-alone corporate social responsibility reporting to integrated reporting. Research in Accounting Regulation, 29, 172-176. 10. Tlili, M., Othman, H.B. and Hussainey, K. (2019). Does integrated reporting enhances the value relevance of organisational capital? Evidence from the South African context. Journal of Intellectual Capital, 20, 642-661. 11. Vaz, N., Fernandez-Feijoo, B. and Ruiz, S. (2016). Integrated reporting: An international overview. Business Ethics: A European Review, 25, 577-591. 12. Velte, P. and Stawinoga, M. (2017). Integrated Reporting: The current state of empirical research, limitations and future research implications. Journal of Management Control, 28, 275-320. 13. Vitolla, F., Raimo, N. and Rubino, M. (2019). Appreciations, criticism, determinants, and effects of integrated reporting: a systematic literature review. Corporate Social Responsibility and Environmental Management, 26, 518-528. 14. Zambon, S., Marasca, S. and Chiucchi, M. S. (2019). Special issue on “the role of intellectual capital and integrated reporting in management and governance: a performative perspective”. Journal of Management and Governance, 23, 291-297. 15. Zhou, S., Simnett, R. and Green, W. (2017). Does Integrated Reporting matter to the capital market? ABACUS A journal of Accounting, Finance and Business studies, 53, 94- 132. Authors: Rajalakshmi J, Kumar P

Paper Title: Gesture Recognition using CNN and RNN Abstract: Gesture Recognition is a major area in Human-Computer Interaction (HCI). HCI allows computers to capture and interpret human gestures as commands. A real-time Hand Gesture Recognition System is implemented and is used for operating electronic appliances. This system is implemented using the deep learning models such as the Convolution Neural Network (CNN) and the Recurrent Neural Network (RNN). The combined model will effectively recognize both static and dynamic hand gestures. Also the model accuracy while using VGG16 pre-trained CNN model is investigated.

Keywords: Convolution Neural Network (CNN), Human-Computer Interaction (HCI), Recurrent Neural Network (RNN).

References: 230-233 1. Sanmukh Kaur, Anuranjana (2018), “Electronic Device Control Using Hand Gesture Recognition System for Differently Abled”, 8th International Conference on Cloud Computing, Data Science & Engineering. 2. Youngwook Kim And Brian Toomajian (2016), “Hand Gesture Recognition Using Micro-Doppler Signatures With Convolutional Neural Network”, IEEE Access Vol. 4. 3. Soeb Hussain, Rupal Saxena, Xie Han, Jameel Ahmed Khan, Hyunchul Shin (2017), “Hand Gesture Recognition Using Deep Learning”, International SoC Design Conference. 4. Md Rashedul Islam, Rasel Ahmed Bhuiyan, Ummey Kulsum Mitu, Jungpil Shin (2018), “Hand Gesture Feature Extraction Using Deep Convolutional Neural Network for Recognizing American Sign Language”, 4th International Conference on Frontiers of Signal Processing. 5. Jing-Hao Sun, Shu-Bin Zhang (2018), “Research on the Hand Gesture Recognition Based on Deep Learning”, 12th International Symposium on Antennas, Propagation and EM Theory. 6. Anush Ananthakumar (2018), “Efficient Face and Gesture Recognition for Time Sensitive Application”, IEEE Southwest 46. Symposium on Image Analysis and Interpretation. 7. Saransh Sharma, Samyak Jain, Khushboo (2019), “A Static Hand Gesture and Face Recognition System for Blind People”, 6th International Conference on Signal Processing and Integrated Networks. 8. Mohd. Baqir Khan, Kavya Mishra, Mohammed Abdul Qadeer (2017), “Gesture Recognition using Open-CV”, 7th International Conference on Communication Systems and Network Technologies. 9. Arathi P.N, S.Arthika, S.Ponmithra, K.Srinivasan, V.Rukkumani (2017), “Gesture Based Home Automation System”, 2017 International Conference on Nextgen Electronic Technologies: Silicon to Software. 10. Kenneth Lai and Svetlana N. Yanushkevich (2018), “CNN+RNN Depth and Skeleton based Dynamic Hand Gesture Recognition”, 24th International Conference on Pattern Recognition. 11. P Raghu Veera Chowdary, M Nagendra Babu, Thadigotla Venkata Subbareddy, Bommepalli Madhava Reddy, V Elamaran (2014), “Image Processing Algorithms for Gesture Recognition using MATLAB”, IEEE International Conference on Advanced Communication Control and Computing Technologies. 12. Chih-Hung Wu and Chang Hong Lin (2013), “Depth-based hand gesture recognition for home appliance control”, IEEE 17th International Symposium on Consumer Electronics. 13. Ransalu Senanayake, Sisil Kumarawadu (2012), “A Robust Vision-based Hand Gesture Recognition System for Appliance Control in Smart Homes”, IEEE International Conference on Signal Processing, Communication and Computing. 14. Archana S. Ghotkar, Rucha Khatal, Sanjana Khupase, Surbhi Asati &Mithila Hadap (2012), “Hand Gesture Recognition for Indian Sign Language”, International Conference on Computer Communication and Informatics. 15. Lan Tiantian, Shen Jinyuan, Liu Runjie, Guo Yingying (2015), “Hand Gesture Recognition Based on Improved Histograms of Oriented Gradients”, The 27th Chinese Control and Decision Conference. 16. Guillaume Plouffe and Ana-Maria Cretu (2016), “Static and Dynamic Hand Gesture Recognition in Depth Data Using Dynamic Time Warping”, IEEE Transactions on Instrumentation and Measurement, Vol. 65, Issue: 2. 17. Dong-Woo Lee, Jeong-Mook Lim, John Sunwoo, Il-Yeon Cho and Cheol-Hoon Lee (2009), “Actual Remote Control: A Universal Remote Control using Hand Motions on a Virtual Menu”, IEEE Transactions on Consumer Electronics, Vol. 55, Issue: 3. 18. Sruthy Skaria, Student, Akram Al-Hourani, Margaret Lech, and Robin J. Evans (2019), “Hand-Gesture Recognition Using Two- Antenna Doppler Radar With Deep Convolutional Neural Networks”, IEEE Sensors Journal, Vol. 19, Issue: 8. 19. Guangming Zhu, Liang Zhang, Peiyi Shen, Juan Song, Syed Afaq Ali Shah, and Mohammed Bennamoun (2019), “Continuous Gesture Segmentation and Recognition Using 3DCNN and Convolutional LSTM”, IEEE Transaction on Multimedia, Vol. 21, NO. 4. 20. Bin Xie1, Xiaoyu He1, Yi Li1 (2018), “RGB-D static gesture recognition based on convolutional neural network”, The Journal of Engineering, Vol. 2018, Issue: 16. Authors: Deepjyot Kaur Ryait, Manmohan Sharma

Paper Title: To Eiminate the threat of a Single Point of Failure in the SDN by using the Multiple Controllers

47. Abstract: The Software Defined Network (SDN) provides an innovative paradigm for networking, which improve the programmability and flexibility of the network. Due to the separation between the control and data plane, all the control logic transfer to the controller. In SDN, the controller, which provides a global view of the 234-241 whole network. That is why it acts as the “Network Brain” of the network. Because the controller has the capability to configure or reconfigure the forwarding devices by customizing their policies in a dynamic manner. Thus, the controller provides a centralized logical view of the entire network. Therefore, all manipulation and implementation in the network are control by the single controller in the SDN, which increases the maximum chance of a single point of failure (SPOF) in the network. As a consequence, it collapses the entire network. Therefore, a fault tolerance mechanism is required which reduce single point of failure in the network by using multiple controllers. As a significance, this mechanism also increases the scalability, reliability, and high availability of services in the network. The three different roles of multiple controllers are equal, master and slave exist in the SDN. In the simulation, the Ryu SDN controller and Mininet tool are utilized. During the simulation to analysis, what is happen when a single point of failure (SPOF) occur in the network and how to use the different roles of the multiple controllers (such as equal, master and slave) which reduces the threat of single point of failure in SDN network.

Keywords: SDN, SPOF, Mininet, OpenFlow, Ryu.

References:

1. D. Kreutz, F. M. Ramos, P. E. Verissimo, C. E. Rothenberg, S. Azodolmolky, and S. Uhlig, “Software-Defined Networking: A Comprehensive Survey,” in Proceedings of the IEEE, vol. 103, no. 1, pp. 14–76, 2015. 2. W. Braun and M. Menth, “Software-Defined Networking Using OpenFlow: Protocols, Applications and Architectural Design Choices,” Open Access Future Internet, vol. 6, no. 2, pp. 302-336, 2014. 3. Y. Yu, X. Li, X. Leng, L. Song, K. Bu, J. Yang, Y. Chen, L. Zhang, K. Cheng and X. Xiao, “Fault Management in Software- Defined Networking: A Survey,” IEEE Communications Surveys & Tutorials, vol. 21, no. 1, pp 349-392, 2018. 4. C. M. Duran, E. A. Leal and J. F. Botero, “Improving fault tolerance in critical networks through OpenFlow,” in IEEE Colombian Conference on Communications and Computing (COLCOM), IEEE, 2017. 5. A. Malik, B. Aziz, A. Al-Haj and M. Adda, “Software-Defined Networks: A Walkthrough Guide From Occurrence To Data Plane Fault Tolerance,” Open Access, pp. 1-26, 2019. 6. J. Chen, J. Chen, F. Xu, M. Yin and W. Zhang, “When Software Defined Networks Meet Fault Tolerance: A Survey,” G. Wang et al. (Eds): International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP), Part III, vol. 9530, pp. 351-368, 2015. 7. M. R. Parsaei, S. H. Khalilian and R. Javidan, “A Comparative Study on Fault Tolerance Methods in IP Networks versus Software Defined Networks,” International Academic Journal of Science and Engineering, vol. 3, no. 4, pp. 146-154, 2016. 8. B. Isong, I. Mathebula and N. Dladlu, “SDN-SDWSN Controller Fault Tolerance Framework for Small to Medium Sized Networks,” in the 19th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), IEEE Computer Society, pp. 43-51, 2018. 9. L. Sidki, Y. Ben-Shimol and A. Sadovski, “Fault Tolerant Mechanisms for SDN Controllers,” in IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN), IEEE, pp. 1-6, 2016. 10. Y. Zhang, L. Cui, W. Wang and Y. Zhang, “A Survey on Software Defined Networking with Multiple Controllers,” Journal of Network and Computer Applications (Elsevier), pp. 1-58, 2017. 11. D. Gopi, S. Cheng and R. Huck, “Comparative Analysis of SDN and Conventional Networks using Routing Protocols,” in IEEE International Conference on Computer, Information and Telecommunication Systems (CITS), IEEE, 2017. 12. A. J. Gonzalez, G. Nencioni, B. E. Helvik and A. Kamisinski, “A Fault-689+Tolerant and Consistent SDN Controller,” in IEEE Global Communications Conference (GLOBECOM), IEEE, 2016. 13. M. Z. Abdullah, N. A. Al-awad and F. W. Hussein, “Performance Evaluation and Comparison of Software Defined Controllers,” International Journal of Scientific Engineering and Science, vol. 2, no. 11, pp. 45-50, 2018. 14. Y. E. Oktian, S. Lee, H. Lee and J. Lam, “Distributed SDN controller system: A survey on design choice,” Elsevier Computer Networks, vol. 121, pp. 100-111, 2017. 15. M. Karakus and A. Durresi, “A survey: Control Plane Scalability Issues and approaches in Software-Defined Networking (SDN),” Elsevier Computer Networks, vol. 112, pp. 279-293, 2017. 16. F. Bannour, S. Souihi and A. Mellouk, “Distributed SDN Control: Survey, Taxonomy and Challenges,” IEEE Communications Surveys & Tutorials, vol. 20, no. 1, pp. 333-354, 2017. 17. M. Paliwal, D. Shrimankar and O. Tembhurne, “Controllers in SDN: A Review Report,” IEEE Access, vol. 6, pp. 36256-36270, 2018. 18. A. Mahjoubi, O. Zeynalpour, B. Eslami and N. Yazdani, “LBFT: Load Balancing and Fault Tolerance in distributed controllers,” in 2019 International Symposium on Networks, Computers and Communications (ISNCC), IEEE, 2019. 19. A. U. Rehman, R. L. Aguiar and J. P. Barraca, “Fault- Tolerance in the Scope of Software-Defined Networking (SDN),” IEEE Access, vol. 7, pp. 1-18, 2019. 20. S. Asadollahi, B. Goswami and M. Sameer, “Ryu Controller’s Scalability Experiment on Software Defined Networks,” in 2018 IEEE International Conference on Current Trends in Advanced Computing (ICCTAC), IEEE, 2018. 21. P. Dutta and R. Chatterjee, “A Novel Solution for Controller Based Software Defined Network (SDN),” in Communications in Computer and Information Science, Springer Singapore, 2018. 22. “Ryu SDN Controller,” [Online]. Available: https://osrg.github.io/ryu/. [ Accessed: 20- Apr-2020]. 23. “Getting-Started-Ryu 4.30 documentation,” [Online]. Available: https://ryu.readthedocs.io/en/latest/getting_started.html. [Accessed: 20- Apr-2020]. 24. “Mininet,” [Online]. Available: http://mininet.org/. [Accessed: 20- Apr-2020]. Authors: Doniyor A. Akhmedov, Davron I. Xashimov, Munavvar X. Rashidova, Dilnoza B. Namozova

Paper Title: Simulation of the Effect of Turning Steering Wheel Intensity on the Vehicle Stability Abstract: In this article, a mathematical model has been developed to show the effect of the drivers’ steering wheel turning intensity on the vehicle’s stability. The developed mathematical model was compared with the results of experiment and its adequacy was evaluated. 3 conditional drivers turn the steering wheel of the vehicle 48. at different speeds. When the conditional drivers were analyzed in the “J-turn” maneuver, it was determined that the indicators of 1,2,3 - conditional drivers are close to the standard. The conditional 2-driver recorded an 242-247 indicator close to the standard. As for the “Single Lane Change” maneuver, the value of the smallest quadratic deviation from the trajectory of conditional 1-driver was recorded, the correlation index was equal to 0.102, respectively, 0.88.

Keywords: vehicle stability, “J-turn”, “Single Lane Change” maneuver, trajectory, conditional driver, steering wheel turning intensity.

References:

1. Borisov B. I. On the issue of classification of causes of road accidents / / Vestnik SSTU. -2013. - N-2(71). Issue 2. – pp.. 366- 369.(in Russian) 2. S. Ikehaga, “Active Suspension Control of Ground Vehicle based on a Full-Vehicle Model”, American Control Conference, pp.4019-4024, 2000. 3. Guo L, Ge PS, Yue M, et al. Lane changing trajectory planning and tracking controller design for intelligent vehicle running on curved road. Math Probl Eng 2014; 2014: 1–9 4. Yoshida H, Shinohara S and Nagai M. Lane change steering maneuvers using model predictive control theory. Veh Syst Dynam 2008; 46: 669–681. 5. Motor vehicles. Manageability and stability. Test method. Interstate standard. GOST 31507-2012. Moscow: standardinform, 2013. (in Russian) 6. ISO 3888-1:1999 Passenger vehicles – Test track for a severe lane-change ma-noeuvre – Part 1: Double line-change. 7. ISO 3888-2:2002 Passenger vehicles – Test track for a severe lane-change ma-noeuvre – Part 2: Obstacle avoidance. 8. Antonov D. A. Theory of stability of movement of multi-axis cars. Moscow: Mashinostroenie, 1987, p.216 (in Russian) Authors: Jothilakshmi R, Sharanesh R

Paper Title: Automated Plant Disease Detection using Deep Learning Architectures with Autonomous Rover Abstract: Agriculture is the backbone and plays a vital role in many Asian countries. Farmers mainly depend on their agricultural produce for their living. A report says one-third of the farmers income account’s for the agricultural loss which is primarily due to plant diseases. To combat this farmers are in need of a early plant disease identification mechanism. Observation of individual plants in the farm for detecting the disease is labor- intensive and time consuming work, if the farm is vast and multiple plants are cultivated then it’s even worse. To solve such issues, current technologies like the Internet of Things (IoT) and artificial intelligence (AI) and Machine Learning (ML) are used to predict the diseases more effectively. Farmers usually detect plant diseases with the help of images captured manually and analyzed separately by experts. The proposed system renders an efficient solution for detecting multiple diseases in several plant varieties. The system is designed to detect and recognize several plant varieties, specifically pepper, grapes, and strawberry. The proposed system discovers various plant’s various diseases based on the inputs obtained by capturing images from a built-in camera present in the Autonomous rover. The rover also record’s it’s GPS location and makes a map of the entire farm traced and checked by the robot. The images are processed and are classified into their respective categories using deep learning algorithms. Convolutional neural networks the powerful methodology for image classification is the underlying principle applied. The deep learning model’s architecture namely, VGG16 and InceptionResNetV2, are used to train the model. These models are primarily made of convolutional layers. On testing, we recorded am accuracy of 93.21% was obtained from VGG16, and 95.24% from InceptionResNetV2.

Keywords: Deep learning, Disease detection, Precision farming, Convolutional Neural Networks(CNN), Location mapping, VGG16, InceptionResNetV2 Graphical Abstract:

References: 49. 1. Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Alex Alemi,”Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning”,Computer Vision and Pattern Recognition, Cornell University,2016. 248-254 2. Devie Rosa Anamisa , Muhammad Yusuf, Wahyudi Agustiono ,”Technologies, Methods, and Approaches on Detection System of Plant Pests and Diseases”, EECSI 2019 3. Edna Chebet Too,Li Yujian, SamNjuki, Liu Yingchun,”A comparative study of fine-tuning deep learning models for plant disease identification”Computers and Electronics in Agriculture, 4. Halil Durmus, Ece Olcay Gune, Murvet Koro, “Disease Detection on the Leaves of the Tomato Plants by Using Deep Learning”, 6th International Conference on Agro-Geoinformatics, 2017. 5. Hilman F. Pardede, Endang Suryawati, Rika Sustika, and Vicky Zilvan, “Unsupervised Convolutional Autoencoder-Based Feature Learning for Automatic Detection of Plant Diseases “, International Conference on Computer, Control, Informatics and its Applications, 2018. 6. Hussam Qassim , Abhishek Verma ,David ,”Compressed residual-VGG16 CNN model for big data places image recognition”, IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC),2018. 7. Jayme Garcia Arnal Barbedo,”Impact of dataset size and variety on the effectiveness of deep learning and transfer learning for plant disease classification”Computers and Electronics in Agriculture Volume 153, 2018, Pages 46-53 8. Kamlesh Golhania, Siva.K.Balasundram, Ganesan Vadamalai, Biswajeet Pradhan,”A review of neural networks in plant disease detection using hyperspectral data”Information Processing in Agriculture,Volume 5, Issue 3, 2018, Pages 354-371 9. Konstantinos P.Ferentinos,”Deep learning models for plant disease detection and diagnosis”,Computers and Electronics in Agriculture,Volume 145, 2018, Pp: 311-318 10. L. Sherly Puspha Annabel, V. Muthulakshmi, “AI-Powered Image-Based Tomato Leaf Disease Detection”, Proceedings of the Third International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), 2019 11. Liqiong Tang , Phillip Abplanalp,”GPS guided farm mapping and waypoint tracking mobile robotic system”,9th IEEE Conference on Industrial Electronics and Applications,2014. 12. Long D. Nguyen, Dongyun Lin, Zhiping Lin,Jiuwen Cao,”Deep CNNs for microscopic image classification by exploiting transfer learning and feature concatenation” IEEE International Symposium on Circuits and Systems (ISCAS),2018. 13. Nuttakarn Kitpo,Masahiro Inoue ,”Early Rice Disease Detection and Position Mapping System using Drone and IoT Architecture”, 12th South East Asian Technical University Consortium Sysmposium (SEATUC), Yogyakarta, Indonesia,2018. 14. Parul Sharma, Yash Paul Singh Berwal, Wiqas Ghai, “ KrishiMitr (Farmer’s Friend): Using Machine Learning to Identify Diseases in plants“ , IEEE International Conference on Internet of Things and Intelligence System (IoTaIS),2018. 15. Poojan Panchal, Vignesh Charan Raman, Shamla Mantri,”Plant Diseases Detection and Classification using Machine Learning Models “, 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS), 2019. 16. Sammy V. Militante, Bobby D. Gerardo, Nanette V. Dionisio, “Plant Leaf Detection and Disease Recognition using Deep Learning “,IEEE Eurasia Conference on IOT, Communication and Engineering,2019 17. Sammy V. Militante, Bobby D. Gerardo, Ruji P. Medina,”Sugarcane Disease Recognition using Deep Learning”, IEEE Eurasia Conference on IOT, Communication and Engineering,2019. 18. Shamse Tasnim Cynthia, Kazi Md. Shahrukh Hossain, Md. Nazmul Hasan, Md. Asaduzzaman, Amit Kumar Das,” Automated Detection of Plant Diseases Using Image Processing and Faster R-CNN Algorithm “, International Conference on Sustainable Technologies for Industry 4.0 (STI), 2019. 19. Sharada P. Mohanty,David P. Hughes,Marcel Salathe,”Using Deep Learning for Image-Based Plant Disease Detection”Frontiers in plant science, 2016 20. Suyash S. Patil, Sandeep A. Thorat,”Early Detection of Grapes Diseases Using Machine Learning and IoT“,Second International Conference on Cognitive Computing and Information Processing (CCIP),2016 21. https://towardsdatascience.com/illustrated-10-cnn-architectures-95d78ace614d ; Raimi Karim; July 29,2019. 22. https://medium.com/@mannasiladittya/building-inception-resnet-v2-in-keras-from-scratch-a3546c4d93f0; Siladittya Manna; April 12, 2019. 23. https://neurohive.io/en/popular-networks/vgg16/ ; Muneeb ul Hassan; November 20, 2018. 24. https://towardsdatascience.com/step-by-step-vgg16-implementation-in-keras-for-beginners-a833c686ae6c ;Rohit Thakur; August 16,2019 25. https://cinescopophilia.com/the-worlds-first-remote-dolly-for-360o-vr-cameras-coming-to-nab-2016/ ; Cinescopophilia; 26. https://cs231n.github.io/convolutional-networks/#conv ; CS231 Stanford University 27. https://towardsdatascience.com/applied-deep-learning-part-4-convolutional-neural-networks-584bc134c1e2 ; Arden Dertat; November8, 2017 28. https://towardsdatascience.com/a-simple-guide-to-the-versions-of-the-inception-network-7fc52b863202 ; Bharath Raj; March 29,2018 29. https://towardsdatascience.com/residual-blocks-building-blocks-of-resnet-fd90ca15d6ec ;Sabyasachi Sahoo; November 27, 2018 Authors: Rishabh Kansal, Lavanya Yadav, Aju D

Paper Title: Waste Sorting Mobile Application for Interactive AI Based Waste Management System Abstract: A lot of us are misinformed about trash and how it can be managed. We don’t know what kind of trash can be repurposed or be turned into usable items such as manure. We tend to throw everything out together without segregating at the source of generation. Through our app, they can simply point towards objects and understand what to do with that trash. The goal is to sensitize people not only to segregate trash but also reduce their waste regeneration through practices like composting and reusing. We should first try to understand the plight of waste management in India and how it can be made better. In this project, we will create a mobile app, Python Server with Flask, and Watson Visual Recognition. This mobile app sends pictures of waste and garbage to be analyzed by a server app, using Watson Visual Recognition. The server application will use pictures of common trash to train Watson Visual Recognition to identify various categories of waste, e.g. recycle, compost, or landfill. The resultant smart dustbin is very effective as people have an urge of curiosity and after learning more about their waste, they always tend to make the right decisions. We aim to make a difference in the society through our app and help in making a cleaner and better tomorrow. 50. Keywords: Waste Management, CNN, IBM Watson, iOS Mobile Application, AI. 255-260 References:

1. Hazardous wastes mounting at the rate of 2-5 % per year-ASSOCHAM-PwC. (n.d.). Retrieved from https://www.assocham.org/newsdetail.php?id=6296 2. Kumar, S., Smith, S. R., Fowler, G., Velis, C., Kumar, J., Arya, S., … Cheeseman, C. (n.d.). Challenges and opportunities associated with waste management in India. Retrieved from https://royalsocietypublishing.org/doi/full/10.1098/rsos.160764 3. Ahluwalia, I. J., & Patel, U. (n.d.). Solid Waste Management in India An Assessment of Resource Recovery and Environmental Impact. Retrieved from https://icrier.org/pdf/Working_Paper_356.pdf 4. O'Shea, Keiron & Nash, Ryan. (2015). An Introduction to Convolutional Neural Networks. ArXiv e-prints. 5. Chauhan, Rahul & Ghanshala, Kamal & Joshi, R.. (2018). Convolutional Neural Network (CNN) for Image Detection and Recognition. 278-282. 10.1109/ICSCCC.2018.8703316. 6. Sultana, Farhana & Sufian, A. & Dutta, Paramartha. (2018). Image Classification using CNN. 7. Aivaliotis, Panagiotis & Zampetis, A. & Michalos, George & Makris, S.. (2017). A Machine Learning Approach for Visual Recognition of Complex Parts in Robotic Manipulation. Procedia Manufacturing. 11. 423-430. 10.1016/j.promfg.2017.07.130. 8. Amasuomo, Ebikapade & Baird, Jim. (2016). The Concept of Waste and Waste Management. Journal of Management and Sustainability. 6. 88. 10.5539/jms.v6n4p88 Authors: S.S.Khaydarov, N.M.Islambekova, U.N. Azamatov, G.A.Yusupxodjayeva, B.I. Abrorqulov Research of Preparation of Defect Cocoons for Unreeling and Technology for Producing Silk-Raw Paper Title: with High Linear Density Abstract: The article examines the yield of defective cocoons according to the feeding season of the silk moth and hybrids, determines the increase in the yield of defective cocoons in the second and third season by 2 and 6% compared to the first season. It is proved that in order to obtain raw silk of grade 4A; cocoons must be 51. unwound separately by grades. Determining the indicators of defective cocoons after processing, the possibilities of unwinding with a speed of 110-130 m / min were revealed. 261-263 Studying the time of filling cocoons in a rose while regulating leukemic density, the compaction time was determined for raw silk with a high linear density and it was recommended to use the obtained raw silk for the production of silk carpets.

Keywords: Cocoon, rodent, defective cocoon, raw silk, high density silk, cocoon rate, hybrids.

References:

1. Islambekova N.M. Umurzakova H.H. Improving the properties and improving the unwinding of defective cocoons. // Science and World International Scientific Journal.-Volgograd-2014, Volume 1.-No.10 (14). 2. Islambekova N.M., Azamatov U.M. The influence of water hardness on the unwinding cocoons.// The way of science. ISSN 2311-2158. -2018. -№4 (50) p.23-27. 3. Islambekova N.M. Wettability of cocoon shell modified with surfactant.//Composite materials.- Tashkent. -2010. -№4.-s-6-9. 4. Islambekova N.M. Azamatov U.N. Axmedov J.A. Khaydarov S.S. Yusupxodjayeva G.A. Muxiddiniv N. Investigation of Unwinding Speed Based on the Process of Separating the Thread from the Surface of the Cocoons. IJARSET Vol. 6, Issue 5, May 2019 9136-9142 India 5. Babu, K.M. Silk: Processing, properties and applications 2013. 6. lib26.ru/index.php?id=69183 Authors: Vimal Sen, Krishna Gupta

Paper Title: Diabetic Prediction using Classification Method Abstract: Prediction analysis of diabetes mellitus is the main focus of this work. There are mainly three tasks involved in prediction analysis. These tasks are input dataset, feature extraction and classification. The earlier framework makes use of SVM and naïve bayes approaches for predicting this disease. This study implements voting classifier for prediction purpose. It is an ensemble approach. This classifier combines three classification models. These models are SVM, naïve bayes and decision tree. The implementation of available and new technique is carried out in python tool. These approaches give outcomes in terms of different performance parameters. In contrast to other classification models, proposed classification model performs better.

Keywords: Diabetic, SVM, Naïve Bayes, Feature Extraction.

References:

1. Azhar Rauf, Mahfooz, Shah Khusro and Huma Javed (2012), “Enhanced K-Mean Clustering Algorithm to Reduce Number of Iterations and Time Complexity”, Middle-East Journal of Scientific Research, vol. 12, 2012, pp. 959-963. 2. Osamor VC, Adebiyi EF, Oyelade JO and Doumbia S (2012), “Reducing the Time Requirement of K-Means Algorithm” PLoS ONE, vol. 7, 2012, pp-56-62. 3. Azhar Rauf, Sheeba, Saeed Mahfooz, Shah Khusro and Huma Javed (2012), “Enhanced K-Mean Clustering Algorithm to Reduce Number of Iterations and Time Complexity,” Middle-East Journal of Scientific Research, vol. 5, 2012, pp. 959-963 4. Ankur Goyala, Vivek Kumar Sharmaa, “Improving the MANET Routing algorithm by GC-Efficient Neighbor Selection Algorithm”, International Conference on Advancements in Computing & Management (ICACM-2019),April 13-14, 2019, 52. Jagannath University, Jaipur, India, SSRN: 3446673 5. Ankur Goyal, Dr. Vivek Sharma, “Study of Position Based Greedy Routing algorithm with Interference in the MANET”, 2nd International Conference on Emerging Trends in Engineering & Applied Science (ICETEAS' 19) Volume: 5 Issue: 1, ISSN: 2454-4248 04-06, January 2019. 264-267 6. Ankur Goyal, Vivek Kumar Sharma, “Modifying the MANET Routing algorithm by GBR CNR-Efficient Neighbor Selection Algorithm”, International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-8 Issue-10, August 2019. 7. Min Chen, Yixue Hao, Kai Hwang, Fellow, IEEE, Lu Wang, and Lin Wang (2017), “Disease Prediction by Machine Learning over Big Data from Healthcare Communities”, 2017, IEEE, vol. 15, 2017, pp- 215-227 8. Akhilesh Kumar Yadav, Divya Tomar and Sonali Agarwal (2014), “Clustering of Lung Cancer Data Using Foggy K-Means”, International Conference on Recent Trends in Information Technology (ICRTIT), vol. 21, 2013, pp.121-126. 9. Sanjay Chakrabotry, Prof. N.K Nigwani and Lop Dey (2014), “Weather Forecasting using Incremental K-means Clustering”, vol. 8, 2014, pp. 142-147. 10. Ioannis Kavakiotis, Olga Tsave, Athanasios Salifoglou, Nicos Maglaveras, Ioannis Vlahavas, Ioanna Chouvarda, “Machine Learning and Data Mining Methods in Diabetes Research”, Computational and Structural Biotechnology Journal 15 (2017) 104– 116 11. Bayu Adhi Tama,1 Afriyan Firdaus,2 Rodiyatul FS, “Detection of Type 2 Diabetes Mellitus with Data Mining Approach Using Support Vector Machine”, Vol. 11, issue 3, pp. 12-23, 2008. 12. Yu-Xuan Wang, QiHui Sun, Ting-Ying Chien, Po-Chun Huang, “Using Data Mining and Machine Learning Techniques for System Design Space Exploration and Automatized Optimization”, Proceedings of the 2017 IEEE International Conference on Applied System Innovation, vol. 15, pp. 1079-1082, 2017. 13. Zhiqiang Ge, Zhihuan Song, Steven X. Ding, Biao Huang, “Data Mining and Analytics in the Process Industry: The Role of Machine Learning”, 2017 IEEE. Translations and content mining are permitted for academic research only, vol. 5, pp. 20590- 20616, 2017. 14. Jahin Majumdar, Anwesha Mal, Shruti Gupta, “Heuristic Model to Improve Feature Selection Based on Machine Learning in Data Mining”, 2016 6th International Conference - Cloud System and Big Data Engineering (Confluence), vol. 3, pp. 73-77, 2016. 15. MS.Tejashri n. Giri, prof. S.r.todamal, “data mining approach for diagnosing type 2 diabetes”, international journal of science, engineering and technology, vol. 2 issue 8, 2014. Authors: Poonkuzhali R

Paper Title: Smart Wheel Chair for Elderly People Abstract: Healthcare is a labour intensive industry. A substantial amount of money and resources are spent on 53. hiring caretakers and nurses for patients who need constant attention for their sustenance. The proposed system can be used for a broad spectrum of patients but specifically focuses on the elderly, the bedridden, and the ones 268-275 with limited mobility. The proposed work provides a solution to get a full-fledged working system that automates every aspect of patient monitoring to reduce errors introduced by human intervention. It provides a framework of seamless interaction with the patient and, finally, to deliver external assistance for mobility. This proposed system relies on embedded computers, ECG, IoT, RTC, HMM, machine learning, and other sensors used in the healthcare industry.

Keywords: Embedded Computing, ECG, RTC, IoT, HMM, Healthcare, labour, machine learning, mobility, patient monitoring, automation, sensors.

References:

1. P. Leijdekkers and V. Gay, "Personal heart monitoring and rehabilitation system using smartphones," in Mobile Business, 2006. ICMB '06. International Conference on, 26-27 2006, pp. 29 -29. 2. J. Ko, C. Lu, M. B. Srivastava, J. A. Stankovic, A. Terzis and M. Welsh, "Wireless Sensor Networks for Healthcare," in Proceedings of the IEEE, vol. 98, no. 11, pp. 1947-1960, Nov. 2010. 3. P. Benavidez, M. Kumar, S. Agaian and M. Jamshidi, "Design of a home multi-robot system for the elderly and disabled," 2015 10th System of Systems Engineering Conference (SoSE), San Antonio, TX, 2015, pp. 392-397. 4. K. Natarajan, B. Prasath, "Smart Health Care System Using Internet of Things", Journal of Network Communications and Emerging Technologies (JNCET) Volume 6, Issue 3, March (2016) 5. Lin Yang, Yanhong Ge, Wenfeng Li, Wenbi Rao, "A Home Mobile Healthcare System for Wheelchair Users", IEEE 18th International Conference on Computer Supported Cooperative Work in Design (CSCWD) (2014) 6. Sashanka Kumar Pramanik, Zishan Ahmed Onik, Nadia Anam, MdMahsinUllah, Ahmed Saiful, Sumaiya Sultana, "A Voice Controlled Robot for Continuous Patient Assistance", International Conference on Medical Engineering, Health Informatics and Technology, (2016) 7. Encarnação, P. (Ed.), Cook, A. (Ed.). (2017). Robotic Assistive Technologies. Boca Raton: CRC Press.------c5, c8, c9 8. A. Kumar and F. Rahman, "Wireless health alert and monitoring system," in Biomedical and Pharmaceutical Engineering, 2006. ICBPE 2006. International Conference on, 11-14 2006, pp. 241 -245 9. M. Hassanalieragh et al., "Health Monitoring and Management Using Internet-of-Things (IoT) Sensing with Cloud-Based Processing: Opportunities and Challenges," 2015 IEEE International Conference on Services Computing (SCC), New York City, NY, USA, 2015, pp. 285-292. 10. Abdallah Kassem, Mustapha Hamad and Chady El Moucary, Elias Nawafal and Alain Aoun, "MedBed: Smart Medical Bed", Fourth International Conference on Advances in Biomedical Engineering (ICABME) (2017) 11. Linh L.H., Hai N.T., Van Thuyen N., Mai T.T., Van Toi V. (2015) MFCC-DTW Algorithm for Speech Recognition in an Intelligent Wheelchair. In: Toi V., Lien Phuong T. (eds) 5th International Conference on Biomedical Engineering in Vietnam. IFMBE Proceedings, vol 46. Springer, Cham 12. M. Hassanalieragh et al., "Health Monitoring and Management Using Internet-of-Things (IoT) Sensing with Cloud-Based Processing: Opportunities and Challenges," 2015 IEEE International Conference on Services Computing (SCC), New York City, NY, USA, 2015, pp. 285-292. 13. N. Oliver and F. Flores-Mangas, "Healthgear: a realtime wearable system for monitoring and analyzing physiological signals," in Wearable and Implantable Body Sensor Networks, 2006. BSN 2006. International Workshop on, 3-5 2006, pp. 4 pp. -64. 14. IEEE Std., Health Informatics—Personal Health Device Communication—Device specialization Pulse oximeter,11073-10404 TM, 2008.s 15. Ashish Bhatia, Mohit Matwani, Pawan Karira, Rahul Sidhwani, Asha Bharambe, "A Heart Disease Prediction System using Artificial Neural Network and Naive Bayes" IJSRD - International Journal for Scientific Research & Development, Vol. 4, Issue 03, 2016. 16. "Medication Adherence by Using a Hybrid Automatic Reminder Machine "Ying-Wen Bai and Ting-Hsuan Kuo.IEEE International Conference on Consumer Electronics (ICCE) 2016 17. W. Antoun, A. Abdo, S. Al-Yaman, A. Kassem, M. Hamad and C. El-Moucary, "Smart Medicine Dispenser (SMD)," 2018 IEEE 4th Middle East Conference on Biomedical Engineering (MECBME), Tunis, Tunisia, 2018, pp. 20-23. 18. Speech Recognition, https://en.wikipedia.org/wiki/Speech_recognition, last accessed 17/3/2018 19. Lamere, Paul, et al. "The CMU SPHINX-4 speech recognition system." IEEE Intl. Conf. on Acoustics, Speech, and Signal Processing (ICASSP 2003), Hong Kong. Vol. 1. 2003. 20. Bourlard, Hervé A., and Nelson Morgan. "Hidden Markov models." Connectionist Speech Recognition. Springer, Boston, MA, 27-58(1994) 21. Rabiner, Lawrence R. "A tutorial on hidden Markov models and selected applications in speech recognition." Proceedings of the IEEE 77.2, 257-286(1989) 22. eSpeak text to speech URL- http://espeak.sourceforge.net/index.html 23. Hussain, I., Chen, L., Mirza, H.T. et al. Right mix of speech and non-speech: hybrid auditory feedback in mobility assistance of the visually impaired, Univ Access Inf Soc (2015) 14: 527 24. Ling Chen, Sen Wang, Huosheng Hu, Dongbing Gu, Ian Dukes, Chapter 17 - Voice-directed autonomous navigation of a smart- wheelchair, Editor(s): Pablo Diez, Smart Wheelchairs and Brain-Computer Interfaces, Academic Press, 2008, Pages 405-424, ISBN 9780128128923 25. Aditya A. Shinde, Sharad N. Kale, Rahul M. Samant, Atharva S. Naik, "Heart Disease Prediction System using Multilayered Feed Forward Neural Network and Back Propagation Neural Network," International Journal of Computer Applications (0975 – 8887) Volume 166 – No.7, May 2017. 26. Durairaj M, Revathi V, "Prediction Of Heart Disease Using Back Propagation MLP Algorithm" International Journal of Scientific & Technology Research Vol. 4, Issue 08, August 2015. 27. M. A. Rahman Apu, I. Fahad, S. A. Fattah and C. Shahnaz, "Eye Blink Controlled Low Cost Smart Wheel Chair Aiding Disabled People," 2019 IEEE R10 Humanitarian Technology Conference (R10-HTC)(47129), Depok, West Java, Indonesia, 2019, pp. 99- 103. 28. Y. T. K. Yunior and Kusrini, "Integration System of Voice Recognition and DC motor control using Fuzzy Logic on Smart Wheelchair," IOP Conf.series, Journal of Physics, 1140 (2018) 012053. 29. T. Hossain, Md Sabbir-Ul-Alam Sabbir, Asma Mariam, Islam, M.N., Sabir, M.S.U, Toki T Inan, Khairul Mahbub, Muhammad Tawsif Sazid., "Towards Developing an Intelligent Wheelchair for People with Congenital Disabilities and Mobility Impairment," 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), Dhaka, Bangladesh, 2019, pp. 1-7. 30. S. Chauhan, S. Kothari, Y. Malu and S. Mhamane, "Smart Guide for Patients in Hospital using Microcontroller Interrupts," 2018 3rd International Conference on Inventive Computation Technologies (ICICT), Coimbatore, India, 2018, pp. 288-292. Authors: M. Nagaraja, Sushma Prashanth, Jayadev Pattar, H.M. Mahesh

54. Paper Title: Electrical, Structural and Gas Sensing Properties of Polyaniline/DBSA-Fullerene Nanocomposite Abstract: By employing chemical route PANI/DBSA-C60 (Polyaniline/Dodechyl benzene sulfonic acid – 276-279 Fullerene) nanocomposite is synthesized by using ammonium persulfate and DBSA as oxidizing agent and acid dopant respectively. Synthesized samples are characterized for FTIR (Fourier Transform Infrared Spectroscopy), SEM (Scanning Electron Microscope), XRD (X-ray diffraction), electrical conductivity using standard four probe method and methanol gas sensing properties. The FTIR spectrum illustrates the existence of interaction between polyaniline and fullerene. XRD spectra prove the formation of PANI/DBSA-C60. In collaboration with these, SEM images also indicate the highly branched chain structure of the PANI in the presence of C60. The PANI/DBSA-C60 showed the electrical conductivity more than over pure PANI/DBSA. The gas response of the PANI/DBSA-C60 nanocomposite towards different concentration of methanol was examined and compared with that of the pure PANI/DBSA. The PANI/DBSA-C60 was observed higher methanol gas sensing capacity compared to pure PANI/DBSA.

Keywords: Conductivity, Nanocomposite, Polyaniline, Sensor.

References:

1. R.P. Tandon, M.R. Tripathi, K.A. Arora, S. Hotchandani, Gas and humidity response of iron oxide-Polypyrrole nanocomposites, Sens. Actuators B. 114 (2006) 768-773. 2. W.S. Huang, A.G. MacDiarmid, Optical properties of polyaniline, Polymer. 34 (1993)1833-1845. 3. N.S. Sariciftci, L. Smilowitz, A.J. Heeger, F. Wudl, Photoinduced electron transfer from a conducting polymer to buckminsterfullerene. Science. 258 (1992) 1474-1476. 4. N.S. Sariciftci, A.J. Heeger, Handbook of Organic Conductive Molecules and Polymers (S. Halwa, H, ed.), vol. 1, Wiley, New York, 1997,p. 437 5. N.S. Sarciftci, D. Braun, C. Zhang, V. Srdanov, A.J. Heeger, G. Stucky, F. Wudl, Semiconducting polymer-buckminsterfullerene heterojunctions - Diodes, photodiodes, and photovoltaic cells, Appl. Phys. Lett. 62 (1992) 585-587. 6. F.L. Zhang, M. Johansson, M.R. Andersson, J.C. Hummelen, O. Inganas, Polymer solar cells based on MEH-PPV and PCBM, Syn. Met. 137 (2003) 1401-1402. 7. T. Fromherz, F. Padinger, D. Gebeyehu, C. Brabec, J.C. Hummelen, N.S. Sariciftci, Comparison of photovoltaic devices containing various blends of polymer and fullerene derivatives, Solar Energ. Mater. Solar Cells. 63 (2000) 61-68. 8. D. Mendoza, G. Gonzalez, R. Escudero, Clusters of C60 Molecules, Adv. Mater, 11(1999) 31-33. 9. P. Topart, P. Hourquebie, Infrared switching electroemissive devices based on highly conducting polymers, Thin Solid Films, 352 (1999) 243-248. 10. S.A. Chen, K.R. Chuang, C.I. Chao, H.T. Lee, White-light emission from electroluminescence diode with polyaniline as the emitting layer, Synth. Met, 82 (1996) 207-210. 11. G. Gustafsson, Y. Cao, G.M. Treacy, F. Klavetter, N. Colaneri, A.J. Heeger, Flexible light-emitting diodes made from soluble conducting polymers, Nature, 357 (1992) 477-479. 12. N.S. Sariciftci, Role of Buckminsterfullerene C60 in organic photoelectric devices, J. Prog. Quant. Electr. 19 (1995) 131-159. 13. L. Cheng, A. Chakraborty, Effects of dimensions on the sensitivity of a conducting polymer microwire sensor, Microelectronics Journal, 40 (2009) 912-920. 14. T. Kazuyoshi, M. Yukihito, O. Yoshiaki, Y. Tokio, Doping effect of C60 on soluble polyaniline, Synthetic Metals, 66 (1994) 193- 196. 15. J. McElvain, M. Keshavarz, H. Wang, F. Wudi, A.J. Heeger, “Fullerene-based polymer grid triodes,. J. Appl. Phys, 81 (1997) 6468. 16. L.I. Ming, W. Meixiang, Doped polyaniline with C60, Sol. Stat. Comm. 93 (1995) 681-684. 17. M. Nagaraja, H.M. Mahesh, J. Manjanna, K. Rajanna, M.Z. Kurian, S.V. Lokesh, Effect of multiwall carbon nanotubes on electrical and structural properties of polyaniline, J. Electr. Mater, 41 (2012) 1882-1885. 18. K. Tanigaki, H.S. Nalwa, Handbook of organic conductive molecules and polymers, vol. 1. New York: Wiley, 1997. p. 298 19. W. Feng, X.D. Bai, Y.Q. Lian, J. Liang, X.G. Wang, K. Yoshino, Well-Aligned Polyaniline/CarbonNanotube Composite Films Grown by in-Situ Aniline Polymerization, Carbon, 41 (2003) 1551-1557. 20. N. Serdar Sariciftci, Role of buckministerfullerene C60 in organic photoelectric devices, Prog. Quant. Electr. 19 (1995) 131-159. 21. H.Y. Lim, S.K. Jeong, , J.S. Suh, E.J. Oh, Y.W. Park, K.S. Ryu, C.H. Yo, Preparation and properties of fullerene doped polyaniline, Synth. Met. 70 (1995) 1463-1464. 22. Z. Huseyin, Z. Wensheng, J. Jianyong, C. Richard, D.W. Smith, L. Echegoyen, D.L. Carroll, S.H. Foulger, J. Ballato, Adv. Mat, 14 (2000) 1480 23. B. Lundberg, B. Sundqvist, Resistivity of a composite conducting polymer as a function of temperature, pressure, and environment: Applications as a pressure and gas concentration transducer. J. Appl. Phys, 60 (1986) 1074. 24. M.F. Mabrook, C. Pearson, M.C. Petty, An inkjet-printed chemical fuse, Appl. Phys. Lett. 86 (2005) 013507. 25. J. Jang, M. Chang, H. Yoon, Chemical sensors based on highly conductive Poly(3,4-ethylenedioxythiophene) nanorods, Adv. Mater, 17 (2005) 1616-1620. Authors: Srilakshmi R, Chayapathi V, Anitha G S

Paper Title: Voltage Profile Improvement & Loss Reduction using Optimal Allocation of Svc Based on Fvsi Abstract: Power sector is one of the factors that play an eminent role in the economic progress of a country and India has got a distinguished power sector with sources of power generation from feasible renewable energy resources to non-renewable energy resources. Owing to the fact of increasing day to day demand in power sector, integration of renewable energy sources, increased usage of non linear loads etc. results in voltage fluctuations, which in turn affects most of the customer’s load and their electricity bills too. If these voltage 55. problems are not treated properly it may lead to serious conditions like voltage instability. Hence firstly in order to meet the increase in demand either new transmission line should be opted or the existing transmission line should be analyzed whether it is capable of handling increased load or not. Secondly to resolve Voltage 280-283 fluctuation issues compensating devices should be used. Hence in this paper an effort is made to address both of the aforementioned issues, a 10-bus system is checked for existing transmission line performance under normal condition then for increased load condition , followed by an effort to shed light on the voltage profile improvement and loss reduction using SVC and also enlightens about the optimal allocation of SVC based on FVSI in a compact form.

Keywords: FACTS, FVSI, Load flow Analysis, Optimum allocation, SVC, voltage profile.

References:

1. ShraddhaUdgir, SarikaVarshney,Laxmi Srivastava“Optimal Placement and Sizing of SVC for Voltage Security Enhancement”, International Journal of Power System Operation and Energy Management, Volume-1, Issue-2, pp: 54-58, 2011. 2. Ranjit Kumar Bindal: “A Review of Benefits of FACTS Devices in Power System”, International Journal of Engineering and Advanced Technology (IJEAT), Volume-3, Issue-4, pp. 105-108, April 2014 3. R. Mohan mathur, Rajiv k. Varma “Thyristor-based facts controllersFor electrical transmission systems”, A John Wiley & Sons, Inc. Publication 4. Sandesh Jain, Shivendra Singh Thakur: “Voltage Control of Transmission System Using Static Var Compensator”, International Journal of Science and Engineering Applications (IJSEA) Volume 1, Issue 2, pp. 107-109, 201 5. Claudia Reis, F.P. Maciel Barbosa: “A Comparison of Voltage Stability Indices”, IEEE Melecon 2006, May 16-19, pp. 1007- 1010. 6. H. Iyer, Student Member, S. Ray: “Voltage Profile Improvement with Distributed Generation”, IEEE 2005, pp. 1-8. 7. A.A. Alabduljabbar, J.V. Milanovic: “Assessment of techno-economic contribution of FACTS devices to power system operation”, Electric power Systems Research 80, Elsevier, pp.1247–1255, 23 May 2010. Authors: Sugandha Agarwal, Upendra Singh

Paper Title: An Association Between Emotional Intelligence and Performance of Workforce Abstract: The undertaken subject could be considered controversial, some people take ‘emotional intelligence’ as a non-existent matter, while others see it as having a huge impact on employee performance. The present research analyses the association and impact of emotional intelligence (EI) on workforce performance while taking into account demographic features along with individual clusters of emotional intelligence. The Emotional Competence Inventory model is considered to explore EI and its four dimensions- self-awareness, self-management, social-awareness and relationship management. The domain of study is UAE, where hardly such a topic is being explored earlier. A quantitative study is employed with a sample of 119 participants accessed through convenience approach from diverse sectors such as banking, education, health, engineering and recruitment of UAE. The statistical tools such as Cronbach’s Alpha, Chi-square test, Correlation and Regression analysis including ANOVA are put to analyse the primary data to serve the basis for results & discussion. Results reflect that emotional intelligence is independent of age, gender, qualification and designation of employees. It also statistically proves that all the dimensions of emotional intelligence are not equally significant or even considerate to affect employee performance.

Keywords: Emotional Intelligence, Workforce Performance, Demographic Features, Self and Social Awareness, Self and Relationship Management.

References:

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(1999).Emotional intelligence meets traditional standards for an intelligence. Intelligence, 27(4),267-298. 45. Mayer, J.D., Roberts, R.D., &Barsade, S. G. (2008).Human abilities: Emotional intelligence. Annual Review of Psychology, 59, 507-536. 46. Mayer, J.D., Salovey, P., & Caruso, D.R. (2004).Emotional intelligence: theory, findings, and implications. Psychological Inquiry, 15(3),197-215. 47. Mayer, J. D., Salovey, P., & Caruso, D. R. (2000).Emotional intelligence as zeitgeist, as personality, and as a mental ability. In R. Bar-On & J. D. A. Parker (Eds.), The handbook of emotional intelligence: Theory, development, assessment, and application at home, school, and in the workplace (pp. 92-117). San Francisco, CA, US: Jossey-Bass. 48. Mayer, J.D., Salovey, P. & Caruso, D.R. (2008).Emotional intelligence: New ability or electric traits. American Psychological Association, 63(6), 503-517. 49. Mohamad, M. &Jais, J. (2016).Emotional intelligence and job performance: a study among Malaysian teachers. Procedia Economics and Finance, 35,674-682. 50. Noel, A., &Mosoti, Z. (2016).The effect of emotional intelligence on employees performance in the private sector: A case of Kinyara Sugar Limited. Journal of Business and Management (IOSR-JBM), 18(12), 1-10. 51. Nunnally, J. & Bernstein L. (1994).Psychometric Theory. New York: McGraw-Hill Higher, INC. 52. O'Boyle, E.H., Humphrey, R.H., Pollack, J.M., Hawver, T.H. & Story, P.A. (2011).The relation between emotional intelligence and job performance: A meta-analysis. Journal of organizational Behavior, 32(5),788-818. 53. O'Leary, P., Tsui, MS.&Ruch, G. (2012).The boundaries of the social work relationship revisited: towards a connected, inclusive and dynamic conceptualisation. British Journal of Social Work,43(1),135-153. 54. Osisioma, H.E., Hope, Nzewi, N., Nnenne, &Nnabuife, I. (2016).Emotional Intelligence and Employee Performance in Selected Commercial Banks in Anambra State, Nigeria.European Journal of Business, Economics and Accountancy,4(3), 1-10. 55. Pekaar, K.A., Linden, D., Bakker, A.B. & Born, M.P. (2017). Emotional intelligence and job performance: The role of enactment and focus on others’ emotions. Human Performance, 30(2-3),135-153. 56. Petrides, K.V. (2011). Ability and Trait Emotional Intelligence. In T. Chamorro-Premuzic, A. Furnham, & S. Von-Stumm (Ed.),The Blackwell-Wiley handbook of individual differences(pp. 656-678).New York: Blackwell Publishing Ltd. 57. Petrides, K.V. &Furnham, A. (2000).Gender differences in measured and self-estimated trait emotional intelligence.Sex Roles, 42(5),449-461. 58. Pradhan, R.K. & Jena, L.K. (2017).Employee performance at workplace: conceptual model and empirical validation. Business Perspectives and Research, 5(1), 69–85. 59. Rangarajan, R. &Jayamala, C. (2014).The impact of emotional intelligence on employee performance an epigrammatic survey.SUMEDHA Journal of management,3(1), 76-81. 60. Rexhepi, G. &Berisha, B. (2017).The effect of emotional intelligence on employee performance. International Journal of Business and Globalization, 18(4), 467-479. 61. Rothstein, M.G. & Burke, R.J. (2010).Self-Management and Leadership Development. UK: Edward Elgar Publishing Limited. 62. San Lam, C. & O’Higgins, E. (2012). Enhancing employee outcomes: The interrelated influences of managers’ emotional intelligence and leadership style.Leadership & Organization Development Journal, 33(2),149-174. 63. Sallie-Dosunmu, M. (2016).Using Emotional Intelligence in the Workplace.Career Development, 33(1612), 1-20. 64. Salovey, P. & Mayer, J.D. (1990).Emotional intelligence.Imagination, Cognition, and Personality, 9, 185-211. 65. Schutte, N.S. &Malouff, J.M. (1999).Measuring Emotional Intelligence and Related Constructs.United States: E. Mellen Press. 66. Shamsuddin, N. &Rahman, R.A. (2014).The Relationship between emotional intelligence and job performance of call centre agents. Procedia-Social and Behavioral Sciences, 129, 75-81. 67. Sony, M. &Mekoth, N. (2016).The relationship between emotional intelligence, frontline employee adaptability, job satisfaction and job performance.Journal of Retailing and Consumer Services, 30,20-32. 68. Tavakol, M. &Dennick, R. (2011).Making sense of Cronbach’s alpha. International Journal of Medical Education, 2, 53-55. 69. Treadway, C.D., Breland, W.J., Williams, M.L., Cho, J., Yang, J. and Ferris, R.G. (2013).Social influence and interpersonal power in organizations: roles of performance and political skill in two studies. Journal of Management,39(6),1529-1553. 70. Welikala, DSM. &Dayarantha, NWKDK. (2015). The Impact of Emotional Intelligence on Employee Job Performance: An Empirical Study Based on The Commercial Bank in Central Province. Human Resource Management Journal, 3(1),33-41. 71. Whiteoak, J. & Manning, R.L. (2011).Emotional intelligence and its implications on individual and group performance: a study investigating employee perceptions in the United Arab Emirates. The International Journal of Human Resource Management, 23(8),1660-1687. 72. Winkelman, M. (1994).Cultural Shock and Adaptation.Journal of Counseling and Development: JCD, 73(2), 121-126. 73. Wolff, S.B. (2005). Emotional Competence Inventory (ECI)-Technical Manual. Hay Acquisition Company: McClelland Center for Research and Innovation. Retrieved from http://www.eiconsortium.org/pdf/ECI_2_0_Technical_Manual_v2.pdf 74. Wong, C.S. & Law, K.S. (2002).The effects of leader and follower emotional intelligence on performance and attitude: An exploratory study. The Leadership Quarterly,13,243-274. 75. Zeidner, M., Roberts, R.D., & Matthews, G. (2008).The science of emotional intelligence: Current consensus and controversies. European Psychologist,13,64–78. Authors: Sreesha. S, Esakkiraj. P, Sreevidya. V Development of Self Curing Geopolymer Concrete Incorporating Expanded Polystyrene, Recycled Paper Title: Coarse Aggregate and Rubber Crumbs Abstract: Sustainable building production includes the effective usage of natural materials by the processing of waste materials. The present work aims to use different waste materials, such as fly ash, industrial waste pond ash, rubber crumbs from rubber tires, recycled coarse aggregate from building waste. In doing so, the goal of reducing building costs will be achieved and can help to solve the issues connected with its disposal, particularly the environmental concerns of the area. Throughout this project, Rubber Crumbs (RC) and Recycled Coarse Aggregate (RCA) were partly substituted instead of coarse aggregate with a percentage of 10, 15, 20, and 5, 10, 15, which were found to improve the flexural strength of concrete. Such products may also be used for renewable building purposes.

Keywords: Geopolymer, fly ash, GGBS, Pond ash, Recycled Coarse aggregate, Rubber Crumbs, Expanded Polystyrene

References:

1. BapugoudaPatil, Veerendra Kumar M, Dr. H Narendra (2015), “Durability Studies on Sustainable Geopolymer Concrete”. International Research Journal of Engineering and Technology. 57. 2. Chetna M. Vyas, JayeshkumarPitroda (2013), “Fly Ash and Recycled Coarse Aggregate in Concrete: New Era for Construction Industries”.International Journal of Engineering Trends and Technology.4(5), 1781-1787 3. Hanbing Liu, Xianqiang Wang, Yubo Jiao, and Tao Sha (2016), “Experimental Investigation of the Mechanical and Durability Properties of Crumb Rubber Concrete”. Materials. 292-296 4. Lee Yee Loon, Dr. R. N. Krishna (2014), “Biomass Aggregate Geopolymer Concrete”International Journal of Civil Engineering and Technology (IJCIET).5(3), 340-356. 5. Mahesh H. Vaniya, Ankur C. Bhogayata, Dr. N. K. Arora (2015), “A Review on Utilization of Crumb Rubber in Geopolymer Concrete” International Journal of Advance Engineering and Research Development. 6. Preethy K Thomas, Binu M Issac, Deepak John Peter (2015), “Assessment of Demolished Concrete as Coarse Aggregate in Geopolymer Concrete”. International Journal of Advance Research in Science and Engineering. 7. P. Saravanakumar (2018), “Strength and Durability Studies on Geopolymer Recycled Aggregate Concrete”. International Journal of Engineering & Technology. 8. Shivakumar P. A., Maneeth P. D., Dr.ShreenivasReddy Shahapur, Ravikumar H. (2016), “Use of Pond Ash (Waste) as Partial Replacement to Fine Aggregate In Self Compacting Concrete”. International Research Journal of Engineering and Technology. 9. SofiYasir and Gull Iftekar,(2018) “Study of Properties of Fly Ash Based Geopolymer Concrete”. International Journal of Engineering Research. 10. S. Vaidya, E. I. Diaz, E. N. Allouche, (2011) “Experimental Evaluation of Self-Cure Geopolymer Concrete for Mass Pour Applications”. World of Coal Ash (WCOA) Conference. http://www.flyash.info/ 11. Vandhiyan. R, RanjithBabu. B, Nagarajan. M (2016), “A study on mechanical properties of concrete by replacing Aggregate with expanded polystyrene beads”. Global Journal of Engineering Science and Researches. 12. Z Yahya, M M A B Abdullah, S N H Ramli, M G Minciuna, and R AbdRazak (2018), “Durability of Fly Ash Based Geopolymer Concrete Infilled with Rubber Crumb in Seawater Exposure”. IOP Conference Series: Materials Science and Engineering.374 012069. 13. Dr.V. Sreevidya, Dr. B.V. Rangan, Dr. Anuradha, R. Venkatasubramani (2011), “Modified Guidelines for Geopolymer Concrete Mix Design Using Indian Standard”. Asian Journal of Civil Engineering (Building and Housing). 14. Subhash V. Patankar, Yuwaraj M. Ghugal And Sanjay S. Jamkar (2015), “Mix Design of Fly Ash Based Geopolymer Concrete”. Authors: Akabom I. Asuquo, Nicholas O. Dan, Glory T. Effiong

Paper Title: Impact of Information Technology on Accounting Line of Works Abstract: Title: The study was carried out on the impact of information technology on the accounting line of work. Purpose: The purpose was to determine the impact of information technology on the accounting line of works in the global system. Methodology: A survey methodology was adopted. In the course of gathering 58. Information, questionnaires were constructed, validated, and dispensed to sampled elements of the targeted area, which had the relevant knowledge in the area of inquiry. Appropriate statistical tools were adopted to evaluate 297-302 the raw facts obtained. Results/Conclusion: Results of the investigation had shown that information technology has a substantial influence on the accounting line of work and was therefore concluded that accounting line of work has changed from what it used to be before now to a line of works that developed in alliance with the trend in technical improvement and a globalized structure. Furthermore as inferred from the findings of the study, there is a great call for prompt and concerted efforts on several fronts in order to find ways of coping with the growing degree of window dressing account, the malady of accounting noise and fraud skyrocketing syndrome in the business and the non-business world due to non-adhering to tenets of information technology when carrying out an accounting line of works. Recommendation: It was consequently recommended that the accounting line of work will be greatly enhanced if information technology is allowed to penetrate and dominate accounting practices and operations.

Keywords: Accounting, Accounting profession, Global system, Information, Line, Technology, Works.

References:

1. J. T. Anthony, Computer Information System: A vital component of the Business views and management, 1st ed. Calabar; Wusen Press, 1993, p. 30. 2. B. A. Akpan, Computer and accounting information system, 2nd ed., Nigeria: PZW Publishers, 2003, pp.20-24. 3. O. Amana, Information Technology. This Day Reviews, volume 15 (number 14), 1999, pp. 15-16. 4. A. I. Asuquo, Analysis of the impact of information technology on forensic accounting practices in Cross River State-Nigeria. International Journal of Scientific and Technology Research, volume 1 (number 7), 2012, pp. 28-30. 5. A. Bamidele, Influence of information technology on accounting and auditing firm performance. Journal of Accounting Information Systems in Nigeria, volume 6 (number 2), 2009, pp. 220-229. 6. A. C. Chukwu, Exactly how to hand-pick accurate IT program for accounting practices. Journal of College of Accountancy, Nigeria volume 8 (number 3), 2001, pp. 67-68. 7. O. Clem, Information Technology and the Bank Seminar paper (Unpublished), 1993, pp. 20-24. 8. H. S. Dordick, The Emerging World of Information Business, London: Evans Book, 1983, pp.120-122. 9. A. M. Franklin, Computer software procurement: A business stratagem scrutiny. African Journal of information technology, volume 2 (issue 3), 2014, pp. 154-162. 10. T. Forester, The Information Technology Revolution, Oxford: Basil Blackwell, 1985, pp. 50-65. 11. M. Hammer, Re-engineering Work: Don’t Automate, Obliterate. Harvard Business Review, 1990, July –August, pp.104-12. 12. A. Hopwood, The Archaeology of Accounting Systems, NY: Oxford Press, 1987, pp. 206-208. 13. R. P. James, Enterprise Resource Program software. International Journal of Research & Development Institute, Accra, volume 3 (issue 4), 2012, pp. 124-137. 14. A. Moghaddam, S. Baygi, R. Rahmani and M. Vahediyan, The impact of information technology on accounting scope in Iran. Middle-East Journal of Scientific Research, volume 12 (number 10), 2012, pp. 13-48. 15. N. C. Osisioma, Problems and the prospect of the accounting profession in the new Millennium, Benin City: Presented at NAA, 1998, pp. 7-12. 16. B. C. Osisioma and H. E. Osisioma, Management Practice Manual for Professional Accounting Students, Jos: College of Accountancy Publication, 2002, pp. 56-70. 17. G. Robert, Management Information System for Modern Management, 2nd ed. USA: Prentice-Hall Press, 1993, pp. 123-134. 18. S. O. Samuel, Management Information System for Academic and professional students, 2nd ed. Calabar: Wusen Press, 2002, pp. 30-34. 19. S. T. Sunday, Accounting information system: Easy approach, 2nd ed., Nigeria: Index Educational Publishers, 2013, pp.40-42. 20. D. A. Sampson, Impact of information on Accounting profession and practices in the 21st century. Devons Journal of Accounting and Business Management, volume 3 (number 4), 2006, pp.100-122. 21. C. T. Solomon, Accounting practices: The new Dimensions, 2nd ed. Nigeria: QA & Co. Press, 2003, p.34. 22. E. Tafewa, Management accounting information: Evidence from decision making. Journal of Accounting Research, volume 2 (number 6), 2012, pp.101-106. 23. C. M. Udoka, Management information and computer systems. Journal of Financial and Accounting Research, volume 4 (number 2), 2012, pp.192-210. 24. N. A. Vincent and Y. M. Joseph, Technological development: Managerial line of attack. International Journal of Business and Management, volume 6 (number 5 200, pp.256- 270. 25. K. M. Victor, Easy way out of manual accounting practices, 2nd ed. Calabar: King & Son Printing, 2001, pp. 58-84. 26. P. W. Watson, Empirical evaluation of the problems and prospects of information technology. Journal of Accounting Research, Duncan Series, volume 2 (number 4), 2004, pp. 78-98. 27. A. P. Williams, Effect of ICT on accounting practices. MBA project, University of Calabar, 2001, (Unpublished). 28. E. B. Williamson, Developing an accounting information system for rapid decisions. Training workshop operative papers for accounting trainees in Governments, 1996, (Unpublished). 29. A. D. Wusen, The accounting profession in the new millennium: Problems and prospects. Journal of Association of National Accountants of Nigeria, volume 4 (number 5), 2000, pp. 50-51. 30. D. A. Xapher, Developing computer software for operational accounting, 2nd ed. Ibadan: Modern Press and Artworks, 013, pp.65. Authors: Liubov Syniavska, Oksana Grytsyna, Heorhiy Cherevko, Olha Sholudko, Ruslana Sodoma

Paper Title: Direct Taxation of Agricultural Enterprises Abstract: Financial support of the current activity and perspective economic development of the agricultural enterprises in Ukraine still remains a significant problem nowadays. Under the condition of limited budget resources, the necessity of activation of mechanism of government control of economic processes, that is conducted by the methods of legal, administrative and financial economic influence, deserves special attention. Key position in the government economic regulation belongs to the methods of tax adjustment which include the 303-307 usage of budget, tax, customs, money credit, price making and investment policies means. The study of tax 59. regulation of the agrarian sector of the economy is getting special significance, taking into account the lack of financial resources of agricultural enterprises, tendency to the reduction of the budgetary expenditures on the industry development, intensification of socio-economic problems because of political and economic instability

in the country and integration of Ukraine into the world economic community. One of the most acute issues of the current development of the agrarian sector of the economy is the creation of the efficient mechanism of taxation. Today the situation is such that despite the growth of state budget revenues, agrarian business is constantly exposed to tax changes. Though, the practice of law application shows that amendments to the Ukrainian laws concerning taxation very often cause new problems. They relate primarily to the growth of the tax burden on small producers, in particular, farms. Agroholdings minimize their tax payments by acquiring the status of taxpayers of the 4th group. This allows them to accumulate significant funds for investment. Agricultural companies do not legally pay income tax on the land they use. Instead, they pay a meager tax under the simplified system of taxation in agriculture, the amount of which is many times smaller than if they paid under the general system of taxation. The scientific novelty of the obtained results is presented by a set of theoretical and practical aspects of the study, namely proposals for the current state of taxation of agricultural enterprises and recommendations for improving the security of the Ukraine`s taxation.

Keywords: agricultural producers, taxation system, direct taxation, tax relations.

References:

1. Barbone, L., Bird, R. M., & Vázguez-Caro, J. (2012). The Costs of VAT: A Review of the literature. [The Costs of VAT: A Review of the literature] Center for Economic and Social Research, Case Networ Reports No. 106/2012. doi: https://doi.org/10.2139/ssrn.2024880 2. Hellerstein, W., & Grills, T. H. (2010, April 26). The VAT in the European Union. Tax Analysts, 461-471. Retrieved from https://taxprof.typepad.com/files/127tn0461.pdf 3. Keen, M. (2012). Taxation and Development - Again. International Monetary Fund, Fiscal Affairs Department. IMF Working Paper12/220. Retrieved from https://www.imf.org/ external/pubs/ft/wp/2012/wp12220.pdf 4. Killington, K., & Dylewski, P. (2017, June 15). VAT deduction and noneconomic activities: art or science? Tax Journal, 14-15. Retrieved from https://home.kpmg.com/content/ dam/kpmg/uk/pdf/2017/06/vat-deduction-and-non-economic-activities.pdf 5. Kosova, T., Slobodyanyuk, N., Polzikova, H., & Šatanová, A. (2018) Tax gap management: theory and practice. Economic Annals-XXI, 174(11-12), 22-28 Retrieved from https://doi.org/10.21003/ea.V174-04 6. Lupenko Yu.O (2014) Special taxation modes in the Ukrainian agricultural sector. (The CAP and competitiveness of the Polish and European food sectors) 6:106–117Retrieved from http://soskin.info/ea/2010/3-4/201022zmist.html [In Ukrainian] 7. Ministry of Finance of Ukraine (2019). Richnij zvit 2019. Available at: https://mof.gov.ua/uk/set-of-summarizing-tax- consultations Retrieved from https://mof.gov.ua/uk [In Ukrainian] 8. Simović, H., & Deskar-Škrbić, M. (2016). Value Added Tax Efficiency in Croatia. EFZG Working paper series, 16-02. Zagreb: Faculty of Economics. Retrieved from https://hrcak.srce.hr/file/223529 [In Croatian] 9. Sоskіn, О. І. (2010). Transformations of the tax system in the context of the modern economic model of Ukraine. Economic Annals-XXI, 3-4, 7-14. Retrieved from http://soskin.info/ea/2010/3-4/201022zmist.html [In Ukrainian] 10. Sinchak V. P. (2014) Taxation of agricultural enterprises - owners of vehicles. Economics of AIC 15-26. Retrieved from http://bigpo.ru/potr/ agricultural enterprises /part-4.html 11. The Law of Ukraine On fixed agricultural tax. (of 17 December 1998 № 320-XIV) http://zakon0.rada.gov.ua/laws/show/320-14. [In Ukrainian] 12. The Law of Ukraine On making changes to the article 9 of the Law of Ukraine “On fixed agricultural tax”. (of 03.02.99 № 414- XIV) http://zakon0.rada.gov.ua/laws/show/414-14. [In Ukrainian] 13. The Law of Ukraine On making changes to the Law of Ukraine “On fixed agricultural tax”. (№ 659-IV of 03.04.2003) http://zakon0.rada.gov.ua/laws/show/659-15. [In Ukrainian] 14. The Law of Ukraine On making changes to some laws of Ukraine as to activity regulation in the agrarian sector of the economy. The Law of Ukraine (№ 974-IV of 19.06.2003). http://zakon0.rada.gov.ua/laws/show/974-15. [In Ukrainian] 15. The Law of Ukraine On making changes to some Laws of Ukraine as to the taxation of agricultural enterprises and support of social standards of their workers. (№ 1878-IV of 24.06.2004). http://zakon0.rada.gov.ua/laws/show/1878-15. [In Ukrainian] 16. The Law of Ukraine On making changes to the Internal Revenue Code of Ukraine and some legislative acts of Ukraine as to tax reform. (№71-19 of 28.12.2014). http://zakon0.rada.gov.ua/laws/show/71-19. [In Ukrainian] 17. The Law of Ukraine On making changes to the Internal Revenue Code of Ukraine and some legislative acts of Ukraine as to provision of balance of budgetary receipts in 2016. (№ 909-VIII of 24.12.2015) http://zakon0.rada.gov.ua/laws/show/909- 19/page [In Ukrainian] 18. Tulush LD (2014) Enforcement of regulatory functions of the direct taxation in agribusiness Scientific notes of “KROK” University. (J Economics) 35, 85-93 Retrieved from http://nbuv.gov.ua/UJRN/Vzuk_2014_35_14. [In Ukrainian] 19. Tulush L. D (2015) Tax policy efficiency in agriculture of Ukraine. Economic Annals-XXI (5-6), 49-52 Retrieved from http://soskin.info/en/ea/2015/5-6/contents_12.html 20. Yakubiv, V.; Sodoma, R.; Hrytsyna, O.; Pavlikha, N.; Shmatkovska, T.; Tsymbaliuk, I.; Marcus, O.; Brodska, I. (2019). Development of electronic banking: a case study of Ukraine. [Development of electronic banking: a case study of Ukraine]. Entrepreneurship and Sustainability Issues, 7(1): 219-232. Retrieved from http://doi.org/10.9770/jesi.2019.7.1(17) [In English] Authors: Rohan Namdeo, Sahil Sharma, Varun Anand, Chanchal Lohi

Paper Title: Smart Automated Surveillance System using Raspberry Pi Abstract: With the fast-growing world, frequent attacks and burglaries are increased. Therefore, the need for an effective and reliable surveillance security system has become an indispensable necessity to fulfill various security aspects and add quality to human life. The existing security systems use CCTV cameras and computers. It also consumes a lot of memory because of continuous recording and needed manpower to detect unauthorized activities and instant notification is also not possible in these surveillance security systems. So we researched the surveillance part and mainly on the burglary part where during the absence of the owner the camera will detect motion and will send instant notification to the user when motion is detected. Compared to existing surveillance 60. systems, the use of Raspberry pi is effective because of its size, low power, and memory consumption, wireless features, and many more effective aspects. In this paper, we proposed an IoT based surveillance security system that can be accessed remotely with the use of the internet. This framework can be used in homes and personal 308-311 offices. The framework works best in confined spaces and when the space in which it is being used has the absence of the owner. This is because the system will detect any movement occurring in the space. Keywords: Smart surveillance, Raspberry Pi, Web Camera, Wireless, MotionEye.

References:

1. International Journal of Applied Information Systems (IJAIS)–ISSN: 2249-0868 Foundation of Computer Science FCS, New York, USA Volume 10 –No.5, February 2016 –www.ijais.org 2. An Internet of things approach for motion detection using Raspberry Pi,978-1-4799-7534-1/14$31.00©2015 IEEE 3. Raspberry Pi. Raspberry Pi, n.d. Web. Oct. 2013. http://www.raspberrypi.org 4. Simon Monk, Raspberry pi Cookbook, First edition, ISBN: 978-1-449-36522-6. Authors: Jyoti, Peri Arjun

Paper Title: Diabetes Mellitus Prediction using Ensemble Machine Learning Techniques Abstract: The healthcare industry is inflicted with the plethora of patient data which is being supplemented each day manifold. Researchers have been continually using this data to help the healthcare industry improve upon the way major diseases could be handled. They are even working upon the way the patients could be informed timely of the symptoms that could avoid the major hazards related to them. Diabetes is one such disease that is growing at an alarming rate today. In fact, it can inflict numerous severe damages; blurred vision, myopia, burning extremities, kidney and heart failure. It occurs when sugar levels reach a certain threshold, or the human body cannot contain enough insulin to regulate the threshold. Therefore, patients affected by Diabetes must be informed so that proper treatments can be taken to control Diabetes. For this reason, early prediction and classification of Diabetes are significant. This work makes use of Machine Learning algorithms to improve the accuracy of prediction of the Diabetes. A dataset obtained as an output of K-Mean Clustering Algorithm was fed to an ensemble model with principal component analysis and K-means clustering. Our ensemble method produced only eight incorrectly classified instances, which was lowest compared to other methods. The experiments also showed that ensemble classifier models performed better than the base classifiers alone. Its result was compared with the same Dataset being applied on specific methods like random forest, Support Vector Machine, Decision Tree, Multilayer perceptron, and Naïve Bayes classification methods. All methods were run using 10k fold cross-validation.

Keywords: Diabetes, Machine learning, Ensemble, Dataset.

References:

1. Insulin and Diabetes, Diabetes UK (2019). https://www.diabetes.org.uk/guide-todiabetes/ managing-your-diabetes/treating-your- diabetes/insulin. Accessed 30 May 2020 2. Narges Razavian, Saul Blecker, Ann Marie Schmidt, Aaron Smith-McLallen, Somesh Nigam, and David Sontag.Big Data.Dec 2015.277-287.http://doi.org/10.1089/big.2015.00 3. T.G. Dietterich. Ensemble methods in machine learning. In J. Kittler and F. Roli, editors, Multiple Classi_er Systems. First 61. International Workshop,MCS 2000, Cagliari, Italy, volume 1857 of Lecture Notes in Computer Science,pages 1{15. Springer- Verlag, 2000. [48] 4. L.I. Kuncheva. Combining Pattern Classifers: Methods and Algorithms. Wiley-Interscience, New York, 2004. [116] 312-316 5. Han L, Diao L, Yu S, et al. The Genomic Landscape and Clinical Relevance of A-to-I RNA Editing in Human Cancers. Cancer Cell. 2015;28(4):515‐528. doi:10.1016/j.ccell.2015.08.013 6. Polat K, Güneş S. A hybrid approach to medical decision support systems: combining feature selection, fuzzy weighted preprocessing and AIRS. Comput Methods Programs Biomed. 2007;88(2):164‐174. doi:10.1016/j.cmpb.2007.07.013 7. Duygu Çalişir and Esin Doğantekin. 2011. An automatic diabetes diagnosis system based on LDA-Wavelet Support Vector Machine Classifier. Expert Syst. Appl. 38, 7 (July, 2011), 8311–8315. DOI:https://doi.org/10.1016/j.eswa.2011.01.017 8. Razavian N, Blecker S, Schmidt AM, Smith-McLallen A, Nigam S, Sontag D. Population-Level Prediction of Type 2 Diabetes From Claims Data and Analysis of Risk Factors. Big Data. 2015;3(4):277‐287. doi:10.1089/big.2015.0020 9. Kavakiotis, Ioannis & Tsave, Olga & Salifoglou, Athanasios & Maglaveras, N. & Vlahavas, I. & Chouvarda, Ioanna. (2017). Machine Learning and Data Mining Methods in Diabetes Research. Computational and Structural Biotechnology Journal. 15. 10.1016/j.csbj.2016.12.005. 10. Eleni I. Georga, Vasilios C. Protopappas, Diego Ardigò, Demosthenes Polyzos, and Dimitrios I. Fotiadis.Diabetes Technology & Therapeutics.Aug 2013.634-643.http://doi.org/10.1089/dia.2012.0285 11. Ozcift A, Gulten A. Classifier ensemble construction with rotation forest to improve medical diagnosis performance of machine learning algorithms. Comput Methods Programs Biomed. 2011;104(3):443‐451. doi:10.1016/j.cmpb.2011.03.018 12. Han, J., Kamber, M., Pei, J.: Data Mining Concepts and Techniques, 3rd edn, pp.370–382. Morgan Kaufmann, Burlington (2011) 13. Wolpert, David. (1992). Stacked Generalization. Neural Networks. 5. 241-259. 10.1016/S0893-6080(05)80023-1. 14. Witten, I., Frank, E., Hall, M.: Data Mining Practical Machine Learning Tools and Techniques, 3rd edn, pp. 166–580. Morgan Kaufmann, Burlington (2011). 15. Yue, C., Xin, L., Kewen, X., and Chang, S. (2008). “An intelligent diagnosis to type 2 diabetes based on QPSO algorithm and WLS-SVM,” in Proceedings of the 2008 IEEE International Symposium on Intelligent Information Technology Application Workshops, Washington, DC. doi: 10.1109/IITA.Workshops.2008.36 16. N.H. Barakat, A.P. Bradley, M.N.H. Barakat. (2010). Intelligible SupportVector Machines for Diagnosis of Diabetes Mellitus. IEEE Transactions on Information Technology in Biomedicine. 14(4), pp.1114-1120. 17. Larabi-Marie-Sainte, S.; Aburahmah, L.; Almohaini, R.; Saba, T. Current Techniques for Diabetes Prediction: Review and Case Study. Appl. Sci. 2019, 9, 4604. 18. Nguyen, BK, Patel, NM, Arianpour, K, et al. Characteristics and management of sinonasal paragangliomas: a systematic review. Int Forum Allergy Rhinol. 2019; 9: 413– 426. 19. Bradley, P., & Fayyad, U. (1998). Refining initial points for k-means clustering. Proc. 15th International Conf. on Machine Learning. 20. Chris Ding and Xiaofeng He. 2004. K-means clustering via principal component analysis. In Proceedings of the twenty-first international conference on Machine learning (ICML ’04). Association for Computing Machinery, New York, NY, USA, 29. DOI:https://doi.org/10.1145/1015330.1015408 Authors: Indra Jeet Yadav, Rudra Pratap Singh Effect of Welding Current and Electrodes on Reinforcement Height in Shielded Metal Arc Welding Paper Title: 62. Process Abstract: Joining of materials is the need of modern industries and stuctures. Shielded metal arc welding process is one of the most popular and commonly used method of joining materials. The weld reinforcement 317-320 height should be optimum for mechanical properties of the weld. If the reinforcement height is less or negative, it is not recommended considering strength of weld as surface area will be reduced and if the reinforcement height is more, it will produce stress concentration which is not recommended. In the present work the investigation of the effect of three different types of electrodes at three different welding currents in shielded metal arc welding process utilizing Low Carbon Steel plate of API 5L Grade X 52, was done for reinforcement height. The three different electrodes as E 6013, E 7016 and E 7018 and the varying currents as 90 A, 100 A and 110 A. Total 18 pieces were used to obtain 9 welds which were used to analyze the effect of current and the electrode on reinforcement height. The dimensions of the work pieces were taken as 75 mm x 50 mm x 5 mm. The values of reinforcement height in each weld were written in a table and respective diagrams were drawn to make clear the effect of welding current on reinforcement height for the three different electrodes.

Keywords: Electrode, Current, Structure, reinforcement height, Arc.

References:

1. Kim I. S., Son J. S., Park C. E. and Kim I. J., Kim H. H. (2005) An investigation into an intelligent system for predicting bead geometry in GMA welding process. Journal of Materials Processing Technology. 159 (2005) 113 – 118. 2. Tewari S. P., Ankur Gupta and Jyoti Prakash (2010). Effect of welding parameters on the weldability of materials. International journal of engineering science and technology volume 2(4), 512-516. 3. Bahman, A.R. (2010). Change in Hardness, Yield Strength and UTS of Welded Joints Reduced in ST 37 Grade Steel. Indian Journal of Science and technology, 1162-1164. 4. Abson, D. J., & Pargeter, R. J. (2013). Factors influencing as-deposited strength, microstructure, and toughness of manual metal arc welds suitable for C-Mn steel fabrications. International Metals Reviews, 31(1), 141-196. 5. Maksuti, R. Impact Of The Acicular Ferrite On The Charpy V-Notch Toughness Of Submerged Arc Weld Metal Deposits. International Journal of Scientific & Engineering Research, Volume 7, Issue 8, August-2016. 1149-1155. 6. Sumardiyanto, D., Susilowati, S. E., & Cahyo, A. (2018). Effect of Cutting Parameter on Surface Roughness Carbon 7. Steel S45C. Journal of Mechanical Engineering and Automation, 8(1), 1-6. Authors: Surapu Ramlal, Ponnana RamPrasad, Dora Prudhvi Raju Experimental Inspection on Shear Capacity in RCC Beams with Partial Replacement of Recycled Paper Title: Coarse Aggregates Abstract: Demolition waste increasing day by day. The old damaged building materials can be used in present buildings or other construction works. Especially the recycled aggregates are useful to the concrete structures. The experimental studies on the use of recycled coarse aggregate has been going on for many countries. This publication focuses on the relationship between the shear capacity and the flexural cracking load of reinforced recycled concrete beams with stirrups, this experimental Inspection with partial replacement of natural coarse aggregates (NAC) with recycled coarse aggregates (RAC) at different ages as 10, 20 and 30 years in various proportions as 20 per cent, 30 per cent, 40 per cent. For this, M30 grade of concrete is consider. Curing of specimens were done for 7 day and 28 days to conclude the maximum strengths. The obtained results of concrete with partial replacement of recycled aggregates of 10,20and 30 years age group conclude maximum compressive strength of 35.84 N/mm2 at 40% replacement of NCA with RCA of age group (10 years) and 34.12 N/mm2 at 30% replacement of NCA whit RCA of (20 years) age group and 36.14 N/mm2 20% replacement of NCA with RCA of age group (30 years). After the compressive strength, beam specimens were casted for 7day and 28 days. Based on test results of 8 beams, the relationship between the cracking load that causes a beam to crack in the middle of the shear span and the beam's shear capacity is confident. All beams are reinforced in the longitudinal 63. direction only and only tested under two-point loading conditions. The average analytical cracking load ratio is 0.60.the mid-shear span at cracking load (Vcr-a/2) in comparison with the observed shear capacity (Vexp). The analytical cracking load ratio. The analytical cracking’s load was used in this exploration as it is more reliable 321-326 than the observed cracking load. At mid-span, the shear capacity of most of the beams was shown to be 50%. The average shear capacity ratio to the related test crack load in the center of the shear span 0.43. The analysis showed that cracking loads are strongly related to the shear capacity of the members. This relationship can be used to develop recycled reinforced beam members ' shear design process.

Keywords: Shear cracking load, shear capacity, recycled coarse aggregate, compressive strength.

References:

1. M.S.Alam, A.Hussein, “Relationship between the shear capacity and the flexural cracking load of FRP reinforced concrete beams” construction and building materials, 2017-Elsevier. 2. Ponnana Ramprasad, Allu Manikanta,“ An Experimental Research on partial Replacement of Coarse Aggregate with Recycled Aggregate And Fine Aggregate with Granite Powder.”IJITEE ISSN:2278-3075,Volume-8,Issue-9S2, July 2019 3. S.Ramlal, Pravesh Jha, “Strength Behavior of M25 Grade Concrete Mixed with Two Natural Fibers in Both Curing and Without curing condition”IJITEE ISSN:2278-3075,Volume-8,Issue-9S2,July 2019 4. IS: 456-2000: Plain and Reinforced concrete code of Practice, BIS, and New Delhi. 5. IS: 383-1970: Specification for Coarse and Fine Aggregate From natural source For Concrete, BIS, New Delhi-1970. 6. IS:2386(part 1,3)-1963: Methods of Test for Aggregates for Concrete, BIS, NEW Delhi-1963. Authors: Vinit Kumar Singh, Ashu Verma, T. S. Bhatti

64. Paper Title: Small Scale Stability of Isolated Rural Microgrid Based on Load Characteristics Abstract: Rural areas are either weakly connected to grid or have no grid access. Therefore, hybrid system is 327-334 only solution for continuous undisrupted power supply. Also these rural microgrids have specific types of load. Small systems are more vulnerable to load disturbances and therefore, frequency and voltage variation have post disturbance effect on the system stability. Four different types of loads having exponential voltage and frequency characteristics are considered for the study. EPRI Load modeling has been considered based on aggregate index calculation. The system is studied for step increase/decrease in load as well as input power to the wind energy system and also the post effect of voltage & frequency change on the load with its influence on the system is considered for study purpose. The isolated microgrid proposed has wind energy system with PMSG and biogas genset (BG) with synchronous generator (SG). The system is designed in such a way that any increase/decrease in load and input power to wind is taken up by the biogas system. No additional reactive power compensator is envisaged. Accordingly, the controllers are tuned using Integral Square Error criterion for mitigate variation in load voltage & frequency.

Keywords: Microgrid, renewable energy, wind energy system(WES), biogas generator(BG), synchronous generator, permanent magnet synchronous generator.

References:

1. Robert Lasseter, Abbas Akhil, Chris Marnay, John Stephens, “White Paper on Integration of Distributed Energy Resources, The CERTS Microgrid Concept”, CERTS, April, 2002. 2. N.H.S. Ray, M.K. Mohanty and R.C. Mohanty, “A Study on application of biogas as fuel in compression ignition engine”, IJIET, Vol.-3, Issue-1, Oct 2013. 3. M. Orabi , F. El-Sousy , H. Godah , M.Z.Youssef, “High-performance induction generator-wind turbine connected to utility grid”, INTELEC 2004, 26th Annual International Telecommunications Energy Conference, IEEE. 4. Sagar P. Burud , Trishul B. Patil , Uday S. Mirje, Somesh S. More, Gauri S. Mane , Snehal S. Mulik, “Requirement of Minimum Capacitor to Build-Up and Maintain the Voltage in Self Excited Induction Generator”, 2018-International Conference On Advances in Communication and Computing Technology (ICACCT), IEEE. 5. Aakriti Pandey, Ashok Kumar Pandey, “Reactive power compensation for a doubly fed induction generator based WECS”, 2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), IEEE. 6. P Sharma and T.S. Bhatti, “Performance Investigation of Isolated Wind–Diesel Hybrid Power Systems With WECS Having PMIG”, IEEE Trans. on Industrial Electronics, Vol. 60, No. 4, April 2013. 7. Suji Muhammed, Krishnakumari V, “Performance Analysis of a PMSG Based Wind Energy Conversion System”, International Journal of Engineering Research & Technology (IJERT), Vol. 3 Issue 8, August – 2014. 8. Alepuz, S., Calle, A.,Busquets-Monge, S., Kouro, S., Wu, B., “Use of stored energy in PMSG rotor inertia for low-voltage ride- through in back-to-back NPC converter-based wind power systems”, IEEE Trans. Ind. Electron. 2013, 60, 1787–1796. 9. Revel, G., Leon, A.E., Alonso, D.M., Moiola, J.L., Dynamics and stability analysis of a power system with a PMSG-based wind farm performing ancillary services”, IEEE Trans. Circuits Syst. 2014, 61, 2182–2193. 10. D. K. Yadav, T. S. Bhatti and Ashu Verma, “Study of Integrated Rural Electrification System Using Wind-Biogas Based Hybrid System and Limited Grid Supply System”, International Journal of Renewable Energy Research, Vol.7, No.1, 2017. 11. L. Wang, and Y. Lin, “Analysis of a commercial biogas generation system using a gas engine-induction generator set”, IEEE Transaction on Energy Conversion, vol. 24, no.1, pp. 230-239, 2009. 12. S. R. Arya, R. Niwas, K. Kant Bhalla, B. Singh, A. Chandra and K. A. Haddad, "Power Quality Improvement in Isolated Distributed Power Generating System Using DSTATCOM," IEEE Transactions on Industry Applications, vol. 51, no. 6, pp. 4766-4774, 2015. 13. B. V. Ga and T. V. Nam, "Appropriate structural parameters of biogas SI engine converted from diesel engine," IET Renewable Power Generation, vol. 9, no. 3, pp. 255-261, 2015. 14. B. J. Seshaprasad and D. Mikkelsen, “Load Models for Power System Stability Studies”, www.iitk.ac.in /npsc/papers. 15. Load representation for dynamic performance analysis [of power systems], IEEE Trans. Power Systems, Vol.8, no.2, pp. 472- 482, May 1993. 16. M.Farrohabadi and K.Bhattachraya, “Frequency Control in Isolated/Islanded Microgrids through voltage regulation”, IEEE Transaction, Smart Grid, Sept 2015. 17. M. Yang, S. Yuanyuan, F. Yang, S. Xiangjing, W. Chengshan , W. Jianhui,“Frequency and Voltage Coordinated Control for Isolated Wind–Diesel Power System Based on Adaptive Sliding Mode and Disturbance Observer”, IEEE Transactions on Sustainable Energy, Volume: 10, Issue: 4,2019. 18. S. Mishra, C.K. Panigrahia and D.P. Kothari, “Design and simulation of a solar–wind–biogas hybrid system architecture using HOMER in India”, International Journal of Ambient Energy 37(2):1-8 · December 2014. A M Razmy, Faisal Mohamed Ababneh, Ahmed Al-Hadhrami, Mohammad Zakir Hossain, Sadoon Authors: Abdullah Ibrahim Al-Obaidy Paper Title: Performance of Joint Quality Monitoring Schemes under Gaussian distribution Abstract: Jointly monitoring the process mean and variance has become a well-known topic in statistical quality control literature after it is considered as a bivariate problem. Many joint monitoring schemes have been proposed by using the Shewhart, cumulative sum and exponentially weighted moving average techniques. In this paper, best performing schemes from each technique has been selected and compared for their performance using average run length properties. It was found that selection of better joint monitoring scheme based on the shift in mean and variance to be detected quickly. In particular, the Shewhart distance joint monitoring scheme 65. performs well when there is larger shifts in mean, variance or in both. In addition, the Shewhart distance joint monitoring scheme performs specific when there is no shift in mean and decrease in variance. For the smaller 335-340 shifts in mean, variance or in both, cumulative sum and exponentially weighted moving average joint monitoring schemes can be recommended. At this scenario exponentially weighted moving average joint monitoring scheme performs marginally better than the cumulative sum scheme.

Keywords: Average run length, Control chart, Cumulative sum, Exponentially weighted moving average, Joint monitoring scheme, Shewhart scheme.

References:

1. F.F. Gan,. “Joint Monitoring of process mean and variance,” Nonlinear Analysis, Theory, Methods and Applications, vol. 30, series 7, pp. 4017–4024, 1997. 2. A.K. McCracken, and S. Chakaborthi,. “Control Charts for Joint Monitoring of mean and variance: An overview,” Journal of Quality Technology & Quantitative Management, vol. 10 series 1, pp. 17–36, 2013. 3. F.F. Gan, K.W. Ting, and T. Chang,. “Interval charting schemes for joint monitoring of process mean and variance,” Quality and Reliability Engineering International, vol. 20 series 4, pp. 291 – 303, 2004. 4. A. Haq, J. Brown, and E. Moltchanova. “Effect of measurement error on exponentially weighted moving average control charts under ranked set sampling schemes”. Journal of Statistical Computation and Simulation 85: 1224–1246, 2015. 5. M.R Maleki, and A. Amiri. “Simultaneous monitoring of multivariate-attribute process mean and variability using artificial neural networks”. Journal of Quality Engineering and Production Optimization 1: 43–54, 2015. 6. A. Amiri, R. Ghashghaei, and M.R. Maleki. “On the effect of measurement errors in simultaneous monitoring of mean vector and covariance matrix of multivariate processes”. Transactions of the Institute of Measurement and Control, 40(1), 318–330, 2018. 7. A.M. Razmy. “Joint Monitoring of Process Mean and Variance with Shewhart Distance Scheme,” Sri Lankan Journal of Applied Statistics, vol. 2, pp. 14-26, 2010. 8. A.M. Razmy, and T.S.G. Peiris. “Performance Comparison of Shewhart Joint Monitoring Schemes for Mean and Variance”. National Engineering Conference, 2013, 19th ERU Symposium, Faculty of Engineering, University of Moratuwa, Sri Lanka. Vol. 19, 99 -104, 2013. 9. A.K. McCracken, S. Chakraborti, and A. Mukherjee. “Control Charts for Simultaneous Monitoring of Unknown Mean and Variance of Normally Distributed Processes”. Journal of Quality Technology Vol. 45 No. 4, pp. 360-376, 2013. 10. T.C. Chang, and F.F. Gan. A cumulative sum control chart for monitoring process variance. Journal of Quality Technology, 27, 109–119, 1995. 11. F.F. Gan. “Joint Monitoring of process mean and variance using exponentially weighted moving average control charts”. Technometrics, 37, 446–453, 1995. 12. S.V. Crowder. “Design of exponentially weighted moving average schemes”. Journal of Quality Technology, 21, 155–162, 1989. 13. T.C. Chang, and F.F. Gan. “Optimal designs of one-sided EWMA charts for monitoring a process variance”. Journal of Statistical Computing & Simulations, 49, 33–48, 1993. 14. A.M. Razmy, and T.S.G. Peiris. “A Standard method to Compare the combined Quality Monitoring Schemes using Average Run Length Properties,” Second International Symposium, South Eastern University of Sri Lanka, 2012, pp. 172-173, 2012. Authors: Vinay V, Adiseshu S, Chandramouli S

Paper Title: Field Application of Pervious Concerte for Recharge of Groundwater Abstract: In the present study, an attempt has been made to investigate strength and permeability of pervious concrete made with different combinations of aggregate sizes (20mm,12.5mm and 10mm) and different mix proportions using flyash and super platiciciser). The main objective of this investigation is to apply the pervious concrete through a footpath to improve groundwater recharge by finding out the best combination of grading of aggregates and also the mix proportion with fly ash for obtaining optimal permeability and strength. The effect of partial replacement of cement with fly ash and super plasticizer on the compressive strength and the water permeability of pervious concrete are investigated. The analysis of the test results indicated that the proposed combination of materials have increased the compressive strength significantly and also, the water permeability. Even though, the individual performances (maximum strength and maximum permeability) of some of the combinations obtained are good, but it is expected in the study to have reasonable values for both to use pervious concrete in the field. Hence, in this study, it is considered the intersection point on the strength versus permeability graph as the best combination. So, the combination with 40% of 20mm, 30% of 12.5 mm and 10mm and 10%flyash with 90% opc (53 grade) without super plasticizer considered as the best which gives 24 MPa and 15.6 mm/s permeability.. A footpath of size 1.2 m (width) x 0.25 m (thickness) x 19 m (length) is selected for laying pervious concrete in the form of number of panels. A constant discharge is applied on to the footpath in lateral direction and it is found that the absorption capacity of the laid mix in the field is 115.52 litres/ m length.

66. Keywords: Pervious concrete, supplementary cement materials, fly ash, permeability, compressive strength

341-350 References:

1. Aoki, Y Sri Ravindrarajah R. and Khabbaz, H. (2012). "Properties of Pervious Concrete Containing Fly Ash", Road Materials and Pavement Design, Vol.13, No.1,PP:1-11, Taylor and Francis Group. 2. AnushKChandrappa and Krishna PrapoornaBiligiri (2016). “Pervious concrete as a sustainable pavement material – Research findings and future prospects: A state of the art review”, Journal of Construction and Building Materials (Elseveir), Vol.111, PP.262-274. 3. Deepashri S, Mohanraj. N and Krishnaraj C. (2016). “An Experimental Study on The Durability Characteristics of Pervious Concrete”, ARPN journal of Engineering and Applied sicences, Vol.11, No.9,PP:6006-6009. 4. Jing Yang Guoliang Jiang (2003). “ Experimental study on properties of pervious concrete pavement materials”, Journal of Cement and concrete research, Vol.33, PP.381-386. 5. Jiusu Li Yi Zhang Guanlan Liu XinghaiPeng (2016). “Preparation and performance evaluation of an innovative pervious concrete pavement”, Journal of Construction and Building Materials (Elseveir), Vol.138, PP.479-485. 6. K Cosic L Korat V Ducman I Netinger (2015). “Influence of aggregate type and size on properties of pervious concrete”, Journal of Construction and Building Materials (Elseveir), Vol.78, PP.69-76. 7. Mohammed Sonebi, Mohammad Bassuoni and AmmarYahia (2016).” Pervious concrte mix design, properties and applications”, RILEM technical letters, Vol.1, Issue:1, PP;109-115. 8. RuiZhong Kay Wille (2016). “Compression response of normal and high strength pervious concrete”, Journal of Construction and Building Materials (Elseveir), Vol.109, PP.177-187. 9. Shahrul Azwan Shakrani, Afizah Ayob and Mohd AsriAb Rahim (2017). “Applications of waste material in the pervious concrete pavement: A review”, Proceedings of 3rd electronic and green materials International Conference. Vol 1885, Thailand. 10. Yu Chen Kejin Wang Xuhao Wang Wenfang Zhou (2013). “Strength, fracture and fatigue of pervious concrete”, Journal of Construction and Building Materials (Elseveir), Vol.42, PP.97-104. 11. Baoshan Huang, Hao Wu, Xiang Shu, Edwin G. Burdette (2010). “Laboratory evaluation of permeability and strength of polymer-modified pervious concrete”, Journal of Construction and Building Materials (Elseveir), Vol.42, PP.97-104. 12. TawatchaiTho-in, VanchaiSata, PrinyaChindaprasirt, Chai Jaturapitakkul (2012). “Pervious high-calcium fly ash geopolymer concrete”, Journal of Construction and Building Materials (Elseveir), Vol.30, PP.366-371. 13. YuwadeeZaetang, AmpolWongsa, VanchaiSata, PrinyaChindaprasirt (2015).”Use of coal ash as geopolymer binder and coarse aggregate in pervious concrete”, Journal of Construction and Building Materials (Elseveir), Vol.96, PP.289-295. 14. L. K. Crouch, P.E; Jordan Pitt; and Ryan Hewitt (2007), “Aggregate Effects on Pervious Portland Cement Concrete Static Modulus of Elasticity”, Journal of Materials in Civil Engineering (ASCE),Vol. 19(7), PP561-568. 15. Liv M. Haselbach, M (2010),”Potential for Clay Clogging of Pervious Concrete under Extreme Conditions”, Journal of Hydrologic Engineering (ASCE), Vol. 15(1), PP 67-69. 16. M.UmaMaguesvari, V.L. Narasimha,”Studies on Characterization of Pervious Concrete for Pavement Applications”, 2nd Conference of Transportation Research Group of India (Elsevier), Procedia - Social and Behavioral Sciences 104 ,PP.198 – 207. 17. William D. Martin III, Nigel B. Kaye, Bradley J. Putman (2014),” Impact of vertical porosity distribution on the permeability of pervious concrete”, Journal of Construction and Building Materials (Elsevier), Vol. 59, PP.78-84. 18. M. AamerRafiqueBhutta, K. Tsuruta , J. Mirza,”Evaluation of high-performance porous concrete properties”, Journal of Construction and Building Materials (Elseveir),Vol.31,PP.67-73 Authors: Shahnas S, Sreeletha S H

Paper Title: Generating Images of Face Poses for Pose Varying Face Recognition Abstract: Deep learning has attracted several researchers in the field of computer vision due to its ability to perform face and object recognition tasks with high accuracy than the traditional shallow learning systems. The convolutional layers present in the deep learning systems help to successfully capture the distinctive features of the face. For biometric authentication, face recognition (FR) has been preferred due to its passive nature. Processing face images are accompanied by a series of complexities, like variation of pose, light, face expression, and make up. Although all aspects are important, the one that impacts the most face-related computer vision applications is pose. In face recognition, it has been long desired to have a method capable of bringing faces to the same pose, usually a frontal view, in order to ease recognition. Synthesizing different views of a face is still a great challenge, mostly because in non-frontal face images there are loss of information when one side of the face occludes the other. Most solutions for FR fail to perform well in cases involving extreme pose variations as in such scenarios, the convolutional layers of the deep models are unable to find discriminative parts of the face for extracting information. Most of the architectures proposed earlier deal with the scenarios where the face images used for training as well as testing the deep learning models are frontal and nearfrontal. On the contrary, here a limited number of face images at different poses is used to train the model, where a number of separate generator models learn to map a single face image at any arbitrary pose to specific poses and the discriminator performs the task of face recognition along with discriminating a synthetic face from a realworld sample. To this end, this paper proposes a representation learning by rotating the face. Here an encoder-decoder structure of the generator enables to learn a representation that is both generative and discriminative, which can be used for face image synthesis and pose-invariant face recognition. This representation is explicitly disentangled from other face variations such as pose, through the pose code provided to the decoder and pose estimation in the discriminator.

67. Keywords: Pose variation, face recognition, generative adversarial network, adversarial loss.

References: 351-356

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68. Authors: M.E. Ojewumi, O.R. Obanla, G.P. Ekanem, P.C. Ogele, E.O. Ojewumi Paper Title: Anaerobic Decomposition of Cattle Manure Blended with Food Waste for Biogas Production Abstract: The concern on how food and livestock waste should be managed and recycled has greatly increased in the world. This research investigated the anaerobic decomposition (digestion) process for biogas production on dairy cattle manure (CM) and food waste (FW) using a bacteria as inoculum - Pseudomonas aeruginosa. CM and FW were co-digested with bacteria (P. aeruginosa) as the substrate. FW was allowed to decompose separately without inoculum for 30 days. Digesters (Bioreactor) were prepared in five places to monitor the maximum biogas production, generation rate of methane and number of days for the production of biogas. 1 to ratio 5ml and 10ml of FW were co-digested with P. aeruginosa (bacteria) in 2 proportion and also Cow manure with 1 to ratio 1 and 0.5ml in 2 proportions [ 1:5ml; 1:10ml and 1:1; 1:5ml]. Batch process operation was used under mesophilic condition (35⁰C) for the digesters/bioreactor. Production of biogas was notices on the third and fourth day after commencement for the digesters with cattle manure, fourth to fifth day for the digester (bioreactor) with bacteria and third day for the digester with only FW. FW and CM generated highest cumulative biogas with volume of 88.5g/kg.

Keyword: Decomposition, food waste, Pseudomonas aeruginosa, inoculum, methane, cattle manure, bioreactor.

References:

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Anaerobic 357-365 digestion of municipal solid waste and agricultural waste and the effect of co-digestion with dairy cow manure. Bioresource technology, 99, 8288-8293. 11. NAYONO, S. E. 2010. Anaerobic digestion of organic solid waste for energy production, KIT Scientific Publishing. 12. OJEWUMI, M.E. 2016. Optimizing the Conditions and processes for the production of Protein Nutrient from Parkia biglobosa seeds. A thesis submitted in partial fulfillment of the award of the degree of Ph.D in Chemical Engineering, Covenant University, Nigeria. 13. OJEWUMI, M.E., Omoleye, J.A., Ajayi, A.A.. 2017. Optimization of Fermentation Conditions for the Production of Protein Composition in Parkia biglobosa Seeds using Response Surface Methodology. International Journal of Applied Engineering Research, 12, 12852-12859. 14. OJEWUMI, M.E., EMETERE, M.E., AMAEFULE, C.., DURODOLA, B. & ADENIYI, O. D. 2018a. Bioconversion of Orange Peel Waste by Escherichia Coli and Saccharomyces Cerevisiae to Ethanol. International Journal of Pharmaceutical Sciences and Research, 10(3): 1246-1252. 15. OJEWUMI, M.,E., OGELE, P.C., OYEKUNLE, D.T., OMOLEYE, J.., TAIWO, S. O & OBAFEMI, Y.D. Co-digestion of cow dung with organic kitchen waste to produce biogas using Pseudomonas aeruginosa. Journal of Physics: Conference Series, 2019a. IOP Publishing, 012011. 16. OJEWUMI, M.E., OMOLEYE, J.A. & AJAYI, A.A. 2016a. The Effect of Different Starter Cultures on the Protein Content in Fermented African Locust Bean (Parkia Biglobosa) Seeds. International Journal of Engineering Research & Technology (IJERT), 5, 249-255. 17. OJEWUMI, M. E., OMOLEYE, J.A., NYINGIFA, S.A. 2018a. Biological and chemical changes during the aerobic and anaerobic fermentation of African locust bean. International Journal of Chemistry Studies, 2, 25-30. 18. OJEWUMI, M. E., E.V. ANENIH, TAIWO, O.S., ADEKEYE, B.T., AWOLU, O.O., OJEWUMI, E.O. 2018b. A Bioremediation Study of Raw and Treated Crude Petroleum Oil Polluted Soil with Aspergillus niger and Pseudomonas aeruginosa. Journal of Ecological Engineering, 19, 226-235. 19. OJEWUMI, M. E., AKWAYO, I. J., TAIWO, O. S., OBANLA, O. M., AYOOLA, A. A., OJEWUMI, E. O. & OYENIYI, E. A. 2018b. Bio-Conversion of Sweet Potato Peel Waste to BioEthanol Using Saccharomyces Cerevisiae. Bio-Conversion of Sweet Potato Peel Waste to BioEthanol Using Saccharomyces Cerevisiae, 8, 46-54. 20. OJEWUMI, M. E., AYOMIDE, A. A., OBANLA, O. M. & OJEWUMI, E.O. 2014. Pozzolanic properties of Waste Agricultural Biomass-African Locust Bean Pod Waste. World Journal of Environmental Biosciences, 6, 1-7. 21. OJEWUMI, M. E., EMETERE, M. E., BABATUNDE, D. E. & OKENIYI, J. O. 2017. In Situ Bioremediation of Crude Petroleum Oil Polluted Soil Using Mathematical Experimentation. International Journal of Chemical Engineering, Volume 2017, Article ID 5184760, 11 pages, https://doi.org/10.1155/2017/5184760 22. OJEWUMI, M. E., KOLAWOLE, O. E., OYEKUNLE, D., TAIWO, O. S. & ADEYEMI, A. 2019b. Bioconversion of Waste Foolscap and Newspaper to Fermentable Sugar. Journal of Ecological Engineering, 20, 35-41. 23. OJEWUMI, M. E., OBIELUE, B. I., EMETERE, M. E., AWOLU, O. O. & OJEWUMI, E. O. 2018c. Alkaline Pre-Treatment and Enzymatic Hydrolysis of Waste Papers to Fermentable Sugar. Journal of Ecological Engineering, 19, 211-217. 24. OJEWUMI, M. E., OKENIYI, J. O., IKOTUN, J. O., OKENIYI, E. T., EJEMEN, V. A. & POPOOLA, A. P. I. 2018d. Bioremediation: Data on Pseudomonas aeruginosa effects on the bioremediation of crude oil polluted soil. Data in Brief, 19, 101- 113. 25. OJEWUMI, M. E., OKENIYI, J. O., OKENIYI, E. T., IKOTUN, J. O., EJEMEN, V. A. & AKINLABI, E. T. 2018e. Bioremediation: Data on Biologically-Mediated Remediation of Crude Oil (Escravos Light) Polluted Soil using Aspergillus niger. Chemical Data Collections 17–18 (2018) 196–204. 26. OJEWUMI, M. E., OMOLEYE, J.A. & AJAYI, A.A. 2016b. The Study of the Effect of Moisture Content on the Biochemical Deterioration of Stored Fermented Parkia Biglobosa Seeds. Open Journal of Engineering Research and Technology, 1, 14-22. 27. OWOLABI, R.U., OSIYEMI, N.A., AMOSA, M.K. & OJEWUMI, M.E. 2011. Biodiesel from household/restaurant waste cooking oil (WCO). J Chem Eng Process Technol, 2 2:112. doi:10.4172/2157-7048.10001 1 2 28. TASNIM, F., IQBAL, S. A. & CHOWDHURY, A. R. 2017. Biogas production from anaerobic co-digestion of cow manure with kitchen waste and Water Hyacinth. Renewable Energy, 109, 434-439. 29. TEMITAYO, O. D. 2017. Optimization of Oil Extraction from Thevetia Peruviana (Yellow Oleander) Seeds: A Case Study of Two Statistical Models. 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Paper Title: Automatic Tap-Changer with TRIAC Switch for Constant Voltage and Current Measurements Abstract: This research article demonstrates how to protect the power transformer from over load time. It is to controlling our desired voltage on various load times but the research gives some solution to avoid over voltage problem by automatic tapping technique. In this research tapping of transformer is selected automatically with the help of Peripheral Interface Controller (PIC) microcontroller. Effective use of potential transformer is to find out the precision in the system. The load voltage is measured by potential transformer which is given to precision rectifier. It will produce the accurate voltage levels. Here the things are using PIC microcontroller which is a flash type reprogrammable device in which system was already programmed. The Model was fabricated with Triode for Alternating Current (TRIAC) switches just as a switching devices and Peripheral Interface Controller microcontroller just as a control unit. In this research automatic control sections by using tap changer. In conventional methods industrialist used mechanical on load tap changer which is highly risk, because it does not produces constant output voltage and can create voltage dips. In this research it will be overcome above problem by using automatic electronic tap changer. It will produce constant voltage regulation and to measure variation of current.

69. Keywords: Triode for Alternating Current (TRIAC) switch, current measurement, Peripheral Interface Controller (PIC) microcontroller, load tap changing transformer. 366-367 References:

1. Li xiaoming & liao qingfen, “A New on load tap changing system with power electronics elements for power transformer”, international conference on power system technology ,2002. 2. P.Bauer & S.W.H.de.Haan, “Electronic tap changer for 500kv\10kvdistribution transformer design experimental results and impact in distribution networks”. Industry application conference, 1998. 3. Osman demirci and David A. Tory ,” A new approach to solid state on load tap changing transformer”,IEEE transaction on power delivery. 4. Tommy Larson & Reijo Innanen, “Static Electronic Tap Changer for Fast Phase Voltage control”. Power system technology, 2000 5. Eric Back, Marcos Ferreira, Dave Hanson, Edis Osmanbasic, “TDA: Tap-changer Dual Assessment”, TechCon USA, Chicago, paper D12, 2012 6. J. Faiz and B. Siahkolah, Electronic Tap-changers of Distribution Transformers, Springer, Heidelberg/New York, 2011. 7. M. Thomson, “Automatic-voltage-control relays and embedded generation,” Power Engineering Journal, No. 14, pp. 71-76, 2000. 8. J.H. Harlow, “Load Tap Changing Control,” presented to the National Rural Electric Co-operative Association (NRECA), Houston, Texas 1996. 9. D. J. Rogers, T. C. Green, R. W. Silversides, “A low-wear on load tap changer diverter switch for frequent voltage control on distribution networks,” IEEE Trans. on Power Delivery, vol. 29, no. 2, pp. 860-869, Apr. 2014 Authors: Appasani Geetha, Pasumarthi Sai Ramya, Chenikala Sravani, M.Ramesh

Paper Title: Real Time Air Quality Index from Various Locations Abstract: The surveys regarding air pollution shows that there has been a hasty growth due to the emission of fuels and exhaust gases from factories. The Air Quality Index (AQI) has been launched to note the contemporary status of the air quality. The intent of AQI is to aid every individual know how the regional air quality will make 70. an impact on them. The Environmental Protection Agency assess the AQI for five major air pollutants namely Nitrogen dioxide (NO2), ground-level ozone (O3), particle pollution (PM10, PM2.5), carbon monoxide (CO), 368-372 and sulphur dioxide (SO2). The intent of the project is to congregate real-time Air Quality Index from distinct monitoring stations across India, analysing the data and reporting on it. Collect the real-time data using the API key provided by Open Government Data (OGD) platform India. This is done by making use of Microsoft Business Intelligence (MSBI) and Power BI Tools to transform, analyse and visualize the data. This project can be utilized to develop various programs like Ozone today in Europe and in mobile applications which acts as an alert system that can protect people from air pollution.

Keywords: Air Pollution, Air Quality Index, Business Intelligence, Power BI, pollutants, SSIS, SSAS.

References:

1. Nikila Varshini, Sreeha.MR, Lhavanya Roobini. VN, Vijayarangam.J, Sujithra.M Coimbatore Institute of Technology, “Analysis of Air Quality Index” 2. Kanchan, Amit Kumar Gorai, and Pramila Goyal , “A Review on Air Quality Indexing System”, Asian Journal of Atmospheric Environment, Vol. 92, pp. 101-113, June 2015 3. Bezuglaya, E.Y., Shchutskaya, A.B., Smirnova, “I.V. Air Pollution Index and Interpretation of Measurements of Toxic Pollutant Concentrations. Atmospheric Environment” Vol. 27, pp. 773-779 4. Martand Pratap Singh, R. Dileep Kumar, “A Visualization Approach of Air Quality Index using R Review Paper”, International Journal for Research in Applied Science & Engineering Technology (IJRASET), Volume 7 Issue V,ISSN: 2321-9653 May 2019 5. Biswanath Bishoi, Amit Prakash, V.K. Jain, Bishoi et al., “A Comparative Study of Air Quality Index Based on Factor Analysis and US-EPA Methods for an Urban Environment”, Aerosol and Air Quality Research, Vol. 9, No. 1, pp. 1-17, 2009 6. Amit Kumar Gorai, Pramila Goyal, “A Review on Air Quality Indexing System”, Asian Journal of Atmospheric Environment, 9(2):101-113 • June 2015, 7. Dalila Taieb and Ammar Ben Brahim, Int. J. Renewable Energy Technology, “Methodology for developing an air quality index (AQI) for Tunisia”, Vol. 4, No. 1, 2013, 8. Gufran Beig Sachin D. Ghude and Aparna Deshpande, “Scientific Evaluation of Air Quality Standards and Defining Air Quality Index for India Indian Institute of Tropical Meteorology, Pune, India Ministry of Earth Sciences, Govt. of India”, August 2010 9. Yuzhe Yang, Zijie Zheng, Student Member, IEEE, Kaigui Bian, Member, IEEE, Lingyang Song, Senior Member, “Realtime Profiling of Fine- Grained Air Quality Index Distribution using UAV Sensing”, IEEE, 8 Nov 2017 10. Gowtham. Sarella, Mrs. Dr. Anjali. K. Khambete, “AMbient Air Quality Analysis using Air Quality Index – A Case Study of Vapi”, IJIRST – International Journal for Innovative Research in Science & Technology| Volume 1 | Issue 10 | March 2015, 11. Sef van den Elshout, Hans Bartelds, Hermann Heich, Karine Leger, citeair_ii_caqi_update.doc, May 23, 2012 12. US Environmental Protection Agency (USEPA) (2008), “Air quality index: a guide to air quality and your health. Environmental Protection Agency, Office of Air Quality Planning and Standards, Research Triangle Park” Authors: B Sandeep Kumar, R Sivakumar Urban Sprawl Mapping through Geo-Informatics and Impact on Land use and Landcover of Paper Title: Kakinada Municipal Corporation in Andhra Pradesh, India. Abstract: Urban infrastructure and urban sprawl required the idea of preparing a proper management plan to avoid the unwanted environmental and economic impacts that come with it. The main objectives of the research is to map the urban sprawl using Geospatial technology and t its impact on land use and land cover. The increase in the rate of population over the last two decades is equally responsible for the urban expansion and subsequent infrastructure development. The results of the integrated geospatial study shows that the urban expansion of Kakinada Municipal Corporation was largely caused by the increase in built-up area from 29.67% in 1995, 44.86% in 2011 to 51.34% in 2017 to 62.84% in 2019 out of Kakinada’s township area of 189552.6 ha mainly due to natural increase of the population and rural ward migration. Vegetation area was 50.68% in 1995 and has declined to 37.82% in 2011. However, the percentage of vegetation experienced a hike and covered 40.23% in 2017 and then went downhill with a land cover percentage of 34.04% of the total township by the year 2019. Over the last two decades the water-body and the dry land were largely converted into built-up areas. The decline of 49151 ha of water-body due mainly because of the urban expansion and the dry-land lost nearly 27200.79 ha of its land cover to the built-up areas. Therefore, controlling and monitoring of urban expansion using GIS and remote sensing technologies are vital solutions to assess the impact of urban expansion of land use and land cover.

Keywords: Change detection, Land-use/Land-cover, Geospatial analysis, Urban Sprawl. 71. References: 373-378 1. Anderson, J.; Hardy, E.; Roach, J.; Witner, R. A Land Use and Land Cover Classification System for Use with Remote Sensor Data; US Geological Survey Professional Paper 964, USGS: Washington, DC, USA, 1976. 2. Abubakar M and Anjide A (2012) Analysis of Land Use/Land Cover Changes to Monitor Urban Sprawl in Keffi-Nigeria. Environmental Research Journal, 6, 130–135. 3. Al-Rowaily SL, El-Bana MI, Al-Dujain, FA (2012) Changes in vegetation composition and diversity in relation to morphometry, soil and grazing on a hyper-arid watershed in the central Saudi Arabia. CATENA, 97, 41–49. doi:10.1016/j.catena.2012.05.004 4. Al-Rubaiay TA, Al-Ma′amar AA, Hattab, AA (2010) Integration of remotely sensed data and GIS techniques to study Lesser Zab River Basin. Internalreport No. 3328, Geosurv, Iraq, Baghdad, 73 P. Baghdad.. 5. Batty,M , P Longley,P and Fotheringham S 1989, Urban Growth and Form: Scaling, Fractal Geometry, and Diffusion-Limited Aggregation Volume: 21 issue: 11, page(s): 1447- 1472 6. Civco, D.; Hurd, J.; Wilson, E.; Son, M; Zhang, Y. A comparison of land use and land cover change detection methods. In Proceedings of American Society for Photogrammetry and Remote Sensing/American Congress on Surveying and Mapping, Washington, DC, USA, April 2002. 7. Herold, M.; Goldstein, N.; Clarke, K. The spatio-temporal form of urban growth: Measurement, analysis and modeling. Remote Sens. Environ. 2003, 85, 95-105. 8. Lata, M.; Prasad, K.; Bandarinath, K.; Raghavaswamy, R.; Rao, S. Measuring urban sprawl: A case study of Hyderabad. GIS Development 2001, 5. Available online: http://www.gisdevelopment.net/application/urban/sprawl/urbans0004.htm (accessed on December 15, 2008). 9. Lakshmi KantaKumar N, Nikhil G Sawant, Shamita Kumar, 2011, Forecasting urban growth based on GIS, RS and SLEUTH model in Pune metropolitan area, International Journal of Geomatics and Geosciences, Volume 2, No 2, 2011, pp: 568-579 10. Mallouk A , Elhadrachi. H, Malaainine M.E. and Rhinane, H, 2019, Using the SLEUTH urban growth model coupled with a GIS to simulate and predict the future urban expansion of Casablanca region, Morocco, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, V, Volume XLII-4/W1 11. Moeller, M.S.; Stefanov, W.L.; Netband, M. Characterizing land cover changes in a rapidly growing metropolitan area using long term satellite imagery. In Proceedings of the 2004 Annual Meeting of the American Society for Photogrammetry and Remote Sensing, Denver, CO, USA, 2004. 12. Mohamed Al-shalabi, Lawal Billa, Biswajeet Pradhan, Shattri Mansor and Abubakr A. A. Al-Sharif , 2013 Modelling urban growth evolution and land-use changes using GIS based cellular automata and SLEUTH models: the case of Sana’a metropolitan city, Yemen, Environmental Earth Sciences , 70, pp:425–437 Indian science congress Association 13. Muzein, B.S. Remote sensing and GIS for land cover/land use change detection and analysis in the semi-natural ecosystems and agriculture landscapes of the central Ethiopian Rift Valley. Ph.D. Dissertation, Techniche Universität Dresden, Dresden, Germany, 2006 14. Neda Bihamta, Alireza Soffianian, Sima Fakheran and Mehdi Gholamalifard, 2014, Using the SLEUTH Urban Growth Model to Simulate Future Urban Expansion of the Isfahan Metropolitan Area, Iran, Journal of the Indian Society of Remote Sensing volume 43, pages 407–414 15. Qi, Lingrui (2012),Urban land expansion model based on SLEUTH a case study in Dongguan city, China Ph.D. Dissertation, Lund University 16. Sun, H.; Forsythe, W.; Waters, N. Modeling urban land use change and urban sprawl: Calgary, Alberta, Canada. Networks Spatial Econ. 2007, 7, 353-376. 17. Yea-Chung Ding, Yong- Kui Zhang, 2007 , The Simulation Of Urban Growth Applying Sleuth Ca Model To The Yilan Delta In Taiwan, Jurnal Alam Bina, Jilid 09, No: 01, 2007.pp 95-107 18. Yeh, A.; Li, X. Measurement and monitoring of urban sprawl in a rapidly growing region using entropy. Photogramm. Eng. Remote Sensing 2001, 67, 83-90. 19. Yuan, F.; Sawaya, K.; Loeffelholz, B.; Bauer, M. Land cover classification and change analysis of the Twin cities (Minnesota) Metropolitan Area by multitemporal Landsat Remote Sens. Environ.. Remote Sens. Environ. 2005, 98, 317-328. Authors: R. Hasibuan, R. Sundari, A.S. Wicaksono, R. Anggraini

Paper Title: Statistical Examination of Uncaria Gambir Roxb Drying Modeling Abstract: This study investigates the drying modeling of Uncaria gambir Roxb using convective desiccant examined by statistical parameters. Three types of drying modeling are investigated, i.e. the Newton, Page and Henderson-Pabis models. The drying conditions of Uncaria gambir Roxb were set at 35oC, 45oC and 55oC and air velocity of 1.2 m/s. The results show that the Page modeling is the best fit model for this investigation based on values of R2 (coefficient of determinant), RMSE (root mean square error) and χ2 (chi-square) goodness of fit test derived from (MR) moisture ratio equation. The Page modeling shows R2 value nearest to unity and lowest values of RMSE and χ2 are obtained for all given temperatures (35oC, 45oC and 55oC) at air velocity of 1.2 m/s. The drying modeling is useful for optimization in design process encountered with product quality and cost of production.

Keywords: Henderson-Pabis model, Newton model, Page model, statistics parameter.

References:

72. 1. S. Naderinezhad, N. Etesami, A.P. Najafbady, and M.G. Falavarjani, “Mathematical modeling of drying of potato slices in a forced convective dryer based on importance parameters”, Food Sci. Nutrition, vol. 4, 2016, pp. 110-118. 2. D.I. Onwude, N. Hanshim, R.B. Janius, N.M. Nawi, and K. Abdan, “Modeling the thin layer drying of fruits and vegetables: A 379-382 Review”, Comprehensive Rev. Food Sci. and Food Safety, vol. 15, 2016, pp. 299-618. 3. J.A.K.M. Fernando, and A.D.U.S. Amarasinghe, “Drying kinetics and mathematical modeling of hot air drying of coconut coir pith”, Springer Plus, vol. 5, 2016, pp. 807. 4. P.C.Coradi, E.D.C. Melo, and R.P.D. Rocha, “Mathematical modeling of the drying kinetics of the leaves of lemon grass (Cymbopogon citrates Stapf) and its effects on quality”, IDESIA (Chile), vol. 32, 2014, pp. 43-56. 5. A.O. Omolola, A.I.O. Jideani, and P.F. Kapila, “Drying kinetics of banana (Musa Spp.)”, Interciencia, vol. 40, 2015, pp. 374-380. 6. S. Darici, and S. Sen, “Experimental investigation of convective drying kinetics of kiwi under different conditions”, Heat Mass Transfer, vol. 51, 2015, pp. 1167-1176. 7. D.A. Tzempelikos, A.P. Vouros, A.V. Bardakas, A.E. Filios, and D.P. Margaris, “Experimental study of convective drying of quince slices and evaluation of thin layer drying models”, Eng. Agri. Environ. and Food, vol. 8, 2015, pp. 169-177. 8. T.J. Afolabi, T.Y. Tunde-Akintunde, and J.A. Adeyanju, “Mathematical modeling of kinetics of untreated and pre-treated cocoyam slices”, J. Food Sci. Technol., vol. 52, 2015, pp. 2731-2740. 9. R.Hasibuan, R. Manurung, S.Alva, R.Anggraini, and R. Sundari, “The study of drying kinetics of Uncaria gambir Roxb leaves applying convective desiccant drying”, Int. J. Adv. Sci. and Technol., vol. 29, 2020, pp. 739-749. 10. U.E. Inyang, I.O. Oboh, and B.R. Etuk, “Kinetic models for drying techniques – food materials”, Adv. Chem. Eng. and Sci., vol. 8, 2018, pp. 27-48. 11. I.U. Ekwere, E.B. Reuben, and O.I. Oseribho, “Mathematical and kinetic modeling for convective hot air drying of sweet potatoes (Ipomoea batatas L)”, American J. Chem. Eng., vol. 7, 2019, pp. 22-31. 12. K. Rayaguru, and W. Routray, “Mathematical modeling of thin layer drying kinetics of stone apple slices”, Int. Food Res. J., vol. 19, 2012, pp. 1503-1510. Authors: G. Mallikharjuna Rao

Paper Title: Information Security using Cryptography and Image Steganography Abstract: Typically, hackers are ready to hack the confidential documents for their vested interests. The main challenge is to construct a secure relation between the secret message and image quality. To avoid dangerous 73. illegal attacks by the third person, a scheme is proposed to have a combination of cryptography and image steganography techniques. This scheme will enable the security, secret message and image cannot be extracted. The International Data Encryption Algorithm (IDEA) cryptographic algorithms and Discrete Cosine Transform 383-392 (DCT) based steganography algorithm is chosen for the functionality. Cryptography is used to encrypt and decrypt the document. Steganography to hide document inside an image with increasing payload for the secure transmission of confidential data across the internet. In this paper we present a single application to hide the information by the sender, which is so important document and confidential in the form of files, it will be invisible to unauthorized person. The results of a suggested scheme with respect to PSNR of 90.06 dB with a payload of 52,400 bytes of information in an image.

Keywords: Data Hiding, cryptography, steganography, Image processing, DCT, IDEA.

References:

1. Zeyad SafaaYounus, Mohammed KhaireHussain, " Image steganography using exploiting modification direction for compressed encrypted data", Journal of King Saud University - Computer and Information Sciences, pp. 1-13, 2019.. 2. Aiswarya, S. Gomathi, R., "Review on Cryptography and Steganography Techniques in Video" 2018 IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2018, pp. 119-122, 2018. 3. Morkel, T., Eloff, J., Olivier, M., 2005. An overview of image steganography. Proceedings of the Fifth Annual Information Security South Africa Conference (ISSA2005). Sandston, South Africa. 2005. 4. Kumar, V., Kumar, D., Performance evaluation of modified color image steganography using discrete wavelet transform. J. Intell. Syst., 2017. 5. Wang, H., Wang, S., “Cyber warfare: steganography vs. steganalysis. Commun.” ACM 47, 10. 2004. 6. Kalra, M., Singh, P., “EMD techniques of image steganography a comparative study”. Int. J. Technol. Explor. Learn. 3 (2). 2014. 7. Hashim, M., Rahim, M., “Image steganography based on odd/even pixels distribution scheme and two parameters random function” J. Theor. Appl. Inf. Technol. 95 (22), pp. 5977–5986, 2017. 8. Chang, C., Tai, W., Chen, K., Improvements of EMD embedding for large payloads. In: Third International Conference on International Information Hiding and Multimedia Signal Processing (IIH-MSP 2007). ACM, pp. 473–476, 2007, 9. Chan, C., Cheng, L., Hiding data in images by simple LSB substitution. Pattern Recogn. 37, pp. 469–474, 2004, 10. Shjul, A., Kulkarni, U., “A secure skin tone-based steganography using wavelet transform. Int. J. Comput. Theory Eng. 3 (1), 16– 22. 11. Lee, C., Wang, Y., Chang, C., “A steganography method with high capacity by improving exploiting modification direction. In: IEEE Third International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIHMSP), pp. 497– 500, 2007, 12. Jung, K., Yoo, K., “Improved exploiting modification direction method by modulus operation”, Int. J. Signal Process. Image Process. Pattern. 2 (1), pp. 79–87, 2009. 13. Lin, K., Hong, W., Chen, J., Chen, T., Chiang, W., n et al. “Data hiding by exploiting modification direction technique using optimal pixel grouping. In: IEEE 2010 2nd international Conference on Education Technology and Computer (ICETC). 2010. 14. Mohsin, A.T., A New Steganography Technique Using Knight’s Tour Algorithm, Affine Cipher and Huffman Coding (Master Thesis). Faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia. 2013. 15. Ahmad, A., Sulong, G., Rehman, A., Alkawaz, M., Saba, T., Data hiding based on improved exploiting modification direction method and huffman coding. J. Intell. Syst. 23 (4), pp.451–459, 2014. 16. Alsaffawi, Z.S.Y., Image steganography by using exploiting modification direction and knight tour algorithm. J. Al- Qadissiya Comput. Sci. Math. 8 (1), pp.1– 11, 2016. 17. Lee, C., Chang, C., Pai, P., Liu, C., Adjustment hiding method based on exploiting modification direction. Int. J. Netw. Security 17 (5), pp. 607–618, 2015. 18. Saha, S., Ghosal, S., Chakraborty, A., Dhargupta, S., Sarkar, R., Mandal, J., Improved exploiting modification direction-based steganography using dynamic weightage array. Electron. Lett. 54 (8), pp. 498–500, 2018. 19. Zhang, X., Wang, S., Efficient stenographic embedding by exploiting modification direction. IEEE Commun. Lett. 10 (11), pp.781–783, 2006. 20. Mawengkang, H., Sitepu, I., Efendi, S., Security analysis in file with combinations One Time Pad Algorithm and Vigenere Algorithm. In: IOP Conf. Series: Materials Science and Engineering (2018) 2nd Nommensen nternational Conference on Technology and Engineering, pp. 21–29, 2018. 21. Mediacrypt AG, The IDEA Block Cipher, submission to the NESSIE Project, http://cryptonessie.org 22. USC-SIPI Image Database. Available from: https://sipi.usc.edu/database/. 23. Lin, K., Hong, W., Chen, J., Chen, T., Chiang, W., n et al., Data hiding by exploiting modification direction technique using optimal pixel grouping. In: IEEE 2010 2nd international Conference on Education Technology and Computer (ICETC). 2010. 24. Naidu, Deeraj Ananda Kumar, K. S. Jadav, Shwetha L. Sinchana, M. N. " Multilayer Security in Protecting and Hiding Multimedia Data using Cryptography and Steganography Techniques", 2019 4th IEEE International Conference on Recent trends on Electronics, Information, Communication and Technology, RTEICT 2019 – Proceedings, pp. 1360-136, 2019. Authors: G. Pradeep, M. Ramulu, G. Budi, V.M.S.R. Murthy

Paper Title: Smart Software Assessment of Effects of Rock Properties on Blast-Induced Ground Vibrations Abstract: The drilling and blasting method considered most economical method in civil and urban construction process. Ground vibrations are one of the major problem in blasting activity. Velocity of blast induced ground vibrations influenced by mainly three parameters such as properties of the rock mass properties, the explosive characteristics and the blast design and execution. In those parameters, rock mass properties of blasting area are unchangeable, so study of influence of rock properties very essential to minimize the effect of vibration on nearby structure. This study investigated the effect of rock strength parameters on vibration velocity. In this study, the blasting vibration monitored at a blasting site with different rock masses. This paper, presented a review on prediction models and rock properties influence on peak particle velocity. This paper also presented 74. the relation between peak particle velocities different mines with their respective rock properties. This paper critical analysis on previous studies. This paper presented correlation between rock strength properties like 393-397 compressive strength and tensile strength on vibration velocity.

Keywords: References:

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(Eds.): ICONIP, (pp. 1008-1013), Berlin Heidelberg: Springer-Verlag. 7. Devine, J.F., Beck, R.H., Meyer, A.V.C and Duvall, W.I., (1966), Effect of charge weight on vibration levels from quarry blasting, USBM, RI-6774, pp. 37-38. 8. Du Pont, E.I., 1977, Blasters hand book, 175th Anniversary edition, E.I. du Pont de Nemours, Inc., Wilmington, Delaware. 9. Duvall WI, Petkof B. (1959) Spherical propagation of explosion generated strain pulses in rock. U.S. Department of the Interior, Bureau of Mines; 1959. 10. Ghosh, A.A. and Daemen, J.J.K. (1983) A new analytical predictor of ground vibrations induced by blasting, Volume IV, Report to the office of surface mining, Research Grants G5105010 and G5115041, OFR 105(4), pp.84. 11. Gong QM, Zhao J, Jiao YY. (2005)Numerical modelling of the effects of joint orientation on rock fragmentation by TBM cutters. Tunnell Undergr Space Tech; 20:183–91. 12. Hagan, T. N. and Just, J. D., 1974, Rock breakage by explosives-theory optimisation, Proc. 3rd Cong. Rock Mech., 2, pp.1349- 1358. 13. Hajihassani M, Jahed Armaghani D, Marto A, Tonnizam Mohamad E (2014) Ground vibration prediction in quarry blasting through an artificial neural network optimized by imperialist competitive algorithm. Bull Eng Geol Environ. doi:10.1007/ s10064-014-0657-x 14. Hao H, Wu Y, Ma G, Zhou Y (2001) Characteristics of surface ground motions induced by blasts in jointed rock mass. Soil Dynamics and Earthquake Engineering 21(2):85-87. 15. Iphar M, Yavuz M, Ak Hakan (2008) Prediction of ground vibrations resulting from the blasting operations in an open-pit mine by adaptive neuro-fuzzy inference system. Environ Geol56:97–107 16. Iramina, W. S., Sansone, E. C., Wichers, M., Wahyudi, S., Eston, S. M. de, Shimada, H., & Sasaoka, T. (2018). Comparing blast- induced ground vibration models using ANN and empirical geomechanical relationships. 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Malek, Manoj Khandelwal, (2013), Evaluation of effect of blast design parameters on flyrock using artificial neural networks, Neural Computing and Applications, Volume 23, Issue 2, pp 349-356 24. Mahdi Hasanipanah, Saeid Bagheri Golzar , Iman Abbasi Larki , Masoume Yazdanpanah Maryaki , Tade Ghahremanians,(2017)Estimation of blast-induced ground vibration through a soft computing framework, Engineering with Computers, v.33 n.4, p.951-959, October 2017 25. Mohamed, M.T. (2009) ‘Artificial neural network for prediction and control of blasting vibrations in Assiut (Egypt) limestone quarry’, Int J Rock Mech Min Sci, Vol. 46, pp. 426– 43. 26. Monjezi M, Hasanipanah M, Khandelwal M (2013) Evaluation and prediction of blast-induced ground vibration at Shur River Dam, Iran, by artificial neural network. Neural Comput Appl 22:1637–1643 27. Nateghi R. (2011) Prediction of ground vibration level induced by blasting at different rock units. International Journal of Rock echanics and Mining Sciences 4(6):899-908. 28. Nicholls HR, Charles FJ, Duvall WI. (1971) Blasting vibrations and their effects on structures. U.S. Department of the Interior, Bureau of Mines. 29. Nicholson RF.(2005) Determination of blast vibrations using peak particle velocity at Bengal Quarry, in St Ann, Jamaica. MS Thesis. Lulea, Sweden: Department of Civil and Environmental Engineering, Division of Rock Engineering, Lulea Uni-versity of Technology. 30. Olson, J.J., Fogelson, D.E. and Fletcher, L.R., (1970), Mine roof vibrations from production blasts, Shullsburg mine, Shullsburg, Wis., USBM RI 7462, pp. 3. 31. Ozer U. (2008) Environmental impacts of ground vibration induced by blasting at different rock units on the Kadikoye Kartal metro tunnel. Engineering Geology 100(1-2):82-90. 32. Pal RP (2005) Rock blasting. IBH Publishing, New Delhi Roy PP (1991) Vibration control in an opencast mine based on improved blast vibration predictors. Min Sci Technol 12:157–165 33. Pal Roy P. (1993) Putting ground vibration predictors into practice. Colliery Guardian 241(2):63-7. 34. Rai R, Singh TN. (2004)A new predictor for ground vibration prediction and its comparisonwith other predictors. Indian Journal of Engineering and Material Sciences11:178e84. 35. Ramulu More (2004) investigation into the influence of burden distance on blast induced ground vibrations and air overpressure. M.tech. Dissertation, Visvesvaraya National Institute of Technology, 36. Ranjan Kumar, Deepankar Choudhury, and Kapilesh Bhargava,. (2016). Determination of blast induced ground vibration equations for rocks using mechanical and geological properties. Journal of Rock Mechanics and Geotechnical Engineering. 8. 10.1016/j.jrmge.2015.10.009. 37. Sambuelli, L. (2009). Theoretical derivation of a peak particle velocity-distance law for the prediction of vibrations from blasting. Rock Mechanics and Rock Engineering, vol. 42, no. 3. pp. 547-556 38. Singh RB, Pal Roy P (1993) Blasting in ground excavations and mines. Rotterdam: A.A. Balkema. 39. Singh, T. N. and Singh, V. (2005) ‘An intelligent approach to prediction and control ground vibration in mines’, Geotech Geo. Eng J, Vol. 23, pp.249–262. 40. Siskind, D. E., Stagg, M. S., Kopp, J. W. and Dowding, C. H., (1980) Structure response and damage produced by ground vibration from surface mine blasting. USBM RI 8507, pp.74. 41. SR Dindarloo (2015)’ Peak particle velocity prediction using support vector machines: a surface blasting case study ‘, Journal of the Southern African Institute of Mining and Metallurgy 115 42. Tang, H., Shi, Y., Li, H., Li, J., Wang, X., and Jiang, P. (2007) ‘Prediction of peak velocity of blasting vibration based on neural network’, Chinese J Rock Mech and Engg, Volume 26, Issue Suppl. 1, pp. 3533-3539. 43. Wayne Fong (2019) Excel Vs. Python For Data Analysis has retrieved from https://xccelerate.co/blog/excel-vs-python-for-data- analysis 44. Wiss, J. F. and Linehan, P.W. (1978) Control of vibration and blast noise from surface mining, Wiss, Janney and Elstner and Associates Contract Report J0255022 for USBM, pp. 1111. Authors: Manjunath K Gowda, Priya D A Simulator for Reporting Performance Data from Network Element To Network Management Paper Title: System Abstract: Now a days, the biggest challenge for Mobile Network Operators is to provide broadband service with high performance.4G(VoLTE) has been developed to meet user requirement by offering high speed data transfer services using IMS network. The key performance indicators (KPI) are used to monitor and optimize mobile network performance in order to provide high quality services using counters. The indicators are standardized by third-generation partnership project(3GPP). Simulators are used in network element so that we can check the capacity of each VM and calculated using counters from element management system to network management system.

Keywords: Key Performance Indicators, Voice over Long Term Evolution, third Generation Partnership Project, IP Multimedia Subsystem.

References:

1. 3GPP TS 22.228 V12.9.0, Service requirements for the Internet Protocol (IP) Multimedia core network Subsystem (IMS). 2. 3GPP TS 22.173 V9.5.0 (2010-03): “IP Multimedia Core Network Subsystem (IMS) Multimedia Telephony Service and supplementary services; Stage 1 (Release 9)” 3. GSMA, IR.92 IMS Profile for Voice and SMS, Version 7.0, 03 March 2013. 4. GSMA, IR.94 IMS Profile for Conversational Video Service Version 5.0 04 March 2013. 5. A. Elnashar, M. A. El-Saidny, “Looking at LTE in Practice: A Performance Analysis of the LTE System based on Field Test Results,” IEEE Vehicular Technology Magazine, Vol. 8, Issue 3, pp. 81:92, Sept. 2013. 6. Ayman Elnashar, Mohamed Al-saidny, and Mahmoud Sherif “Design, Deployment, and Performance of 4G-LTE Networks: A 75. practical Approach,” Wiley, May 2014. 7. 3GPP TS 26.441, “Codec for Enhanced Voice Services (EVS); General overview”, version 12.0.0 Release 12 8. Myasar R. Tabany and Chris G. Guy, “Performance Analysis and Deployment of VoLTE Mechanisms over 3GPP LTE-based 398-401 Networks,” International Journal of Computer Science and Telecommunications, Volume 4, Issue 10, October 2013. 9. Myasar R. Tabany, Chris G. Guy, An End-to-End QoS Performance Evaluation of VoLTE in 4G E-UTRAN-based Wireless Networks, ICWMC 2014, pp. 90:97. 10. J. Calle-Sánchez, M. Molina-García, J. I. Alonso, and A. FernándezDurán, “Long term evolution in high speed railway environments: feasibility and challenges,” Bell Labs Tech. J., vol. 18, no. 2, pp. 237– 253, 2013. 11. Sniady, A.; Sonderskov, M.; Soler, J., VoLTE Performance in Railway Scenarios: Investigating VoLTE as a Viable Replacement for GSM-R, IEEE Vehicular Technology Magazine, 2015, Volume: 10, Issue: 3, Pages: 60 – 70 12. “Circuit Switched (CS) fallback in Evolved Packet System (EPS); Stage 2”,3GPP TS 23.272 V9.14.0 13. Miikka Poikselkä et al, “Voice over LTE (VoLTE)”, Wiley, 2012 14. A. Elnashar, Mohamed A. El-Saidny, “Practical Guide to LTE-A, VoLTE and IoT: Paving the way towards 5G,” Wiley, July 2018. 15. Ayman Elnashar, Mohamed A. El-Saidny, Mohamed Mahmoud, “Practical Performance Analyses of Circuit Switched Fallback (CSFB) and Voice over LTE (VoLTE),” IEEE Transactions on Vehicular Technology, Vol. 66, Issue 2, pp. 1748 – 1759 16. Andreas Maeder and Arne Felber, “Performance Evaluation of ROHC Reliable and Optimistic Mode for Voice over LTE,” In Proc. Vehicular Technology Conference (VTC Spring), 2013 IEEE 77th. 17. TR 45.820 v13.1.0, “Cellular system support for ultra-low complexity and low throughput internet of things,” Nov. 2015. 18. Elnashar A, Al-Saidny M, Sherif M (2014) Design, deployment, and performance of 4G-LTE networks: a practical approach. Wiley, Hoboken. 19. Tabany MR, Guy CG (2013) Performance analysis and deployment of VoLTE mechanisms over 3GPP LTE-based networks. Int J Comput Sci Telecommunication. 20. 3GPP TS 23.002 3rd Generation Partnership Project; Technical Specification Group Services and Systems Aspects; Network architecture. Available via www.3gpp.org/ [accessed March 29, 2018]. 21. 3GPP TS 29.228 Digital cellular telecommunications system; Universal Mobile Telecommunications System; LTE; IP Multimedia Subsystem Cx and Dx Interfaces; Signaling flows and message contents. Available via www.3gpp.org/ [accessed March 29, 2018]. 22. ITU Recommendation G.109 Definition of categories of speech transmission quality. Available via https://www.itu.int/ [accessed March 29, 2018]. Authors: Poojya J Bhat, Priya D

Paper Title: Modern Messaging Queues - RabbitMQ, NATS and NATS Streaming Abstract: Distributed messaging structures shape the core of massive microservice architecture , cloud native applications and data streaming as they are used to communicate between different application services. With actual-time crucial programs there is a developing need for well-constructed messaging platform this is fault tolerant, has low latency and scalable. This paper surveys various message broker that are in vogue today. These modern message brokers have their own adavantages and disadvantages that have come up lately. There is need for the comparative study to decide which broker is most suitable for a specific appalication. An in-depth study 76. is required to decide which features of a messaging system meet the needs of the application. The paper outlines information about three messaging systems – RabbitMq, Nats and Nats-Streaming and explores the features they offer as well as their performance under varied testing workloads. 402-408

Keywords: NATS, NATS-Streaming, RabbitMQ, distributed messaging systems. References:

1. R. Rocha, L. L. Ferreira, C. Maia, P. Souto and P. Varga, "Improving the performance of a Publish-Subscribe message broker," 2019 IEEE 22nd International Symposium on Real-Time Distributed Computing (ISORC), Valencia, Spain, 2019, pp. 91-92. 2. R. Rocha, L. L. Ferreira, C. Maia, P. Souto and P. Varga, "Improving the performance of a Publish-Subscribe message broker," 2019 IEEE 22nd International Symposium on Real-Time Distributed Computing (ISORC), Valencia, Spain, 2019, pp. 91-92. 3. P. T. Eugster, P. A. Felber, R. Guerraoui, and A.-M. Kermarrec. “The Many Faces of Publish/Subscribe” ACM Computing Surveys (CSUR), vol. 35, pp. 114–131, 2003 4. Gracioli, Giovani & Dunne, Murray & Fischmeister, Sebastian “A Comparison of Data Streaming Frameworks for Anomaly Detection in Embedded Systems” 1st International Workshop on Security and Privacy for the Internet-of-Things (IoTSec),2018 5. S. Stoja, S. Vukmirovic and B. Jelacic, "Publisher/Subscriber Implementation in Cloud Environment," 2013 Eighth International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, Compiegne, 2013, pp. 677-682. 6. Banavar, Guruduth & Deepak Chandra, Tushar & Strom, Robert & C. Sturman, Daniel. “A Case for Message Oriented Middleware.”,DISC Proceedings,Slovak Republic,1999, pp. 1-18. 7. B. R. Hiraman, C. V. M and C. Karve Abhijeet, "A Study of Apache Kafka in Big Data Stream Processing," 2018 International Conference on Information , Communication, Engineering and Technology (ICICET), Pune, 2018, pp. 1-3. 8. Jay Kreps, Neha Narkhede, Jun Rao, “Kafka: A Distributed Messaging System for Log Processing”,at NetDB workshop,2011 9. RabbitMQ,https:// 2019. 10. Apache Kafka website, April 2020. Available online: https://kafka.apache.org/. 11. Redis website, April 2020. Available online: https://redis.io/. 12. P. Bellavista, A. Corradi and A. Reale, "Quality of Service in Wide Scale Publish—Subscribe Systems," in IEEE Communications Surveys & Tutorials, vol. 16, no. 3, pp. 1591-1616, 2014. 13. F. Araujo and L. Rodrigues, "On QoS-aware publish- subscribe," Proceedings 22nd International Conference on Distributed Computing Systems Workshops, Vienna, Austria, 2002, pp. 511-515. 14. P. Dobbelaere and K. S. Esmaili. “Kafka versus RabbitMQ: A comparative study of two industry reference publish/subscribe implementations” In Proceedings of the 11th ACM International Conference on Distributed and Event-based Systems, pages 227–238. ACM, 2017. 15. NATS, https://nats.io/documentation/, 2019 16. RabbitMQ, https://2019 17. John, Vineet & Liu, Xia. A Survey of Distributed Message Broker Queues.2017, arXiv:1704.00411 18. T. Treat. Benchmarking NATS Streaming and Apache Kafka, https://dzone.com/articles/benchmarking-nats-streaming-and- apache- kafka, 2016 19. T. Treat. Benchmarking Message Queue Latency,201s6 20. M. Rostanski, K. Grochla and A. Seman, "Evaluation of highly available and fault-tolerant middleware clustered architectures using RabbitMQ," 2014 Federated Conference on Computer Science and Information Systems, Warsaw, 2014, pp. 879-884 21. S. Skeirik, R. B. Bobba and J. Meseguer, "Formal Analysis of Fault- tolerant Group Key Management Using ZooKeeper," 2013 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing, Delft, 2013, pp. 636-641. 22. Martin Toshev, “Learning RabbitMQ” Birmingham, UK: Packt Publishing, Ltd, 2015 23. B. R. Hiraman, C. V. M and C. Karve Abhijeet, "A Study of Apache Kafka in Big Data Stream Processing," 2018 International Conference on Information , Communication, Engineering and Technology (ICICET), Pune, 2018, pp. 1-3. 24. S. Patro, M. Potey and A. Golhani, "Comparative study of middleware solutions for control and monitoring systems," 2017 Second International Conference on Electrical, Computer andCommunication Technologies (ICECCT), Coimbatore, 2017, pp. 1- 10. 25. Kamburugamuve, Supun & Fox, Geoffrey. (2016). “Survey of Distributed Stream Processing.” 10.13140/RG.2.1.3856.2968. 26. Kamburugamuve, Supun, Leif Christiansen and Geoffrey C. Fox. “A Framework for Real Time Processing of Sensor Data in the Cloud.” J. Sensors 2015 (2015): 468047:1-468047:11. 27. Z. Wang et al., "Kafka and Its Using in High-throughput and Reliable Message Distribution," 2015 8th International Conference on Intelligent Networks and Intelligent Systems (ICINIS), Tianjin, 2015, pp. 117-120. 28. P. Le Noac'h, A. Costan and L. Bougé, "A performance evaluation of Apache Kafka in support of big data streaming applications," 2017 IEEE International Conference on Big Data (Big Data), Boston, MA, 2017, pp. 4803-4806. Authors: Shashwat Dwivedi, A.S. Yadav, Rajesh Tyagi

Paper Title: A Flexible Renewable Smart Production with RDG, Considering Power Quality Abstract: This report largely focused on the influence on the delivery system of the Renewable Distributed Generations (RDGs). DG's intercourse showed that the suggested the traditional method of radial distribution into a multiple DG scheme. The main contribution of this study is to reduce total power losses and increase the distribution system's power quality using RDGs. The Loss sensitivity factor (LSF) is used to find the RDGs. A heuristic search novel The Modified Bat Algorithm (MBA) is used to define the amount of the RDGs. MBA is largely focused on microbats' higher elastic modulus. The proposed MBA is measured on standard bus test systems IEEE 33 and 69.

Keywords: Renewable Distributed Generation (RDGs), Modified Bat Algorithm (MBA), Loss Sensitivity Factor (LSF).

References: 77. 1. Veera, D.P.R.P.V. and Gowri, R.T. Ant Lion optimization algorithm for optimal sizing of. Electrical Power & Energy Systems 28 (2017) 669-678. 409-415 2. Ackermann, T., Andersson, G. and Söder, L. Distributed generation: a definition1. Electric power systems research 57 (3) (2001) 195-204. 3. Adefarati, T. and Bansal, R.C. Integration of renewable distributed generators into the distribution system: a review. IET Renewable Power Generation 10 (7) (2016) 873-884. 4. Acharya, N., Mahat, P. and Mithulananthan, N. An analytical approach for DG allocation in primary distribution network. International Journal of Electrical Power & Energy Systems 28 (10) (2006) 669-678. 5. Yuvaraj, T., Devabalaji, K.R. and Ravi, K. Optimal placement and sizing of DSTATCOM using harmony search algorithm. Energy Procedia 79 (2015), 759-765. 6. Viral, R. and Khatod, D.K. Optimal planning of distributed generation systems in distribution system: A review. Renewable and Sustainable Energy Reviews 16 (7) (2012) 5146-5165. 7. Hung, D.Q. and Mithulananthan, N. Multiple distributed generator placement in primary distribution networks for loss reduction. IEEE Transactions on industrial electronics 60 (4) (2013) 1700-1708. 8. Devi, S. and Geethanjali, M. Application of modified bacterial foraging optimization algorithm for optimal placement and sizing of distributed generation. Expert Systems with Applications 41 (6) (2014) 2772-2781. 9. Doagou-Mojarrad, H., Gharehpetian, G.B., Rastegar, H and Olamaei, J. Optimal placement and sizing of DG (distributed generation) units in distribution networks by novel hybrid evolutionary algorithm. Energy 54 (2013) 129-138. 10. Hung, D.Q., Mithulananthan, N. and Bansal, R.C. Analytical expressions for DG allocation in primary distribution networks. IEEE Transactions on energy conversion 25 (3) (2010) 814-820. 11. El-Fergany, A. Study impact of various load models on DG placement and sizing using backtracking search algorithm. Applied Soft Computing 30 (2015) 803-811. 12. Esmaeilian, H.R. and Fadaeinedjad, R. Energy loss minimization in distribution systems utilizing an enhanced reconfiguration method integrating distributed generation. IEEE Systems Journal 9 (4) (2015) 1430-1439. 13. Viral, R. and Khatod, D.K. An analytical approach for sizing and siting of DGs in balanced radial distribution networks for loss minimization. International Journal of Electrical Power & Energy Systems 67 (2015) 191-201. 14. Karaki, S.H., Chedid, R.B. and Ramadan, R. Probabilistic performance assessment of autonomous solar-wind energy conversion systems. IEEE Transactions on Energy Conversion 14 (3) (1999) 766-772. 15. Atwa, Y.M., El-Saadany, E.F., Salama, M.M.A. and Seethapathy, R. Optimal renewable resources mix for distribution system energy loss minimization. IEEE Transactions on Power Systems 25 (1) (2010) 360-370. 16. Khatod, D. K., Pant, V. and Sharma, J. Evolutionary programming based optimal placement of renewable distributed generators. IEEE Transactions on Power systems 28 (2) (2013) 683-695. 17. Yuvaraj, T., Ravi, K. and Devabalaji, K.R. Optimal allocation of DG and DSTATCOM in radial distribution system using cuckoo search optimization algorithm. Modelling and Simulation in Engineering (2017). 18. Thangaraj, Y. and Kuppan, R. Multi-objective simultaneous placement of DG and DSTATCOM using novel lightning search algorithm. Journal of Applied Research and Technology 15 (5) (2017) 477-491. 19. Tan, W.S., Hassan, M.Y., Majid, M.S. and Rahman, H.A. Allocation and sizing of DG using cuckoo search algorithm. IEEE International Conference on Power and Energy (PECon), 2012, 133-138. 20. Shukla, T. N., Singh, S. P., Srinivasarao, V. and Naik, K.B. Optimal sizing of distributed generation placed on radial distribution systems. Electric power components and systems 38 (3) (2010) 260-274. 21. Hassan, A.A., Fahmy, F.H., Nafeh, A.E.S.A. and Abu-elmagd, M.A. Genetic single objective optimisation for sizing and allocation of renewable DG systems. International Journal of Sustainable Energy 36 (6) (2017) 545-562. 22. Manafi, H., Ghadimi, N., Ojaroudi, M. and Farhadi, P. Optimal placement of distributed generations in radial distribution systems using various PSO and DE algorithms. Elektronika ir Elektrotechnika 19 (10) (2013) 53-57. 23. Baran, M. and Wu, F.F. Optimal sizing of capacitors placed on a radial distribution system. IEEE Transactions on power Delivery 4 (1) (1989) 735-743. 24. Abdelaziz, A.Y., Hegazy, Y.G., El-Khattam, W. and Othman, M.M. A multi-objective optimization for sizing and placement of voltage-controlled distributed generation using supervised big bang–big crunch method. Electric Power Components and Systems 43 (1) (2015) 105-117. 25. Abu-Mouti, F.S. and El-Hawary, M.E. Optimal distributed generation allocation and sizing in distribution systems via artificial bee colony algorithm. IEEE transactions on power delivery 26 (4) (2011) 2090-2101. 26. Gözel, T. and Hocaoglu, M.H. An analytical method for the sizing and siting of distributed generators in radial systems. Electric Power Systems Research 79 (6) (2009) 912-918. 27. Yuvaraj, T., Ravi, K. and Devabalaji, K.R. DSTATCOM allocation in distribution networks considering load variations using bat algorithm. Ain Shams Engineering Journal (2015). 28. Devabalaji, K.R., Imran, A.M., Yuvaraj, T. and Ravi, K. Power loss minimization in radial distribution system. Energy Procedia 79 (2015) 917-923. 29. Devabalaji, K.R., Yuvaraj, T. and Ravi, K. An efficient method for solving the optimal sitting and sizing problem of capacitor banks based on cuckoo search algorithm. Ain Shams Engineering Journal (2016). 30. Yuvaraj, T., Ravi, K. and Devabalaji, K.R. DSTATCOM Allocation in the Radial Distribution Networks with Different Stability Indices using Bat Algorithm. Gazi University Journal of Science 30 (4) (2017) 314-328. 31. Yuvaraj, T., Devabalaji, K.R. and Ravi, K. Optimal Allocation of DG in the Radial Distribution Network Using Bat Optimization Algorithm. Advances in Power Systems and Energy Management, 2018, 563-569. 32. Yang, X.S. and Hossein Gandomi, A. Bat algorithm: a novel approach for global engineering optimization. Engineering Computations 29 (5) (2012) 464-483. 33. Yılmaz, S., Kucuksille, E.U. and Cengiz, Y. Modified bat algorithm. Elektronika ir Elektrotechnika 20 (2) (2014) 71-78. 34. Sahoo, N.C. and Prasad, K. A fuzzy genetic approach for network reconfiguration to enhance voltage stability in radial distribution systems. Energy conversion and management 47 (18-19) (2006) 3288-3306. Authors: Nirav Satani, Smit Patel, Sandip Patel

Paper Title: AI Powered Glasses for Visually Impaired Person Abstract: This dissertation presents a system that can assist a person with a visual impairment in both navigation and movability. Meanwhile, number of solutions are available in current time. We described some of them in the later part of the paper. But to date, a reliable and cost-effective solution has not been put forward to replace the legacy devices currently used in mobilizing on a daily basis for people with a visual impairment. This report first examines the problem at hand and the motivation behind addressing it. Later, it explores relative current technologies and research in the assistive technologies industry. Finally, it proposes a system design and implementation for the assistance of visually impaired people. The proposed device is equipped with hardware like raspberry pi processor, camera, battery, goggles, earphone, power bank and connector. Objects will be captured with the help of camera. Image processing and detecting would be done with the help of deep learning, R-CNN like modules on the device itself. However, final output would be delivered by the earphone into the 78. visually impaired person’s ear. The research work contains the methodology and the solutions of above mention problem. The research works can be used in practical use cases, for visually impaired person. The system 416-421 proposed in this project includes the use of a region based convolutional neural network as well as the use of a raspberry pi for processing the image data. System includes tesseract library of programming language python for OCR and give output to the user. The detailed methodology and result are elaborated later in this paper.

Keywords: OCR, R-CNN, Transfer learning.

References:

1. Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. “Imagenet classification with deep convolutional neural networks”. In: Advances in neural information processing systems. 2012, pp. 1097–1105 2. Frieden, B. Roy. "Image enhancement and restoration." Picture processing and digital filtering. Springer, Berlin, Heidelberg, 1975. 177-248. 3. Ilag, Balu N., and Yogesh Athave. "A Design review of Smart Stick for the Blind Equipped with Obstacle Detection and Identification using Artificial Intelligence." International Journal of Computer Applications 975: 8887. 4. J. Donahue, R. Girshick, T. Darrell and J. Malik, "Rich feature hierarchies for accurate object detection and semantic segmentation," IEEE Int. Conf. on Computer Vision and Pattern Recognition, Columbus, 2014. 5. Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. “Deep residual learning for image recognition”. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016, pp. 770–778 6. Karen Simonyan and Andrew Zisserman. “Very deep convolutional networks for large-scale image recognition”. In: arXiv preprint arXiv:1409.1556 (2014). 7. Li, Xiang, et al. "Cross-Safe: A computer vision-based approach to make all intersection-related pedestrian signals accessible for the visually impaired." Science and Information Conference. Springer, Cham, 2019. 8. Liu, Fayao, Chunhua Shen, and Guosheng Lin. "Deep convolutional neural fields for depth estimation from a single image." Proceedings of the IEEE conference on computer vision and pattern recognition. 2015. 9. Matthew D Zeiler and Rob Fergus. “Visualizing and understanding convolutional networks”. In: European Conference on Computer Vision. Springer. 2014, pp. 818–833 10. Maxime Oquab, Leon Bottou, Ivan Laptev, and Josef Sivic. “Learning and transferring mid-level image representations using convolutional neural networks”. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2014, pp. 1717–1724. 11. Mithe, Ravina, Supriya Indalkar, and Nilam Divekar. "Optical character recognition." International journal of recent technology and engineering (IJRTE) 2.1 (2013): 72-75. 12. Raspberry Pi information, 26 April 2018, https://components101.com/microcontrollers/raspberrypi-3-pinout-features-datasheet 13. R. Girshick, "Fast R-CNN," IEEE International Conference on Computer Vision, Santiago, 2015 14. Sakhardande, Jayant, Pratik Pattanayak, Mita Bhowmick. “Smart Cane Assisted Mobility for the Visually Impaired.” International Journal of Electrical and Computer Engineering, vol. 6, no. 10, 2012, pp. 9-20 15. Sandnes, Frode Eika. "What do low-vision users really want from smart glasses? Faces, text and perhaps no glasses at all." International Conference on Computers Helping People with Special Needs. Springer, Cham, 2016. 16. Takefuji, Yoshiyasu. Neural network parallel computing. Vol. 164. Springer Science & Business Media, 2012. 17. Viraktamath, S. V., et al. "Face detection and tracking using OpenCV." The SIJ Transactions on Computer Networks & Communication Engineering (CNCE) 1.3 (2013): 45-50. 18. World Health Organization (WHO). “Blindness and Vision Impairment.” WHO, 11 October 2018, https://www.who.int/newsroom/factsheets/detail/blindness-and-visual-impairment 19. Xing Luo, Oscar. "Deep Learning for Speech Enhancement: A Study on WaveNet, GANs and General CNN-RNN Architectures." (2019). 20. Yann LeCun, Leon Bottou, Yoshua Bengio, and Patrick Haffner. “Gradient-based learning applied to document ´ recognition”. In: Proceedings of the IEEE 86.11 (1998), pp. 2278–2324 Authors: Fatima Patel, Saniya Shinde, Shivani Lingwat, Komal Bavle, Shweta Koparde

Paper Title: Harnessing Feature Extraction Techniques alongside CNN for Diabetic Retinopathy Detection Abstract: Diabetes mellitus is a disorder that inhibits your body from properly using the energy from the food you consume. The blood vessels and blood are responsible for the transport of sugar. A hormone called insulin helps cells to take in sugar to be used as energy. Deficiency in insulin causes the disease of diabetes mellitus. One of the side effect of diabetes mellitus is diabetic retinopathy. Diabetic retinopathy is the medical condition that causes the principal vision or in rare cases entire vision loss. Diabetic retinopathy has frequent occurrences in people among 20 to 60 years. Addressing this problem, we have developed an application that saves time and gives the result of the stage of the disease. This research paper presents a CNN based system that classifies the patients in four classes as 0-no DR, 1-Mild DR, 2-Moderate DR, 3-Severe DR. The system takes the input as an image taken from a fundus camera. Image processing techniques and machine learning algorithms are used for feature extraction. The Automated screening of the retinal images would assist the doctors to easily identify the patient's condition more precisely. With this we can easily distinguish between normal and abnormal images of the retina, this will reduce the number of inspections for the doctors.

79. Keywords: Diabetic Retinopathy, Image processing, K means clustering algorithm, Convolutional neural networks. 422-425

References:

1. K. S. Argade, K. A. Deshmukh, M. M. Narkhede, N. N. Sonawane and S. Jore, "Automatic detection of diabetic retinopathy using image processing and data mining techniques". 2. S. Yu, D. Xiao and Y. Kanagasingam, "Exudate detection for diabetic retinopathy with convolutional neural networks". 3. N. Chakrabarty, "A Deep Learning Method for the detection of Diabetic Retinopathy". 4. E. V. Carrera, A. González and R. Carrera, "Automated detection of diabetic retinopathy using SVM". 5. D. Doshi, A. Shenoy, D. Sidhpura and P. Gharpure, "Diabetic retinopathy detection using deep convolutional neural networks". 6. S. Kumar and B. Kumar, "Diabetic Retinopathy Detection by Extracting Area and Number of Microaneurysm from Colour Fundus Image". 7. N. H. Harun, Y. Yusof, F. Hassan and Z. Embong, "Classification of Fundus Images For Diabetic Retinopathy using Artificial Neural Network". 8. K. K. Palavalasa and B. Sambaturu, "Automatic Diabetic Retinopathy Detection Using Digital Image Processing". 9. M. Y. Esmail and M. A. Alzain, "Mobile based Tele- medicine Diabetic Retinopathy Screening". 10. H. Thanati, R. J. Chalakkal and W. H. Abdulla, "On Deep Learning based algorithms for Detection of Diabetic Retinopathy". 11. C. Bhardwaj, S. Jain and M. Sood, "Appraisal of Pre- processing Techniques for Automated Detection of Diabetic Retinopathy". Authors: Esakkiraj. P, Sreesha. S, Sreevidya. V, Antony Jeyendran. S

Paper Title: Development of High Volume Fly Ash Concrete Incorporating Steel Fibre 80. Abstract: Concrete is most frequently used composite material. Concrete is homogeneous mix of fine aggregate, Coarse aggregate and binding medium of concrete paste .Due to `high demand of cement Co2 426-433 emission is very high, It leads to global warming. So in this project high volume fly ash concrete was incorporated. Fly ash is the waste material obtained from thermal power plant. In this paper we investigated about high volume fly ash in different percentage of replacement 55, 60, 75 percentage. Layered pavement is incorporated with Steel fiber in a different aspect ratio (15, 30, 40).layered pavement will give good thermal expansive properties. By varying fly ash content and Steel fibers Aspect ratio of different mixes were arrived hardened properties of these nine mixes were arrived such as Compression test, Split tensile test and Flexural test. Keywords: HVFAC, Fly Ash, M Sand, Compression, Split, Flexural Test.

References:

1. Binod Kumar,G. K. Tike, P.K. Nanda, “Evaluation of properties of high-volume fly-ash concrete for pavements”,Journals of material in Civil engineering, Volume 19, Issue 10, October 2004. 2. Cengiz Duran Atis, “High-volume fly ash concrete with high strength and low drying shrinkage’’, Journal of materials in Civil engineering (ASCE), March/April- 2003. 3. Cengiz Duran Atis; Okan Karahan; Kamuran Ari; ozlemCelik Sola; and CahitBilim, “Relation between strength properties flexural and compressive… and abrasion resistance of fiber„steel and polypropylene…-reinforced fly ash concrete”, Journals of material in Civil engineering, Volume 21, Issue 8, August-2009. 4. Cengiz Duran Atise, “High-volume fly ash concrete with high strength and low drying shrinkage”, Journals of material in Civil engineering ( ASCE), Volume 15,Issue 2, April-2003. 5. Cengiz Duran Atiseon, “High volume fly ash abrasion resistant concrete” Journals of material in civil engineering (ASCE),Volume 14,Issue 3, June-2002. 6. Jesus Larralde, Wai-Fah Chen/M. ASCE (Reviewed by the Highway Division), “estimation mechanical deterioration of highway rigid pavements”,Journal of Transportation engineering, Volume 113, Issue 2, March-1987. 7. Mark Reiner and Kevin Renson , “High volume fly ash concrete analysis and application”, Practice periodical on Structural design and Construction, Volume 11, Issue 1, Feb 2006. 8. Nithyalakshmiy,Nivetha, “A study on hybrid fibre reinforced fly ash base concrete”,International Journal of Scientific & Engineering Research, Volume 7, Issue 4, April-2016. 9. R.Narayanan, IYS.Darwish, “Shear in mortar beams containing fibers and fly ash”, Journal of Structural engineering, Volume 114, Issue Jan-1988. 10. Rooban Chakravarthy, Srikanth Venkatesan, Indubhushan Patnaikuni , “Review on hybrid fiber reinforced high performance high volume flyash concrete”, International Journal of Structural and Civil Engineering Research ,Volume 5, No. 1, Feb-2016 . 11. Rooban Chakravarthy, Srikanth Venkatesan , “Mechanical properties of high volume fly ash concrete”, Advances in Materials Science and Engineering, Volume 10 , Nov- 2016. 12. S. Binil Sundar, E. Santhosh Kirubhakaran, “Experimental study on fibre reinforced high volume fly ash concrete for rcc construction”, International Journal for Research in Applied Science & Engineering Technology (IJRASET), Volume 4 Issue 4, April 2016. 13. Samarul Huda,Anwar Ahmad, “An experimental study of fly ash concrete with steel fiber hooked ends to obtain strength of m30 grade”, International Journal of Civil Engineering and Technology (IJCIET) ,Volume 8, Issue 3, March 2017. 14. Shailendra Gilhare,Dr. Ajay Swarup, “Analysis of behaviour of steel fibre reinforced fly ash concrete” Advances in Civil Engineering, Volume 3,2012. 15. Shivakumara.B,Dr. Prabhakara H R, “Strength characteristics of fiber reinforced high volume fly ash concrete”, International Journal of Engineering Research & Technology (IJERT) , Vol. 2 Issue 9, September – 2013. 16. Sukhvarshjerath,m.asce and nicholas hanson , “Effect of fly ash content and aggregate gradation on the durability of concretepavements”, Journal of material in civil Engineering Volume 19,Issue 4, May-2007. 17. Tarun R.Naik,Shiw S.Singh, “Effect of source of fly ash on abrasion resistance of concrete”, Journal of material in Civil Engineering volume 14,Issue 5,Oct-2002. 18. Zhi Pin Loh, Chee Ghuan Tan, “Behaviour of fibre-reinforce cementitious composite containing high volume”, Sadhana, Volume 45 ,Issue 177, 2018. Authors: Sneha Murali, Arjun Mahesh, Rajesh N.

Paper Title: Design of a Compact Microstrip Patch Antenna with Dual Notched Characteristics Abstract: The paper presents a microstrip patch antenna with dual band-notched characteristics. The proposed antenna is fed by a thin microstrip patch and provides band-notched characteristics by etching 2 symmetric C- shaped notches and one hollow cylindrical arc-shaped notch and notched partial ground. The antenna size was 30x19 mm2, covering a range from 2.8-12.3 GHz. The UWB covers a large band of frequencies including WLAN band (4.75-5 GHz) and the X-Band (7.75-8.35 GHz). To prevent interference with the operation of our antenna and devices operating in these bands, the frequencies are rejected through notches in the antenna. The simulated results show that the VSWR of the antenna is <-10 in the radiating bandwidth. This can be used in wireless applications.

81. Keywords: Dual band notch, frequency notch, monopole antenna, UWB antenna

434-438 References:

1. Scholtz, R. (1982). The origins of spread-spectrum communications. IEEE transactions on Communications, 30(5), 822-854. 2. Kshetrimayum, R. S. (2009). An introduction to UWB communication systems. IEEE Potentials, 28(2), 9-13. 3. Yang, Y., Wang, Y., & Fathy, A. E. (2008). Design of compact Vivaldi antenna arrays for UWB see through wall applications. Progress in Electromagnetics Research, 82, 401-418. 4. Li, D., Quan, S., & Wang, Z. (2008, December). Design of a planar ultra-wideband monopole antenna with WLAN band- Notched Characteristic. In 2008 Asia-Pacific Microwave Conference (pp. 1-4). IEEE. 5. Sarsamba, M. C., & Yanamshetti, R. (2017, September). Micro strip antenna application for WiMAX, WLAN, mobile communiction: A review. In 2017 IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI) (pp. 988-991). IEEE. 6. Hedfi, A., & Hayouni, M. (2014, December). Band-notched UWB antennas: Recent trends and development. In Proceedings of 2014 Mediterranean Microwave Symposium (MMS2014) (pp. 1-4). IEEE. Authors: Shweta Sadwani, Vaibhavi Sangawar, Rushabh Sanap, Akanksha Kakade, Minakshi Vharkate

Paper Title: Application of CBIR in E-commerce Abstract: The rise of technology and the rapidly increasing inventions in Science have completely changed many aspects of the world today. Many sectors such as communication, banking, media, etc. have gained momentum because of the internet. Online shopping is one such sector that has flourished in recent times because of the internet. This paper presents a method which employs the system of Content Based Image Retrieval (CBIR) in online shopping. Using this system, the time required to shop online will be reduced. CBIR is the activity of fetching images from the database which have some similarity to the given query image. Traditionally customers would have to search from different categories and apply various filters to buy the product that they want. But in this system, they will be provided with an option to directly upload the image of the product that they wish to buy. If similar products are available, it will be displayed to the customer immediately. Thus, the time required for a customer to buy a product reduces considerably thereby making the shopping experience fun, easy and convenient. The system works in a way such that when an image is uploaded, the features of this image are extracted by using the deep learning method of Convolutional Neural Network (CNN). These extracted features are compared with the features of the available images stored in the database. Then, the similarity measure is calculated and images that are akin to the query image are found and are set out as result. This method significantly helps in reducing the time required to search for a particular product.

Keywords: classification and indexing, Content based Image Retrieval, Convolutional Neural Network, deep learning, image processing. 82. References: 439-444 1. Mutasem Alsmadi, “An efficient similarity measure for Content Based Image Retrieval using memetic algorithm”, Taylor and Francis, 2019 2. Ruigang Fu, Biao Li, Yinghui, Ping Wang - ATRKey Lab, “Content- Based Image Retrieval Based on CNN and SVM” - National University of Defense Technology, 2nd IEEE International Conference on Computer and Communications, 2016 3. Amjad Shah, Rashid Naseem, Sadia, Shahid Iqbal, and Muhammad Arif Shah,“Improving CBIR Accuracy using Convolutional Neural Network for Feature Extraction”- Department Of Computer Science, City University of Science and Information Technology 4. Joel Pyykko and Dorota G, “Interactive Content-Based Image Retrieval with Deep Neural Networks”, LNCS 9961, pp. 77–88, 2017 5. GuoyongDuana, Jing Yanga, Yilong Yanga,“Content-Based Image Retrieval Research”, International Conference on Physics Science and Technology (ICPST 2011) 6. OuhdaMohamed, El Asnaoui Khalid, “Content-Based Image Retrieval Using Convolutional Neural Networks”, Springer- 2019 7. HuijingZhan ,Boxin Shi, Ling-Yu Duan, Alex C. Kot, “DeepShoe: An improved Multi-Task View-invariant CNN for street-to- shop shoe retrieval”, Science Direct, Computer Vision and Image understanding 180 (2019) 23-33 8. S.Rubini, R.Divya, G.Divyalakshmi, Mr T.M. Senthil Ganesan, “Content Based Image Retrieval”, International Research Journal of Engineering and Technology, Volume: 05 Issue: 03, Mar-2018 9. Y. Jing, D. Liu, D. Kislyuk, A. Zhai, J. Xu, J. Donahue, and S. Tavel,“Visual search at pinterest,” in Proceedings of the 21th ACM SIGKDDInternational Conference on Knowledge Discovery and Data Mining.ACM, 2015 10. K. Lin, H.-F. Yang, K.-H. Liu, J.-H. Hsiao, and C.-S. Chen, “Rapid clothing retrieval via deep learning of binary codes and hierarchical search,” in Proceedings of the 5th ACM on International Conference on Multimedia Retrieval. ACM, 2015 11. “Alex net VGG and Inception Architectures, Deep Learning in Computer Vision”, National Research University Higher School of Economics, Coursera 12. Fei-Fei Li & Justin Johnson & Serena Yeung , “Lecture 9 – CNN Architecture”, Stanford University, 30 April 2019 13. Aqeel Anwar, “Difference between AlexNet, VGGNet, ResNet and Inception”, medium- Towards Data Science, Jun 7, 2019 Authors: Rupinder Kaur, Gaurav Gupta

Paper Title: Heart Attack Prediction with Hybrid Technique of Weighted K-Mean and Logistic Regression Abstract: Data analytics is the main focusing point for different fields. Medical field is the new entrant to the data analytics. It specifically picks the data related to patient different parameters and evaluates the parameters with different machine learning algorithms. In proposed technique the heart attack prediction based on different parameters has been evaluated. These parameters are related to patient different aspects like blood pressure, blood sugar, age, physical activities etc. The proposed technique for the prediction is k-mean and logistic regression. The proposed technique is showing better results in terms of accuracy, precision and recall. The accuracy improvement is around 1.67%, Recall is improved by 1.15% and precision is improved by 3.15%.

83. Keywords: Prediction, Logistic regression, k-mean 445-448 References:

1. Mackay J, Mensah G. Atlas of heart disease and stroke. Nonserial Publication; 2004. 2. Vasighi Mahdi, Ali Zahraei, Bagheri Saeed, Vafaeimanesh Jamshid. Diagnosis of coronary heart disease based on Hnmr spectra of human blood plasma using genetic algorithm-based feature selection. Wiley Online Library; 2013. p. 318–22. 3. Amin Mohammed Shafennor, et al. Identification of Significant features and data mining techniques in predicting heart disease. Telematics Inf 2019:82–93. 4. Nahar J, Imam T, Tickle KS, Chen YPP. Computational intelligence for heart disease diagnosis: a medical knowledge driven approach. Expert Syst Appl 2013;40(1):96–104. 5. Guru Niti, Dahiya Anil, NavinRajpal. Decision support system for heart disease diagnosis using neural network, Delhi Business Review. 2007;8(1). January-June. 6. Detrano Robert. Cleveland heart disease database. V.A. Medical Center, Long Beach and Cleveland Clinic Foundation; 1989. 7. Patil SB, Kumaraswamy YS. Extraction of significant patterns from heart disease warehouses for heart attack prediction. Int. J. Comput. Sci. Netw. Secur(IJCSNS) 2009;9(2):228–35. 8. Chauhan Shraddha, Aeri Bani T. The rising incidence of cardiovascular diseases in India: assessing its economic impact. J. Prev. Cardiol. 2015;4(4):735–40. 9. Vanisree K, JyothiSingaraju. Decision support system for congenital heart disease diagnosis based on signs and symptoms using neural networks. Int J Comput Appl April 2011;19(6). (0975 8887). 10. Verma L, Srivastava S, Negi PC. A hybrid data mining model to predict coronary artery disease cases using non-invasive clinical data. J Med Syst 2016;40(7):1–7. 11. Liu Xiao, Wang Xiaoli, Su Qiang, Zhang Mo, Zhu Yanhong, Wang Qiugen, Wang Qian. A hybrid classification system for heart disease diagnosis based on the RFRS method. Comput. Math. Methods Med. 2017;2017:1–11. 12. Xing Yanwei, Wang Jie, Yonghong Gao Zhihong Zhao. Combination data mining methods with new medical data to predicting outcome of Coronary Heart Disease. Convergence Information Technology. 2007. p. 868–72. Authors: Mohamed S.S. Basyoni

Paper Title: A Fast Model Based on Genetic Algorithm to Construct Fuzzy Rules Abstract: Fuzzy rule has been used extensively in data mining. This paper presents a fast and flexible method based on genetic algorithm to construct fuzzy decision rule with considering criteria of accuracy. First, the algorithm determines the width that divides each attribute into “n” intervals according to the number of fuzzy sets, after that calculates the parameters width according to that width. Rough Sets Model Based on Database Systems technique used to reduce the number of attributes if there exists then we use the algorithm for extracting initial fuzzy rules from fuzzy table using SQL statements with a smaller number of rules than the other models without needing to use a genetic algorithm – Based Rule Selection approach to select a small number of significant rules, then it calculates their accuracy and the confidence.. Multiobjective evolutionary algorithms (EAs) that use nondominated sorting and sharing have been criticized mainly for computational complexity and needing for specifying a sharing parameter but in our genetic model each fuzzy set represented by “Real number” from 0 to 9 forming a gene on chromosome (individual). Our genetic model is used to improve the accuracy of the initial rules and calculates the accuracy of the new rules again which be higher than the old rules The proposed approach is applied on the Iris dataset and the results compared with other models: Preselection with niches, ENORA and NSGA to show its validity.

Keywords: Genetic algorithm, Fuzzy logic, Rough set, SQL statements, Accuracy.

References:

84. 1. Yan-Qing Yao, Ju-Sheng Mi, Zhou-Jun Li. Attribute reduction based on Generalized fuzzy evidence theory in fuzzy sets and systems. Fuzzy Sets and Systems, (2011) 509-517. 2. Laumanns, M., Zitzler, E., Thiele, L.: On the Effects of Archiving, Elitism, and Density Based Selection in Evolutionary Multi- 449-456 objective Optimization. In: Zitzler, E., Deb, K., Thiele, L., Coello Coello, C.A., Corne, D.W. (eds.) EMO 2001. LNCS, vol. 1993, pp. 181–196. Springer, Heidelberg (2001) 3. Cordon, O., Herrera, F., Hoffmann, F., Magdalena, L. (2001): Genetic Fuzzy Systems: Evolutionary Tuning and Learning of Fuzzy Knowledge Bases. World Scientific Publishers, Singapore. 4. Fernando Jiménez, Gracia Sánchez, Fuzzy Classification with Multi-objective Evolutionary Algorithms, Springer-Verlag Berlin Heidelberg 730–738, (2008). 5. I.H. Toroslu, Y. Arslanoglu, Genetic algorithm for the personnel assignment problem with multiple objectives, Information Sciences 177 (2007) 787–803. 6. Hu, X., Lin, T., Han, J, " A New Rough Sets Model Based on Database Systems" , Fundamenta Information 59 no. 2-3 pp.135- 152 ., 2004 7. Jiménez, F., Gómez-Skarmeta, A.F., Sánchez, G., Deb, K.: An evolutionary algorithm for constrained multi-objective optimization. In: Proceedings IEEE World Congress on Evolutionary Computation (2002). 8. Yusliza Yusoff*, Mohd Salihin, Overview of NSGA-II for Optimizing Machining Process Parameters, Published by Elsevier Ltd (2007). 9. Deb, K.: Multi-Objective Optimization using Evolutionary Algorithms. John Wiley and Sons, LTD, Chichester (2001) 10. Hisao Ishibuchi, Tomoharu Nakashima, Manabu Nii. Classification and Modeling with Linguistic Information Granules. Springer Berline Heidelberg New York (1998). 11. Gómez-Skarmeta, A.F., Jiménez, F.: Fuzzy modeling with hibrid systems. Fuzzy Sets and Systems 104, 199–208 (1999) 12. Gómez-Skarmeta, A.F., Jiménez, F., Sánchez, G.: Improving Interpretability in Approximative Fuzzy Models via Multiobjective Evolutionary Algorithms. International Journal of Intelligent Systems 22, 943–969 (2007) 13. Ishibuchi, H., Nakashima, T., Murata, T.: Performance evaluation of fuzzy classifier systems for multidimensional pattern classification problems. IEEE Transactions on Systems, Man, and Cubernetics - Part B: Cybernetics 29(5), 601–618 (1999) 14. Deb, K., Pratap, A., Agarwal, S. and Meyarivan, T. A fast and elitist multiobjective genetic algorithm: NSGA-II. Evolutionary Computation, IEEE Transactions on, 6, 2 (2002), 182-197. 15. Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C.M., Grunert da Fonseca, V.: Performance Assessment of Multiobjective Optimizers: An Analysis and Review. IEEE Transactions on Evolutionary Computation 7(2), 117–132 (2003). Authors: Manash Dey, Harshit Bisht, Rishab kumar, Abhinav Kumar, Aman Arora, Jatin Modelling and Assembling Rocker Rover and Its Implementation in the Field of Agriculture: Paper Title: Designing Abstract: The Project work “ROCKER ROVER AND ITS IMPLEMENTATION IN THE FIELD OF 85. AGRICULTURE: DESIGNING” deal with the important aspect of modification of traditional rocker rover to make it more affordable and accessible in the field of Agriculture in order to make the backbone of the Indian economy progress towards automation making it more efficient. The rocker rover has to operate on rough and 457-461 harsh environments like exploring the Moon’s surface and other expeditions alike for which it was designed. But the implementation of the rocker rover can be further extended in the areas of work where the land upon which the operations needs to be executed like in the field of Agricultural farming. The rover has been completely made from PVC to increase its capacity to withstand shocks, vibrations and mechanical failures caused by working on the large rough fields where it is operated on. Using SOLIDWORKS software the design of the rover has been fine-tuned and by experimenting with prototypes and models of the rover in the experimental setup of the live test, improvements and feature were included into the rocker rover.

Keyword: Farming, Agriculture, Automation, PVC Pipe, Suspension less system, all-terrain, SOLIDWORKS, Design, Driverless.

References:

1. K. HARISH CHANDU1, P. HARI NARAYANA2, K. C. CHARAN TEJA3, B. SAI4, Y. MURALI MOHAN5 “Design and Fabrication of Rocker Bogie Mechanism” (ISSN 2319-8885 Vol.07,Issue.04, April- 2018, Pages:0781-0784) 2. Mohammed Arbaz1, Mohammed Zayan Damda, Mohammed Suhail, Mohammed Fazil “Multi-Function Rover Based on Rocker- Bogie Mechanism” (Volume: 06 Issue: 05, Irjet.net) 3. M. Vigneshwaran, R. Siddharthaa, G. Vijay and S. Pravin Kumar “DESIGN OF ALL TERRAIN VEHICLE USING ROCKER BOGIE MECHANISM” (IJMET Volume 10, Issue 03, March 2019, pp. 214–219, Article ID: IJMET_10_03_022 ) 4. Abhisek Verma, Chandrajeet Yadav, Bandana Singh , Arpit Gupta, Jaya Mishra, Abhishek Saxena “Design of Rocker-Bogie Mechanism” (International Journal of Innovative Science and Research Technology ISSN No: - 2456 - 2165) 5. P. Panigrahi, A. Barik, Rajneesh R. & R. K. Sahu, “Introduction of Mechanical Gear Type Steering Mechanism to Rocker Bogie” (Imperial Journal of Interdisciplinary Research (IJIR) Vol-2, Issue-5, ISSN: 2454- 1362,2016) 6. Design analysis of Rocker Bogie Suspension System and Access the possibility to implement in Front Loading Vehicles (e-ISSN: 2278- 1684,p-ISSN: 2320-334X, Volume 12, Issue 3 Ver. III ) 7. B. D. Harrington and C. Voorhees, “The Challenges of Designing the Rocker-Bogie Suspension for the Mars Exploration Rover”, Proceedings of the 37th Aerospace Mechanisms Symposium, Johnson Space Center, page No. 185-1985, May 19-21, 2004. 8. Sathiesh Kumar V, Gogul I, Deepan Raj M, Pragadesh S.K, Sarathkumar Sebastin J “Smart Autonomous Gardening Rover with Plant Recognition using Neural Networks” (6th International Conference On Advances In Computing & Communications, ICACC 2016, 6-8 September 2016, Cochin, India) 9. Jotheess S, Hari Ragul K, Abhilash K, Govendan M “Design and Optimization of a Mars Rover’s Rocker-Bogie Mechanism” (e- ISSN: 2278-1684,p-ISSN: 2320-334X, Volume 14, Issue 5 Ver. III) 10. Manash Dey, Harshit Bisht, Rishab Kumar, Abhinav Kumar, Aman Arora, Jatin “Rocker Rover And Its Implementation In The Field Of Agriculture: A Review” (JETIR June 2019, Volume 6, Issue 6 ISSN-2349-5162) Authors: B Sri Vyshnavi, S Nandha Kishor, G N Kodanda Ramaiah

Paper Title: Electronic Shopping Cart Abstract: The present paper is discussing the project's main goal and therefore the plan of proposing an answer to the present downside within the present state of affairs all the medium to massive size supermarkets/grocery stores. Therefore, it pretends to be a significant improvement in these things, and time is saved as an answer for the shoppers whereas buying these places. It is as a result of the massive progress and improvement of the IT trade throughout the past 55 years (and far more throughout the last decade), that this project had the chance to be developed and enforced. Currently, a day’s personal computers are getting terribly smaller and smaller, their processing is even quicker and higher (much additional efficient), and it's additionally less expensive than it absolutely was twenty-five years agone. This personal plan (and consequently, this project); which is explained very well during this document, tries to be a significant improvement within the retail business. The project development and implementation were complete focusings not solely on the grocery stores, and food supermarkets/butcher retailers, and additionally massive malls. However, these ideas and styles may be cipher too in many totally different sectors, like the textile trade, entertainment-related business (videogames, music, movies…), books, toys… and no matter the alternative trade of products that the retail company is commercialism. therefore the initiative and section of this document are that the rationalization of the most motivations and things that originated this concept, therefore as its analysis, design, and implementation, for in a while showing the plausible enhancements which may be enclosed within the system and therefore the real 86. implementation of the first plan is additionally been mentioned within the paper itself.

Keywords: Embedded, E-Shopping Cart, Barcode, and Raspberry pi. 462-465

References:

1. Dr. Mary Cherian, “Bill Smart- A Smart Billing System using Raspberry Pi and RFID”, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 5, Issue 5, May 2017 2. Ravindra Jogekar,” Automated Shopping Trolley System Using Raspberry Pi Device”, International Journal Of Research Culture Society, Volume - 2, Issue – 2 Feb – 2018. 3. Dr. Suryaprasad J, “A Novel Low-Cost Intelligent Shopping Cart”, Proceedings of the 2nd IEEE International Conference on Networked Embedded Systems for Enterprise Applications (NESEA) Dec, 2011. 4. Ms.Vrinda, Niharika, “Novel Model for Automating Purchases using Intelligent Cart,” e-ISSN: 2278-0661, p- ISSN: 1; 22788727Volume16, Issue 1, Ver. VII (Feb. 2014), PP 23-30. 5. Mr. Chandrasekhar and Ms.T. Sangeetha “Smart Shopping Cart with Automatic Billing System through RFID and ZigBee”, IEEE, 2014. 6. Ankush Yewatkar, Faiz Inamdar, Raj Singh, Ayushya, Amol Bandal “Smart Cart with Automatic Billing, Product Information, Product Recommendation Using RFID & Zigbee with Anti-Theft” 2016. 7. Thakur Prerana, Shikha Ranjan, Prachi Kaushik “Smart Shopping Cart For Automatic Billing In Supermarket” 2017 IJEDR. 8. Yasir Ahmed and Taha Yaseen “Intelligent Shopping: Improved Smart Shopping in Malls through Image Recognition via Internet of Things (IoT)” IJETCSE march 2017. 9. P.Jey Praveen Raj, P.M.Mohamed Fuge, R.Paul Caleb, G.Natarajan “Design and Fabrication of Stair Climbing Trolley” IJETCSE march 2016. 10. B. N. Arathi and M. Shona “An Elegant Shopping using Smart Trolley” Indian Journal of Science and Technology, January 2017. 11. Rahul Chaudhari, Sunil Bhagat, Shubham Kanfade, Mayuri Taklikar, Snehal Bhajikhaye, Prof. S. P. Chaware “Smart Trolley in Shopping Mall” International Journal of Innovations in Engineering and Science, 2018. Authors: Rajesh Goel

Paper Title: Performance Analysis of Subcarrier Multiplexed Optical Transmission System for QPSK Data Abstract: This paper focuses on the impact of different parameters on the performance of the Subcarrier Multiplexed Optical Transmission System for the application on radio link via optical fiber. Performance results are evaluated for QPSK data format for ODSB and OSSB modulation of Microwave subcarriers with digital NRZ coded random data patterns. The four subsystems of QPSK modulators are at 400, 500, 600, 700 MHz subcarrier frequencies with frequency spacing of 100 MHz. The power of subcarriers is decreasing with increasing the link distance due to dispersion and attenuation. By using dispersion compensation fiber, the link distance has been enhanced from 100 km to 240 km successfully. The impact of chromatic dispersion has been reduced in OSSB by using dual-electrode MZM. The constellation diagram also confirms that the phase of the signal after traveling through the link is changing due to dispersion. The phase is the same for subcarrier 600 MHz & 700 MHz for ODSB and OSSB in QPSK SCM. The impact of linewidth and responsivity on SNR has also analyzed to evaluate the performance. It is concluded that the maximum SNR is decreasing with increase in the linewidth of laser source and increasing with the increase in responsivity of PIN diode for the same fiber length in SCM transmission.

Keywords: Eye Diagram, SCM, ODSB, OSSB, QPSK,

References:

1. Adnan Hussein Ali and Alaa Desher Farhood, Design and Performance Analysis of the WDM Schemes for Radio over Fiber System With Different Fiber Propagation Losses, Fibers 2019, 7, 19; doi:10.3390/fib7030019 2. Rajesh, Analysis of impact of laser line width and chromatic dispersion on Carrier to Noise Ratio in Radio over Fiber, 87. International Journal of Recent Technology and Engineering, ISSN: 2277-3878, Volume-8, Issue-6, March 2020, pp 3969-3972. 3. Md. Shamsul Arefin, Nusrat Tazin, Munsury Rahman and Tajrian Mollick, Performance Evaluation of a SCM Optical Transmission System, IOSR Journal of Engineering (IOSRJEN) e-ISSN: 2250-3021, p-ISSN: 2278-8719, Vol. 3, Issue 4 (April. 466-476 2013), PP 37-53 4. Arief M., Idrus and S.M. Alifah, The SCM/WDM system model for radio over fiber communication link, RF and Microwave Conference, IEEE International, pp 344-347, 2-4 December 2008. 5. Md. Shamim Reza, Md. Maruf Hossain, Adnan Ahmed Chowdhury, S. M. Shamim Reza and Md. Moshiur Rahman, Performance Evaluation of SCM-WDM System Using Different Linecoding, Journal of Telecommunications, Vol. 2, Issue 1, April 2010. 6. Shijun Xiao & Andrew M. Weiner, Four-User ~3-GHz-Spaced Subcarrier Multiplexing (SCM) Using Optical Direct-Detection via Hyperfine WDM, IEEE Photonics Technology Letters, Vol. 17, No. 10, October 2005. 7. Cheng Juang, Shaw Tzuu Huang, Chin Yueh Liu, Wei ChungWang, Tsung Min Hwang, Jonq Juang and WenWei Lin, Subcarrier Multiplexing by Chaotic Multitone Modulation, IEEE Journal of Quantum Electronics, Vol. 39, No. 10, October 2003. 8. M.T. Al-Qdah, H.A. Abdul-Rashid, K. Dimyati, B.M. Ali and M. Khazani, Effect of Optical Beat Interference in SCM/WDM Optical Networks in Presence of FWM, KMITL Sci. Tech. J. Vol. 5, No. 3, July-December 2005. 9. Hui R., Zhu B., Huang R., Allen C., Demarest K., and Richard D., (2001), 10Gb/s SCM system using optical single sideband modulation,” In proc. OFC’01, Anaheim, CA, March 2001, MM4. 10. Flood E. A., (2002), Demonstration of 20 Gbits/s subcarrier multiplexed transmission system, Electronics Letters vol. 38, no. 9, 25th April 2002. 11. Rajesh, Performance Analysis of Radio over Fiber with ODSB and OSSB modulation techniques using ASK, 64QAM and 8DPSK data, International Journal of Control and Automation, ISSN: 2005-4297 IJCA, Vol. 13, No. 2. (2020), pp. 81-89. 12. Sieben M., Conradi J., Dodds D., Davis B., and Walklin S., (1997), Optical signal sideband (OSSB) transmission for dispersion avoidance and electrical dispersion compensation in microwave subcarrier and baseband digital systems, Electron. Lett., vol. 33, pp. 971–973, May 1997. 13. Smith G. H., Novak D. and Ahmed Z., (1996), Optimization of link distance in fiber-radio systems incorporating external modulators, in Proc. Australian Conf. Opt. Fiber Technol., Gold Coast, Australia, Dec. 1996, pp. 141–144. 14. Smith G., Novak D., Ahmed Z., (1997), Overcoming Chromatic-Dispersion Effects in Fiber-Wireless Systems Incorporating External Modulators, IEEE Transactions on Microwave Theory and Techniques, vol. 45, no. 8, Pages 1410-1415, August 1997. 15. Woodward Sheryl L., Senior member IEEE, and Phillips Mary R., Member IEEE, (2004), Optimizing Subcarrier Multiplexed WDM Transmission Links, Journal of Lightwave Technology, vol. 22, no.3, March, 2004. Authors: Fatna El Mahdi, Mohammed Souidi, Halim Berradi, Ahmed Habbani

Paper Title: Enhancing Security in Smart Cities using Dynamic Multipath Algorithm Abstract: The world of today is an ultimate connected world, where all types of physical devices and virtual objects can communicate in order to exchange information or provide services. Internet of Things (IoT) is one of the leading areas that make this worldwide connection possible, by integrating and enabling different solutions and communication technologies. Wireless mobile networks are increasing very fast and giving more perspectives in the telecommunication field. 88. Nevertheless, some problems are still facing this development. The most important and mandatory issue, among them, is the security of the network. In this paper, we will introduce some related works about security concept 477-482 for mobile networks and we present our solution that provides a new dynamic approach to find a variable number of multiple paths according to the neighborhood, the density and to the mobility of nodes in the network. In order to evaluate the impact of our solution on network performances, we implement our algorithm on one of the most known multipath protocols (MP-OLSR).

Keywords: Smart cities, Internet of Things, Dynamic Multipath, Security.

References:

1. D. Fang, Y. Qian, and R. Q. Hu, "Security for 5G mobile wireless networks," IEEE Access, vol. 6, pp. 4850-4874, 2017. 2. A. Khan, J. Abdullah, N. Khan, A. Julahi, and S. Tarmizi, "Quantum-Elliptic curve Cryptography for Multihop Communication in 5G Networks," International Journal of Computer Science and Network Security (IJCSNS), vol. 17, pp. 357-365, 2017. 3. B. Ying and A. Nayak, "Lightweight remote user authentication protocol for multi-server 5G networks using self-certified public key cryptography," Journal of Network and Computer Applications, vol. 131, pp. 66-74, 2019. 4. L. Goubin and A. Martinelli, "Protecting AES with Shamir’s secret sharing scheme," in International Workshop on Cryptographic Hardware and Embedded Systems, 2011, pp. 79-94. 5. W. Lou, W. Liu, Y. Zhang, and Y. Fang, "SPREAD: Improving network security by multipath routing in mobile ad hoc networks," Wireless Networks, vol. 15, pp. 279-294, 2009. 6. F. El Mahdi, A. Habbani, Z. Kartit, and B. Bouamoud, "Optimized Scheme to Secure IoT Systems Based on Sharing Secret in Multipath Protocol," Wireless Communications and Mobile Computing, vol. 2020, 2020. 7. K. Witrisal, P. Meissner, E. Leitinger, Y. Shen, C. Gustafson, F. Tufvesson, et al., "High-accuracy localization for assisted living: 5G systems will turn multipath channels from foe to friend," IEEE Signal Processing Magazine, vol. 33, pp. 59-70, 2016. 8. S.-J. Lee and M. Gerla, "Split multipath routing with maximally disjoint paths in ad hoc networks," in ICC 2001. IEEE International Conference on Communications. Conference Record (Cat. No. 01CH37240), 2001, pp. 3201-3205. 9. P. Papadimitratos, Z. J. Haas, and E. G. Sirer, "Path set selection in mobile ad hoc networks," in Proceedings of the 3rd ACM international symposium on Mobile ad hoc networking & computing, 2002, pp. 1-11. 10. J. Yi and B. Parrein, "Multipath Extension for the Optimized Link State Routing Protocol Version 2 (OLSRv2)," 2017. 11. F. El Mahdi, B. Bouamoud, and A. Habbani, "Analyzing security in Smart cities networking and implementing link quality metric," in 2019 Third International Conference on Intelligent Computing in Data Sciences (ICDS), 2019, pp. 1-8. 12. M. Souidi, A. Habbani, H. Berradi, and F. El Mahdi, "Geographic forwarding rules to reduce broadcast redundancy in mobile ad hoc wireless networks," Personal and Ubiquitous Computing, vol. 23, pp. 765-775, 2019. 13. J. Yi, E. Cizeron, S. Hamma, and B. Parrein, "Simulation and performance analysis of MP-OLSR for mobile ad hoc networks," in 2008 IEEE Wireless Communications and Networking Conference, 2008, pp. 2235-2240. 14. J. Yi, A. Adnane, S. David, and B. Parrein, "Multipath optimized link state routing for mobile ad hoc networks," Ad hoc networks, vol. 9, pp. 28-47, 2011. 15. L.-W. Chen, W. Chu, Y.-C. Tseng, and J.-J. Wu, "Route throughput analysis with spectral reuse for multi-rate mobile ad hoc networks," Journal of information science and engineering, vol. 25, pp. 1593-1604, 2009. 16. D. S. J. De Couto, "High-throughput routing for multi-hop wireless networks," Massachusetts Institute of Technology, 2004. 17. Z. Zaidi, T. Y. Tan, and Y. Cheng, "ETX could result in lower throughput," in 2009 Proceedings of 18th International Conference on Computer Communications and Networks, 2009, pp. 1-6. 18. F. E. Mahdi, A. Habbani, N. Mouchfiq, and B. Essaid, "Study of security in MANETs and evaluation of network performance using ETX metric," in Proceedings of the 2017 International Conference on Smart Digital Environment, 2017, pp. 220-228. 19. N. Liu and W. K. Seah, "Performance evaluation of routing metrics for community wireless mesh networks," in 2011 Seventh International Conference on Intelligent Sensors, Sensor Networks and Information Processing, 2011, pp. 556-561. Authors: K.Narasimha Raju, Dekka Satish, G.Sita Ratnam, G.Ravindranath, T.M.L Prasanna

Paper Title: Development of a Priority based System for Emergency Vehicles to Reduce Accidents in VANETs Abstract: Due to tremendous increase of vehicles in number leads to excessive congestion of vehicles at intersection of roads. It causing inconvenience to emergency vehicles like Ambulance and Fire brigade etc, ultimately which is the cost of human life To avoid this, Emergency Vehicles will have to give high priority to overcome from the congestion. Vehicular Ad-Hoc Networks (VANETs) is a network which is used to create a temporary communication among the vehicles. In this paper, priority based vehicle movement system is proposed to give high priority to emergency vehicles and establishing communication among the vehicles through VANET. Due to this high priority, there is no necessity to wait for the emergency vehicles at the traffic signals to get the green signal while communicating with traffic controller. In this paper, SUMO simulator is used for experimental analysis. The result indicates that the proposed methodology reduces the waiting time when compared to the existing system.

Keywords: Emergency vehicles, Priority, VANET.

89. References: 483-486 1. Priority based Algorithm for Traffic Intersections Streaming Using VANET Mohamed Akram Ameddah, Bhaskar Das, Jalal Almhana University de Moncton Department of Computer Science Moncton, NB, Canada E1A 3E9 Email: {ema3944, bhaskar.das, jalal.almhana}@umoncton.ca 2. Simulations and Result analysis of VANET Based Self Adaptive Prioritized Traffic Signal Control Shaikh Sharique Ahmad* , Prof. Hiralal Solunke Department of Computer Science Engineering, G.H. Raisoni Institute of Engineering and Managements, Jalgaon (M.S), India 3. Vehicular Ad-hoc Network (VANET): Review Muhammad Rizwan Ghori, Kamal Z. Zamli, Nik Quosthoni, Muhammad Hisyam, Mohamed Montaser Faculty of Computer Systems and Software Engineering, University of Malaysia Pahang, Gambang Campus, Kuantan, Malaysia 4. VANET-based Vehicle Priority Scheduling System at Multi Intersection Seyed Vorya hosseini 1 , Jamshid Bagherzadeh2 1,2 Computer Engineering Department, Urmia University, Urmia, IRAN [email protected],[email protected] 5. Vanet Based Traffic Management System Development And Testing Using Aodv Routing Protocol. PATIL V.P. Smt. Indira Gandhi college of Engineering, New Mumbai, INDIA 6. VANET Based Remote Diagnostic Safety System Using Cloud Shruti Kamatekar1 , Prof. Balachandra G.C 2 1(Computer Engineering, K.L.E.I.T College, Hubballi) 2 (Computer Engineering, K.L.E.I.T College, Hubballi) 7. A Comprehensive Survey on VANET Security Services in Traffic Management System Muhammad Sameer Sheikh and Jun Liang 8. A Research on VANET: Various Broadcasting and Clustering Techniques D. Kalaivani, Rajkumar S Authors: Solomon B Selvaraj, Narasimhan K, Daniel Abishai L, Krishna Kaanth M, Ashish Daniel 90. Paper Title: The Impact of Slicing Softwares on the Mechanical Properties of 3D Printed Parts Abstract: “Slicing tool” or “Slicing Software” computes the intersection curves of models and slicing planes. They improve the quality of the model being printed when given in the form of STL file. Upon analyzing a specimen that has been printed using two different slicing tools, there was a drastic variation on account of the mechanical properties of the specimen. The ultimate tensile strength and the surface roughness of the material vary from one tool to another. This paper reports an investigation and analysis of the variation in the ultimate tensile strength and the surface roughness of the specimen, given that the 3D printer and the model being printed is the same, with a variation of usage of slicing software. This analysis includes ReplicatorG, Flashprint as the two different slicing tools that are used for slicing of the model. The variation in the ultimate tensile strength and the surface roughness are measured and represented statistically through graphs. An appropriate decisive conclusion was drawn on the basis of the observations and analysis of the experiment on relevance to the behavior and mechanical properties of the specimen.

Keywords: FFF, Slicing software, STL file, UTS, Surface roughness, Wanhao Duplicator 4S-printer

References:

1. Low Cost 3D Printing for Rapid Prototyping and its Application, Taha Hasan, Masood Siddique, Iqra Samiy, Malik Zohaib Nisarz, Mashal Naeemx, Abid Karim and Muhammad Usman arXiv:1911.10758v1 [ 2. cs.RO] 25 Nov 2019 IEEE 3. 3D Metal Printing Technology, Thomas Duda et al. / IFAC-PapersOnLine 49-29 (2016) 103–110 4. Utility and challenges of 3 D Printing Aman Sharma, Harish Garg, IOSR Journal of Mechanical and Civil Engineering (IOSR- JMCE), e-ISSN: 2278-1684, p-ISSN: 2320–334X 5. Lebon N, Tapie L, Duret F, et al. Understanding dental CAD/CAM for restorations—dental milling machines from a mechanical engineering viewpoint. Part A: chairside milling machines. Int J Comput Dent. 2016;19:45–62. 6. Lebon N, Tapie L, Duret F, et al. Understanding dental CAD/CAM for restorations—dental milling machines from a mechanical engineering viewpoint. Part B: labside milling machines. Int J Comput Dent. 2016;19:115–34 7. Hugo I. Medellín-Castillo, Joel Esau Pedraza Torres, rapid prototyping and manufacturing: a review of current technologies 8. 3d printed model of complex anatomy in cardiovascular diseases, openventio publishers, open journal, volume 2: issue 3, article ref.# : 1000hroj2118 Sun z, Squelch a. 3d printed models of complex anatomy in cardiovascular disease. heart res open j. 2015; 2(3): 103-108. doi: 10.17140/hroj- 2-118 9. 3d printing ‘s role in transformation of plastic industry, Haishang Wu, Waseda university, international journal of mechanical engineering and technology (ijmet), volume 10, issue 03, march 2019, pp. 1623–1629, article id: ijmet_10_03_163, issn print: 487-494 0976-6340 and issn online: 0976-6359 10. Improvement of the Traditional Techniques of Artistic Casting through the Development of Open Source 3D Printing Technologies Based on Digital Ultraviolet Light Processing (DLP), Drago Díaz Alemán, Jose Luis Saorín Pérez, Cecile Meier, Itahisa Pérez Conesa, Jorge de la Torre-Cantero, Amsterdam The Netherlands May 14-15, 2019, Part IV. 11. Study on Design and Manufacture of 3D Printer based on Fused Deposition Modeling Technique Ngoc-Hien Tran, Van-Cuong Nguyen, Van-Nghia Nguyen. International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958, Volume-6 Issue-6, August 2017 12. The Impact and Application of 3D Printing Technology Thabiso Peter Mpofu, Cephas Mawere, Macdonald Mukosera. International Journal of Science and Research (IJSR) ISSN (Online): 2319-7064, Impact Factor (2012): 3.358 13. Evaluating the Mechanical Properties of Commonly Used 3d Printed ABS and PLA Polymers with Multi Layered Polymers, Shabana, R.V.Nikhil Santosh, J.Sarojini, K.Arun Vikram, V.V.K.Lakshmi, International Journal of Engineering and Advanced Technology (IJEAT). ISSN: 2249 – 8958, Volume-8 Issue-6, August 2019 14. Pang, Xuan & Zhuang, Xiuli & Tang, Zhaohui & Chen, Xuesi. (2010). Polylactic acid (PLA): Research, development and industrialization. Biotechnology journal. 5. 1125-36. 10.1002/biot.201000135 15. Nagendra G. Tanikella, Ben Wittbrodt and Joshua M. Pearce. Tensile Strength of Commercial Polymer Materials for Fused Filament Fabrication 3-D Printing. Additive Manufacturing 15: pp. 40–47 (2017). DOI: 10.1016/j.addma.2017.03.005 16. 3D printing of multiple metallic materials via modified selective laser melting, Chao Wei, Lin Li (1)*, Xiaoji Zhang, Yuan-Hui Chueh, Laser Processing Research Centre, School of Mechanical, Aerospace and Civil Engineering, The University of Manchester, Manchester M13 9PL, UK 17. ASTM. Committee F42 on Additive Manufacturing Technologies, West Conshohocken, Pa. 2009 Standard terminology for additive manufacturing—general principles and terminology. ISO/ASTM52900–15. 18. Sabourin E, Houser SA, Bøhn JH (1996) Adaptive slicing using stepwise uniform refinement. Rapid Prototyp J 2(4):20–26. https://doi.org/10.1108/13552549610153370 19. Tyberg J, Bøhn JH (1998) Local adaptive slicing. Rapid Prototyp J 4(3):118–127. https://doi.org/10.1108/13552549810222993 20. M.Y.Zhou, “STEP-based approach for direct slicing of CAD models for layered manufacturing”, International journal of production research, vol.43, no.15: pp.3273-3285, 2005. 21. Boschetto A and Bottini L 2014 Accuracy prediction in fused deposition modeling, The International Journal of Advanced Manufacturing Technology, 73(5-8) 913-928 22. Influence of slicing tools on quality of 3D printed parts Felix Baumann, Halil Bugdayci, Jonas Grunert, Fabian Keller and Dieter Roller. University of Stuttgart COMPUTER AIDEDDESIGN&APPLICATIONS,2016 VOL.13,NO.1,14–31 http://dx.doi.org/10.1080/16864360.2015.1059184 23. Improvement of quality of 3D printed objects by elimination of microscopic structural defects in fused deposition modeling Evgeniy G. Gordeev, Alexey S. Galushko, Valentine P. Ananikov* PLOS ONE https://doi.org/10.1371/journal.pone.0198370 June 7, 2018 Authors: R. Priyadharshini, R.Ramkumar

Paper Title: Faster R-CNN Network Based on Multi Feature Fusion for Efficient Face Detection Abstract: The putting into effect of deep learning gave all attention on deep convolutionary neural networks has 91. gained great condition of having general approval in face discovery in near in time years. One of the still in the same way open tasks however is to make out small-scale points. The distance down of the convolutionary network can cause a quick becoming-smaller of the sent out point map for small faces, and most scale 495-499 unchanging views can hardly grip less than 15x15 bit of picture faces. There are few types of Haar-like rectangle points, which send in to the hard question that the training time for the classifier is too long because of, in relation to the greatly sized number of point amounts needed to put in order the small faces. In order to get answer to this hard question, we offer the MB-LBP (Multi-scale Local based on good example) features and joined rotation-invariant LBP (nearby based on good example) features based on the quicker R-CNN classifier called convolutional neural network (put) in middle in the gave greater value to quicker field, range (EFR-CNN). Despite the shortcomings of MB-LBP point and rotation-invariant LBP point on face edge knowledge, the canny operator-based edge azimuth field purpose, use is grouped together with the above features to make statement of the sense of words of the face news given. In place, based on the grouped together point group, a given to overmuch pleasure R-CNN network of parallel form is suggested. The proposals are put on one side to three being like (in some way) given to overmuch pleasure R-CNN networks according to the different rates on a hundred that they cover on the pictures. The three networks are separated by the measures of the map and (make, become, be) different in the weight of the concatenation 8 of purpose, use maps from one another. The offered EFR-CNN system gets done giving undertaking operation on common points of comparison, including FDDB, AFW, PASCAL faces, And wider face, made a comparison of with state-of - the-art face discovery methods such as UnitBox, hyperface Fastcnn.

Keywords: LBP features, Faster R-CNN, face detection, MB-LBP and Feature map.

References:

1. Zhou, Z.: ‘Fixed-point optimization algorithm of AdaBoost face detection’, J. Univ. Electron. Sci. Technol. China, 2015, 44, (4), pp. 589–590. 2. Ye, J., Zhang, Z.: ‘Face detection based on DS-adaboost algorithm’, Comput. Sci., 2013, 40, (11A), pp. 318–319. 3. Lienhart, R., Kuranov, A., Pisarevsky, V.: ‘Empirical analysis of detection cascades of boosted classifiers for rapid object detection’, Dagm, 2003, 2781, pp. 297–304. 4. Fang, C., Su, T.: ‘Floatboost algorithm for face detection based on improved LBP features’, J. Nanjing Univ. Posts Telecommun. (Natural Science), 2014, 34, (6), pp. 76–79. 5. Luan, F., Guo, S., Song, X.: ‘Adaboost fast training algorithm based on multifeature fusion for face detection’, J. Chi. Comput. Syst., 2015, 36, (7), pp. 1613–1616. 6. X. X. Zhu and D. Ramanan, “Face detection, pose estimation, and landmark localization in the wild,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2012, pp. 2879–2886. 7. J. J. Yan, X. X. Zhu, Z. Lei, and S. Li, “Face detection by structural models,” J. Image Vis. Comput., vol. 32, no. 10, pp. 790– 799, 2014. 8. M. Mathias, R. Benenson, M. Pedersoli, and L. V. Gool, “Face detection without bells and whistles,” in Proc. Eur. Conf. Comput. Vis., Sep. 2014, pp. 720–735. 9. H. X. Li, Z. Lin, X. H. Shen, J. Brandt, and G. Hua, “A convolutional neural network cascade for face detection,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2015, pp. 5325–5334. 10. H. W. Qin, J. J. Yan, X. Li, and X. L. Hu, “Joint training of cascaded CNN for face detection,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2016, pp. 3456–3465. Authors: Naol Bakala Defersha

Paper Title: Ambo University Student’s Case Classification Models using Support Vector Machine Abstract: The main objective of ambo university is to provide quality education and improve the overall performance of an students by looking at individual students’ problems cases. One way to analysis students’ cases personally is to identify the problems causes and guide the students to solve the problems. Following this, the department Academic council and Academic Commission is whole authorized people to make the decision manually so this will consume more time and energy. This research focused to learning classification models for predicting students problems cases using support vector classification techniques. Finally, performance of the model evaluated using precision, recall and F-measure evaluation parameters.

Keywords: Decision making, Evaluation Parameters, Machine learning algorithms, Prediction Model, student 92. cases, Support Vector approach.

References: 500-504

1. Ambo University Legislation 2019," Ambo Universitty, Ambo, 2019. 2. N. P. N. S. B. T. Priyanka Bhilare, "Predicting Outcome of Judicial Cases and Analysis using Machine Learning," nternational Research Journal of Engineering and Technology (IRJET), vol. 06, no. 03, p. 2, 2019. 3. C. M. W. L. Wenjian Wanga, "Online prediction model based on support vector machine," Elsevier, vol. 71, 2007. 4. N. Bakala, "Information Retrieval System by using Vector Space Model," INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH, vol. 1, no. 1, p. 1, 2018. 5. E. G. Cyril Goutte, "A Probabilistic Interpretation of Precision, Recall and F-Score, with Implication for Evaluation," in Researc gate, Meylan, 2014. 6. K. S. J. S. Raikwal, "Performance Evaluation of SVM and K-Nearest Neighbor Algorithm over Medical Data set," International Journal of Computer Applications (0975 – 8887) , vol. 50, p. 3, 2012. 7. https://en.wikipedia.org/wiki/Ambo_University". Authors: Safiullah Noori, Nirav Raja

Paper Title: Energy Efficiency Improvement in LEACH Protocol for Wireless Sensor Network 93. Abstract: The most important reason for saving energy on wireless sensor networks is communication. Data transmission consumes about 70% of the energy of the sensor node. Effective use of energy on sensor nodes is a 505-508 good way to increase the lifetime of WSN. In order to extend the life of the network, energy-saving routing protocols must be designed. In this article, I will discuss (LEACH), which is the first and most popular energy- saving hierarchical clustering algorithm for WSN and an improvement to Leach and VLeach that attempts to eliminate the shortcoming of V-LEACH and LEACH protocols, In this method, initially, the "sub-cluster head" and "cluster head" are selected according to the energy and distance parameters. The head of the cluster and the vice president of the cluster make decisions based on the distance and the remaining energy of the sensor nodes. Compared with standard leaching, this algorithm can provide better network life, efficiency and performance.

Keywords: DVLEACH, LEACH, VLEACH

References:

1. Rathi and Viswanathan, “TWO PHASE CLUSTERING METHOD FOR LEACH PROTOCOL FOR EFFECTIVE CLUSTER HEAD SELECTION”, Journal of Computer Science, Volume 10, Issue 3,2014 2. S. Ananda Kumar, P. Ilango, Grover Harsh Dinesh,” A Modified LEACH Protocol for Increasing Lifetime ' of the Wireless Sensor Network”, CYBERNETICS AND INFORMATION TECHNOLOGIES, 2016 3. Md Arif Ali, Abha Kiran Rajpoot, “Development of energy efficient routing protocol using Hop PEGASIS in Wireless Sensor Networks”, IJCSET, 2014 4. Dr. Neeraj Bhargava, Dr. Ritu Bhargava, Shilpi Gupta, K.Kumar Jyotiyana , “A survey of congestion control with cross layer approach using OLSR on MANET”, International Conference on Engineering and Technology (ICET),2016 5. Arnab Nandi, Baishalee Sonowal, Dimbeswar Rabha,Akshay Vaibhav,” Centered Sink LEACH Protocol for Enhanced Performance of Wireless Sensor Network”, IEEE,2019 6. Asha Ahlawat ; Vineeta Malik,” An Extended Vice-Cluster Selection Approach to Improve V Leach Protocol in WSN”, IEEE,2013 7. Ge Ran , Huazhong Zhang , Shulan Gong,” Improving on LEACH Protocol of Wireless Sensor Networks Using Fuzzy Logic”, Journal of Information & Computational Science,2010 8. Sara Al-Sodairi ,Ridha Ouni, “Reliable and energy-efficient multi-hop LEACH-based clustering protocol for wireless sensor networks”, Elsevier, 2018 9. Priti K. Hirani ,Manali Singh, “Improved LEACH Protocol using vice Cluster in Wireless Sensor Networks ”, International Journal of Innovative Computer Science & Engineering, 2015 10. Arun, Parbhat Verma, “Improvement of Energy efficient leach protocol in WSN ”, IJEDR, 2016 11. Jyoti Singh, Bhanu Pratap Singh, Subhadra Bose Shaw, “A Survey on LEACH-based Hierarchical Routing Protocols in Wireless Sensor Network”, International Journal of Engineering Research & Technology (IJERT),2014 12. Wendi Rabiner Heinzelman, Anantha Chandrakasan, and Hari Balakrishnan, “Energy Efficient Communication Protocol for Wireless Micro sensor Networks”, Published in the Proceedings of the Hawaii International Conference on System Sciences, January 4-7, 2000, 13. Maui, Hawaii. S. Lindsey and C. Raghavendra, “PEGASIS: Power-Efficient Gathering in Sensor Information Systems”, Proceedings of the IEEE Aerospace Conference, vol. 3, pp. 1125-1130, Big Sky, MT, USA, March 2002 Authors: Neelam Shrivastava, Abhishek Jain, Kopal Garg, Abhishekh Pratap Singh, Abhishek Sharma

Paper Title: Sign Language Translation using Hand Gesture Detection Abstract: This Paper demonstrate a module on a sign to speech (voice) converter for auto Conversion of American sign language (ASL) to English Speech and text. It minimizes the gap of communication between speech impaired and other humans. It could be used to understand a speech impaired person’s thoughts or views which he communicates with others through its ASL gestures but failing to communicate due to a large communication gap between them. It also work as a translator for person who do not understand the sign language and allows the communication in the natural way of speaking. The proposed module is an interactive application module developed using Python and its Advanced Libraries. This module uses inbuilt camera of the system to get the images and perform analysis on those images to predict the meaning of that gesture and provide the output as text on screen and speech through speaker of the system that makes this module very much cost effective. This module recognizes one handed ASL gestures of alphabets (A-Z) with highly consistent, 94. fairly high precision and accuracy.

Keywords: ASL, Sign Language, Gesture, Image, Communication. 509-512

References:

1. Andreas Domingo, Rini Akmeliawati, Kuang Ye Chow ‘Pattern Matching for Automatic Sign Language Translation System using LabVIEW’, International Conference on Intelligent and Advanced Systems 2007. 2. Beifang Yi Dr. Frederick C. Harris ‘A Framework for a Sign Language Interfacing System’, A dissertation submitted in partial fulllment of the requirements for the degree of Doctor of Philosophy in Computer Science and Engineering May 2006 University of Nevada, Reno. 3. Jose L. Hernandez-Rebollar1, Nicholas Kyriakopoulos1, Robert W. Lindeman2 ‘A New Instrumented Approach For Translating American Sign Language Into Sound And Text’, Proceedings of the Sixth IEEE International Conference on Automatic Face and Gesture Recognition (FGR’04) 0-7695- 2122-3/04 $ 20.00 © 2004 IEEE. 4. Helene Brashear & Thad Starner ‘Using multiple sensors for Mobile Sign Recognition’. ETH- Swiss Federal Institute of Technology, Zurich,Switzerland. Authors: T. Aravinda Babu, K.S.R.S.Jyothsna

Paper Title: Iot Based Human Health Monitoring System 95. Abstract: IOT has plays important role in the upcoming technologies. It is connecting appliance each other over the internet. The main purpose of this paper is to give better services for remote place patient. Our plan is to 513-518 design the Human Health Monitoring System based on IOT is measure the patient’s Heart rate, temperature, drop down detection and giving the caution in critical situation. It can be used to promote basic nursing care in the hospital environment by improving the quality of care and patient safety. Rural area of India is lack behind from the proper patient monitoring system. The main objective is to give the awareness instruction and also remote monitoring by sharing proper information in an authenticated manner and decreases valuable time of doctors. They don’t need to wait for the reports because sensors are giving real time data. It is useful rural areas people.

Keywords: Android phone, ESP 8266 WiFi module, LM35 temperature sensor, MEMS sensor, Heart sensor, Arduino module.

References:

1. S. M. Riazul Islam, DaehanKwak, MD.Humaun Kabir “The Internet of Things for Health Care: A Comprehensive Survey”, June, 2015. 2. Vandana Milind Rohokale, Neeli Rashmi Prasad, Ramjee Prasad, “Cooperative Internet of Things (IoT) for Rural Healthcare Monitoring and Control”, IEEE, 2011. 3. S. Pradeep Kumar, Vemuri Richard Ranjan Samson, U. Bharath Sai, P L S D. Malleswara Rao, K. KedarEswar, "From Smart Health Monitoring System of Patient Through IoT", International conference on I-SMAC, pp. 551-556, 2017. 4. M.C. Hornbrook, V.J. Stevens, D.J. Wingfield, J.F. Hollis, M.R. Greenlick, and M.G. Ory, ”Preventing falls among community- dwelling older persons: Results from a randomized trial,” The Gerontologist 16-23 5. K. Natarajan, B. Prasath, P. Kokila, "Smart Health Care System Using Internet of Things", Journal of Network Communications and Emerging Technologies (JNCET), 2016. 6. Abhirup khanna, “IOT based smart parking system”, IEEE international conference on internet of things and applications (IOTA), 08 September 2016. 7. G. Revathi, “Smart parking systems and sensors”, IEEE international conference on computing, communication and applications (ICCCA), 05 April 2012. Authors: Padmalatha N, Deepa T, Susila M

Paper Title: Design of Miniaturized Flexible Planar Antenna for Biomedical Application Abstract: The proposed design of a Microstrip patch antenna for biomedical applications is represented in this paper which is operating at 2.4GHz frequency range in ISM Band. The significant feature of the proposed antenna is to make it perfect for on-body biomedical applications. The Polyimide substrate which has dielectric constant (εr) of 3.5, thickness of 0.0508 mm and loss tangent 0.0027 is used. The proposed antenna dimension is 21 mm x 17 mm x 0.0508 mm length, width and height respectively. The radiating patch has the length and width is 20 mm and 16.5 mm respectively. The antenna is designed using 3D EM simulation tool and S11 response is of less than -10dB. The designed and developed antenna has been fabricated in the laboratory and measured using RF equipment for parameters like reflection coefficient, VSWR and input impedance. The experimental results agree well with simulation results which proves that it is a good candidate for Biomedical applications.

Keywords: Miniaturized antenna, ISM Band, Meandering, Reflection coefficient, Radiation pattern.

References:

1. Ashyap, Adel YI, Zuhairiah Zainal Abidin, Samsul Haimi Dahlan, Huda A. Majid, A. M. A. Waddah, Muhammad Ramlee Kamarudin, George Adeyinka Oguntala, Raed A. Abd-Alhameed, and James M. Noras. "Inverted E-shaped wearable textile antenna for medical applications." IEEE Access 6 (2018): 35214-35222. 2. Ketavath, Kumar Naik, Dattatreya Gopi, and Sriram Sandhya Rani. "In-Vitro Test of Miniaturized CPW-Fed Implantable 96. Conformal Patch Antenna at ISM Band for Biomedical Applications." IEEE Access 7 (2019): 43547-43554. 3. Fallahpour, Mojtaba, and Reza Zoughi. "Antenna miniaturization techniques: A review of topology-and material-based methods." IEEE Antennas and Propagation Magazine 60, no. 1 (2017): 38-50. 519-522 4. Kapoor, Janak. "Miniaturization of microstrip patch antenna obtained by patch meandering and shorting pin loading technique." J. Nat. Phys. Sci 24 (2011): 5-9. 5. Haque, Sk Moinul, and Khan Masood Parvez. "Slot antenna miniaturization using slit, strip, and loop loading techniques." IEEE Transactions on Antennas and Propagation 65, no. 5 (2017): 2215-2221. 6. Wang, Chien‐Jen, and Christina F. Jou. "Compact microstrip meander antenna." Microwave and Optical Technology Letters 22, no. 6 (1999): 413-414. 7. Kuo, Jieh‐Sen, and Kin‐Lu Wong. "A compact microstrip antenna with meandering slots in the ground plane." Microwave and Optical Technology Letters 29, no. 2 (2001): 95-97. 8. Sarkar, S., A. Das Majumdar, S. Mondal, S. Biswas, D. Sarkar, and P. P. Sarkar. 9. "Miniaturization of rectangular microstrip patch antenna using optimized single‐slotted ground plane." Microwave and Optical Technology Letters 53, no. 1 (2011): 111-115. 10. Ullah, Mohammad H., Mohammad T. Islam, Mandeep S. Jit, and Norbahiah Misran. "A three-stacked patch antenna using high- dielectric ceramic material substrate." Journal of Intelligent Material Systems and Structures 23, no. 16 (2012): 1827-1832. 11. Kumar, Raj, P. Malathi, and J. P. Shinde. "Design of miniaturized fractal antenna." In 2007 European Microwave Conference, pp. 474-477. IEEE, 2007. 12. Ripin, N., W. M. A. W. Saidy, A. A. Sulaiman, N. E. A. Rashid, and M. F. Hussin. "Miniaturization of microstrip patch antenna through metamaterial approach." In 2013 IEEE Student Conference on Research and Developement, pp. 365-369. IEEE, 2013. 13. Kiourti, Asimina, and Konstantina S. Nikita. "A review of in-body biotelemetry devices: implantables, ingestibles, and injectables." IEEE Transactions on Biomedical Engineering 64, no. 7 (2017): 1422-1430. 14. Afridi, Muhammad Aamir. "Microstrip patch antenna− designing at 2.4 GHz frequency." Biological and chemical research 2015 (2015): 128-132. 15. Memon, Mosin I., and Anurag Paliwal. "Microstrip Patch Antenna Design Calculator." In International Journal of Engineering Research & Technology, vol. 2, no. 9, pp. 3080-3084. 2013. 16. Holub, Alois, and Milan Polivka. "A novel microstrip patch antenna miniaturization technique: A meanderly folded shorted-patch antenna." In 2008 14th Conference on Microwave Techniques, pp. 1-4. IEEE, 2008. 17. Starodubov, Andrei V., Viktor V. Galushka, Alexey A. Serdobintsev, Anton M. Pavlov, Galina A. Korshunova, Peter V. Ryabukho, and Sergey Yu Gorodkov. "A novel approach for fabrication of flexible antennas for biomedical applications." In 2018 18th Mediterranean Microwave Symposium (MMS), pp. 303-306. IEEE, 2018. 18. Raad, Haider Khaleel, Hussain M. Al-Rizzo, Ayman Issac Abbosh, and Ali I. Hammoodi. "A compact dual band polyimide based antenna for wearable and flexible telemedicine devices." Progress In Electromagnetics Research 63 (2016): 153-161. 19. Delaveaud, C., and S. Sufyar. "A miniaturization technique of a compact omnidirectional antenna." In 2009 3rd European Conference on Antennas and Propagation, pp. 384-388. IEEE, 2009. 20. Biswas, Akash, Akib Jayed Islam, Khandoker Tanjim Ahammad, and Bithi Barua. "A miniaturized on-body matched antenna design and its performance evaluation at ISM band." In 2017 3rd International Conference on Electrical Information and Communication Technology (EICT), pp. 1-6. IEEE, 2017. 21. Christina, G., A. Rajeswari, M. Lavanya, J. Keerthana, K. Ilamathi, and V. Manoranjitha. "Design and development of wearable antennas for tele-medicine applications." In 2016 International Conference on Communication and Signal Processing (ICCSP), pp. 2033-2037. IEEE, 2016. 22. Liu, Chen, Ying-Qi Jiang, Shuo Ji, Shao-Long Chang, Hong-Fei Li, Yu-Xing Ding, Kwok L. Chung, and Wei-Hua Zong. "A Novel Flexible Implantable Antenna at ISM Band." In 2018 IEEE International Workshop on Electromagnetics: Applications and Student Innovation Competition (iWEM), pp. 1-2. IEEE, 2018. 23. C. A. Balanis, Antenna Theory: Analysis and Design. 2005. 24. CST Microwave Studio [Online]Available:http/www.cst.com. Authors: Amira H. Hussein, Dalia Elfiky Qualification of Operational Amplifier used in Satellite Subsystem using Picosecond Pulsed Laser Paper Title: System Abstract: Picosecond Pulsed Laser System (PPLS) was used to simulate the single event effects (SEE) on satellite electronic components. Single event transients effect induced in an operational amplifier (LM324) to determine how transient amplitude and charge collection varied with pulsed laser energies. The wavelength and the focused spot size are the primary factors generating the resultant charge density profile. The degradation performance of LM324 induced by pulsed laser irradiation with two wavelength (1064nm, 532nm) is determined as a function of laser cross section. The transient voltage changed due to pulsed laser hitting specific transistors. This research shows the sensitivity mapping of LM324 under the effect of fundamental and second harmonic wavelengths. Determine the threshold energy of the SET in both wavelength, and compare the laser cross section of 1064 nm beam and 532 nm beam.

Keywords: Pulsed Laser Single Event Effect System, Single Event Transient, operational amplifier.

References:

1. Handbook of Radiation Effects.; Oxford Science Publications; 1993. 2. A. H. C. on the S. S. R. E. and N. V. for S. E. A. Workshop, S. S. Board, D. on E. and P. Sciences, and N. R. Council, Space Radiation Hazards and the Vision for Space Exploration: Report of a Workshop. National Academies Press, 2006. 97. 3. A. . Chumakov, “Simulation of Space Radiation Effects in Microelectronic Parts,” in Effects of Space Weather on Technology Infrastructure, Springer Netherlands, 2004, pp. 165–184. 4. D. M. Fleetwood, P. S. Winokur, and P. E. Dodd, “An overview of radiation effects on electronics in the space 523-527 telecommunications environment,” Microelectron. Reliab., vol. 40, no. 1, pp. 17–26, Jan. 2000. 5. S. Duzellier, “Radiation effects on electronic devices in space,” Aerosp. Sci. Technol., vol. 9, no. 1, pp. 93–99, Jan. 2005. 6. Y. Boulghassoul et al., “Circuit modelling of the M124 operational amplifier for analogue single-event transient analysis,” EEE Trans. Nucl. Sci., vol. 49, pp. 3090–3096, Dec. 2002. 7. R. Koga et al., “Single event upset (SEU) sensitivity dependence of linear integrated circuits ( Cs) on bias conditions,” EEE Trans. ucl. Sci., vol. 44, no. 6Pt1, pp. 2325–2332, 1997. 8. B. Alpat et al., “The radiation sensitivity mapping of Cs using an R pulsed laser system.” 9. R. Koga et al., “Observation of single-event upsets in analogue microcircuits,” EEE Trans. ucl. Sci., vol. 40, no. 6, pp. 1838– 1844, 1993. 10. S. Buchner et al., “Comparison of Single Event Transients Generated at Four Pulsed-Laser Test Facilities-NRL, IMS, EADS, JP ,” EEE Trans. Nucl. Sci., vol. 59, no. 4, pp. 988–998, Aug. 2012. 11. S. Buchner, J. Howard, C. Poivey, D. McMorrow, and R. Pease, “Pulsed-laser testing methodology for single event transients in linear devices,” EEE Trans. ucl. Sci., vol. 51, no. 6, pp. 3716–3722, Dec. 2004. 12. M. Green and M. Keevers, “Optical properties of intrinsic silicon at 300 K,” Prog. Photovolt. Res. Appl., vol. 3, pp. 189–192, 1995. 13. ESCC Basic Specification No. 25300,esa, issue 2, December 2014. 14. I. Lopez-Calle, “SEE aser Testing guidelines.” ESA. 15. R. . Pease et al., “Critical charge for single-event transients (SETs) in bipolar linear circuits,” EEE Trans. ucl. Sci., vol. 48, no. 6, pp. 1966–1972, Dec. 2001. 16. Stephen Buchner, Nicholas Roche, Laurent Dusseau, and Ron L. Pease , " The Effects of Low Dose-Rate Ionizing Radiation on the Shapes of Transients in the LM124 Operational Amplifier"; IEEE T Trans. Nucl. Sci., Vol. 55 , no. 6 , pp. 3314 - 3320, Dec. 2008. Authors: Haitham Akah, Dalia Elfiky

Paper Title: ARM based Telemetry Subsystems Qualification for Micro-Satellite Abstract: Developing spacecraft telemetry subsystem utilizing commercial of the shelf (COTs) components to 98. meet the technical design requirements with low-cost is big challenge for designers, due to the considerations of harmed ionizing space radiation effect, specially the total ionizing dose effect (TID). This effect induces performance degradation and failure in satellite electronic components (ECs). Because of the complexity of 528-532 microcontrollers and their various integrated functionality, they present a hardness assurance encounter. A careful technique was followed in analyzing the space radiation effects. Then rigorous tests should be conducted to test the performance of the candidate microcontrollers under these effects. This paper presents the predicted dose depth curve and the total ionizing does test results for a commercial ARM microcontroller for Low Earth Orbit (LEO) satellites. Such test results help estimate the effect of space environment on the microcontroller and decide if such microcontroller is an accepted candidate for LEO missions or not.

Keywords: Microcontroller, COTS, TID, satellite, ARM.

References:

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Paper Title: Detection of Frauds in Financial Reporting Abstract: Many researches have been done on annual reports to detect whether it is fraud or not by the analytical and empirical part of the report. Annual reports provide information on a company’s activities throughout a year. By analyzing the annual report, we can identify the condition of the company whether it is in crisis or operating perfectly. This research deals with the data that can be obtained from the reports’ text to determine the probability of being a fraudulent annual report. The verbal content of the report which determines the linguistic features are being analyzed using natural language processing tools to distinguish fraud financial reports from non-fraud financial reports. A set of 60 annual reports were taken for the study. Out of which 30 annual reports are labelled as fraud and the other 30 is labelled as non-fraud. The set of fraudulent companies were selected on the basis of a reporting case of fraudulency of another company or the same company in any other year of non-reporting of cases. The features are selected using a wrapper method search algorithm. A neural network model of MLP (Multi-Layer Perceptron) algorithm is used to classify the data with an accuracy of 85.1%. Classifiers like SVM (Support Vector Machines), Logistic Regression, Naïve Bayes and Random Forest algorithms were also used to identify the best classifier out of all the algorithms. Performance of all the techniques used in this paper are being analyzed and presented in terms of accuracy, precision, recall, F1 score, TN rate and FN rate. 99. Keywords: fraud detection, MLP classifier, linguistic features, neural network model 533-540 References:

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From Spin to Swindle: Identifying Falsification in Financial Text. Cognitive Computation. 8. 10.1007/s12559-016-9413-9. 19. Nguyen, Khanh. “Financial Statement Fraud: Motives, Methods, Cases and Detection.” (2010). 20. Samociuk, Martin, Nigel K. Iyer, and Helenne Doody. A Short Guide to Fraud Risk: Fraud Resistance and Detection. Farnham, Surrey: Gower, 2010. 21. Public Company Accounting Oversight Board (PCAOB). (Nov. 15, 2007). Auditing Standards (A4). 22. Goel, Sunita & Gangolly, Jagdish & Faerman, Sue. (2010). Can Linguistic Predictors Detect Fraudulent Financial Filings?. Journal of Emerging Technologies in Accounting. 7. 10.2308/jeta.2010.7.1.25. 23. Katerattanakul, Nitsawan, "A pilot study in an application of text mining to learning system evaluation" (2010). Masters Theses. 4771. 24. Piramuthu, Selwyn & Shaw, Michael & Gentry, James. (1994). A classification approach using multi-layered neural networks. Decision Support Systems. 11. 509-525. 10.1016/0167-9236(94)90022-1. 25. Le, C. C., Prasad, P. W. C., Alsadoon, A., Pham, L., & Elchouemi, A. (2019). Text classification: Naïve bayes classifier with sentiment Lexicon. IAENG International Journal of Computer Science, 46(2), 141-148. 26. Jasneet Kaur, 2 Seema Bhagla (2016). News Classification Using Naïve Baye’s Claassifier IJARCS International Journal of Advanced Research in Computer Science and Software Engineering. 27. Rajeswari R.P, Kavitha Juliet, Dr.Aradhana "Text Classification for Student Data Set using Naive Bayes Classifier and KNN Classifier". International Journal of Computer Trends and Technology (IJCTT) V43(1):8-12, January 2017. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group. 28. Wulandini, Fatimah and Anto Satriyo Nugroho. “Text Classification Using Support Vector Machine for Webmining Based Spatio Temporal Analysis of the Spread of Tropical Diseases.” (2009). 29. Batoul Aljaddouh, Nishith A. 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Support Vector Machines for Classification. 10.1007/978-1-4302-5990-9_3. 39. https://www.sec.gov/spotlight/fcpa/fcpa-cases.shtml] 40. https://machinelearningmastery.com/clean-text-machine-learning-python/ 41. https://pathmind.com/wiki/multilayer-perceptron 42. Mai, Florian & Galke, Lukas & Scherp, Ansgar. (2018). Using Deep Learning for Title-Based Semantic Subject Indexing to Reach Competitive Performance to Full-Text. 169-178. 10.1145/3197026.3197039. 43. Batoul Aljaddouh, Nishith A. Kotak, "Document Text Classification Using Support Vector Machine", International Journal of Engineering Development and Research (IJEDR), ISSN:2321-9939, Volume.8, Issue 1, pp.138-142, January 2020 44. Srivastava, Durgesh & Bhambhu, L.. (2010). Data classification using support vector machine. Journal of Theoretical and Applied Information Technology. 12. 1-7. 45. Liparas, Dimitris & HaCohen-Kerner, Yaakov & Moumtzidou, Anastasia & Vrochidis, Stefanos & Kompatsiaris, Ioannis. (2014). News Articles Classification Using Random Forests and Weighted Multimodal Features. LNCS. 8849. 10.1007/978-3-319- 12979-2_6. 46. Elagamy, Mazen & Stanier, Clare & Sharp, Bernadette. (2018). Stock market random forest-text mining system mining critical indicators of stock market movements. 1-8. 10.1109/ICNLSP.2018.8374370. Authors: Law Choon Chuan, Herman Wahid, Dirman Hanafi, Seriaznita Haji Mat Said Performance Examination of Low-Power Thermoelectric Sensor Arrays for Energy Harvesting Paper Title: 100. From Human Body Heat Abstract: Thermoelectric energy harvester is known as a type of energy harvesting technologies which extracts waste heat from a target device or object to generate electrical power. The low power generation from 541-547 thermoelectric energy harvester, though, is always a critical consideration in designing a self-sustaining system. The energy harvesting system is usually aided by a power management solution to further enhance the power generation for better performance. Therefore, maximizing the power generated from the thermoelectric sensor itself is essential in order to select the most suitable power management approach. This paper presumed the methodology to maximize power generation of thermoelectric and further discussion is reviewed in the report.

Keywords: Thermoelectric, energy harvester, optimal array, performance, self-sustain system.

References:

1. R. Kappel, Pachler, W., Auer, M., Pribyl, W., Hofer G. and Holweg, G. “Using Thermoelectric Energy Harvesting to Power a Self-Sustaining Temperature Sensor in Body Area Network.” IEEE 2013: 787-792, 2013. 2. A. Montecucco, Siviter, J. and Knox, A.R. “The Effect of Temperature Mismatch on Thermoelectric Generators Connected in Series and Parallel.” Elsevier Journal of Applied Energ.: 47-54, 2015. 3. A. Chen, “Thermal Energy Harvesting with Themoelectrics for Self-powered Sensors: With Applications to Implantable Medical Devices, Body Sensor Networks and Aging in Place.” Thesis, University of California, 2011. 4. X. Niu, Yu, J. and Wang, S. “Experimental Study on Low-Temperature Waste Heat Thermoelectric Generator.” Elsevier Journal of Power Sources. Vol. 188, Issue 2, 15 March 2009: 621-626, 2008. 5. C.J. Udalagama, “Electrical Energy Generation From Body Heat.” IEEE ICSET, 2010. 6-9 Dec 2010. 6. M.K. Kim, Kim, M.S., Jo, S.E., Kim, H.L., Lee, S.M. and Kim, Y.J. “Wearable Thermoelectric Generator for Human Clothing Applications.” IEEE Transducer 2013, Barcelona, Spain 16-20 June 2013. 7. Z. Luo, Z. “A Simple Method to Estimate The Physcical Characteristics of A Thermoelectric Cooler,” from Vendor Datasheets. Electronics Cooling, 2010. 8. D.K. Aswal, R. Basu and A. Singh, “Key issues in development of thermoelectric power generators: Highfigure-of-merit materials and their highly conducting interfaces withmetallic interconnects,” Energy Conversion and Management, vol. 114. pp. 50-67, 2016. 9. G. Wu, Yu, X. “System Design on Thermoelectric Energy Harvesting from Body Heat.” 39th Annual Northeast Bioengineering Conference. : 157 – 158, 2013. 10. Q. Brogan, O'Connor, T. and Ha, D.S. “Solar and Thermal Energy Harvesting with a Wearable Jacket.” IEEE, 2014: 1412 – 1415, 2014. 11. Z.H. Abdul Rahman, Md Khir, M.H., Burhanudin, Z.A., et. al. “CMOS based Thermal Energy Generator For Low Power Devices.” IJSER, Vol. 4, Issue 5, May 2013. 12. T.M.M.A.I Omer, “Development of Solar Thermoelectric Generator.” European Scientific Journal, Edition Vol. 10, No. 9, March 2014 : 123 – 134, 2014. 13. C.C. Law, C.C., Wahid, H., Abdul Rahim, H. and Abdul Rahim, R. “A Review of Thermoelectric Energy Harvester and Its Power Management Approach in Electronic Applications.” Jurnal Teknologi, Vol. 73(3) : 153-159, 2015. 14. C.C. Law, Wahid, H. and Leow, P.L. (2015). A Charge Pump-based Power Conditioning Circuit for Low Powered Thermoelectric Generator (TEG). The 10th Asian Control Conference 2015, Sabah, Malaysia (appear in IEEE Xplore), 2015. 15. R. Yusof, H. Wahid, M. H. I. Ishak, D. Hanafi, R. Ghazali, “Performance Examination and Design Optimization of Thermoelectric Sensor Configuration for Energy Harvesting From Air Conditioner Waste Heat,” International Journal of Recent Technology and Engineering (IJRTE), Vol. 8(3S2), October 2019. Authors: S Kiruthika, Sowmyarani C N Credit Card Fraud Detection using Machine Learning and Deployment of Model in Public Cloud as Paper Title: a Web Service Abstract: In recent times, usage of credit cards has increased exponentially which has given way to an increase in the number of cybercrimes related to transactions using credit cards. In this paper, the aim is to reduce the fraudulent credit card transactions happening around the world. Latest technologies like machine learning algorithms, cloud computing and web service implementation has been used in this paper. The model uses Local outlier factor algorithm and Isolation forest algorithm to develop the credit card fraud detection model using unsupervised learning techniques. The model has been implemented as a Web service to make the solution integratable with other applications and clients across the world. A third party prototype application is developed and integrated to the Fraud Detection Model using Web Services. The complete Fraud Detection System is deployed on the cloud. The Fraud Detection Model shows exceptionally high accuracy when compared to other models already existing.

Keywords: Fraud detection model, machine learning, local outlier factor, isolation forest, web service, prototype application, public cloud, amazon web services, EC2 instance 101. References: 548-552

1. Andrea, Dal, Pazzolo; Oliver, Caelen; Reid, A, Johnson; Gianluca, Bontempi . (2015). Calibrating Probability with Undersampling for Unbalanced Classification. IEEE Symposium Series on Computational Intelligence. 2. Dal, P. A., & Johnson, A. R. (2014). Using HDDT to avoid instances propagation in unbalanced and evolving data streams. Proceedings of international joint conference on neural networks. 3. Datta, S., & Arputharaj, A. (2018). An Analysis of Several Machine Learning Algorithms for Imbalanced Classes. 5th International Conference on Soft Computing and Machine Intelligence. 4. Divya, I., Arti, M., Sneha, J., Dhanashree, R., & Amrutha, S. (2011). Credit Card Fraud Detection using Hidden Markvo Model. World Congress on Information and Communication Technologies. 5. Jon, T. S., & Sriganesh, M. (2008). Real-time credit card fraud detection using computational intelligence. Expert Systems with Applications , 35 (4), 1721-1732. 6. L.U. Orghenekaro, C. U. (2016). A Novel Machine Learning Approach to Credit Card Fraud Detection. International Journal of Computer Applications , 140 (5). 7. Lin, W. C., Tsai, C. F., Hu, Y. H., & Jhang, J. S. (2017). Clustering-based undersampling in class-imbalanced data. Information Sciences , 409-410, 17-26 8. Roberston, D. (2016). Investments &, Acquisitions – September 2016 Top Card Issues in Asia – Pacific Card Fraud Losses reach $21.84 Billion. 9. Samaneh, S., Zahra, Z., Reza, E. A., & Amir, H. M. (2016). A Survey of Credit Card Fraud Detection Techniques: Data and Technique Oriented Perspective. IOT Security . 10. Suvasini, P., Amlan, K., Shamik, S., & Manjumdar, A. (2009). Credit card fraud detection: A fusion approach using Dempster- Shafer theory and Bayesian learning. Information Fusion , 10, 354-363. 11. Wang, H., Zhu, P., Zou, X., & Qin, S. (2018). An Ensemble Learning Framework for Credit Card Fraud Detection based on Training Set Partitioning and Clustering. IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovations, (pp. 94-98). 12. Xuan, Z., Liu, G., Li, Z., Zheng, L., Wang, S., & Surname, G. (2018). Random Forest for Credit Card Fraud . 15th International Conference Networking Sensor Control. 13. Yusuf, S., Serol, B., & Ekram, D. (2013). A cost-sensitive decision tree approach for fraud detection. Expert Systems with Applications, 5916-5923. Authors: D.Ramesh Kumar, D.Gayathiry Consumer Perception and Satisfaction Towards Food Delvery Service (with Special Reference to Paper Title: Coimbatore City) Abstract: India is developing country in recent years many industries are growing and significant increase in the employment, due to this disposable income is increased, more urbanisation, lifestyle are changing. Participation of women in all areas is increasing. They are not preferred traditional way of preparing food, mostly preferred for prepared food. Increasing internet and development of digital world online food services providing easy way of access a prepared food, through this customer has enjoying more benefit such as doorstep delivery, various payment options, attractive discounts, cash back offers this would lead to increasing online food services day by day. The online food ordering market in India is likely to grow at over 16 percent annually to touch US$ 17.02 billion by 2023, according to a study by business consultancy firm Market Research Future. This paper has exhibit the customer perception on the online food services and their satisfaction.

Keywords: Development, Food service, Industries, Online, Urbanization.

References:

1. Jyotishman Das (2018), “Consumer perception towards ‘Online Food ordering and delivery services’:An empirical study” Volume 5, Issue 5, September-October 2018, pp. 155–163. 2. Mrs I.Karthika, Miss. A.Manojanaranjani (2018) “ A Study on the various food ordering apps based on consumer preference” 102. International Journal Peer Reviewed Journal Refereed Journal Indexed Journal, WWJMRD 2018; 4(11): 88-89 E-ISSN: 2454- 6615. 3. Suryadev Singh Rathore, 2Mahik Chaudhary (2018) “Consumer's Perception on Online Food Ordering” International Journal of 553-556 Management & Busin ess Studies, Vol. 8, Iss ue 4, Oct - Dec 2018,pp 12-17. 4. Minal Kashyap, KomalKashyap, Dr. Anil Sarda(2013) “A Study of Growth of Fast Food Industry with Reference to Shift in Consumer’s Buying Habits in Nagpur City” International Journal of Application or Innovation in Engineering & Management (IJAIEM) Special Issue for National Conference On Recent Advances in Technology and Management for Integrated Growth 2013 (RATMIG 2013). 5. 5. Anitharaj M.S(2018) “ A Study on Buying Behaviour of Youngsters towards Fast Food Restaurants” International Journal of Emerging Research in Management &Technology ISSN: 2278-9359 (Volume-7, Issue-1) pp1-7 6. Donkoh S. A, Quainoo A. K, Cudjoe E. and Kaba N. C(2012) Customer satisfaction and perceptions about food services on the University for Development Studies Campus, GhanaAfrican Journal of Food Science Vol. 6(8), pp. 216-223. 7. H.S. Sethu, Bhavya Saini (2016), “Customer Perception and Satisfaction on Ordering Food via Internet, a Case on Foodzoned.Com” Proceedings of the Seventh Asia-Pacific Conference on Global Business, Economics, Finance and Social Sciences(AP16Malaysia Conference) ISBN: 978-1-943579-81-5 Kuala Lumpur, Malaysia. 15-17, July 2016. Paper ID: KL631. 8. Dr. Mitali Gupta(2019), “A Study on Impact of Online Food delivery app on Restaurant Business special reference to zomato and swiggy” IJRAR- International Journal of Research and Analytical Reviews, VOLUME 6 I ISSUE 1 I JAN. – MARCH 2019,PP:889- 893 9. https://www.indiaretailing.com/2019/03/26/food/food-service/online-food-market-to-grow-16-pc-by-2023-us-17-billion- opportunity-thanks-to-rise-in-working-women/ 10. https://www.kenresearch.com/blog/2018/12/growing-demand-for-online-food-in-india- market-outlook-ken-research/ 11. https://yourstory.com/mystory/a688ed0a54-the-benefits-of-online-food-delivery-system- 12. https://www.televisory.com/blogs/-/blogs/rapidly-growing-indian-online-food-delivery- industry-and-its-unrealised-profits# 13. K.Vengatesan,M.Kalaivanan Assistant Professor, Department of CSE Published a paper on, “Recommendation System Based on Statistical Analysis of Ranking From User” Information Communication and Embedded Systems (ICICES) 29 April 2013, pp. 479-484 Authors: Nitin Bansal, Nistha Pareek, Abhinav Nigam The Impact of Awareness of Password Management of Digital Banking Services on Customer’s Paper Title: Adoption in India Abstract: Password management is a highly decisive component while adopting digital banking services. Password composition strategies facilitate the users to construct a safe, secure and strong password which is difficult to crack and misuse your confidential information. There should be an institutional structure to enhance the awareness level of password management of users to minimize the probability of cyber attack. This study has 103. focused on the analysis of impact of awareness of password management of digital banking services on customer adoption in India. To analyze the applicability and reliability of scale, factor analysis has been administered before the use of stepwise method (forward selection) of multiple regression. Primary data on 5 point Likert 557-563 scale has collected from the Delhi region with the help of questionnaires through a self administered approach. Stratified random sampling technique was used and total 432 useful schedules were considered for analysis of data using SPSS version 23. The results of the study show that there is a positive impact of awareness of password management of digital banking services on customer’s adoption in India.

Keywords: Password security, Password management, Password composition strategy, Digital banking services, Common password, Awareness of password management, Cyber frauds

References:

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Paper Title: Assessment of Service Quality Dimensions in Higher Education Sector of North India Abstract: The current paper discusses the importance of service quality in higher education among the Indian universities. In this modern world, education is a way to shift the country’s economy to knowledge economy and emerges as an important sector for the increasing the county’s economy and wealth. Therefore, assessment of service quality among the universities is gaining the preference from the researchers around the world. In this connection present study collected the data of355 post graduate student and analysed with the help of Structure Equation Modeling. The present paper found that all dimensions of SERVQUAL model (reliability, tangible, assurance, responsiveness and empathy) are important for the service quality assessment and it is affecting highly. Among all the dimensions reliability is having a great impact on service quality among the private universities of north India. Education managers can enhance of quality of service in their universities with the help of the current study.

Keywords: Higher education, SERVQUAL, perceptions.

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Paper Title: Energy Performance Modelling and Consumption Forecasting in Built Environments Abstract: For all sector of the economy including the construction sector, energy consumption forecasting is critical for future planning. The building sector accounts for a staggering 30% of the world’s energy use and one-third of associated greenhouse gas (GHG) emissions worldwide. Modeling of building energy performance and consumption forecasting is significant for energy policy formulation, fixing targets and control energy usage to provide a long term energy security. Many energy models are accessible now, but the area is still under development and needs perfection on several counts. To select the most suitable and appropriate model for a specific purpose, it is often hard to evaluate the various models and their characteristics. This article provides a broad analysis of modeling methods, classification, and applications in constructed settings with an improved focus. A critical assessment of various models is also provided based on their composition, input-output relationships, strengths, and weaknesses to define study gaps and provide directions for future studies.

Keywords: Artificial neural network, Bottom-up, Energy consumption, Energy forecasting, Energy performance modeling, Machine learning Top-down etc.

References:

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Machine Logic Program Development and Electrical Design of H Gantry Automation System for Paper Title: Compressor Housing Abstract: Automation is one of the growing fields ,and is being used at levels from small scale industries to very a large scale industries due to the advantage of increase in productivity and quality, along with it recently new revolution in the automation 4.0enabling the data monitoring possible which helps in better control and monitoring of machineries and equipments. The objective of the work is to design electrical circuit and perform 106. suitable control actions such as loading and unloading of heavier components to machines, indexing, providing suitable safety for the devices and improvising productivity and quality rate in the production line. These activities are being done by a control system adopted to gantry system, in such control systems there will be 576-580 provision for manual control, auto mode, jog mode & edit mode to enable the work to be carried out smoothly and effectively. Such complete system is designed to reach the customer required production cycle time with 6 axis (y,z1,z2,x1,x2,c), these axis are controlled by CNC controller and all other Stations like IPC, OPC, tilting stations are controlled by the PMC controller, which is the part of CNC.

Keywords: Industrial Automation; Gantry Systems; Productivity; compressor housing component, CNC, IPC, OPC, PM

References:

1. Chandan ,Koushik, “Design And Analysis Of Gantry Automation System For Wheel Hub Machining In VMC” Ijirae,2018, pp. 218-224. 2. Rajendra Rajput, Dr. Ajay Kumar Sarathe,“Study of CNC Controllers used in CNC Milling Machine”,American Journal of Engineering Research, volume-5, issue-4, pp. 54-62. 3. Kyung Chang Lee, and Suk Lee, “Implementation of Networked Control System using a Profibus-DP Network”,International Journal of the Korean Societyof PrecisionEngineering, July 2002 Vol. 3, No. 3. 4. M. S. Morse, "Designing for electrical safety that can withstand legal scrutiny," IEEE IAS Electrical Safety Workshop, 2009, pp. 1-3. 5. G. Gabor, C. Pintilie, C. Dumitrescu, N. Costica and A. T. Plesca, "Application of Industrial PROFIBUS-DP Protocol," International Conference and Exposition on Electrical and Power Engineering, Iasi, 2018, pp. 0614-0617. 6. T. Ernst, "Application of multi-function motor protection relays to variable frequency drive connected motors," 61st IEEE Pulp and Paper Industry Conference, Milwaukee, WI, 2015, pp.1-6. Authors: Vandana B V, Ramya M V

Paper Title: V2V Communication using fog Computing for Safe Commute Abstract: In this real-world, there is a great necessity to satisfy the demands in all the communication systems to experience the best ever convenience and flexibility. Advancement in the IoT concept in the form of IoV has been a solution to overcome all the difficulties experienced during driving vehicles. The complete potential of IoV addresses many challenges of traffic monitoring and road safety measures by forming a distributed network of vehicles and collaborate between heterogeneous vehicular systems. IoV refers to the integration of networks that transmit information periodically between vehicles to vehicles, vehicles to roadside units and it is intended to play an essential role in this. The prevailing solutions like VANET, Vehicular Cloud Computing (VFC), and Mobile Cloud Computing are not ideal as there is high latency and delay in responsiveness. The collaboration of vehicular networks and fog computing forms a promising paradigm called Vehicular Fog Computing (VFC) which serves as a effective yet alternative method for VANETS’s. This paradigm consists of multiple near-end devices to carry out communication and computation of every vehicle. This paper presents certain scenarios of moving and idle state of vehicles by adapting VFC methods, wherein as a result of this communication and computation infrastructure, it showcases the capabilities of VFC. The objective here is to present four different scenarios of a vehicle in motion and in idle state, which brings out an interesting relationship between the communication capability and connectivity of the vehicles.

Keywords: Internet of Things (IoT), Internet of Vehicles (IoV), Vehicular Cloud Computing (VCC), Vehicular Fog Computing (VFC)

References:

1. Zijiang Hao, Zhengrui Qin, and Qun Li Shanhe Yi, "Fog Computing: Platform and Applications," 2015 Third IEEE Workshop on Hot Topics in Web Systems and Technologies, pp. 73-78, 2015.W.-K. Chen, Linear Networks and Systems (Book style). 107. Belmont, CA: Wadsworth, 1993, pp. 123–135. 2. Robert J.Walters 1 and Gary B. Wills 1 Hany F. Atlam, "Fog Computing and the Internet of Things: A Review," Big data and Cognitive Computing, vol. 2, no. 2, pp. 1-18, April 2018. 581-585 3. Member, IEEE, Lei Shu, Senior Member, IEEE, and Di Wang Mithun Mukherjee, "Survey of Fog Computing: Fundamental, Network," IEEE Communications Surveys & Tutorials, vol. 20, no. 3, pp. 1826-1857, 2018. 4. Ivan Stojmenovic and Sheng Wen, "The Fog Computing Paradigm: Scenarios and Security Issues," in Proceedings of the 2014 Federated Conference on Computer Science and Information Systems, WARSAW, 2014, pp. 1-8. 5. Varun G Menon, "Moving From Vehicular Cloud Computing to Vehicular Fog Computing:Issues and Challenges," International Journal on Computer Science and Engineering (IJCSE), vol. 9, no. 2, pp. 14-18, Feb 2017. 6. Jiejian Cai, Yunshi Luo, Fenglin Zheng, Jianwei Zhang, Qiao Luo Baoling Qin, "Research and Application of Intelligent Internet of Vehicles Model Based on Fog Computing," IEEE 3rd Information Technology,Networking,Electronic and Automation Control Conference (ITNEC 2019), pp. 1777-1783, 2019. 7. Kashif Naseer Qureshi and Hanan Abdullah, "A Survey on Intelligent Transportation Systems," Research Gate, pp. 629-642, January 2013.M. Young, The Techincal Writers Handbook. Mill Valley, CA: University Science, 1989. 8. Mario Sller and Ivan Huerta Hector Jalil Desirena Lopez, "Internet of Vehicles: Cloud and Fog Computing Approaches," IEEE International Conference on Service Operations and Logistics, and Informatics, pp. 211-216, September 2017. 9. Khaled B. Letaief Jun Zhang, "Mobile Edge Intelligence and Computing for the Internet of Vehicles," in Proceedings of the IEEE, vol. 108, Hong Kong, 2020, pp. 246-261. 10. Vallidevi Krishnamurthy, Raj an Alwan Tej Tharang Dandala, "Internet of Vehicles (10 V) for Traffic Management," IEEE International Conference on Computer, Communication and Signal Processing (lCCCSP-2017), pp. 1-4, 2017. 11. Sibaram Khara Indu, "Internet of Vehicles (IOV): Evolution, Architectures, Security Issues and Trust Aspects," International Journal of Recent Technology and Engineering (IJRTE), vol. 7, no. 6, pp. 268-280, March 2019. 12. Ming Liu, Wei Louy, Guihai Chenz and Jiannong Caoy Nianbo Liu, "PVA in VANETs: Stopped Cars Are Not Silent," in Proceedings IEEE INFOCOM, Shanghai, 2011, pp. 431-435. 13. Member IEEE, Abdul Hanan Abdullah, Member IEEE, Yue Cao, Member IEEE, Ayman Omprakash Kaiwartya, "Internet of Vehicles: Motivation Layered Architecture, Network Model Challenges and Future Aspects," IEEE Access, vol. 4, pp. 5356- 5373, 2016. 14. Saif Ul Islam, Ikram Ud Din, and Mohsen Guizani Hasan Ali Khattak, "Integrating Fog Computing with VANETs: A Consumer Perspective," IEEE Communications Standards Magazine, vol. 3, no. 1, pp. 19-25, March 2019. 15. J. Li, J. Wu, W. Yang and Z. Guan S. Liao, "Fog-enabled Vehicle as a Service for Computing Geographical Migration in Smart Cities," IEEE Access, vol. 4, pp. 8726-8736, 2019. 16. Xueshi & Li, Yong & Chen, Min & Wu, Di & Jin, Depeng & Chen, Sheng Hou, "Vehicular Fog Computing: A Viewpoint of Vehicles as the Infrastructures," IEEE Transactions on Vehicular Technology, vol. 65, no. 6, pp. 3860-3873, 2016. 17. Mohammad Aazam and Eui-Nam Huh, "E-HAMC: Leveraging Fog Computing for Emergency Alert Service," IEEE International Conference on Pervasive Computing and Communication Workshops, pp. 518-523, 2015. 18. Z. Ning and L. Wang X. Wang, "Offloading in Internet of Vehicles: A Fog-Enabled Real-Time Traffic Management System," IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, vol. 14, no. 10, pp. 4568-4578, October 2018. 19. "Fog Computing for Detecting Vehicular Congestion, an Internet of Vehicles Based Approach: A Review," IEEE Intelligent Transportation Systems Magazine, vol. 11, no. 2, pp. 8-16, 2019. 20. Mehdi Sookhak, Abdullah Gani, Rajkumar Buyya Md Whaiduzzaman, "A survey on vehicular cloud computing," Journal of Network and Computer Applications, vol. 40, pp. 325-344, August 2013. 21. S. Shin, S. Seo, S. Eom, J. Jung and K. Lee S. Chun, "A Pub/Sub-Based Fog Computing Architecture for Internet-of-Vehicles," IEEE International Conference on Cloud Computing Technology and Science (CloudCom), pp. 90-93, 2016. 22. E. Lee, G. Pau and U. Lee M. Gerla, "Internet of vehicles: From intelligent grid to autonomous cars and vehicular clouds," IEEE World Forum on Internet of Things (WF-IoT), pp. 241-246, 2014. 23. R. Lu and K. R. Choo C. Huang, "Vehicular Fog Computing: Architecture, Use Case, and Security and Forensic Challenges," IEEE Communications Magazine, vol. 55, pp. 105-111, November 2017. 24. J. Huang and X. Wang Z. Ning, "Vehicular Fog Computing: Enabling Real-Time Traffic Management for Smart Cities," IEEE Wireless Communication, vol. 26, no. 1, pp. 87-93, February 2019. 25. T. S. J. Darwish and K. Abu Bakar, "Fog Based Intelligent Transportation Big Data Analytics in The Internet of Vehicles Environment: Motivations, Architecture, Challenges, and Critical Issues," IEEE Access, vol. 6, pp. 15679-15701, 2018. Authors: Ajay Shelar, Amit Mahindrakar, VIT Vellore

Paper Title: Abrasion Resistance of Concrete with Crushed Sand Abstract: The studies measure Abrasion Resistance of Concrete with Crushed Sand. Pune is developing in construction activity so Pune province was chosen as a case study. Due to increase in Automobile and IT Industry increases Infrastructure faculty in Pune. Increases concrete demands for construction of Infrastructure faculty such as Fly over, Metro rails & Ring road around Pune city. To Gratifications in the demand of concrete and to supply this concrete demand increase in demand of natural sand Construction activity of Infrastructure & heavy traffic in Pune. Natural sand are replace with Crushed Fine and Coarse Aggregate Concrete (CCA )

Keywords: Crushed Fine and Coarse Aggregate, Engineering properties, Natural aggregate.

References:

1. Abhay Shelar, D. Neeraja, Amit B. Mahindrakar,(2018) “ Properties of Concrete made with Recycled Aggregate from Partially Hydrated Old Concrete”, International Journal of Research in Engineering Science and Technology 3(08), 9-14 2. Abhay Shelar, D. Neeraja, and Amit B. Mahindrakar (2019) “Experimental Study on Ready Mix Concrete Plant Waste Concrete as a Aggregate for Structural Concrete” Springer Nature Singapore, 1(01), 112–118, 3. Abhay Shelar, D. Neeraja, Amit B. Mahindrakar(2019) “Effect of Polycarboxylate Ether on Properties of Ready mix concrete plant waste concrete as a aggregate Concrete ( RMWCA)” Journal of Emerging Technologies and Innovative Research. 6(1), 10- 19. 4. Abhay Shelar, Amit Mahindrakar (2019) “Practical Scrutiny for Concrete Strength Properties of Ready-Mix Concrete Plant from 108. Waste Concrete Aggregate” International Journal of Innovative Technology and Exploring Engineering . 8(10), 2931-2935. 5. Abhay Shelar, D. Neeraja and Amit B. Mahindrakar (2018) “Experiential investigation of nitric acid concentration on the compressive strength of ready mix concrete plant waste concrete aggregate” International Journal of Civil Engineering and 586-589 Technology (IJCIET), 9(08) ,1353-1364. 6. Abhay Shelar, D. Neeraja and Amit B. Mahindrakar (2018) “Experimental investigation of strength of concrete by using concrete and concrete related product plants waste water use in concrete” International Journal of Civil Engineering and Technology .9(09), 1302–1308, 7. Abhay Shelar, D. Neeraja, Amit B. Mahindrakar (2018) “Experimentation of rapid chloride permeability test on ready mix concrete plant waste aggregate concrete” International Journal of Engineering Science Invention Research & Development; 4(12) ,395-401. 8. Abhay Shelar, Amit B. Mahindrakar,(2020) “Impact Resistance For Ready Mix Concrete Plant Waste Concrete Aggregate” International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-9 Issue-1, May 2020 9. Ajay Shelar, Dr. D. Neeraja, Dr.Amit B. Mahindrakar “Experimental Study Of Superplasticizer on Fresh and Hardened Properties of Concrete Using Crushed Sand” International Journal of Civil Engineering and Technology (IJCIET) Volume 9, Issue 8, August 2018, pp. 1407-1413 10. Ajay Shelar, Dr. D. Neeraja and Dr. Amit B. Mahindrakar “ Experimental Investigation of Crushed Sand In Strength Supporting Self- Actualization Of Concrete “ International Journal of Civil Engineering and Technology (IJCIET) Volume 9, Issue 9, September 2018, pp. 1504–1513 11. Ajay Shelar, Dr. D. Neeraja, Dr. Amit B. Mahindrakar “Durability Studies on Concrete with Crushed Sand as A Partial Replacement of Fine Aggregate in Hydrochloric acid Solution” Journal of Emerging Technologies and Innovative Research (JETIR) Volume 6, Issue 1 January 2019, 12. Ajay Shelar , Dr. D. Neeraja, Dr. Amit B. Mahindrakar “Effect of Admixture In The Partial Replacement of Natural Sand By Crushed Sand In Concrete ” International Journal of Engineering Science Invention Research & Development; Vol. IV, Issue XII, JUNE 2018 13. Ajay Shelar , D. Neeraja, Amit B. Mahindrakar “ An Experimental Examination to Achieve of High Strength Concrete Using Crushed Sand ” Springer Nature Singapore, 1(01), 28–32 Authors: Ashwini Gade, Rasika Khollam, Karenca Fichardo

Paper Title: Intelligent Transportation System Abstract: This paper proposes a low cost, portable and flexible vehicle security system. It bestows the use of an 109. embedded micro – web server in Raspberry pi -3B microcontroller, with IP connectivity for remotely controlling the devices from another location. The proposed system does not require a dedicated server PC with respect to 590-593 similar systems and offers an offbeat channel to record and implicate the vehicle environment with more than just the switching functionality. The system for it’s the feasibility and effectiveness will be integrated with external devices such as alcohol sensor, gas sensor, ultrasonic sensor and pressure sensors. All of the above features will predict the system to form an intelligent transportation system for a smarter and more secure way of travelling.

Keywords: Security system, Raspberry pi, Integrated system, Sensors.

References:

1. G. Eason, B. Noble, and I.N. Sneddon, “On certain integrals of Lipschitz-Hankel type involving products of Bessel functions,” Phil. Trans. Roy. Soc. London, vol. A247, pp. 529-551, April 1955. 2. J. Clerk Maxwell, A Treatise on Electricity and Magnetism, 3rd ed., vol. 2. Oxford: Clarendon, 1892, pp.68-73. 3. I.S. Jacobs and C.P. Bean, “Fine particles, thin films and exchange anisotropy,” in Magnetism, vol. III, G.T. Rado and H. Suhl, Eds. New York: Academic, 1963, pp. 271-350. 4. K. Elissa, “Title of paper if known,” unpublished. 5. R. Nicole, “Title of paper with only first word capitalized,” J. Name Stand. Abbrev., in press. 6. Y. Yorozu, M. Hirano, K. Oka, and Y. Tagawa, “Electron spectroscopy studies on magneto-optical media and plastic substrate interface,” IEEE Transl. J. Magn. Japan, vol. 2, pp. 740-741, August 1987 [Digests 9th Annual Conf. Magnetics Japan, p. 301, 1982]. 7. M. Young, The Technical Writer’s Handbook. Mill Valley, CA: University Science, 1989. 8. www.pololu.com/file/0J309/MQ2.pdf 9. https://www.pololu.com/file/0J310/MQ3.pdf 10. http://wiki.friendlyarm.com/wiki/index.php/Matrix_Pressure_and_Temperature_Sensor_mcp308 11. https://www.raspberrypispy.co.uk/2013/10/analogue-sensors-on-the-r aspberry-piusing-an 12. https://www.electroschematics.com/hc-sr04-datasheet Authors: Mehariw Belay Gelagay, Amanpreet Singh

Paper Title: External and Internal Factors on Export Performance in Ethiopian Manufacturing Firms Abstract: The study described external and internal factors that affect export performance in Ethiopian manufacturing firms. In this study, self-administered survey was administered to manufacturing exporters in Ethiopia. Descriptive analysis was made in order to show the effect of internal and external factors on export performance in Ethiopia using SPSS version 21. The study disclosed that external uncontrollable forces and internal controllable factors are the causes of export performance in Ethiopian exporting firms. It was therefore, recommended that exporting manufacturing firms in Ethiopia should engage in maximizing their opportunities and work on their strengths to enhance export performance.

Keywords: External Forces, Internal Factors and Export Performance.

References:

1. Debel Gemechu (2002): Exports and Economic Growth in Ethiopia, An Empirical Investigation: Addis Ababa University, Addis Ababa. 2. Anagaw, Belayneh Kassa, and Wondaferahu Mullugeta Demissie. "Determinants of Export Performance in Ethiopia: VAR Model Analysis." Natl. Mon. Refereed J. Res. Commer. Manag 2 (2013): 5- 94. 3. McGuinness, N.W. and Little B. (1981)The influence of product characteristics on the export performance of new industrial products. Journal of Marketing.45 (2): 110-122 4. Tookey, D. (1964) Factors associated with success in exporting. Journal of Management Studies.1 (1): 48-66. 5. Medalla, E. M., Tecson, G. R., Bautista, R. M., & Power, J. H. (1996). Philippine Trade and Industrial Policies: Catching up with Asia’s Tigers, Volumes I and II. Makati City: Philippine Institute for Development Studies. 6. Hunger, T. L. (2012). Strategic Management and Business Policy: Toward Global Sustainability, 13rd,. Boston: Pearson 7. Percin, S., &. Ayan, T. Y (2005). A structural analysis of the determinants of export performance: Evidence from Turkey. 110. Innovative Marketing, 1(2), 106-120. 8. Loo, G. V. (2016). Ethiopian Business Review. (T. E. Review, Interviewer) 9. Lee, E. (1996). The Hand Book of Channel Marketing. Edwin Lee. 594-597 10. Dwyer, F. R., & Tanner, J. F. (2002). Business marketing: Connecting strategy, relationships, and learning. New York: McGraw- Hill. 11. Czinkota Michael, R., & Ronkainen Ilkka, A. (2007). International marketing. 12. Clerides,S.,S.Lach,and J.Tybout (1998).‘Is Learning-by-Exporting Important? Micro-Dynamic eevidence from Colombia, Mexico, and Morocco’. Quarterly Journal of Economics, 113(3): 903–47. 13. Aaby N. and Slater, S.F. (1989) Management influences on export performance: A review of the empirical literature 1978–1988. International Marketing Review.6 (4): 7–23. 14. Gemünden (1991) H.G. (1991) Success factors in export marketing. In: New perspectives in international marketing. Paliwoda SJ (edt). Routledge: London; 33–62 15. Zou,S.,& Stan,S.(1998).The determinants of export performance: a review of the empirical literature between1987and1997.International Marketing Review,15(5),333-356. 16. Leonidou,L.,Katsikeas,C.,& Samiee,S.(2002).Marketing strategy determinants of export performance: a meta-analysis. Journal of Business Research,55(1),51-67 17. Cooper, R.G and Kleinschmidt, E.J. (1985). The impact of export strategy on export sales performance. Journal of International Business Studies.16 (1): 37-55. 18. Madsen, T.K. (1989) Empirical export performance of studies: A review of conceptualizations and findings. Advances in international marketing, Cavusgil ST (edt). JAI Press 19. Madsen, T.K. (1988) Empirical export performance of studies: A review of conceptualizations and findings. Advances in international marketing, Cavusgil ST (edt). JAI Press 20. Axinn CN. 1988. Export performance: Do managerial perceptions make a difference? International Marketing Review. 5 (2): 61– 71 21. Brooks, M. R., & Rosson, P. J. (1982). A study of export behavior of small and medium-sized manufacturing firms in three Canadian provinces. Export management: An international context, 39-54. 22. Medalla, Tecson, Bautista, and Power and Associates (1996 23. Styles, C& Amber (1994) Export performance measures in Australia and the UK. Journal of International Marketing.6 (3): 12– 36. 24. Johansson, J. K. (2009). Global Marketing Foreing entry, Local Marketing & Global Management (5Th Edition ed.). Boston: Mc Graw-Hill Irwin. 25. Roberts, M.,and J.Tybout (1997).‘The Decision to Export in Colombia: An Empirical Model of Entry with Sunk Costs’. American Economic Review, 87(4):545–64. Authors: Uditi Chaudhary, Tanisha Kumari, Sara Bamiri, B.R.G. Robert Sanitary Gravity Sewer Design using Sewer GEMS Software Connect Edition for Utsav Vihar, Paper Title: Karala Abstract: The paper presents modeling, design and analysis of sewage network of Utsav Vihar by means of SewerGEMS software which helps in the achievement of project results in a shorter period of time in an effective way and at reasonable prices. In the present study the sewerage was designed for Utsav Vihar area in North – West Delhi District. SewerGEMS software eases the designing for engineers because of unique features to offer a fully dynamic and multi-platform sanitary and combined sewer modeling solution which otherwise tends to consume a lot of time and energy. The software uses derived equations and theorems for calculating the hydraulic model. It enables engineers to analyze all sanitary and combined sewer networks in a single package. 598-602 The hydraulic design section includes the calculation and determination of the transit, total flow and hydraulic modeling for network pipes diameters or slopes. The application generates reports, layouts, longitudinal or transverse cross section.

111. Keywords: curvilinear method, Utsav Vihar, SewerGEMS, rainfall estimation, peak flow, sanitary loading.

References:

1. Terence J. McGhee, ‘’Water Supply and Sewerage”, Lafayeette College, 6th ed., McGraw-Hill,Inc., 1991, pp.8. 2. CPHEEO, Manual on Sewerage Treatment Systems, Part A- Engineering: Ministry of Housing and Urban Affairs Government of India, 2013, chapter 3 pp. 1-9. Available: http://cpheeo.gov.in/upload/uploadfiles/files/engineering_chapter3.pdf 3. CPHEEO, Manual on Storm Water Drainage Systems, vol. I, Ministry of Housing and Urban Affairs Government of India, 2019, pp. 58-61. Available: http://cpheeo.gov.in/cms/manual-on-storm-water-drainage-systems---2019.php 4. Ministry of Statistics & Programme Implementation, “Rainfall- Statistical Year Book India 2016’’, Table 34.1(B) & 34.2, Government of India. 5. Delhi Jal Board, “ Report on Feasibility Study & Detailed Cost Estimates (Hydraulic Sheet), Preparation of Sewerage Master Plan Delhi- 2031’’ by AECOM-WAPCOS. 6. Directorate of Economics & Statistics Office of Chief Registrar, ‘District of Tehsil wise Final Result of Census 2001’ , Government of NCT of delhi. 7. Census of India 2011, ‘District Census Handbook of All Nine Districts’, Series 06, Part XII- B, Directorate of Census operations, Delhi, pp.41. Available: https://www.censusindia.gov.in Authors: Lim Zi Hao, Mafas Raheem, Seetha Letchumy

Paper Title: Predicting Type 2 Diabetes: A Machine Learning Approach Abstract: Diabetes is a well-known common disease among people around the world. Diabetes causes many anomalies in the body and results in the patients to become under a long term medication. Detecting diabetes has been done via hectic medical tests and causes a delay for the patients to get to know their test results. However, data mining and machine learning approaches are in the frontline supporting the health care domain to make effective predictions in this regard. This paper elaborates about predicting Type 2 Diabetes Mellitus using classification models. A suitable secondary dataset was used to build classification models and the more suitable model was selected via the valid performance measures. In this line, the Random Forest, Support Vector Machine, Naïve Bayes and Artificial Neural Network models were built. Based on the performance measures, Random Forest has been identified as the more suitable classifier with the accuracy of 90%, the recall and precision value of 0.90.

Keywords: diabetes prediction, machine learning, predictive models, optimization, model tuning.

References: 112. 1. International Diabetes Federation, “International Diabetes Federation - Type 2 diabetes”, 2019. Accessed on July 1, 2019. [Online]. Available: https://www.idf.org/aboutdiabetes/type-2-diabetes.html 603-608 2. Walley, A. J., Blakemore, A. I., Froguel, P., “Genetics of obesity and the prediction of risk for health”, Human Molecular Genetics, Vol. 15, No. 2, pp. R124-R130, 2006. 3. Belkina, A. C., Denis, G. V., “Obesity genes and insulin resistance”, Curr Opin Endocrinol Diabetes Obes., Vol. 17, No. 5, pp. 472-477, 2010. 4. Diabetes Care, “Gestational Diabetes Mellitus”, Diabetes Care, Vol, 25, No. 1, pp. S94-S96, 2002. 5. Bellamy, L., Casas, J. P., Hingorani, A. D., Williams, D., “Type 2 diabetes mellitus after gestational diabetes: a systematic review and meta-analysis”, The Lancet, Vol. 373, No. 9677, pp. 1773-1779, 2009. 6. The National Institute of Diabetes and Digestive and Kidney Diseases, “The A1C Test & Diabetes NIDDK”, 2018. Accessed on January 30, 2020. [Online]. Available: https://www.niddk.nih.gov/health-information/diabetes/overview/tests-diagnosis/a1c-test. 7. Villines, Z., “Fasting blood sugar: Normal levels and testing”, 2019. Accessed on January 30, 2020. [Online]. Available: https://www.medicalnewstoday.com/articles/317466. 8. Medline Plus, “Glucose tolerance test - non-pregnant: MedlinePlus Medical Encyclopedia”, 2019. Accessed on January 30, 2020. [Online]. Available: https://medlineplus.gov/ency/article/003466.htm. 9. Xue-Hui, M., Yi-Xiang, H., Dong-Ping, R., Qiu, Z., Qing, L., “Comparison of three data mining models for predicting diabetes or prediabetes by risk factors”, The Kaohsiung Journal of Medical Science, Vol. 29, No. 2, pp. 93-99. 2013. 10. Kaur, G., Chhabra, A., “Improved J48 Classification Algorithm for the Prediction of Diabetes”, International Journal of Computer Applications, Vol. 98, No. 22, pp. 13-17. 2014. 11. Iyer, A., Jeyalatha, S., Sumbaly, R., “Diagnosis of Diabetes Using Classification Mining Techniques”, International Journal of Data Mining & Knowledge Management Process (IJDKP), Vol. 5, No. 1, pp. 1-14. 2015. 12. Feurer, M., Hutter, F., Kotthoff, L., Vanschoren, J., “Automated Machine Learning”, 1st ed. Cham: Springer. 2019. 13. Scikit-Learn Devs, “Tuning the hyper-parameters of an estimator — scikit-learn 0.22.2 documentation”, 2019. Accessed on January 30, 2020. [Online]. Available: https://scikit-learn.org/stable/modules/grid_search.html. 14. Bergstra, J., Bengio, Y., “Random Search for Hyper-Parameter Optimization”, Journal of Machine Learning Research, Vol. 13, pp. 281-305. 2012. 15. Ray, S., “6 Easy Steps to Learn Naive Bayes Algorithm”, 2017. Accessed on February 20, 2020. [Online]. Available: https://www.analyticsvidhya.com/blog/2017/09/naive-bayes-explained/. 16. Yiu, T., “Understanding Random Forest - Towards Data Science”, 2019. Accessed on February 20, 2020. [Online]. Available: https://towardsdatascience.com/understanding-random-forest-58381e0602d2. 17. Ray, S., “Understanding Support Vector Machines(SVM) algorithm”, 2017. Accessed on February 20, 2020. [Online]. Available: https://www.analyticsvidhya.com/blog/2017/09/understaing-support-vector-machine-example-code/. 18. Dormehl, L., “What is an artificial neural network? Here's everything you need to know”, 2019. Accessed on February 21, 2020. [Online]. Available: https://www.digitaltrends.com/cool-tech/what-is-an-artificial-neural-network/. Authors: Suleman Ali Eyal Awwad, Nik Mohd Norfadzilah, Faruk Abdullah

Paper Title: Impact of Audit Committees on the Financial Performance: Evidence from Jordan Abstract: This study examines the effects of the correlation between corporate governance mechanisms (audit committees) on the financial performance in the Jordanian companies, the sample comprises of 115 companies, 690 observations, listed in ASE for the period from (2010-2015), thus our goal is the correlation between the audit committees and financial performance to financial data analysis quality test and effect on the earnings. The results of this research expose a strong relationship both the audit committees and performance, and the results of this study have significant inclusion that supports encouraging the enforcement of the principles of governance and controlling the behavior of these committees, the reliability of statements, financial information, and reports issued by companies can be enhanced. The current study bridges the previous literature gap by providing empirical evidence on the quality and efficiency of audit committees in Jordan as an emerging economy and its impact on financial performance.

Keywords: Corporate Governance, Audit Committee, Financial Performance, Earnings Quality.

References:

1. Aanu, O. S., Odianonsen, I. F., & Foyeke, O. I. (2014). Effectiveness of audit committee and firm financial performance in Nigeria: an empirical analysis. Journal of Accounting and Auditing, 2014, 1. 2. Abbadi, S. S., Hijazi, Q. F., & Al-Rahahleh, A. S. (2016). Corporate governance quality and earnings management: Evidence from Jordan. Australasian Accounting, Business and Finance Journal, 10(2), 54-75. 3. Abed, S., Al-Attar, A., & Suwaidan, M. (2012). Corporate governance and earnings management: Jordanian evidence. International Business Research, 5(1), 216. 4. Ahmed Haji, A. (2015). The role of audit committee attributes in intellectual capital disclosures: Evidence from Malaysia. Managerial Auditing Journal, 30(8/9), 756-784. 5. Alzoubi, E. S. S. (2016). Audit quality and earnings management: evidence from Jordan. Journal of Applied Accounting Research, 17(2), 170-189. 6. Allegrini, M., & Greco, G. (2013). Corporate boards, audit committees and voluntary disclosure: Evidence from Italian listed 113. companies. Journal of Management & Governance, 17(1), 187-216. 7. Al‐Najjar, B. (2010). Corporate governance and institutional ownership: evidence from Jordan. Corporate Governance: The international journal of business in society. 609-613 8. Alsufy, F. J. H. (2019). The Impact of Audit Committees Controls Commitment on Strengthening Corporate Governance: Evidence from Jordan. International Journal of Economics and Finance, 11(3), 69-76. 9. Barr-Pulliam, D. (2017). The effects of continuous auditing and functional alignment on internal auditors’ perceptions of the likelihood of earnings management and their likelihood of reporting: Working Paper. 10. Bansal, N., & Sharma, A. K. (2016). Audit committee, corporate governance and firm performance: Empirical evidence from India. International Journal of Economics and Finance, 8(3), 103. 11. Bromilow, C. L., & Berlin, B. (2005). Audit Committee Effectiveness. Corporate Board, 155, 16. 12. Chaudhry, N. I., Roomi, M. A., & Aftab, I. (2020). Impact of expertise of audit committee chair and nomination committee chair on financial performance of firm. Corporate Governance: The International Journal of Business in Society. 13. Eisenhardt, K. M. (1989). Agency theory: An assessment and review. Academy of management Review, 14(1), 57-74. 14. Farouk, M. A., & Hassan, S. U. (2014). Impact of audit quality and financial performance of quoted cement firms in Nigeria. International Journal of Accounting and Taxation, 2(2), 1-22. 15. Felo, A. J., Krishnamurthy, S., & Solieri, S. A. (2003). Audit committee characteristics and the perceived quality of financial reporting: an empirical analysis. Available at SSRN 401240. 16. Ghazali, A. W., Shafie, N. A., & Sanusi, Z. M. (2015). Earnings management: An analysis of opportunistic behaviour, monitoring mechanism and financial distress. Procedia Economics and Finance, 28, 190-201. 17. Guthrie, J., & Turnbull, S. (1995). Audit committees: is there a role for corporate senates and/or stakeholders councils? Corporate Governance: An International Review, 3(2), 78-89. 18. Hayes, R. M. (2014). Discussion of “Audit committee financial expertise and earnings management: The role of status” by Badolato, Donelson, and Ege (2014). Journal of Accounting and Economics, 58(2-3), 231-239. 19. Kalbers, L. P., & Fogarty, T. J. (1993). Audit committee effectiveness: An empirical investigation of the contribution of power. Auditing, 12(1), 24. 20. Khalid, K., Abdullah, H. H., & Kumar M, D. (2012). Get along with quantitative research process. International Journal of Research in Management. 21. Klein, A. (2002). Audit committee, board of director characteristics, and earnings management. Journal of Accounting and Economics, 33(3), 375-400. 22. Kwon, S. S., & Wild, J. J. (1994). Informativeness of annual reports for firms in financial distress. Contemporary Accounting Research, 11(1), 331-351. 23. Nelson, S. P., & Shukeri, S. N. (2011). Corporate governance and audit report timeliness: evidence from Malaysia. Research in Accounting in Emerging Economies, 11(1), 109-127. 24. Owens‐Jackson, L. A., Robinson, D., & Shelton, S. W. (2009). The association between audit committee characteristics, the contracting process and fraudulent financial reporting. American Journal of Business. 25. Phan, T., Lai, L., Le, T., & Tran, D. (2020). The impact of audit quality on performance of enterprises listed on Hanoi Stock Exchange. Management Science Letters, 10(1), 217-224. 26. Rittenberg, L. E., & Nair, R. (1993). Audit Committees: Is There an Expectations Gap? The Expectations Gap Standards: Progress, Implementation Issues, Research Opportunities. New York, NY: American Institute of Certified Public Accountants, 59-85. 27. Sekaran, U. Bougie.(2010). Research Methods for Business: A Skill Building Approach: USA: Wiley. 28. Spira, L. F. (2007). The audit committee: performing corporate governance: Springer Science & Business Media. 29. Ziaee, M. (2014). The effect of audit quality on the performance of listed companies in Tehran Stock Exchange. International Letters of Social and Humanistic Sciences(21), 36-43. 30. Zraiq, M., & Fadzil, F. (2018). The impact of audit committee characteristics on firm performance: Evidence from Jordan. Sch J Appl Sci Res, 1(5), 39-42. Authors: Santus Kumar Deb

Paper Title: Evaluation of Mobile Applications in eTourism: an Innovative Outlook Abstract: Today technology plays an important part in eTourism services around the country in the world. The theme of the manuscript is to examine the critical success factors of mobile applications to adopt eTourism. The study used a triangulation process in which literature review, exploratory investigation from expert interview, and focus group discussion, along with descriptive study for the identification critical success factor. Quantitative approaches based on survey data like descriptive statistics and regression analysis is to discover relative important critical success factors. Among 250 respondents 55 percent of the respondents are male and rest of them are female respectively. Majority of the tourists’ preferred to usage mobile to perform tourism related activities. The study found that eVisa Processing, eTour guide, eReservation, eItenary, eTicketing, and Virtual Tourism are statistically significant and also reliable. The study implied that timely and user-friendly mobile applications are expected for reliability and security of transaction/payment, and other tourism services for tourists. This study will make a good lesson for Mobile manufacturers and tourism stakeholders to develop business product. Conclusively portals make the traveler self-reliant and give all details with a single click.

Keywords: Mobile phone, Tourists, eTourism, Online Services, Adoption

References:

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Hoehle, and V. Venkatesh, “Mobile application usability: conceptualization and instrument development”. MIS Quarterly, 39(2), 2015, pp. 435-472. 17. G. Inkpen, Information Technology for Travel and Tourism. Addison Wesley Logman: Essex UK, 1998. 18. R. Kramer, M. Modsching, M., K. Hagen, and U. Gretzel, “Behavioural impacts of mobile tour guides.” In ENTER, 2007, pp. 109-118. 19. M. Kenteris, D. Gavalas, and D. Economou, An innovative mobile electronic tourist guide application. Personal and Ubiquitous Computing, 13(2), 2009, pp. 103–118. 20. R. Qi. Law, and D. Buhalis, “Progress in Tourism Management: A Review of Website Evaluation in Tourism Research”, Tourism Management. 31, 2010, pp. 297-313. 21. R. Ling, “The mobile connection: The cell phone's impact on society.” Elsevier. 2008 22. J. M. Lopez-Bonilla, and L. M. Lopez-Bonilla, “Self-service technology versus traditional service: Examining cognitive factors in the purchase of the airline ticket.” Journal of Travel & Tourism Marketing, 30(5), 2013, pp. 497-513. 23. M. F. Luo, Information search behavior and tourist characteristics: The Internet vis-a-vis other information sources. Journal of Travel & Tourism Marketing, 17, 2004, pp. 15–25 24. M. Mia, M. Rahaman, and M. Uddin, “E-Banking: Evolution, Status and Prospects.” The Cost and Management, 35(1), 2007, pp. 36-48 25. N. K. Malhotra, “Marketing research: an applied orientation.” New Jersey, Prentice Hall: Upper Saddle River, 2007. 26. S. McCabe, and C. Foster, “The role and function of narrative in tourist interaction.” Journal of Tourism and Cultural Change, 4(3), 2006, pp. 194-215. 27. J. Nunnally, “Psychometric theory.” New York: McGraw-Hill, 1998. 28. B. Pan, and D. Fesenmaier, Online Information Search: Vacation Planning Process. Annals of Tourism Research, 33, 2006, pp. 809-832. 29. A. Poon, “Tourism, Technology and Competitive Strategies,” Cab International, 1993 30. J. Rasinger, M. Fuchs, W, Hopken, “Information search with mobile tourist guides: A survey of usage intention.” Information Technology & Tourism, 3(4), 2007, pp. 177–194. 31. J. R. Ritche, and C. R. Goodrich, “Book Reviews : Travel, Tourism, and Hospitality Research: A handbook for Managers and Researchers” Edited by J. R. Brent Ritchie and Charles R. Goeldner (John Wiley and Sons, Inc., 605 Third Avenue, New York, NY 10158, 1994, Second Edition, 614 Pages.” Journal of Travel Research ,Vol. 33(2), 71–71. doi:10.1177/0047287594033002126. 32. Sayar, C. and Wolfe, S. (2007). Internet banking market performance: Turkey versus the UK". International Journal of Bank Marketing, 25(3), 122-141. 33. Sebastiani, F. (2002). Machine learning in automated text categorization. ACM computing surveys (CSUR), 34(1), 1–47. 34. P. Sheldon, “Tourism Information Technology.” Wallingford, UK and New York, USA: CA International, 1997. 35. H. Werthner, “E-Commerce and Tourism. Communications of the ACM, 47, 2004, pp. 101–105. 36. H. Werthner, and S. Klein, “Information Technology and Tourism – A Challenging Relationship”, Springer, Wien and New York, 1999 37. A. G. Woodside and C. Dubelaar,“A General Theory of Tourism Consumptions System”. J. Travel Res. 41(2); 2002, pp. 120- 132. Authors: B.Arivuselvam, M.Madhushalini, T.Nivetha, M.G.Rufus

Paper Title: Underwater Image Restoration Based on Image Fusion Abstract: The haziness in underwater images occurs due to two major phenomena namely absorption and scattering of light. Hence, we have proposed an image fusion-based approach to improve the visibility of images obtained underwater. The proposed method uses a single hazy image. Initially the colour corrected and contrast improved versions of the image are obtained. Further, Laplace transform is applied which is followed by replication and saliency mapping on each of the derived images. Multi-scale image fusion technique has been used to combine the inputs. This enables each of the fused images to contribute the most essential feature to obtain the resultant image. Thus, the proposed method significantly restores the quality of the input distorted images.

Keywords: Adaptive histogram equalization, Image fusion, Laplace Transform, Underwater image.

References:

1. J. Baneerjee, R. Ray, S.R.K. Vadali, S.N. Shome and S. Nandy, “Realtime underwater image enhancement: An improved approach for imaging with AUV-150,” Sadhana, vol. 41, no. 2, pp. 225-238, Feb. 2. Malathi V, Manikandan A, “An Enhancement of Underwater Images using DCP and CLAHE Algorithm”, International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958, Volume-9 Issue-2, December, 2019. 3. C. Ancuti, et al., “Enhancing underwater images and videos by Fusion,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 81-88, 2012. 4. J. Jaffe, “Computer modeling and the design of optimal underwater imaging systems,” IEEE J. Oceanic Eng., vol. 15, no. 2, pp. 101-111, 1990 115. 5. W. Hou, S. Woods, E. Jarosz, et al., “Optical turbulence on underwater image degradation in natural environments,” Appl. Opt., vol. 15, no. 14, pp. 2678-2686, 2012. 1 6. K. Iqbal, R.A. Salam, A. Osman, and A.Z. Talib, “Underwater Image Enhancement Using an Integrated Colour Model,” Int. J. Comput. Sci., vol. 32, no. 2, pp. 239—244, Nov. 2007. 621-626 7. C.O. Ancuti, C. Ancuti, and P. Bekaert, “Effective single image dehazing by fusion,” in Proc. Int. Conf. on Image Processing, ICIP, 2010, pp. 3541–3544. 8. Nicolas Limare1 , Jose-Luis Lisani2 , Jean-Michel Morel1 , Ana Bel´en Petro2 , Catalina Sbert2,” Simplest Color Balance”, Published in Image Processing On Line on 2011–10–24. 9. W. Wang, Q. Lai, H. Fu, et al., “Salient object detection in the deep learning era: An in-depth survey,” in arXiv preprint arXiv: 1904.09146, 2019. 1 10. C. Li, R. Cong, J. Hou, et al., “Nested network with two-stream pyramid for salient object detection in optical remote sensing images,” IEEE Trans. Geosci. Remote Sens., vol. 57, no. 11, pp. 9156-9166, 2019. 1 11. C.O. Ancuti, C. Ancuti, T. Haber, and P. Bekaert, “Fusion-based restoration of the underwater images,” in Proc. Int. Conf. on Image Processing, ICIP, 2011, pp. 1557–1560. 12. Ritu Singh, Dr. Mantosh Biswas, “Fusion Technique for Hazy Underwater Image Enhancement”, 2016 IEEE International Conference on Computational Intelligence and Computing Research. 13. Rajni Sethi, Sreedevi Indu, “Fusion of Underwater Image Enhancement and Restoration”, International Journal of Pattern Recognition and Artifcial Intelligence Vol. 34, No. 3 (2020) 2054007. 14. K. He, J. Sun and X. Tang, “Single image haze removal using dark channel prior”, IEEE Trans. Pattern Anal. Mach. Intell. 33(12) (2011) 2341–2353. 15. A. Galdran, et al., “Automatic red-channel underwater image restoration,” Elsevier Journal of Visual Communication and Image Representation, vol. 26, pp. 132-145, 2015. 16. C. O. Ancuti, C. Ancuti, C. D. Vleeschouwer and P. Bekaert, Color balance and fusion for underwater image enhancement, IEEE Trans. Image Process. 27 (2018) 379–393. 17. Galdran, D. Pardo, A. Picón and A. Alvarez-Gila, “Automatic red-channel underwater image restoration”, J. Vis. Commun. Image Represent. 26 (2015) 132–145. 18. Keming Cao, Yan-Tsung Peng and Pamela C. Cosman(2018),” Underwater Image Restoration using Deep Networks to Estimate Background Light and Scene Depth”,2018 IEEE Southwest Symposium on Image Analysis and Interpretation(SSIAI). 19. Amjad Khan, Syed Saad Azhar Ali*, Aamir Saeed Malik, Atif Anwer,” Underwater Image Enhancement by Wavelet Based Fusion”, 2016 IEEE 6th International Conference on Underwater System Technology.

116. Authors: Umaya Ramadhani Putri Nst, Sutarman, Pahala Sirait Paper Title: Analysis of Representative Values in Clustering using the CURE Algorithm Abstract: The collection of data that everyone has on earth has a fully agreed upon value of knowledge. Analysis of a collection of data that can accommodate a long processing time, for this we need an algorithm that can provide a comparison of the acceleration of the analysis process. One process of data analysis is clustering, which is a process of grouping large amounts of data so that it is easy to understand. One of the algorithms in the clustering process is CURE (Clustering Using Representative) where CURE random sample-based data bases partition the data using representative points called representative points. Sample-based process will provide better processing time acceleration because it will only be done on the data collection, not the whole data. This representative point determines the processing time of the testing carried out in the input. Values, representative values, and shrinkage values will provide a faster settlement process for the values inputted according to the correct conditions.

Keywords: Representative, CURE algorithm

References:

1. Shirkhorsidi, A.S., Aghabozorghi, S., Wah, T.Y., & Herawan, T. 2014. Big data Clustering. International Conference 627-630 Computational Science and Applications, pp. 707-720. 2. Rani, Y., Manju & Rohil, H. 2014. Comparative Analysis of BIRCH and CURE Hierarchical Clustering Algorithm using WEKA 3.6.9. Computer Science Engineering and Application 2: 25-29. 3. Jian, S., Pang, G., & Cao, L. 2018. CURE: Flexible Categorical Data Representation by Hierarchical Coupling Learning. IEEE Transactions on Knowledge and Data Engineer. Vol. 14. No.8 4. Eick, Christoph F., Zeidat, N., & Vilalta, R. 2004. Using Representative-Based Clustering for Nearest Neighbor Dataset Editing. Proceedings of the fourth IEEE International Conference on Data Mining, pp. 2142-2145. 5. Putra, A.K.P., Purwanto, Y., & Novianty, A. 2015. Analisis Sistem Deteksi Anomali Traffic Menggunakan Algoritma CURE dengan Koefisien Silhoutte dalam Validasi Clustering. Procedings of engineering, pp. 3837-3842. 6. Han, J., Kamber, M., & Pei, J. 2012. Data Mining: Concepts and Techniques Third. Elsevier: USA. 7. Manjula, V. & Nandakumar, A.N. 2018. An Effective Cure Clustering Algorithm in Education Data Mining Techniques to Valuate Student’s Performance. International Journal of Applied Engineering Research 10: 7493-7498. 8. Meng, H.-D., Song, Y.-C., Song, F.-Y., & Wang, S. L. 2009. Clustering for Complex and Massive Data. International Conference on Information Engineering and Computer Science, pp. 4244-4994. 9. Rani, Y., Manju & Rohil, H. 2014. Comparative Analysis of BIRCH and CURE Hierarchical Clustering Algorithm using WEKA 3.6.9. Computer Science Engineering and Application 2: 25-29. 10. Yin, J., Tan, Z., Ren, J., & Chen, Y. 2005. An Efficient Clustering Algorithm Mixed Type Attributes in Large Dataset. Proceedings of the Fourth International Conference on Machine Learning and Cybernatics, pp. 7803-9091. Authors: Balaji N, Karthik Pai B H, Shivam Khandelwal, Ramya R Nayak, Vadiraj M Kale

Paper Title: An Integrated Application for Tracking, Maintaining Health and Fitness Abstract: Health and fitness are two such important things in everyone’s life today. Everyone wants to see themselves fit and healthy so that can lead a happy disease-free life without any worry. But sometimes this cannot be achieved by few. Unhealthy and unfit can lead to many types of disease, one such disease is described in this work. Gestational diabetes refers to type of disease that is caused to pregnant women. Due to uneven diet and no proper exercise this disease can affects the pregnancy of the women. It can be controlled by doing regular workouts, having a proper diet, keeping the track of steps walked, nutrition intake, etc. In order to develop motivation among the people motivational videos are added in the application so that they can view it and give them with a clear dedication of maintaining a good health and bringing fitness to life. The user-interface of the application discussed in this project is light and can be easily understandable, easy to use for the users. The dataset was created by consulting to doctor who helped to understand the nutrition and fitness related conditions for each trimester and how a healthy body can be maintained.

Keywords: GDM, Gestational diabetes, Low glycemic index food, Pregnancy, Tri-semester. 117. References: 631-637

1. Farhad Ahamad and Farnaz Farid, “Applying Internet of Things and Machine Learning for Personalized Healthcare: Issues and Challenges”, International Conference on Machine Learning and Data Engineering (iCMLDE), 2018. 2. Affreen Ara and Aftab Ara, “Case study: Integrating IoT, streaming analytics and machine learning to improve intelligent diabetes management system”, International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS), 2017. 3. Steven T. Johnson, Ana B. Mladenovic, Nonsikelelo Mathe, Margie H. Davenport, Sonia Butalia, Weiyu Qiu, Jeffrey A. Johnson, “Healthy eating and active living after gestational diabetes mellitus (HEALD-GDM): Rationale, design, and proposed evaluation of a randomized controlled trial Contemporary Clinical Trials”, Volume 61, October 2017, Pages 23-28. 4. Deepti Sisodia, Dilip Singh Sisodia, ”Prediction of Diabetes using Classification Algorithms”, Procedia Computer Science, Volume 132, 2018, Pages 1578-1585. 5. Nathan Y. Weltman Med, Susan A. Saliba PhD, PT, ATC, Eugene J. Barrett MD, Ph. D, Arthur Weltman Ph. D, FACSM, “The Use of Exercise in the Management of Type 1 and Type 2 Diabetes Clinics in Sports Medicine”, Volume 28, Issue 3, July 2009, Pages 423-439. 6. Neda Dolatkhah, M.D., Ph.D., Majid Hajifaraji, Ph.D., and Seyed Kazem Shakouri, M.D, “Nutrition Therapy in Managing Pregnant Women With Gestational Diabetes Mellitus: A Literature Review”, J Family Reprod Health, June 2018. 7. J Zhong C, Li X, Chen R, Zhou X, Liu C, Wu J, et al., “Greater early and mid-pregnancy gestational weight gain are associated with increased risk of gestational diabetes mellitus: A prospective cohort study”, Clinical nutrition ESPEN, December, 2017. 8. Chen Z, Watanabe RM, Stram DO, Buchanan TA, Xiang AH, “High Calorie Intake Is Associated With Worsening Insulin Resistance and Beta-Cell Function in Hispanic Women After Gestational Diabetes Mellitus”, Diabetic Care, December 2014. 9. Duarte-Gardea MO, Gonzales-Pacheco DM, Reader DM, Thomas AM, Wang SR, Gregory RP, et al., “Academy of Nutrition and Dietetics Gestational Diabetes Evidence-Based Nutrition Practice Guideline”, J Acad Nutr Diet, September 2018. 10. I Nematy M, Haghani M, Akhavan R, Babazadeh S, Safarian M, Abdi M, et al., “Determination of the Glycemic Index of the most popular Iranian rice-Tarom-in two cooking methods: Boiled and Steamed”, International Journal of Health and Life Sciences. 2015. Authors: Karthik Pai B. H, Balaji N.

Paper Title: An Ontology Based Information Retrieval System Abstract: Ontology provide a structured way of describing knowledge. Ontology's are usually repositories of concepts and relations between them, so using them in information retrieval seems to be a reasonable goal. The main objective in this report is to provide efficient means to move from keyword-based to concept-based information retrieval utilizing ontology's for conceptual definitions [1]. In this paper, we present the skeleton of such an IR system which works on a collection of domain specific documents and exploits the use of a domain specific ontology to improve the overall number of relevant documents retrieved. In this system, a user enters a query from which the meaningful concepts are extracted; using these concepts and domain ontology, query expansion is performed. We propose a system that matches the query terms in the ontology/schema graph and exploits the surrounding knowledge to derive an enhanced query. The enhanced query is given to the underlying basic keyword search system LUCENE [2]. In this approach we try to make use of more ontological Knowledge 118. than IS-A and HAS-A relationships and synonyms for information retrieval.

Keywords: Ontology, Semantic, DSA, IR System. 638-643

References:

1. A. Macfarlane J. Bhogal and P. Smith, A Review of Ontology based Query Expansion, Information Process Management, Elsevier, 2007. 2. Lucene Search: http://lucene.apache.org 3. Rohit Rathore Priyamvada Singh Rashmi Chauhan, Rayan Goudar and Sreenivasa Rao, Ontology based automatic query expansion for semantic retrieval in sports domain. ICECCS, pages 422–433, 2012. 4. Norbert Fuhr. Probabilistic models in information retrieval, The Computer Journal, 35, 1992. 5. Vinu E V and Rajeev. Data structures and algorithms ontology, AIDB, Research Lab, IIT-M, 2015. 6. Wordnet wordnet 2.1 reference manual, http://wordnet.princeton.edu/man/, Cognitive Science Laboratory, Princeton University, 54, 2005. 7. Z. Wu and M. Palmet, Verb semantics and lexical selection, Proceedings of the 32nd Annual meeting of the Association of Computational Linguistics, pages 133-138, 1994. Authors: Pulkit Tiwari

Paper Title: Mechanical Properties of Concrete Containing Manufactured Steel Fiber & Lathe Waste Fiber Abstract: Concrete is the most versatile construction material in the construction industry with its high compressive strength though it has comparatively low tensile strength and high brittleness properties and hence the concept of fiber reinforcement comes into the picture to empower its brittleness and tensile strength properties. There are various types of fiber have been used in concrete like glass fiber, polypropylene, Asbestos fiber, carbon fiber, organic fiber, steel fibers, etc. Similarly, the lathe waste fiber can be used in concrete to replace the manufactured steel fibers as they lead to manufacturing cost while lathe waste fiber does not require any manufacturing cost and thus saving the cost of manufacturing. This paper aims to study and compare the mechanical behavior of concrete by using lathe waste fiber and manufactured steel fiber in concrete. The fibers to be used in the study are lathe waste fibers and manufactured steel fibers. Lathe waste fibers are the scraps from lathe workshops and industries. Experimental study and analysis of results are to be conducted to study the compressive and tensile behavior of composite concrete with varying percentages of such fibers added to it. The concrete mix to be adopted is M-60 with varying percentages of fibers ranging from 0, 0.5, 1, 1.5 & 2%. 119. Keywords: Manufactured Steel fiber Reinforced Concrete (SFRC), Lathe Fiber Reinforced Concrete (LFRC), 644-648 Compressive strength, Split Tensile Strength, and Flexural Behavior of Concrete, Economy in Construction.

References:

1. Vasudev R, Dr. B G Vishnuram (2013).Studies on Steel Fibre Reinforced Concrete – A Sustainable Approach, International Journal of Scientific & Engineering Research, Volume 4, Issue 5, May-2013 1941 ISSN 2229-5518. 2. Ashish Kumar Parashar, Rinku Parashar (2012).The Effect of Size of Fibres on Compressive Strength of M-20 Concrete Mix, International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue 4, July- August 2012, pp.1232-1236. 3. G.Murali, C.M.Vivek Vardhan, R.Prabu, Z.MohammedSadaquath Ali Khan, T.Aarif Mohamed, and T.Suresh (2012).EXPERIMENTAL INVESTIGATION ON FIBRE REINFORCED CONCRETE USING WASTE MATERIALS International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue 2, Mar- Apr 2012, pp.278-283. 4. Zeeshan Nissar Qureshi, Yawar Mushtaq Raina, Syed MohdAsgarRufaie(2016).Strength Characteristics Analysis of Concrete Reinforced With Lathe Machine Scrap, International Journal of Engineering Research and General Science Volume 4, Issue 4, July-August, 2016 ISSN 2091-2730. 5. Harshit Garg, Saurabh Suman,AnishJain,Saurabh Prakash Srivastava, ShobhitBhadauriya, Dr. Mrs. V.S SohoniI(2017), International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 Volume: 04 Issue: 05 | May-2017 www.irjet.net p-ISSN: 2395-0072. 6. SushanB.K, Purandara k(2018).Investigation of Performance of Concrete on Addition of Lathe Steel Scraps Under Elevated Temperatures, Technical Research Organization India. 7. Saranya C. V(2016), Experimental Investigation On Standard Concrete By Using Lathe Industry Waste & Waste Flux Sheet, International Journal of Science and Engineering Research (IJ0SER), Vol 4 Issue 11 November -2016 3221 5687, (P) 3221 568X. 8. Sheetal Chinnu James, Dr. Mini Mathew, Ms. Anitta Jose (2015).Experimental Study on Concrete Using Lathe Scrap Fiber, International Journal of Advanced Technologies in Engineering and Science, Volume no. 3, Issue no. 01. 9. Sajad Ahmad Mir, Kshipra Kapoor, Mukesh Kumar, Mohit Kansal(2017).An Experimental Investigation of Scrap Steel Reinforced with M 20 Concrete”, International Journal For Technological Research In Engineering Volume 4, Issue 8, ISSN (Online): 2347 – 4718. 10. Shaik Mohammed Arshad a, Alisha K. Nawaz Nadaf a, G. Manikumar Reddy a, Dr. D. Neeraja (2017). Evaluation of Workability and Strength of Green Concrete Using Waste Steel Scrap”, Article in Materials Science and Engineering. 11. Vasudev R., Dr. B. G. Vishnuram (2014). Experimental Studies of the Application of Turn Steel Scraps as Fibres in Concrete – A Rehabilitative Approach, International Journal of Engineering and Technology. 12. Jais Joy Rajesh Rajeev(2015).Performance of Steel Scrap in Concrete”, IJSRD - International Journal for Scientific Research & Development| Vol. 2, Issue 12, | ISSN (online): 2321-0613, 2015. 13. PoorvaHaldkar, Ashwini Salunke(2016). Analysis of Effect of Addition of Lathe Scrap on the Mechanical Properties of Concrete”, International Journal of Science and Research (IJSR) ISSN (Online): 2319-7064 Index Copernicus Value (2013): 6.14 | Impact Factor 6.391. 14. Shirule Pravin Ashok., Swami Suman and Nilesh Chincholkar (2012).Reuse of Steel Scrap from Lathe Machine as Reinforced Material to Enhance Properties of Concrete, Global J. of Engg. & Appl. Sciences, 2012: 2 (2)164 ISSN 2249-2631(online): 2249- 2623(Print) - Rising Research Journal Publication Research paper: Shirule Pravin Ashok et al., Pp.164-167. 15. Ashish Kumar Parashar, Rinku Parashar (2012).Utility of Wastage Material as Steel Fibre in Concrete Mix M-20,International Journal of Advancements in Research & Technology, Volume 3, Issue1, ISSN: 0976-4860. 16. Abhishek Mandloi Dr. K. K. Pathak (2015). Utilization of Waste Steel Scrap for Increase in Strength of Concrete- Waste Management, IJSRD - International Journal for Scientific Research & Development| Vol. 3, Issue 09, ISSN (online): 2321-061. 17. Gopalsamy.P, Sundar V, Yasar Arafath, N HarifKhan, Rajeshkumar.T(2017),Experimental Investigation of Steel Waste Generated From Lathes as Fiber Reinforced Concrete”, IJSRD - International Journal for Scientific Research & Development| Vol. 5, Issue 01, | ISSN (online): 2321-0613. 18. Namrata M. Mannade, Prof. A.P.Khatri (2018). Experimental Investigation on Use of Lathe Scraps Steel Fibers in Rigid Pavement”, International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 6, Issue 4 PP. 08-11. 19. Hemanth Tunga G N, Rasina K V, Akshatha S P(2018).Experimental Investigation on Concrete with Replacement of Fine Aggregate by Lathe Waste”, ISSN (Print): 2319-8613 ISSN (Online): 0975-4024 Hemanth Tunga G N et al. / International Journal of Engineering and Technology (IJET). 20. G Vijayakumar, P Senthilnathan, K Pandurangan, and G Ramakrishna (2012).Impact and Energy Absorption Characteristics of Lathe Scrap Reinforced Concrete. International Journal of Structural & Civil Engineering, ISSN 2319 – 6009 www.ijscer.com Vol. 1, No. 1, IJSCER. 21. Dinesh W. Gawatre, PoorvaHaldkar, Shraddha Nanaware,AshwiniSalunke, Meherunnisa Shaikh, Anita Patil(2016).Study on Addition of Lathe Scrap to Improve The Mechanical Properties of Concrete” International Journal of Innovative Research in Science, Engineering and Technology(An ISO 3297: 2007 Certified Organization)Vol. 5, Issue 5. 22. Seetharam. P. G, et. al. (2017).Studies on Properties of Concrete Replacing Lathe Scrap, International Journal of Engineering Research & Technology (IJERT) http://www.ijert.org ISSN: 2278-0181 IJERTV6IS030377 (This work is licensed under a Creative Commons Attribution 4.0 International License.) Published by:www.ijert.org Vol. 6 Issue 03. 23. Taha A. El-Sayed(2019).Flexural behavior of RC beams containing recycled industrial wastes as steel fibers”, Elsevier Construction and Building Materials 212 27–38. 24. Abbas Hadi Abbas (2011).Management of Steel Solid Waste Generated from Lathes as Fiber Reinforced Concrete”, European Journal of Scientific Research ISSN 1450-216X Vol.50 No.4 (2011), pp. 481-485 © Euro Journals Publishing. 25. Abdul Rahman, Syed Mustafa Ali, Syed Azeemuddin (2017).IOSR Journal of Mechanical and Civil Engineering (IOSR-JMCE) e-ISSN: 2278-1684,p-ISSN: 2320-334X, Volume 14, Issue 2 Ver. VII. Himakar Sai Chowdary Maddipati, Manoj Kumar Alluri,Venkata Sai Maneesh Morampudi, Authors: Likhitha Kolupuri, Anuradha Chinta Paper Title: Mobile Hearing Aid Abstract: This is about developing an android app for replicating the mechanism of hearing aid machine. Majority of the Hearing Impaired people cannot afford hearing aids due to higher cost of the Instruments. Similarly Audiometry Test for assessing the Deafness levels are also costly. Affordable Smartphone are available with majority of the Hearing Impaired people in Indian. So, we propose a Mobile Application which consists of the following three Features. This APP enables Ear phones of Phone to function as Hearing Aid for people with hearing disability. This APP converts speech to Text so that Hearing impaired people can know what other people are talking without using SIGN Language. This APP provides Pure-Tone AudiometryTest to assess level of Hearing Loss.

120. Keywords: Hearing aid, Audiometry Test, Pure tone audio, Audiogram, Android Media API, Android Speech Recognizer API. 649-651

References:

1. Design and Implementation of Software Based Audiometer System- BY Mehmet Cem Catalbas and Hasan Guler – Journal of Image and Graphics, Vol. 5, No. 1, June 2017 2. Development and Evaluation of a Portable Audiometer for High- Frequency Screening of Hearing Loss From Ototoxicity in Homes/Clinics –BY Peter G. Jacobs*, Member, IEEE, Grayson Silaski, Debra Wilmington, Samuel Gordon, Member, IEEE, Wendy Helt, Garnett McMillan, Stephen A. Fausti, and Marilyn Dille 3. VOISEE COMMUNICATOR: An Android Mobile Application for Hearing-impaired and Blind Communications-BY Junar Arciete Landi- cho ,Mindanao University of Science and Technology, Philippines- International Journal of Interactive Mobile Technologies. 4. ANDROID BASED AID FOR THE DEAF-BY Apeksha Khilari ,Manasi Marathe, Aishwarya Parab, Manita Rajput-International Journal of Technical Research and Applications e-ISSN: 2320-8163. 5. Hearing Aids -by Gerald R. Popelka , Brian C.J. Moore, Richard R. Fay,Arthur N. Popper. 6. Professional Android 4 Application Development- by Reto Meier 7. https://developer.android.com/reference/android/media 8. https://developer.android.com/reference/android/speech/SpeechRecognizer 9. HEARING MEASUREMENT- BY John R. Franks, Ph.D. 10. Data Structures and Algorithms using Java-O’Reilly School of Technology course 11. The Scientist and Engineer’s Guide to Digital Signal Processing -By Steven W. Smith, Ph.D. 12. Audiometry Screening and Interpretation -JENNIFER JUNNILA WALKER, MD, MPH, U.S. Army Health Clinic, Schofield Barracks,Hawaii Authors: P.Sivashankari, R.Ranjithkumar, K.Kailash, K.R.Kavitha, J.Lilly Mercy, S.Prakash

Paper Title: Modelling of Automobile Radiator by Varying Structure and Materials Abstract: Radiators used in the automotive application are a class of heat exchangers whose main purpose is to cool the coolant coming from the internal combustion engines. These coolants flow through tubes covered with fins that facilitate a faster way of heat transfer to the surrounding more efficiently. With the increase in efficiency of the engine cooling system it directly helps in the longevity of the engine in other words, the life of the internal combustion engine increases multifold times. Upon investigating we found different shapes that can be used to optimize the radiators efficiency. There are several other ways to improve the efficiency of a radiator. And these can be achieved by improving the surface area of the radiator, improving airflow through it, improving coolant property which flows through these tubes covered with fin all around and at last using alternate materials that prove to be more efficient than the present ones that are being used. The demand of the current times of climate change and energy crisis have paved way for improved heat transfer rates and designing radiators in smaller dimensions and sizes at the same time being more efficient than the previous generation of radiators. With the above conditions in mind, it has been found out that with a simple modification of changing the existing rectangular-shaped radiators into spiral-shaped ones thereby improving efficiency to improved levels, which finds its use in the current generation of vehicles which are benefitting from the improved rate of heat transfer taking place. The spiral radiator of copper tube used here is wound in two coils connected centrally. Spiral tubes of the radiator have circumferential fins. In this type of configuration, heat transfer rate will increase because of having a circumferential fin across the length of the spiral tube through which water flows. These design considerations have been done keeping in mind the major aims to achieve for this type of design and they are improving heat transfer rate and achieving compactness of shape of radiator. We also did Computational Fluid Dynamics or CFD Analysis to test the material properties for the application of heat transfer and how it fares against old materials.

Keywords: Spiral, radiator, circumferential fins, Heat transfer rate. 121.

References: 652-656

1. Akhilnandh Ramesh, M. Jaya ArunPrasanth, A.Kirthivasan, M.Suresh, (2015), Heat Transfer Studies on Air Cooled Spiral Radiator with Circumferential Fins, Procedia Engineering, 127,333 – 339 2. SandipSawant, S.Shastri , Imran Quazi,SandipSawan, S. Shastri Imran Quazi (2017),Experimental Study & Heat Transfer Analysis on Spiral Radiator with Circumferential Fins 3. P. Ragupathi, DebabrataBarik, R. Pradeepkannan, P. Senthilmuthu (2018) , Analysis of Heat Transfer in Spiral Heat Exchanger with Circumferential Fins, E-ISSN: 2321-9637 4. Chavan d. K , Tasgaonkar g. S, Study, analysis and design of automobile radiator (heat exchanger) proposed with cad drawings and geometrical model of the fan, vol. 3, issue 2, jun 2013, 137-146 5. A. R. EsmaeiliSany M. H. Saidi J. Neyestani, (2010), Experimental Prediction of Nusselt Number and Coolant Heat Transfer Coefficient in Compact Heat Exchanger Performed with ε-NTU Method, The Journal of Engine Research, 18, Spring 201 6. JP Yadavand ,Bharat Raj Singh, “Study on performance evaluation of automotive radiator”, S-JPSET:ISSN : 2229-7111, Vol. 2, Issue 2,(2011) 7. Pawan S. Amrutkar, Sangram R. Patil Automotive Radiator Performance – Review. International Journal of Engineering and Ad- vanced Technology. 2013; 2(3):563–5. 8. Suman Shah, K. Kiran Kumar , Experimental Study & Heat Transfer Analysis on Copper Spiral Heat Exchanger Using Water Based SiO2 Nanofluid as Coolant Vol.08 No.04(2018), Article ID:89004,12 pages 9. Ebin Jose , A.V Ramesh ,Nidheesh P , Optimization of Circular Shaped AutomobileRadiator, Vol.4, Special Issue 12, September 2015 10. RanamPrathyusha , K.Aparna, G.Vinod Reddy , Flow Analysis on Automobile Radiator ,volume no :4,issue no:7, (2017) 11. C. Oliet, A. Oliva, J. Castro, C.D. Preez-Segarra (2007), Parametric studies on automotive radiators, Applied Thermal Engineering, 27, 20332043 12. Ram JatanYadav, Kashish Singh Pilyal, Devansh Gupta, Shivam Sharma , Design and material selection of an automobile radiator issn 0973-4562 Volume 14, Number 10, 2019 13. P.Sivashankari,K.R.Kavitha,J.LillyMercy,A.Krishnamoorthy,S.Prakash,Modelling of automobile radiator by varying structure of fin and coolant,ISSN:2277-3878,Volume-8,Issue-2,July2019 14. P.Sivashankari, A.Krishnamoorthy,Evaluation of mechanical characteristics magnesium foam by varying the prcentage of foaming agent, ISSN:2277-3878,Volume-8,Issue-3,Sepetember2019 15. P.Sivashankari,A.Krishnamoorthy,S.Prakash,Wear behaviour and metallurgical characteristics of aluminium copper and zinc alloys,11(2),176-179 ISSN:0975-3060 Authors: Pradeep H S

122. Paper Title: Triple Band Slotted Patch Antenna with a Notch for Wi-MAX Communications Abstract: A conventional probe fed square patch antenna with a triangular notch & a rectangular slot with 657-660 perturbation is designed and simulated for triple band operation for applications in communication systems like Wi-MAX systems is presented. The antenna is designed on the glass epoxy FR4 substrate. With the fixed feed point location, the notch angle is varied from 1800 to 1400 resulting in single and dual resonance behavior of patch antenna. With the notch angle from 1800 to 1650, antenna resonates at a single frequency but on further reduction of notch angle from 1650 to 1400, dual resonance behavior is noticed with improved bandwidth. The optimum performance is obtained for a notch angle of 1500. The maximum radiation efficiency of ⁓ 40% is obtained. Further, a rectangular slot with perturbation at shorter edges give rise to additional resonant frequency. The other performance parameters of antenna are analyzed by varying notch angle and its position.

Keywords: Triple band, probe feed, radiation efficiency,Wi-MAX.

References:

1. Wong.K.L, ‘Compact and Broadband Microstrip Antennas’(John Wiley & Sons, 2003). 2. James.J.R, ‘Handbook of Microstrip Antenna’ (Peter Peregrinus Ltd, 1989). 3. Garg.R, Bhartia.P, Bahl.I,J, Ittipiboon.A, ‘Microstrip Antenna Design Handbook’ (Artech House, 2001). 4. Hui.J.Y, Shi.Z.S, ‘Notched triangular microstrip antenna for dual frequency operation’, J. Shanghai Univ., 7,(4), pp. 375 – 378 (English Edn), 2007. 5. Chen.Z.N, Chia M.Y.W, ‘Broadband probe-fed notched plate antenna’, Electron. Lett., 36, (7), pp 599 – 600, 2003. 6. Shakelfford.A.K, et al., ‘Simulation of a probe-fed notched patch antenna with a shorting post’, IEEE Antennas and Propagation Society Int. Symp., vol. 2, pp. 708 – 711, 2001. 7. Singhal.P.K, Srivastava.L, ‘On the investigations of a wide band proximity fed bow tie shaped microstrip antenna’, J. Microw. Optoelectron., 3, (4), pp. 87 – 98, 2004. 8. Bhardwaj.D, et al., ‘Design of square patch antenna with a notch on FR4 substrate’, Proc. Asia Pacific Microwave Conf. (APMC -2007), vol. 2, pp. 975 -977, 2007. 9. Amit.A.D, et al., ‘Analysis of Proximity Fed Circularly Polarized Triangular Notch Cut Square Patch Antennas’, IEEE International Conference on Research in Computational Intelligence and Communication Networks, 2015. 10. R.Jothi Chitra, K.Jeyanthi, V.Nagarajan, ‘Design of E Slot Rectangular Microstrip Slot Antenna for WiMAX Application’, International conference on Communication and Signal Processing, Melmaruvathur, India, 2013. 11. P.Surendra Kumar & B.Chandra Mohan, ‘Dual-Frequency Vertex-Fed Pentagonal Slot On Rectangular Patch For WLAN/WiMAX Applications’, IEEE Annual India Conference (INDICON), pp. 2325-9418, Bangalore, 2016. 12. Shilpa Jangid, Mithilesh Kumar, ‘A Novel UWB Band Notched Rectangular Patch Antenna with Square slot’, Fourth International Conference on Computational Intelligence and Communication Networks, Mathura, India, 2012. 13. Rajat Srivastava, et al., ‘Dual Band Rectangular and Circular Slot Loaded Microstrip Antenna for WLAN/GPS/WiMax Applications’, Fourth International Conference on Communication Systems and Network Technologies, Bhopal, 2014. 14. Atul Kumar Dwivedi, et al., ‘Design of Tapered Shape Notch Cut Multi-slotted patch Antenna for Wi-Max/Wi-Fi Applications’, First International Conference on Power, Control and Computing Technologies (ICPCCT), Raipur, India, 2020. 15. Saminur Rahman & Usha Kiran K., ‘A Compact Triangular Slotted Microstrip Antenna for Multiband Operation’, International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), Chennai, India, 2016. 16. V.V. Reddy and NVSN Sarma, ‘Tri-Band Circularly-Polarized Koch Fractal Boundary Microstrip Antenna’, IEEE Antennas and Wireless Propagation Letters, 2014. 17. Pritam Singh Bakariya, et al., ‘Proximity Coupled Multiband Microstrip Antenna for Wireless Applications’, IEEE Antennas and Wireless Propagation Letters, 2014. Authors: P.T. Juwono, R. Asmaranto, A. Murdhianti

Paper Title: Useful Life of Ngancar Reservoir Due to Erosion and Sedimentation Abstract: Ngancar Dam is located in Ngancar Village, Batuwarno Sub district, Wonogiri Regency Central Java Province. This dam was built in 1944-1946 and is classified as an old dam in Indonesia which has a function to meet the needs of 1300 ha of irrigation water. Ngancar Dam is a type of rock fill dam with a soil core, has a height of 19.40 m above the riverbed and 25.40 m above quarry. The length of the dam peak is 181.00 m and the width is 5.00 m while the reservoir volume at the normal water conditions is 1.64 million m3. Because the Ngancar Dam has been operating for a long time then required the evaluation on the service age of the reservoir due to sedimentation so that it effects on the operating pattern and safety of the dam. Based on the results of hydrological analysis and reservoir bathymetry analysis obtained the information that the reservoir sedimentation rate equal to 4,266.08 m3/year or 11.9 m3/day and this requires mechanical and non-mechanical handling efforts to reduce the sedimentation rate.

123. Keywords: Ngancar Dam, erosion rate, sedimentation, sediment delivery ratio.

References: 661-667

1. Asmaranto,runi; Suryono,Antonius; Hidayat, Muhammad Nurjati, Inspections of Hydro-Geotechnical on Ngancar Dam. Civil and Environmental Science Journal, [S.l.], v. 2, n. 2, p. pp.117-127, oct. 2019. ISSN 2620-6218. Available at: . Date accessed: 10 Feb. 2020. Doi:https://doi.org/10.21776/ub.civense.2019.00202.5. 2. A Palmieria F. Shah A. Dinar. Economics of reservoir sedimentation and sustainable management of dams. Journal of Environmental Management, Volume 61, Issue 2, February 2001, Pages 149-163. https://doi.org/10.1006/jema.2000.0392. 3. Lily Montarcih Limantara, Donny H. Harisuseno, Vita A.K. Dewi. Modelling of rainfall intensity in a watershed: A case study in Amprong watershed, Kedungkandang, Malang, East Java of Indonesia. JOURNAL OF WATER AND LAND DEVELOPMENT 2018, No. 38 (VII–IX): 75–84 PL ISSN 1429–7426, e-ISSN 2083-4535. DOI: 10.2478/jwld-2018-0044. 4. Ian M. Brodie & Shahjahan Khan, A direct analysis of flood interval probability using approximately 100-year streamflow datasets, Hydrological Sciences Journal, (2016) 61:12, 2213-2225, DOI: 10.1080/02626667.2015.1099790 5. Darlington Ogbonna, Boniface Chidi Okoro, Joachim Chinonyerem Osuagwu, Application of Flood Routing Model for Flood Mitigation in Orashi River, South-East Nigeria. Journal of Geoscience and Environment Protection, (2017), 5, 31-42 http://www.scirp.org/journal/gep ISSN Online: 2327-4344 ISSN Print: 2327-4336. 6. Asmaranto, runi, The Effect of Soil Physical Properties Change on Soil Erodibility Factor in Manting Basin-Mojokerto. Disertation. Sepuluh Nopember Insititute of Technology. (2013) 7. Yupi, H.M. Studi model WEPP (water erosion prediction project) dalam upaya pengaturan fungsi kawasan pada sub DAS Lesti berbasis sistem informasi geografis (SIG), Thesis Program Magister, Universitas Brawijaya Malang (2006). 8. Julio Cesar Neves dos Santos, Eunice Maia de Andrade, Pedro Henrique Augusto Medeiros, Helba Araújo de Queiroz Palácio and José Ribeiro de Araújo Neto, Sediment delivery ratio in a small semi-arid watershed under conditions of low connectivity. Revista Ciência Agronômica, v. 48, n. 1, p. 49-58, jan-mar, (2017) Centro de Ciências Agrárias - Universidade Federal do Ceará, Fortaleza, CE www.ccarevista.ufc.b 9. WISCHMEIER, W. H.; SMITH, D. D. Predicting rainfall erosion losses: a guide to conservation planning. Washington: United States Department of Agriculture, (1978). 58 p. 10. Naharuddin, Abdul Wahid, Rukmi, Sustri, Erosion Hazard Assessment in Forest and Land Rehabilitation for Managing the Tambun Watershed in Sulawesi, Indonesia. Journal of Chinese Soil and Water Conservation, 50 (3): 124-130 (2019) DOI: 10.29417/JCSWC.201909_50(3).0004 11. Blanco, A.C., and K. Nadaoka, A Comparative Assessment and Estimation of Potential Soil Erosion Rates and Patterns in Laguna Lake Watershed Using Three Models: Towards Development of an Erosion Index System for Integrated Watershed-Lake Management. Symposium on Infrastructure Development and the Environment (2006). SEAMEO-INNOTECH University of the Philippines, Diliman, Quezon City, Philippines. Authors: Jayant Singh, U.K. Singh, R.K. Singh

Paper Title: Stabilization of Clayey Soil using Gypsum and Calcium Chloride Abstract: The engineering strength properties of expensive soils (clayey soil) such as compaction characteristics and bearing capacity can be improved by stabilization process of the soil. These properties can be improved by controlled compaction using the mechanical equipment’s or by addition of suitable admixtures like cement, fly ash, lime, gypsum or by reinforcing the soil with shredded tyre, crumb rubber, plastic waste etc. But gypsum is used now a days to enhance the geotechnical properties. So, in this research paper gypsum and calcium chloride has been used to improve the various strength properties of natural soil. The objective of this research paper is to examine the strength properties of natural clayey soil reinforced with different percentage of gypsum by the weight of soil and fixed percentage of calcium chloride(Cacl2) as a binding material. A series of Standard Proctor tests(for calculation of MDD and OMC) and California Bearing Ratio (C.B.R) tests are conducted on both raw clayey soil and reinforced soil with different percentages of gypsum (2%, 4%, 6% and 8%) by weight and with fixed percentage of calcium chloride (0.75%). A comparison between properties of raw clayey soil, raw clayey soil mixed with gypsum and raw clayey soil mixed with gypsum and calcium chloride (CaCl2) are performed. It is found that the properties of clayey soil mixed with gypsum and calcium chloride (CaCl2) are suitably enhanced.

Keywords: Gpysum, Soil stabilization,Cacl2,Standard Proctor Test.

References: 124.

1. Yasser I.O. and H Alsharie, “Stabilization of clayey soil using cement, gypsum and recycling concrete in Jaresh city-Jordan”, 668-673 INTERNATIONAL JOURNAL OF CURRENT RESEARCH, Vol. 10, Issue 10, pp 74135-74137, October2018. 2. Tamadher T, Abood, Anuar Bin Kasa and Zamri Bin Chik, “Stabilization of silty clay soil using chemical compounds” Journal of Engineering Science and Technology,2007, Vol. 2, No. 1 Pages 102-110. 3. Arash B.,” The effect of adding iron powder on atterberg limits of clay soils”. International Research Journal of Applied and Basic Sciences, Intl. Res. J. Appl. Basic. Sci. Vol. 3, Issue 11, 2012. 4. Ramadas, T.L., Kumar, N.D., and Yesuratnam, G., 2012. A study on strength and swelling characteristics of three expansive soils treated with CaCl2. International Journal of Advances in Civil Engineering and Architecture. 1(1):77-86. 5. Aly Ahmed and Usama H.Iss, “Stability of soft clay soil with recycled gypsum in a wet environment”. Soils and Foundations, 2014, 54(3), Pages 405–416. 6. Dina Kuttah and Kenichi Sato, “Review on the effect of gypsum content on soil behavior”. Transportation Geotechnics, September 2015, Volume 4, Pages 28-37. 7. A.Goyal, “Soil stabilization of clayey soil using jute fibre and gypsum” International Journal of Innovative Research in Science, Engineering and Technology, Vol. 5, Issue 8, pp 15513-15519, August 2016. 8. B.P. Singh Sikarwar,” Stabilization of clayey soil by using gypsum” IJSRD – International Journal for Scientific Research & Development, Vol.5, Issue03, pp886- 888, 2017. 9. Peddaiah and K.Suresh, “Experimental study on effect of gypsum and Nacl in improvement of engineering properties of clayey soil” International Journal of Engineering and Technology (IJET), Vol. 9, Issue 4, pp 2771-2778, Aug-Sep2017. 10. IS: 2720-Part 3-1980, Bureau of Indian Standards New Delhi, Feb (1981).Determination of Specific Gravity of Soil Solids. 11. IS: 2720-Part 16-1987, Bureau of Indian Standards New Delhi, May (1988). Laboratory determination of C.B.R Value. 12. IS: 2720-Part 7-1980, Bureau of Indian Standards New Delhi, December (1980). Laboratory method for Standard Proctor Test. 13. IS: 2720-Part 5-1985, Bureau of Indian Standards New Delhi, August (1985). Laboratory method for determination of LL and PL of soil. 14. IS: 2720-Part 10-1991, Bureau of Indian Standards New Delhi, May (1992). Laboratory method for determination of Unconfined Compressive Strength of Soil. Khairul Eahsun Fahim, Md. Sakib Hossain, Monzurul Karim Afgani, Shaikh Mohammad Farabi, Authors: Soad Shajid Paper Title: Modelling and Simulation of DC-DC Boost Converter using Sliding Mode Control Abstract: DC-to-DC converter is an electronic circuit that converts direct current (DC) from a given voltage to 125. another. DC-DC converters have a broad range of applications, starting from electronic gadgets to household equipment, adapters of mobile phone and laptops, aero plane control frameworks and communication hardware. This paper illustrates the practical application of DC-DC boost converter using Sliding Mode Control (SMC). 674-678 DC-DC converters can be categorized into different categories in terms of mechanical, electrical and electronic features. SMC DC-DC converters show better performance compared to other converters under certain conditions. This nonlinear control system is especially well suited for Variable Structure Systems. The most significant advantage of Sliding Mode Control over conventional control systems is its robustness against load, line and parametric uncertainties.

Keywords: Sliding Mode Control, Boost Converter, Power Electronics, Control Systems.

References:

1. P. Mattavelli, L. Rossetto, G. Spiazzi, and P. Tenti, “General-purpose sliding-mode controller for dc/dc converter applications,” in IEEE Power Electronics Specialists Conf. Rec. (PESC), 1993, pp. 609–615 2. P. Mattavelli, L. Rossetto, G. Spiazzi, and P. Tenti, “General-purpose sliding-mode controller for dc/dc converter applications,” in IEEE Power Electronics Specialists Conf. Rec. (PESC), 1993, pp. 609–615 3. S. C. Tan, Y. M. Lai, and C. K. Tse, “Adaptive feed forward and feedback control schemes for sliding mode controlled power converters,” IEEE Trans. Power Electron. vol. 21, no. 1, pp. 182–192, Jan.2006. 4. S.C. Tan, Y. M. Lai, and C. K. Tse, “Adaptive feed forward and feedback control schemes for sliding mode controlled power converters,” IEEE Trans. Power Electron., vol. 21, no. 1, pp. 182–192, Jan.2006 5. Q. Valter, Pulse Width Modulated (PWM) Power Supplies. New York:Elsevier, 1993 Authors: K. P. K Devan, Sruthi Prabakaran P, Tamizhazhagan S, Vaishnavi S

Paper Title: One-Word Answer Correction using Deep Learning Models and OCR Abstract: Examinations/Assessments are a way to assess the understanding of a student on a particular subject. Even today many educational organizations prefer to conduct exams by offline mode (pen and paper). And evaluating them is a time-consuming process. There is no effectual model to evaluate Offline descriptive answers automatically. The traditional method involves staff assessing the content manually. In place of this process, a new approach using image captioning by using deep learning algorithms can be implemented. Handwritten Text Recognition (HTR) can be used to evaluate descriptive answers. One-word Answers captured as images are pre-processed to extract the text features using deep learning models and pytesseract. This paper presents a comparison between the CNN-RNN hybrid model and Optical Character Recognition (OCR) to predict a score for one-word answers.

Keywords: Convolutional Neural Network (CNN), Handwritten Text Recognition (HTR), Optical Character Recognition (OCR), Recurrent Neural Network (RNN).

References:

126. 1. Piyush Patil, Sachin Patil, Vaibhav Miniyar, Amol Bandal, “Subjective Answer Evaluation Using Machine Learning”, International Journal of Pure and Applied Mathematics, Volume 118 No. 24, 2018. 2. V. Nandini, P. Uma Maheswari, “Automatic assessment of descriptive answers in the online examination system using semantic 679-682 relational features”, The Journal of Supercomputing, Springer, 2018. 3. Maram F. Al-Jouiea, Aqil M. Azmia, “Automated Evaluation of School Children Essays in Arabic, 3rd International Conference on Arabic Computational Linguistics”, ACLing, Elsevier, 2017, pp:19-22. 4. Haiqing Ren, Weiqiang Wang, Chenglin Liu, “Recognizing online handwritten Chinese characters using RNNs with new computing architectures”, Pattern Recognition, Elsevier, 2019, pp:179-192. 5. Raymond Ptucha, Felipe Petroski Such, Suhas Pillai, Frank Brockler, Vatsala Singh, Paul Hutkowski, “Intelligent character recognition using fully convolutional neural networks”, Pattern Recognition, Elsevier, 2019, pp:604-613. 6. J.Ignacio Toledoa, Manuel Carbonell, Alicia Fornés, Josep Lladós, “Information extraction from historical handwritten document images with a context-aware neural model”, Pattern Recognition, Elsevier, 2019, pp:27-36. 7. Noman Islam, Zeeshan Islam, Nazia Noor, “A Survey on Optical Character Recognition System”, Journal of Information & Communication Technology-JICT, Vol. 10 Issue. 2, 2016. 8. Alla Defallah Alrehily, Muazzam Ahmed Siddiqui, Seyed M Buhari, “Intelligent Electronic Assessment for Subjective Exams”, ACSIT, ICITE, SIPM, 2018, pp: 47-63. 9. Meena.K, Lawrance Raj,” Evaluation of the Descriptive type answers using Hyperspace Analog to Language and Self-organizing Map”, IEEE International Conference on Computational Intelligence and Computing Research, IEEE, 2014. 10. Meena.K, Lawrance.R, “Semantic Similarity Based Assessment of Descriptive Type Answers”, IEEE, 2016. 11. Rohan Vaidya, Darshan Trivedi, Sagar Satra, Prof. Mrunalini Pimpale, “Handwritten Character Recognition Using Deep- Learning”, Proceedings of the 2nd International Conference on Inventive Communication and Computational Technologies, IEEE, 2018 12. Pratik Madhukar Manwatkar, Dr. Kavita R. Singh, “A Technical Review on Text Recognition from Images”, IEEE 9th International Conference on Intelligent Systems and Control (ISCO), IEEE, 2015. Authors: Suhas B.G, Kavadiki Veerabhadrappa Theoretical Formulation of the Thermodynamic Properties of Ammonia-Water and Ammonia- Paper Title: Water-Lithium Bromide Solutions Abstract: System of Ammonia-Water-Lithium Bromide overcomes the disadvantage of Ammonia-Water absorption refrigeration system. Addition of lithium bromide reduces the formation of water vapour thus presents its entry into the condenser. A set of computational formulations of thermodynamic and 127. Thermophysical properties of Ammonia-Water, Water-Lithium bromide and Ammonia-Water-Lithium Bromide solutions at different pressures and temperatures are presented in the paper. Obtained results are validated with the experimental data which were available in the literature. It is found to be in good agreement with those 683-687 values of the literature.

Keywords: Ammonia water, Binary mixture, Lithium bromide, Ternary mixture

References:

1. Zhe Yuan & Keith E. Herold, Thermodynamic Properties of Aqueous Lithium Bromide Using Multiproperty Free Energy Correlation, HVAC&R Research, 2005, vol.11:3, 377-393 2. Soleimani and Alamdari Simple, Equations for Predicting Entropy of Ammonia-Water Mixture, IJE Transactions B: Applications Vol. 20, No. 1, April 2007, pp.9-105. 3. Han Yuan, Ji Zhang, Xiankun Huang, Ning Mei, Experimental investigation on binary ammonia–water and ternary ammonia– water–lithium bromide mixture-based absorption refrigeration systems for fishing ships, Energy Conversion and Management vol.166, 2018, pp.13–22 4. Konwara, Gogoia and Das, Multi-objective optimization of double effect series and parallel flow water–lithium chloride and water–lithium bromide absorption refrigeration systems, Energy Conversion and Management vol.180, 2019, pp.425–441. 5. Berdasco, Valles.M and Coronas.A, Thermodynamic analysis of an ammonia/water absorption–resorption refrigeration system, International Journal of Refrigeration, vol.103, 2019, pp.51–60 6. Anurag Goyal and Srinivas Garimella, Computing thermodynamic properties of ammonia–water mixtures using artificial neural networks, International Journal of Refrigeration vol.100,2019, pp.315–325. 7. Arun, M. & Maiya, M. & Murthy, Performance comparison of double-effect parallel-flow and series flow water–lithium bromide absorption systems. Applied Thermal Engineering , 2001 (01)00005-9. 8. G.P. Xu, Y.Q. Dai, Theoretical analysis and optimization of a double-effect parallel-flow-type absorption chiller, Applied Thermal Engineering. Vol.17, 1997, pp.157-170. 9. Yuyuan Wu, Yan Chen and Tiehui Wu, Experimental researches on characteristics of vapor–liquid equilibrium of NH3–H2O– LiBr system, International Journal of Refrigeration, vol.29,2006, pp.328–335 10. Abhishek Ghodeshwar and Mr.Prashant Sharma Thermodynamic Analysis of Lithium Bromide-Water(LiBr-H2O) Vapor Absorption Refrigeration System Based on Solar Energy, International Research Journal of Engineering and Technology Vol. 05 Issue: 01 | Jan-2018,pp.1365-137 Authors: Alok Mishra, P. K. Dwivedi, Kamlesh Singh

Paper Title: Tensor Decomposition of KUKA Industrial Robot (KR16-2) in Rotational System Abstract: This paper refers to study of industrial robot (KUKA KR16-2), in which we have considered the matrix decomposition and tensor decomposition model in rotational motion. We have considered robotic matrix & Tensor and defined a modal product between robot rotation matrix and a tensor Further we have proposed the third order tensor for the motion of Industrial robot and tried to find out the useful result. At last we have shown that the tensor model is providing alternate way to find the solution.

Keywords:

References: 128. 1. G.H. Golub and C.F. Van Loan, Matrix Computations, (The Johns Hopkins University Press 2013). 688-690 2. HongfeiWang et al, \Quantitative spectral and orientational analysis in surface sum frequency generation vibrational spectroscopy (SFG-VS)", 3. International Reviews in Physical Chemistry (2005), (24) no. 2, 191-256. 4. Kolda, T.G. and Bader, B.W., \Tensor Decompositions and Applications", SIAM Review (2009), (51) no. 3, 455-500. 5. Carroll, J.Douglas and Chang, Jih-Jie, \Analysis of individual di_erences in multidimensional scaling via an n-way generalization of Eckart-Young decomposition", Psychometrika (1970), (35) no. 3, 283-319. 6. P. Paatero, \A weighted non-negative least squares algorithm for three-way PARAFAC factor analysis", Chemometrics and Intelligent Laboratory Systems (1997), (38) no. 2, 223-242. 7. N. K. M. Faber and R. Bro, and P. K. Hopke, \Recent developments in CANDECOMP/PARAFAC algorithms: A critical review", Chemometrics and Intelligent Laboratory Systems (2003), (65), 119-137. 8. J. B. Kruskal, \Rank, decomposition, and uniqueness for 3-way and N-way arrays, in Multiway Data Analysis", 7-18. 9. Tyler Ueltschi, Third-Order Tensor Decompositions and Their 10. Application in Quantum Chemistry, in University of Puget SoundTacoma, Washington, USA", 1-9. Authors: Kura Ranjeeth Kumar, Rajulapati Sudha, Kunta Srikanth A Design Modification to Improve the Charge and Discharge Profile of a Super Capacitor in Electric Paper Title: Vehicles Abstract: As the Super Capacitors have Higher Specific power density, longer life time and safer operation when compared with any other energy source in Electric Vehicles, thus have more attention in the present market. Thus presently Super Capacitors are used to act as intermittent source. This study would deal with the performance analysis of Electric Vehicles by adopting Super Capacitor as the only Energy source without any Backup. The SC can charge and discharge at a very faster rate, the time taken for the charge is also equal to the time taken for discharge. Thus the SC will discharge at a very faster rate, in this regard to improve its discharging rate we would like to do certain Design Modifications. The Main objective of this study is to replace 129. the energy source in the EV’s with Super Capacitor Bank which would reduce the weight, Maintenance, and increase the performance of EV’s in a long run. 691-694 Keywords: Super Capacitor (SC), Energy Storage, Performance, Design Modifications, Electric Vehicles (EV’s).

References:

1. Sarang R Sony, Chetan D Upadhyay, Hina Chandwani, “Analysis of Battery-Super capacitor based storage for electric vehicle”, International Conference on Energy Economics and Environment (ICEEE), 2015. 2. X.D. Xue, K.W.E Cheng, Raghu Raman S, Jones Chan, J. Mei, and C.D. Xu, “Performance Prediction of Light Electric Vehicles powered by Body-Integrated Super-Capacitors”, International Conference on Electrical Systems for Aircraft, Railway, Ship Propulsion and Road Vehicles & International Transportation Electrification Conference (ESARS-ITEC), 2016. 3. Ms. Sujata Rawale, Prof. Chandan Kamble, “Study and Analysis of Super capacitor with its Applications”, International Research Journal of Engineering and Technology (IRJET), 2015. 4. Biswajita.Panda ; Indu.Dwivedi ; Krishna.Priya ; P.B.Karandikar ; P. S. Mandake, “Analysis of Aqueous Supercapacitor with various current collectors, Binders and Adhesives”, Biennial International Conference on Power and Energy Systems: Towards Sustainable Energy (PESTSE), 2016. 5. Yuya.Maeyama ; Hideki.Omori ; Noriyuki.Kimura ; Toshimitsu Morizane ; Mutsuo Nakaoka, “A Novel Type of Ultracapacitor Electric scooter with EDLC Super Rapid Charging System for Home Use”, International Power Electronics and Application Conference and Exposition, 2014. 6. Zdenek Cerovsky; Pavel Mindl, “Regenerative Braking by electric hybrid vehicles using super capacitor and power splitting generator”, 2015. Authors: Ajay Kumar Tripathi, Pankaj Kumar Shrivastava

Paper Title: Factors Influencing Capacity Utilization of Mechanical Resources in Construction Industries Abstract: Construction industries in present world do face several challenges from project feasibility status to handing over of the project. With the unbounded scope and inevitable need for automation and machine tools, the success of any project heavily relies on the effective and uninterrupted performance of mechanical resources used in the projects. Companies spend huge amounts to own, operate and maintain these resources. The optimum utilization and failure-free performance of these resources are expected from the stake holder’s perspective. An experiment is carried out using the principles of Design of Experiments to decide as which are the most influential factors affecting the utilization of the mechanical resource with the help of Mini Tab 130. software and the results are discussed.

Keywords: Break-down, Mechanical resources, Resource utilization, Work availability. 695-698

References:

1. Johansen I. Production functions and the concept of capacity. Recentes sur la function de production. Economic Mathemaique et Econometric 1968; 2:52. 2. Marucheck A, McClelland M. Planning capacity utilization in an assemble to order environment. International Journal of Operations and Production Management 1992; 12(9):18–38. 3. Achi Z, Hausen J, Nick A, Pfeffer JL, Verhaeghe P. Managing capacity in basic materials’. McKinsey Quarterly 1996; 1:58–65. 4. Hand Book on Road Construction Machinery, Published by the Indian Road Congress on behalf of the Government of India, Ministry of Shipping and Transport (Roads Wing), June 1985. Authors: Nurain Mohamad Ansor, Zety Sharizat Hamidi, Nur Nafhatun Md Shariff

Paper Title: The Effectiveness of E-CALLISTO System in Predicting Geomagnetic Disturbance Abstract: This study focuses on analyzation of solar radio burst (SRB) data obtained by e-CALLISTO system in order to predict the occurrence of geomagnetic storm. E-CALLISTO is a global network of solar spectrometer that continually generates the observed radio signals in a form of spectrographs. Previous studies have strongly proved that the source of geomagnetic disturbance turned out to be type IV SRB which can be detected by CALLISTO instrument. Therefore, we selected 4 stations located at different locations to study the effectiveness and consistency of this system in detecting type IV bursts associated to solar storm during solar maximum. The data chosen was on 10th September 2014 where type IV bursts were formed at 1727 UT until 1745 UT within a frequency range of 135MHz to 390MHz. Accompanying the bursts was a halo CME prior to the bursts’ formation and an X1.6 flare was registered. From the results obtained by all stations, the pattern of the bursts depicts the same characteristic as theory says, by which, they emit a broadband continuum in a zebra pattern with varying fine structures. The formation of the bursts is due to magnetic reconnection and disruption of magnetic loops during large flares on Sept. 10th. As a consequence to type IV bursts associated to a vigorous CME, a major G2 storm was reported by NOAA a couple of days later. The presented results have shown a parallel correlation between type IV bursts detected by those 4 stations and the commencement of geomagnetic 131. disturbance which took place 2 days afterwards.

Keywords: Coronal mass ejection, E-callisto, Geomagnetic storm, Solar radio burst type IV. 699-702

References:

1. H. Koskinen, E. Tanskanen, R. Pirjola, A. Pulkkinen, C. Dyer, D. Rodgers, P. Cannon, J. C. Mandeville and D. Boscher, Spaceweather effects catalogue. ESWS-FMI-RP- 0001 , no. 2, 2001. 2. N. A. M. Norsham and Z. S. Hamidi, "The Relationship between Solar Radio Burst Type III and Geomagnetic Storm on the Earth that Occurred on 4th October 2017.," Journal of Physics: Conference Series Vol. 1152, p. 012019, 2019. 3. N. Mohamad Ansor, Z. S. Hamidi and N. N. M. Shariff, "The Impact on Climate Change Due to the Effect of Global Electromagnetic Waves of Solar Flare and Coronal Mass Ejections (CMEs) Phenomena.," Journal of Physics: Conference Series Vol. 12, 2019. 4. N. Gopalswamy, "Coronal mass ejections of solar cycle 23," J. Astrophys. Astron. 27, p. 243, 2006. 5. M. Dumbović, A. Devos, B. Vršnak, D. Sudar, L. Rodriguez, D. Ruždjak, K. Leer, S. Vennerstrøm and A. Veronig, "Geoeffectiveness of coronalmass ejections in the SOHO Era," Sol. Phys. 290 (2), p. 579, 2015. 6. J. T. Gosling, D. J. McComas, J. L. Phillips and S. J. Bame, "Geomagnetic activity associated with earth passage of interplanetary shock disturbances and coronal mass ejections," J. Geophys. Res. 96, p. 7831, 1991. 7. M. Mierla, D. Besliu-Ionescu, O. Chiricuta, C. Oprea and G. Maris, "Studies of coronal mass ejections that have produced major geomagnetic storms," Sun Geosph. 7 (1), p. 13, 2012. 8. C. -C. Wu and R. P. Lepping, "Relationships among geomagnetic storms, interplanetary shocks, magnetic clouds, and sunspot number during 1995–2012," Sol. Phys. 291, p. 265., 2016. 9. N. N. M. Shariff, Z. S. Hamidi and N. H. Zainol, "Analysis of slow partial Halo CME events with velocity of 100-200 km/s: an observation through CALLISTO system.," in IEEE, Seoul, 2017. 10. Z. S. Hamidi, M. F. M. Noh, W. N. Toni and N. N. M. Shariff, "An Analysis of Active Region as a Trigger of Solar Flares.," Journal of Physics: Conference Series Vol. 1298, 2019. 11. Z. S. Hamidi, N. N. M. Shariff, Z. A. Ibrahim, C. Monstein, W. W. Zulkifli, M. B. Ibrahim and N. A. Amran, "Magnetic Reconnection of Solar Flare Detected by Solar Radio Burst Type III.," Journal of Physics: Conference Series Vol. 539, 2014. 12. H. Liu, Y. Chen, K. Cho, S. Feng, V. Vasanth, A. Koval, G. Du, Z. Wu and C. Li, "A Solar Stationary Type IV Radio Burst and Its Radiation Mechanism," Solar Phys 293:58, 2018. 13. J. S. Hey, S. J. Parsons and J. W. Phillips, Monthly Notices of the Royal Astronomical Society 108, pp. 354-371, 1948. 14. Z. S. Hamidi, N. N. M. Shariff, C. Monstein and Z. A. Ibrahim, "Space Weather: The Significance of e-CALLISTO (Malaysia) As One of Contributor of Solar Radio Burst Due To Solar Activity," International Letters of Chemistry, Physics and Astronomy 7, pp. 37-44, 2014. 15. Z. S. Hamidi, N. M. Anim, N. N. M. Shariff, Z. Z. Abidin, Z. A. Ibrahim and C. Monstein, "Dynamical structure of solar radio burst type III as evidence of energy of solar flares," AIP Conference Proceedings Vol. 1528, 2013. 16. M. Pick, "Sur differents aspects du rayonnement des sursauts de type IV In: Evans, J.W. (ed.) The Solar Corona," IAU Symposium 16, p. 247, 1963. 17. S. M. White, "Solar Radio Bursts and Space Weather," pp. 1-18. 18. Z. S. Hamidi and N. N. M. Shariff, "Detailed Investigation of a Moving Solar Burst Type IV Radio Emission in on Broadband Frequency," International Letters of Chemistry, Physics and Astronomy Vol. 26, pp. 30-36, 2014. 19. V. V. Zheleznyakov, "Radio Emission of the Sun and Planets," 1970. 20. M. Pick, "Observations of radio continua and terminology," Solar Phys. 104, p. 19, 1986. 21. N. H. Zainol, S. N. U. Sabri, Z. S. Hamidi, M. O. Ali, N. N. M. Shariff, N. Husien and M. S. Faid, "Effective data collection and analysis of solar radio burst type II event using automated CALLISTO network system," in IEEE, Jeju, 2016. 22. Z. S. Hamidi, N. N. M. Shariff, Z. Z. Abidin, Z. Ibrahim and C. Monstein, "ECallisto Collaboration: Some Progress Solar Burst Studies Associated with Solar Flare Research Status in Malaysia," Malaysian Journal of Science and Technology Studies 9, pp. 15-22, 2013. 23. A. O. Benz, M. Guedel, H. Isliker, S. Miszkowicz and W. Stehling, "A broadband spectrometer for decimetric and microwave radio bursts first results," Sol. Phys. 133, pp. 385-393, 1991. Authors: Zoya Fatma, Tarana Afrin Chandel, Mohd Yusuf Yasin

Paper Title: Design Modeling and Simulation of 150 KW Solar Photovoltaic Systems: A Review Abstract: Sun is the source of energy. Renewable energy is a clean eco-system free of harmful gasses such as carbon dioxide, and all the other harmful gases which are produced from the fossil. Many technologies have been used to design and manufacture the photovoltaic module. In this paper we have reviewed the design of solar photovoltaic system of using MatLab/Simulink. Characteristics of I-V and P-V are parameter of great importance in solar photovoltaic system. The effects of temperature and irradiation at the output of solar system that is current, voltage and output power have also been reviewed and studied.

Keywords: Irradiance, Solar cell, Fill factor, Maximum Power, MatLab/Simulink

References:

1. Mohan et al, Modeling and Simulation of Solar PV Cell With Matlab/Simulink and Its Experimental Verification, Journal of Innovative Research and Solution, Print ISSN: 2320 1932 / Online ISSN – 2348 3636 Volume No: 1, Issue No.1. Page No: 118 - 124, JUL – DEC: 2014 2 2. Tarana Afrin Chandel et-al, Modeling and Simulation of Photovoltaic Cell using Single Diode Solar Cell and Double Diode Solar Cell Model, International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-8 Issue-10, August 2019. 3. Tarana Afrin Chandel et al, Performance of Partially Shaded Solar Photovoltaic System, International Journal of Recent Technology and Engineering, Volume-7 Issue-6, March 2019 4. samlex solar RV power products, https://www.samlexsolar.com/learning-center/solar-cell-module-array. aspx 132. 5. Tarek Selmi et al, Analysis and Investigation of a Two-Diode Solar Cell Using MATLAB/Simulink, International Journal Of Renewable Energy Research, Vol.4, No.1, 2014 6. https://energyeducation.ca/encyclopedia/Photovoltaic_effect 703-706 7. https://en.wikipedia.org/wiki/Charge_controller 8. Science Direct, Stand-Alone Photovoltaic Systems, Photovoltaic System 9. Conversion Lana El Chaar, in Alternative Energy in Power Electronics, 2011,https://www.sciencedirect.com/topics/engineering/stand-alone-p hotovoltaic-systems 10. Tarana Afrin Chandel et al, Oxidation: A dominant source for reduced efficiency of silicon solar photovoltaic modules. Materials today: Proceedings, Journal Elsevier, Volume 27, Part 2, pp: 1092 – 1098, https://doi.org/10.1016/j.matpr.2020.01.473 URL: https://www.sciencedirect.com/science/article/pii/S22147853203057 82 11. https://www.physics-and-radio-electronics.com/electronic-devices-an d-circuits/semiconductor/generation-and- recombination.html 12. Saeed Babkair, Charge Transport Mechanisms and Device Parameters of CdS/CdTe Solar Cells Fabricated by Thermal Evaporation, Journal of King Abdulaziz University-Science 22(1):21-33 · January 2010. 13. Tsai, H.L, Tu, C.S and Su, Y.J. „Development of Generalized Photovoltaic Model Using MATLAB/Simulink‟, Proceedings of the World Congress on Engineering and Computer Science (WCECS ‘08) San Francisco (USA), 2008 14. Nema, S., Nema, R.K and Agnihotri, G. MATLAB/Simulink Based Study of Photovoltaic Cells / Modules / Array and their Experimental Verification, International Journal of Energy and Environment Vol.1, No. 3, PP 487-500, 2010 15. Kumari, S. and Babu,S, Mathematical Modelling and Simulation of Photovoltaic Cell using MATLAB/Simulink Environment, International Journal of Electrical and Computer Engineering (IJECE) Vol. 2, No.1, PP. 26-34, 2012 16. Bikaneria, J., Joshi, S.P and Joshi, A.R., Modelling and simulation of PV cell using one diode model, International Journal of Scientific and research publications, Vol.3, No.10, pp.1-4, 2013 17. Venkateswarlu, G. and Raju, P.S Simcape Module of photovoltaic cell International journal of advanced research in electrical in, electronic and instrumentation engineering Vol. 2, No.5, pp.1766-1772, 2013 18. Physics of solar cells, http://depts.washington.edu/cmditr/modules/opv/physics_of_sola r_cells.html 19. Amardeep Chaudhary, Shriya Gupta, Dhriti Pande, Fazal Mahfooz, Gunjan Varshney, “Effect of partial shading on characteristics of PV panel using Simscape, Int. Journal of Engineering Research and Applications, October 2015, pp.85-89. 20. Tarana Afrin Chandel et al, Performance of Rooftop Grid Connected Solar Photovoltaic System, International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-9 Issue-1, May 2020 21. JA. Ramos-Hernanz et al, Obtaining the characteristics curves of a photocell by different methods, International Conference on Renewable Energies and Power Quality (ICREPQ’13) Bilbao (Spain), 20th to 22th March, 2013 exÇxãtuÄx XÇxÜzç tÇw cÉãxÜ dâtÄ|àç ]ÉâÜÇtÄ (RE&PQJ) ISSN 2172-038 X, No.11, March 2013 22. Kavita Singh, Tarana Afrin Chandel, Saif Ahmad, Effect of Bypass Diode under Partial Shading in SPV Module: A Review, International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-8, Issue-1, May 2019 23. Kavita Singh, Tarana Afrin Chandel, Mohd. Khursheed Siddiqui, Md. Arifuddin Mallick, Effect of Bypass Diode under Partial Shading in SPV Module, International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249-8958, Volume-8 Issue-5, June 2019 24. https://www.pveducation.org/pvcdrom/solar-cell-operation/fill-factor 25. http://depts.washington.edu/cmditr/modules/opv/physics_of_solar_ce lls.html, Utkarsh Kamble, Shubham Paturkar, Pooja Swami, Shivam Malwade, M. S.Dholkawala, Anand Authors: Bhise Paper Title: Design and Analysis of Inclined Belt Conveyor System for Coal Loading for Weight Reduction Abstract: Belt conveyor is used for the transportation of material from one location to another. Belt conveyor has high load carrying capacity, large length of conveying path, simple design, easy maintenance and high reliability of operation. This paper discuss about study of design procedure and analysis of inclined type belt conveyor system for coal loading application.1 The paper shows design calculations of conveyor, trajectory of the material on conveyor, power and belt design and stresses on pulley due to belt tensions at and slack and tight side. The results comprises of capacity, power calculations on pulley, stress analysis on pulley drive shaft, on components of belt conveyor and its effect. The Belt conveyor used for coal processing industry is considered to have a design capacity is 250 TPH and speed of the conveyor to be 115 ft. /min. Geometrical modelling has been done using Catia V5R20 and finite element analysis is done in Solid works 2018. This paper discusses the conveyor design and weight optimization. Material weight reduction is accomplished using ASHBY charts and ASME standards and finally weight optimisation and performance index has been discussed.

Keywords: Inclined Belt Conveyor, Ashby chart, Performance index, Weight Optimization, Analysis, 133. modelling

References: 707-711

1. Ms. Sayali Todkar, PG Student, Prof. Milind Ramgir, Associate Professor, JSPMs RSCOE Tathwade “Design of Belt Conveyor System”, International Journal of Science, Engineering and Technology Research,(IJSETR) Volume 7, Issue 7, July 2018, ISSN: 2278 -7798. 2. “Design and Analysis of Belt Conveyor System of Sugar Industry for Weight Reduction”, JETIR (ISSN-2349-5162) 3. “Design, Analysis and Weight Optimization of Belt Conveyor”, International Journal of Scientific Engineering and Applied Science (IJSEAS) - Volume-1, Issue-5, August 2015 ISSN:2395-3470 4. V.B.Bhandari “Design of Machine Element,” Tata McGraw Hill publishing company, eighth edition (2003). 5. Susmitha “Design of shaft using Concept In design of machine element”. 6. CEMA (Conveyor Equipment Manufacturers Association) “Belt Conveyors for Bulk Materials”, Chaners Publishing Company, Inc. 7. Konakalla Naga Sri Ananth , Vaitla Rakesh , Pothamsetty Kasi Visweswarao,” Design And Selecting the Proper Conveyor-Belt” International Journal of Advanced Engineering Technology 8. Lubos Kudelas, “Energy Requirements and Optimization the Transport Route of Belt Conveyors”,The International Journal of Transport & Logistics 9. Miss S. S. Vanamane and Dr. K. H. Inamdar, “Design of Belt Conveyor System used for Cooling of Mould”, International Conference on Sunrise Technologies. Authors: Ram Jatan Yadav, Lakshay Saini, Devashish, Rishabh Tomar, Vipul Rana

Paper Title: Domestic Solar Panel Cleaning System and effect of Enviranmental Dust in PV Modules Abstract: In this research paper, various studies revolving around how dirt and dust affect the performance of solar panels depending upon different regions, as different areas have different soil compositions and how they are different from one another. A new way of approach to the domestic solar panel cleaning system, researchers have proposed many ways to improve and classify the object and present it in an image in the past. However, there have few projects related to domestic cleaning of solar panels. Due to the inconsistencies in cleaning especially in region where rain is not the most convenient option for cleaning. On continuous using of solar panels, a layer of accumulated dust particles is settled on the surface of solar panels or PV panels which affect the result of decreasing in efficiency by 50 %. By cleaning on regular intervals it decreases this soil loss. Various 134. data have been collected which shows the importance of domestic solar panel cleaning for future generation.

Keywords: Solar panels, Efficiency, 3D printer, Cleaning, Soil loss, Dust accumulation. 712-715

References: 1. Arvind chhabra , India today- India's first solar power plant opens in Punjab : December 15,2009. 2. Pandey S, Singh VS. "Determinants of success for promoting solar energy in India," Renewable Sustainable Energy Review, 16, pp. 3593–98, 2012. 3. Silicon in Soils and Plants, Brenda Servaz Tubaña and Joseph Raymond Heckman K. Elissa, © Springer International Publishing Switzerland 2015 7 F.A. Rodrigues, L.E. Datnoff (eds.), Silicon and Plant Diseases, DOI 10.1007/978-3-319-22930-0_2. 4. Design and fabrication of Automatic Solar Panel Cleaning System , International Journal of Innovative Research in Science, Engineering and Technology, Vol. 8, Issue 3, March 2019. 5. Aslan Gholami, Ali Akbar Alemrajabi, Ahmad Saboonchi, “Experimental study of self-cleaning property of titanium dioxide and Nanospray coatings in solar applications” paper published in sciencedirect.com, journal 2017. 6. Influence of Environmental Dust on the Operating Characteristics of Solar PV Module in Tripura [ISSN:2319-6890),2347-5013 Volume No.4, Issue No.3, pp : 141 - 144 01 March. 2015] 7. F. Mejia, J. Kleissl & J. L. Bosch, 2013. The Effect Of Dust On Solar Photovoltaic Systems, Energy Procedia 49 (2014), pp. 2370 – 2376. Authors: Kehdinga George Fomunyam

Paper Title: Engineering Education and the Drive for Social Justice in Africa Abstract: Engineering social justice education (ESJ) is an emerging core subject in engineering education (EE)and profession. However, several EE institutions are yet to incorporate social justice (SJ) into engineering courses, leading to strong advocacy for EE review of programmes. This paradigm shift is align with ESJ revised curricula to increase the power of engineering knowledge integrated with SJ, which explicitly harnessed in serving vulnerable society, thereby addressing injustices and inequalities; hence the crux of this paper. This paper was guided by Nancy Fraser’s theory of SJ that elucidates that a more equitable distribution of resources is interrelated with equal recognition of different identities/groups within a society. This theory looks at how individuals are prevented from participating as equals by denying them of available resources to do so. This paper takes a broad look at the impact of integrating SJ in EE in Africa, while examining the extent EE has addressed numerous inequalities and, exploring how engineering practitioners can work towards a more just and equitable society. The significance of SJ in EE in the 21st century were discussed among others. Thus, to address social justice in EE, collaboration amongst educational sector and engineering industrialists are central in building and revising EE curriculum inclusive of SJ themes to consolidate engineering professional ethics. This will transform the way educators think about ESJ through creating or converting existing core curriculum courses to attract, retain, and motivate engineering students to become professionals to enact SJ in engineering field.

Keywords: equitable distribution, Fraser’s theory, inequalities, professional, social justice

References: 1. Accreditation Board for Engineering and Technology (ABET), (2010). Annual report. Accessed on June 10th, 2020 from http://www.abet.org/uploadedFiles/Publications /Annual_Report /abet-2010-annual-report.pdf. 2. Accreditation Board for Engineering and Technology (ABET), (2012). Criteria for evaluating engineering programs, 2012– 2013. Accessed on June 12th, 2020 from http://www.abet .org/engine 3. Adam, T. 2020. Between Social Justice and Decolonisation: Exploring South African MOOC designers’ conceptualizations and approaches to addressing injustices. Journal of Interactive Media in Education, 2020(1). 4. Anderson, B., Hartman, C. and Knijn, T. (2017). Report on the Conceptualization and Articulation of Justice: Justice in Social Theory. Available at https://www.ethos-europe.eu/publications Accessed 27 September, 2018. 5. Anikina Z (Ed.), (2020). Integrating Engineering Education and Humanities for Global Intercultural Perspectives. Proceedings of the Conference “Integrating Engineering Education and Humanities for Global Intercultural Perspectives”, 25-27 March 2020, St. 135. Petersburg, Russia. Springer Nature Switzerland AG, 2020. 6. Barry B. (2005). Why social justice matters . Cambridge/Malden: Polity. 7. Bielefeldt AR, Polmear M, Knight D, Swan C, Canney N. (2017). Disciplinary variations in ethic and societal impacts education practices and sufficiency perceptions among engineering and computing educators. Science and Engineering Ethics. In review. 716-722 Submitted Nov. 21; 2017. 8. Boni A, Perez-Foguet A. (2008). Introducing development education in technical universities: Successful experiences in Spain. European Journal of Engineering Education;33 (3):343-354. 9. Brooks, T. (2008). The global justice reader . Oxford: Blackwell Pub. 10. Brown A, Wisby E. (eds.), (2020). Knowledge, Policy and Practice in Education and the Struggle for Social Justice: Essays Inspired by the Work of Geoff Whitty. London: UCL. Press. 11. Catalano G, Baillie C, Riley D, Nieusma D, Byrne C, Bailey M, Maralampides K. (2010). Integrating social justice ideas into a numerical methods course in bioengineering. In: Proceedings of the American Society for Engineering Education (ASEE) Annual Conference & Exposition; 20-23 June 2010. Louisville KY: ASEE; 2010. p. 7. 12. Cech EA, Waidzunas TJ. (2011). Navigating the heteronormativity of engineering: The experiences of lesbian, gay, and bisexual students. Engineering Studies; 3 (1): 1–24. 13. Chasmar J, Wichman I. (2017). ‘Social Justice Warriors’ are Destroying Engineering. The Washington Times. Accessed on June 11th from https://www.washingtontimes.com /news /2017/a ug/9/indrek-wichman-msu-professor-says-social-justice. 14. Fraser N. 1995. “From Redistribution to Recognition? Dilemmas of Justice in a ‘Post-Socialist’ Age.” New Left Review 212 (July/August 1995): 68–93. 15. Fraser N. 1996. “Social Justice in the Age of Identity Politics: Redistribution, Recognition and Participation.” The Tanner Lectures on Human Values, Stanford University, April 30 – June 2. http://tannerlectures.utah.edu/_documents/a-to- z/f/Fraser98.pdf. 16. Fraser N. (1997). Justice interruptus: Critical reflections on the ‘post-socialist’ condition. New York, NY: Routledge. P. 14 17. Fraser N. (2000). “Rethinking Recognition.” New Left Review 3, May–June 2000: 107–120. 18. Fraser N (2003). Axel Honneth. redistribution or recognition? A political-philosophical exchange (p.36). In J. Golb., J. Ingram, & C. Wilke (Trans.). Verso. 19. Fraser N. (2007). Feminist politics in the age of recognition: A two-dimensional approach to gender justice. Studies in Social Justice, 1(1), 23–35. 20. Fraser N. (2008). Scales of justice reimagining political space in a globalizing world. Polity Press. 21. Fraser N. (2008a). Rethinking recognition: Overcoming displacement and reification in cultural politics. In K. Olson (Ed.), Adding insult to injury: Nancy Fraser debates her critics (pp. 129–141). London: Verso. 22. Fraser N. (2008b). Reframing justice in a globalizing world. In K. Olson (Ed.), Adding insult to injury: Nancy Fraser debates her critics (pp. 273–294). London: Verso. 23. Herkert J. (2011). Yogi meets Moses: Ethics, progress, and the grand challenges for engineering. In Proceedings of the 2011 American Society for Engineering Education Annual Conference and Exposition . Washington, DC: ASEE. 24. Kabo J, Tang X, Nieusma D, Currie J, Hu W, Baillie C. (2012). Visions of social competence: Comparing engineering education accreditation in Australia, China, Sweden, and the United States. In Proceedings of the 2012 American Society for Engineering Education Annual Conference and Exposition . Washington, DC: ASEE. 25. Keddie, A. 2012. Schooling and social justice through the lenses of Nancy Fraser. Critical Studies in Education, 53(3): 263–279. 26. Lambert S, Czerniewicz L. (2020). Approaches to Open Education and Social Justice Research. Journal of Interactive Media in Education; (1): 1. 27. Leef G. (2017). Social Justice has Invaded Engineering. National Review Online. August 2nd, 2017. Accessed on 10th June, 2020 from http://www.nationalreview.com/corner/ 450075/martin-center-articl esocial-justice-engineering. 28. Leibowitz, B and Bozalek, V. 2016. The scholarship of teaching and learning from a social justice perspective. Teaching in Higher Education, 21(2): 109–122. 29. Leydens JA, Lucena JC. 2018. Engineering justice: transforming engineering education and practice. IEEE PCS Professional Engineering Communication Series, IEEE Press WILEY. Published by John Wiley & Sons, Inc. Hoboken, New Jersey. Pp. 223. 30. Lucena J, Schneider J, Leydens JA. (2010). Engineering and sustainable community development. San Rafael: Morgan & Claypool Publishers. 31. McLoughlin L. (2012). Community colleges, engineering, and social justice. In Engineering and social justice: University and beyond. West Lafayette: Purdue University Press. 32. Mladenov T. (2016) Disability and social justice. Disability & Society; 31:9: 1226-124. 33. Nasser RN, Romanowski M. (2016): Social justice and the Engineering Profession: challenging engineering education to move beyond the technical. In book: Advances in Engineering Education in the Middle East and North Africa: Current Status, and Future Insights. Chapter 17. Publisher: Springer. Editors: Abdulwahed, Mahmoud, Hasna, Mazen O., Froyd, Jeffrey E. 34. National Academy of Engineering (NAE), (2004). The engineer of 2020: Visions of engineering in the new century . Washington, DC: The National Academies Press. 35. National Academy of Engineers (NAE). (2008). Grand challenges for engineering . Washington, DC: The National Academies. 36. Nieusma D, Riley D. (2010). Designs on development: Engineering, globalization, and social justice. Engineering Studies; 2 (1): 29–59. 37. Riley D. (2008). Engineering and Social Justice. San Rafael, CA: Morgan & Claypool; p. 164. 38. Riley D. (2012). Engineering thermodynamics and 21st century energy problems: A textbook Sandekian R, Chinowsky P, Amadei B. (2014). Engineering for developing communities at the University of Colorado Boulder: A ten year retrospective. International Journal for Service Learning in Engineering. 2014: Fall Special Edition: 62-77. 39. Sandekian R, Chinowsky P, Amadei B. Engineering for developing communities at the University of Colorado Boulder: A ten year retrospective. International Journal for Service Learning in Engineering. 2014: fall special edition:62-77. 40. Shutaleva AV, Kartasheva AA. (2018). Humanities Education as way of forming social responsibility of engineer. In: The European Proceedings of Social & Behavioural Sciences; vol. 50, Future Academy, London. Pp. 1088-1097. 41. Teschler L. (2010). 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Paper Title: Identification of Road Accident by using Black Spot Method Between Panthaghati to Dhalli Road Abstract: In India road accidents are very serious problem because of large population and high traffic density of vehicles. Most of the road accidents occur mainly due to the negligence of driver and poor infrastructure only a few accidents occur due to the technical error of vehicles. The main purpose of this research paper is prevention of road traffic accidents and improvement of road safety in Shimla. Road safety is very important aspect of today’s life, so it is important that everybody should aware about road safety. To do this study a section of 12km length is chosen between Panthaghati to Dhalli in district Shimla on NH 5 where accidents black spots are identified for the section by analyzing secondary data used to prevent road accidents. In this study primary data is used for observing the road conditions and secondary data is used to find accidents black spot. Black Spot is a point or a place on the road where road accident occurs repeatedly one after another which is known as accident black spot. To identify these black spots we use weighted severity index (WSI) method. It is one the most reliable and effective method for determining the most proven accidents black spots. Shimla is a hilly area and it has narrow roads, blind curve and black spots which increase the chances of road traffic accidents. In past recent years road traffic accidents are increasing in Shimla and this study deals with identification of major 136. issues causing road traffic accidents. This research paper helps to improve the road safety in Shimla because in this study the analysis has been done to identify the major problems responsible for gradually increasing road 723-728 accidents. This research paper is also used in future research paper as reference purpose and it will also provide an overview to other researchers who want do their research on similar kind of topics.

Keywords: Economy, Road accident, Black Spots and Road Safety Secondary Data, Mitigation.

References: 1. “Road Accidents in India 2018” (2019). Government of India Ministry of Road Transport and Highway Transport Research Wing, New Delhi, September 2019 2. Road safety annual report 2019 by international transport form (ITF) 3. Global Status Report on Road Safety 2018: summary. Geneva: World Health. 4. The Newspaper publication Times of India and The Tribune 5. Google map and Google earth applications are used to showing the picture of study area and black spot areas. 6. Road Accident data source Dhalli Police Chowaki and Chotta Shimla East Police Station. 7. S. Tanwer and S.Dass “Identification of accidents black spot on NH 65” published by International Research Journal of Engineering and Technology. Vol. 4, Issue 2, Feb. (2017). 8. M. Mishra, A. Mahajan and J. Singh.“Technical analysis of accidents on NH-22 in Hilly terrain: Causes and Remedies”. Published by journal of Civil Engineering and Environmental Technology. Vol. 1, Number 2: 2014, pp. 23 -27. P.M. Shivakumaraswamy, S.T. Veerabhadrappa, Priyanka D, Srinidhi S K, Sonia K, Anika Anjani Authors: Gowda 137. Paper Title: Non – Invasive Haemoglobin Measurement Abstract: Haemoglobin is one of the main constituents in characterizing the physiological condition of a human 729-733 body. Currently, invasive techniques which are being used, are not suitable for real-time continuous monitoring and also include delay. On the other hand, the non-invasive method of haemoglobin measurement ensures painless and continuous real-time monitoring. This paper discusses the method and technique involved in designing a prototype for the non-invasive measurement of haemoglobin.

Keywords: Anaemia, Haemoglobin, Non-invasive, Photo-plethysmography,Polycythaemia, Spectrophotometry.

References:

1. Kumar, G. V. P., Jagadeesh, B., & Ravi, T. (2019, October). IoT Based Dual-wavelength Non-Invasive Haemoglobin Sensor System. In IOP Conference Series: Materials Science and Engineering (Vol. 590, No. 1, p. 012042). IOP Publishing. https://iopscience.iop.org/article/10.1088/1757-899X/590/1/012042/meta 2. Bhatia, K., & Singh, M. (2019). Towards development of portable instantaneous smart optical device for hemoglobin detection non invasively. Health and Technology, 9(1), 17-23. https://link.springer.com/article/10.1007/s12553-018-0247-1 3. Joseph, B., Haider, A., & Rhee, P. (2016). Non-invasive hemoglobin monitoring. International Journal of Surgery, 33, 254-257. https://www.sciencedirect.com/science/article/pii/S1743919115013667 4. Ismail, R., & Rahim, H. A. (2016, March). Near-infrared spectroscopy (NIRS) applications in medical: non-invasive and invasive leukemia screening. In 2016 IEEE 12th International Colloquium on Signal Processing & Its Applications (CSPA) (pp. 305-310). IEEE. https://ieeexplore.ieee.org/abstract/document/7515851 5. Galvagno, S. M., Hu, P., Yang, S., Gao, C., Hanna, D., Shackelford, S., & Mackenzie, C. (2015). Accuracy of continuous noninvasive hemoglobin monitoring for the prediction of blood transfusions in trauma patients. Journal of clinical monitoring and computing, 29(6), 815-821. https://link.springer.com/article/10.1007/s10877-015-9671-1 6. Bhatia, K., & Singh, M. (2015). Non-Invasive techniques for detection of haemoglobin in blood: a review. Int J Sci Eng Technol Res (IJSETR), 4(6), 1946-9. 7. Chugh, S., & Kaur, J. (2015, November). Non-invasive hemoglobin monitoring device. In 2015 International Conference on Control Communication & Computing India (ICCC) (pp. 380-383). IEEE. https://www.researchgate.net/profile/Soumil_Chugh3/publication/284673164_Non_Invasive_Hemoglobin_Monitoring_Device/links/56 79210708aeaf87ed8afa6d/Non-Invasive-Hemoglobin-Monitoring-Device.pdf 8. Chaudhary, R., Dubey, A., & Sonker, A. (2017). Techniques used for the screening of hemoglobin levels in blood donors: current insights and future directions. Journal of blood medicine, 8, 75. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5503668/ 9. Priti V Bhagat, Rohit Singhal "A Review Paper on Non-Invasive Methods for Determination of Anemia" Published in International Journal of Trend in Research and Development (IJTRD), ISSN: 2394-9333, Volume-5 | Issue-2 , April 2018. 10. Jeon K. J., Kim S. J., Park K. K., Kim J. W. & Yoon G. (2002). Non-invasive total haemoglobin measurement. Journal of biomedical optics, 7(1), 45-51. Authors: Prashant G. Mungase, Omkar S. Phand, Akash R. Jagtap, Bhavna A. Shelar, Mahesh P. Joshi Experimental Investigation of CI Engine Fuelled With Waste Agricultural Biodiesel at Higher Paper Title: Compression Ratios Abstract: In this study, the performance, combustion and emissions characteristics of compression ignition engine were calculated and analysed using a waste agricultural biodiesel . The tests were performed at steady state conditions for a four-stroke single cylinder diesel engine loaded at engine speed of 1500 rpm. The present experimental investigation evaluates the effects of using BD20 blend of biodiesel. During experimental testing of CI engine using biofuel blend, the engine was maintained at various compression ratio i.e., 18, 19 and 20 respectively. Engine load is varied from zero to full load condition. Design of experiment is done with Taguchi method. The main objective is to check the optimum compression ratio and to obtain minimum specific fuel consumption, better efficiency and lesser emission with higher compression ratio. Results shows that Brake thermal efficiency and cylinder pressure of CI engine increases with increase in compression ratio and load. Specific fuel consumption, emission of hydrocarbon and carbon monoxide decreases as we increase compression ratio. Nitrogen oxide follows the reverse trend and found to be increased as we increase compression ratio and load on engine. The analysis shows optimum performance with lower emission at a CR of 20 and load 100%.

Keywords: Biodiesel, compression ratio, emission, load, Taguchi.

138. References: 734-741 1. Wail M. Adaileh and Khaled S. AlQdah (2012) ‘Performance of Diesel Engine Fuelled by a Biodiesel Extracted from A Waste Cocking Oil’, Energy Procedia, Vol.18, pp.1317 – 1334. 2. Goutam Pohit and Dipten Misra (2013) ‘Optimization of Performance and Emission Characteristics of Diesel Engine with Biodiesel Using Grey-Taguchi Method’, Hindawi Publishing Corporation Journal of Engineering, Vol.2013, Article ID 915357. 3. Puneet Verma and Varinder Mohan Singh (2014) ‘Assessment of Diesel Engine performance using Cotton Seed Biodiesel’, Integrated Research Advances, Vol.1 No.1, pp.1-4. 4. Milind A. Pelagade, Madhavi S. Harne and Dr. Ramakant Shrivastava (2017) ‘Performance Evaluation of CI Engine Using Cottonseed Oil as an Alternate Fuel’, IJSRST, Vol.3 No.6, pp.288-293. 5. G. Antony Miraculas , N. Bose and R. Edwin Raj (2015) ‘Optimization of Biofuel Blends and Compression Ratio of a Diesel Engine Fuelled with Calophyllum inophyllum Oil Methyl Ester’, Arab Jr. Sci., DOI 10.1007/s13369-015-1942-0. 6. L. Yüksek, O. Özener and H. Kaleli (2013) ‘Determination of Optimum Compression Ratio: A Tribological Aspect’, Tribology in Industry, Vol. 35 No. 4, pp.270‐275. 7. V. Hariram and R. Vagesh Shangar (2015), ‘Influence of compression ratio on combustion and performance characteristics of direct injection compression ignition engine’, Alexandria Engineering Journal, Vol.54, pp.807–814 8. S. Sivaganesan, M Chandrasekaran and M Ruban (2017) ‘Impact of Various Compression Ratio on the Compression Ignition Engine with Diesel and Jatropha Biodiesel’, IOP Conf. Ser.: Mater. Sci. Eng. 183 012039, DOI :10.1088/1757- 899X/183/1/012039 9. Mr. Sandip S. Shelkar, Prof. Amol B. Varandal and Prof. N. C. Ghuge (2017) ‘Performance Analysis of Diesel Engine using Biodiesel for Variable Compressible Ratio’, IJERT, Vol.5 No.2, ISSN: 2278-0181. 10. V. Gnanamoorthi and G. Devaradjane(2014) ‘Effect of Compression Ratio on the Performance, Combustion and Emission of DI Diesel Engine Fuelled with Ethanol – Diesel Blend’, Energy Institute, Vol.88, pp.19-26. 11. Satendra Singh, Ashish Nayyar, Rahul Goyal and Mahesh Saini (2019), ‘Experimentally Optimization of a Variable Compression Ratio Engine Performance Using Different Blends of Cotton Seed with Diesel Fuel at Different Compression Ratio’, IJMERR, Vol. 8, No. 1, pp.121-128 12. H.Raheman and S.V. Ghadge (2008) 'Performance of diesel engine with biodiesel at varying compression ratio and ignition timing', Elsevier, pp.2661-2664 13. Chandramaulisinh A. Parmar, Dr. Tushar M Patel, Pragna R Patel and Gaurav P Rathod (2017) 'Parametric Optimization of Variable Compression Ratio Diesel Engine Fueled with Palm Seed Biodiesel and its Blend using the Taguchi Method for SFC', IOSR-JMCE, pp.109-114 Authors: Rawan Alharbi, Haya Almagwashi An Exploration of the Wearable Internet of Things (WIoT) Users‟ Privacy Concerns in Healthcare Paper Title: Domain Abstract: Internet of things (IoT) is earning a significant role in the health care domain. Though the growing benefits to improve the health process and services and the large use of the Wearable Internet of Things (WIoT) devices, the patient’s privacy issue remains a big concern. While IoT devices and its applications are more exposed to privacy risks, there is a need for a stick and strict guidelines and solutions to assist and solve this issue and minimize these risks. The aim of this paper is to survey the end-user concerns of the privacy issues related to WIoT then we review conducted on current solutions that are worked toward preserving privacy in the healthcare domain, and finally, we state our solution. This paper aims to survey end users’ privacy and security concerns and issues related to WIoT.

Keywords: Internet of Things (IoT); preserving-privacy; Wearable IoT; Privacy; differential privacy

References:

1. R. Farzad Kamrani, Mikael Wedlin, “Internet of Things: Security and Privacy Issues,” 2017. 2. M. B. Yassein, M. Q. Shatnawi, D. Al-zoubi, and 2016 International Conference on Engineering & M I S (ICEMIS), “Application layer protocols for the Internet of Things: A survey,” pp. 1–4, 2016. 3. S. Kumar, “Technological and business perspective of wearable technology.,” 2017. 4. C. Maple, “Security and privacy in the internet of things,” J. Cyber Policy J. Cyber Policy, vol. 2, no. 2, pp. 155–184, 2017. 5. O. Arias, J. Wurm, K. Hoang, and Y. Jin, “Privacy and Security in Internet of Things and Wearable Devices,” IEEE Trans. Multi- Scale Comp. Syst. IEEE Trans. Multi-Scale Comput. Syst., vol. 1, no. 2, pp. 99–109, 2015. 139. 6. M. UGUR, Naciye. BARUTCU, “A Critical analysis on Internet of Things: Features and vulnerabilities. Sakarya University.,” 2018. 7. Z. Guan, Z. Lv, X. Du, L. Wu, M. Guizani, "Achieving data utility-privacy tradeoff in Internet of medical things: A machine 742-749 learning approach", Future Gener. Comput. Syst., vol. 98, pp. 60-68, Sep. 2019. 8. Sagirlar, G., Carminati, B., & Ferrari, E. (2018). Decentralizing privacy enforcement for Internet of Things smart objects. Computer Networks, 143, 112–125. doi:10.1016/j.comnet.2018.07.019 9. Aivaloglou, E., Gritzalis, S., & Skianis, C. Requirements and Challenges in the Design of Privacy-aware Sensor Networks. Retrieved from http://ieeexplore.ieee.org/document/4150864/. 2006. 10. Al-mawee, W. Privacy and Security Issues in IoT Healthcare Applications for the Disabled Users a Survey. Retrieved from http://scholarworks.wmich.edu/cgi/viewcontent.cgi?article=1661&context=masters_theses . 2012 11. Ruback, T. (2015). Understanding the Differences Between Privacy and Security Online. Retrieved from https://www.ghostery.com/intelligence/business-blog/privacy/understanding-thedifferences-between-privacy-and-/ 12. Comparative Assessment on Privacy Preservation in Health Care Sectors coupled with IoT 13. Dwivedi, A.D.; Srivastava, G.; Dhar, S.; Singh, R. A Decentralized Privacy-Preserving Healthcare Blockchain for IoT. Sensors 2019, 19, 326. 14. E. Luo et al., "PrivacyProtector: Privacy-protected patient data collection in IoT-based healthcare systems", IEEE Commun. Mag., vol. 56, no. 2, pp. 163-168, Feb. 2018. 15. Hsu, C. L., Lee, M. R., and Su, C. H., The role of privacy protection in healthcare information systems adoption. J Med Sys 37(5):9966, 2013. doi:10.1007/s10916-013-9966-z. 16. Logrippo, Luigi. Abdelouadoud, Stambouli. “Configuring Data Flows in the Internet of Things for Security and Privacy Requirements”. 2019. DOI: 10.1007/978-3-030-18419-3_8 17. Alsamiri1, Jadel. Alsubhi, Khaled. Internet of Things Cyber Attacks Detection using Machine Learning. International Journal of Advanced Computer Science and Applications, Vol. 10, No. 12, 2019 18. Zheng, Mengyao. Jiang, Linshan. Challenges of Privacy-Preserving Machine Learning in IoT. 2019. 19. M. Du, K. Wang, Y. Chen, X. Wang and Y. Sun, "Big Data Privacy Preserving in Multi-Access Edge Computing for Heterogeneous Internet of Things," in IEEE Communications Magazine, vol. 56, no. 8, pp. 62-67, August 2018. 20. Homas W. Edgar, David O. Manz, in Research Methods for Cyber Security, 2017. 21. Alharbi, Rawan. Almagwashi, Haya. The Privacy requirments for wearable IoT devices in healthcare domain. 2019 7th International Conference on Future Internet of Things and Cloud Workshops (FiCloudW). 2019 Authors: T.Chellatamilan, B.Valarmathi, K.Santhi

Paper Title: Research Trends on Deep Transformation Neural Models for Text Analysis in NLP Applications Abstract: In the recent few years, text analyses with neural models have become more popular due its versatile usages in different software applications. In order to improve the performance of text analytics, there is a huge collection of methods that have been identified and justified by the researchers. Most of these techniques have been efficiently used for text categorization, text generation, text summarization, query formulation, query 140. answering, sentiment analysis and etc. In this review paper, we consolidate a recent literature along with the technical survey on different neural models such as Neural Language Model (NLM), sequence to sequence 750-758 model (seq2seq), text generation, Bidirectional Encoder Representations from Transformers (BERT), machine translation model (MT), transformation model, attention model from the perception of applying deep machine learning algorithms for text analysis. Applied extensive experiments were conducted on the deep learning model such as Recurrent Neural Network (RNN) / Long Short-Term Memory (LSTM) / Convolutional Neural Network (CNN) and Attentive Transformation model to examine the efficacy of different neural models with the implementation using tensor flow and keras.

Keywords: BERT, RNN, CNN, Language model, seq2seq, text summarization, text generation, text mining.

References:

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Chellatamilan, and L. J. Deborah, “Word based language model using long short term memory for disabilities,” Int. J. Psychosoc. Rehabil., vol. 24, no. 6, pp. 6509–6513, 2020. 63. B. Valarmathi, K. Santhi, R. Chandrika, P. Goel, and B. Bagwe, “Performance analysis of genetic algorithm, particle swarm optimization and ant colony optimization for solving the travelling salesman problem,” Int. J. Recent Technol. Eng., vol. 8, no. 2 Special Issue 4, pp. 91–95, 2019. Authors: S. Kavitha, B. Mathivanan Consumer Perception: Factors Influencing E-Banking Services Towards Private Sector Banks In Paper Title: Tiruchirappalli District Abstract: In olden days customers were transacting money through traditional banking. Traditional banking is the process of handling with common bank transactions like making withdrawals and deposits in a bank that maintains a physical location. In traditional banking system, the customer can contact the staff for their transactions. These include opening a new account, check deposit, withdrawing funds and applying for a loan etc. The main feature of traditional banking is the customer can ask their queries and clarify their doubts immediately to the staff who is working in the bank. After a big boon in the banking industry, banks realized that the rising popularity of the World Wide Web involves consumers using the Internet to access their bank account and to undertake the banking transactions. From the bank’s perspective, the online banking platform provides low-cost channel for both transactions and building relationships.

141. Keywords: (Easy Access, Convenience, Quick transfer, Money saving)

References: 759-765

1. MamilaRajasekar., CaddapahAnit&NamaMadhavi (2015).“Impact of service quality of SBIs Internet Banking and its Customers Rural India”, .International conference on advances in Engineering Sciences & Applied Mathematics, March 23, 2015, London: PP.106-111. 2. S.Arunkumar (2016).“A study on attitude and intention towards Internet Banking with reference to Malaysian consumers in Klang valley region”.The International Journal of Applied Management and Technology, Vol.16, No.1, PP.1-32. 3. GarimaSrivastav&Arun Mittal (2016).“Impact of Internet Banking on consumer satisfaction in private and public sectors banks”, Indian Journal of Marketing, February 2016, ISSN 0973-8703, Vol.46, issue.2, PP.36-49. 4. R.D. Priyangika., M.S. Perera&D.P.Rajapakshe (2016).“An Empirical Investigation on Customer Attitude and Intention towards Internet Banking: A Case of Licensed Commercial banks in Colombo District, Srilanka”.International Conference on Business Management, PP.718-732. 5. www.google scholar.com 6. www.researchgate.com 7. www.elsivier.com Authors: Suyamburaja Arulselvan, G..Puvaneswari 142. Effect of Embedding Reinforcement on Brick Works Built by Three Different Types Bricks and Two Paper Title: Different Mortar Ratios Abstract: Experimental investigations have been conducted on brick prism specimens to study its performance with the presence of reinforcement. Brick prisms were constructed using red bricks, fly ash bricks and concrete bricks with and without embedding steel reinforcement. Cement mortar with 1:5 and 1: 6 mixes have been used to build prisms. Concrete bricks of same sizes were casted in the lab and used after proper curing. Brick prisms were subjected to compressive force by Universal Testing Machine. Compressive strength of different types of brick prisms were compared and plotted. Compressive strengths were improved by embedding steel reinforcement in the brick works. Reinforced Concrete brick prisms contributed higher strength. Reinforced fly ash brick prism contributed higher compressive strength than the red brick. By embedding reinforcements in the brick works, load carrying capacity and stability of brick works have been improved. Due to ductile properties of steel reinforcement, steel embedding brick works led to ductile and reduce brittle cracks. Overall performance of brick works improved by embedding steel reinforcement.

Keywords: Red bricks, fly ash bricks, concrete bricks, normal brick prisms, reinforced brick prisms, compressive strength. 766-770

References:

1. Amin Al-Fakih, Bashar S M , Md Shahir L., “Behavior of the Dry Bed Joint in the Mortarless Interlocking Masonry System: an Overview. Civil Eng Res J. 2018; 4(3): 555639.. DOI: 10.19080/ CERJ. 2018.04.555639 2. Anna Brignola, Sara Frumento, Sergio Lagomarsino, Stefano Podestà, “Identification of Shear Parameters of Masonry Panels Through the In-Situ Diagonal Compression Test”, International Journal of Architectural Heritage 3(1):52-73, 2008, DOI.10.1080/ 15583050802138634 3. 3. Antonio Borri, Giulio Castori, Marco Corradi, Emanuela Speranzini, “Shear behavior of unreinforced and reinforced masonry panels subjected to in situ diagonal compression tests”, Construction and Building Materials 25 (12):4403-4414, 2011, DOI: 10.1016/ j.conbuildmat. 2011.01.009 4. Antonio Borri, Marco Corradi, Romina Sisti, Cinzia Buratti, Elisa Belloni, Elisa Moretti, “Masonry wall panels retrofitted with thermal-insulating GFRP-reinforced jacketing”, Materials and Structures, October 2016, Volume 49, Issue 10, pp 3957–3968, DOI: 10.1617/s11527-015-0766-4 5. Ayed HB, Limam O, Aidi M, Jelidi A (2016) Experimental and numerical study of Interlocking Stabilized Earth Blocks mechanical behavior. J Build Eng 7: 207-216. 6. Başak ZENGİN1 , Ali KOÇAK, “The Effect of the Bricks used in Masonry Aalls on Characteristic Properties”, Sigma J Eng & Nat Sci 35 (4), 2017, 667-677, 7. Dawei Huang, Oriol Pons and Albert Albareda, “Bond Strength Tests under Pure Shear and Tension between Masonry and Sprayed Mortar”, Materials, 13, 2183, 2020, 1-19 8. Gumaste, K.S., Nanjunda Rao, K.S., Venkatarama Reddy, B.V, Jagadish, K.S, “ Strength and elasticity of brick masonry prisms and wallettes under compression”, Materials and Structures, March 2007, Volume 40, Issue 2, pp 241–253, DOI: 10.1617/ s11527-006-9141-9 9. Jasiński R., Drobiec Ł. Study of Autoclaved Aerated Concrete Masonry Walls with Horizontal Reinforcement under Compression and Shear. Procedia Eng. 2016; 161:918–924. doi: 10.1016/ j.proeng. 2016.08.758. 10. Jose M. Adam , Antonio Brencich, Tim G. Hughes, Tony Jefferson, “ Micromodelling of eccentricallyloaded brickwork: Study of masonry wallettes”, Engineering Structures, Volume 3, Issue 5, May 2010, Pages 1244–1251, doi:10.1016/j.engstruct.2009.12.050 11. Kaushik, H., Rai, D., and Jain, S. (2007). "Stress-Strain Characteristics of Clay Brick Masonry under Uniaxial Compression." J. Mater. Civ. Eng., 10.1061/ (ASCE) 0899-1561 (2007)19:9 (728), 728-739. 12. Kunasegaram Sajanthan, Balasingam Balagasan, and Navaratnarajah Sathiparan, “Prediction of Compressive Strength of Stabilized Earth Block Masonry”, Advances in Civil Engineering Volume 2019, Article ID 2072430, 13 pages , doi.org/ 10.1155/ 2019/2072430 13. Marco Corradi, Cristina Tedeschi, Luigia Ada Binda, Antonio Borri, “Experimental evaluation of shear and compression strength of masonry wall before and after reinforcement: Deep repointing”, Construction and Building Materials 22(4):463-472, 2008, DOI: 10.1016/j.conbuildmat.2006.11.021 14. McNary, W. and Abrams, D. (1985). "Mechanics of Masonry in Compression." J. Struct. Eng., 10.1061/ (ASCE) 0733-9445 (1985) 111:4 (857), 857-870. 15. Nassif Nazeer Thaickavil Job Thomas, “Behaviour and strength assessment of masonry prisms”, Case Studies in Construction Materials, Volume 8, 2018, Pages 23-38, doi.org/ 10.1016/ j.cscm.2017.12.007 16. Piyawat Foytong, Maetee Boonpichetvong, Natthapong Areemit, Jaruek Teerawong, “Effect of Brick Types on Compressive Strength of Masonry Prisms”, International Journal of Technology 7(7):1171, 2016, DOI. 10.14716/ijtech.v7i7.4640 17. Radosław Jasiński, “Research on the Influence of Bed Joint Reinforcement on Strength and Deformability of Masonry Shear Walls”, Materials, 12(16): 2543, 2019, doi: 10.3390/ ma12162543 18. Sarangapani, G., Venkatarama Reddy, B., and Jagadish, K. (2005). "Brick-Mortar Bond and Masonry Compressive Strength." J. Mater. Civ. Eng., 10.1061/(ASCE)0899-1561(2005)17:2(229), 229-237. 19. S Valerio Alecci, Mario Fagone, Tommaso Rotunno, Mario De Stefano, “Shear strength of brick masonry walls assembled with different types of mortar”, Construction and Building Materials , 40:1038-1045, March 2013, DOI: 10.1016/j.conbuildmat.2012.11.107 20. Valluzzi M.R., Tinazzi D., Modena C. “Shear behavior of masonry panels strengthened by FRP laminates”, Constr. Build. Master. 2002; 16:409–416. doi: 10.1016/S0950-0618 (02)00043-0 Authors: Shanelle Fernandes, Rushia Fernandes, Jessica Kakkanad

Paper Title: Wireless Gesture Control Wheelchair Abstract: Wheelchairs have been used by patients who suffer from various physical disabilities to help them with locomotion and cater their day to day needs with ease. But there are some cases where the movement of a 143. wheelchair is dependent on another individual as is the case with patients who lack the required arm strength and movement to properly push the wheels forward such as quadriplegics, paraplegics, stroke patients, elders etc. 771-775 Joystick oriented wheelchairs, thought to be a solution to those kinds of patients, can pose different problems as it requires basic shoulder movement. It is not always possible for the aforementioned types of patients. In addition, our solution doesn't have the positional constraints that a joystick wheelchair might have as it is wireless and can be worn on either hand which allows the patient to sit in their preferred position for minimum discomfort. This project is an attempt to help the disabled move around independently. Thus, in this research work, we present a prototype of a wireless gesture-based wheelchair which can be controlled via hand gestures. The framework consists of a transmitter and a receiver that communicate with each other wirelessly. For wireless transmission, 433Mhz RF Transmitter and Receiver Unit has been used as it transmits data through an antenna at the speed of 1Kbps - 10Kbps and the range can be adjusted as required. The transmitter unit consists of an Arduino LilyPad microcontroller and an accelerometer that has been attached to a hand glove. The accelerometer sensor has been used to register the position of the hand while creating a gesture. This glove is supposed to be worn by the patient allowing them to move their hand conveniently, sending signals to the receiver unit connected to the wheelchair leading to the movement of the wheels in the desired direction. The receiver unit consist of motor drivers that convert the voltage as needed by the wheels. This paper presents an alternative to the commercial wheelchairs as it is cost effective, easy to control and efficient. The working and assembly of the system has been explained in the paper.

Keywords: Accelerometer, Gesture Recognition, Handicapped Assistance, Radio-Frequency Transceiver, Wheelchair

References:

1. Ababneh, M., et al. “Gesture Controlled Mobile Robotic Arm for Elderly and Wheelchair People Assistance Using Kinect Sensor.” 2018 15th International Multi-Conference on Systems, Signals & Devices (SSD). IEEE, 2018 2. Megalingam, Rajesh Kannan, et al. “Wireless gesture controlled wheelchair.” 2017 4th International Conference on Advanced Computing and Communication Systems (ICACCS). IEEE, 2017. 3. Khadilkar, Shraddha Uddhav and Narendra Wagdarikar “Android phone voice, gesture and touch screen operated smart wheelchair.” 2015 International Conference on Pervasive Computing (ICPC). IEEE, 2015. 4. Gao, Xiang, Lei Shi, and Qiang Wang. “The design of robotic wheelchair control system based on hand gesture control for the disabled.” 2017 International Conference on Robotics and Automation Sciences (ICRAS). IEEE, 2017. 5. Jain, Yash M., Saurabh S. Labde and Sunil Karamchandani. “Gesture controlled wheelchair for quadriplegic children.” 2016 3rd International Conference on Systems and Informatics (ICSAI). IEEE, 2016. 6. Megalingam, Rajesh Kannan, et al. “IR sensor-based gesture control wheelchair for stroke and SCI patients.” IEEE Sensors Journal 16.17 (2016): 6755-6765. 7. Lu Tao “A motion control method of intelligent wheelchair based on hand gesture recognition.” 2013 IEEE 8th Conference on Industrial Electronics and Applications (ICIEA). IEEE, 2013. Authors: Mohanraj A, Karthiga N, Durgadevi S, Dhivyabharathi S, Senthilkumar V

Paper Title: Design and Implementation of ANN Based SCC with GGBS using Auromix 400 Abstract: Concrete is the main building material. When concrete becomes hard, it gives strength to the structure. Many times it is a difficult task to pour the concrete into the formwork and compact it perfectly. This has been overcome by using Self-Compacting Concrete (SCC). Such a concrete is one of the advanced building materials in the field of construction industry. Unlike the other type of concrete, this kind of concrete compact’s effectively under its own weight. There is no need of any external vibration or compaction procedure to minimal the concrete in formwork. It can easily flow in every corners of the formwork without blocking. This project deals with SCC in which, the binary material used is Ground Granulated Blast Furnace Slag (GGBS) as mineral admixture at various percentage of replacement. To reduce the measure of water used in concrete, Auromix-400 is used as Super Plasticizer at a constant dosage. Several tests were carried out to study the behavior of fresh and hardened concrete. Test for fresh concrete includes slump flow, V Funnel test. Similarly, the properties of concrete were also determined by conducting compression and Spit tensile test. At the same time the simulation model was also developed to test the proposed system using the artificial neural network (ANN) protocol. The ANN model is built on six objects with multiple output-multiple. Single Output Type - In the second method, the artificial neural network model is a single input neural network that is built on top of multiple inputs - where multiple inputs has been predicted separately based on various types of neural function - Secondly, the ANN 144. model is built on multiple inputs. The results indicate the superiority of the neural network method in terms of the accuracy of publication prediction results. 776-781 Keywords: SCC, ANN, Workability, Compressive strength, Split tensile

References:

1. P. Dinakar, P.S. Kali, C.S. Umesh, “Design of self-compacting concrete with ground granulated blast furnace slag”, Journal of Material and Design, Vol. 43, Jan 2013, pp. 161–169 2. EFNARC, ―Specification and guidelines for Self Compacting Concrete, 2002. 3. Nan Su., Kung-Chung Hsu., and His-Wen Chai., “A simple mix design method for self compacting concrete”, Journal of Cement Concrete Research Vol. 31, No. 12, Dec 2001, pp 1799-1807 4. G R. Siddique “Properties of self-compacting concrete containing class F fly ash”, Journal of Materials and Design, Vol. 32, 2011, pp. 1501-1507 5. B.H.V Pai, M. K.H. Nandy,P.K. Sarkar, C.P. Ganapathy, “Experimental study on self compacting concrete containing industrial by-products” , European Scientific Journal, vol 12, , April 2014, ISSN 1875-7881 6. Jyoti R. Mali, Piyush Bagul, Yogesh Biyani, Pradip Pandhare, Hemant Bafna “Partical replacement of fine aggregate with GGBS” IJESC, 7 (3), (2017), pp: 4912 – 4914. 7. K.H. Khayat, H. Monty ―Stability of SCC, advantages and Potential applications, In: Proceedings of RILEM international conference on self-compacting concrete, Rilem Publications SARL, 143-152 pp. 1999. 8. IS: 12269 (53 Grade Ordinary Portland cement Specifications), Indian Standard Code, 1987. 9. IS: 383 (Specification for coarse and fine aggregates from natural sources for concrete), Indian Standard Code of Practice, 1970. 10. IS: 12089 (Specification for Granulated Slag for Manufacture of Portland Slag Cement), Indian Standard Code of Practice, 1987. 11. Yeh, “Modeling of strength of high-performance concrete using artificial neural networks”, Cem. Concr. Res. 28 (12) (1998) pp. 1797– 1808. 12. S. Lai, M. Serra, “Concrete strength prediction by means of neural network”, Constr. Build. Mater. 11 (2) (1997) pp. 93–98. 13. M. Nehdi, H.E. Chabib, M.H.E. Naggar, “Predicting performance of self-compacting concrete mixtures using artificial neural”, ACI Mater. J. 98 (5) (2001), pp.349–401. 14. X. Wu, J. Ghaboussi, J.H. Garrett Jr., “Use of neural network s in detection of structural damage”, Comput. Struct. 42 (4) (1992) pp. 649–659. 15. C. Pal, I. Hagiwara, N. Kayaba, S. “Morishita, Dynamic system identification by neural network: a new, fast learning method based on error back propagation”, J. Intell. Mater. Syst. Struct. 5 (1) (1994), pp. 127–135. 16. J. Ghaboussi, J.H. Garrett Jr., X. Wu, “Knowledge-based modeling of material behavior with neural networks”, ASCE J. Eng. Mech. 117 (1), 1991, pp. 132–153. 17. S. Ranjithan, J.W. Eheart, “Neural network–based screening for groundwater reclamation under uncertainty”, Water Resour. Res. 29 (3), (1993), pp. 563-547. 18. H. Adeli, H.S. Park, “A neural dynamic model for structural optimization – theory”, Comput. Struct. 57 (3), (1995), pp. 383–390. 19. H.E. Chabib, M. Nehdi, M. Sonebi, “Artificial intelligence model for flowable concrete mixtures used in underwater construction and repair”, ACI Mater. J. 100 (2) (2003), pp. 164–173. 20. A.M. Diab, H.E. Elyamany, A.M. AbdElmoaty, A.H. Shalan, “Prediction of concrete compressive strength due to long term sulfate attack using neural network”, Alexandria Eng. J. 53, 2014, pp. 627–642. 21. O.A. Hodhod, H.I. Ahmed, “Developing an artificial neural network model to evaluate chloride diffusivity in high performance concrete”, HBRC J. Vol 9, 2013, pp. 15–21. 22. R.A. Tarefder, L. White, M. Zaman, “Neural network model for asphalt concrete permeability”, ASCE J. Mater. Civ. Eng. Vol 17, 2005, pp. 468–473. Authors: Prajval Mohan, Tejas Jambhale, Lakshya Sharma, Simran Koul, Simriti Koul

Paper Title: Load Balancing using Docker and Kubernetes: A Comparative Study Abstract: Still in its early years, containers are increasingly being used in production environments. Containers offer a streamlined approach, easy deployment, and secure method of implementing infrastructure requirements also provide a much-improved alternative to virtual machines. A load balancer is required to distribute traffic across clusters. And now, with multiple container environments becoming widespread, load balancers are becoming a necessity to distribute traffic and reduce server load. Different load balancing algorithms provide a solution to this with varying efficiency. This paper presents a study on the latest methods which are being implemented to perform effective load balancing on containers. Docker Swarm and Kubernetes are the most widely used systems for deploying and managing a cluster of containers in an environment. The paper further demonstrates how Docker Swarm and Kubernetes can be used to minimize load traffic through load balancing techniques. We have introduced load balancing and different algorithms. Also, we have shown the implementations of load balancing algorithms in Docker and Kubernetes and finally compared the results. The paper finally concludes why Kubernetes is often preferred over Docker Swarm for load balancing.

Keywords: Docker, Docker Swarm, Kubernetes, Ingress, Load balancing, NodePort, LoadBalancer, Nginx.

References:

1. Netto, Hylson V., et al. "State machine replication in containers managed by Kubernetes." Journal of Systems Architecture 73 (2017): 53-59. 2. Rusek, Marian, Grzegorz Dwornicki, and Arkadiusz Orłowski. "A decentralized system for load balancing of containerized microservices in the cloud." International Conference on Systems Science. Springer, Cham, 2016. 145. 3. Sajjan, Rajani. (2017). Load Balancing and its Algorithms in Cloud Computing: A Survey. 4. Cito, Jürgen & Ferme, Vincenzo & C. Gall, Harald. (2016). Using Docker Containers to Improve Reproducibility in Software and Web Engineering Research. 609-612. 10.1007/978-3-319-38791-8_58. 782-792 5. C. Cérin, T. Menouer, W. Saad and W. B. Abdallah, "A New Docker Swarm Scheduling Strategy," 2017 IEEE 7th International Symposium on Cloud and Service Computing (SC2), Kanazawa, 2017, pp. 112-117. doi: 10.1109/SC2.2017.24 6. A Scheduling Strategy on Load Balancing of Virtual Machine Resources in Cloud Computing Environment. 7. Wei-guo, Zhang & Xi-lin, Ma & Jin-zhong, Zhang. (2018). Research on Kubernetes' Resource Scheduling Scheme. 144-148. 8. 10.1145/3290480.3290507. 9. Bui, Thanh. "Analysis of Docker security." arXiv preprint arXiv:1501.02967 (2015). 10. Di Tommaso, Paolo, et al. "The impact of Docker containers on the performance of genomic pipelines." PeerJ 3 (2015): e1273. 11. Harter, Tyler, et al. "Slacker: Fast distribution with lazy Docker containers." 14th {USENIX} Conference on File and Storage Technologies ({FAST} 16). 2016. 12. Naik, Nitin. "Applying computational intelligence for enhancing the dependability of multi-cloud systems using Docker swarm." 2016 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2016. 13. Nguyen, Nikyle, and Doina Bein. "Distributed mpi cluster with Docker swarm mode." 2017 ieee 7th annual computing and communication workshop and conference (ccwc). IEEE, 2017 14. Ismail, Bukhary Ikhwan, et al. "Evaluation of Docker as edge computing platform." 2015 IEEE Conference on Open Systems (ICOS). IEEE, 2015. 15. Kherani, Foram F. "Prof. Jignesh Vania, "Load Balancing in cloud computing"." International Journal of Engineering Development and Research 2.1 (2014). 16. James, Jasmin, and Bhupendra Verma. "Efficient VM load balancing algorithm for a cloud computing environment." International Journal on Computer Science and Engineering 4.9 (2012): 1658. 17. Yagoubi, Belabbas, and Yahya Slimani. "Dynamic load balancing strategy for grid computing." Transactions on Engineering, Computing and Technology 13.2006 (2006): 260-265. 18. Data storage and load Balancing in cloud computing using container clustering Trapti Gupta & Abhishek Dwivedi 19. Ren, Xiaona, Rongheng Lin, and Hua Zou. "A dynamic load balancing strategy for cloud computing platform based on exponential smoothing forecast." 2011 IEEE International Conference on Cloud Computing and Intelligence Systems. IEEE, 2011. 20. Kaewkasi, Chanwit, and Kornrathak Chuenmuneewong. "Improvement of container scheduling for Docker using ant colony optimization." 2017 9th international conference on knowledge and smart technology (KST). IEEE, 2017. 21. Research and implementation of Docker performance service in distributed platform Liu Lijuan 22. Rad, Babak Bashari, Harrison John Bhatti, and Mohammad Ahmadi. "An introduction to Docker and analysis of its performance." International Journal of Computer Science and Network Security (IJCSNS) 17.3 (2017): 228. 23. Zhang, Dongsheng. Resilience enhancement of container-based cloud load balancing service. No. e26875v1. PeerJ Preprints, 2018 24. Value-Based Allocation of Docker Containers by Piotr Dziurzanski, and Leandro Soares Indrusiak 25. Takahashi, K., Aida, K., Tanjo, T., & Sun, J. (2018). A Portable Load Balancer for Kubernetes Cluster. Proceedings of the International Conference on High Performance Computing in Asia-Pacific Region - HPC Asia 2018. doi:10.1145/3149457.3149473 26. Cloud Resource Orchestration: A Data-Centric Approach By Changbin Liu, Yun Mao, Jacobus E. Van der Merwe, Mary F. Fernández 27. Prajval Mohan, Adiksha Sood, Lakshya Sharma, Simran Koul, Simriti Koul. ‘PC-SWT: A Hybrid Image Fusion Algorithm of Stationary Wavelet Transform and Principal Component Analysis.’ International Journal of Engineering and Advanced Technology (IJEAT). ISSN: 2249-8958 (Online), Volume-9 Issue-5, June 2020, Page No.700-705. 28. Polyphony: A Workflow Orchestration Framework for Cloud Computing Khawaja S Shams, Dr. Mark W. Powell., Tom M. Crockett, Dr. Jeffrey S. Norris, Ryan Rossi, Tom Soderstrom 29. SDN orchestration architectures and their integration with Cloud Computing applications By Arturo Mayoral, Ricard Vilalta, Raul Muñoz, Ramon Casellas, Ricardo Martínez 30. An in-depth analysis and study of Load balancing techniques in the cloud computing environment. By Geethu Gopinath P P, Shriram K Vasudevan 31. Dynamic Balance Strategy of High Concurrent Web Cluster Based on Docker Container. Weizheng Ren et al 2018 IOP Conf. Ser.: Mater. Sci. Eng. 466 012011 32. Prajval Mohan, Pranav Narayan, Lakshya Sharma, Tejas Jambhale, Simran Koul, "Iterative SARSA: The Modified SARSA Algorithm for Finding the Optimal Path". International Journal of Recent Technology and Engineering (IJRTE). ISSN: 2277- 3878, Volume-8 Issue-6, March 2020 Authors: Mirzaev Pulat, Umarov Kadir, Mirzaev Shavkat Loop-Free Tie-Down Node with an Anchor Rod-Dowel for a Hollow-Core Floor Slab of Formwork- Paper Title: Free Shaping Abstract: When designing buildings and structures for various purposes, the specialists should find new decisions for the possibility of using structures made using the long-line formwork-free technology, including hollow-core floor slabs, namely: installation without free lengths of reinforcement, lifting without the tie-down loops, joint units with other structure elements, configuration of sections with new geometrical parameters. As the hollow-core slabs of formwork-free shaping are produced without tie-down loops, the problems of installation and transportation of these slabs are discussed. Examples of tie-down nodes installation in the slabs produced by the formwork-free shaping technology were considered in the paper, with the justification of the complexity of their installation in such slabs and the increased metal consumption. The aim is to reduce the laboriousness of rigging work and to provide a gripping device for a hollow-core slab of formwork-free shaping when removing it from a long-line pallet, storing, loading and installing this slab during the construction process. A constructive-technological decision was proposed for a tie-down node installed in the slab body without the use of a tie-down loop and having only an anchor rod-dowel through which the tie-down of a slab could be directly done without the use of traditional tie-down loops. The node was designed with reduced metal consumption and does not change the technology of manufacturing hollow-core slabs of formwork-free shaping. The theoretical basis for calculating the bearing capacity of the proposed tie-down node, installed in a hollow- core slab of formwork-free shaping was determined and summarized. It was revealed that the bearing capacity of the proposed tie-down node installed in the body of a hollow-core slab, under the action of lifting loads, depends on the splitting force of concrete protective layer located above the anchor rod-dowel of this node (all things being equal). The theoretical data of the study were validated by full-scale tests of slabs with tie-down nodes 146. installed in their body, carried out in accordance with the proposed structural-technological development. The operational suitability of the proposed tie-down nodes with an anchor rod-dowel for the hollow-core slabs of formwork-free shaping and the possibility of their implementation at other enterprises of the country having 793-798 production lines for long-line formwork-free shaping were stated. A tie-down node with an anchor rod-dowel, proposed to be installed in a hollow-core slab of formwork-free shaping, can be used in other reinforced concrete structures produced by the technology of long-line formwork-free shaping. A patent for a utility model has been received for the development of a loop-free tie-down node for a hollow-core slab of formwork-free shaping. Keywords:

References:

1. Goncharova M.A., Ivashkin A.N., Costa A.A. Selection and optimization of concrete compositions for the production of hollow- core slabs of formwork-free shaping. Construction materials. Moscow. No. 3 (2017). P.35-38. 2. Yumasheva E. N., Sapacheva L. V. House-building industry and the social control of time. Construction materials. Moscow. No. 10 (2014). P. 3-11. 3. Mirzaev Pulat, Mirzaev Shavkat. Optimization of Geometrical Parameters of Hollow-core Slabs by Formwork-free Shaping for Construction in Seismic Areas. International Journal of Recent Technology and Engineering (IJRTE). ISSN: 2277-3878. Volume- 8. Issue-6. March. (2020). P. 4973-4977. 4. Mirzaev Pulat, Umarov Kadir, Mirzaev Shavkat. Anti-seismic bracing in a hollow-core slab of formwork-free shaping for the possibility of creating a rigid disk in a building floor slab. International Journal of Recent Technology and Engineering (IJRTE). ISSN: 2277-3878, Volume-9 Issue-1, May 2020. P. 1409-1413. 5. Mirzaev Pulat, Umarov Kadir, Mirzaev Shavkat. Strength Calculation Features and Tests Results on Bearing Capacity and Operational Serviceability of Hollow-Core Floor Slabs of Formwork-Free Shaping in Seismic Areas. International Journal of Recent Technology and Engineering (IJRTE). ISSN: 2277-3878, Volume-9 Issue-1, May 2020. P. 2219-2225. 6. node В, fig. 2]. Barkov Yu. V., Zakharov V.F. Crosswise loops for reinforced concrete hollow-core floor slabs of formwork-free long-line shaping. Housing construction. Moscow. No. 10 (2008). P. 24-27. 7. Pachesa A. V., Barauskas Ya. A. Improvement of loop-free tie-downing of prefabricated structures. Concrete and reinforced concrete. Moscow. No. 4 (1984). P. 45-46. 8. fig.3]. Barkov Yu. V., Golofast S. I., Zakharov V. F., Smirnov L. Yu. Hollow-core building product and the method for its manufacture. Patent for invention RU 2 204 665 C1 (05/20/2003). 9. Anferov V. A. Hollow-core building product and the method for its manufacture. Patent for invention RU 2.263 748 C1. (10/11/2005). 10. fig. 3]. Degotkov O. V., Krivikov A. N. Hollow-core building product and the method for its manufacture. Patent for invention RU 2 313 639 C1 (12/27/2007). 11. fig. 2] Serbinovsky A. V., Pesotsky E. A., Pinevich S. S. Reinforced concrete hollow-core slab of long-line formwork-free shaping. Utility Model Patent RU 65 917 U1. (March 27, 2007). 12. Shashin A. F., Kalinichev N. N., Minkin B. R. Closed lifting loops in the products built from heavy concrete. Concrete and reinforced concrete. Moscow. No. 9 (1986). P. 5-6. 13. fig. 4]. Grinev V.D., Popkov Yu.V., Gil A.I. Hollow-core reinforced concrete slabs with modified lifting loops. Bulletin of Polotsk State University. Applied Sciences. Series F. Construction. No. 16 (2011). P. 67-70. 14. formula 4.6]. Ashkinadze G.N., Sokolov M.E., Martynova L.D., Lishak V.I. (USSR), Tassnos F., Tsukantas S., Plainis P., Skarpas A. (Greece). Reinforced concrete walls of earthquake-resistant buildings: Research and design basics. Moscow: Stroyizdat. (1988). 504 p. - ISBN 5-274-00212-5. 15. Rasmussen B. H. Strength of transversely loaded boltsand dowels cast into concrete. Laboratoriet for Bygningastatik, Denmark Technical University, Meddelelse. vol. 34. № 2, 1962. 16. Utesher G. and Herrmann M. Vesushe zur Ermittlung der Tragfahig Keit in beton eingespannter Rundstahldollen aus nichetrostondem austenitischem Stahi. Deutscher Ausschuss fur stahlbeton, Heft 346, Berlin, 1983, p 49-104. 17. Gustafsson P. J. and Hillerborg A. Improvements in concrete design achieved through the application of fracture mechanics. NATO ARW: Fracture Mechanics in concrete. Northwestern University, September, 1984. 18. Mirzaev P.T., Umarov K.S., Mirzaev S.P., Shukhratkhodzhaev Zh.M. Hollow-core slab of formwork-free shaping. Utility Model Patent UZFAP01501 (05/29/2020). Authors: Dipesh B. Pardeshi, Anupama Deshpande

Paper Title: ETAP Based Power System Analysis of a 6 MW Biomass Power Plant Abstract: To meet needs of electricity in rural India, there is an alternative option to be searched as the traditional ways and present approach which is renewable energy based and decentralized. While considering for options in the renewable energy sector for this purpose, such as bio-energy technologies are being explored. This work basically is intended to minimize the problems and difficulties involved in the generation of power by biomass combustion technique. The work behind this paper is to conduct a power system analysis of a 6 MW biomass power plant to calculate an impinging force advantageously placed distributed or embedded generation on distribution systems using an iterative power system simulation tools as regards the load flows, short circuit or fault studies, relative protective relay co-ordinations. This paper is based on modeling of single line diagram and simulates it through Electrical Transient Analyzer Program (ETAP) software. This work is investigated and resolved the problem to build the required confidence that a high penetration of biomass power plant connected to the grid is both realistic and secure. 147. Keywords: Biomass, ETAP, Load Flow Analysis, Short Circuit Analysis. 799-804 References:

1. A. Panyosam, B. R. Oswald, ―Modified Newton Raphson Load Flow Analysis for Integrated AC/DC Power System‖, Institute of Electric Power Systems, University of Hannover, Germany. 2. Grainger, J.; Stevenson, W., ―Power System Analysis‖, New York McGraw-Hill. 3. Hadi Saadat, "Power System Analysis", Tata McGraw Hill, 2001.. 4. IEEE Brown Book: IEEE Std. 399:1997 ―IEEE Recommended Practice for Industrial and Commercial Power Systems Analysis‖, Power Systems Engineering Committee of the Industrial and Commercial Power Systems Department of the IEEE Industry Applications Society. 5. Jignesh S. Patel Manish N. Sinha. ― Power System Transient Stability Analysis Using ETAP Software‖, National Conference on Recent Trends in Engineering & Technology, B. V. M. Engineering College, V. V. Nagar, Gujarat, India, 13-14 May 2011. 6. Rohit Kapahi ―Load Flow Analysis of 132 kV substation using ETAP Software‖, International Journal of Scientific and Engineering Research, Volume 4, Issue 2, February-2013 ISSN 2229-5518. 7. ETAP 16.0.0 Documentation and help files. 8. Sunil S. Rao, ―Switchgear and Protection‖, Dhanpat Rai Publications. Authors: P. Kanaka, K. Thiyagu, K. Anbarasi, S. Southamirajan, K. Vengatesh

Paper Title: Behaviour of Paver Block with Sugarcane Effluent Abstract: Populace blast combined with urbanization has raised the interest for water bringing about its shortage. With industrialization, the quantum of waste water produced too has taken off up justifying proper measures for use of the equivalent. We are here putting a stage forward to use the mechanical effluents in development industry. Practically all businesses dismiss there effluents either into rural terrains or into normal 148. water bodies. We are thinking about the gushing waste water which is being placed in rural terrains from sugarcane businesses. Since the use of sugarcane modern waste water was not successfully done as such far, we accepting this task as a test. We gathered water tests and tests were directed to know its qualities. We directed 805-808 tests to know pH and the outcome was contrasted and the Indian guidelines IS 10500 (2012) for drinking water. As the water utilized for drinking reason that the sugarcane modern waste water can be used for development purposes. As a second step we need to cast lab scale solid squares of M20 grade and ought to be tried for different new concrete and solidified solid tests. The side-effects of sugarcane like bag gash, press mud are utilized as substitutions of totals; we without a doubt accept that our venture will become achievement. With the ebb and flow water shortage in India there is a need to search for interchange hotspot for solid creation. By doing this undertaking we can move the solid business towards zero release office, and in this manner decreasing the wastage of a valuable normal asset.

Keywords: Paverblock, Samples, Waste water, Water scarcity.

References:

1. Joulani, Nabil, and Riyad Abdel-Karim Awad. "The Effect of Using Wastewater from Stone Industry in Replacement of Fresh Water on the Properties of Concrete." Journal of Environmental Protection 10, no. 02 (2019): 276. 2. Reddy, M. Vijaya Sekhar, K. Ashalatha, M. Madhuri, and P. Sumalatha. "Utilization of sugarcane bagasse ash (SCBA) in concrete by partial replacement of cement." IOSR Journal of Mechanical and Civil Engineering 12, no. 6 (2015): 12-16. 3. Kadir, Aeslina Abdul, Shahiron Shahidan, Lau Hai Yee, Mohd Ikhmal Haqeem Hassan, and Mohd Mustafa Al Bakri Abdullah. "The effect on slurry water as a fresh water replacement in concrete properties." In IOP Conference Series: Materials Science and Engineering, vol. 133, no. 1, p. 012041. IOP Publishing, 2016. 4. Al-Ghusain, Ibrahim, and M. Terro. "Use of treated wastewater for concrete mixing in Kuwait." Kuwait journal of science and Engineering 30, no. 1 (2003): 213-228. 5. Al-Jabri, K. S., A. H. Al-Saidy, Rb Taha, and A. J. Al-Kemyani. "Effect of using wastewater on the properties of high strength concrete." Procedia Engineering 14 (2011): 370-376. 6. Tay, Joo-Hwa, and Woon-Kwong Yip. "Use of reclaimed wastewater for concrete mixing." Journal of environmental engineering 113, no. 5 (1987): 1156-1161. 7. Gadzama, E., O. J. Ekele, V. E. Anametemfiok, and A. U. Abubakar. "Effects of sugar factory wastewater as mixing water on the properties of normal strength concrete." Civil Engineering Department. Modibbo Adama University of Technology, PMB 2076 (2015). 8. Meena, Khushboo, and Salmabanu Luhar. "Effect of wastewater on properties of concrete." Journal of Building Engineering 21 (2019): 106-112. 9. BIS (Bureau of Indian Standards). "Methods of tests for strength of concrete." (1959). 10. Standard, Indian. "IS 10262: 2009, Concrete Mix Proportioning Guidelines." Bureau of Indian Standards (2009). 11. Indian Standard, I. S. "IS 456 2000: Plain and Reinforced Concrete. Code of Practice (4th revision)." New Delhi (2000). 12. Nataraja, M. C., and Lelin Das. "A study on the strength properties of paver blocks made from unconventional materials." IOSR Journal of Mechanical and Civil Engineering (2006): 1-5. Authors: G. Venkata Hari Prasad, Lakshmi Narayana Thalluri

Paper Title: Enhanced Performance of PCG Signal using Effective Feature Extraction Method Abstract: Phonocardiography (PCG) is the realistic portrayal of sounds created in the heart auscultation. PCG is an improvement for ECG. Particularly in observing of patient and biomedical research, these signals need to do the diagnosis. This paper deals with the processing of heart sound signals i.e., Phonocardiography (PCG) Signals. The primary goal of analyzing these heart sound signals is to separate the signals from the noisy background and to extract some parameters which are used for patient monitoring and for other researches. Various momentum explore ventures are going on biomedical signal processing and its applications. The performance of the PCG signal will comprise of sectioning the signal into S1 and S2 and then compare, whether the PCG is normal or abnormal. In the previous framework the different change approaches are utilized to break down the PCG signal.In the primary stage, for include extraction; acquired heart sound signals were isolated to its sub-groups utilizing discrete wavelet change with Level-1 to Level-10. This upgraded strategy proposes a best component for Heart Signal Features, which are removed and changed in to other area to arrange signals. This enhanced method proposes a best feature for Heart Signal Features, which are extracted and transformed in to other domain to classify signals. In the proposed strategy the Wavelet is utilized for highlight extraction and different Statistical strategies are utilized. InformationGain (IG), Mutual Information (MI) and so on. Feature selection techniques are compared using classifiers like kNN(k-Nearest Neighbor), Naïve Bayes, C4.5 and 149. Support Vector Machines (SVMs). MATLAB & WEKA Soft wares are used for analysis Purpose. In this paper, coiffelet technique is utilized to analyze the synthetic PCG and the classifier parameters are compared with one another. 809-813

Keywords: Heart Sounds, Wavelets, Feature Extraction, Mutual Information, Information Gain (IG).

References:

1. G. Venkata Hari Prasad, P. Rajesh Kumar. "Performance analysis of feature selection methods for feature extracted PCG signals", 2015, 13th International Conference on Electromagnetic Interference and Compatibility (INCEMIC), 2015 2. Anita Devi, Abhishek Misaland Dr. G.R.Sinha, “Performance Analysis of DWT at different levels for Feature Extraction of PCG Signals,” International Conference on Microelectronics, Communication and Renewable Energy (ICMiCR-2013), pp. 1-5. 3. “Denoising of PCG Signal by using Wavelet Transforms” Abhishek missal, Advances in Computational Research ISSN: 0975- 3273 & E- ISSN: 0975-9085, Volume 4, Issue 1, 2012. 4. A Single channel Phonocardiograph Processing using EMD, SVD, and EFICA, Anil Dada Warbhe, 2010 IEEE. 5. Bai Fang-Fang, Miao Chang-yun, Zang Cheng,Gan Jing-Meng (2010) ICAP,1797-1800 6. Detection of Heart Diseases by Mathematical Artificial Intelligence Algorithm Using Phonocardiogram Signals, Prakash D, Uma Mageshwari T, Prabakaran K, and Suguna A, International Journal of Innovation and Applied Studies ISSN 2028-9324 Vol. 3 No. 1 May 2013. 7. Wang, Q., Guan, Y., Wang, X., & Xu, Z. (2006). A novel feature selection method based on category information analysis for class prejudging in text classification. Proceedings of the International Journal of Computer Science and Network Security, 6(1), 113-119.

150. Authors: Jitendra Mohan Empirical Validation of Unified Theory of Acceptance and Use of Technology (UTAUT) Model for Paper Title: m-Agriculture Service in India Abstract: With an increase in the mobile penetration in India, m-Agriculture is getting more popular among Farmers to get information about weather, crops, and market prices. The basic issues are that of information asymmetry and individual user’s acceptance for the m-Agriculture among Indian farmers as an effective information sharing tool. The present study focuses on validating Unified Theory of Acceptance and Use of Technology (UTAUT) model for m-Agriculture among India Farmers and acceptance of m-Agricultural services. The study is being conducted in Western Uttar Pradesh and adjacent districts of Haryana in India. The region of India known for green revolution and cash crop farming and contributes a large quantity of food grains to Indian granary. The study also ascertains the benefit of mobile services by the Indian farmers.

Keywords: E-Service, Indian Farmers, m-Agriculture Rural Sector, Technology Adoption, UTAUT model .

References:

1. Adams, Dennis A., Nelson, Ryan R., Todd, Peter A. ―Perceived Usefulness, Ease of Use, and Usage of Information Technology: A Replication,‖ MIS Quarterly, 16 (1992): 227-247. 2. Chattopadyay, B.N. (undated) Information and Communication Technologies for Sustainable Development, http://agropedia.iitk.ac.in/openaccess/sites/default/files/WS%205.pdf. 3. Compeau, Deborah R., Higgins, Christopher A., Huff, Sid. ―Social Cognitive Theory and Individual Reactions to Computing Technology: A Longitudinal Study,‖ MIS Quarterly, 23 (1999): 145-158. 4. Cronbach, L. J., Meehl, P. E. ―Construct Validity in Psychological Tests,‖ Psychological Bulletin, 4 (1955): 281-302. 5. Doll, William J., Xia, Weidong, Torkzadeh, Gholamreza. ―A Confirmatory Factor Analysis of the End-User Computing Satisfaction Instrument,‖ MIS Quarterly, 18 (1994): 453-461. 6. Elgers, Pieter T. ―Accounting-Based Risk Predictions: A Re-Examination,‖ MIS Quarterly, 55 (1980): 389-408. 814-818 7. H.S. Kwon and L. A. Chidambaram, ―Test of the Technology Acceptance Model, the Case of Cellular Telephone Adoption‖. Proceedings of the 33rd Hawaii International Conference on System Sciences, USA, 2000. 8. http://censusindia.gov.in/2011-prov-results/data_files/india/Final_PPT _2011_chapter6.pdf 9. http://www.ibef.org/industry/agriculture-india.aspx 10. Kacmar, Michele K., Bozeman, Dennis P., Carlson, Dawn S., Anthony, William P. ―An Examination of the Perceptions of Organizational Politics Model: Replication and Extension,‖ Human Relations, 52 (1999): 383-416. 11. Mokkink, L. B., Terwee, C. B., Patrick, D. L., Alonso, J., Stratford, P. W., Knol, D. L., … de Vet, H. C. W. (2010). The COSMIN study reached international consensus on taxonomy, terminology, and definitions of measurement properties for health‐related patient‐reported outcomes. J Clin Epidemiol, 63(7), 737– 745. https://doi.org/10.1016/j.jclinepi.2010.02.006 12. Mohan, J. ( 2016) , " Importance of mobile in dissemination of Agriculture Information among Indian Farmers" IJETCAS, 15(1), December, 2015- February,2016, pp. 75-79 13. Segars, Albert H., Grover, Varun. ―Re-Examining Perceived Ease of Use and Usefulness: a Confirmatory Factor Analysis,‖ MIS Quarterly, 17 (1993): 517-525. 14. Straub, Detmar W. ―Validating Instruments in MIS Research,‖ MIS Quarterly, 13 (1989): 147-169. 15. Straub, Detmar W., Boudreau, Marie-Claude. ―Validation Guidelines for IS Positivist Research,‖ Communications of the Association for Information Systems, 13 (2004): 380-427. 16. Szajna, Bernadette. ―Empirical Evaluation of the Revised Technology Acceptance Model,‖ Management Science, 42 (1996): 85- 92. 17. Venkatesh, Viswanath, David, Fred D., ―A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies,‖ Management Science, 46 (2000): 186-204. 18. Venkatesh, Viswanath, Morris, Michael G., Davis, Gordon B., Davis, Fred D. ―User Acceptance of Information Technology: Toward a Unified View,‖ MIS Quarterly, 27 (2003): 425-478. 19. Yunfu Huo , Lin Ma , Deli Yang , "The strength of trust: discussion on the influencing factors of the Chinese farmer’s adoption of mobile agriculture science and technology knowledge service‖ International Journal of Innovative Computing, Information and Control, ICIC International 2011 ISSN 1349-4198 Volume 7, Number 12, December 2011 pp. 6979-6989 Authors: Aji Rilamsyah, Fauzi Abdillah, Muhamad Rifaldy Ramadhan, Sambudi Hamali Factors Influencing Supply Chain Performance through Sharing Information on PT Sinar Sosro Paper Title: KPW Banten Abstract: The purpose of this study is to know the influence of supplier integration, internal integration, and customer integration on supply chain performance through sharing information at PT. Sinar Sosro KPW Banten. The study method used is quantitative method by collecting data through questionnaire. Data analysis using SEM-PLS with WarpPLS 5.0 software. The results show that supplier integration and internal integration have no effect on supply chain performance. Supplier integration, internal integration, and costumer integration affect the sharing information. And customer integration and sharing information have an effect on supply chain performance. The current conclusion of this result, managers need to improve customer integration and information sharing. (AR, FA, MRR) 151. Keywords: Customer Integration, Internal Integration, Sharing Information, Supplier Integration, Supply Chain Performance. 819-822

References:

1. Badan Pusat Statistik, “Produk Bruto Domestik Triwulan,” September, 2017. [Online]. Available: https://www.bps.go.id/publication/2017/10/02/a169618dd6a187b5029ab668/produk-domestik-bruto-triwulanan-2013---2017. 2. Kementrian Perindustrian Republik Indonesia, “Artikel,” Agustus 12, 2017. [Online]. Available: http://www.kemenperin.go.id/artikel/17984/Tertinggi,-Kontribusi-Industri-Makanan-dan-Minuman-Capai-34,17-Persen. 3. B. Asamoah, D., Andoh-Baidoo, F. K., & Agyei-Owusu, “Examining the Relationships between Supply Chain Integration, Information Sharing, and Supply Chain Performance: A Replication Study,” Twenty-second Am. Conf. Inf. Syst., pp. 1–9, 2016. 4. P. Chatzoudes, D., & Chatzoglou, “Supply Chain Integration (SCI) measured from an information sharing perspective: Examining its impact on business success,” Res. Challenges Inf. Sci., pp. 52–63, 2015. 5. Sugiyono, Metode Penelitian Bisnis. Bandung: Alfabeta, 2014. 6. R. Sekaran, U., & Bougie, Research Methods for Business 7th Edition. West Sussex: John Wiley & Sons. Ltd, 2016. 7. N. Kock, WarpPLS 5.0 User Manual. Texas: ScriptWarp Systems, 2015. 8. D. Sholihin, M., & Ratmono, Analisis SEM-PLS dengan WarpPLS 3.0. Yogyakarta: Indonesia Journal of Accounting Research, 2013. 9. M. Hair, Jr, J. F., M. Hult, G. T., Ringle, C. M., & Sarstedt, A Primer On Partial Least Squares Structural Equation Modeling (PLS-SEM). California: SAGE Publications, Inc, 2014. Authors: Biancha Yusputri Aniendita, Cindy Ladiesta Erientania, Shavira Prita Nuraissa, Teguh Sriwidadi Minimizing Defective Products of Motorcycle and Car Exhaust Products using Six Sigma Method in Paper Title: PT Berlindo Mitra Utama Abstract: The purpose of this study is to determine the level of sigma, find out what factors cause defects, and find out the right way to reduce defective products. The research methodology used is quantitative. The analysis used is the Six Sigma method. The results achieved from this research are the level of sigma produced in the motorcycle exhaust of 4.3 and in the car exhaust 4.46, the factors that cause defects are mold, material, human, method, engine, and the right way to reduce the defective product is to check the machine carefully before and after use. The conclusion obtained is to minimize defective products on motorcycle and car exhaust products at PT Berlindo Mitra Utama by using the Six Sigma method.

Keywords: Defective Products, Quality Control, Six Sigma 152. References: 823-829 1. Kompas.com, “Advertorial : Tantangan Industri Otomotif Nasional,” September 14, 2017. [Online]. Available: https://biz.kompas.com. [Accessed: 14-Mar-2018]. 2. Y. Wicaksono, K., & Herawati, “Honda RI Tarik Ratusan Ribu Mobil karena Cacat Produksi,” January 26, 2018. [Online]. Available: https://www.viva.co.id. [Accessed: 05-Jun-2018]. 3. Sugiyono, Metode Penelitian Pendidikan Pendekatan Kuantitatif, Kualitatif dan R&D. Bandung: Alfabeta, 2014. 4. J. Friedli, T., Basu, P., Bellm, D., & Werani, Leading Pharmaceutical Operational Excellence. London: Springer-Verlag Berlin Heidelberg, 2013. 5. F. B. Pranata, Business Intellegence Cockpit. Jakarta: PT. Alex Media Komputindo Kelompok Gramedia, 2014. 6. B. Kho, “Pengertian DPMO (Defects Per Million Opportunities) Six Sigma dan Cara Menghitungnya,” November 11, 2016. [Online].Available: https://ilmumanajemenindustri.com/pengertian-dpmo-defects-per-million-opportunities-six-sigma-cara- menghitung-dpmo/. [Accessed: 20-Apr-2018]. 7. B. W. Russel, R. S., & Taylor, Operation Management. New Jersey: John Wiley and Sons, Inc, 2011. 8. M. M. Weeden, Failure Mode and Effects Analysis (FMEAs) for Small Business Owners and Non-Engineers. Milwaukee: Quality Press, 2015. Authors: Dian Siti Sundari, Shanissa Diandi, Danang Prihandoko The Controlling of Raw Materials Inventory using Material Requirement Planning Method in PT Paper Title: Anugrah Mandiri Abstract: The purpose of this study is to determine the most appropriate forecasting to predict the demand for raw materials of laundry in 2017, determine the results of the Material Requirement Planning calculation using lot permits, and to determine the total cost-efficiency of the conventional calculation method with the Material Requirement Planning method. The research method used is quantitative research, descriptive research type, and time horizon is cross-sectional for all data collection. This study uses Exponential smoothing (QM for Windows) forecasting, as an illustration, to determine the number of raw material requirements by using the analysis method, Material Requirement Planning with lot sizing measurements used are Lot for Lot, Economic Order Quantity, and Period Order Quantity. Of the three methods, the analysis results illustrate that the Lot for Lot method produces the lowest total cost of IDR. 56,160,000, compared to the total costs incurred using the 153. company's conventional method of IDR. 175,985,288,2, So, the Material Requirement Planning method can reduce the company's inventory costs. 830-835 Keywords: Economic Order Quantity (EOQ), Forecasting, Lot for Lot, Material Requirement Planning (MRP), Period Order Quantity (POQ).

References:

1. W. Sungkono, A.A., Sulistyowati, “Perencanaan dan Pengendalian Bahan Baku Untuk Meningkatkan Efisiensi Produksi Dengan Metode Material Requirement Planning dan Analytical Hierarchy Process di PT. XYZ,” Spektrum Ind., vol. 14, no. 1, 2016. 2. M. Imitieg, A, A., Lutovac, “Project Scheduling Method with time using MRP System,” Eur. J. Appl. Econ., 2015. 3. C. Heizer, J., Render, B., Munson, Operation Management Sustainability and Supply Chain Management. United States: Pearson Education, 2017. 4. B. Russell, R. S., & Taylor, Operations Management: Creating Value along the supply chain, 6th ed. Hoboken, New Jersey: John Wiley & Sons, 2009. Authors: Marco Adi Pati, Mochamad Rizky Drestiyandi, Raffi Razak Dhamhory, Danang Prihandoko The Use of Distribution Requirement Planning Method in Rice Distribution System of CV. Usaha Paper Title: Milla Gesit for Cost Efficiency 154. Abstract: The purpose of this research to provide a method of Distribution Requirement Planning on the form of CV. Usaha Milla Gesit’s rice to efficient the cost of the material distribution. This type of research is descriptive research with a time horizon that use cross-sectional for data collection .The technique of data collection during 836-840 this research is directly interviewing company’s oprational manager. The data that acquired processed with Distribution Requirement Planning and then compared with the results of company’s conventional system. From the results of the comparison Distribution Requirement Planning can be efficient the cost of rice distribution as many as 26.76 %. With this proves Distribution Requirement Planning can efficient distribution total cost, Forecasting aims to know the period from March 2018 - February 2019 and manage it with Distribution Requirement Planning to know distribution total cost from March 2018 - February 2019.

Keywords: Distribution Cost, Distribution Requirement Planning, Economic Order Quantity, Efficient, Forecasting, Safety Stock.

References:

1. I. Invesment, “Komuditas Indonesia,” 2017. [Online]. Available: https://www.indonesia- investments.com/id/bisnis/komoditas/item75? [Accessed: 28-Jan-2018]. 2. D. Sutoni, A., & Agustian, “Penjadwalan Pengiriman Produk Kaos Oleh CV Chronicle Mart Kepada Sub Distributor Cianjur Dengan Mengunakan Metoda DRP (Distribution Requirement Planning),” J. Mananjemen Ind. Dan Logistik, vol. 1, no. 2, pp. 42–52, 2017. 3. I. Hardi, S., & Sudarso, “Analisis Simulated Annealing (SA) dan Rancang Bangun Sistem Penjadwalan Aktivitas Distribusi Dengan Menggunakan Distribution Requirement Planning (DRP),” 2017. Authors: Hendry Christian, Elsa Irani Putri, Riska Fitra Dewi, Sevenpri Candra The Influence of Product Innovation and Marketing Tools on The Competitive Advantage of Paper Title: Fashion Products in Jakarta Barat Area Abstract: Fashion is a stylish dress equipped with accessories used every day by someone, be it in their daily life or at a particular event to support the appearance. This research was conducted with the knowledge to know the effects of product innovation and marketing tools to the competitive advantage of fashion products in the West Jakarta area. This study uses quantitative data, where the data use is primary data in the form of questionnaires and secondary data obtained from websites and institutions. The technique of collecting data using a survey with a Likert scale. The population is students who come from private universities in the area of West Jakarta. Samples of the study were 628 people and tested the instrument as many as 60 people. Data analysis methods used in this research include descriptive inferential, validity test, reliability test, normality test, correlation test and simple regression analysis, and multiple regression analysis using software processed by IBM SPSS Statistics 22. The results showed that product innovation and marketing tools have an effect on the competitive advantage of fashion products in the West Jakarta region with both have medium criteria. This means product innovation and marketing must be maintained and upgraded to achieve a High Competitive 155. Advantage on Fashion products.

Keywords: Product Innovation, Marketing Tools, Competitive Advantage, Fashion. 841-843

References:

1. C. Selang, “MARKETING MIX INFLUENCE ON CONSUMER LOYALTY ON FRESH MART SHOULDER MALL MANADO,” pp. 71–80, 2013. 2. H. Yusof, Y., Roddin, R., & Awang, “What Students Need, and What Teacher Did: the Impact of Teacher’s Teaching Approaches to the Development of Students’ Generic Competences,” pp. 36–44, 2015. 3. P. Keller, & Kotler, Marketing Management, 13th ed. New Jersey: Pearson, 2012. 4. D. Kotler, P., Armstrong, G., Ang, G., Leong, S., Tan, C., & Tse, Principles of Marketing: An Asian Perspective. Singapore: Pearson Prentice Hall., 2005. 5. Saiman, Entrepreneurship (Theory, Practice, and Cases), 2nd ed. Jakarta: Salemba Empat, 2014. 6. J. Creswell, QUALITATIVE INQUIRY AND RESEARCH DESIGN: CHOOSING AMONG FIVE TRADITIONS. London: Sage, 2014. 7. D. Robert V. Krejcie, “Determining Sample Size for Research Activities,” pp. 607–610, 1970. 8. Ristekdikti, Data on the Number of Private Universities and the number of students in the West Jakarta area. Jakarta: Ministry of Research, Technology, and Higher Education, 2016. Authors: Sandip Kumar Singh Modak, Vijay Kumar Jha A Comparative Performance Analysis of Multimodal-Multialgorithm System Framework Based on Paper Title: Rank Level Fusion Abstract: The Unimodal biometric framework have various fundamental issues, for example, intra-class alteration, noisy data, failure-to-enroll, spoofing attacks, unacceptable error rate and non-universality. To defeat this shortcoming multibiometric is a decent alternative where we can utilize at least two individual modalities. This paper gives a comparative analysis of multi-algorithm and multimodal system framework based on rank level fusion. An effective combination strategy that integrates information given by different domain specialist 156. dependent on rank level fusion approach is utilized to enhance the presentation of the framework. The rank of individual matcher is combined using the highest rank, Borda count, weighted Borda count, nonlinear weighted 844-853 approach and Bucklin combination approach. The outcomes of the results show there is a noteworthy exhibition enhancement in the identification accuracy can be accomplished when contrasted those from unimodal frameworks. The outcomes also reveal that combination of individual modalities can enhance the biometric system performance. The experiment based on multimodal (NIST BSSR1 multimodal database of fingerprint and face) and multialgorithm (Hong Kong Polytechnic University database of palmprint) system shows an improvement in term of the Rank-1 identification rate of the system.

Keywords: Unimodal; Multibiometric; Rank level fusion; Highest rank; Borda count; Weighted Borda count; Nonlinear weighted; Bucklin; Multi-algorithm; Multimodal;Rank-1 identification.

References:

1. Jain, Anil K and Ross, Arun and Prabhakar, Salil. (2004) ‘An Introduction to biometric recognition’, IEEE Transaction on Circuits and Systems for Video Technology. Special Issue on Image and Video-Based Biometrics, Vol .14, no.1, pp. 4-20. 2. Ross, A., and Jain, A. (2003) ‘Information fusion in biometrics’, Pattern recognition letters, Vol.24, no.13, pp.2115-2125. 3. Jain, A., Nandakumar, K., and Ross, A. (2005) ‘Score normalization in multimodal biometric systems’, Pattern recognition, Vol.38, no.12, pp.2270-2285. 4. Ross, A. A., Nandakumar, K., and Jain, A. K. (2006) ‘Handbook of multibiometrics ‘, Springer Science & Business Media, Vol.6. 5. Liau, H. F., and Isa, D. (2011) ‘Feature selection for support vector machine-based face-iris multimodal biometric system’, Expert Systems with Applications, Vol.38, no.9, pp.11105-11111. 6. Mezai, L., and Hachouf, F. (2015) ‘Score-level fusion of face and voice using particle swarm optimization and belief functions’, IEEE Transactions on Human-Machine Systems, Vol. 45,no.6, pp.761-772. 7. Eskandari, M., Toygar, Ö., and Demirel, H. (2014)’ Feature extractor selection for face–iris multimodal recognition’, Signal, image and video processing ,Vol. 8,no.6, pp.1189-1198. 8. He, M., Horng, S. J., Fan, P., Run, R. S., Chen, R. J., Lai, J. L., ... and Sentosa, K. O. (2010) ‘Performance evaluation of score level fusion in multimodal biometric systems’, Pattern Recognition, Vol.43, no.5,pp.1789-1800. 9. Monwar, M. M., and Gavrilova, M. L. (2009) ‘Multimodal biometric system using rank-level fusion approach’, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), Vol. 39,no.4, pp.867-878. 10. Nandakumar, K., Chen, Y., Dass, S. C., and Jain, A. K. (2008)’ Likelihood ratio-based biometric score fusion’, IEEE Trans. Pattern Anal. Mach. Intell., Vol.30, no.2, pp.342-347. 11. Bhatnagar, J., Kumar, A., and Saggar, N. (2007) ‘A novel approach to improve biometric recognition using rank level fusion’, In Computer Vision and Pattern Recognition, 2007. CVPR'07. IEEE Conference on, pp. 1-6. 12. Hanmandlu, M., Kumar, A., Madasu, V. K., and Yarlagadda, P. (2008) ‘Fusion of hand based biometrics using particle swarm optimization’, In Information Technology: New Generations, 2008. ITNG 2008. Fifth International Conference on, pp. 783-788. 13. Marasco, E., Abaza, A., Lugini, L., and Cukic, B. (2013) ‘ Impact of biometric data quality on rank-level fusion schemes’ ,In International Conference on Algorithms and Architectures for Parallel Processing ,pp. 209-216, Springer, Cham. 14. Li, C., Hu, J., Pieprzyk, J., and Susilo, W. (2015) ‘A new biocryptosystem-oriented security analysis framework and implementation of multibiometric cryptosystems based on decision level fusion’, IEEE transactions on Information Forensics and Security, Vol.10, no.6, pp.1193-1206. 15. Noore, A., Singh, R., and Vatsa, M. (2007) ‘Robust memory-efficient data level information fusion of multi-modal biometric images’, Information Fusion, Vol. 8,no.4, pp.337-346. 16. Wang, J. G., Yau, W. Y., Suwandy, A., and Sung, E. (2008) ‘ Person recognition by fusing palmprint and palm vein images based on “Laplacianpalm” representation’, Pattern Recognition, Vol.41,no.5, pp.1514-1527. 17. Kisku, D. R., Sing, J. K., Tistarelli, M., and Gupta, P. (2009) ‘Multisensor biometric evidence fusion for person authentication using wavelet decomposition and monotonic-decreasing graph’, In 2009 seventh international conference on advances in pattern recognition ,pp. 205-208, IEEE. 18. Kumar, A., Wong, D. C., Shen, H. C., and Jain, A. K. (2003) ‘ Personal verification using palmprint and hand geometry biometric’, In International Conference on Audio-and Video-Based Biometric Person Authentication ,pp. 668-678, Springer, Berlin, Heidelberg. 19. Feng, G., Dong, K., Hu, D., and Zhang, D. (2004) ‘When faces are combined with palmprints: A novel biometric fusion strategy’, In Biometric authentication, pp. 701-707, Springer, Berlin, Heidelberg. 20. Valentine Azom, Aderemi Adewumi, Jules-Raymond Tapamo (2015) ‘Face and iris biometric personal identification using hybrid fusion at feature and score level’, Pattern Recognition Association of South Africa and Robotics and Mechatronics. 21. Kumar, A., and Kumar, A. (2016) ‘Adaptive management of multimodal biometrics fusion using ant colony optimization’, Information Fusion, Vol.32, pp. 49-63. 22. Liang, Y., Ding, X., Liu, C., and Xue, J. H. (2016) ‘Combining multiple biometric traits with an order-preserving score fusion algorithm’, Neurocomputing, Vol.171, pp.252-261. 23. Yu, P., Xu, D., Zhou, H., and Li, H. (2009) ‘Decision fusion for hand biometric authentication’, In Intelligent Computing and Intelligent Systems, ICIS 2009. IEEE International Conference on, Vol. 4, pp. 486-490. 24. Kumar, A., Hanmandlu, M., Sanghvi, H., and Gupta, H. M. (2010) ‘Decision level biometric fusion using Ant Colony Optimization’, In Image Processing (ICIP), 2010 17th IEEE International Conference on, pp. 3105-3108. 25. Abaza, A., and Ross, A. (2009) ‘Quality based rank-level fusion in multibiometric systems’, In Biometrics: Theory, Applications, and Systems, 2009. BTAS'09. IEEE 3rd International Conference on, pp. 1-6. 26. Kumar, A., and Shekhar, S. (2011) ‘Personal identification using multibiometrics rank-level fusion’, IEEE Transactions on Systems, Man and Cybernetics, Part C: Applications and Reviews, Vol.41, no.5, pp.743-752. 27. Monwar, M. M., and Gavrilova, M. (2013) ‘Markov chain model for multimodal biometric rank fusion’, Signal, Image and Video Processing, Vol.7, no.1, pp.137-149. 28. Sharma, R., Das, S., and Joshi, P. (2015) ‘ Rank level fusion in multibiometric systems’, In Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG), 2015 Fifth National Conference on ,pp. 1-4. 29. Phillips, P. J., Flynn, P. J., Scruggs, T., Bowyer, K. W., Chang, J., Hoffman, K., and Worek, W. (2005) ‘Overview of the face recognition grand challenge’, In Computer vision and pattern recognition, 2005. CVPR 2005. IEEE computer society conference on Vol. 1, pp. 947-954. 30. Borade, S. N., Deshmukh, R. R., and Ramu, S. (2016) ‘Face recognition using fusion of PCA and LDA: Borda count approach’, In Control and Automation (MED), 2016 24th Mediterranean Conference on, pp. 1164-1167. 31. Li, S. Z. (2009) ‘Encyclopedia of Biometrics: I-Z ‘, Vol. 2, Springer Science & Business Media. 32. Ho, T. K., Hull, J. J., and Srihari, S. N. (1994) ‘Decision combination in multiple classifier systems’, IEEE transactions on pattern analysis and machine intelligence, Vol.16, no.1, pp.66-75. 33. Gonzalez, R.C. and Woods, R.E (2003) ‘Digital Image Processing’, 2nd ed., Pearson Publication, India. 34. Mallat, S., and Zhong, S. (1992) ‘Characterization of signals from multiscale edges’, IEEE Transactions on Pattern Analysis & Machine Intelligence, Vol. 7, pp.710-732. Authors: Udit Barde, Archana Ghotkar

Paper Title: Sign Language Recognition-A Survey of Techniques 157. Abstract: Sign language is the only method of communication for the hearing and speech impaired people around the world. Most of the speech and hearing-impaired people understand single sign language. Thus, there 854-857 is an increasing demand for sign language interpreters. For regular people learning sign language is difficult, and for speech and hearing-impaired person, learning spoken language is impossible. There is a lot of research being done in the domain of automatic sign language recognition. Different methods such as, computer vision, data glove, depth sensors can be used to train a computer to interpret sign language. The interpretation is being done from sign to text, text to sign, speech to sign and sign to speech. Different countries use different sign languages, the signers of different sign languages are unable to communicate with each other. Analyzing the characteristic features of gestures provides insights about the sign language, some common features in sign languages gestures will help in designing a sign language recognition system. This type of system will help in reducing the communication gap between sign language users and spoken language users.

Keywords: American Sign Language, gesture recognition, Indian Sign language, sign language recognition, Sign language translation.

References:

1. Ghotkar, Archana & Gajanan, K & Kharate, Gajanan. (2014). Study of vision-based hand gesture recognition using Indian sign language. International journal on smart sensing and Intelligent System. 2. Raheja, J L., Mishra, A. & Chaudhary, “A. Indian sign language recognition using SVM.” Pattern Recognit. Image Anal. 26, 434–441 (2016) 3. Kumar, P., Saini, R., Roy, P.P. et al., “A position and rotation invariant framework for sign language recognition (SLR) using Kinect”, Multimedia Tools Applications (2018) 77: 8823. 4. Hisham, B. & Hamouda, “Supervised learning classifiers for Arabic gestures recognition using Kinect V2”, Springer Nature Applied Sciences, 2019 1: 768. 5. Molina, J., Pajuelo, J.A. & Mart´ınez, “Real-time Motion-based Hand Gestures Recognition from Time-of-Flight Video”, Journal of Signal Process Syst (2017) 86: 17. 6. Hasler, B.S., Salomon, O., Tuchman, P. et al., “Real-time gesture translation in intercultural communication”, AI & Soc (2017) 32: 25. 7. J. Joy, K. Balakrishnan, Sreeraj M, SignQuiz: “A Quiz Based Tool for Learning Fingerspelled Signs in Indian Sign Language Using ASLR”, IEEE Access Vol 7, 2019, pp 28363-28371. 8. A. S. C. S. Sastry, P. V. V. Kishore, D. Anil Kumar, E. Kiran Kumar, “Sign Language Conversion Tool (SLCTooL) Between 30 World Sign Languages”, Smart Computing and Informatics, 2018, pp 701-711. 9. E. Song, H. Lee, J. Choi and S. Lee, "AHD: Thermal Image-Based Adaptive Hand Detection for Enhanced Tracking System," in IEEE Access, vol. 6, pp. 12156-12166, 2018. 10. T. L. Baldi, S. Scheggi, L. Meli, M. Mohammadi and D. Prattichizzo, "GESTO: A Glove for Enhanced Sensing and Touching Based on Inertial and Magnetic Sensors for Hand Tracking and Cutaneous Feedback," in IEEE Transactions on Human-Machine Systems, vol. 47, no. 6, (Dec. 2017) pp. 1066-1076, 11. Simon T, Joo H, Matthews I, Sheikh Y. Hand keypoint detection in single images using Multiview bootstrapping. In Proceedings of the IEEE conference on Computer Vision and Pattern Recognition 2017 (pp. 1145-1153). 12. X. Deng, Y. Zhang, S. Yang, P. Tan, L. Chang, Y. Yuan, and H. Wang, “Joint Hand Detection and Rotation Estimation Using CNN”, IEEE Transactions on Image Processing, vol. 27, no. 4, (April 2018) pp. 1888-1900. 13. American Sign Language [Internet]. NIDCD. 2019 [cited 25 December 2019]. Available from: https://www.nidcd.nih.gov/health/american-sign-language 14. Jane E. Johnson and Russell J. Johnson. 2008. Assessment of Regional Language Varieties in Indian Sign Language. SIL Electronic Survey Reports 2008-006. 1-121. 15. "Interpreters and translators: employees U.S. 2018 | Statista", Statista, 2020. [Online]. Available: https://www.statista.com/statistics/320340/number-of-employees-in-interpreting-and-translating-services-us/. [Accessed: 06- Mar- 2020]. 16. ANNUAL REPORT. New Delhi: INDIAN SIGN LANGUAGE RESEARCH AND TRAINING CENTER, 2017, p. 14. 17. "Help & Resources - British Deaf Association", British Deaf Association, 2020. [Online]. Available: https://bda.org.uk/help- resources/#statistics. [Accessed: 06- Mar- 2020] Ivan Alexander, Nanda Adytiansyah, Oei Kurniawan Utomo, Sembada Denrineksa Bimorogo, Erick Authors: Pinenka Paper Title: Success Factors and Challenges in Behavior Driven Development Abstract: Behavior Driven Development (BDD) is a software development process that combines the general techniques and principles of Test Driven Development (TDD) with ideas from Domain Driven Design (DDD) and Object Oriented (OO) analysis. It describes a cycle of interactions with well-defined outputs, resulting in the deliverable, tested working software. Today, BDD has evolved into an established agile practice. However, compared to other agile methodology frameworks, such as Scrum and Kanban, BDD is a relatively new. Thus, available resources explaining BDD is still limited and the BDD approach is still under development. Based on this observation, this literature review aims to provide the key of success as well as the challenge that lies on the implementation process of BDD in IT Project. We identified 3 success factors and 5 challenges. The success factors are focusing in product value, having a thorough system behavior definition, and using the right BDD 158. supporting tools. Meanwhile, the most challenging part are the difficulties in writing BDD scenario and automating the test case to maintain the system quality. 858-861

Keywords: Behavior Driven Development, BDD, IT Project, IT Software.

References:

1. D. North, “Introducing BDD,” Better Software, March, 2006. 2. W. Bissi, A. G. Serra Seca Neto, and M. C. F. P. Emer, “The effects of test driven development on internal quality, external quality and productivity: A systematic review,” Information and Software Technology. 2016. 3. C. Solís and X. Wang, “A study of the characteristics of behaviour driven development,” in Proceedings - 37th EUROMICRO Conference on Software Engineering and Advanced Applications, SEAA 2011, 2011. 4. R. A. De Carvalho, R. S. Manhães, and F. L. D. C. E. Silva, “Filling the Gap between Business Process Modeling and Behavior Driven Development,” New York, 2010. 5. R. A. De Carvalho and R. S. Manhaes, “Mapping Business Process Modeling constructs to Behavior Driven Development Ubiquitous Language,” arXiv10064892v1, 2010. 6. M. Diepenbeck and R. Drechsler, “Behavior Driven Development for Tests and Verification,” in Formal Modeling and Verification of Cyber-Physical Systems, Wiesbaden: Springer Fachmedien Wiesbaden, 2015, pp. 275–277. 7. J. Sutherland, “Agile development: Lessons learned from the first scrum,” Cut. Agil. Proj. Manag. Advis. Serv. …, 2003. 8. M. Olausson, J. Rossberg, J. Ehn, and M. Sköld, “Kanban,” in Pro Team Foundation Service, Berkeley, CA: Apress, 2013, pp. 91–99. 9. R. Vallon, B. J. da Silva Estácio, R. Prikladnicki, and T. Grechenig, “Systematic literature review on agile practices in global software development,” Inf. Softw. Technol., 2018. 10. C. de O. Melo et al., “The evolution of agile software development in Brazil,” J. Brazilian Comput. Soc., vol. 19, no. 4, pp. 523– 552, Nov. 2013. 11. V. Garousi and J. Zhi, “A survey of software testing practices in Canada,” Journal of Systems and Software. 2013. 12. D. Zayan, A. Sarkar, M. Antkiewicz, R. S. P. Maciel, and K. Czarnecki, “Example-driven modeling: on effects of using examples on structural model comprehension, what makes them useful, and how to create them,” Softw. Syst. Model., pp. 1–27, Jan. 2018. 13. P. Palla, G. Frau, L. Vargiu, and P. Rodriguez-Tomé, “QTREDS: A ruby on rails-based platform for omics laboratories,” BMC Bioinformatics, 2014. 14. J. M. Alberola, V. Botti, and J. M. Such, “Advances in infrastructures and tools for multiagent systems,” Inf. Syst. Front., vol. 16, no. 2, pp. 163–167, Apr. 2014. 15. G. Karagöz and H. Sözer, “Reproducing failures based on semiformal failure scenario descriptions,” Softw. Qual. J., 2017. 16. E. Bjarnason, M. Unterkalmsteiner, M. Borg, and E. Engström, “A multi-case study of agile requirements engineering and the use of test cases as requirements,” Inf. Softw. Technol., 2016. 17. S. Mäkinen et al., “Improving the delivery cycle: A multiple-case study of the toolchains in Finnish software intensive enterprises,” Inf. Softw. Technol., vol. 80, pp. 175–194, Dec. 2016. 18. R. O. Sinnott and W. Voorsluys, “A scalable Cloud-based system for data-intensive spatial analysis,” Int. J. Softw. Tools Technol. Transf., 2016. 19. T. Mens, A. Decan, and N. I. Spanoudakis, “A method for testing and validating executable statechart models,” Softw. Syst. Model., 2018. 20. E. Pyshkin, “In the right order of brush strokes: a sketch of a software philosophy retrospective,” Springerplus, vol. 3, no. 1, p. 186, Dec. 2014. 21. P. Arcaini, A. Gargantini, and E. Riccobene, “Rigorous development process of a safety-critical system: from ASM models to Java code,” Int. J. Softw. Tools Technol. Transf., 2017. 22. S. Willuweit and L. Roewer, “The new y chromosome haplotype reference database,” Forensic Sci. Int. Genet., 2015. 23. D. Lubke and T. Van Lessen, “Modeling Test Cases in BPMN for Behavior-Driven Development,” IEEE Softw., 2016. 24. Á. Carrera, C. A. Iglesias, and M. Garijo, “Beast methodology: An agile testing methodology for multi-agent systems based on behaviour driven development,” Inf. Syst. Front., 2014. 25. S. G. Yaman et al., “Introducing continuous experimentation in large software-intensive product and service organisations,” J. Syst. Softw., vol. 133, pp. 195–211, Nov. 2017. 26. W. M. Watanabe, R. P. M. Fortes, and A. L. Dias, “Acceptance tests for validating ARIA requirements in widgets,” Univers. Access Inf. Soc., 2017. Authors: Akshay Babrekar, Rohini G. Pise

Paper Title: Public Key Encryption for Cloud Storage Attack using Blockchain Abstract: Cloud storage enables user to store data and make it available when it is requested by user. Data generated electronically is very important and it must be encrypted to make sure that the data is tramper-proof. There are two important points to be considered, keyword guessing attack and making cloud storage secure from hackers. In Keyword guessing attack the Keywords search by user are encrypted using secure mechanism and securing the cloud storage means use such techniques which assured to give Confidentiality, Integrity and Accessibility using Blockchain Technology. It is decentralized cloud storage which assist different security mechanisms to protect data. Decentralized cloud storage is itself secure than centralized cloud storage. Because the concept of decentralized is not to store data on single storage device but to store on multiple servers. While storing the data on different location it divided into small parts, and at the time of retrieving data it is available as a complete single block of original data. Whereas in centralized cloud storage data is stored on single storage device. As technology progress the risk from fraudulent users also increases. For this reason, we need some encryption, decryption and authentication mechanism to verify user and if it is authenticated allow access to use its data. There are some techniques also available where user made request on cloud server to receive data which makes cloud server to learn keywords except resulting data. In this paper we make an attempt to review 159. encryption and decryption for cloud storage using blockchain technology to improve security of data.

Keywords: Cloud storage, Keyword guessing attack, securing the cloud, Blockchain, Encryption. 862-867

References:

1. Yuan Zhang, Chunxiang Xu, Jianbing Ni, Hongwei Li, Xuemin Shen - Blockchain-assisted Public-key Encryption with Keyword Search against Keyword Guessing Attacks for Cloud Storage, IEEE Transactions on Cloud Computing, vol. 2168-7161 (c) 2019. 2. Sabrina De Capitani di Vimercati, Sara Foresti, Stefano Paraboschi, Marco Rosa, Pierangela Samarati - Securing Resources in Decentralized Cloud Storage, Ieee Transactions on Information Forensics and Security, Vol. Xx, No. Yy, Month 2019. 3. Shangping Wang, Xu Wang, And Yaling Zhang - A Secure Cloud Storage Framework with Access Control Based on Blockchain, IEEE Access, Volume 7, 2019. 4. Yinghui Zhang, Robert H. Deng, Jiangang Shu, Kan Yang, Dong Zheng - TKSE: Trustworthy Keyword Search Over Encrypted Data with Two-Side Verifiability via Blockchain, IEEE Access, Volume 6, 2018. 5. G. Abinaya, Preksha Kothari, Alex Pavithran KP, Manasi Biswas, Farheen Khan - Block Chain Based Decentralized Cloud Storage, International Journal of 6. Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958, Volume-8 Issue-4, April 2019. 7. Zibin Zheng1, Shaoan Xie1, Hongning Dai2, Xiangping Chen4, and Huaimin Wang - An Overview of Blockchain Technology: Architecture, Consensus, and Future Trends, 978-1-5386-1996-4/17 $31.00 © 2017 IEEE DOI 10.1109/BigDataCongress.2017.85. 8. Simanta Shekhar Sarmah - Application of Block chain in Cloud Computing, IEEE Computer Society, 978-1-5386-1996-4/17 $31.00 © 2017 IEEE DOI 10.1109/BigDataCongress.2017.85. 9. Edoardo Gaetani, Leonardo Aniello, Roberto Baldoni, Federico Lombardi, Andrea Margheri, and Vladimiro Sassone - Blockchain-based Database to Ensure Data Integrity in Cloud Computing Environments, e First Italian Conference on Cybersecurity (ITASEC17), Venice, Italy, 2017. 10. Jin Ho Park, Jong Hyuk Park - Blockchain Security in Cloud Computing: Use Cases, Challenges and Solutions, Symmetry 2017, 9, 164; DOI:10.3390/sym9080164. 11. Vidhya Ramani, Tanesh Kumar, An Braeken, Madhusanka Liyanage, Mika Ylianttila - Secure and Efficient Data Accessibility in Blockchain based Healthcare Systems, Research gate, DOI: 10.1109/GLOCOM.2018.86472221. 12. Yuan Zhang, Yunlong Mao, Minze Xu, Fengyuan Xu and Sheng Zhong - Towards Thwarting Template Side-channel Attacks in Secure Cloud Deduplications, DOI 10.1109/TDSC.2019.2911502, IEEE Transactions on Dependable and Secure Computing. Authors: L. D. P Cuong, Wang Dong, D. T. Hoang, L. M. N Uyen Breast Cancer Prediction based on Deep Neural Network Model Implemented AWS Machine Paper Title: Learning Platform Abstract: Breast cancer in women is one of the most dangerous cancers leading to death in women by developing breast tissue. In this work, the application of the Deep Neural Network (DNN) model is implemented on AWS machine learning platform, besides, a comparison with other ML techniques includes XGBoost and Random Forest on a public dataset. Breast cancer prediction based on DNN model with Hyperparameter tuning has the best results of the plot of model accuracy for the training and validation sets and performance evaluation metrics to test the model.

Keywords: Breast cancer, Deep Neural Network, Deep Learning, AWS SageMaker, Docker containers.

References:

1. Dhanalakshmi, G., Keerthana, P., Rohini, M. and Karunamoorthy, Y. (2019). Decision Support System for Breast Cancer Predicttion. IJRASET, 7(3), March 2019, pp 816–821. Retrieved from http://doi.org/10.22214/ijraset.2019.3142 2. Subashini, A., Thamarai, S. M. and Meyyappan, T. (2019). Advanced Weather Forecasting Prediction using Deep Learning. IJRASET, 7(8), Aug 2019, pp 939-945. Retrieved from http://doi.org/10.22214/ijraset.2019.8139 3. Specht, D. F. A general regression neural network, IEEE transactions on neural networks, 2 (6), 1991 4. Niaei, A., Towfighi, J., Khataee, A. R. and Rostamizadeh, K. (2007). The use of ANN and the mathematical model for prediction of the main product yields in the thermal cracking of naphtha. Petroleum science and technology, 25 (8), Aug 2007, pp 967-982. Retrieved from http://doi.org/10.1080/10916460500423304 5. Cloud AutoML https://cloud.google.com/automl/ 6. L. Faes et al (2019). Automated deep learning design for medical image classification by health-care professionals with no coding experience: a feasibility study, The Lancet Digital Health, 1(5), 2019 160. 7. Bisong, E. (2019). Google AutoML: Cloud Vision, Building Machine Learning and Deep Learning Models on Google Cloud Platform. 2019 8. Hoang, D. T., Yang, P. L., Cuong, L. D. P., Trung, P. D., Tu, N. H., Truong, L. V., ... and Nha, V. T. (2020). Weather prediction 868-873 based on LSTM model implemented AWS Machine Learning Platform. IJRASET, 8(5), May 2020, pp 283–290. Retrieved from http://doi.org/10.22214/ijraset.2020.5046 9. Medisetty, H. K., Kunjam, N. R. (2018). Prediction of Breast Cancer using Machine Learning techniques. IJMTE, 8(12), December 2018, pp 5261-5269. Retrieved from http://doi.org/16.10089.IJMTE.2018.V8I12.17.2594 10. Sivapriya, J., Aravind, K. V., Siddarth, S. S. and Sriram, S. (2019). Breast Cancer Prediction using Machine Learning. IJRTE, 8(4), November 2019, pp 4879-4881. Retrieved from http://doi.org/10.35940/ijrte.D8292.118419 11. Yeulkar, K., Sheikh, R. (2017). Utilization of Data Mining Techniques for Analysis of Breast Cancer Dataset Using R. IJRASET, 5(3), March 2017, pp 406-410. Retrieved from http://doi.org/10.22214/ijraset.2017.3074 12. Atrey, K., Sharma, Y., Bodhey, N. K., & Singh, B. K. (2019). Breast cancer prediction using dominance-based feature filtering approach: A comparative investigation in machine learning archetype. Brazilian Archives of Biology and Technology, 2019, 62. Retrieved from http://dx.doi.org/10.1590/1678-4324-2019180486 13. Dhahri, H., Al Maghayreh, E., Mahmood, A., Elkilani, W., & Faisal Nagi, M. (2019). Automated Breast Cancer Diagnosis Based on Machine Learning Algorithms. Journal of Healthcare Engineering, 2019. Retrieved from https://doi.org/10.1155/2019/4253641 14. AWS https://aws.amazon.com/vi/ 15. Amazon Sagemaker https://aws.amazon.com/vi/sagemaker/ 16. Moolayil, J., Moolayil, J., and John, S. (2019). Learn Keras for Deep Neural Networks. Apress. 17. Ketkar, N. (2017). Introduction to keras. In Deep learning with Python Apress, Berkeley, CA, 2017, pp 97-111. 18. Ma, X., et al. (2018). Study on a prediction of P2P network loan default based on the machine learning LightGBM and XGboost algorithms according to different high dimensional data cleaning, Electronic Commerce Research and Applications, 2018, 31, 24- 39. 19. Choi, D. K (2019). Data-Driven Materials Modeling with XGBoost Algorithm and Statistical Inference Analysis for Prediction of Fatigue Strength of Steels, International Journal of Precision Engineering and Manufacturing, 2019, 20(1), pp 129-138. 20. Jean, S. et al. (2020), Breast Cancer Classification and Prediction using Machine Learning, IJERT, February 2020, 9(2). Retrieved from http://dx.doi.org/10.17577/IJERTV9IS020280 21. Cito, J., Ferme, V. and Gall, H. C. (2016). Using Docker containers to improve reproducibility in software and web engineering research, International Conference on Web Engineering, 2016. Authors: Archana Sharma Internet of Things: Impact of IoT in Business Environment and Challenges in Secure Paper Title: Implementation Abstract: The Internet of Things as new paradigm is interconnection of computing ,physical and mechanical device together with people with the ability to transfer the data over network. The embedded devices may gather 161. and swap over data with the support of network connectivity, sensors and electronics. The diversified deployment area of IoT are not limited to smart home application, health care industry, education industry and 874-879 agriculture, etc. It also taking step ahead in developing new products or services in business. With the help of this emerging technology business will have the major impact by improved customer engagement, productivity enhancement, and better access to data, enhanced inventory tracking and security. Whereas the rapid growth rate of IoT network is getting attention of the cyber criminals. In recent advancement, different types of embedded IoT devices are connected together with wireless network and continuously access internet for communication. Cyber criminals are finding vulnerabilities on IoT devices and compromise them to launch massive attacks (e.g. DDoS, Spamming, MITM, RFID Skimming) to destroy the network. IoT devices having default authentication credentials are easy target. This paper highlights that how IoT may introduce the better opportunities in business and challenges of secure IoT connection and while communicating the IoT device. This research also highlights the security aspects of existing technologies and existing technologies and challenges with implementation.

Keywords: IoT, sensors, tracking device, embedded system, digital signature, DSA, RSA

References:

1. Haller, S., Karnouskos, S., & Schroth, C. (2009). The Internet Of Things In An Enterprise Context Springer Berlin, Heidelberg, pp. 14-28. 2. Mahadevan, B., (2000) Business models for Internet-based e-commerce: An anatomy, alifornia Management Review, vol. 4, pp. 55-69. 3. Baden-Fuller C., and Stefan Haefliger S., Business models and technological innovation, Long Range Planning, vol. 46/6, 2013. p. 419-426. 4. Narasimha Murthy, D., Vijaya Kumar, B., (2015) Internet Of Things (Iot): Is Iot A Disruptive Technology Or A Disruptive Business Model? Indian Journal of Marketing, vol. 45/8, pp. 18-27. 5. Lopez Research LLC, An Introduction to the Internet of Things (IoT), 2013 6. Meola, A., IoT for small business: Effects, opportunities & platforms, Business Insider, http://www.businessinsider.com/internet- of-things-small-business-opportunities-platforms-2016-18 7. Rashid, H.,Securing the Internet of Things, A Technical Seminar Report submitted for fulfilment of the requirements for the Degree of Bachelor of Technology, Biju Pattnaik University of Technology, 2012 8. Roman, R., Najera, P., Lopez, J., Securing the internet of things, Computer, vol. 44, pp. 51-58, 2011. 9. Zhang, W. (2010, April). Integrated security framework for secure web services. In 2010 Third International Symposium on Intelligent Information Technology and Security Informatics (pp. 178-183). IEEE. 10. Geer, D. (2003). Taking steps to secure web services. Computer, 36(10), 14-16. 11. Hassan, R., & Qamar, T. (2010). Asymmetric-key cryptography for contiki. 12. Wong, C. K., & Lam, S. S. (1998, October). Digital signatures for flows and multicasts. In Proceedings Sixth International Conference on Network Protocols (Cat. No. 98TB100256) (pp. 198-209). IEEE. 13. ZhannaLyasota (Aug, 2018). A Guide to Digital Signature Algorithms [Online]. Available: https://dzone.com/articles/digital- signature-1. Authors: B. Sesha Sai, B. Satya Sai

Paper Title: Understanding Reactive Power and Its Importance in Power Systems Abstract: The main intent and purpose of this paper is to flaunt the rudimentary and homespun idea of reactive power on electrical power systems. Electrical machines such as motors and generators require reactive power for the production of magnetic field. Transformers and transmission lines too obligatory reactive power while they bring up with resistance and inductance contend with the flow of current. The Voltage profile must be uplifted to push this power through line inductance. Suitable reactive power when not used can cause contemplative black- outs.

Keywords: Reactive power, Voltage profile, Black-out. 162. References: 880-883 1. Electrical power systems by C L Wadhwa 2. Reactive Power Control in AC Power Systems : Fundamentals and Current Issues by Naser Mahdavi Tabatabaei 3. Electrical power transmission sytem engineering : Analysis and Design by Turan Gonen 4. P. L. Noferi and L. Paris "Effects of Voltage and Reactive Power Constraints on Power System Reliability" IEEE Trans. on Power App. and Syst. vol. PAS-94 no. 2 pp. 482-490 Mar. 1975. 5. M. Giuntoli P. Pelacchi and D. Poli "On the use of Simplified Reactive Power Flow Equations for Purposes of Fast Reliability Assessment" in IEEE EUROCON Zagreb Croatia pp. 992-997 July 2013. M. Benidris S. Elsaiah and J. Mitra "Reliability and Sensitivity Analysis of Composite Power Systems Considering Voltage and Reactive Power Constraints" to appear in IET Generation Transmission and Distribution 2015. 6. M. Benidris and J. Mitra "Consideration of the Effects of Voltage and Reactive Power Constraints on Composite System Reliability" North American Power Symposium (NAPS) pp. 1-6 7–9 Sept. 2014. 7. www.energy.gov Authors: MZA Yazid, Munzir Razak

Paper Title: Influence of Tool Path Strategies and Pocket Geometry on Surface Roughness in Pocket Milling Abstract: This paper discusses the effect of tool path strategies and pocket geometry to surface roughness due to pocket milling process. The machining processes have been performed on mould steel DF2 using carbide insert end mill as the cutting tool. The cutting parameters for this experiment were kept constant while the variables 163. were cutting tool, path strategies and pocket geometries at three levels each. The effectiveness of different tool path strategy and different pocket geometry is evaluated in terms of measured surface roughness (Ra) of the 884-888 workpiece. The grade of a pocket is directly proportional with its surface roughness. The lowest surface roughness measurement was produced by pocket geometry B with parallel spiral cutting tool path strategy.

Keywords: Tool path strategy, pocket geometry, surface roughness, pocket milling.

References:

1. Elkeran, A., El-Midany, T. T., & Tawfik, H. (2006). Toolpath Pattern Comparison: Contour-Parallel with Direction-Parallel . Geometric Modeling and Imaging--New Trends, 06, 77 - 82. 2. Krar, S. F., & Gill, A. (2011). Technology of machine tools (7th ed.). New York, NY: McGraw-Hill. 3. Toh, C.K., 2004. A study of the effects of cutter path strategies and orientationsin milling. J. Mater. Process. Technol. 152, 346– 356. 4. Gologlu, C., & Sakarya, N. (2008). The effects of cutter path strategies on surface roughness of pocket milling of 1.2738 steel based on Taguchi method. Journal of Materials Processing Technology, 206(1–3), 7-15. 5. Wang, M.Y., Chang, H.Y., 2004. Experimental study of surface roughness in slot end milling AL2014-T6. Int. J. Mach. Tools Manuf. 44, 51–57. 6. Chartakom, S. & Butdee, S. (2008). Journal of Achievements in Materials and Manufacturing Engineering. Experiment on High Speed Machining Parametres for Sport Shoe Sole Mold Making Using Aluminium Alloy 5083, 6163 and 7075, 31(2), 307-307. Retrieved from Papers_vol31_2/31221.pdf. 7. Gokler, M.I., Ozanozgu , A.M., 2000. Experimental investigation of effects of cutting parameters on surface roughness in the WEDM process. Int. J. Mach. Tools Manuf. 40 (13), 1831–1848.. 8. ASSAB DF-3. (n.d.). DF3_Brochure. Retrieved March 4, 2014, from http://assabmalaysia.com/DF3_Brochure_090619_Ed.pdf. 9. Shamsuddin, K. A., Ab - Kadir, A. R., & Osman, M. H. (2013). A Comparison of Milling Cutting Path Strategies for Thin- Walled Aluminium Alloys Fabrication. The International Journal Of Engineering And Science (IJES) , 2(3), 01-08. 10. Ganesh, S. (1997). Software review: A review of Minitab – Release 11. Journal of Applied Mathematics and Decision Sciences, 1(1), 73-78. 11. Romero, P., Dorado, R., Díaz, F., & Rubio, E. (2013). Influence of Pocket Geometry and Tool Path Strategy in Pocket Milling of UNS A96063 Alloy. Procedia Engineering, 63, 523-531. Authors: M. V. Satish Kumar, M. Pradeep Kumar, S. Vamshi Krishna, K. Vikram Kumar Optimization of CNC Turning Parameters in Machining EN19 using Face Centered Central Paper Title: Composite Design Based RSM Abstract: Manufacturing a defect free (quality) product is playing a vital role in today’s globally competitive, customer oriented era. Meeting the demand of the market by producing sufficient quantity is another challenge. Achieving greater production rates without compromising on quality, increases the complexity of the task. Adopting modern manufacturing methods like CNC turning are essential to meet the above requirements. EN19 is an important member in the family of alloy steels, which has a wide variety of applications in automobile and machine tool industries. Optimization of machining parameters is crucial in obtaining the required outputs such as quality and productivity. In this work, optimization of CNC turning parameters for machining EN19 alloy steel is performed. The number of experiments was designed using face centred central composite based response surface methodology with varied independent process parameters namely cutting speed, feed and depth of cut. After designing the experiments, the performance measures such as surface roughness of the test samples and Material Removal Rate (MRR) is calculated using the existing formulae. The influence of parameters on MRR and surface roughness are determined by analysis of variance (ANOVA) and for significance interactions of the process parameters are also considered. Using MINITAB 17 software analysis is performed. Further, regression analysis has been done and second order mathematical model is obtained. Using desirability approach, optimization is carried out.

Keywords: Optimization, Response surface methodology, Machining.

References:

164. 1. Singaram Lakshmanan and Mahesh Kumar (2013)“Optimization of EDM parameters using Response Surface Methodology for EN31 Tool Steel Machining”. International Journal of Engineering Science and Innovative Technology (IJESIT) 2013; 2 (5): 64- 71. 889-896 2. Pratik A. Patil and C.A. Waghmare (2014). “Optimization of process parameters in wire-edm using response surface methodology”. International Journal of Mechanical and Production Engineering 2014; 2(8):15-20. 3. Gaurav chaudhary, Manoj kumar, Santosh verma and Anupam srivastav (2014) “Optimization of drilling parameters of hybrid metal matrix composites using response surface methodology”. Procedia Materials Science 2014; 6:229 – 237. 4. Paresh D.Patel (2015) “Box–Behnken response surface methodology for optimization of operational parameters of compression ignition engine fuelled with a blend of diesel, biodiesel and diethyl ether”. Taylor & Francis 2015; 1-14. 5. Funda kahraman (2015) “Application of the response surface methodology in the ball burnishing process for the prediction and analysis of surface hardness of the aluminum alloy AA 7075”. Materials Testing 2015; 57(4):311-315. 6. Mr. Somanath M.kale and Mr. D.S khedekar (2016) “Optimization of Process parameters in EDM for Machining of Inconel 718 using Response Surface Methodology”. International Journal of Innovations in Engineering and Technology 2016; 7(3):188-193. 7. Sushanth Kumar Behera, Himanshu Meena, Sudipto Chakraborty and B.C.Meikap (2018) “Application of response surface methodology (RSM) for optimization of leaching parameters for ash reduction from low-grade coal”. International Journal of Mining Science and Technology 2018; 28:621–629. 8. N.Indhusekaran, Pradeep M, Raguraman M, Process Parameter Optimization on H11 Steel in CNC Drilling Process through Coated and Uncoated Drill Bits, International Journal of Innovative Research in Science, Engineering and Technology, Vol 5(6), ISSN: 2347-6710 9. Dennis K. Muriithi, J. K. Arap Koske, Geofrey K. Gathungu. “Application of Central Composite Design Based Response Surface Methodology in Parameter Optimization of Watermelon Fruit Weight Using Organic Manure”. American Journal of Theoretical and Applied Statistics 2017; 6(2): 108-116. 10. Singaram Lakshmanan, Prakash Chinnakutti, Mahesh Kumar Namballa. “Optimization of Surface Roughness using Response Surface Methodology for EN31 Tool Steel EDM Machining”. International Journal of Recent Development in Engineering and Technology 2013; 1(3):33-37. 11. Seyed Mojib Zahraee, Ghasem Rezai,Ashkan Memari and Jafar Afshar. “Teaching the Design of Experiment and Response Surface Methodology Using Paper Helicopter Experiment”. Conference paper 2013. 12. Khairul Anwar Mohamad Said and Mohamed Afizal Mohamed Amin. “Overview on the Response Surface Methodology (RSM) in Extraction Processes”. Journal of Applied Science & Process Engineering 2015; 2(1):8-17. 13. Sahoo, P. “Optimization of turning parameters for surface roughness using RSM and GA”. Advances in Production Engineering & Management 2011; 6(3):197-208. 14. Vishwajeet Kumar and Prof. Rakesh. “Analysis of EDM Process Parameters Using Response Surface Methodology and Grey Relational Analysis”. International Journal of Science Research and Education 2016; 4(9):5907-5921. 15. L.B.Abhang and M.Hameedullah. “Optimization of Machining Parameters in Steel Turning Operation by Taguchi Method”. Procedia Engineering 2012; 38:40-48. 16. Rahul Davis, Vikrant Singh, Shaluza Priyanka. “Optimization of Process Parameters of Turning Operation of EN 24 Steel using Taguchi Design of Experiment Method”. Proceedings of the World Congress on Engineering 2014; 2. 17. Tanveer Hossain Bhuiyan and Imtiaz Ahmed. “Optimization of Cutting Parameters in Turning Process," SAE Int. J. Mater. Manf. 2014; 7(1). 18. R.Babu, D.S.Robinson Smart, G.Mahesh and M. Shanmugam. “Effect of Machining Parameters and Optimization of Machining Time in Facing Operation using Response Surface Methodology and Genetic Algorithm”. Indian journal of science and technology 2015; 8(36):3-9. 19. D.Vishnu Vardhan Reddy, N. Jaya krishna and N.Bhaskar. “Optimization Of Cutting Parameters In Turning Of En-19 By Using Taguchi And Genetic Algorithm”. International Journal of Engineering and Technical Research 2016; 5(1). 20. A.Chabbi, M.A.Yallese, M.Nouioua, I. Meddour, T. Mabrouki and Francois Girardin. “Modeling and optimization of turning process parameters during the cutting of polymer (POM C) based on RSM, ANN, and DF methods”. Int J Adv Manuf Technol (2017) 91:2267–2290. 21. Sudhir Kumar, Pradeep Kumar, and H. S. Shan. “Effect of Process Parameters on the Solidification Time of Al-7%Si Alloy Castings Produced by VAEPC Process”. Materials and Manufacturing Processes 2007; 22:879-886. 22. Mohan Kumar Pradhan and Chandan Kumar Biswas. “Modelling of machining parameters for MRR in EDM using response surface methodology”. Proceedings of NCMSTA’08 Conference, National Conference on Mechanism Science and Technology: from Theory to Application 2008; National Institute of Technology, Hamirpur.535-542. 23. Gökhan Orhan, Gökçe Hapçı, Özgül Keleş. “Application of Response Surface Methodology (RSM) to Evaluate the Influence of Deposition Parameters on the Electrolytic Cu-Zn Alloy Powder”. International Journal of Electrochemical Science 2011; 6:3966 – 3981. 24. Dr. M. Naga Phani Sastry, K. Devaki Devi, Dr, K. Madhava Reddy. “Analysis and Optimization of Machining Process Parameters Using Design of Experiments”. Industrial Engineering Letters 2012; 2(9):23-32. Authors: A. Eliwa Gad, M. Helmy A. Raouf, A. A. Ammar

Paper Title: Design of Programmable Inductive Voltage Divider using Minimum Inductive Elements Abstract: In this paper, a proposed inductive voltage divider is described to be operated manually and automatically, which is not provided by the ordinary decade inductive voltage dividers. Design of that programmable inductive voltage divider (PIVD) is investigated and presented in detail. The introduced PIVD mainly consists of inductive elements, relays, microcontroller to get the required output voltage ratios through its three stages. This PIVD is designed to produce 999 steps in the range from 1×10-3 to 999×10-3 output ratios using minimum inductive elements and reed relays. Simulation of this PIVD has been performed and illustrated as well as practical design components are discussed in detail.

Keywords: Programmable inductive voltage divider; Reed relays; simulation; Microcontroller; voltage ratio

References:

1. Grubmüller, B. Schweighofer, and H. Wegleiter, “Characterization of a Resistive Voltage Divider Design for Wideband Power Measurements”, IEEE SENSORS, pp. 1332–1335, Nov. 2014. 2. W. Wang, Y. Yang, L. Huang, and Z. Zhang, “Establishing of a 1000 V Multi-Decade Inductive Voltage Divider Standard at 165. NIM”, IEEE Access, vol. 6, pp. 58594–58599, 2018. 3. K. Suzuki, “A New Self-Calibration Method for a Decade Inductive Voltage Divider by Using Bifilar Windings as an Essential Standard at Wide Frequency”, IEEE Transactions on Instrumentation and Measurement, vol. 58, no. 4, pp. 985–992, Apr. 2009. 897-901 4. S. Kon and T. Yamada, “Load Characteristics of Two-staged Inductive Voltage Dividers”, 29th Conference on Precision Electromagnetic Measurements (CPEM 2014), pp. 758–759, Aug. 2014. 5. B. C. Waltrip, A. D. Koffman, and S. Avramov-Zamurovic, “The Design and Self-Calibration of Inductive Voltage Dividers for an Automated Impedance Scaling Bridge”, Conference Proceedings IMTC, vol. 2, pp. 1191–1195, May 2002. 6. T. Yamada, S. Kon, and N. Sakamoto, “Evaluations of a Wideband Inductive Voltage Divider and Non-sinusoidal Power Measurement System”, Conference on Precision Electromagnetic Measurements (CPEM 2010), Daejeon, Korea (South), pp. 239–240, Jun. 2010. 7. V. L. Kim, V. N. Dainakov, and A. B. lljin, “Extending of the Frequency Range of a Multidecade Inductive Voltage Divider”, 8th Russian-Korean International Symposium on Science and Technology, KORUS, Tomsk, Russia, vol. 1, pp. 241–244, 2004. 8. D. Filipović-Grčić, B. Filipović-Grčić, and K. Capuder, “Modeling of Three-phase Autotransformer for Short-circuit Studies”, International Journal of Electrical Power & Energy Systems, vol. 56, pp. 228–234, Mar. 2014. 9. A. Dutta and S. S. Ang, “Effects of Parasitic Parameters on Electromagnetic interference of power electronic modules”, IEEE Applied Power Electronics Conference and Exposition (APEC), pp. 2706–2710, Mar. 2017. 10. J. Zhang et al., “Design and Self-calibration of Cascaded Inductive Voltage Divider with Ratio of 2n:1”, Conference on Precision Electromagnetic Measurements (CPEM 2016), pp. 1–2, Jul. 2016. 11. M. Helmy A. Raouf, A. Eliwa Gad, El-Sayed Soliman A. Said, and M. A. Elwany, “Fully Automated Inductance Measuring System Using New Fabricated Inductance Box”, MAPAN-Journal of Metrology Society of India, vol. 32, no. 3, pp. 199–205, Sep. 2017. 12. “IEC 60618:1978/AMD2:1997- Inductive Voltage Dividers | IEC Webstore.” https://webstore.iec.ch/publication/2725 (accessed Apr. 11, 2019). Authors: Mallikarjun Aralimarad, Meena S M, Jayashree D Mallapur

Paper Title: A Comprehensive Survey on Human Action Recognition Abstract: The present The present situation is having many challenges in security and surveillance of Human 166. Action recognition (HAR). HAR has many fields and many techniques to provide modern and technical action implementation. We have studied multiple parameters and techniques used in HAR. We have come out with a 902-908 list of outcomes and drawbacks of each technique present in different researches. This paper presents the survey on the complete process of recognition of human activity and provides survey on different Motion History Imaging (MHI) methods, model based, multiview and multiple feature extraction based recognition methods.

Keywords: Computer Vision, HAR, , Histogram of Oriented Gradients(HOG), MHI.

References:

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167. Authors: Ruth Chweya, Samuel-Soma M. Ajibade, Abba Kyari Buba, Moveh Samuel Paper Title: IoT and Big Data Technologies: Opportunities and Challenges for Higher Learning Abstract: Internet of Things (IoT) and Big data have been speculated to result in various opportunities and challenges to higher learning. The two mentioned technologies inclusive of cloud computing, pervasive learning and 3D printer can improve teaching and learning beyond classrooms. Furthermore, the above-mentioned technologies have led to enhancements and valuable living in many sectors like education, medicine, agriculture, and even security. Taking into consideration the predictions made for IoT and Big Data, it is beneficial to provision for confidence in this current world. IoT and Big Data have therefore emerged as innovations to change the education sector for quality training, education, and research. However, there have been challenges accruing to the mentioned technologies. This study gave an approach of how both IoT and Big Data could play similar beneficial roles to higher learning. This paper suggests that IoT and Big data can be incorporated in learning as they complement each other in supporting intelligent connections. The outcome is an enriched learner with effective and efficient study surrounding. Further, from the study, both technologies also share similar issues like security, transparency, and huge gathered information. Hence, this paper concentrates on the relationship between IoT and Big Data, examines their opportunities in education and mentions their challenges to improve the education sector.

Keywords: Big Data, Education, Higher Learning, Internet of Things.

References:

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The study collects the primary data for analysis through a structured questionnaire distributed among the employees working in multi-national corporations operating in Andhra Pradesh. The study results reveal there is a significant relationship towards appraisal system and career advancement. However, the appraisal system and job satisfaction and organizational commitment are certainly found as insignificant for career advancement in MNCs. Many Indian companies have been borrowing modern appraisal methods from the domestic and foreign organization in order to improve the best performance appraisal climate among selected multinational corporations operating in Sri City at Nellore, Andhra Pradesh.

Keywords: Performance Appraisal Practices, Career advancement, Job Satisfaction and Organizational Commitment.

168. References:

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Paper Title: Motivational Factors for Knowledge Sharing and Trust Abstract: sharing knowledge is transmission of knowledge (implicit or tacit) from an organization, group, or person to another one. Through sharing knowledge, organizations are able to improve their effectiveness, saves cost of training and moderate risks due to lack of certainty. While managing knowledge, organizations find it difficult to motivate employees for sharing knowledge with others. Therefore, it is essential to recognize the elements impacting information sharing and trust. This paper attempts to understand trust and persuasive variables that impact information sharing conduct in associations. It is huge that there are a not many investigations because of inspirational factors on information sharing conduct through trust as an arbitrator. Right now, specialist proposed a hypothetical system that consolidated inspirational elements with Theory of Reasoned Action (TRA) to depict the relationship among inspiration (extraneous and inherent), trust and demeanors toward information sharing. This paper will be important to the experts as it gives a premise of understanding persuasive elements for information sharing and trust.

Keywords: Knowledge sharing, Trust, Motivational Factor, TRA

References:

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The study of barrier factors in knowledge sharing: A case study in public university. Management Science and Engineering, 5(1), 59. 170. Authors: Simriti Koul Contribution of Cognitive Science and Artificial Intelligence in the Simulation of the Complex Paper Title: Human Mind Abstract: The research incorporated encircles the interdisciplinary theory of cognitive science in the branch of artificial intelligence. It has always been the end goal that better understanding of the idea can be guaranteed. Besides, a portion of the real-time uses of cognitive science artificial intelligence have been taken into consideration as the establishment for more enhancements. Before going into the scopes of future, there are many complexities that occur in real-time which have been uncovered. Cognitive science is the interdisciplinary, scientific study of the brain and its procedures. It inspects the nature, the activities, and the elements of cognition. Cognitive researchers study intelligence and behavior, with an emphasis on how sensory systems speak to, process, and change data. Intellectual capacities of concern to cognitive researchers incorporate recognition, language, memory, alertness, thinking, and feeling; to comprehend these resources, cognitive researchers acquire from fields, for example, psychology, artificial intelligence, philosophy, neuroscience, semantics, and anthropology. The analytic study of cognitive science ranges numerous degrees of association, from learning and choice to logic and planning; from neural hardware to modular mind organization. The crucial idea of cognitive science is that "thinking can best be understood in terms of representational structures in the mind and computational procedures that operate on those structures."

Keywords: Artificial intelligence, ACT-R, Android’s feature of Text-to-speech, bimodal stimuli, cognitive science, Microsoft Azure, SOAR, Turing test.

References:

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Hassabis, D., Kumaran, D., Summerfield, C., et al.: „Neuroscience-inspired artificial intelligence‟, Neuron, 2017, 95, (2), pp. 245–258. 6. Geman, D., Geman, S., Hallonquist, N., et al.: „Visual turing test for computer vision systems‟, Proc. Natl. Acad. Sci., 2015, 112, (12), pp. 3618–3623. 7. Luber, S.: „Cognitive science artificial intelligence: simulating the human mind to achieve goals‟. 2011 3rd Int. Conf. on 928-932 Computer Research and Development, Shanghai, China, March 2011, vol. 1, pp. 207–210. 8. Hua, H., Johansson, B., Magnusson, L., et al.: „Speech recognition and cognitive skills in bimodal cochlear implant users‟, J. Speech Lang. Hear. Res., 2017, 60, (9), pp. 2752–2763. 9. Vandierendonck, A.: „A working memory system with distributed executive control‟, Perspect. Psychol. Sci., 2016, 11, (1), pp. 74–100. 10. 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Beaty, R.E., Kaufman, S.B., Benedek, M., et al.: „Personality and complex brain networks: the role of openness to experience in default network efficiency‟, Hum. Brain Mapp., 2016, 37, (2), pp. 773–779. 15. Di Nuovo, A., Varrasi, S., Conti, D., et al.: „Usability evaluation of a robotic system for cognitive testing‟. 2019 14th ACM/IEEE Int. Conf. on HumanRobot Interaction (HRI), Daegu, Korea, March 2019, pp. 588–589. 16. Arora, M.R., Sharma, J., Mali, U., et al.: „Microsoft cognitive services‟, Int. J. Eng. Sci., 2018, 8, (4), p. 17323. 17. Li, D., Du, Y.: „Artificial intelligence with uncertainty‟ (CRC press, Florida, USA, 2017). 18. Dzyubenko, E., Gottschling, C., Faissner, A.: „Neuron-glia interactions in neural plasticity: contributions of neural extracellular matrix and perineuronal nets‟, Neural Plast., 2016, 2016, pp. 170–195. 19. Gaohua, L., Neuhoff, S., Johnson, T.N., et al.: „Development of a permeability-limited model of the human brain and cerebrospinal fluid (CSF) to integrate known physiological and biological knowledge: estimating time varying CSF drug concentrations and their variability using in vitro data‟, Drug Metab. Pharmacokinet., 2016, 31, (3), pp. 224-237. 20. Prajval Mohan, Pranav Narayan, Lakshya Sharma, Tejas Jambhale, Simran Koul, "Iterative SARSA: The Modified SARSA Algorithm for Finding the Optimal Path". International Journal of Recent Technology and Engineering (IJRTE). ISSN: 2277- 3878, Volume-8 Issue-6, March 2020. 21. Prajval Mohan, Adiksha Sood, Lakshya Sharma, Simran Koul, Simriti Koul, “PC-SWT: A Hybrid Image Fusion Algorithm of Stationary Wavelet Transform and Principal Component Analysis”. „International Journal of Engineering and Advanced Technology (IJEAT)‟, ISSN: 2249-8958, Volume-9 Issue-5, June 2020. 22. Simran Koul, “Contribution of Artificial Intelligence and Virtual Worlds Towards Development of Super Intelligent AI Agents”, „International Journal of Engineering and Advanced Technology (IJEAT)‟, ISSN: 2249-8958, Volume-9 Issue-5, 30 June 2020. 23. Simran Koul, Yash Raj, Simriti Koul, “Analyzing Cyber Trends in OnlineFinancial Frauds using Digital Forensics Techniques”, „International Journalof Innovative Technology and Exploring Engineering (IJITEE)‟, ISSN: 2278-3075, Volume-9 Issue-9, 15 July 2020. Authors: Ajmal Paktiawal, Mehtab Alam Effect of Alkali Resistant Glass Fiber on Performance of Cement Concrete - A Review of 171. Paper Title: Experimental Investigations Abstract: Cement concrete, a universally accepted construction material is enough strong in compression but 933-950 weak in tension with limited ductility and therefore its resistance to cracking is low attributed to the inherent presence of micro-internal cracks. In order to improve its resistance to cracking, and post peak response, alkali- resistant glass fiber (ARGF) as an additive is one of the options. This is added in certain percentage during mixing and the concrete is generally called alkali-resistant glass fiber reinforced concrete (ARGFRC). This paper presents review of the experimental research works carried out on use of type with regards to aspect ratio and quantity of ARGF in cement concrete. Type and quantity both influence the properties of fresh and hardened ARGFRC. The dosage effect with quantity of the fiber on fresh concrete such as workability, and mechanical performance, seismic vulnerability evaluation, microstructural analysis, durability aspects on hardened concrete have been reviewed. It is found that alkali-resistant glass fiber together with silica fume can be utilized to enhance the toughness and load carrying capacity and improve the stiffness of the composite concrete.

Keywords: Alkali Resistant Glass Fiber; Alkali Resistant Glass Fiber Reinforced Concrete; Durability; Microstructural analysis; Admixture.

References:

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Branco, The use of glass fiber reinforced concrete as a structural material, Society for Experimental Mechanics. (2007) 64-73. doi: 10.1111/j.1747-1567.2007. 00153.x 8. Shang-Lin Gao, Edith Ma¨der, Anwar Abdkader, Peter Offermann, Sizings on Alkali-Resistant Glass Fibers: Environmental Effects on Mechanical Properties, Institute of Textile and Clothing Technology, Dresden University of Technology, Dresden, Germany Langmuir. 19 (2003) 2496-2506. 9. Mahmoud Mazen Hilles, Mohammed M. Ziara, Ahmed Y.Aboshama, Mechanical behavior of high strength concrete reinforced with glass, Engineering Science and Technology,an International Journal. 22 (2019) 920-928. 10. Tejal Desai, Rimpal Shah, Alav Peled, Barzin Mobasher, Mechanical Properties of Concrete Reinforced with AR-Glass Fibers, ZTUREK RSI and Woodhead Publ., Warsaw. (2003) 1-10. 11. 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Chang, Alkali-resistant glass fiber reinforced high strength concrete in simulated aggressive environment, Materials de Construcción. 329 (2017) 1-14. 17. WU Huijun, ZHAO Jing, WANG Zhongchang, SONG Ting, Damage Action of Alkali-resistant Glass Fiber in Cement-based Material, Journal of Wuhan University of Technology-Mater. (2013) 761-765. 18. Shashidhara Marikunte, Corina Aldea, Surendra P. Shah, Durability of Glass Fiber Reinforced Cement Composites, NSF Center for Advanced Cement-Based Materials, Northwestern University, Evanston, Illinois. 5 (1997) 101-107. 19. A. Peled, J. Jones, S.P. Shah, Effect of matrix modification on durability of glass fiber reinforced cement composites. RILEM TC TRC 'Textile reinforced concrete. 38 (2004) 163-171. 20. X. Qian, B. Shen, B. Mu, Z. Li, Enhancement of aging resistance of glass fiber reinforced cement, Materials and Structures / Matdriaux et Constructions. 36 (2003) 323-329. 21. Arabi Nourredine, Influence of curing conditions on durability of alkali-resistant glass fibres in cement matrix, Bull. Mater. Sci. 34 (2011) 775-783. 22. Rose Mary Georg, Bibhuti Bhusan Das, Sharan Kumar Goudar, Durability Studies on Glass Fiber Reinforced Concrete, Sustainable Construction and Building Materials, Lecture Notes in Civil Engineerin. (2019) 747-756. 23. ASTM C995-01, Standard test method for time of flow of fiber-reinforced concrete through inverted slump cone, American Society for Testing and Materials. (2001) 1-2. 24. IS 10510-2004, Method of tests for Vee-Bee Consistometer, Bureau of Indian Standards, New Delhi. 25. S. U. Kannan, Selvamony C, M. S. Ravikumar, S. Basil Gnanappa, Investigation and study on the effect of AR Glass polymer fiber in self-compacting self-curing concrete. ARPN Journal of Engineering and Applied Sciences. 2 (2010) 41-45. 26. S. Jagan, R. Gokul Kannan, S. Prasanth, Effect of chopped glass fiber on strength and durability of concrete, International Journal of Civil Engineering and Technology. 11 (2017) 600–608. 27. B.S. Krishnamurthy, R.Balamuralikrishnan, Mohammed Shakil, An experimental work on alkaline resistance glass fiber reinforced concrete, International Journal of Advanced Engineering Management and Science. 7 (2017) 730-737. 28. Yuwaraj M. Ghugal, Santosh B. Deshmukh, Performance of alkali-resistant glass fiber reinforced concrete, Journal of Reinforced Plastics and composites. 6 (2006) 617-630. 29. Hanuma Kasagani, C.B.K Rao, Effect of graded fibers on stress strain behavior glass fiber reinforced concrete in tension. www.elsevier.com. (2018) 593-604. 30. N. Arabi, Static and cyclic performance of cementitious composites reinforced with glass-fibers, Materiales de ConstruCCión. 329 (2017) 1-12. 31. Sujit V. Pati, and N. J. Pathak, the experimental study on compressive strength of concrete using AR glass fibers and partial replacement of cement with GGBS with effect of magnetic water, International Journal of Engineering Technology, Management and Applied Sciences. 8 (2016) 2349-4476. 32. Erhan Guneyisi, Yahya R. Atewi, Mustafa F. Hasan, Fresh and rheological properties of glass fiber reinforced self-compacting concrete with nanosilica and fly ash blended, Construction and Building Materials. 211 (2019) 349-362. 33. S. A. Yildizel, O.Timur, A. U. Ozturk, Abrasion resistance and mechanical properties of waste glass fiber reinforced roller compacted concrete, Mechanics of Composite Materials. 54 (2018) 251-256. 34. Dayalan J, a study on strength characteristics of glass fiber reinforced high-performance concrete, International Research Journal of Engineering and Technology. 4 (2017) 1-5. 35. B. Rath, S. Deo, G. 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Prasanth, Effect of chopped glass fiber on strength and durability of concrete, International Journal of Civil Engineering and Technology. 11 (2017) 600–608. 41. V.R.Rathi, A.V.Ghogare ,S.R.Nawale, Experimental study on glass fiber reinforced concrete moderate beam, International Journal of Innovative Research in Science Engineering and Technology. 3 (2014) 10639-10645. 42. Widodo Kushartomo, RichardIvan, Effect of glass fiber on compressive, flexural and splitting strength of reactive powder concrete, MATEC Web of Conferences. 138 (2017) 1-6. 43. P. Sangeetha, Study on the compression and impact strength of GFRC with combination of admixtures, Journal of Engineering Research and Studies. 2 (2011) 36-40. 44. B.S. Krishnamurthy, R.Balamuralikrishnan, Mohammed Shakil, An experimental work on alkaline resistance glass fiber reinforced concrete, International Journal of Advanced Engineering Management and Science. 7 (2017) 730-737. 45. Yuwaraj M. Ghugal, Santosh B. 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Paper Title: Annual Rainfall Prediction of Various States in India using Linear Regression Abstract: Rainfall prediction is a significant part in agriculture, so prediction of rainfall is essential for the best financial development of our nation. In this paper, we represent the linear regression method to predict the yearly rainfall in different states of India. To predict the estimate of yearly rainfall, the linear regression is implemented on the data set and the coefficients are used to predict the yearly rainfall based on the corresponding parameter values. Finally an estimate value of what the rainfall might be at a given values and places can be establish easily. In this paper, we demonstrate how to predict the yearly rainfall in all the states from the year 1901 to 2015 by using simple multi linear regression concepts. Then we train the model using train _test_ split and analyze various performance measures like Mean squared error, Root mean squared error, R^2 and we visualize the data using scatter plots, box plots, expected and predicted values.

Keywords: Rainfall Prediction, Linear Regression, Learning Process, Machine Learning.

References: 172. 1. Goswami.P and Srividya, “A novel Neural network design for long range prediction of rainfall pattern,” Current Sci.(Bangalore), vol. 70, no. 6, pp. 447-457, 1996. 951-954 2. Venkatesanet.C, S. D. Raskar , S. S. Tambe , B. D. Kulkarni , and R.N. Keshavamurty , “Prediction of all India summer monsoon rainfall using Error Back-Propagation Neural Networks,” Meteorology and Atmospheric Physics, pp. 225-240, 1997. 3. Sahai.A. K., M. K. Soman, and V. Satyan, “All India summer monsoon rainfall prediction using an Artificial Neural Network,” Climate dynamics, vol. 16, no. 4, pp. 291-302, 2000. 4. Philip.N.S. and K. B. Joseph, “On the predictability of rainfall in Kerala-An application of ABF neural network,” Computational Science- ICCS, Springer Berlin Heidelberg, pp. 1-12, 2001. 5. Somvanshi.V. K., O. P. Pandey, P. K. Agrawal, N.V.Kalanker1, M.Ravi Prakash, and Ramesh Chand, “Modeling and prediction of rainfall using Artificial neural Network and ARIMA techniques,” J. Ind. Geophys. Union, vol.10, no.2, pp. 141-151, 2006. 6. Chattopadhyay.S. and G. Chattopadhyay, “Comparative study among different neural net learning algorithms applied to rainfall time series”, Meteorological applicat., vol. 15, no. 2, pp. 273-280, 2008. 7. Wu. C. L, K. W. Chau, and C. Fan, “Prediction of rainfall time series using Modular Artificial Neural Networks coupled with data preprocessing techniques," J. of hydrology, vol. 389, no. 1, pp. 146-167, 2010. 8. Htike. K. K and O. O. Khalifa, “Rainfall forecasting models using Focused Time-Delay Neural Networks,” Comput. and Commun. Eng.(ICCCE), Int. Conf. on IEEE, 2010. 9. Kannan.M., S.Prabhakaran, P.Ramachandran, “Rainfall Forecasting Using Data Mining Technique”, International Journal of Engineering and Technology, Vol.2 (6), 397-401, 2010. 10. G. Geetha and R. S. Selvaraj, “Prediction of monthly rainfall in Chennai using Back Propagation Neural Network model,” Int. J. of Eng. Sci. and Technology, vol. 3, no. 1, pp. 211 213, 2011. Authors: Jayprakash Umap, Shantanu Shelke, Satyam Tripathi, Omkar Karande, Abhimanyu Chandgude 173. Manufacturing and Optimization of Process Parameters by using Abrasive Water Jet Machining of Paper Title: Carbon Fibre Reinforced Polymer Composites Abstract: In the last decade, invention of new material is the point of interest of researchers. Carbon composite epoxy is one of those materials, which are currently used in transportation, aerospace, structural as well as naval applications. It is very difficult to machine those carbon composite materials using traditional methods, so an updated solution for this issue is machining using Abrasive Water Jet. Significant input parameters namely Standoff Distance, Abrasive Mass Flow Rate and Traverse Rate are varied for various outputs, out of which kerf width is main point of focus. For précised values, Kerf Width measurement is carried out using Profile Projector. The process parameters were further optimized using GRA and Taguchi method. Regression models were developed for correlation with actual generated data using experiments. The result obtained using Optimization technique and Taguchi method is confirmed using confirmation experiment. The parameters were optimized for Kerf Width of carbon fibre with reference to input parameters by using AWJM.

Keywords: Abrasive Mass Flow Rate, Abrasive Water Jet Machining, Carbon Fibre Reinforced Polymer, Kerf Width, Standoff Distance, Traverse Speed.

References:

1. M. El-Hofy, M. O. Helmy, G. Escobar-Palafox, K. Kerrigan, R. Scaife, H. El-Hofy. Abrasive Water Jet Machining of Multidirectional CFRP Laminates.19th CIRP Conference on Electro Physical and Chemical Machining, 23-27 April 2018, Bilbao, Spain. 2. S vighneshwaran, M uthayakumar, V Arumugaprabu. Abrasive water jet machining of fiber- reinforced composite materials. Journal of Reinforced Plastics and Composites 2017. 3. Raul Ruiz Garcia, Pedro F. Mayuet, Juan Manuel Vazquez Martinez, Jorge Salguero Gomez. Influence of Abrasive Waterjet 955-961 Parameters on the Cutting and Drilling of CFRP Preprints.org -2018. 4. Harsh Pandey, Shatbadan Soni. Analysis and Optimization of Machining Parameters of AJM on Composite Fiber Reinforced Polymer. IJLTEMAS- 2018 5. Prasad D.Unde, M. D. Gayakwad, N. G. Patil, R. S. Pawade,D. G. Thakur, P. K. Brahmankar. Experimental Investigations into Abrasive Waterjet Machining of Carbon Fiber Reinforced Plastic. Hindawi Publishing Corporation Journal of Composites-2015 6. B Jagadeesh, P Dinesh Babu, M Nalla Mohamed, P Marimuthu. Experimental investigation and optimization of abrasive water jet cutting parameters for the improvement of cut quality in carbon fiber reinforced plastic laminates. SAGE Journal – 2018. 7. Saleel Visal, Swapnil U. Deokar. A Review Paper on Properties of Carbon Fiber Reinforced Polymers. IJIRST - 2016. 8. Adel ABIDI, Sahbi Ben SALEM, Mohamed Athman YALLESE. Experimental and Analysis in Abrasive Water jet cutting of carbon fiber reinforced plastics. Congrès Français de Mécanique – 2019. 9. Binduk Potom, S. Madhu, S.Kannan, P. Prathap. Performance Analysis of Abrasive Water Jet Cutting Process in Carbon Fiber Epoxy Polymer Composite. International Conference on Materials Engineering and Characterisation – 2014. 10. Fuji Wang, Xiaonan Wang, Rui Yang, Hanqing GAO, Youliang Su and Guangjian Bi. Research on the carbon fibre-reinforced plastic (CFRP) cutting mechanism using macroscopic and microscopic numerical simulations. Journal of Reinforced Plastics and Composites – 2016. 11. Mayur M. Mhamunkar and Niyati Raut. Process Parameter Optimization of CNC Abrasive Water Jet Machine for Titanium Ti6Al4V. International Journal of Advance Industrial Engineering - 2017 12. M.Sreenivasa Rao, S.Ravinder and A.Seshu Kumar Parametric Optimization of Abrasive Waterjet Machining for Mild Steel Taguchi Approach. International Journal of Current Engineering and Technology - 2014 13. D. Sidda Reddy, A. Seshu Kumar, M. Sreenivasa Rao, Parametric Optimization of Abrasive Water Jet Machining of Inconel 800H Using Taguchi Methodology, Universal Journal of Mechanical Engineering - 2014 14. P.P. Badgujar, M.G. Rathi. Mathematical modelling and optimization of machining parameter by using Taguchi approach for Aluminum and Stainless-Steel materials. International Journal of Engineering and Advanced Technology (IJEAT)-2014 15. Vinod B. Patel, Prof. V. A. Patel on implementation of Taguchi approach for optimization of abrasive water jet machining process parameters on Aluminum material by ANOVA Technique of optimization. (IJERA) – 2012 Authors: Deya Aldeen Bakheet, Azrul Amri Jamal

Paper Title: Internet of Things Applications Abstract: Internet of Things technology has become one of the most advanced technologies that will change and change many traditional things and add many advantages to many systems and environment and improve the level of services provided to many users, whether in the field of smart health care or smart homes or smart energy etc.In this paper, we will introduce the concept of the Internet of Things, its applications and components for a broader understanding of (I oT) and try to enable and support the Internet of Things and develop applications that support and improve the efficiency of services and other functions. And the importance of integrating Internet of Things with artificial intelligence and machine learning to assist in the analysis and processing of the enormous data collected by wireless sensors from all fields in order to provide sufficient information and help solve many problems such as the problem of energy consumption and the problem of 174. traffic congestion and pollution, etc.

Keywords: Internet of Things, Smart Cities, Smart Energy, Cloud Computing 962-968

References:

1. Filman, R. E. (2005). Internet computing. In IEEE Internet Computing (Vol. 9). https://doi.org/10.1109/MIC.2005.125. 2. Tamizan, M., Bakar, A., Jamal, A. A., & Madi, E. N. (2019). Resource Discovery in High-Volume Internet of Things: Systematic Research. International Journal of Innovative Technology and Exploring Engineering, 8(12S2), 437–444. https://doi.org/10.35940/ijitee.l1085.10812s219. 3. Osuwa, A. A., Ekhoragbon, E. B., & Fat, L. T. (2018). Application of artificial intelligence in the Internet of Things. Proceedings - 9th International Conference on Computational Intelligence and Communication Networks, CICN 2017, 2018-Janua, 169–173. https://doi.org/10.1109/CICN.2017.8319379. 4. Al-mandhari, I. S., Guan, L., & Edirisinghe, E. A. (2019). Advances in Information and Communication Networks (Vol. 886). Springer International Publishing. https://doi.org/10.1007/978-3-030-03402-3. 5. Ghosh, A., Chakraborty, D., & Law, A. (2018). Artificial intelligence in the Internet of things. CAAI Transactions on Intelligence Technology, 3(4), 208–218. https://doi.org/10.1049/trit.2018.1008. 6. Ali, M. F., Kwame, A. B., Nam, I. F., Svetlik, M. V, & Zhong, Y. (2019). The Internet of Things and Benefits at a Glance, (July). 7. Vashi, S., Ram, J., Modi, J., Verma, S., & Prakash, C. (2017). Internet of Things (IoT): A vision, architectural elements, and security issues. Proceedings of the International Conference on IoT in Social, Mobile, Analytics, and Cloud, I-SMAC 2017, (December), 492–496. https://doi.org/10.1109/I-SMAC.2017.8058399. 8. Ray, P. P. (2018). A survey on Internet of Things architectures. Journal of King Saud University - Computer and Information Sciences, 30(3), 291–319. https://doi.org/10.1016/j.jksuci.2016.10.003. 9. Worlu, C., Jamal, A. A., & Mahiddin, N. A. (2019). Wireless Sensor Networks, Internet of Things, and Their Challenges. International Journal of Innovative Technology and Exploring Engineering, 8(12S2), 556– 566.https://doi.org/10.35940/ijitee.l1102.10812s219. 10. Rad, B. B., & Ahmada, H. A. (2018). Internet of Things : Trends , Opportunities , and Challenges (July 2017). 11. Abu Bakar, M. T., & Jamal, A. A. (2020). Latency issues in internet of things: A review of literature and solution. International Journal of Advanced Trends in Computer Science and Engineering, 9(1.3 Special Issue), 83–91. https://doi.org/10.30534/ijatcse/2020/1291.32020. 12. Silva, B. N., Khan, M., & Han, K. (2017). Internet of Things: A Comprehensive Review of Enabling Technologies, Architecture, and Challenges. IETE Technical Review, 0(0), 1–16. https://doi.org/10.1080/02564602.2016.1276416 13. Qureshi, Z., Agrawal, N., & Chouhan, D. (2018). Cloud-based IoT : Architecture , Application, Challenge, and Future, 3(7), 359– 368. 14. F., H., H., E., & A., A. (2019). Internet of Things Applications and its Security. International Journal of Computer Applications, 182(41), 9–11. https://doi.org/10.5120/ijca2019918475. 15. Daniel, M., & Benedict, O. (2018). Internet of Things Applications for Smart Cities. Internet of Things A to Z, 507–528. https://doi.org/10.1002/9781119456735.ch12. 16. Alavi, A. H., Jiao, P., Buttlar, W. G., & Lajnef, N. (2018). Internet of Things-enabled smart cities: State-of-the-art and future trends.Measurement: Journal of the International Measurement Confederation, 129(July), 589–606. https://doi.org/10.1016/j.measurement.2018.07.067. 17. Kim, T. hoon, Ramos, C., & Mohammed, S. (2017). Smart City and IoT. Future Generation Computer Systems, 76(July 2014), 159–162. https://doi.org/10.1016/j.future.2017.03.034. 18. Joy, A., & Manivannan, D. (2017). Smart Energy Management and Scheduling using the Internet of Things. Indian Journal of Science and Technology, 9(48). https://doi.org/10.17485/ijst/2016/v9i48/108001. 19. Nayanatara, C., Divya, S., & Mahalakshmi, E. K. (2018). Micro-Grid Management Strategy with the Integration of Renewable Energy Using IoT. 7th IEEE International Conference on Computation of Power, Energy, Information and Communication, ICCPEIC 2018, 160–165. https://doi.org/10.1109/ICCPEIC.2018.8525205. 20. Kumar, N. M., Manoj Kumar, N., & Dash, A. (2017). The Internet of Things: An Opportunity for Transportation and Logistics (November 2017). Retrieved from https://www.researchgate.net/publication/321242420. 21. Dlodlo, N. (2015). The internet of things in transport management in South Africa. Proceedings of 2015 International Conference on Emerging Trends in Networks and Computer Communications, ETNCC 2015,19– 26,https://doi.org/10.1109/ETNCC.2015.7184802. 22. Tripathi, V., & Shakeel, F. (2018). Monitoring health care system using the internet of things-an immaculate pairing. Proceedings - 2017 International Conference on Next Generation Computing and Information Systems, ICNGCIS 2017, (April), 164–167. https://doi.org/10.1109/ICNGCIS.2017.26. 23. Hdowkfduh, R. I., Xvlqj, V., Ri, Q., Frp, V. V. J., Vxuyh, S. D., Ydulrxv, R. I., … Wkhp, R. (2018). Survey of Smart Healthcare Systems using the Internet of Things(IoT), 6, 508–513. 24. Kalaivanan, S., & Manoharan, S. (2016). Monitoring and controlling of smart homes using IoT and low power wireless technology. Indian Journal of Science and Technology, 9(31). https://doi.org/10.17485/ijst/2016/v9i31/92701. 25. Ravinder, B., & Raju, K. S. (2018). An Application of Internet of Things for Smart Home, (July). https://doi.org/10.22161/ijaers/si.28. 26. Mei, G., Xu, N., Qin, J., Wang, B., & Qi, P. (2019). A Survey of Internet of Things (IoT) for Geohazards Prevention: Applications, Technologies, and Challenges. IEEE Internet of Things Journal, PP(0), 1–1. https://doi.org/10.1109/jiot.2019.2952593. 27. Raun, N. F. (2016). Smart environment using the internet of things(IOTS) - A review. 7th IEEE Annual Information Technology, Electronics, and Mobile Communication Conference, IEEE ICON 2016. https://doi.org/10.1109/IEMCON.2016.7746313. 28. González García, C., Núñez-Valdez, E., García-Díaz, V., Pelayo G-Bustelo, C., & Cueva-Lovelle, J. M. (2018). A Review of Artificial Intelligence in the Internet of Things. International Journal of Interactive Multimedia and Artificial Intelligence, 5(4), 9. https://doi.org/10.9781/ijimai.2018.03.004. 29. Madakam, S., Ramaswamy, R., & Tripathi, S. (2015). Internet of Things (IoT): A Literature Review. Journal of Computer and Communications, (May), 164–173. https://doi.org/10.4236/jcc.2015.35021. Authors: Nijil Raj N, Anandu S Ram, Aneeta Binoo Joseph, Shabna S

Paper Title: Deep Learning Based Indian Currency Detection for Visually Challenged using VGG16 Abstract: Banknote recognition is a major problem faced by visually Challenged people. So we propose a system to help the visually Challenged people to identify the different types of Indian currencies through deep learning technique. In our proposed project, bank notes with different positions are directly fed into VGG 16, a pretrained model of convolution neural network which extracts deep features. From our work the visually impaired people will be able to recognize different types if Indian Currencies.

Keywords: Deep Learning, VGG16 175. References: 969-972 1. Gouri Sanjay Tele, Akshay Prakash Kathalkar, Sneha Mahakalkar, Bharat Sahoo, and Vaishnavi Dhamane. Detection of fake indian currency. International Journal of Advance Research, Ideas and Innovations in Technology, 4(2):170–176, 2018. 2. Naga Sri Ram B Yamini Radha V Rajarajeshwari P Navya Krishna G, Sai Pooja G. Recognition of fake currency note using convolutional neural networks. 4(2):182–186, 2018. 3. [3]N. A. J. Sufri, N. A. Rahmad, N. F. Ghazali, N. Shahar, and M. A. As’ari. Vision based system for banknote recognition using different machine learning and deep learning approach. In 2019 IEEE 10th Control and System Graduate Research Colloquium (ICSGRC), pages 5–8, 2019. 4. Qian Zhang, Wei Qi Yan, and Mohan Kankanhalli. Overview of currency recognition using deep learning. Journal of Banking and Financial Technology, 3(1):59–69, 2019. 5. Hung-Cuong Trinh, Hoang-Thanh Vo, Van-Huy Pham, Bhagawan Nath, and Van-Dung Hoang. Currency recognition based on deep feature selection and classification. In Asian Conference on Intelligent Information and Database Systems, pages 273–281. Springer, 2020. 6. Kan Chen, Jiang Wang, Liang-Chieh Chen, Haoyuan Gao, Wei Xu, and Ram Nevatia. Abc-cnn: An attention based convolutional neural network for visual question answering. arXiv preprint arXiv:1511.05960, 2015. 7. NA Jasmin Sufri, NA Rahmad, MA As’ari, NA Zakaria, MN Jamaludin, LH Ismail, and NH Mahmood. Image based ringgit banknote recognition for visually impaired. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(3- 9):103–111, 2017. 8. Snigdha Kamal, Simarpreet Singh Chawla, Nidhi Goel, and Balasubra- manian Raman. Feature extraction and identification of indian currency notes. In 2015 Fifth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG), pages 1–4. IEEE, 2015. Authors: D Anil Kumar, T.Rama Subba Reddy, A.Hari Prasad, P Sirisha

Paper Title: Automatic Working Phase Picker for Domestic load from Three phase supply Abstract: Absence of phase is a very common and serious problem in every sector, at home or workplace. Many times, one or two phases in three phase supply cannot be live. Regardless of this, certain electrical equipment in one room and OFF in another room would be on, several times. This causes considerable disturbance to our routine work. This paper is intended to test the availability of any live phase, and will only link the load to the specific live phase. There is only one phase available, and then the load will still be ON. The idea is conceived with ARDUINO. This controller continually checks the live state of all connected phases, using a Relay the load is connected to active phase by controller. Transistor is operated the relay.When two or more phases are active 76. but load is only connected to phase 1,that active phase number is display in LCD for observation.

Keywords: Controller, LCD, Relay, ARDUINO. 973-975

References:

1. Su Chen, GCza Jobs, ‘‘Series and Shunt Active Power Conditioners for Compensating Distribution System Faults’’, Vol. 142, No. 1 , Jan. 1955. 2. Anu P,Divya R, Dr. Manjula G Nair, ‘‘STATCOM Based Controller for a Three Phase System Feeding Single Phase Loads’’ 2015 IEEE International Conference L.S. Ezema, B.U. Peter, O.O. Harris, 3. ‘‘DESIGN OFAUTOMATIC CHANGE OVER SWITCH WITH GENERATOR CONTROL MECHANISM’’,ISSN: 2223-9944 Vol. 3, No. 3, November 2012 4. Ahmed,M.S.,Mohammed,A.S.,Agusiobo,O.B, ‘‘Single Phase Automatic Change-Over Switch, AU J.T.10(1): 68-74)’’ Authors: Syed. Siddik, G. JogaRao, D.V.N. Ananth Application of CUK and ZETA Dc-Dc Choppers for Power Factor Correction and Power Quality Paper Title: Improvement of BLDC Motor Abstract: For industrial, electrical vehicles, drives, and grid applications ac-dc based inverters with power factor correction is considered as a key element to meet the power system firm regulatory rules. Among these dc-ac inverters, bridgeless converters play a vital role. In this paper, Bridgeless cuk and zeta converter-fed brushless dc (BLDC) motor drive is studied for power factor correction (PFC) and power quality improvement. The major objectives of this work are (i) dc bulk capacitance and inductance use is decreased, so that permanence film capacitors can be utilized, (ii) overall efficiency of BLDC drive system is improved due to decreased losses in the switches and conducting devices and (iii) guaranteed high or unity source power factor. The motor and source parameters are examined in this study for PFC using CUK and ZETA based dc-dc converters with MATLAB software. The work is compared with bridgeless ZETA, CUK and.

Keywords: bridgeless converter, ZETA dc-dc converter, CUK dc-dc converter, power factor correction.

References:

1. Du, Yi, Liang Du, Bin Lu, Ronald Harley, and Thomas Habetler. "A review of identification and monitoring methods for electric 77. loads in commercial and residential buildings." In 2010 IEEE Energy Conversion Congress and Exposition, pp. 4527-4533. IEEE, 2010. 2. Fu, Dianbo, Fred C. Lee, Yang Qiu, and Fred Wang. "A novel high-power-density three-level LCC resonant converter with 976-983 constant-power-factor-control for charging applications." IEEE Transactions on Power Electronics 23, no. 5 (2008): 2411-2420. 3. Zeng, Zheng, Huan Yang, Shengqing Tang, and Rongxiang Zhao. "Objective-oriented power quality compensation of multifunctional grid-tied inverters and its application in microgrids." IEEE transactions on power electronics 30, no. 3 (2014): 1255-1265. 4. Garland, David. "Penal power in America: Forms, functions and foundations." Journal of the British Academy 5 (2017): 1-35. 5. Bist, Vashist, and Bhim Singh. "An adjustable-speed PFC bridgeless buck–boost converter-fed BLDC motor drive." IEEE Transactions on Industrial Electronics 61, no. 6 (2013): 2665-2677. 6. Gopinath, M., and D. Yogeetha. "Efficiency analysis of bridgeless PFC boost converter with the conventional method." International Journal of Electronic Engineering Research 1, no. 3 (2009): 213_221. 7. Yadav, Apoorva, and Arunima Verma. "Sepic DC–DC Converter: Review of Different Voltage Boosting Techniques and Applications." In 2020 2nd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), pp. 733- 739. IEEE, 2020. 8. Musavi, Fariborz, Wilson Eberle, and William G. Dunford. "A phase-shifted gating technique with simplified current sensing for the semi-bridgeless AC–DC converter." IEEE Transactions on Vehicular Technology 62, no. 4 (2012): 1568-1576. 9. Siu, Ken KM, and Carl NM Ho. "A critical review of Bridgeless PFC boost rectifiers with common-mode voltage mitigation." In IECON 2016-42nd Annual Conference of the IEEE Industrial Electronics Society, pp. 3654-3659. IEEE, 2016. 10. Vishvanath, M., and R. Balamurugan. "An Review of Power Factor Correction in SRM Drives Using Bridgeless Converters." Telkomnika Indonesian Journal of Electrical Engineering 14 (2015). 11. Ma, Hongbo, Yuan Li, Jih-Sheng Lai, Cong Zheng, and Jianping Xu. "An improved bridgeless SEPIC converter without circulating losses and input-voltage sensing." IEEE Journal of Emerging and Selected Topics in Power Electronics 6, no. 3 (2017): 1447-1455. 12. Mahdavi, Mohammad, and Hosein Farzanehfard. "Bridgeless SEPIC PFC rectifier with reduced components and conduction losses." IEEE Transactions on Industrial Electronics 58, no. 9 (2010): 4153-4160. 13. Singh, Bhim, and Vashist Bist. "Power-quality improvement in PFC bridgeless SEPIC-fed BLDC motor drive." International Journal of Emerging Electric Power Systems 14, no. 3 (2013): 285-296. 14. Singh, Bhim, and Vashist Bist. "Improved power quality bridgeless Cuk converter fed brushless DC motor drive for air conditioning system." IET Power Electronics 6, no. 5 (2013): 902-913. 15. Yang, Hong-Tzer, Hsin-Wei Chiang, and Chung-Yu Chen. "Implementation of bridgeless Cuk power factor corrector with positive output voltage." IEEE Transactions on Industry Applications 51, no. 4 (2015): 3325-3333. 16. Singh, Bhim, Vashist Bist, Ambrish Chandra, and Kamal Al-Haddad. "Power factor correction in bridgeless-Luo converter-fed BLDC motor drive." IEEE Transactions on Industry Applications 51, no. 2 (2014): 1179-1188. 17. Singh, Bhim, and Radha Kushwaha. "An EV battery charger with power factor corrected bridgeless zeta converter topology." In 2016 7th India International Conference on Power Electronics (IICPE), pp. 1-6. IEEE, 2016. 18. Khan, Shakil Ahamed, Nasrudin Abd Rahim, Ab Halim Abu Bakar, and C. K. Tan. "Single-phase bridgeless Zeta PFC converter with reduced conduction losses." Journal of Power Electronics 15, no. 2 (2015): 356-365. 19. Singh, Bhim, and Vashist Bist. "Power quality improvements in a zeta converter for brushless DC motor drives." IET Science, Measurement & Technology 9, no. 3 (2014): 351-361. 20. Salehifar, Mehdi, Ghanim Putrus, and Peter Barras. "Analysis and comparison of conventional two-stage converter and single stage bridgeless ac-dc converter for off-road battery charger application." (2016): 7-7. 21. G. Joga Rao, D.V.N. Ananth, P.Kiran Kumar, P.RamReddy, “Performance Enhancement of PMBLDC Motor Drive by Multi- Carrier Modulation Technique”, International Journal of Innovative Technology and Exploring Engineering (IJITEE), 8 (7), May, 2019. 22. Bist, Vashist, and Bhim Singh. "A brushless DC motor drive with power factor correction using isolated zeta converter." IEEE Transactions on Industrial Informatics 10, no. 4 (2014): 2064-2072. 23. Kushwaha, Radha, and Bhim Singh. "UPF-isolated zeta converter-based battery charger for electric vehicle." IET Electrical Systems in Transportation 9, no. 3 (2019): 103-112. Authors: Swati Sharma, Mamta Bansal

Paper Title: Multilingual Lexicon based Approach for Real-Time Sentiment Analysis Abstract: The information on WWW has mounted to a greater height, overriding to fledgling analysis in the direction of sentiments using Artificial Intelligence. Sentiment Analysis deals with the calculus exploration of sentiments, opinions and subjectivity. In this paper, multilingual tweets are analyzed for identifying the polarities of various political parties like AAP, BJP, Samajwadi, BSP and Congress; so that the users will get an idea that to which party they should give their vote. The data is being analyzed using Natural Language Processing. Using different smoothening techniques, noise is removed from data, classified by using Machine learning algorithms and then the accuracy of the system is gauged using various evaluation precision measures. The central premise of this research is to benignant common people and politicians both. For common people; is for deciding their precious vote, to which party to give will be good for themselves and nation too. For politicians; they will have an idea about themselves i.e. after seeking the polarities of different parties, the politicians will have an idea which party is preferable and which is not preferable, so that the politicians can work accordingly. The system shows comparison among VADER and SVM algorithm; and SVM algorithm showed 90% accuracy.

Keywords: Lexicon, NLP, SVM, VADER

References:

1. Bing Liu, Sentiment Analysis and Opinion Mining, Synthesis Lectures on Human Language Technologies Morgan and Claypool 78. Publishers May (2012). 2. B.J. Jansen, M. Zhang, K. Sobel and A. Chowdury, Micro-blogging as online word of mouth branding., CHI’09 Extended Abstracts on Human Factors in Computing Systems, Boston, MA, USA, (2009) : 3859-3864. 984-989 3. Arti Buche, Dr.M.B.Chandak, Akshay Zadgoanakar, Opinion Mining and Analysis: A Survey, International Journal on Natural Language Computing (IJNLC) Vol 2 No 3 June (2013) : 39-48. 4. S. Asur and B.A. Huberman, Predicting the future with social media, Proc. of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, Washington, DC, USA: IEEE Computer Society, vol. 1 (2010) : 492-499. 5. Bucur Cristian, Aspects regarding detection of sentiment in web content, International Journal of Sustainable Economies Management (IJSEM), Volume 3, issue 4, p.24-32, ISSN: 2160-9659 (2014). 6. S Chandrakala, C M Sindh, Opinion Mining and Sentiment Classification: A Survey, SOCO DOI: 10.21917/ijsc.2012.0065 (2012). 7. Amrita Kaur, Neelam Duhan, A Survey on Sentiment Analysis and Opinion Mining, International Journal of Innovations & Advancement in Computer Science, IJIACS USSN 2347 – 8616 Volume 4, May (2015). 8. B.O. Connor, R. Balasubramanyan, B. R. Routledge and N. A. Smith, From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series, Proc. of the Fourth International AAAI Conference on Weblogs and Social Media, pp. 122–129 (2010) : 122-129. 9. B. Pang, L. Lee and S. Vaithyanathan, Thumbs up? sentiment classification using machine learning techniques, Proc. of the Conference on Empirical Methods on Natural Language Processing, (2002) : 79-86. Huizhi Liang , Umarani Ganeshbabu , Thomas Thorne. A Dynamic Bayesian Network Approach for Analysing Topic-Sentiment Evolution, Page(s): 54164 – 54174, 06 March 2020 10. Huyen Trang Phan , Van Cuong Tran , Ngoc Thanh Nguyen , Dosam Hwang . Improving the Performance of Sentiment Analysis of Tweets Containing Fuzzy Sentiment Using the Feature Ensemble Model, Page: 14630 – 14641, 03 January 2020, ISSN: 2169- 3536, INSPEC Accession Number: 19313387 11. L. Belcastro, R. Cantini, F. Marozzo, D. Talia, and P. Trun_o. Discovering political polarization on social media: A case study, in Proc. 15th Int. Conf. Semantics, Knowl. Grids, Guangzhou, China, 2019, pp. 1_8. 12. K. Jaidka, S. Ahmed, M. Skoric, and M. Hilbert. Predicting elections from social media: A three-country, three-method comparative study, Asian J. Commun., vol. 29, no. 3, pp. 252_273, May 2019.

79. Authors: Shikha Bhardwaj, Gitanjali Pandove, Pawan Kumar Dahiya A Journey from basic Image Features to Lofty Human Intelligence in Content-based Image Paper Title: Retrieval: Motivation, Applications and Future Trends Abstract: Due to a remarkable increase in the complexity of the multimedia content, there is a cumulative enhancement of digital images both online and offline. For the purpose of retrieving images from a vast storehouse of images, there is an urgent requirement of an effectual image retrieval system and the most effective system in this domain is denoted as content-based image retrieval (CBIR) system. CBIR system is generally based on the extraction of basic image attributes like texture, color, shape, spatial information, etc. from an image. But, there exists a semantic gap between the basic image features and high-level human perception and to reduce this gap various techniques can be used. This paper presents a detailed study about the various basic techniques with an emphasis on different intelligent techniques like, the usage of machine learning, deep learning, relevance feedback, etc., which can be used to achieve a high level semantic information in CBIR systems. In addition, a detailed outline regarding the framework of a basic CBIR system, various benchmark datasets, similarity measures, evaluation metrics have been also discussed. Finally, solution to some research issues and future trends have also been given in this paper.

Keywords: Deep Learning, Feature extraction, Image retrieval, Relevance Feedback, Similarity Matching.

References:

1. A. K. Naveena, N. K. Narayanan, “Image Retrieval using combination of Color, Texture and Shape Descriptor”. In: IEEE International Conf. on Next Generation Intelligent Systems (ICNGIS), 2017, pp.1-5 2. L. Piras, G. Giacinto, “Information fusion in content based image retrieval: A comprehensive overview” J. of Inf. Fusion, Vol. 25, 2017, pp. 1-44 3. J. M. Patel JM, “A Review on Feature Extraction Techniques in Content Based Image Retrieval” In: IEEE Conference on WiSPNET, 2016, pp. 2259–2263 4. A. Alzu’bi, A. Amira, N. Ramzan, “Semantic content-based image retrieval: A comprehensive study” J. Vis. Commun. Image Represent., Vol. 32(July), 2016, pp. 20–54 5. Y. Mistry, D. T. Ingole, “Content based image retrieval using hybrid features and various distance metric” J. Electr. Syst. Inf. Technol., 2016, pp. 1–15 6. P. C. Kuo, “Bridging the Semantic Gap in Content Based Image Retrieval” Int. J. Innov. Res. Comput. Commun. Eng. Peer Rev. Journal), Vol. 4, no. 5, 2016, pp. 1-8 7. V. Vinayak, “CBIR System using Color Moment and Color Auto-Correlogram with Block Truncation Coding” International Journal of Computer Applications. Vol. 161, no. 9, 2016, pp. 1–7 8. S. Fadaei, R. Amirfattahi, M. R. Ahmadzadeh, “New content-based image retrieval system based on optimised integration of DCD, wavelet and curvelet features” IET Image Process., Vol. 11, no. 2, 2017, pp. 89-98 9. L. K. Pavithra, T.S. Sharmila, “An efficient framework for image retrieval using color, texture and edge features” Comput. Electr. Eng., Vol. 0, 2017, pp. 1–14 10. C. Singh, K. Preet Kaur, “A fast and efficient image retrieval system based on color and texture features.” J. Vis. Commun. Image Represent., Vol.41(October), 2016, pp. 225-238 990-998 11. N.S.T. Sai, R. Patil, S. Sangle, B. Nemade B, “Truncated DCT and Decomposed DWT SVD Features for Image Retrieval” Procedia Computer Science, Vol. 79, 2016, pp. 579-588 12. A. Singla, M. Garg M, “CBIR Approach Based On Combined HSV , Auto Correlogram , Color Moments and Gabor Wavelet” Int. J. Eng. Comput. Sci. IJECS, Vol. 3, no. 10, 2014, pp. 9007–9012 13. M. Kaipravan, “A Novel CBIR System Based on Combination of Color Moment and Gabor Filter” In: IEEE International Conference on Data Mining and Advanced Computing (SAPIENCE) 1990, pp. 1-5 14. S. Bhardwaj, G. Pandove, P. K. Dahiya, “An Intelligent Multi-resolution and Co-occuring local pattern generator for Image Retrieval” EAI Endorsed Transactions on Scalable Information System. Vol. 6, no. 22, 2019, pp. 1–12. 15. N. Neelima, E. Sreenivasa Reddy, N. Kalpitha, “An Efficient QBIR System Using Adaptive Segmentation and Multiple Features” Procedia Comput. Sci., Vol. 87, 2016, pp. 134–139 16. A. Amanatiadis, V. G. Kaburlasos, A. Gasteratos, S. E. Papadakis, “Evaluation of shape descriptors for shape-based image retrieval” IET Image Processing, Vol. 5, no. 5, 2011, pp. 493-499 17. V. Naghashi, “Co-occurrence of adjacent sparse local ternary patterns : A feature descriptor for texture and face image retrieval” Opt. - Int. J. Light Electron Opt., Vol. 157, 2018, pp. 877–889 18. P. Sharma, “Improved shape matching and retrieval using robust histograms of spatially distributed points and angular radial transform” Opt. - Int. J. Light Electron Opt., Vol. 145, 2017, pp. 346–364 19. P. Chandana, P. Srinivas Rao, C. H. Satyanarayanan, Y. Srinivas, A. Gauthami Latha, “An Efficient Content-Based Image Retrieval (CBIR) Using GLCM for Feature Extraction”. Recent Developments in Intelligent Computing, Communication and Devices, Advances in Intelligent Systems and Computing, 2017, pp. 21-30 20. A. Anandh, K. Mala, S. Suganya S, “Content based image retrieval system based on semantic information using color, texture and shape features” Comput. In: IEEE Int. Conf. on Technol. Intell. Data Eng. (ICCTIDE), 2016, pp. 1–8 21. T. Pranckevičius, V. Marcinkevičius, “Comparison of Naïve Bayes , Random Forest , Decision Tree , Support Vector Machines and Logistic Regression Classifiers for Text Reviews Classification” Baltic J. Modern Computing, Vol. 5, no. 2, 2017, pp. 221– 232 22. M. Tzelepi, A. Tefas, “Deep convolutional image retrieval : A general framework” Signal Process. Image Commun., Vol. 63(August), 2018, pp. 30–43 23. O. A. Adegbola, D. O. Aborisade, S.I. Popoola, O. A. Amole, A. A. Atayero, P. Zheng, “Modified one-class support vector machine for content-based image retrieval with relevance feedback” Cogent Eng., Vol. 5, no. 1, 2018, 1–20 24. M. A. Ansari, M. Dixit, D. Kurchaniya, P. K. Johari, “An Effective Approach to an Image Retrieval using SVM Classifier”, International Journal of Computer Sciences and Engineering, August, 2018, pp. 62-72 25. N. Bhosle, M. Kokare, “Random forest based long-term learning for content based image retrieval” In: IEEE Int. Conf. on Signal and Information Processing (IConSIP), 2016, pp. 1-4 26. V. K. Govindan, K. S. Arun, “ A Hybrid Deep Learning Architecture for Latent Topic-based Image Retrieval” Data Sci. Eng., Vol. 3, no. 2, pp. 166–195 27. W. Liu, Z. Wang, X. Liu, N. Zeng, Y. Liu, F. E. Alsaadi, “A survey of deep neural network architectures and their applications” Neurocomputing, Vol. 234, 2017, pp. 11–26 28. P. Shamna, V. K. Govindan, K. A. Abdul Nazeer, “Content-based medical image retrieval by spatial matching of visual words” J. King Saud Univ. - Comput. Inf. Sci., Vol. xxx, 2018, pp. 1-14 29. N. Bouchra, A. Aouatif, N. Mohammed, H. Nabil, “Deep Belief Network and Auto-Encoder for Face Classification” Int. j. of interactive multimedia and artificial intelligence, 2018, http:// dx.doi.org /10.9781/ ijimai.2018.06.004, 1–9 30. T. Kinnunen, J. Kamarainen, L. Lensu, J. Lankinen, K. Heikki, "Making Visual Object Categorization More Challenging : Randomized Caltech-101 Data Set. In: Int. conf. on Pattern recognition, 2010, pp. 1–4 31. C. Iakovidou, N. Anagnostopoulos, A. Kapoutsis, Y. Boutalis, M. Lux, S. A. Chatzichristofis, “Localizing global descriptors for content-based image retrieva”. EURASIP Journal on Advances in Signal Processing, 2010, 10.1186/s13634-015-0262-6, pp. 1-20 32. R. Das, J. K. Dash, S. Mukhopadhyay, “Rotation invariant textural feature extraction for image retrieval using eigen value analysis of intensity gradients and multi-resolution analysis” Pattern recognition, vol. 46, 2013, pp. 3256–3267 33. 33. C. Che, K. P. Chung, C. C. Fung, “Relevance feedback and intelligent technologies in content-based Image Retrieval System for Medical Applications” Processing, Vol. 8(September), 2004, pp. 113- 122. Authors: Keerthi Kumar Narayan, Sharan Kumar Paratala Rajagopal

Paper Title: Anatomy of Model Based Testing Abstract: In a typical Software Development Life Cycle (SDLC), the software testing life cycle consists of reviewing of the requirements, test planning for design, development and execution. Test designing phase is considered as the most vital and foundational in deriving the test cases against the software or the application to be validate. The known fact is that in order to derive an effective test suite generally consumes a lot of manual efforts and good amount of expertise as well. [1] When the testers validate an application for its correct and required behavior, then that system is known as System Under Test (aka SUT), the most common term used in software testing process. Since, this is purely based on a manual approach and testers may not be able to validate all the possible and required scenarios, there may be risk of putting the system for validation. Because, the application may break under a particular use case. This can be overcome by applying Model Based Testing 80. (MBT).

Keywords: MBT, Model Based Testing, Testing, Model Based Design 999-1002

References:

1. Eckard Bringmann, Andreas Krämer PikeTec GmbH, Germany [email protected], [email protected]://files.piketec.com/downloads/papers/Kraemer2008 Model_based_testing_of_automotive_systems.pdf 2. Royal Cyber, 20-Feb-2019 https://www.royalcyber.com/wp-content/uploads/2019/03/Flexible-Approach-to-Test-Automation- with-TEAF-WhitePaper.pdf 3. Zoltan Micskei – Model Based Testing [MBT] http://mit.bme.hu/~micskeiz/pages/modelbased_testing.html 4. Greg Sypolt, 2018 https://saucelabs.com/blog/the-challenges-and-benefits-of-model-based-testing, 5. Shafique, Muhammad & Labiche, Yvan. (2010). A systematic review of model based testing tool support. Shafique, Yvan Labiche, May 2010 Authors: N.M. Jothi Swaroopan, A.Mano Garan, T.Surya Annamalai, E.Sai Teja

Paper Title: Smart Monitoring and Analyzing Process Level of Boiler Water Treatment Plant Abstract: Field instruments used in boiler water treatment plant are not able to access remotely and only able to control through central location Also the data are able to access through the internet only in the particular area. If any data lag or system failure occurs then it can be control only in the plant area. This becomes complex and cannot be applicable for all situation. To avoid such complexity happened due to system error and to monitor the system parameter remotely, a cloud based system from Siemen is used to connects plant, system and machines by IoT with advanced analytics and practically tested in Technocart Automation plant located in Ambattur, TamilNadu, India. This gives advance solution for monitoring the industrial parameter anywhere using Siemens Mindsphere and Mind connect nano Software.

81. Keywords: IOT (Internet of Things), Siemens S7-400, Siemens Mindsphere and Mind connect nano Software, DCS(Distributed Control System), SCADA(Supervisory Control & Data Acquisition). 1003- 1006 References:

1. Mr. Maldar Aman Malikamber, Mr. Tamhankar S.G., “Implementing SCADA system for industrial environment using „IEEE C37.1‟ standard”, IEEE C37.1: IEEE standard for SCADA & Automation system. 2. Zafar Aydogmus, Omur Aydogmus, “A Web Based Remote Access Laboratory Using SCADA” IEEE Transactions on Education, Volume 52, No.1, February, 2009. 3. Prof. Jaikaran Singh, Prof. Mukesh Tiwari, Mr. Manish shrivastava,” Industrial Automation-A Review”, Int. Journal of Advanced Engineering Tends And Technology Vol. 4, Issue 8, August 2013. 4. Bulipe Srinivas Rao, Prof. Dr. K. Srinivas Rao, Mr. N Ome,“ Internet of Things (IOT) based Weather Monitoring System”, Int. Journal of Advanced Research in Computer and Communication Engineering Vol. 5, Issue 9, September 2016. 5. Nashwa El-Bendary, Mohamed Mostafa M. Fouad, Rabies A Ramadan, Soumya Banerjee and Aboul Ella Hassanien, “Smart Environmental Monitoring Using Wireless Sensor Network” March 2019. Authors: Anitha. T, Madhu Mitha. R, Priyanka.T, Sanchya.V, Saranya. S

Paper Title: Smart Mobility in Urban Spaces

182. Abstract: There is a need to make a smart city nowadays, to increase population in cities. Today public transport is of vitaly importance. Conveyance we plan to wait for a bus to hit our place on a long time. In proposed 1007- method, people's waiting time is reduced. The RFID tag reads the person who enters and exits the bus. In the 1010 MQTT dash application, the number of persons entering and leaving details is shown. The smartcard's tag debits the number. It is a system that is in real time. Using the LCD display, all bus information such as bus number, routes, stops and timings are shown in bus stops.

Keywords: Public Transport, Bus tracking, GPS, RFID tag, RFID reader.

References:

1. Surendranath. H,Sai Ram. B.(2019) ‘Smart bus tracking system’ International Journal of Engineering Research in Electronics and Communication Engineering, Vol.6,Issue:02, pp.70-77. 2. Niveditha. N, Swathy. S.(2019) ‘Bus Tracking with QR code and RFID 3. ’ International Journal of Innovative Technology and Exploring Engineering Vol.2,Issue:05, pp. 325-327. 4. Mohini S. Shirsath ,Pooja M. Chinchole. (2018) ‘A Review on Smart Bus Ticketing System using QR-Code’, International Research Journal of Engineering and Technology , Vol.4, Issue:06, pp. 99-115. 5. Virendra Patil, Kapish Kaith.(2018) ‘Smart Bus for Smart City using IOT Technology’, International Journal of Advanced Research in Computer and Communication Engineering, Vol.2,Issue:04, pp. 22-26. 6. Zhang Benhua, L. Li Chenghua, Z and Sun Shiming, M.(2017) ‘ IOT 7. Based Smart Public Transport System ’ IEEE journal, Vol.2, Issue:05,pp. 72-74. 8. Kamisan, A.A.Aziz, W.R.WAhmadand N.Khairuddin (2017) ‘ Campus bus tracking system using Arduino based and smart phone application ’ 9. ,IEEE student conference research development(SCoReD),Vol.5, Issue:03,pp.45-50. 10. Pravin A.Kamble and Rambabu (2017) ) ‘Bus Tracking and Monitoring using RFID’, 4th international conference on image processing(ICIIP),Vol.4,Issue:03,pp.45-56 11. Maria Anu.v and Keerthy.(2015) ‘An RFID based system for bus location 12. for tracking and display’ International conference on innovation information in computing technology(ICIICT) ,V0l.3,Issue:06,pp 87-89. 13. Girish L.Deshmukh and Dr.S.P Metkar(2015) ‘information processing RTOS based Vehicle Tracking system’ IEEE journal, Vol.2, Issue:05,pp. 72-74. 14. T. Le-Tien, V. Phung(2010) ‘ Routing and Tracking System for Mobile Vehicles in Large Area, Fifth IEEE International Symposium on Electronic Design’, Test & Applications, Vol.4,Issue:03 pp. 297-300. Authors: Urvi Oza, Pankaj Kumar

Paper Title: Empirical Examination of Color Spaces in Deep Convolution Networks Abstract: In this paper we present an empirical examination of deep convolution neural network (DCNN) performance in different color spaces for the classical problem of image recognition/classification. Most such deep learning architectures or networks are applied on RGB color space image data set, so our objective is to study DCNNs performance in other color spaces. We describe the design of our novel experiment and present results on whether deep learning networks for image recognition task is invariant to color spaces or not. In this study, we have analyzed the performance of 3 popular DCNNs (VGGNet, ResNet, GoogleNet) by providing input images in 5 different color spaces(RGB, normalized RGB, YCbCr, HSV , CIE-Lab) and compared performance in terms of test accuracy, test loss, and validation loss. All these combination of networks and color spaces are investigated on two datasets- CIFAR 10 and LINNAEUS 5. Our experimental results show that CNNs are variant to color spaces as different color spaces have different performance results for image classification task.

Keywords: CIFAR 10, Color spaces, Convolution neural networks, LINNAEUS 5, Object recognition.

References:

183. 1. S. Belongie, J. Malik, and J. Puzicha, ―Shape matching and object recog-nition using shape contexts,‖IEEE Transactions on Pattern Analysis &Machine Intelligence, no. 4, pp. 509–522, 2002.W.-K. Chen, Linear Networks and Systems (Book style). Belmont, CA: Wadsworth, 1993, pp. 123–135. 1011- 2. D. G. Lowe, ―Object recognition from local scale-invariant features,‖ inComputer vision, 1999. The proceedings of the seventh 1018 IEEE interna-tional conference on, vol. 2. Ieee, 1999, pp. 1150–1157.B. Smith, ―An approach to graphs of linear forms (Unpublished work style),‖ unpublished. 3. D. G. Lowe, Distinctive image features from scale-invariant keypoints,‖Inter-national journal of computer vision, vol. 60, no. 2, pp. 91–110, 2004. 4. J. Talmi, R. Mechrez, and L. Zelnik-Manor, ―Template matching withdeformable diversity similarity,‖ in2017 IEEE Conference on ComputerVision and Pattern Recognition (CVPR). IEEE, 2017, pp. 1311–1319.. 5. M. J. Swain and D. H. Ballard, ―Color indexing,‖International journalof computer vision, vol. 7, no. 1, pp. 11–32, 1991. 6. M. Mercimek, K. Gulez, and T. V. Mumcu, ―Real object recognitionusing moment invariants,‖sadhana, vol. 30, no. 6, pp. 765– 775, 2005. 7. Matas, O. Chum, M. Urban, and T. Pajdla, ―Robust wide-baselinestereo from maximally stable extremal regions,‖Image and visioncomputing, vol. 22, no. 10, pp. 761–767, 2004. 8. Kumar, P.; Dick, A. Adaptive earth movers distance-based Bayesian multi-target tracking. IET Computer Vision 2013, 7, 246– 257. 9. P. Kumar and S. J. Miklavcic, ―Analytical study of colour spaces forplant pixel detection,‖Journal of Imaging, vol. 4, no. 2, 2018. [Online].Available: http://www.mdpi.com/2313-433X/4/2/42 10. S. L. Phung, A. Bouzerdoum, and D. Chai, ―Skin segmentation usingcolor pixel classification: analysis and comparison,‖IEEE transactionson pattern analysis and machine intelligence, vol. 27, no. 1, pp. 148–154, 2005. 11. K Shaik,., Packyanathan, G., Kalist, V., B.S, S., Merlin Mary Jenitha,J.: Comparative study of skin color detection and segmentation in hsv and ycbcr color space. Procedia Computer Science57, 41–48 (12 2015). https://doi.org/10.1016/j.procs.2015.07.362 12. P. Kumar, K. Sengupta, and A. Lee, ―A comparative study of differentcolor spaces for foreground and shadow detection for traffic monitoringsystem,‖ inIntelligent Transportation Systems, 2002. Proceedings. TheIEEE 5th International Conference On. IEEE, 2002, pp. 100–105. 13. D. Cires ̧An, U. Meier, J. Masci, and J. Schmidhuber, ―Multi-columndeep neural network for traffic sign classification,‖Neural networks,vol. 32, pp. 333–338, 2012. 14. D. Khattab, H. M. Ebied, A. S. Hussein, and M. F. Tolba, ―Color imagesegmentation based on different color space models using automaticgrabcut,‖The Scientific World Journal, vol. 2014, 2014. 15. S.N Gowda , C Yuan, ColorNet: Investigating the importance of color spaces for image classification, 2019, [arXiv:cs.CV/1902.00267]. 16. W. contributors, ―List of color spaces and their uses — Wikipedia,the free encyclopedia,‖ https://en.wikipedia.org/w/index.php?title=Listofcolorspacesandtheiruses&oldid=886629037, 2019. 17. K. Simonyan and A. Zisserman, ―Very deep convolutional networks forlarge-scale image recognition,‖arXiv preprint arXiv:1409.1556, 2014. 18. A. Krizhevsky, I. Sutskever, and G. E. Hinton, ―Imagenet classificationwith deep convolutional neural networks,‖Commun. ACM, vol. 60,no. 6, pp. 84–90, May 2017. [Online]. Available: http://doi.acm.org/10.1145/3065386 19. C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan,V. Vanhoucke, and A. Rabinovich, ―Going deeper with convolutions,‖arXiv preprint arXiv:1409.4842, 2014. 20. K. He, X. Zhang, S. Ren, and J. Sun, ―Deep residual learning for imagerecognition,‖2016 IEEE Conference on Computer Vision and PatternRecognition (CVPR), pp. 770–778, 2016. 21. A. Krizhevsky, ―Learning multiple layers of features from tiny images,‖Tech. Rep., 2009. 22. J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, ―ImageNet:A Large-Scale Hierarchical Image Database,‖ inCVPR09, 2009. 23. G.K.L. Chaladze, ―Linnaeus 5 Dataset for Machine Learning‖,Tech. Rep., 2017. 24. D. P. Kingma and J. Ba, ―Adam: A method for stochastic optimization,‖ arXiv preprint arXiv:1412.6980, 2014. 25. Kumar P., Shingala M. (2021) Native Monkey Detection Using Deep Convolution Neural Network. In: Hassanien A., Bhatnagar R., Darwish A. (eds) Advanced Machine Learning Technologies and Applications. AMLTA 2020. Advances in Intelligent Systems and Computing, vol 1141. Springer, Singapore 26. Doshi N, Oza U, Kumar P. Diabetic Retinopathy Classification using Downscaling Algorithms and Deep Learning. In2020 7th International Conference on Signal Processing and Integrated Networks (SPIN) 2020 Feb 27 (pp. 950-955). IEEE. Authors: Muhammed Anaz Khan, A Vivek Anand, Lokasani Bhanuprakash, A Ravindra, V Hariprasad

Paper Title: Mechanical Properties of E-Glass Fiber - Basalt Fiber Reinforced Polymer Matrix Composite Abstract: Composite materials have significant role in automobile and aerospace applications because of their attractive mechanical properties compared. This fascinating properties attracted several industries especially automotive sectors. In contrast to metallic alloys, composite materials composed of individual constituent elements with distinguishable interfaces and chemical identities, however, when combined e-glass and basalt fiber, they will produce superior properties. The fundamental advantage of composite materials is their high specific strength and specific stiffness, which emphasis on its weight saving potential in the finished part. Two principal constituent elements of composites are matrix and reinforcement materials. In the present work, an attempt has been made to understand the advancements achieved in the combination of e-glass fiber and basalt fiber composites. Based on the comprehensive literature review, it is observed that broad work was done on the manufacturing techniques and characterization of the composites, however, limited works were carried out in analyzing the tensile, flexural and shear strength properties of differently oriented fibers in the laminated composites. In this paper, focus was given in fabricating and characterizing the glass fiber reinforced epoxy composite laminates with different fiber orientations, thereby, examining the mechanical properties of prepared laminates for tensile and bending strengths.

Keywords: Basalt fiber, polymer matrix composite, tensile strength, bending strength and flexural rigidity. 184. References: 1019- 1022 1. Fiore V, Scalici T, Di Bella G, Valenza A. A review on basalt fibre and its composites. Composites Part B: Engineering. 2015 pp. 74-94. 2. Singha K. A Short Review on Basalt Fiber. International Journal of Textile Science. 2012;1(4) pp. 19-28. 3. Artemenko SE. Polymer Composite Materials Made from Carbon, Basalt and Glass Fibres. Structure and Properties. Fibre Chemistry. 2003;35(3) pp.226-229. 4. Parnas RS, Shaw MT, Liu Q. Basalt Fiber reinforced polymer composites. The New England Transportation Consortium; 2007. 5. Liu Q, Shaw MT, Parnas RS, McDonnel AM. Investigation of basalt fibre composite aging behaviour for applications in transportation. Polymer Composites. 2006; 27(5) pp. 475-483. 6. Banakar P, Shivananda HK, Niranjan HB. Influence of Fiber Orientation and Thickness on Tensile Properties of Laminated Polymer Composites. International Journal of Pure & Applied Sciences and Technology. 2012; 9(1) pp.61-68. 7. Hao LC, Yu WD. Evaluation of thermal protective performance of basalt fibre nonwoven fabrics. Journal of Thermal Analysis and Calorimetry. 2010; 100(2) pp. 551-555. 8. C. J. Kaufman, Rocky Mountain Research Lab., Boulder, CO, private communication, May 1995. 9. Huonnic N, Abdelghani M, Mertiny P, McDonald A. Deposition and characterization of flame-sprayed aluminum on cured glass and basalt fiber-reinforced epoxy tubes. Surface & Coatings Technology. 2010; 205(3) pp. 867-873. 10. Carvalho O. Influence of the stacking sequence of layers on the mechanical behavior of polymeric composite cylinders. [Dissertation]. São Paulo: Instituto de Pesquisas Energéticas e Nucleares (IPEN); 2006. 11. Lapena MH, Marinucci G, De Carvalho O. Mechanical characterization of unidirectional basalt fiber epoxy composite. In: Proceedings of the 16th European Conference on Composite Materials ECCM-16; 2014 Jun pp. 22-26. 12. ASTM International. ASTM D3171 - 15 - Standard Test Methods for Constituent Content of Composite Materials. West Conshohocken: ASTM International; 2015. Authors: Ann Lia Jose, Athira Soman Nair, Joshna Mary Jose, Sameer S., Sherry Varghese George

185. Paper Title: Robotic Walk Along Cart Abstract: Many people all around are having problems by carrying heavy weights on their shoulders and 1023- travelling around with trolleys. In busy airports, shopping malls and many other places people often face 1029 difficulty with this issue. Housekeeping robots that localize on walking person and follow the path of the user can stand as an advantage to the posed problem. The robotic cart proposed here focuses on reducing the load the user carries. It develops a platform for sending and receiving a signal that would provide a simple and practical means for the robot to determine a path by following the user and avoiding obstacles using ultrasonic sensors and also the ability to self-localize. This reduces the tiring experience faced by people and makes them tension free. The key of the design is to use the Wi-Fi technology to transmit the location of the user to the cart and LoRa technology to give the location of the cart. Meanwhile, together with both the technologies, the proposed design achieves the high feasibility and flexibility of the controllable distance as it follows the user. As a result, the proposed system shows efficiency up to 88%. A follower robot application can be extended to a wide range of fields as in the case of porters at railway stations, loading and unloading goods at factories (civil and industrial fields) and as a helping hand for elderly people thus allowing them to avoid the goods they have to carry along.

Keywords: Arduino UNO, LoRa transceiver, Node MCU Wireless communication

References:

1. Dr. M. Ramkumar Raja, R. Arun Kumar, C. Hemanthi, G.A. Keerthana, B. Kirudiha, “Follow Me Trolley”, International Journal of Recent Trends in Engineering & Research (IJRTER), Conference on Electronics, Information and Communication Systems (CELICS’18), March – 2018. 2. Bonda Venna Pradeep ; E. S. Rahul ; Rao R. Bhavani, “Follow me robot using bluetooth-based position estimation”, International Conference on Advances in Computing, Communications and Informatics (ICACCI) 2017. 3. César Nuñez, Alberto García, Raimundo Onetto, Daniel Alonzo, Sabri Tosunoglu, “Electronic Luggage Follower”, Florida Conference on Recent Advances in Robotics, FCRAR 2010 - Jacksonville, Florida, May 20-21, 2010 4. Prof. B. Gopinathan, Saida. A, Kasthuri Priya. R,” Tracking the Remote Location using LoRa”, International Journal of Computer Science Trends and Technology (IJCST), Mar-Apr 2020. 5. Akshay Chalke, Ashwini Bhor, Pradnya Bhoite, Uddhav Jadhav, “Follower Robotic Cart Using Ultrasonic Sensor”, International Journal of Engineering Science and Computing (IJESC), March 2017. 6. Boris Sofman, Ellie Lin Ratliff, J. Andrew (Drew) Bagnell, John Cole, Nicolas Vandapel and Anthony (Tony) Stentz " Improving Robot Navigation Through Self-Supervised Online Learning," Journal of Field Robotics, December, 2006. 7. Sayali N Joshi, Vaishnavi K Patki, Priyanka S Dixit, Husain K Bhaldar, “Design and Development of Human Following Trolley”, International Journal of Innovative Science and Research Technology, April – 2019. Authors: Ajay Mushan, P. S. Vidap

Paper Title: Video Summarization using Keyframe Extraction Methods Abstract: Video summarization plays an important role in too many fields, such as video indexing, video browsing, video compression, video analyzing and so on. One of the fundamental units in the video structure analysis is the keyframe extraction, Keyframe provides meaningful frames from the video. The keyframe consists of the meaningful frame from the videos which help for video summarization. In this proposed model, we presented an approach that is based on Convolutional Neural Network, keyframe extraction from videos and static video summarization. First, the video should be converted to frames. Then we perform redundancy elimination techniques to reduce the redundancy from frames. Then extract the keyframes from video by using the Convolutional Neural Network(CNN) model. From the extracted keyframe, we form a video summarization.

Keywords : Video Summarization, Keyframes, Convolutional Neural Network, key frame extraction, interest point.

References:

1. H. Gharbi, S. Bahroun and Ezzeddine Zagrouba, "A Novel Key Frame Extraction Approach for Video Summarization", VISAPP 186. 2016 -International Conference on Computer Vision Theory and Applications. 2. H. Gharbi, S. Bahroun and Ezzeddine Zagrouba, “Key Frames Extraction Using Graph Modularity Clustering for Effective Video Summarization", IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2017. 1030- 3. Chaohui Lv, Yiyang Huang, “Effective Keyframe Extraction From Personal Video By Using Nearest Neighbor Clustering", 1032 International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI 2018). 4. Muhammad Asim, Noor Almaadeed and Azeddine Beghdadi, “A Key Frame Based Video Summarization using Color Features", Colour and Visual Computing Symposium (CVCS) 2018. 5. Dipti Jadhav and Udhav Bhosle, "SURF based Video Summarization and its Optimization", International Conference on Communication and Signal Processing, April 6-8, 2017, India. 6. S. S. Thomas, S. Gupta and V. K. Subramanian, "Smart surveillance based on video summarization", 2017 IEEE Region 10 Symposium (TENSYMP), Cochin, 2017. 7. H. H. Phadke and H. Mallika, "Key frame extraction, localization and segmentation of caption text in news videos," 2017 2nd IEEE International Conference on Recent Trends in Electronics, Information Communication Technology (RTEICT), Bangalore, 2017. 8. Sanjoy Ghatak, "Key-Frame Extraction Using Threshold Technique", International Journal of Engineering Applied Sciences and Technology, Issue 8, ISSN No. 2455-2143, Pages 51-56, 2016 Vol. 9. Ch, Sujatha & Uma, “A Study on Keyframe Extraction Methods for Video Summary”, Proceedings -2011 International Conference on Computational Intelligence and Communication Systems, CICN 2011. 10.1109/CICN.2011.15. 10. Sebastian Stein and Stephen J. McKenna,“ Combining Embedded Accelerometers with Computer Vision for Recognizing Food Preparation Activities”, the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2013). 11. Sandra E. F. de Avila, Ana P. B. Lopes, Antonio da Luz Jr., Arnaldo de A. Araújo, ”VSUMM: A mechanism designed to produce static video summaries and a novel evaluation method” ,Pattern Recognition Letters, Volume 32, Issue 1, January 2011, pages 56–68. 12. Shaoshuai Lei, Gang Xie, and Gaowei Yan, “A Novel Key-Frame Extraction Approach for Both Video Summary and Video Index” ,Hindawi Publishing Corporation Scientific World Journal Volume 2014. 13. J. Almeida, N. J. Leite, and R. D. S. Torres, “VISON: Video Summarization for ONline applications,” Pattern Recognit. Lett., vol. 33, no. 4, pp. 397–409, 2012. 14. Mundur, Padmavathi & Rao, Yong & Yesha, Yelena. (2006). Keyframe-based video summarization using Delaunay clustering. Int. J.on Digital Libraries. 6. 219-232. 10.1007/s00799-005-0129-9. 15. Shivangi Pandey, Prashant Dwivedy, Sunil Meena, Anjali Potnis. "A survey on key frame extraction methods of a MPEG video", 2017 International Conference on Computing, Communication and Automation (ICCCA), 2017. Authors: D. S. Yerudkar, R. A. Dubal, M Rai Sharma Finite Element Analysis Research on Buckling Behaviour of Unrestrained Stiffened Cold Formed Paper Title: Steel Box Sections Abstract: This research mainly concentrates on ultimate strength and buckling behaviour of cold formed steel (CFS) laterally un-braced longitudinally stiffened box sections under flexure. A total of five various stiffener combinations for box sections has been studied by modifying the shape of a simple end stiffened section by the provision of intermediate stiffeners along web, flange or both along web and flange. The influence of different types of stiffeners with respect to various aspect radio’s (H/T, B/T, C/T and H/B) have been studied using Finite Element Method (FEM), and recommendations have been proposed on provisions of different stiffener’s combinations. This study mainly details with ultimate strength and buckling behaviour of CFS laterally unbraced stiffened box sections made by C sections connected face to face.

Keywords: Cold Formed Steel; Laterally Un-Braced; Stiffened Box Sections; C Sections Connected Face To Face; Finite Element Method.

References:

1. Yu WW (1985). Cold–Formed Steel Structures. John Wiley and Sons, New York. 2. I S 801- (1975). Indian Standard Specification for Cold Formed Steel Structures. 3. AISI (1996). American Iron and Steel Institute, Specification for the Design of Cold-Formed Steel Structural Members. Washington, DC. 4. CSA-S136 (2007). North American Specification for the Design of Cold-Formed Steel Structural Members. Canadian Standard Steel Association, Mississauga, Ontario. 5. NAS (2001). North American Specification for the Design of Cold-Formed Steel Structural Members. American Iron and Steel Institute, Washington, D.C. 6. Pham Cao Hung, Hancock Gregory J. (2010). “Numerical simulation of high strength cold-formed purlins in combined bending and shear.” Journal of Constructional Steel Research, 66, 1205-1217. 7. Silvestre Nuno, Camotim Dinar, Dinis Pedro B. (2012). “Post-buckling behaviour and direct strength design of lipped channel columns experiencing local/distortional interaction.” Journal of Constructional Steel Research, 73, 12-30. 8. Mohammad Reza Haidarali, David A Nethercot (2012). “Local and distortional buckling of cold-formed steel beams with both 187. edge and intermediate stiffeners in their compression flanges.” Thin-Walled Structures, 54, 106-112. 9. Pham Cao Hung, Davis Annabel F, Emmett Bonney R (2014). “Numerical investigation of cold-formed lapped Z purlins under combined bending and shear.” Journal of Constructional Steel Research, 95, 116-125. 1033- 10. Iman Faridmehr, Mahmood Md.Tahir, Mohd Hanim Osman, Ali Farokhi Nejad, Reza Hodjati (2015). “An experimental 1043 investigation of stiffened cold-formed C-channels in pure bending and primarily shear conditions.” Thin-Walled Structures, 96, 39-48. 11. Shi’er Dong, Huiran Li, Qiuping Wen (2015). “Study on distortional buckling performance of cold-formed thin-walled steel flexural members with stiffeners in the flange.” Thin-Walled Structures, 95, 161-169. 12. Zhenyu Yao, Kim J. R. Rasmussen (2017). “Perforated Cold-Formed Steel Members in Compression. I: Parametric Studies.” Journal of Structural Engineering, 143(5): 04016226 13. Jun Ye, Iman Hajirasoulih, Jurgen Becque (2018). “Experimental investigation of local-flexural interactive buckling of cold formed steel channel columns.” Thin-Walled Structures, 125, 245-258. 14. Nima Talebian, Benoit P. Gilbert, Cao Hung Pham, Romain Chariere, Hassan Karampour (2018). “Local and Distortional Biaxial Bending Capacities of Cold-Formed Steel Storage Rack Uprights.” Journal of Structural Engineering, 144(6): 04018062 15. Ju Chen, Man-Tai Chen, Ben Young (2019). “Compression Tests of Cold-Formed Steel C- and Z-Sections with Different Stiffeners.” Journal of Structural Engineering, 145(5): 04019022. 16. S. Yerudkar and G. R. Vesmawala (2017). “Strength and buckling of cold-formed steel laterally unbraced stiffened C and Z sections” Institution of Civil Engineers Structures and Buildings, 171, 101-107. 17. Wang S-Den T, Yost Mike I, Tien Yei L (1977). “Lateral buckling of locally buckled beams using finite element techniques.” Computers and Structures, 7, 469-475. 18. Sivakumaiun KS (1wang989). “Analysis for web crippling behaviour of cold-formed steel members.” Computers and Structures, 32, 707-719. 19. Ren Wei-Xin, Fang Sheng-En, Young Ben (2006). “Finite element simulation and design of cold-formed steel channels subjected to web crippling.” Journal of Structural Engineering, 132 (12), 1967-1975. 20. Ashraf Mahmud, Gardner Leroy, Nethercot David A (2006). “Finite element modeling of structural stainless steel cross- sections.” Thin-Walled Structures, 44, 1048-1062. 21. Wang H, Zhang Y (2009). “Experimental and numerical investigation on cold-formed steel C-section flexural members.” Journal of Constructional Steel Research, 65, 1225-1235. 22. Macdonald M, Don Heiyantuduwa MA, Kotelko M, Rhodes J (2010). “Web crippling behaviour of thin-walled lipped channel beams.” Journal of Thin Walled Structures, doi:10.1016/j.tws. 2010.09.010. 23. Tondini Nicola, Morbioli Andrea (2015). “Cross-sectional flexural capacity of cold-formed laterally-restrained steel rectangular hollow flange beams.” Thin-Walled Structures, 95, 196-207. 24. Yu Chen, Xixiang Chen, Chaoyang Wang (2015). “Experimental and finite element analysis research on cold-formed steel lipped channel beams under web crippling”. Thin-Walled Structures, 87, 41-52. 25. D. S. Yerudkar and G. R. Vesmawala (2016). “Finite Element Analysis Research on Buckling Behaviour of Unrestrained Stiffened Cold Formed Steel Channel and Zed Sections” Asian Journal of Civil Engineering, 18, 1041-1057. Authors: Sharmila K. Wagh, Gaurav Upadhyay, Usha Bakan, Nikita Shinde, Shivani Nimbalkar

188. Paper Title: Cryptography and Steganography Techniques in Video Abstract: Secure data transmission over the unsecured internet is an important aspect in communication. In 1044- recent times, data piracy, unauthorized access, loss of crucial information has been one of the important concern 1048 in secure communication of data. To provide security over data transmission, multiple techniques are provided each having its own benefits such as cryptography, steganography and watermarking. Cryptography is the technique for modification of data for secured transmission in an unreadable format. Steganography is the process of hiding the cover file with another file and transmitting the cover file without knowing the existence of hidden message. Watermarking is protection technique used to shield the information from intruders. Various combinations of cryptography and steganography is used to make data more secure. Such techniques differ in various aspects which are load capacity, security, efficiency, simplicity, and much more. Watermarking is the most used technique used for copyright protection in media files in recent times while cryptography and steganography are used to protect the data during communication so that the original data cannot be altered. In this paper, various techniques of cryptography and steganography are discussed which are used in the project.

Keywords: Cryptography, Steganography, Watermarking, Data Piracy.

References:

1. Aiswarya.S, Gomathi.R, “Review On Cryptography and Steganography Techniques in Video” (2018). 2. Anitha Gnana selvi. J, Maria kalavathy.G, “Probing Image and Video Steganography based On Discrete Wavelet and Discrete Cosine Transform” (2019). 3. Mohammad A. Alia, Khulood Abu Maria, Maher A. Alsarayreh, Eman, Abu Maria, Sally Almanasra, “An Improved Video Steganography: Using Random Key-Dependent” (2019). 4. Arnold Gabriel Benedict, “Improved File Security System Using Multiple Image Steganography” (2019) 5. Samar Kamil, Masri Ayob, Siti Norul Huda Sheikh Abdullah, Zulkifli Ahmad, “Optimized Data Hiding in Complemented or Non-Complemented Form in Video Steganography” (2018) 6. Hemalatha S, U. Dinesh Acharya, Shamathmika, “MP4 Video Steganography in Wavelet Domain” (2017). 7. Shivam Teotia and Prakash Srivastava, “Enhancing Audio and Video Steganography Technique Using Hybrid Algorithm” (2018) 8. Tengfei Li , Huifeng Li , Liang Hu, Hongtu Li, “A Reversible Steganography Method With Statistical Features Maintained Based on the Difference Value” (2019). 9. Weixiang Li, Wenbo Zhou, Weiming Zhang, Chuan Qin, Huanhuan Hu, Nenghai Yu, “Shortening the Cover for Fast JPEG Steganography” (2018) 10. Shailendra Kumar Yadav, Rosepreet Kaur Bhogal, “An Video Steganography in Spatial, Discrete Wavelet Transform and Integer wavelet domain” (2018) 11. Tamer Rabie, Mohammed Baziyad, “The Pixogram: Addressing High Payload Demands for Video Steganography” (2018) 12. Alpa Agath, Chintan Sidpara, Darshan Upadhyay, “Critical Analysis of Cryptography and Steganography” (2018) Authors: Devansh Shah, Ayushi Lodaria, Danish Jain, Lynette D’Mello

Paper Title: Airline Delay Prediction using Machine Learning and Deep Learning Techniques Abstract: In this paper, we have tried to predict flight delays using different machine learning and deep learning techniques. By using such a model it can be easier to predict whether the flight will be delayed or not. Factors like ‘WeatherDelay’, ‘NASDelay’, ‘Destination’, ‘Origin’ play a vital role in this model. Using machine learning algorithms like Random Forest, Support Vector Machine (SVM) and K-Nearest Neighbors (KNN), the f1-score, precision, recall, support and accuracy have been predicted. To add to the model, Long Short-Term Memory (LSTM) RNN architecture has also been employed. In the paper, the dataset from Bureau of Transportation Statistics (BTS) of the ‘Pittsburgh’ is being used. The results computed from the above mentioned algorithms have been compared. Further, the results were visualized for various airlines to find maximum delay and AUC-ROC curve has been plotted for Random Forest Algorithm. The aim of our research work is to predict the delay so as to minimize loses and increase customer satisfaction.

Keywords: Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forest, Long Short-Term Memory (LSTM), RNN.

189. References: 1049- 1. Yogita Borse , Dhruvin Jain , Shreyash Sharma , Viral Vora, Aakash Zaveri, 2020, Flight Delay Prediction System, International Journal Of Engineering Research & Technology (IJERT) Volume 09, Issue 03 (March 2020). 1054 2. Ye, B.; Liu, B.; Tian, Y.; Wan, L. A Methodology for Predicting Aggregate Flight Departure Delays in Airports Based on Supervised Learning. Sustainability 2020, 12, 2749. 3. Y. J. Kim, S. Choi, S. Briceno and D. Mavris, "A deep learning approach to flight delay prediction," 2016 IEEE/AIAA 35th Digital Avionics Systems Conference (DASC), Sacramento, CA, 2016, pp. 1-6, doi: 10.1109/DASC.2016.7778092. 4. Chakrabarty, Navoneel. “A Data Mining Approach to Flight Arrival Delay Prediction for American Airlines.” 2019 9th Annual Information Technology, Electromechanical Engineering and Microelectronics Conference (IEMECON) (2019): 102-107. 5. Musaddi, Roshni & Jaiswal, Anny & Girdonia, Mansvi & Sanjudharan, M S Minu. (2018). Flight Delay Prediction using Binary Classification. 6. 34-38. 6. B. Thiagarajan, L. Srinivasan, A. V. Sharma, D. Sreekanthan and V. Vijayaraghavan, "A machine learning approach for prediction of on-time performance of flights," 2017 IEEE/AIAA 36th Digital Avionics Systems Conference (DASC), St. Petersburg, FL, 2017, pp. 1-6, doi: 10.1109/DASC.2017.8102138. 7. V. Natarajan, S. Meenakshisundaram, G. Balasubramanian and S. Sinha, "A Novel Approach: Airline Delay Prediction Using Machine Learning," 2018 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, NV, USA, 2018, pp. 1081-1086, doi: 10.1109/CSCI46756.2018.00210. 8. Sternberg, Alice & Soares, Jorge & Carvalho, Diego & Ogasawara, Eduardo. (2017). A Review on Flight Delay Prediction. 9. Bureau of Transportation Statistics. Available online: https://www.bts.gov/ 10. Chakrabarty, Navoneel. “A Data Mining Approach to Flight Arrival Delay Prediction for American Airlines.” 2019 9th Annual Information Technology, Electromechanical Engineering and Microelectronics Conference (IEMECON) (2019): 102-107. 11. V. Venkatesh, A. Arya, P. Agarwal, S. Lakshmi and S. Balana, "Iterative machine and deep learning approach for aviation delay prediction," 2017 4th IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics (UPCON), Mathura, 2017, pp. 562-567, doi: 10.1109/UPCON.2017.8251111. 12. Leo Breiman - Random Forests (dept. Of statistics, University of California, Berkeley) 13. Rokach, Lior & Maimon, Oded. (2005) - Decision Trees. 10.1007/0-387-25465-X_9. 14. Yang, Ning & Li, Tianrui & Song, Jing. (2007). Construction of Decision Trees based Entropy and Rough Sets under Tolerance Relation. International Journal of Computational Intelligence Systems. 10.2991/iske.2007.258. 15. Liu Y., Wang Y., Zhang J. (2012) New Machine Learning Algorithm: Random Forest. In: Liu B., Ma M., Chang J. (eds) Information Computing and Applications. ICICA 2012. Lecture Notes in Computer Science, vol 7473. Springer, Berlin, Heidelberg 16. Durgesh K.Srivastava, Lekha Bhambu – Data Classification using Support Vector Machine 17. B. E. Boser, I. M. Guyon, and V. N. Vapnik. A training algorithm for optimal margin classifiers. In D. Haussler, editor, 5th Annual ACM Workshop on COLT, pages 144-152, Pittsburgh, PA, 1992. ACM Press 18. Guo, Gongde & Wang, Hui & Bell, David & Bi, Yaxin. (2004). KNN Model-Based Approach in Classification. 19. Alka Lamba, Dharmender Kumar. Survey on KNN and its Variants. International Journal of Advanced Research in Computer and Communication Engineering Vol.5,Issue 5, May 2016. 20. Geeks For Geeks – Available: https://www.geeksforgeeks.org/k-nearest-neighbours/ 21. Sherstinsky, Alex. “Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network.” Physica D: Nonlinear Phenomena 404 (2020): 132306. Crossref. Web. 22. Hasim Sak, Andrew Senior, Francoise Beaufays. Long Short-Term Memory Recurrent Neural Network Architectures for Large Scale Acoustic Modeling 23. Towards DataScience – Available: https://towardsdatascience.com/understanding-auc-roc-curve-68b2303cc9c5 24. Available - https://blog.exsilio.com/all/accuracy-precision-recall-f1-score-interpretation-of-performance-measures/ Authors: Deepika Srinivasan, Mahmoud Yousef

Paper Title: Apple Fruit Detection and Maturity Status Classification Abstract: Identifying the quality of fresh produce while procuring is a major task that involves time and human effort in the retail industry. The main objective of this project is to identify and classify whether the apple fruit is fresh or rotten using Convolutional Neural Networks. The outcome of our study resulted in 97.92 percent accuracy for the 2 classes of approximately 5031 images in the classification, by identifying apples using Resnet 50 and then classifying them using the proposed model.

Keywords: Convolution Neural Network, Resnet50, Fresh Produce, Flask, and Tkinter.

References:

1. A. Wajid, N. K. Singh, P. Junjun and M. A. Mughal, ―Recognition of ripe, unripe and scaled condition of orange citrus based on decision tree classification,‖ 2018 International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), Sukkur, 2018, pp. 1-4. 2. M. Ronald and M. Evans, ―Classification of Selected Apple Fruit Varieties Using Naïve Bayes,‖ Indian Journal of Computer Science and Engineering (IJCSE), vol. 7, no. 1, pp. 13–19, 2016. 3. B. Tiger and T. Verma, "Identification and classification of normal and infected apples using neural network,'' International Journal of Sciences and Research, vol. 2, Issue. 6, pp. 160-163, 2013. 190. 4. P. Moallem, A. Serajoddin, and H. Pourghassem, ―Computer vision-based apple grading for golden delicious apples based on surface features,‖ Information Processing in Agriculture, vol. 4, pp. 33–40, 2017. 1055- 5. N. M. S Iswari, Wella and Ranny, "Fruitylicious: Mobile application for fruit ripeness determination based on fruit image," 2017 10th International Conference on Human System Interactions (HSI), Ulsan, 2017, pp. 183-187. 1059 6. U. P. Singh, S. S. Chouhan, S. Jain, and S. Jain, "Multilayer Convolution Neural Network for the Classification of Mango Leaves Infected by Anthracnose Disease," in IEEE Access, vol. 7, pp. 43721-43729, 2019. 7. S. Lu, Z. Lu, S. Aok and L. Graham, "Fruit Classification Based on Six Layer Convolutional Neural Network," 2018 IEEE 23rd International Conference on Digital Signal Processing (DSP), Shanghai, China, 2018, pp. 1-5. 8. Z. M. Khaing, Y. Naung and P. H. Htut, "Development of control system for fruit classification based on convolutional neural network," 2018 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), Moscow, 2018, pp. 1805-1807. 9. A. Singla, L. Yuan, and T. Ebrahimi, T, ―Food/Non-food Image Classification and Food Categorization using Pre-Trained GoogLeNet Model,‖ Proceedings of the 2nd International Workshop on Multimedia Assisted Dietary Management - MADiMa, 2016, pp. 3-11. 10. Y. Zhang, Z. Dong, X. Chen, W. Jia, S. Du, K. Muhammad, S. Wang, ―Image based fruit category classification by 13-layer deep convolutional neural network and data augmentation‖, Multimed Tools Appl 78, 3613–3632, 2019. 11. L. Hou, Q. Wu, Q. Sun, H. Yang and P. Li, "Fruit recognition based on convolution neural network," 2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), Changsha, 2016, pp. 18-22. 12. Kaggle.com. 2018. Fruits Fresh and Rotten For Classification. [online] Available at: [Accessed 1 February 2020]. 13. Elitedatascience.Com Keras Tutorial: The Ultimate Beginner‘s Guide to Deep Learning in Python. [online] Available at: [Accessed 19th July 2020] 14. Towardsdatascience.Com. 2019.Understanding and Coding a ResNet in Keras. [online] Available at: [Accessed 19th July 2020] Authors: Shariq Haseeb, Aisha Hassan A. Hashim, Othman O. Khalifa, Ahmad Faris Ismail Performance Analysis of Virtual Communication Between Thermal Cameras and Contact-tracing Paper Title: Applications for COVID-19 Abstract: Late 2019 and a significant part of early 2020 have witnessed the outbreak of Coronavirus disease-19 191. (COVID-19) across the world. As a desperate attempt to control the virus spread, many countries are enforcing measures to restrict large concentration of people at one place and are implementing some form of contact 1060- tracing mobile application to quickly track close interactions between people. Furthermore, as businesses come 1066 out of lockdowns, they are required to record the temperature of all visitors and staffs that move in and out of their premises. Some businesses are employing medical grade contactless thermal imaging cameras for scanning temperature. Communication and sharing of information between the contact tracing application and thermal cameras would make an effective tool against COVID-19. However, there is a disconnect between the contact tracing applications and contactless thermal imaging solutions because they employ different communication stacks, platforms, data formats, and protocols. Furthermore, any kind of middleware to mediate between the cameras and the mobile applications would render the solution useless because of the induced latencies. In this paper, we are proposing to virtualize the communication between the cameras and mobile applications so that they could communicate and interoperate over a common protocol stack. We further model and simulate the proposed virtualized communication algorithm, under various topologies and configurations to comprehensively evaluate the performance, scalability, and deployment feasibility. The simulation aptly and efficiently evaluates the results for latency, energy, and bandwidth consumption parameters.

Keywords: Cloud, COVID-19, Fog computing, Virtual devices and protocols.

References:

1. J. Louis, “Using tech to fight COVID-19,” pp. 1–5, 2020. 2. F. S. (Apple), “Apple and Google partner on COVID-19 contact tracing technology,” 2020. [Online]. Available: https://www.apple.com/newsroom/2020/04/apple-and-google-partner-on-covid-19-contact-tracing-technology/. [Accessed: 03- May-2020]. 3. S. Gov, “TraceTogether.” [Online]. Available: https://www.tracetogether.gov.sg/. [Accessed: 03-May-2020]. 4. S. Kwan, “App-ly via Gerak Malaysia | The Star Online,” The Star, 2020. [Online]. Available: https://www.thestar.com.my/news/nation/2020/04/26/app-ly-via-gerak-malaysia. [Accessed: 03-May-2020]. 5. S. Kwan, “Covid-19: Hong Kong’s wristbands allow quarantined to wander free | The Star Online,” The Star, 2020. 6. WHO, “Interim guidance,” 2020. 7. A. Chio, G. Bouloukakis, C. H. Hsu, S. Mehrotra, and N. Venkatasubramanian, “Adaptive mediation for data exchange in IoT systems,” in ARM 2019 - Proceedings of the 2019 18th Workshop on Adaptive and Reflexive Middleware, Part of Middleware 2019, 2019, pp. 1–6. 8. A. M. Mohd, A. Suhaimi, Q. S. M. Faisal, and S. Haseeb, “Evaluating QoS performance of Streaming Video On both IPv4 and IPv6 Protocols,” Proc. Spring Simulait. Multiconference, vol. 1, pp. 109–116, 2007. 9. G. Bouloukakis, N. Georgantas, P. Ntumba, and V. Issarny, “Automated synthesis of mediators for middleware-layer protocol interoperability in the IoT,” Futur. Gener. Comput. Syst., vol. 101, pp. 1271–1294, Dec. 2019. 10. Nagasai, “Classification of IoT Devices - CISO Platform.” CISO Platforn, Bangalore, 2017. 11. M. Noura, M. Atiquzzaman, and M. Gaedke, “Interoperability in Internet of Things: Taxonomies and Open Challenges,” Mob. Networks Appl., pp. 1–14, Jul. 2018. 12. R. Sutaria and R. Govindachari, “Making sense of interoperability: Protocols and Standardization initiatives in IOT,” in 2nd International Workshop on Computing and Networking for Internet of Things (CoMNet-IoT) held in conjunction with 14th International Conference on Distributed Computing and Networking (ICDCN 2013), 2013, pp. 2–5. 13. RFC, “Terminology for Constrained-Node Networks [RFC 7228],” Ietf Lwig. pp. 1–17, 2014. 14. T. Pflanzner and A. Kertesz, “A survey of IoT cloud providers,” in 2016 39th International Convention on Information and Communication Technology, Electronics and Microelectronics, MIPRO 2016 - Proceedings, 2016, pp. 730–735. 15. Interoperability and Open-Source Solutions for the Internet of Things, vol. 10218. 2017. 16. R. Morabito, I. Farris, A. Iera, and T. Taleb, “Evaluating Performance of Containerized IoT Services for Clustered Devices at the Network Edge,” IEEE Internet Things J., vol. 4, no. 4, pp. 1019–1030, 2017. 17. S. Haseeb, A. H. A. Hashim, O. O. Khalifa, and A. F. Ismail, “Connectivity, interoperability and manageability challenges in internet of things,” in AIP Conference Proceedings, 2017, vol. 1883. 18. R. Sanchez-Iborra and M. D. Cano, “State of the art in LP-WAN solutions for industrial IoT services,” Sensors (Switzerland), vol. 16, no. 5. 2016. 19. M. A. S. Mosleh, G. Radhamani, M. A. G. Hazber, and S. H. Hasan, “Adaptive Cost-Based Task Scheduling in Cloud Environment,” Sci. Program., vol. 2016, 2016. 20. L. Davoli, M. Antonini, and G. Ferrari, “DiRPL: A RPL-based resource and service discovery algorithm for 6LoWPANs,” Appl. Sci., vol. 9, no. 1, p. 33, Dec. 2018. 21. S. Raza and T. Voigt, “Interconnecting WirelessHART and legacy HART networks,” in DCOSS ’10 - International Conference on Distributed Computing in Sensor Systems, Adjunct Workshop Proceedings: IWSN, MobiSensors, Poster and Demo Sessions, 2010, pp. 1–8. 22. M. M. Feroz and A. K. Kiani, “SHIM6 Assisted Mobility Scheme, an intelligent approach,” 2013 IEEE 10th Consum. Commun. Netw. Conf. CCNC 2013, pp. 725–728, 2013. 23. B. Tank, H. Upadhyay, and H. Patel, “A survey on iot privacy issues and mitigation techniques,” in ACM International Conference Proceeding Series, 2016, vol. 04-05-Marc. 24. O. Gaddour et al., “Demo Abstract: Z-Monitor: A Monitoring Software for IEEE 802.15.4 Wireless Sensor Networks,” 2018. 25. Y. Jararweh, M. Al-Ayyoub, A. Darabseh, E. Benkhelifa, M. Vouk, and A. Rindos, “SDIoT: a software defined based internet of things framework,” J. Ambient Intell. Humaniz. Comput., vol. 6, no. 4, pp. 453–461, Aug. 2015. 26. P. Martinez-julia and A. F. Skarmeta, “Empowering the Internet of Things with Software Defined Networking,” White Pap. IoT6-FP7 Eur. Res. Proj., 2014. 27. S. Bera, S. Misra, S. K. Roy, and M. S. Obaidat, “Soft-WSN: Software-defined WSN management system for IoT applications,” IEEE Syst. J., vol. 12, no. 3, pp. 2074–2081, 2018. 28. A. Gupta and N. Mukherjee, “Rationale behind the virtual sensors and their applications,” 2016 Int. Conf. Adv. Comput. Commun. Informatics, ICACCI 2016, pp. 1608–1614, 2016. 29. 29. A. Gupta and N. Mukherjee, “A Cloudlet Platform with Virtual Sensors for Smart Edge Computing,” IEEE Internet Things J., vol. 6, no. 5, pp. 8455–8462, Oct. 2019. Authors: Yasir A, Aiman Salim, Arya Dileep, Anjana S

Paper Title: Autonomous Car using Raspberry Pi and Ml

192. Abstract: The Proposed system’s goal is to represent an Autonomous car prototype which uses Raspberry Pi as the core functioning chip and our system use Open CV and Machine learning technology. The proposed system 1067- will move automatically without any human help to the destination by itself. The car uses the core processing 1071 system as Raspberry Pi, which is interfaced with the Pi camera module, will stream the video to the Monitor as the local host. Based on which the detection like pedestrians, vehicles or road sign and signals are done and corresponding commands are sent to the Arduino serially to operate the car. The Raspberry Pi has functionalities like, traffic signal detection, vehicle detection, pedestrian detection, road sign detection, which aids the proposed system in arriving the proposed or specified place cautiously and timely. Every process is completed using the Raspberry Pi with C++ programming. The methods used for achieving autonomous movement of car are Gaussian Blur, CED and Region of Interest. Assembled robot body by assembling the chassis and wheels of robot car, soldering the motors and fixed them in the chassis, fixed Raspberry Pi, Arduino Uno, motor driver and made the required circuit connections. The proposed system will be a helping hand in the vehicle industry as it will aid in easing the observation needed and tension taken, thereby easing human efforts while driving and minimizes the chance of collisions arising out of human error or law breaking driving resulting in large mortality rates.

Keywords: Raspberry Pi, Machine Learning.

References:

1. Johann Borenstein Yoram Koren, Obstacle Avoidance with Ultrasonic Sensors, IEEE JOURNAL OF ROBOTICS AND AUTOMATION, VOL. 4, NO. 2, APRIL I988, pp. 213-218. 2. Design and Implementation of self-Driving Car using Raspberry Pi, International Journal of Computer Applications (0975 – 8887) Volume 113 – No. 9, March 2015. 3. Li, M., Zhao, C., Hou, Y. Ren, M. , A New Lane Line Segmentation and Detection Method based on Inverse Perspective Mapping: International Journal of Digital Content Technology and its Applications. Volume 5, Number 4, April 2011, pp. 230- 236. 4. Prof. Z.V. Thorat, Sujit Mahadik, Satyawan Mane, Saurabh Mohite, Aniket Udugade, Self-Driving Car using Raspberry-Pi and Machine. 5. Learning, Department of EXTC, Bharati Vidyapeeth College of Engineering, SEC-7 Opposite to Kharghar Railway Station, CBD Belapur, Navi Mumbai - 400614. 6. Matt Richardson, Shawn Wallace, Getting Started with Raspberry Pi, 2nd Edition, Published by Maker Media, Inc., USA, 2014. Book ISBN: 978-1-457- 18612-7. 7. Tan-Hung Duong, Sun-Tae Chung, Seongwon Cho, Model Based Robust Lane Detection for Driver Assistance, available at http://www.kpubs.org/article/articleMain.kpubsspotTyp e=low article A No=MTMDCW2014v17n6655. 8. Chun-Che Wang, Shih-Shinh Huang and Li-Chen Fu, Pei Yung Hsiao “Driver Assistance System for Lane Detection and Vehicle Recognition with Night Vision”, IEEE. 9. Margrit Betke, Esin Haritaoglu, Larry S Davis, “Real-time multiple vehicle detection and tracking from a moving vehicle , in Machine Vision and Application, IEEE, 2000, pp: 69-83. 10. Aditya Kumar Jain, “Working model of Self-driving car using Convolutional Neural Network, Raspberry Pi and Arduino”, Proceeding of the 2nd International Conference on Electronics, Communication and Aerospace Technology (ICECA), IEEE, 2018, pp: 1630- 1635. Authors: Swechchha Sharma, Gokul Rajan V

Paper Title: Sclera Segmentation Techniques Abstract: Biometric Recognition is process which plays a vital role in many fields like security, authenticity, identification. The term biometric is the biological parts of humans which have a unique feature to isolate the individuals. Iris, fingerprint, palm, vascular pattern, voice, signature, face, DNA are the biometrics which are available in the world. Still there are few more biometric exist with unique feature like sclera, the white area of an eye. Blood vessels in sclera area have the unique pattern for every individual. In order to recognize the individuals features if sclera has also used but still how to isolate the feature from the eye image is a question mark. The challenge is the sclera has been surrounded by iris, eyelid and eyelash. Many procedures and methods has been introduced to segment the sclera form the eye but still the efficiency of the approaches has to be evaluated because segmentation accuracy will affect the recognition accuracy. The comparison has been tabulated and the analysis results are briefed in the result.

Keywords: Digital Image Processing, sclera Recognition, biometrics, feature extraction, Sclera segmentation.

193. References: 1072- 1. S. Alkassar, Student Member, IEEE, W. L. Woo, Senior Member, IEEE, S. S. Dlay, and J. A. Chambers, Fellow, IEEE. “Robust 1076 Sclera Recognition System with Novel Sclera Segmentation and Validation Techniques”. 2. Wei Dong, Han Zhou, Dong Xu, School of Instrumentation Science and Opto-electronics Engineering, Beihang University, Beijing, China, “A New Sclera Segmentation and Vessels Extraction Method for Sclera Recognition”. 3. Petru Radu, James Ferryman and Peter Wild Computational Vision Group, School of Systems Engineering, University of Reading, U.K, “A Robust Sclera Segmentation Algorithm”. 4. Sugandha Agarwal1, Rashmi Dubey2, Sugandh Srivastava3, Prateek Aggarwal4 1(Amity University, Noida), “A Comparative Study of Facial, Retinal, Iris and Sclera Recognition Techniques”. 5. Sinan Alkassar1 , Wai-Lok Woo1, Satnam Dlay1, Jonathon Chambers1, School of Electrical and Electronic Engineering, Newcastle University, Newcastle upon Tyne, UK, “Sclera recognition: on the quality measure and segmentation of degraded images captured under relaxed imaging conditions”. 6. Sheng-Yu He and Chih-Peng Fan, Member, IEEE, Department of Electrical Engineering, National Chung Hsing University, Taiwan, R.O.C, “SIFT Features and SVM Learning based Sclera Recognition Method with Efficient Sclera Segmentation for Identity Identification”. 7. Diego R Lucio, Rayson Laroca, Evair Sever, Alceu S Britto, David Menotti. 2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems(BTAS), 1-7, 2018, “Fully convolutional networks and generative adversarial networks applied to sclera segmentation”. 8. RIZWAN ALI NAQVI, Faculty of Engineering and Technology, The Superior College, Main Raiwind Road, Lahore, Pakistan, 2019. “Sclera-Net: Accurate Sclera Segmentation in Various Sensor Images Based on Residual Encoder and Decoder Network”. 9. O. G. Hastak, Assistant Professor, Priyadarshini college of engg. 2017., “Human Identification Based on Sclera Veins Extraction”. 10. Chih-Peng Fan, Ting-Wing Gu and Sheng-Yu He, 2019, “Feature based Blood Vessel Structure Rapid Matching and Support Vector Machine- Based Sclera Recognition with effective Sclera Segmentation”. 11. S Vijayalakshmi, Gokul Rajan V, “A Novel Approach for Human Identification using Sclera Recognition,” International Journal of Computer Sciences and Engineering, Vol.06, Issue.04, pp.228-235, 2018. 12. Gokul Rajan V, S Vijayalakshmi, “A New Approach for Sclera Segmentation Using Integro Differential Operator”Journal of computational and theoritical nanoscience, Volume 17, pp. 2330-2335(6), 2020. Authors: B.Naufal Ahamed, S.Arulselvan Effect of Aerocon Block, Reinforced Brick Masonry and RCC Diagonal Struct in Two Dimensional Paper Title: Single Bay Two Storey RC Frame under Cyclic Loading Abstract: This paper summarizes the analytical investigation of two dimensional single bays two storeys RC frame with Aerocon block, Reinforced brick masonry and Diagonal Strut under lateral cyclic loading. The paper analytically determines the behaviour of the RC frame with Aerocon block masonry, Reinforced brick masonry and Diagonal Strut under equivalent static lateral cyclic loading conditions. The preliminary test such as compression test on prisms for the infill panels were done experimentally and the values were incorporated in the analytical modeling. Analytical works have been conducted using ANSYS software. The frames were subjected to lateral cyclic loading and the behaviors such as Load carrying capacity of the frames, Load- deflection behaviour, Stiffness degradation, have been determine. The results of all the three frames were compared.

Keywords: Aerocon Block, Reinforced Brick Masonry, Diagonal Strut, cyclic loading, Load-Deflection, Stiffness

References:

1. Adriana Ionescu, Madalina Calbureanu, Mihai Negru, “Static and dynamic simulation in the seismic behavior of a building structure using ANSYS program”, International Journal of Mechanics, Volume 7, Issue 3, 2013. 2. Akkinapalli Vishal kumar, Shaik Mohammad Ali, Marri Bharath Kumar, Koncherla Harsha Kiran, Lingeshwaran Nagarathinam, “Comparitive Analysis on Reinforced and Unreinforced Brick Masonry Walls”, International Journal of Recent Technology and Engineering (IJRTE), Volume-7, Issue-6C2, 2019. 3. Anubama M, Gokul Ram H, Karthick B, “Analytical Study on Seismic Performance of RC Frames InFilled With Masonry Walls Using E-Tabs”, International Journal of Innovative Science, Engineering & Technology, Vol. 3 Issue 6, 2016, 360-368 194. 4. Armin B. Meharbi and P. Benson Shing, “Seismic Analysis of Masonry-Infilled Reinforced Concrete Frames”, TMS Journal Vol.21, No.1, 2003. 5. Arton D. Dautaj, Qani Kadiri, Naser Kabashi, “Experimental study on the contribution of masonry infill in the behavior of RC 1077- frame under seismic loading”, Engineering Structures 183 70–82, 2019, https://doi.org/10.1016/j.engstruct.2018.12.083 1085 6. Arunkumar A.S. and Raghunath, “Response of single bay two storeyed brick masonry infilled RC portal frames under in-plane cyclic loads”, ISET Journal of Earthquake Technology, 527, Vol. 50, No. 2-4, 2013, 73–89 7. Baris Binici, Erdem Canbay, Alper Aldemir, Ismail Ozan Demirel, Ugur Uzgan, Zafer Eryurtlu, Koray Bulbul, Ahmet Yakut, “Seismic behavior and improvement of autoclaved aerated concrete infill walls”, Engineering Structures 193 68–81, May 2019, https://doi.org/10.1016/j.engstruct.2019.05.032 8. Guang Yang, Erfeng Zhao, Xiaoya Li, Emad Norouzzadeh Tochaei, Kan Kan, and Wei Zhang, “Research on Improved Equivalent Diagonal Strut Model for Masonry-Infilled RC Frame with Flexible Connection”, (2019) https://doi.org/10.1155/2019/3725373 9. Hamid N. H,. Adiyanto M. I. and Mohamad M., “Seismic performance of single-bay two-storey RC frame under in-plane lateral cyclic loading”, ARPN Journal of Engineering and Applied Sciences, VOL. 12, NO. 22, 2017, 6502-6510 10. Hrituraj Singh Rathore, Dr.Savita Maru, “Comparative Study of AAC Block and Brick Fully Infill Buildings and Buildings having Soft Storey at Different Floor Subjected to Earthquake: A Review”, International Journal for Research in Applied Science & Engineering Technology (IJRASET), Volume 6 Issue III, 2018. 11. Ionescu A., Calbureanu M., Negru M., “Boussinesq method in seismic analysis of a building structure using ANSYS program” – WSEAS International Conference, Vouliagmeni, Athens, Greece, may 14-16, 2013. 12. Jagadeesan, Reddipally Bhagyamma, “Influence of Diagonal Strut Action in RC Framed Structure”, International Journal for Research in Engineering Application & Management (IJREAM), Vol-04, Issue-02, 2018. 13. Jurko Zovkic, Vladimir Zvonko Sigmund, Ivica Guljas, “Cyclic testing of a single bay reinforced concrete frames with various types of masonry infill” Earthquake Engineering & Structural Dynamics 42(8) 2013, DOI: 10.1002/eqe.2263 14. Ning Ninga, Zhongguo John Ma, Pengpeng Zhang, Dehu Yu, Jianlang Wang, “Influence of masonry infills on seismic response of RC frames under low frequency cyclic load”, Engineering Structures 183, 70–82, 24 2019, https://doi.org/10.1016/ j.engstruct.2018.12.083 15. Vincent Sam Jebadurai S., Tensing, D., Freeda Christy C.,” Enhancing performance of infill masonry with skin reinforcement subjected to cyclic load”, IJE TRANSACTIONS B: Applicatios Vol. 32, No. 2, (2019) 223-228, doi: 10.5829/ije. 2019.32.02b.06 16. Xiaojie Zhou Xiaoyuan Kou, Quanmin Peng and Jintao Cui, “Influence of Infill Wall Configuration on Failure Modes of RC Frames” Shock and Vibration, (2018), https://doi.org/10.1155/2018/6582817 17. Zuowei Wang, Jianchang Zhao, Tingbin Liu, “Bond-slip model for horizontal reinforcing bars in reinforced brick masonry”, Engineering Structures 201, 109770, 2019, https://doi.org/10.1016/j.engstruct.2019.109770 Authors: B.Ratnakanth, K.Venkata Ramana

Paper Title: Systematic Approach to Analyze Attacks on SCM: using Blockchain Abstract: Smart contracts are programs, which are stored in a decentralized network i.e. Block chain. These are 195. written by users to develop decentralized applications using different platform like Ethereum and bitcoin. In current scenario, even though blockchain support features like security and transparency. Because of solidity 1086- language vulnerability, there is a possibility of attacks on smart contracts in blockchain. So, to avoid those 1094 attacks like i.e. Locked Ether, Transaction order dependency and Time stamp dependency. We discussed, analyzed and tested these attacks in this paper. Further, in our project supply chain management for textile industry using block chain technology, we have developed smart contract using solidity language on Ethereum Platform. With the aim of protecting our project from these attacks, we are thoroughly and experimentally analyzed. And based on the experimental observations, we are going to protect our project from these attacks. All the above mentioned attacks are thoroughly studied and experimentally tested on Ganache, Ropston test network, Rinkeby test network using Remix IDE in JVM, injectedweb3and web3 provider environments. Finally, we have suggested security measures to protect from these attacks.

Keywords: blockchain, scm, attacks, smart ontracts, vulnerabilities.

References:

1. Satchain: Secured Autonomous Transactions in Supply Chain using Block Chain, International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-9 Issue-6, April2020,B.Ratnakanth,Dr.K.venkataRamana .https://www.ijitee.org/download/volume-9-issue-6/ . 2. Secure payment system in supply chain management using blockchain technologies, by B.Ratnakanth, dr.k.venkataramana.www.jetir.org (issn-2349-5162),© 2019 jetir june 2019, volume 6, issue 6, 3. Block chain Technology: Supply Chain Insights from ERP, Arnab Banerjee, Advances in Computers # 2018 Elsevier Inc. ISSN 0065-2458 All rights reserved. https://doi.org/10.1016/bs.adcom.2018.03.007. 4. Supply chain and logistics controller – two promising professions for supporting transparency in supply chain management, ISSN: 1359-8546, Publication date: 13 April 2020. 5. The impact of the blockchain on the supply chain: a theory-based research framework and a call for action, Horst Treiblmaier ISSN: 1359-8546, Publication date: 10 September 2018. 6. Block chain in Logistics and Supply Chain: a Lean approach for designing real-world use cases, Guido Perboli1,2,3, Member, IEEE, Stefano Musso1,2, Mariangela Rosano1,2DOI 10.1109/ACCESS.2018.2875782, IEEE Access. 2169-3536 (c) 2018 IEEE 7. Land records on Blockchain for implementation of Land Titling in India, Vinay Thakura, M.N. Dojab, Yogesh K. Dwivedic, Tanvir Ahmadd, Ganesh Khadangae 8. Supply chain re-engineering using blockchain technology: A case of smart contract based tracking process, Received in revised form 21 March 2019; Accepted 26 March 2019 . 0040-1625/ © 2019 Elsevier Inc. All rights reserved. 9. Smart Contracts: Security Patterns in the Ethereum Ecosystem and Solidity, Maximilian Wöhrer and Uwe Zdun 978-1-5386- 5986-1/18 c , 2018 IEEE 10. SmartContractVulnerabilities: DoesAnyone Care? DanielPerez, BenjaminLivshits ImperialCollegeLond, arXiv:1902.06710v3 [cs.CR] 17 May 2019 11. Security Analysis Methods on Ethereum Smart Contract Vulnerabilities — A Survey, Purathani Praitheeshan?, Lei Pan?, Jiangshan Yu†, Joseph Liu†, and Robin Doss?. arXiv:1908.08605v2 [cs.CR] 9 Jan 2020. 12. Smart Contract: Attacks and Protections, sarwar sayeed , hector marco-gisbert , (senior member, ieee), and tom caira, Received December 6, 2019, accepted January 17, 2020, date of publication January 30, 2020, date of current version February 10, 2020.Digital Object Identifier 10.1109/ACCESS.2020.2970495 13. Security Vulnerabilities in Ethereum Smart Contracts, Alexander Mense†, Markus Flatscher, iiWAS '18, November 19–21, 2018, Yogyakarta, Indonesia © 2018 Copyright is held by the owner/author(s). Publication rights licensed to ACM. 14. .Finding the greedy,Prodigal,and suicidal contracts at scale, Ivica Nikolic ,Ashish kolluri, Ilya Surgey,Prateek suxena,Aquinas hobor, arXiv:1908.08605v2 [cs.CR] 9 Jan 2020. 15. On Blockchain Security and Relevant Attacks, Joanna Moubarak,Eric Filiol,Maroun Chamoun, 978-1-5386-1254-5/18/$31.00 ©2018 IEEE. 16. Defects and Vulnerabilities in Smart Contracts, a Classification using the NIST Bugs Framework Authors: Mamatha M.C, H.C. Sateesh Kumar

Paper Title: Adaptive Beamforming Method for MIMO Antenna Array with Constrained Mean Square Error Abstract: The Adaptive beam forming with Multikernel based Bayesian learning method beam forming on Uniform Linear Array (ULA) antennas for better localization. Undetermined source localization problem is solved using the Multikernel Sparse Bayesian Learning framework. Beam forming problem is considered the undetermined source localization problem and solved using the adaptive method. The Degree of Freedom (DOF) is increased using the adaptive nature of the manifold matrix while maintaining the same number of antennas. The response model that adaptively adjusts the manifold matrix in the Sparse Bayesian problem uses the Multikernel framework. MATLAB based implementation thus carried out on the ULA clearly exhibits better results over the single kernel model. The Mean Square Error (MSE) and Root Mean Square Error (RMSE) with Signal to Noise Ratio (SNR) variation is obtained to evaluate the performance of the proposed implementation. The performance obtained is found to be satisfactory and is at par with the recent previous implementation.

Keywords: Direction of Arrival Estimation, Multikernel Sparse Representation, Basis Pursuit Methods 196.

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Paper Title: Exploration of Twitter Sentiments and Classification by using Deep CNN and Naive Bayes Abstract: Sentiment evaluation of tweets help the enterprises to evaluate public emotion towards the activities or products associated with them. Most of the research targeted to obtain sentiment capabilities with the help of analyzing syntactic and lexical features which can be expressed through sentiment phrases, emoticons, exclamation marks etc. In the proposed paper we introduce a phrase embedding received by means of unsupervised learning(deep learning) on large twitter texts which uses contextual semantic relationships and co- occurrence statistical characteristics between words in tweets and also consider the emojis to categorise the emotions whether it is positive or negative by the use of Naive Bayes. In the preceding paper which used usnsupervised learning approach for classification, has an accuracy of 87% and supervised has an accuracy of 89%. According to our context, Naive Bayes has given an accuracy of 100% and CNN has given an accuracy of 100%. As compared to machine learning. It has a higher performance on the accuracy, precision and recall.

Keywords: Tweets, Sentiment analysis, Word Embedding, Convolution Neural Network, Naive Bayes 197.

References: 1100- 1105 1. Bo Pang, Lillian Lee, and Shivakumar Vaithyanathan. Thumbs up? sentiment classification using machine learning techniques. arXiv preprint cs/0205070, 2002. 2. Georgios Paltoglou and Mike Thelwall. Twitter, myspace, digg: Un- supervised sentiment analysis in social media. ACM Transactions on Intelligent Systems and Technology (TIST), 3(4):1–19, 2012. 3. Svetlana Kiritchenko, Xiaodan Zhu, and Saif M Mohammad. Sentiment analysis of short informal texts. Journal of Artificial Intelligence Research, 50:723–762, 2014. 4. Nadia FF da Silva, Eduardo R Hruschka, and Estevam R Hruschka Jr. Tweet sentiment analysis with classifier ensembles. Decision Support Systems, 66:170–179, 2014. 5. Xueliang C Jianqiang Z. Combining semantic and prior polarity for boosting twitter sentiment analysis. In 2015 IEEE International Confer- ence on Smart City/SocialCom/SustainCom (SmartCity), pages 832–837. IEEE, 2015. 6. Hassan Saif, Yulan He, Miriam Fernandez, and Harith Alani. Contextual semantics for sentiment analysis of twitter. Information Processing & Management, 52(1):5–19, 2016. 7. Zhao Jianqiang and Gui Xiaolin. Comparison research on text preprocessing methods on twitter sentiment analysis. IEEE Access, 5:2870– 2879, 2017. 8. Zhao Jianqiang, Gui Xiaolin, and Zhang Xuejun. Deep convolution neural networks for twitter sentiment analysis. IEEE Access, 6:23253–23260, 2018. Authors: P.Pitchaipandi, C.Baskaran The Role and Consumption of Social Networks/Media Research Communication by the Social Paper Title: Science Students and Research Scholars at Alagappa University, Karaikudi, Tamil Nadu. Abstract: The social Networks and Media exchange information, ideas and pictures/videos in virtual communities and networks. The assessment of this study to convey. The role and consumption of Social 198. Networks/Media Research Communication by the Students and Research Scholars’ Social Science at Alagappa University, Karaikudi, Tamilnadu. A sample size of 154 students and Research scholars was selected purposive 1106- sampling method. The data were collected by the make use of the questionnaire. The findings of the study: 1110 (55.2%) of them participants Male. Whereas (30.5%) of them category of age group of 20-25. (33.8%) Post graduates second year students while that reported (31.2%) Economics and Rural Development/ History. It followed by the majority (46.1%) of them participants using Internet browser, Google Chrome, whereas (25.3%) participants using SNs/Media devices for Facebook. (25.3%) participants exhausting CiteULike for Reference Management Software. It followed by using Research Citation Indexes for Google Scholar 72 (46.8%) of them participants respondents respectively.

Keywords: SNs/Media devices, Internet Browser, Reference Management software, Research citation Indexes.

References:

1. Sukula, S. (2012). Web 2.0 and Social Networking weaver Birds in information Superhighway. Darya Gani, New Delhi, India: ESS ESS Publications. 2. Baskaran, C. (2018). Use of Social Networks (SNs) and Medias on Dissemination of Scholarly information among the Research Scholars in Alagappa University, Karaikudi, Tamil Nadu. Journal of Advances in Library and Information Science, 7(3), 257-261. 3. Baskaran, C. (2014). Information Resources Access Pattern at Alagappa University Library, Karaikudi, and Tamilnadu, India. International Journal of Library and Information Studies, 4(1), 19-23. 4. Baskaran, C. (2019). Scholarly Information Share through Social Networks (SNs) and Medias among Social Science Scholars in selected State Universities in Tamilnadu. International Journal of Library and Information Studies, 9(3), 83-92. 5. Baskaran, C., & Prasad, M. (2019). Research Quantify with faculty member's perception and expectations in e-context at the academic sphere. Library Philosophy and Practice, 1-16. 6. Baskaran, C., & Binu, P. C. (2019). Information acceleration into access on acquiring skill under consortium based resources in the selected Universities of Kerala, India. Library Philosophy and Practice, 1-17. 7. Shilpa, V., &Sreekala, P. K. (2019). The Usage and Upshots of Social Networking Sites: A Study among Students of Engineering Colleges in Kozhikode City, Kerala. Asian Journal of Information Science & Technology (AJIST), 9. 8. Pitchaipandi, P. (2020). Impact and Usage of Social Media among the Post Graduate Students of Arts in Alagappa University, Karaikudi, India. In Measuring and Implementing Altmetrics in Library and Information Science Research (pp. 99-110). IGI Global. Authors: Shailendra Giri, Pradeep Kumar Yadav, Harendra Kumar A Neural Network based Model with Lockdown Condition for Checking the Danger Stage Level of Paper Title: COVID-19 Infection Risk Abstract: In December 2019, human history is observing a very strange time fighting an invisible enemy, the novel corona virus (COVID-19). Initially emerged in Wuhan, China and has swiftly spread to other parts of China and a number of foreign countries around the world. There is a current worldwide outbreak of a new type of corona virus (COVID-19). Governments are under increased pressure to stop the outbreak spiralling into a global health emergency. A series of mandatory actions have been taken by the municipal and provincial governments supported by the central government, such as measures to restrict travels across cities, contact tracing and case detection, guidance, quarantine and information to the public etc. At this stage, preparedness, transparency and sharing of information are crucial to risk assessments and beginning outbreak control activities. The main objective of this paper is to develop a neural network based algorithm involving lockdown condition between hidden layers for checking the level of COVID-19 infection risk from the pandemic data. The results show that India is in a good condition in comparison of other countries due to timely implementation of the lockdown

Keywords: Artificial neural network, COVID-19, Corona virus, Lockdown.

References:

1. Al-Hussein, A.-B. A., & Tahir, R. (2020). Epidemiological Characteristics of COVID-19 Ongoing Epidemic in Iraq. https://doi.org/10.2471/BLT.20.251561 2. Anderson, R. M., & May, R. M. (1979). Population biology of infectious diseases: Part I. In Nature (Vol. 280, Issue 5721, pp. 361– 199. 367). Nature Publishing Group. https://doi.org/10.1038/280361a0 3. Bhola, J., Revathi Venkateswaran, V., & Koul, M. (2020). Corona Epidemic in Indian context: Predictive Mathematical Modelling. https://doi.org/10.1101/2020.04.03.20047175 1111- 4. Fitzpatrick, M. C., Bauch, C. T., Townsend, J. P., & Galvani, A. P. (2019). Modelling microbial infection to address global health 1118 challenges. In Nature Microbiology (Vol. 4, Issue 10, pp. 1612–1619). Nature Publishing Group. https://doi.org/10.1038/s41564-019- 0565-8 5. Gros, C., Valenti, R., Schneider, L., Valenti, K., & Gros, D. (2020). Containment efficiency and control strategies for the Corona pandemic costs. 6. Gros, C., Valenti, R., Valenti, K., & Gros, D. (2020). Strategies for controlling the medical and socio-economic costs of the Corona pandemic. http://arxiv.org/abs/2004.00493 7. Guan, W., Ni, Z., Hu, Y., Liang, W., Ou, C., He, J., Liu, L., Shan, H., Lei, C., Hui, D. S. C., Du, B., Li, L., Zeng, G., Yuen, K.-Y., Chen, R., Tang, C., Wang, T., Chen, P., Xiang, J., … Zhong, N. (2020). Clinical Characteristics of Coronavirus Disease 2019 in China. New England Journal of Medicine, 382(18), 1708–1720. https://doi.org/10.1056/NEJMoa2002032 8. Huppert, A., & Katriel, G. (2013). Mathematical modelling and prediction in infectious disease epidemiology. In Clinical Microbiology and Infection (Vol. 19, Issue 11, pp. 999–1005). Blackwell Publishing Ltd. https://doi.org/10.1111/1469-0691.12308 9. Kim, S., Seo, Y. Bin, & Jung, E. (2020). Prediction of COVID-19 transmission dynamics using a mathematical model considering behavior changes. Epidemiology and Health, e2020026. https://doi.org/10.4178/epih.e2020026 10. Kumar H. & Giri S. (2020),, “A neural network-based algorithm for flow shop scheduling problems under fuzzy environment”, International journal Process Management and Benchmarking, Vol. 10(2), pp.282–296, 2020, ISSN: 1741-816X. 11. Kumar H. & Giri S. (2020), “Optimisation of makespan of a flow shop problem using multi layer neural network”, International Journal of Computing Science and Mathematics, Vol. 11(2), pp.107–122, ISSN: 1752-5063, Doi: 10.1504/IJCSM.2020.10028046 12. Kumar H. (2017)., “Some Recent Defuzzification Methods”, Theoretical and Practical Advancements for Fuzzy System Integration, Chapter-2, pp.31-48, IGI-Global USA, ISBN: 9781522518488, Doi: 10.4018/978-1-5225-1848-8.ch002. 13. Kumar H. & Giri S. (2019), “A flowshop scheduling algorithm based on artificial neural network”, Bulletin of Pure and Applied Sciences (Math & Stat.), Vol. 38E, No. 1, pp.62-71, ISSN: 0970-6577, Doi: 10.5958/2320-3226.2019.00007.9 14. Lamba, I. (2020). Why India needs to extend the nationwide lockdown. The American Journal of Emergency Medicine. https://doi.org/10.1016/j.ajem.2020.04.026 15. Lloyd-Smith, J. O. (2007). Maximum likelihood estimation of the negative binomial dispersion parameter for highly overdispersed data, with applications to infectious diseases. PLoS ONE, 2(2). https://doi.org/10.1371/journal.pone.0000180 16. Nishiura, H., Linton, N. M., & Akhmetzhanov, A. R. (2020). Serial interval of novel coronavirus (COVID-19) infections. International Journal of Infectious Diseases, 93, 284–286. https://doi.org/10.1016/j.ijid.2020.02.060 17. Nussbaumer-Streit, B., Mayr, V., Dobrescu, A. I., Chapman, A., Persad, E., Klerings, I., Wagner, G., Siebert, U., Christof, C., Zachariah, C., & Gartlehner, G. (2020). Quarantine alone or in combination with other public health measures to control COVID-19: a rapid review. In The Cochrane database of systematic reviews (Vol. 4, Issue 4, p. CD013574). NLM (Medline). https://doi.org/10.1002/14651858.CD013574 18. Prem, K., Liu, Y., Russell, T. W., Kucharski, A. J., Eggo, R. M., & Davies, N. (2020). Articles The effect of control strategies to reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China: a modelling study. https://doi.org/10.1016/S2468- 2667(20)30073-6i 19. Singh, R., & Adhikari, R. (2020). Age-structured impact of social distancing on the COVID-19 epidemic in India. http://arxiv.org/abs/2003.12055 20. Thieme, H. R. (2003). Mathematics in population biology. Princeton University Press. 21. Zaman, G., Jung, I. H., Torres, D. F. M., & Zeb, A. (2017). Editoral Mathematical Modeling and Control of Infectious Diseases. https://doi.org/10.1155/2017/7149154 Authors: Gouri V, Sreeni K G Generation and Transmission of Solitons at 64Gbps using Multiplexing in Polarization and Paper Title: Wavelength Domains Abstract: The factors which impose an upper limit on data rates in high speed optical systems have been explored. A 64Gbps system has been designed and simulated using a combination of Wavelength Division Multiplexing (WDM) and Polarization Division Multiplexing (PDM) employing soliton(secant hyperbolic pulse) transmission. The possibility of Gordon Haus jitter and adjacent pulse interaction, which curtails performance of very high speed systems, has been ruled out by use of multiplexing in multiple domains. The Polarization Mode Dispersion (PMD) has also been evaded by limiting the data-rate of individual channels to 8 Gbps. This also permits use of components with relaxed specifications, when compared to single channel realizations. In this work a 64 Gbps WDM-PDM based system employing secant hyperbolic pulses at 8Gbps over a distance of 1000km has been simulated. It yields an average bit error rate of 10-10

200. Keywords: Group-Velocity-Dispersion, Polarization-Division- Multiplexing, Secant-Hyperbolic-Pulse, Wavelength-Division-Multiplexing. 1119- 1125 References:

1. J. P. Gordon and H. Kogelnik, “PMD fundamentals: Polarization mode dispersion in optical fibers,” Proceedings of the National Academy of Sciences of the United States of America, vol. 97, no. 9, pp. 4541–4550, 2000. 2. I.Tavakkolnia, M.Safari, “Capacity Analysis of Signaling on the Continuous Spectrum of Nonlinear Optical Fibers”, Journal of Lightwave Technology of vol 35,no 11 , pp. 2086-2097, 2017 3. G.P.Agrawal, Nonlinear fiber optics, Fifth edition .Academic Press, 2013 4. G.P.Agrawal, Fiber Optic communication systems, Third edition, , Wiley Intersciences publishing 2002 5. A.W.Naji et al , “Review of Erbium-Doped Fiber Amplifier”, International Journal of the Physical Sciences Vol. 6 Issue20, pp. 4674- 4689, 23 September, 2011 6. Gerd E. Keiser,“A Review of WDM Technology and Applications”, Optical Fiber Technology, Fifth edition 2013 7. M.Singh, H.S.Saini, “High Performance Soliton WDM Optical Communication System”, Fourth International Conference in Computing and Communication ,IEEE, pp. 20-24, 2014. 8. S.G.Evangelides, Z.F.Mollenauer, J.P.Gordon, N.S.Bergano, ”Polarization Multiplexing with Solitons”, Journal of Lightwave Technology, vol 10, no 1, pp.28-35 ,1992 Authors: Ye-lim Kang, Tae-ho Cho Energy Efficient Operation Cycle Determination Scheme of Fuzzy Based IHA in Air Purification Paper Title: IoT Abstract: Fine dust is a harmful particulate substance floating in the air and is divided into PM10 (which is 10 um in diameter or less) and PM2.5 (which is 2.5 um in diameter or less). Fine dust is a major cause of chronic respiratory diseases, which may occur naturally through forest fires or yellow dust, but it is mainly caused by combustion of fossil fuels such as oil and coal, or by automobile exhaust gases. When this type of bad outdoor fine dust flows into buildings, the indoor air becomes polluted, making it easier for workers or students who spend a lot of time indoors to be at risk for chronic respiratory diseases. To minimize this risk, recent research and development has focused on systems to purify indoor air by filtering fine dust. In this paper, we introduce a Wireless Sensor Networks (WSNs)-based Internet of Things (IoT) air purification system. In the WSNs-based IoT air purification system, it is important to maintain the integrity of the sensing data because the IoT air purifier operates based on the sensing data detected by sensor nodes. To defend the IoT air purifier against false 201. report injection attacks, the existing fuzzy-based Interleaved Hop-by-Hop Authentication (IHA) detects false report injection attacks through Data Calibration. In addition to the existing fuzzy-based IHA sets, the security 1126- limit changes according to the network situation using fuzzy logic and adjusts the security and energy. However, 1131 the existing fuzzy-based IHA executes a fuzzy system every time it detects a normal event or false report injection attack, which requires additional message overhead and increases the transmission/reception energy, which increases the energy burden of the sensor nodes. To address this problem, we propose a method to control the operation cycle of the fuzzy system using the evaluation function. This proposed method has the advantage that the trade-off relationship between energy and security can be appropriately used to adjust the operation cycle and increase the lifetime of the network.

Keywords: Network Security, Internet of Things, Wireless Sensor Networks, Interleaved Hop-by-hop Authentication, False report injection attack

References:

11. Kang, Dongmug, and Jong-Eun Kim. "Fine, ultrafine, and yellow dust: emerging health problems in Korea." Journal of Korean medical science 29.5 (2014): 621-622. 12. Akyildiz, Ian F., et al. "A survey on sensor networks." IEEE Communications magazine 40.8 (2002): 102-114. 13. Al-Fuqaha, Ala, et al. "Internet of things: A survey on enabling technologies, protocols, and applications." IEEE communications surveys & tutorials 17.4 (2015): 2347-2376. 14. Iera, and Giacomo Morabito. "The internet of things: A survey." Computer networks 54.15 (2010): 2787-2805. 15. Ye-lim Kang and Tae-ho Cho. "Fuzzy-based Dynamic Security Parameters Determination Method to Improve Energy Efficiency." International Journal of Engineering and Advanced Technology (IJEAT) 9.4 (2020):1952-1958. 16. Zhu, Sencun, et al. "An interleaved hop-by-hop authentication scheme for filtering of injected false data in sensor networks." IEEE Symposium on Security and Privacy, 2004. Proceedings. 2004. IEEE, 2004. 17. Zhu, Sencun, et al. "Interleaved hop-by-hop authentication against false data injection attacks in sensor networks." ACM Transactions on Sensor Networks (TOSN) 3.3 (2007): 14-es. 18. Jeba, S. Annlin, and B. Paramasivan. "False data injection attack and its countermeasures in wireless sensor networks." European Journal of Scientific Research 82.2 (2012): 248-257. 19. Zadeh, Lotfi Asker. "Fuzzy sets as a basis for a theory of possibility." Fuzzy sets and systems 1.1 (1978): 3-28. 20. Lee, Chuen-Chien. "Fuzzy logic in control systems: fuzzy logic controller. II." IEEE Transactions on systems, man, and cybernetics 20.2 (1990): 419-435. 21. Mamdani, Ebrahim H., and Sedrak Assilian. "An experiment in linguistic synthesis with a fuzzy logic controller." International journal of man-machine studies 7.1 (1975): 1-13. 22. Ye-lim Kang and Tae-ho Cho. “Detection of False Report Injection At Wsns Based on Data Calibration in Iot Environment.” International Journal of Recent Technology and Engineering (IJRTE) 8.4 (2019): 8956-8960. 23. Ye, Fan, et al. "Statistical en-route filtering of injected false data in sensor networks." IEEE Journal on Selected Areas in Communications 23.4 (2005): 839-850. Authors: Samson Wanjala Munialo, Geoffrey Muchiri Muketha, Kelvin Kabeti Omieno

Paper Title: Automated Feature Extraction from UML Images to Measure SOA Size Abstract: Enormous development has been experiences in the field of text and image extraction and classification. This is due to large amount of image data that is generated as a result of document sharing for collaborative software development and electronic storage of design documents. One of the recent technique for analyzing large dataset and discover underlying patterns is Deep learning technique. Deep learning is a branch of Machine learning inspired by human brain functionality for the purpose of analyzing unstructured data including images, sound and text. Unified Model Language (UML) is an architectural design which provides developers with a view of software components and scope. UML contain texts and notations which are mostly analyzed and interpreted manually for the purpose of system implementation and scope or size measurement. Consequently, manual processing of electronic design artifacts is prone to bias, errors and time consuming. Various researchers have attempted to automate the process of reading and interpreting design artifacts but still there is a challenge due to varying style of designing these artifacts. This study propose an automatic tool based on existing deep learning algorithms including ResNet50 CNN to read UML interface and sequence diagrams images to detect UML arrows, EAST test detector to detect text, Tesseract OCR with Long Short-Term Memory (LSTM) to recognize text and Multi-class Support Vector Machine to classify text for the purpose of measuring Service Oriented Architecture size. We subjected the tool to accuracy tests which returned encouraging results.

Keywords: Unified Modeling Language, Machine Learning, Deep Learning, image classification, text extraction.

References: 202. 1. R. Deepa and K. N. Lalwani, “Image Classification and Text Extraction using Machine Learning,” Proc. 3rd Int. Conf. Electron. Commun. Aerosp. Technol. ICECA 2019, pp. 680–684, 2019, doi: 10.1109/ICECA.2019.8821936. 1132- 2. A. M. Intwala, K. Kharade, R. Chaugule, and A. Magikar, “Dimensional arrow detection from CAD drawings,” Indian J. Sci. Technol., 1137 vol. 9, no. 21, pp. 1–7, 2016, doi: 10.17485/ijst/2016/v9i21/89259. 3. W. S. Munialo, M. G. Muketha, and K. . Omieno, “Size Metrics for Service-Oriented Architecture,” Int. J. Softw. Eng. Appl., vol. 10, no. 2, pp. 67–83, 2019. 4. M. Harizi, “The Role of Class Diagram in Estimating Software Size,” Int. J. of Comp. Appl.,vol. 44, no. 5, pp. 31–33, 2012. 5. L. Marcos, “Modelling of Service-Oriented Architectures with UML,” vol. 194, pp. 23–37, 2008, doi: 10.1016/j.entcs.2008.03.097. 6. G. Albrecht, A., Gaffney, “No Title,” A Softw. Sci. Validation, IEEE Trans Softw. Eng., 1983. 7. H. Chindove, L. F. Seymour, and F. I. Van Der Merwe, “Service-oriented Architecture : Describing Benefits from an Organisational and Enterprise Architecture Perspective,” vol. 3, no. Iceis, pp. 483–492, 2017, doi: 10.5220/0006383604830492. 8. S. Bilgaiyan, S. Sagnika, S. Mishra, and M. Das, “A systematic review on software cost estimation in Agile Software Development,” Journal of Engineering Science and Technology Review, vol. 10, no. 4. pp. 51–64, 2017, doi: 10.25103/jestr.104.08. 9. Z. A. Siddiqui and K. Tyagi, “A critical review on effort estimation techniques for service-oriented-architecture-based applications,” Int. J. Comput. Appl., vol. 7074, no. October, pp. 1–10, 2016, doi: 10.1080/1206212X.2016.1237132. 10. COSMIC, Guideline for Sizing SOA Software. 2015. 11. B. Karasneh and M. R. V Chaudron, “Extracting UML Models from Images,” no. March, 2013, doi: 10.1109/CSIT.2013.6588776. 12. K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 2016, pp. 770–778, 2016, doi: 10.1109/CVPR.2016.90. 13. T. Kumuda and L. Basavaraj, “Edge based segmentation approach to extract text from scene images,” Proc. - 7th IEEE Int. Adv. Comput. Conf. IACC 2017, pp. 706–710, 2017, doi: 10.1109/IACC.2017.0147. 14. P. Dong-Chul, “Image Classification Using Naive Bayes Classfier,” Int. J. Comput. Sci. Electron. Eng., vol. 4, no. 3, pp. 2320–4028, 2016, http://www.isaet.org/images/extraimages/P1216004.pdf. 15. S. C. Hsu, I. C. Chen, and C. L. Huang, “Image classification using naive bayes classifier with pairwise local observations,” J. Inf. Sci. Eng., vol. 33, no. 5, pp. 1177–1193, 2017, doi: 10.6688/JISE.2017.33.5.5. 16. L. H. Thai, T. S. Hai, and N. T. Thuy, “Image Classification using Support Vector Machine and Artificial Neural Network,” Int. J. Inf. Technol. Comput. Sci., vol. 4, no. 5, pp. 32–38, 2012, doi: 10.5815/ijitcs.2012.05.05. 17. F. Sultana, A. Sufian, and P. Dutta, “Advancements in image classification using convolutional neural network,” Proc. - 2018 4th IEEE Int. Conf. Res. Comput. Intell. Commun. Networks, ICRCICN 2018, pp. 122–129, 2018, doi: 10.1109/ICRCICN.2018.8718718. 18. Z. Wu, C. Shen, and A. van den Hengel, “Wider or Deeper: Revisiting the ResNet Model for Visual Recognition,” Pattern Recognit., vol. 90, pp. 119–133, 2019, doi: 10.1016/j.patcog.2019.01.006. 19. [19] K. R. Tripathi and R. Kumar, “Image Classification using small convolutional Neural Network,” IEEE, pp. 483–487, 2019, doi: 978-1-5386-5933-5/19. 20. K. Chauhan and S. Ram, “Image Classification with Deep Learning and Comparison between Different Convolutional Neural Network Structures using Tensorflow and Keras,” Int. J. Adv. Eng. Res. Dev., 2018. 21. C. Patel, A. Patel, and D. Patel, “Optical Character Recognition by Open source OCR Tool Tesseract: A Case Study,” Int. J. Comput. Appl., vol. 55, no. 10, pp. 50–56, 2012, doi: 10.5120/8794-2784. 22. K. Kowsari, D. E. Brown, M. Heidarysafa, K. J. Meimandi, M. S. Gerber, and L. E. Barnes, “HDLTex : Hierarchical Deep Learning for Text , Classification,”, IEEE, 2017, doi: 10.1109/ICMLA.2017.0-134. 23. X. Zhou et al., “EAST : An Efficient and Accurate Scene Text Detector,” IEEE, pp. 5551–5560, 2015. Authors: Suspend 203. Paper Title: 1138- 1142 Authors: Anuradha S. Pandit, V.V. Dixit

Paper Title: A Review of Different Methods for Automatic Diagnosis of Oral Cancer Abstract: Oral cancer is having 6th rank out of all cancers in the world. There might be tumor in salivary glands, tonsils and also in neck, head, face and oral cavity. Oral cancer can be diagnosed with methods like biopsy or with screening method. In biopsy method small sample of tissue is being removed from affected part of the body and tested under microscope. But biopsy is invasive and painful. Also pathological analysis of it is time consuming. Screening method is non invasive. Early detection is possible with screening method which is necessary for improvement of survival rate. This paper presents different screening methods for detection of oral cancer. Optical Coherence Tomography and a variety of Machine Learning based techniques, including Artificial Neural Networks (ANNs), Bayesian Networks (BNs), Tree Boost Model are discussed in this paper.

Keywords: OSCC, GLCM, SVM, Optical Coherence Tomography, Neo Maker, Deep Convolution Network.

References:

1. Woonggyu Jung, Jun Zhang, Jungrae Chung, Petra Wilder-Smith, Matt Brenner, J. Stuart Nelson, and Zhongping Chen, “ Advances in Oral Cancer Detection Using Optical Coherence Tomography”, IEEE Journal of Selected Topics in Quantum Electronics, VOL. 11, NO. 4, July/August 2005. 2. Javier A. Jo, Brian E. Applegate, Jesung Park, Sebina Shrestha, Paritosh Pande, Irma B. Gimenez-Conti, and Jimi L. Brandon, “ In Vivo Simultaneous Morphological and Biochemical Optical Imaging of Oral Epithelial Cancer”, IEEE Transactions On Biomedical Engineering, Vol.57, no. 10, Oct 2010. 3. Neha Sharma ,Hari Om, “ Data mining models for predicting oral cancer survivability”, Springer-Verlag Wien 2013, October 2013 4. Dario Salvi, Marco Picone, Marıa Teresa Arredondo, Marıa Fernanda Cabrera-Umpierrez , Angel Esteban, Sebastian Steger, and Tito Poli, “Merging Person-Specific Bio-Markers for Predicting Oral Cancer Recurrence Through an Ontology”, IEEE Transaction on Biomedical Engineering, Vol. 60, No. JAN 2013 5. K. Kalantzaki, E. S. Bei, K. P. Exarchos, M. Zervakis, M. Garofalakis, D. I. Fotiadis, “ Nonparametric Network Design and Analysis 204. of Disease Genes in Oral Cancer Progression”, IEEE Journal of Biomedical & Health Iinformatics, Vol. 18, NO.2, MARCH 2014. 6. XiaoShen Wang, Avraham Eisbruch,” IMRT for head and neck cancer: reducing xerostomia and dysphagia”, Journal of Radiation Research, Vol. 57, No. S1, 2016. 1143- 7. Konstantina Kourou, Costas Papaloukas and Dimitrios I. Fotiadis,” Integration of Pathway Knowledge and Dynamic Bayesian 1145 Networks for the Prediction of Oral Cancer Recurrence”, IEEE Journal of Biomedical and Health Informatics,2016 8. Konstantina Kourou , Themis P. Exarchos , Konstantinos P. Exarchos , Michalis V. Karamouzis , Dimitrios I. Fotiadis, “ Machine learning applications in cancer prognosis and prediction”, K. Kourou et al. / Computational and Structural Biotechnology Journal 13 (2015) 8–17 9. C.R.Muzakkir Ahmed , M.Narayanan , S.Kalaivanan , K.Sathya Narayanan , A.K. Reshmy, “To Detect And Classify Oral Cancer In Mri Image Using Firefly Algorithm And Expectation Maximization Algorithm”, International Journal of Pure and Applied Mathematics, Volume 116 No. 21 2017, 149-154. 10. Rajdeep Mitra, Dr. Menaka R., “Charactersation of Oral Cancer Lesions Using Texture Features”, National Conference on Science, Engineering and Technology, Vol. 4,Issue. 6 Sept. 11. Harikumar Rajaguru, Sunil Kumar Prabhakar, “ Oral Cancer Classification from Hybrid ABC-PSO and Bayesian LDA”, International Conference on Communication and Electronics Systems. 12. D.Padmini Pragna, Sahithi Dandu, Meenakzshi M, C. Jyotsna, Amudha J., “ Health Alert System to Detect Oral Cancer”, International Conference on Inventive Communication and Computational Technologies. 13. Harikumar Rajaguru and Sunil Kumar Prabhakar, “Performance Comparison of Oral Cancer Classification with Gaussian Mixture Measures and Multi Layer Perceptron”, Springer Nature Singapore Pte Ltd. 2017 14. Arianna Strome, Susanne Kossatz, Daniella Karassawa Zanoni, Milind Rajadhyaksha, Snehal Patel, and Thomas Reiner, “Current Practice and Emerging Molecular Imaging Technologies in Oral Cancer Screening”, Molecular Imaging Volume 17, 1-11,2018. 15. Shipu Xu, Chang Liu , Yongshuo Zonu , Sirui Chen , Yiwen Lu , Longzhi Yang , Eddie Y. K. Ng , Yongtong Wang, Yunsheng Wang , Yong Liu, Wenwen Hu2 , And Chenxi Zhang , “An Early Diagnosis of Oral Cancer based on Three-Dimensional Convolutional Neural Networks”, Special Section On Data-Enabled Intelligence For Digital Health, IEEE Access, Vol. 7, 2019. 16. Andrew Emon Heidari , Sumsum P. Sunny, Bonney L. James, Tracie M. Lam , Anne V. Tran, Junxiao Yu , Ravindra D. Ramanjinappa, Uma K, Praveen Birur, Amritha Suresh, Moni A. Kuriakose, Zhongping Chen , and Petra Wilder-Smith, “Optical Coherence Tomography as an Oral Cancer Screening Adjunct in a Low Resource Settings”, IEEE Journal Of Selected Topics In Quantum Electronics, Vol. 25, no. 1,Jan/Feb2019. 17. Angela L. Mazula,b, Graham A. Colditzb , Jose P. Zevallosa , “Factors associated with HPV testing in oropharyngeal cancer in the National Cancer Data Base from 2013 to 2015” Oral Oncology(104), Elsevier,2019 18. Chih-Hung Chan, Tze-Ta Huang, Chih-Yang Chen, Chien-Cheng Lee, Man-Yee Chan, and Pau-Choo Chung, “Texture-Map-Based Branch-Collaborative Network for Oral Cancer Detection”, IEEE Transactions on Biomedical Circuits and Systems, Vol. 13, no. 4, Aug 2019. Sushrutha Bharadwaj M, VG Sangam, Yashodhara Bora, Devaki Menon, Sonal Priyadarshan and Authors: 205. Thrisha Chandru Paper Title: Cognitive Rehabilitation and EEG Analysis: A Review Abstract: Cognition is the capacity of the brain to register and decipher data dependent on information and experience. A portion of the subjective aptitudes are processing, memory and retention, logic and reasoning, attention and so forth. The subjective abilities begin to grow directly from the hour of birth of a person. There are situations where these advancements don't happen at the opportune time or in a proficient manner, which prompts scholarly disorders. The most commonly found intellectual disorders in children are attention deficit hyperactivity disorder (ADHD), epilepsy, encephalitis, autism spectrum disorder (ASD) and speech disorders. There are cognitive tasks and retraining intended for each sort of cognitive issue. These are planned so as to improve the cognitive degrees of the children who experience the ill effects of cognitive issues, for an improvement in their everyday lives. This paper gives an overview of some of the existing techniques for the improvement of cognitive levels along with the techniques of EEG analysis. The activities in the brain can be traced with the help of an electroencephalogram (EEG). Cognitive levels can also be studied with the help of EEG. The study that involves cognition requires careful pre-processing, feature extraction and appropriate analysis. The processed EEG information is analysed utilizing various techniques which can extensively be ordered into time domain, time frequency domain, frequency domain, non-linear methods and artificial neural network methods. Out of every one of these strategies, the frequency domain techniques and time-frequency strategies are most popularly used.

Keywords: Analysis, Brain, Cognition, Electroencephalography (EEG), Rehabilitation

References:

1. Lisa Pauwels, Sima Chalavi, Stephan P. Swinnen, Aging and brain plasticity, Aging 2018 (vol 10) 2. Shahzadi Malhotra, G. Rajender, Vibha Sharma, T.B.Singh, Efficacy of Cognitive Retraining Techniques in Children with Learning Disability, Stroke (vol 12), 2009 3. Institute of Medicine (US) Committee on the Public Health Dimensions of the Epilepsies; England MJ, Liverman CT, Schultz AM, et al., Epilepsy Epidemiology and Prevention, Epilepsy Across the Spectrum: Promoting Health and Understanding, 2012 4. Ponds, Rudolf W.H.M.Hendriks, Mark, Cognitive rehabilitation of memory problems in patients with epilepsy, Seizure(vol 15), 2006 5. Nahid Nasiri, Shervin Shirmohammadi, Ammar Rashed, ‘A Serious Game for Children with Speech Disorders and Hearing Problems’, 2017 IEEE 1146- 6. Bateman, Andrew, Oliver, Clinical, Centre, Zangwill, Rehabilitation after encephalitis By Dr Howard Jackson , Clinical Director and 1152 Nick Morton , Consultant Clinical,National Institute of Neurological Disorders and Stroke 7. Duong Silvia Patel Tejal, Chang Feng, Dementia: What pharmacists need to know, Canadian Pharmacists Journal (vol 150), 2017 8. Carme Carrion, Frans Folkvord , Dimitra Anastasiadou ,Marta Aymerich, Cognitive Therapy for Dementia Patients: A Systematic Review, Dementia and Geriatric Cognitive Disorders(vol 46), 2018 9. Sela et al, Electroencephalography: An introductory Text and Atlas, Cerebral Cortex(vol 62), 2002 10. Regina I Machinskaya, Andrei Kurgansky, Frontal bilateral synchronous theta waves and the resting EEG coherence in children aged 7-8 and 9-10 with learning difficulties, Human Physiology, January 2013 11. J. Noebels, Jasper 's Basic Mechanisms of the Epilepsies, Fourth Edition, 2012 12. Lai CW, et al, EEG in herpes simplex encephalitis. J Clin Neurophysiol. 18 13. Michal, EEG abnormalities, epilepsy and regression in autism: A review, Neuroendocrinology Letters (vol 29), 2008 14. Mrs. Varsha K. Harpale, Dr. Vinayak K. Bairagi, Time and Frequency Domain Analysis of EEG Signals for Seizure Detection: A Review, International Conference on Microelectronics, Computing and Communication, MicroCom 2016 15. Muhammad Tariq Sadiq, Motor imagery EEG signals classification based on mode amplitude and frequency components using empirical wavelet transform, IEEE Access (vol 7), 2019 16. Joseph D. Bronzino, The Biomedical Engineering Handbook, Third Edition 17. Phelan, 乳鼠心肌提取 HHS Public Access, Physiology & behaviour (vol 176), 2018 18. Behnam et al, Analyses of EEG background activity in Autism disorders with fast Fourier transform and short time Fourier measure, 2007 International Conference on Intelligent and Advanced Systems, ICIAS 2007 19. Padmasari, Y.SubbaRao, K. Malini, V, Rao, C. Raghavendra, Linear prediction modelling for the analysis of the epileptic EEG, ACE 2010 - 2010 International Conference on Advances in Computer Engineering 20. P. Vaidyanathan, The theory of linear prediction,2007 21. Nitendra Kumar, Khursheed Alam, and Abul Hasan Siddiqi, Wavelet Transform for classification of EEG signals using SVM and ANN, Biomed Pharmacol J 2017;10(4) 22. Abdulhamit S, M Ismail G, EEG signal classification using PCA, ICA, LDA and support vector machines, Expert Systems with Applications 37(2010), ELSEVIER 23. Andreas J F, Martin JH, Increased EEG power density in alpha and theta bands in adult ADHD patients, Journal of neural transmission, 2008 24. Regina IM, Olga AS, Ksenya AA, Galina AS, Neurophysiological factors associated with cognitive deficits in children with ADHD symptoms: EEG and neurophysiological analysis, Psychology & Neuroscience 2014 25. Alaa EA, Mohamed E, Shervin S, Amer NE, Feasibility of detecting ADHD Patients’ Attention Levels by Classifying their EEG signals, IEEE, 2017 26. Michelle W, Denise R, Using the Virtual Reality Cognitive Rehabilitation Approach to Improve Contextual Processing in Children with Autism, The scientific world journal, 2013 Authors: Aniruth.R, SureshKumar.M, Gokulakrishnan.R, Parthasarathy.M, Krishnan.V.C,

Paper Title: Energy Audit in Households using Machine Learning Abstract: Maintaining the energy usage with minimal power loss throughout the supply chain is of the major 206. issues faced in many small-scale sectors or even in households of today’s world. Even though Power transmission can play a cardinal role in the supply chain, monitoring the transmission lines for energy leakage or 1153- any faulty connections is critically important. There have been several measures taken to come up with a better 1160 solution but, the problem of finding a consistent method for monitoring the power leakage is still at peril. There are actually many ways of saving the energy by mitigating the usage and preventing the loss of energy due to over usage and wastages, for this a thorough monitoring and study of the usage should be done. If the electricity usage pattern of the concerned is identified, then it will be facile to come up with a solution for the problem at hand. The electricity wastage constituted by all the countries aggregated is found out to be around 8.25%, which is considerately large given that many places around the world does not even have access to electricity. So, there is a need to find a better solution for this problem. After conducting a thorough study on the electricity usage pattern of several households we are proposing a method which is an ensemble of machine learning algorithms, Internet of Things, sensors, Embedded systems.Using an IoT device we’ve designed we monitoring and collecting electricity usage in households in a time based manner. These collected data is stored in the database and is processed and fed into machine learning algorithm to predict the upcoming month’s electricity usage. This predicted data is then fed into another algorithm to provide recommendations to the user to reduce the electricity consumption according to their usage interests. Thus reducing the cost significantly.

Keywords: Embedded systems, Energy efficiency, IoT, Linear regression, Machine learning, NodeMCU.

References:

1. R. A. Rashid, L. Chin, M. A. Sarijari, R. Sudirman and T. Ide, "Machine Learning for Smart Energy Monitoring of Home Appliances Using IoT," 2019 Eleventh International Conference on Ubiquitous and Future Networks (ICUFN), Zagreb, Croatia, 2019, pp. 66-71. 2. K. Aurangzeb, "Short Term Power Load Forecasting using Machine Learning Models for energy management in a smart community," 2019 International Conference on Computer and Information Sciences (ICCIS), Sakaka, Saudi Arabia, 2019, pp. 1-6. 3. M. Parthasarathy, R. Aniruth and M. SureshKumar, "Power Efficiency and Automation Based on IoT," 2019 3rd International Conference on Computing and Communications Technologies (ICCCT), Chennai, India, 2019, pp. 316-320. 4. Hebrail, G. and Berard, A. (2012). UCI Machine Learning Repository: Individual household electric power consumption Data Set. [online]. Archive.ics.uci.edu. Available at: http://archive.ics.uci.edu/ml/datasets/Individual+household+electric+power+consumption 5. EvaGarcía-Martín Crefeda, FaviolaRodrigues GrahamRiley, HakanGrahn, ”Estimation of energy consumption in machine learning, Journal of Parallel and Distributed Computing Volume 134, December 2019, Pages 75-88 6. Mihail Lyaskov, Kostadin Popovski, Grisha Valentinov Spasov,” Home Energy Monitoring System based on Open Source Software and Hardware, 17th International Conference on H2020 Smart Cities and Communities, Lighthouse project, June 2016 7. Chao Chen ; Diane J. Cook , “Behavior-Based Home Energy Prediction, 2012 Eighth International Conference on Intelligent Environments, June 2012. 8. Dinh Hoa Nguyen ; Anh Tung Nguyen ,” A Machine Learning-based Approach for The Prediction of Electricity Consumption, 2019 12th Asian Control Conference (ASCC), July 2019. 9. Son Young-Sung, Moon Kyeong-Deok, "Home energy management system based on power line communication", Proc. IEEE International Conference on Consumer Electronics, pp. 115-116, Jan. 2010. 10. Jin-Young Kim and Sung-Bae Cho,” Electric Energy Consumption Prediction by Deep Learning with State Explainable Autoencoder, Energies 2019, 12, 739; doi:10.3390/en12040739 11. L. S. B. Pereira ; R. N. Rodrigues ; G. A. Massuyama ; Neto e E. A. C. Aranha , “Machine Learning Applied to Energy Efficiency of Large Consumers, 2019 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON), 2019 12. MarijanaZekic-Susac, SaSa Mitrovic, Adela Has, “Machine learning based system for managing energy efficiency of public sector as an approach towards smart cities, International Journal of Information Management, January 2020 13. M. SureshKumar, M Parthasarathy ; R Aniruth ,” Power Efficiency and Automation Based on IoT”, Third IEEE International Conference on Computing and Communications Technologies (ICCCT’19), Sri SaiRam Engineering College on February 21-22, 2019. Authors: Khushal Khairnar, Sajidullah Khan

Paper Title: Automatic Early Leaf Spot Disease Segmentation on Cotton Plant Leaf Abstract: Diseases are decreasing production of plants. At present, farmers are identifying, diagnosing diseases and monitoring health in plants by their own knowledge and experience. Naked eye observation by farmers and experts on big plantation areas cannot be possible each time and it can be expensive. Accurate identification of visually observed diseases, symptoms and controls has not studied yet. Therefore a fast automatic, economical and accurate system is an essential research topic that may improve in leaf disease detection of plant disease. The proposed automatic early leaf spot disease segmentation on leaf of cotton plant system is based on image processing and machine learning where segmenting the three major diseases such as Bacterial Blight, Alternaria leaf spot and Cercospora leaf spot. Initially, the infected leaf images are captured from cotton plant fields by using a digital camera. Scaling, background removing and color conversion are done in the preprocessing phase. After preprocessing, the infected region is obtained by using K-means clustering algorithm. The infected region can be applied for detecting the diseases on cotton plant.

207. Keywords: clustering, feature extraction, image processing, segmentation. 1161- References: 1164

1. Khushal Khairnar, Nitin Goje, “Image Processing Based Approach for Diseases Detection and Diagnosis on Cotton Plant Leaf”, Techno-Societal2018, 2020, Springer. 2. Elham Omrani, Benyamin Khoshnevisan, Shahaboddin Shamshirband, Hadi Saboohi, Nor Badrul Anuar, Mohd Hairul Nizam Md Nasir,“Potential of radial basis function-based support vector regression for apple disease detection”, Measurement, vol.55, pg.no.512– 519 , Sept- 2014. 3. Shital Bankar, Ajita Dube,Pranali Kadam, “Plant Disease Detection Techniques Using Canny Edge Detection and Color Histogram in Image Processing”, Shital Bankar et al, (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (2) , 2014, 1165-1168. 4. Mr. Vijay Singh, A. K. Misra “ Detection of Plant Leaf Diseases using Image Segmentation and Soft Computing Techniques”, Elsevier, Information Processing In Agriculture 4 (41-49), 2017. 5. Mr. H. Sabrol and K. Satish, “Tomato Plant Disease Classification in Digital Images using Classification Tree”, International Conference on Communication and Signal Processing, April 6-8, 2016 (IEEE), India 6. Mr. Amar Kumar Dey and M. R. Meshram, “ Image Processing Based Leaf Rot Diseases , Detection of Betal Vine”, Elsevier, International Conference on Computational Modeling and Security (CMS 2016). 7. Mr. Ajay Kaul, “Bacterial Foraging Optimization Based Radial Basis Function Neural Network (BRBFNN) for Identification and Classifica- tion of Plant Leaf Diseases: An Automatic Approach Towards Plant Pathology”, 2169-3536, 2018 IEEE. 8. Mr. Bin Liu,et. , “Identification of Apple Leaf Diseases Based on Deep Convolutional Neural Networks”, www.mdpi.com/journal/ symmetry, Symmetry 2018 9. Serawork Wallelign,et., “Soybean Plant Disease Identification Using Convolutional Neural Network”, The Thirty-First International Florida Artificial Intelligence Research Society Conference, 2018 10. Mr. Guiling Sun,et. , “Plant Diseases Recognition Based on Image Processing Technology”, Hindawi Journal of Electrical and Computer Engg., 7 pages, V.2018, Article ID:6070129 11. Ms. Shima Ramesh,et., ‘’Plant Disease Detection Using Machine Learning”, International Conference on Design Innovations for 3Cs Compute Communicate Control, 2018. 12. Mr.Sardogan, Melike and Tuncer, et, “Plant Leaf Disease Detection and Classification Based on CNN with LVQ Algorithm”, 382-385. 10.1109/UBMK.2018.8566635, (2018). 13. Mr. Wan-jie Liang,et., “Rice Blast Disease Recognition Using a Deep Convolutional Neural Network”, Scientific Reports, 9:2869 https://doi.org/10.1038/s41598-019-38966-0, (2019) https://www.thehindubusinessline.com/economy/agribusiness/201718-cotton- production-seen-rising-1015-on-higheracreage article9799154.ece. 14. http://cotcorp.gov.in/current-cotton.aspx?pageid=4. 15. Khushal Khairnar, Rahul Dahade, "Disease Detection and Diagnosis on Plant using Image Processing- A review", IJCA, volume 108(13): 36-38, 2014. Authors: Aammat ul Ayesha, Dhanalakshmi R

Paper Title: Closed Loop Control of an Isolated semi-SEPIC DC-DC Converter using PID Controller Abstract: This paper presents the closed loop control scheme of the semi-sepic coupled inductor based DC DC converter using a PID controller, enhancing the overall performance of the system. The PID controller usually enhances the transient and steady state performance of the system. The objective here is to maintain the output voltage of the DC/DC converter constant irrespective of the variations in the input/source voltage, components and load current. The implementation of the proposed scheme is done using the blocks of MATLAB Simulink and the model is tested with the controlled voltage source by giving some disturbance at the input where the output is well regulated irrespective of the changes in the input/source voltage. Comparative analysis on the performance of the converter with and without PID controller is carried out using MATLAB Simulink. The output voltage of the converter with PID controller is regulated under disturbed input voltages.

Keywords: DC/DC converter, PID controller, Semi-SEPIC, Voltage Regulation.

References:

208. 1. Hina Fathima.A, Priya. K, Sudakar Babu.T, Devabalaji.K.R, Rekha.M, Rajalakshmi.K and Shilaja.C,” Problems in Conventional Energy Sources and Subsequent shift to Green Energy”, International Journal of Innovative Research in Science, Engineering and Technology, Volume 3, Special Issue 1, February 2014. 1165- 2. Ferdous, S. M., Mohammad Abdul Moin Oninda, Golam Sarowar, Kazin Khairul Islam, and Md Ashraful Hoque. "Non-isolated single 1169 stage PFC based LED driver with THD minimization using Cúk converter." In Electrical and Computer Engineering (ICECE), 2016 9th International Conference on, pp. 471-474. IEEE, 2016. 3. Daniele, Matteo, Praveen K. Jain, and Geza Joos. "A single-stage powerfactor-corrected AC/DC converter." IEEE Transactions on Power Electronics 14, no. 6 (1999): 1046-1055 4. Dave, Mitulkumar R., and K. C. Dave. "Analysis of boostconverter using pi control algorithms." International Journal of Engineering Trends and Technology 3.2 (2012): 71-73. 5. Raviraj, V. S. C., and Paresh C. Sen. "Comparative study of proportional-integral, sliding mode, and fuzzy logic controllers for power converters." IEEE Transactions on Industry Applications 33, no. 2 (1997): 518-524. 6. Meena, Rajendra. "Simulation study of boost converter with various control techniques." International Journal of Science and Research (IJSR) 3, no. 9 (2014): 74-79. 7. L. Mitra and N. Swain, "Closed loop control of solar powered boost converter with PID controller," 2014 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES), Mumbai, 2014, pp. 1-5. 8. Tomaszuk and A. Krupa. High efficiency high step-up DC/DC converters – a review, Power Electronics bulletin of the polish academy of sciences technical sciences, Vol. 59, No. 4, 2011 9. Gopi , R. Saravanakumar. “ High step-up isolated efficient single switch DC-DC converter for renewable energy”. 10. Ali Mostaan, Jing Yuan, Yam P. Siwakoti, Soroush Esmaeili, Frede Blaabjerg, “A Trans-Inverse Coupled-Inductor Semi SEPIC DC/DC Converter with Full Control Range”2019 IEEE Transactions. 11. Ali Mostaan, Jing Yuan, Yam P. Siwakoti, Soroush Esmaeili, Frede Blaabjerg, “Design and Analysis of a Novel Trans-inverse DC- DC Converter”, IEEE Transactions 2019. Authors: Aashika Perunkolam, V. Berlin Hency

Paper Title: Spider Robot for Façade Cleaning Abstract: Most skyscrapers require their glass windows to be cleaned not only on the inside but on the outside as well since dust, rains, birds, etc. can cause stains on them. This cleaning process is often carried out by people who dangle from the top of the building using a rope and carry liquid soap and other cleaning equipment required to manually clean all the windows. Not only is this operation extremely time consuming, but it is also highly dangerous. The proposed work aims at automating this cleaning operation using a spider-like robot. The 209. body of the robot is made of two octagonal plates with six legs at six vertices, each having a degree of freedom of three and a suction cup at its end. The remaining two vertices can have cleaning arms and the bottom of the 1170- robot consists of a rotating cleaner. Cleaning resources like water and soap are placed in-between the two plates. 1174 The robot climbs the wall due to the suction provided by the hydraulic force of steam at high pressure which is controlled and programmed using the Robotic Operating System. Six legs of the robot which comprise of eighteen double shaft NEMA-17 motors are controlled using Arduino Mega and are synchronized using ROS. The tripod gait mechanism is used to move forward and pressurized steam is used to provide the suction required to hold on to the wall. This robot can be extended to perform other applications like painting a wall, or can be used in an industry to detect any gas leak or perform any assembly and transport operations.

Keywords: Hexapod, Hydraulic force, Legged mechanism, Spider-like robot

References:

24. S. Rajesh, P. Janarthanan, G. Pradeep Raj, &A. Jaichandran Design and Optimization of High-Rise Building Cleaner. International Journal of Applied Engineering Research ISSN 0973-4562 Volume 13.2018. 25. Gandhinathan, R., & Ambigai, R. Design and kinematic analysis of tethered guiding vehicle (TGV) for façade window cleaning. Online International Conference on Green Engineering and Technologies (IC-GET). 2016. 26. E. Gambao, M. Hernando, & F. Hernández, E. Pinilla. Cost-Effective Robots for Façade Cleaning.Proceedings of the 21st ISARC, Jeju, South Korea. 2004. 27. Esteban, D. D., Luneckas, M., Luneckas, T., Kriauciunas, J., & Udris, D. Statically stable hexapod robot body construction. IEEE 4th Workshop on Advances in Information, Electronic and Electrical Engineering (AIEEE). 2016. 28. Liu, Y., Ding, L., Liu, Z., Yu, H., Jin, M., Gao, H., & Deng, Z. Effects of stride length and frequency on mobile performance of a hydraulic hexapod robot. International Conference on Fluid Power and Mechatronics (FPM). 2015. 29. Gao Junyao, Duan Xingguang, Huang Qiang, Liu Huaxin, Xu Zhe, Liu Yi, … Sun Wentao. The research of hydraulic quadruped bionic robot design. ICME International Conference on Complex Medical Engineering. 2013. 30. Saranya, R., & Kashwan, K. R. Octapod spider-gait-walking robot implementation using nano processor. International Conference on Advances in Human Machine Interaction (HMI). 2016. 31. Tanaka, G., Takamura, T., Shimura, Y., Motegi, K., & Shiraishi, Y. Development of Simulator and Analysis of Walking for Hexapod Robots. 58th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE). 2019. 32. Nemoto, T., Mohan, R. E., & Iwase, M. Realization of rolling locomotion by a wheel-spider-inspired hexapod robot. Robotics and Biomimetics, a SpringerOpen Journal. 2015. 33. Sun, C., Yuan, M., Li, F., Yang, Z., & Ding, X. Design and Simulation Analysis of Hexapod Bionic Spider Robot. Journal of Physics: Conference Series. 2019. 34. Taniwaki, M., Iida, M., Kang, D., Tanaka, M., Izumi, T., & Umeda, M. Walking behaviour of a hexapod robot using a wind direction detector. Biosystems Engineering, 100(4), 516–523, published by Elsevier Ltd. 2008. 35. Kim, J. H., Ko, H. J., Kim, S. H., Ji, M. G., & Lee, J. B. Intelligent system of the spider robot for the various moving functions. RO- MAN 2009 - The 18th IEEE International Symposium on Robot and Human Interactive Communication. 2009. 36. Ghayour, M., &Zareei, A. Direct Kinematic Analysis of a Hexapod Spider-Like Mobile Robot. Advanced Materials Research, 403- 408, 5053–5060. 2011. 37. Ghayour, M., &Zareei, A. Inverse Kinematic Analysis of a Hexapod Spider-Like Mobile Robot. Advanced Materials Research, 403- 408, 5061–5067. 2011. 38. Vitthal B. Jagtap ; Amol B Jagdale ; Pankaj S. Kadu Hexapod: A Spider for Terrain and Obstacle Shunning by Arduino. International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT). 2016. 39. Marc Manz, Sebastian Bartsch, Frank Kirchner. Mantis - A Robot with Advanced Locomotion and Manipulation Abilities. Symposium on Advanced Space Technologies in Robotics and Automation. 2013. Authors: Yeshaswini. V, Tejaswi. S. P, S. Hema Priyadarshini, Shaik Thahaseen, Varun Kumar

Paper Title: Sensors used to Record Electrocardiogram Abstract: Diagnosing the heart disorders is major challenge because of their short–lasting and intermittent character. The convincing technologies with non–invasive heart rate monitoring systems acquire Electrocardiogram (ECG) have limitations with reference to sensitivity. There are different types of wearable, flexible electrocardiogram sensors that can yield important information about underlying physiological parameters of human for applications related to real time monitoring of health, fitness, and wellness. Sensors with leads are all derived using three electrodes which are used to pick the electrical activity from a different position on the heart muscle. Lead-less sensors are now widely used for acquisition of ECG and related signals for heart rate monitoring which has more advantages compared to other sensors. Henceforth, various sensors are studied to understand their relation with heart rate monitoring. It is inferred that MAX30100 sensor can improve the accuracy of ECG recordings for early detection of cardiovascular diseases.

Keywords: Cardiovascular diseases ECG, heart rate monitoring, MAX30100, sensors

References:

210. 40. Muhammad E. H. Chowdhury, Khawla Alzoubi, Amith Khandakar, Ridab Khallifa, Rayaan Abouhasera, Sirine Koubaa, Rashid Ahmed, and Anwarul Hasan. “Wearable Real-Time Heart Attack Detection and Warning 1175- System to Reduce Road Accidents”. Received 2019 April 12; Accepted 2019 June 10. 1178 41. Hemanth Kapu, Kavisha Saraswat, Yusuf Ozturk, A. Enis Cetin. “Resting heart rate estimation using PIR sensors”. Infrared Physics and Technology, vol. 85, pp. 56-61, 15 May 2017. 42. Zeli Gao, Jie Wu, Jianli Zhou, Wei Jiang. “Design of ECG signal acquisition and processing system”. School of Basic Medical Sciences, Kunming Medical University Kunming, China. 2012 international Conference on Biomedical Engineering and Biotechnology. 43. Fatma Patlar Akbulut. “e-Vital: A Wrist-Worn Wearable Sensor Device for Measuring Vital Parameters”. IEEE, 2019. 44. Haizhou Huang, Shi Su, Nan Wu, Hao Wan, Shu Wan,Heng chang Bi, and Litao Sun.“Graphene-Based Sensors for Human Health Monitoring”. Nanoscience, a section of the journal Frontiers in Chemistry, June 2019, Volume 7, pp. 1-26, Article 399. 45. Filip Kveton, Anna Blsakova, Lenka Lorencova, Monika Je rigova, Dusan Velic, Ola Blixt, Bo Jansson, Peter Kasak, and Jan Tkac. “A Graphene-Based Glycan Biosensor for Electrochemical Label-Free Detection of a Tumor- Associated Antibody”. 10 October 2019; Accepted: 2 December 2019; Published: 9 December 2019. 46. Ayaskanta Mishra, Assistant Professor. Biswarup Chakraborty, Debajyoti Das, Priyankar Bose. “AD8232 based Smart Healthcare System using Internet of Things (IoT)”. UG students, School of Electronics Engineering, KIIT Deemed University, Bhubaneswar, Odisha, India. Vol. 7 Issue 04, pp. 13-16, April- 2018. 47. Miguel Bravo-Zanoguera, Juan Pablo García-Vázquez. “Portable ECG System Design using the AD8232 Microchip and Open-source Platform”. DOI:10.3390/ecsa-6-06584, pp. 42-49. November 2019. 48. Maxim unveils wrist-worn platform for monitoring ECG, Heart Rate, Temperature. 26, 2018. 49. Maurizio Di Paolo Emilio. “A Cuffless Blood-Pressure Measurement Solution”. 11.21.2019. 50. Jeremy Cowan. “Change is coming to ‘stagnant’ wearables market as heart rate sensors claim accurate monitoring”. December 6, 2017. 51. Zhendong Ai, Lijuan Zheng, Hongsheng Qi, Wei Cui1. “Low-Power Wireless Wearable ECG Monitoring System Based on BMD101”. School of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, P. R. China 2. Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, P. R. China. July 25 -27, 2018, Wuhan, China. 52. Hyun-Soo Choi, Byunghan Lee, And Sungroh Yoon. “Biometric Authentication Using Noisy Electrocardiograms Acquired by Mobile Sensors”. Date of publication March 30, 2016, date of current version April 12, VOLUME 4, pp. 1266-1273, IEEE 2016. 53. Qunoot N. Alsahi, Ali F. Marhoon. “Design Health care system using Raspberry Pi and ESP32”. International Journal of Computer Applications (0975 – 8887), Volume 177 – No. 36, pp. 33-39, February 2020. 54. Jiaxi Wan, Yuhua Zou, Ye Li, Jun Wang. “Reflective type blood oxygen saturation detection system based on MAX30100”. 2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC). pp. 615-619. 2017 IEEE. 55. SHREE RAKSHA M P, NANDINI B M. “Remote Monitoring of the Patients using the IoT Widget through Hrut Gati Android Application”. International Research Journal of Engineering and Technology (IRJET). Volume: 07 Issue: 05, pp. 865-869, May 2020. 56. Dipika Vasava, Shripad Deshpande. “Portable Health Monitoring Device”. International Journal of Engineering Research and General Science Volume 4, Issue 3, pp. 807-812, May-June, 2016. Authors: Padmapriya Patil, R. N. Kulkarni

Paper Title: Realization of Parallelism in a Sequential Legacy ‘C’ Program Abstract: In the present era of high speed computation with the multicore and other parallel processors in the computational field, there are still some organizations which rely on their old software systems developed years ago, which over the time have been subjected to continous development by different developers. Even though these softwares persist with the old and little in use technology, they still work to satisfy the operational demands of the organizations and have kept them going in the competetive industry. These systems which have with time grown into legacy, embed the major business functionalities of the organization, which is but effort of years. Hence a methodology is required to rebuild the legacy system to make them suitable for execution on to the present computation systems. The paper discusses a research work, wherein work is done to realize points of latent parallelism in a sequentially executing legacy ‘C’ program which is initially restructured and the design information abstracted. A technique using finite state machine is proposed to identify tasks, events, processes and jobs in the program, which helps to locate functionally independent computational units in the program. Furthur using the slicing technique, slicing is performed to extract out the appropriate lines of codes defined by the slicing criteria, which assembled together form a functionality that can be executed in parallel with other extracted functional modules or computational units on any parallel computational platform.

Keywords: Restructuring, Legacy Software System, Functional Modules, Program Slicing, Functional Dependency, Parallel Computation Systems 211. References: 1179- 1. Rajkumar N. Kulkarni, “Reengineering of the legacy ‘C’ systems, Ph.D. thesis, 2011. 1183 2. Dr Rajkumar N. Kulkarni, Padmapriya Patil, “Restructuring of Legacy ‘C’ Program to be Amenable for Multicore Architecture”, ICRTEST Elsevier energy procedia proceedings, Oct- 2016. 3. Dr Rajkumar N. Kulkarni, Padmapriya Patil, “Abstraction of Information Flow Table from a Restructured Legacy ‘C’ program to be amenable for Multicore Architecture” LNCS Springer 2018 4. Dr R.N Kulkarni, Padmapriya Patil, “Abstraction of Functional Dependency and Information Flow from a Restructured Legacy ‘C’ program for Parallelization” IEEE digital Library-2018. 5. Alcides Fonseca, Bruno Cabral, Joao Rafael, Ivo Correia , Automatic Parallelization: Executing Sequential Programs on a Task-Based Parallel Runtime, International Journal of Parallel Programming, 2016 6. B. Bugeryaa, E. S. Kimb, and M. A. Solovev, Parallelization of Implementations of Purely Sequential Algorithms, ISSN 0361-7688, Programming and Computer Software, 2019 7. Anne Meade, Jim Buckley, J. J. Collins, “Challenges of Evolving Sequential to Parallel Code: An Exploratory Review”, ACM, 2011. 8. Zhen Li, Ali Jannesari, and Felix Wolf, “Discovery of Potential Parallelism in Sequential Programs”, 2014 9. Joao Rafael, Ivo Correia, Alcides Fonseca, “Dependency-Based Automatic Parallelization of Java Applications”, LNCS 8806, Springer, 2014. 10. S. Rul, H. Vandierendonck, and K. De Bosschere, “Function level parallelism driven by data dependencies,” SIGARCH Comput. Archit. News, vol. 35, no. 1, pp. 55–62, Mar. 2007. 11. Miguel Angel Aguilar , Juan Fernando Eusse, Projjol Ray , Rainer Leupers , Gerd Ascheid , Weihua Sheng , Prashant Sharma ,” Parallelism Extraction in Embedded Software for Android Devices”,2015 IEEE. 12. Djordje Kovacevic, Mladen StanojevićVladimir Marinkovic, Miroslav Popovic, A Solution for Automatic Parallelization of Sequential Assembly Code, DOI: 10.2298/SJEE1301091K, Feb 2013 13. Alexandros V. Gerbessiotis, Using parallelism techniques to improve sequential and multi-core sorting performance, Aug 2016 14. Chen Tian · Min Feng · Vijay Nagarajan , Rajiv Gupta, Speculative Parallelization of Sequential Loops on Multicores, springer Int J Parallel Prog (2009) Authors: Nurhasan Syah, Syaiful Haq, Nizwardi Jalinus, Ambiyar

Paper Title: Implementation of Practice Learning During the Covid-19 Pandemic Abstract: This research was done because of the covid-19 pandemic. One regulation impact of this pandemic 212. is social distance, so it disturbed the implementation of learning because students did not allowed to learn face to face in a room or at campus. They must learn from home by online learning. This regulation was not a problem 1184- for some courses, but it was not easy for practice course because students need tools and do demonstration in 1188 laboratory or workshop. This descriptive qualitative reseach was done to observed people who involved in specific teaching method course in the mechanical engineering department of Universitas Negeri Padang (UNP). The key of this research are about data observation, triangulation, careful examination, then data reduction, presentation, and conclusion. The research result stated that; 1) there was no changes in the substance of the learning planning during covid-19, 2) the implementation of practice learning was done by fully learning during covid-19, student and lecture did communication by using whatsapp, e-mail, zoom cloud meeting, UNP’s e- learning, and others tools such as youtube, 3) the assessment of learning was done by testing and observation.

Keywords: Implementation, Online Practice Learning, Pandemic, Covid-19.

References:

1. J. Darmawan, “Menjadi Guru Era Pendidikan 4.0,” Tribunnews.com, 2018. [Online]. Available: http://aceh.tribunnews.com/2018/11/27/menjadi-guru-era-pendidikan-40. 2. D. N. Rakhmah, “Mampukah Pendidikan Kita Beradaptasi dengan Revolusi Industri 4.0,” Kumparan, 2018. [Online]. Available: https://kumparan.com/birokrat-menulis/mengurai-pekerjaan-rumah-pendidikan-indonesia-menyongsong-revolusi-industri-4-0- 1543639076398979922. 3. C. B. Cohen, “Industry 4.0: Are You Ready for the Fourth Industrial Revolution?,” medium.com, 2018. [Online]. Available: https://medium.com/@carmitberdugocohen/industry-4-0-are-you-ready-for-the-fourth-industrial-revolution-464de2dea3b1. 4. K. Schwab, “the Fourth Industrial Revolution (Industry 4.0) a Social Innovation Perspective,” Tạp chí Nghiên cứu dân tộc, vol. 7, no. 23, pp. 12–21, 2018, doi: 10.25073/0866-773x/97. 5. V. s. Shrinath, “IOT Application in Education.,” Int. J. Adv. Res. Dev. (IJARnD), pp. 20–24, 2017. 6. R. Mehta, “Five Ways the Internet of Things is Changing for Education and Learning,” 2018. [Online]. Available: http://customerthink.com/five-ways-the-internet-of-things-is-changing-for-education-and-learning/. 7. S. Ravindra, “Role of IoT in Education.,” KDnuggets, 2018. [Online]. Available: https://www.kdnuggets.com/2018/04/role-iot- education.html. 8. Y. E. Harususilo, “Anies Baswedan: Guru Tidak Dapat Digantikan Teknologi,” Kompas.com, 2018. [Online]. Available: https://edukasi.kompas.com/read/ 2018/09/27/ 18073381/anies-baswedan-guru-tidak-dapat-digantikan-teknologi. 9. Sukardi, M. Giatman, S. Haq, Sarwandi, and Y. F. Pratama, “Effectivity of Online Learning Teaching Materials Model on Innovation Course of Vocational and Technology Education,” J. Phys. Conf. Ser., vol. 1387, no. 1, 2019, doi: 10.1088/1742-6596/1387/1/012131. 10. and A. I. Syaiful Haq, M Giatman, “Evaluation of Teacher Professional Education Program ( Ppg ) Teaching Graduates in Edge Area , Front Area , and Left Side Area of Indonesia ( Sm-3T ) of Universitas Negeri Padang,” Int. J. Educ. Dyn., vol. 1, no. 2, pp. 301–307, 2019. 11. P. L. P. Yuet-Ming Ng, “Coronavirus disease (COVID-19) prevention: Virtual classroom education for hand hygiene,” Nurse Educ. Pract., 2020. 12. H. Setiawan, “Solusi Jitu Pembelajaran Online di Tengah Wabah Virus Korona,” jawapos.com, 2020. [Online]. Available: https://www.jawapos.com/nasional/pendidikan/01/04/2020/solusi-jitu-pembelajaran-online-di-tengah-wabah-virus-korona/. 13. Y. Ainun, “Pembelajaran Online di Tengah Pandemi Covid-19, Tantangan yang Mendewasakan,” timesindonesia.co.id, 2020. [Online]. Available: https://www.timesindonesia.co.id/read/news/261667/pembelajaran-online-di-tengah-pandemi-covid19-tantangan-yang- mendewasakan. 14. L. D. Herliandry and M. E. Suban, “Jurnal Teknologi Pendidikan Pembelajaran Pada Masa Pandemi Covid-19,” vol. 22, no. 1, pp. 65– 70, 2020. 15. N. Syah, S. Haq, Y. F. Pratama, Sarwandi, W. Hutria, and L. Nofianti, “The Effectiveness of Teaching Materials using Project Based Learning (PjBL) in Concrete Stones Practice Course,” in Journal of Physics: Conference Series, 2019, vol. 1387, no. 1, doi: 10.1088/1742-6596/1387/1/012088. 16. N. Jalinus, R. A. Nabawi, and A. Mardin, “The Seven Steps of Project Based Learning Model to Enhance Productive Competences of Vocational Students,” vol. 102, no. Ictvt, pp. 251–256, 2017, doi: 10.2991/ictvt-17.2017.43. 17. Sugiyono, Metode Penelitian Kuantitatif Kualitatif dan R&D. Bandung: Alfabeta, 2012. 18. B. Bungin, Metodologi Penelitian Kualitatif Dan Kuantitatif. Yogyakarta: Gajah Mada Press, 2001. Authors: Aashika Perunkolam, Makshi P Baskaran, Sarrthak Tripathi, Om Prakash Sahu

Paper Title: Smart Water Distribution System using Machine Learning and IoT Abstract: Water Management includes four major processes, namely, estimating the amount of water readily available to be distributed, the measurement of the quality of water, distribution of water to different sectors of the city based on the quality and finally to provide a platform to monitor this distribution from anywhere and by anyone. All these processes, are currently treated as separate modules, but the integration of these four models, enhances water conservation and creates a social awareness since the proposed cloud platform can be accessed by everyone, and they are made aware, in advance, about the quality and amount of water they are going to get for the week, so that they can use the water wisely. In this paper we discuss our new and improved proposed model which not only integrates the existing four modules but also optimizes the distribution path based on algorithms for the fastest coverage. This is turn provides a short and concise solution to water management which is more user friendly and can reach more people, hence spreading more awareness. The lack of coordination between the current quality measurement and distribution system calls for an integrated system. 213. This system predicts the rainfall to prepare the system for the amount of softener required to soften the water which acquires calcium and magnesium as it makes its way into other natural rivers. This water management 1189- system can be setup both on a small scale and a large scale. The smaller water management system setup in 1194 villages can be interconnected to make a larger water management system that can be centrally controlled from cities which helps in ensuring water distribution even in the smallest towns and villages.

Keywords: Forecasting, Knowledge Acquisition, Machine -Learning, Water Distribution Network.

References:

1. Grace, R. K., and Suganya, B. Machine Learning based Rainfall Prediction. 6th International Conference on Advanced Computing and Communication Systems (ICACCS). 2020. 2. Revanth Kondaveti, Akash Reddy, and Supreet Palabtla. 2019 International Conference on Vision Towards Emerging Trends in Communication and Networking (ViTECoN). 2019. 3. Kala, A., & Vaidyanathan, S. G. (2018). Prediction of Rainfall Using Artificial Neural Network. 2018 International Conference on Inventive Research in Computing Applications (ICIRCA). 2018. 4. Thirumalai, C., Harsha, K. S., Deepak, M. L., and Krishna, K. C. Heuristic prediction of rainfall using machine learning techniques. 2017 International Conference on Trends in Electronics and Informatics (ICEI). 2017. 5. Islam, A.-U., & Ripon, S. H. Smart Water System at House Using Arduino Uno and C# Desktop Application to Reduce Water Wastage and Energy Loss. 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT). 2019. 6. Banerjee, I., Tribady, S., Mukherjee, S., Mallick, S., Bhowmik, D. S., & Mazumdar, S. Automated Irrigation System Using Arduino and Cloud. International Conference on Opto-Electronics and Applied Optics (Optronix). 2019. 7. Vaju, D., Vlad, G., & Festila, C. About the Physical Methods Applied by Underground Water Treatment in Food Industry. 2006 IEEE International Conference on Automation, Quality and Testing, Robotics. 2006. 8. Suresh, M., Muthukumar, U., & Chandapillai, J. A novel smart water-meter based on IoT and smartphone app for city distribution management. IEEE Region 10 Symposium (TENSYMP). 2017. 9. M, A., J, A. sugirtharani, P, J. mercy carolina., & C, A. teresa. Smart Water Management in Agricultural Land Using IoT. 5th International Conference on Advanced Computing & Communicaton Systems (ICACCS). 2019. 10. Mr. M. Srihari Intelligent Water Distribution and Management System using Internet of Things International Conference on Inventive Research in Computing Applications (ICIRCA). 2018. 11. Bennet Praba, M. S., Rengaswamy, N., Vishal, & Deepak, O. IoT Based Smart Water System. 3rd International Conference on Communication and Electronics Systems (ICCES). 2018. 12. Budiarti, R. P. N., Tjahjono, A., Hariadi, M., & Purnomo, M. H. Development of IoT for Automated Water Quality Monitoring System. International Conference on Computer Science, Information Technology, and Electrical Engineering (ICOMITEE). 2019. 13. Dinio, C. J. T., Paragon, N. F., Pelena, G. M. A., Valida, A. C., L.Velasco, M. J., Vizcarra, E. D., … Dadios, E. P. Automated Water Source Scheduling System with Flo Control System. 2018 IEEE 10th International Conference on Humanoid, Nanotechnology. 2018. 14. Les Levidowa Daniele Zaccariab Rodrigo Maiac Eduardo Vivasc Mladen Todorovicd AlessandraScardignod Improving water- efficient irrigation: Prospects and difficulties of innovative practices Elsevier Journal, Agricultural Water Management. 2014. 15. Morteza Hadipour, Javad Farrokhi Derakhshandeh, Mohsen AghazadehShiran. An experimental setup of multi-intelligent control system (MICS) of water management using the Internet of Things (IoT) Elsevier Journal, ISA Transactions. 2020. Authors: Rashmi P, Prashanth Kambli

Paper Title: Analysis on Detecting a Bug in a Software using Machine Learning Abstract: In today’s scenario, it is very essential in the development phase of a software, predicting a bug and to obtain a successful software. This can be achieved only through predicting some of the faults in the earlier phase itself such that, it can lead to have a reliable, efficient and a quality software. The challenging task here is to have a well sophisticated model that can predict the bug leading to a cost-effective software. In order to achieve this, few machine learning algorithms are used that produce accuracy with trained and test datasets. A variety of machine learning methodologies have been developed to learn and detect a bug in a software. In this paper, we perform the analysis on detecting a bug in a software using machine learning methods which is very much useful for Software Industries. It summarizes the existing work on detecting a bug in a software by providing the information about various methods involved in bug prediction and points out at the accuracy obtained by the existing methods, advantages, and the drawbacks while working with bug prediction.

Keywords: Software Development, Software Bug Prediction, Quality Software, Machine Learning, Accuracy.

References:

1. S. Delphine Immaculate, M. Farida Begam and M. Floramary, "Software Bug Prediction Using Supervised Machine Learning Algorithms," 2019 International Conference on Data Science and Communication (IconDSC), Bangalore, India, 2019, pp. 1-7, doi: 10.1109/IconDSC.2019.8816965. 2. Feidu Akmel, Ermiyas Birihanu, Bahir Siraj “A Literature Review Study of Software Defect Prediction using Machine Learning Techniques,” IJERMT, ISSN: 2278-9359 (Volume-6, Issue-6), June 2017. 3. Dr. R Beena, N. Kalaivani, “Overview of Software Defect Prediction using Machine Learning Algorithms,” International Journal of Pure and Applied Mathematics Volume 118 No. 20 (2018), 3863-3873, ISSN: 1314-3395. 214. 4. S. Ray, "A Quick Review of Machine Learning Algorithms," 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon), Faridabad, India, 2019, pp. 35-39, doi: 10.1109/COMITCon.2019.8862451. 1195- 5. J. Gao, L. Zhang, F. Zhao and Y. Zhai, "Research on Software Defect Classification," 2019 IEEE 3rd Information Technology, 1199 Networking, Electronic and Automation Control Conference (ITNEC), Chengdu, China, 2019, pp. 748-754, doi: 10.1109/ITNEC.2019.8729440. 6. J. Xu, L. Yan, F. Wang and J. Ai, "A GitHub-Based Data Collection Method for Software Defect Prediction," 2019 6th International Conference on Dependable Systems and Their Applications (DSA), Harbin, China, 2020, pp. 100-108, doi: 10.1109/DSA.2019.00020. 7. S. Reddivari and J. Raman, "Software Quality Prediction: An Investigation Based on Machine Learning," 2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI), Los Angeles, CA, USA, 2019, pp. 115-122, doi: 10.1109/IRI.2019.00030. 8. P. Singh, "Learning from Software defect datasets," 2019 5th International Conference on Signal Processing, Computing and Control (ISPCC), Solan, India, 2019, pp. 58-63, doi: 10.1109/ISPCC48220.2019.8988366. 9. Awni Hammouri, Mustafa Hammad, Mohammad Alnabhan, Fatima Alsarayrah, “Software Bug Prediction using Machine Learning Approach,” IJACSA, Vol. 9, No. 2, 2018. 10. Gupta, D.L., Saxena, K. “Software bug prediction using object-oriented metrics,” Sādhanā 42, 655–669 (2017). https://doi.org/10.1007/s12046-017-0629-5. 11. M. Sujon, M. Shafiuzzaman, M. M. Rahman and R. Rahman, "Characterization and localization of performance-bugs using Naive Bayes approach," 2016 5th International Conference on Informatics, Electronics and Vision (ICIEV), Dhaka, 2016, pp. 791-796, doi: 10.1109/ICIEV.2016.7760110. 12. Ayse Tosun, Ayse Bener, “A Mapping Study on the Bayesian Networks for Software Quality Prediction,” Proceedings of the 3rd International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering, 2014. 13. L. Bao, X. Xia, D. Lo and G. C. Murphy, "A Large Scale Study of Long-Time Contributor Prediction for GitHub Projects," in IEEE Transactions on Software Engineering, doi: 10.1109/TSE.2019.2918536. 14. S. Agarwal, S. Gupta, R. Aggarwal, S. Maheshwari, L. Goel and S. Gupta, "Substantiation of Software Defect Prediction using Statistical Learning: An Empirical Study," 2019 4th International Conference on Internet of Things: Smart Innovation and Usages (IoT-SIU), Ghaziabad, India, 2019, pp. 1-6, doi: 10.1109/IoT-SIU.2019.8777507. 15. H. Osman, M. Ghafari and O. Nierstrasz, "Hyperparameter optimization to improve bug prediction accuracy," 2017 IEEE Workshop on Machine Learning Techniques for Software Quality Evaluation (MaLTeSQuE), Klagenfurt, 2017, pp. 33-38, doi: 10.1109/MALTESQUE.2017.7882014. 16. Sultan Alamri, Bushra Mumtaz, F Faiza Khan, Summrina Kanwal, “Hyper-Parameter Optimization of Classifers, Using an Artificial Immune Network and Its Application to Software Bug Prediction,” IEEE Access (Volume: 8), 2020. 17. K. K. Sabor, M. Nayrolles, A. Trabelsi and A. Hamou-Lhadj, "An Approach for Predicting Bug Report Fields Using a Neural Network Learning Model," 2018 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW), Memphis, TN, 2018, pp. 232-236, doi: 10.1109/ISSREW.2018.00011. 18. Y. Zhou and J. Yan, "A Logistic Regression Based Approach for Software Test Management," 2016 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), Chengdu, 2016, pp. 268-271, doi: 10.1109/CyberC.2016.59. 19. shaniArora, VivekTetarwal, AnjuSaha, “Open Issues found in Software Defect Prediction,” Proceedings of the International Conference on Information and Communication Technologies, ICICT 2014, 3-5 December 2014 at Bolgatty Palace & Island Resort, Kochi, India 20. Subbiah, U and Ramachandran, M and Mahmood, Z (2019) “Software engineering approach to bug prediction models using machine learning as a service (MLaaS),” In: ICSOFT 2018 - 13th International Conference on Software Technologies, 26th to 28th July 2018, Porto, Portugal. 21. Amruthalakshmi, Prashanth Kambli, Naresh E, “Recognition of Handwritten Digits Using Convolutional Neural Network and Linear Binary Pattern,” International Journal of Innovative Technology and Exploring Engineering, ISSN: 2278-3075, Volume-9, Issue-1 November 2019. 22. J. Li, P. He, J. Zhu and M. R. Lyu, "Software Defect Prediction via Convolutional Neural Network," 2017 IEEE International Conference on Software Quality, Reliability and Security (QRS), Prague, 2017, pp. 318-328, doi: 10.1109/QRS.2017.42. Authors: Ritesh Kumar Rai, Anand Kumar Pandey, Anshu Parashar, Sujatha K S

Paper Title: Allocation of PV unit in Distribution Network using Analytical Method Abstract: Inappropriate selection of location and corresponding size of Distributed Generator (DGs) in electrical network may have increased power losses in the system. Application of incorporating DG in system has eased the problem of high power losses, voltage stability, low reliability and poor power quality. This paper suggests a simple and efficient load flow technique known as direct load flow method to find the optimal allocation of Type-3 DG in the distribution system. The presented method was developed and tested in two distribution networks with varying size and complexities and the effect of size and location of DG with respect to real power losses while maintaining the voltage profile of system within limits is examined with verification and discussed in detail.

Keywords: Power system optimization, Distribution system, Optimization techniques. Location and sizing.

References:

1. Ackermann T, Andersson G, Söder L. Distributed generation: a definition.Electr Power Syst Res 2001;57(3):195e204. 2. Chicco G, Mancarella P. Distributed multi-generation: a comprehensive view. Renew SustainEnergyRev 2009;13(3):535e51. 3. Soroudi A, Ehsan M. A distribution network expansion planning model considering distributed generation options and techno- economicalissues.Energy 2010;35(8):3364e74. 4. Acharya, N., Mahat, P., &Mithulananthan, N. (2006). An analytical approach for DG allocation in primary distribution network. International Journal of Electrical Power & Energy Systems, 28(10), 669-678. doi:10.1016/j.ijepes.2006.02.013 5. Anand Kumar Pandey and Sheeraz Kirmani. "Multi-objective optimal location and sizing of hybrid system using analytical crow search optimization algorithm.” International Transactions on Electrical Energy Systems 30, no. 5(2020), e12327 215. 6. Caisheng W, Nehrir MH. Analytical approaches for optimal placement of distributed generation sources in power systems. Power Syst IEEE Trans 2004;19(4):2068e76. 1200- 7. Hung, D. Q., &Mithulananthan, N. (2013). Multiple Distributed Generator Placement in Primary Distribution Networks for Loss Reduction. IEEE Transactions on Industrial Electronics, 60(4), 1700-1708. doi:10.1109/tie.2011.2112316 1207 8. Mithulananthan, N., &Oo, T. (2006). Distributed Generator Placement to Maximize the Loadability of a Distribution System. International Journal of Electrical Engineering & Education, 43(2), 107-118. doi:10.7227/ijeee.43.2.2 9. Aman MM, Jasmon GB, Bakar AHA, Mokhlis H. A new approach for optimum DG placement and sizing based on voltage stability maximization and minimization of power losses. Energy Convers Manag 2013;70(0):202e10. 10. Al Abri RS, El-Saadany EF, Atwa YM. Optimal placement and sizing method to improve the voltage stability margin in a distribution system using distributed generation. Power Syst IEEE Trans 2013;28(1):326e34. 11. Moradi MH, Abedini M. A combination of genetic algorithm and particle swarm optimization for optimal DG location and sizing in distribution systems. Int J Electr Power Energy Syst 2012;34(1):66e74. 12. Borges CLT, Falcão DM. Optimal distributed generation allocation for reliability, losses, and voltage improvement. Int J Electr Power Energy Syst 2006;28(6):413e20. 13. Anand Kumar Pandey and Sheeraz Kirmani. "Multi-Objective Optimal Location and Sizing of Hybrid Photovoltaic System in Distribution Systems Using Crow Search Algorithm." International Journal of Renewable Energy Research (IJRER)” 9.4 (2019): 1681- 1693. 14. Quezada VM, Abbad JR, Roman TGS. Assessment of energy distribution losses for increasing penetration of distributed generation. Power Syst IEEE Trans 2006;21(2):533e40. 15. T.-H. Chen, M.-S. Chen, K.-J. Hwang, P. Kotas, and E. A. Chebli, “Distribution system power flow analysis—A rigid approach,” IEEE Trans. Power Delivery, vol. 6, pp. 1146–1152, July 1991. 16. G. X. Luo and A. Semlyen, “Efficient load flow for large weakly meshed networks,” IEEE Trans. Power Syst., vol. 5, pp. 1309–1316, Nov. 1990. 17. Jen-HaoTeng, “ ADirect Approach for Distribution System Load Flow Solutions,” IEEE Trans. On Power Delivery, Vol. 18, NO. 3, July 2003, pp. 882-887 18. Zhu JZ. Optimal reconfiguration of electrical distribution network using the refined genetic algorithm. Electr Power Syst Res 2002;62(1):37e42. 19. Baran ME, Wu FF. Network reconfiguration in distribution systems for loss reduction and load balancing. IEEE Trans Power Deliv 1989;4(2):1401e7. Authors: Deepak Kourav, Taslima Ahmed, Prashant Mavi

Paper Title: An Efficient Encoding Technique based on Chaotic Cryptography for Digital Image 216. Abstract: Presently advanced India notoriety, associations are proposing various structures concentrating on 1208- computerized encoding procedures. Because of the simplicity of replicating, altering, and altering of 1212 computerized archives and pictures has prompted encoding the data required for transmission and capacity. It clear that the connection between's the picture pixels to its neighborhood district is high, decreasing relationship between's the pixels esteem makes it hard to figure for the first picture and along these lines enhance the security. This paper presents a novel picture encoding strategy which at first improves the picture based on exchanging dim codes and pixel blast. The pixel blast utilizes very much characterized key that switches between the dim code of the picture pixels. Exploratory outcomes would demonstrate that the proposed pixel blast is sufficient for fractional encoding and upgrades security of the information. Further, it could likewise bolster as a deadly implement for any current calculation.

Keywords: Encoding, Gray-Code, Pixel Blast, Authentication

References:

1. Xinyi Zhou, 2Wei Gong, 3WenLong Fu,LianJing Jin “Improved Method for LSB Based Color Image steganography Combined with Cryptography”. IEEE 2016 2. C. C. Ravindranath, Bhatt A K and Bhatt A; “Adaptive Cryptosystem for Digital Images using Chaotic cryptography Bit- Plane Decomposition” International Journal of Computer Applications (0975 – 8887)Volume 65– No.14, March 2013 3. RSA Security. http://www.rsasecurity.com/rsalabs/faq/3-2-6.html 4. DES. http://csrc.nist.gov/publications/fips/fips46-3/fips46-3.pdf. The urlexplains the concept of the Data Encoding Standard. 5. S. S. Maniccam and N. G. Bourbakis,“Image and video encoding using scan patterns,” Pattern Recognition 37, pp. 725-737, 2004. NJ: Prentice Hall, 2003 6. B. Furht, D. Socek, and A.M. Eskicioglu, “Fundamentals of Multimedia Encoding Techniques,” Chapter in Multimedia Security Handbook, pp. 94 – 144, CRC Press, 2005 7. L. C. L. Chuanmu and H. L. H. Lianxi, “A New Image Encoding Scheme based on Hyperchaotic Sequences,” 2007 Int. Work. Anti- Counterfeiting, Secur. Identif., 2007. 8. Y. Zhou, K. Panetta, and S. Agaian, “An image scrambling algorithm using parameter based M-sequences,” in Proceedings of the 7th International Conference on Machine Learning and Cybernetics, ICMLC, 2008, vol. 7, pp. 3695–3698. 9. Y. Zhou, K. Panetta, S. Agaian, and C. L. P. Chen, “Image encoding using P-Chaotic cryptography transform and decomposition,” Opt. Commun., vol. 285, pp. 594–608, 2012. 10. Y. Zhou, K. Panetta, S. Agaian, and C. L. P. Chen, “(n, k, p)-Gray code for image systems,” IEEE Trans. Cybern., vol. 43, pp. 515– 529, 2013. 11. J. Z. J. Zou, R. K. Ward, and D. Q. D. Qi, “The generalized Chaotic cryptography transformations and application to image scrambling,” 2004 IEEE Int. Conf. Acoust. Speech, 12. W. Zou, J. Huang, and C. Zhou, “Digital image scrambling technology based on two dimension chaotic cryptography transformation and its periodicity,” in Proceedings - 3rd International Symposium on Information Science and Engineering, ISISE 2010, 2011, pp. 415–418. 13. J. Z. J. Zou, R. K. Ward, and D. Q. D. Qi, “A new digital image scrambling method based on Chaotic cryptography numbers,” 2004 IEEE Int. Symp. Circuits Syst. (IEEE Cat. No.04CH37512), vol. 3, 2004. 14. Y. Zhou, K. Panetta, and S. Agaian, “Image encoding algorithms based on generalized P-Gray Code bit plane decomposition,” in Conference Record - Asilomar Conference on Signals, Systems and Computers, 2009, pp. 400–404. Authors: Mzuyanda Christian, Phiwe Jiba, Mdoda Lelethu Factors Influencing Rain-Fed Agricultural Land Abandonment in Mnquma and Mbashe Paper Title: Municipalities, Eastern Cape Abstract: Agriculture is one of the imperative segments in the South African economy and it remains the imperative sector for livelihood generation. However, it has been observed that farmers are gradually giving up agriculture in favor of non-agricultural activities. This paper examines the factors influencing agricultural land abandonment in Mnquma and Mbashe Municipalities in Eastern Cape Province. Surveys of 158 semi-structured field interviews were conducted to capture household characteristics, location and farming practices in the study areas. The findings show that limited access to funding, level of education, household size and farming experience seem were the main factors influencing abandonment of rain-fed agricultural land. Another notable reason for agricultural land abandonment was the lack of resources such as storage facilities, transport and access to lucrative markets. Therefore the study recommended that government policies should go beyond supporting primary production and focus also on value adding activities. One of the findings indicated that these areas are dominated by older people (55 years). The study also recommended that focus should be directed on more manageable small plots for older people to increase food production.

217. Keywords: Rain-fed, Agricultural land, Abandonment, Probit regression, Eastern Cape 1213- References: 1219

1. M. Christian, “Impact Analysis of Smallholder Irrigation Schemes on THE choice of rural livelihood strategy and household food security and household food security in Eastern Cape Province”. Unpublished PhD Thesis, Department of Agricultural Economics and Extension, University of Fort Hare, Alice, South Africa, 2017. 2. M. Andrew, A. Ainslie and C. Shackleton, “Land use and Livelihoods. Program for land and Agrarian Studies”. School of Government, University of the Western Cape, 2003. 3. M. N. Baiphethi, “The contribution of subsistence farming to food security in South Africa”. Agrekon Journal South Africa, 48 (4), pp. 22-423, 2009. 4. A. Goldblatt, “Agriculture: Facts and trends, South Africa. In World Wildlife Forum”. WWF-SA: Cape Town South Africa, 2016. 5. Statistics South Africa, “Statistical Release P0318”. In General Household Survey 2018. Statistics SA, Pretoria, 2019. 6. N. M. Mujuru and A. Obi, “Effect of Cultivated Area on Smallholder Farm Profits and Food Security in Rural Communities of the Eastern Cape Province of South Africa”. Sustainability, vol. 12, pp. 3272, 2020. Available from: 10.3390/su12083272. 7. J. Benayas, A. Martins, J. Ncolau and J. Sculz, “Abandonment of agricultural land: an overview of drivers and consequences”. CAB Rev, vol. 2, pp.1-14, 2007. 8. D. MacDonald, J. Crabtree, G. Weisinger, T. Dax, N. Stamou, P. Fleury and L. Gutierrez and A. Gibon, “Agricultural abandonment in mountain areas of Europe: environmental consequences and policy response”. J Environment Manag vol. 59, no. 1, pp. 47-69, 2000. 9. C. Keenleyside, G. Tucker and A. NcConville, “Farmland abandonment in the EU: An assessment of trends and prospects”. London: Institute for European Environmental Policy, 2010. 10. W. Tesfaye and L. Seifu, “Climate change perception and choice of adaptation strategies: Empirical evidence from smallholder farmers in east Ethiopia”. Int. J. Clim. Chang. Strateg. Manag, vol. 8, pp. 253–270, 2016. 11. S. Zakkak, A. Radovic, S. C. Nikolov, S. Shumka, L. Kakalis and V Kati, “Assessing the effect of agricultural land abandonment on bird communities in southern-eastern Europe”. J. Environ. Manag, vol. 164, pp. 171–179, 2015. 12. Statistics South Africa, “Living conditions of households in South Africa. An analysis of household expenditure and income”.2015. 13. Mbhashe Local Municipality IDP 2012-2017. 14. Mbhashe Municipality. 2016. Available from: https://www.mbhashemun.gov.za/2016/07/ 15. P. K. Chauke, M. L. Motlhatlhana, T. K. Pfumayaramba and F. D. K. Anim, “Factors influencing access to credit: A case study of smallholder farmers in the Capricorn district of South Africa”. Afri. J. Agr. Res., vol. 8, no. 7, pp. 582-585, 2013. 16. J. M. Wooldridge, “Introductory Econometrics, a Modern Approach, Fourth Edition”. Michigan State University, 2009. 17. M. Christian and L. Mdoda, “Household Food Security, Dietary Diversity and Coping Strategies amongst Irrigators in Nqamakwe, Eastern Cape”. J Hum Ecol, vol. 68, no. 1-3, pp. 78-88, 2019. Available from:10.31901/24566608.2019/68.1-3.3169. 18. S. Zantsi, J. C. Greyling and N. Vink, “Towards a common understanding of ‘emerging farmer’ in a South African context using data from a survey of three District municipalities in the Eastern Cape Province”. S. Afr. J. Agric. Ext, vol. 47, no. 2, pp. 81 – 93, 2019. 19. G. Chitsa, “Analysis of entrepreneurial behaviour of smallholder irrigation farmers: Empirical evidence from Qamata irrigation scheme”. Published M. Dissertation, University of Fort Hare, Alice, RSA, 2014. 20. M. Aliber and T. G. B. Hart, “Should subsistence agriculture be supported as a strategy to address rural food insecurity”. Agrekon, vol 48, no 4, pp. 434-458, 2009. 21. H. Rajpar, A. Zhang, A. Razzaq, K. Mehmood, M. B. Pirzado and W. Hu, “Agricultural Land Abandonment and Farmers’ Perceptions of Land Use Change in the Indus Plains of Pakistan: A Case Study of Sindh Province”. Sustainability, vol. 11, pp. 4663, 2019. Available from: 10.3390/su11174663. Authors: Ayushi Sharma, Shipra Shukla

Paper Title: An Advanced Machine Learning Model for Disease Prediction Abstract: To settle on right choices and pass on about vital control measures, numerous flare-up expectation models for anticipating COVID-19 are getting utilized all round the world. Straightforward conventional models have indicated extremely less precision rate for future forecast use, because of more significant levels of vulnerability and absence of proper information. Among the different machine learning model algorithms contemplated, an ensembled model was seen as giving the best outcomes. Because of the multifaceted nature of the virus's temperament, this research paper recommends machine learning to be an extremely helpful gadget to consider in case of the ongoing pandemic. This paper gives a colossal benchmark to call attention to the probability of machine learning to be utilized as an instrument for future exploration on pandemic control and its timely prediction. Moreover, this paper delineates that the best prompts for pandemic prediction are frequently comprehended by combining machine learning, predictive analytics and visualisation tools like Tableau. The main purpose of this research is to build a perfect ML model prototype which can be later used when access to appropriate dataset (which is both large and consists of many different features) is available. Also, the secondary aim is to automate the process of reporting so as to facilitate quicker action by the concerned authorities, and help common people reach out to the correct destination for treatment or help. Furthermore, the Tableau analysis performed on the dataset is to provide more analytical depths for people with expertise in the medical domain.

Keywords: COVID-19, machine learning, predictive analytics, Tableau.

References:

1. COVID-19 Outbreak Prediction with Machine Learning Sina F. Ardabili 1, Amir Mosavi 2,3,*, Pedram Ghamisi 4, Filip Ferdinand 2 , Annamaria R. Varkonyi-Koczy 2, Uwe Reuter 3, Timon Rabczuk 5, Peter M. Atkinson 6 218. 2. Dong E, Du H, Gardner L. An interactive web-based dashboard to track COVID-19 in real time. Lancet Infect Dis2020:S1473- 3099(20)30120-1. doi:10.1016/S1473-3099(20)30120-1. pmid:32087114CrossRefPubMedGoogle Scholar 1220- 3. Arabi YM, Murthy S, Webb S. COVID-19: a novel coronavirus and a novel challenge for critical care. Intensive Care Med2020. 1225 doi:10.1007/s00134-020-05955-1. pmid:32125458CrossRefPubMedGoogle Scholar 4. Grasselli G, Pesenti A, Cecconi M. Critical care utilization for the COVID-19 outbreak in Lombardy, Italy: early experience and forecast during an emergency response. JAMA2020. doi:10.1001/jama.2020.4031. pmid:32167538CrossRefPubMedGoogle Scholar 5. Xie J, Tong Z, Guan X, Du B, Qiu H, Slutsky AS. Critical care crisis and some recommendations during the COVID-19 epidemic in China. Intensive Care Med2020. doi:10.1007/s00134-020-05979-7. pmid:32123994 6. Prediction models for diagnosis and prognosis of covid-19 infection: systematic review and critical appraisal. BMJ 2020; 369 doi: https://doi.org/10.1136/bmj.m1328 (Published 07 April 2020) Cite this as: BMJ 2020;369:m1328 7. Predictive Model with Analysis of the Initial Spread of COVID-19 in India Shinjini Ghosh1 Massachusetts Institute of Technology 77 Massachusetts Avenue, MA 02139, USA 8. The APP Solutions. (August 27, 2019 ). How predictive analytics is changing healthcare industry. Medium. https://medium.com/@TheAPPSolutions/how-predictive-analytics-is-changing-healthcare-industry-999646a97d59 9. Ivanov, D. Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case. Transp. Res. Part E Logist. Transp. Rev. 2020, 136, doi:10.1016/j.tre.2020.101922. 10. Koolhof, I.S.; Gibney, K.B.; Bettiol, S.; Charleston, M.; Wiethoelter, A.; Arnold, A.L.; Campbell, P.T.; Neville, P.J.; Aung, P.; Shiga, T., et al. The forecasting of dynamical Ross River virus outbreaks: Victoria, Australia. Epidemics 2020, 30, doi:10.1016/j.epidem.2019.100377. 11. Darwish, A.; Rahhal, Y.; Jafar, A. A comparative study on predicting influenza outbreaks using different feature spaces: application of influenza-like illness data from Early Warning Alert and Response System in Syria. BMC Res. Notes 2020, 13, 33, doi:10.1186/s13104-020-4889-5. 12. The Application of Machine Learning Techniques for Predicting Results in Team Sport: A Review Rory Bunkera,_, Teo Susnjakb aNagoya Institute of Technology. Gokisocho, Showa Ward, Nagoya, Aichi, 466-8555, Japan bMassey University. Massey University East Precinct, Dairy Flat Highway (SH17), 0632, New Zealand 13. Lutins, Evan. (Aug 2, 2017 ). Ensemble Methods in Machine Learning: What are They and Why Use Them?. Towards Data Science. https://towardsdatascience.com/ensemble-methods-in-machine-learning-what-are-they-and-why-use-them-68ec3f9fef5f 14. Singh, Aishwarya. (JUNE 18, 2018). A Comprehensive Guide to Ensemble Learning (with Python codes). Analytics Vidhya. https://www.analyticsvidhya.com/blog/2018/06/comprehensive-guide-for-ensemble- models/ 15. Scikit-learn.org. https://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html 16. Patel, Ashish. (May 15, 2019 ). Ensemble Learning- The heart of Machine learning. Medium. https://medium.com/ml-research- lab/ensemble-learning-the-heart-of-machine-learning-b4f59a5f9777 17. Python.org. https://docs.python.org/3/library/smtplib.html 18. Sen, Arijit. (April 18, 2020 ). Rules to Define India’s COVID-19 Hotspots Are Omitting More Stressed Districts. The Wire. https://science.thewire.in/health/covid-19-hotspot-districts-red-zone/ 19. Shukla, S., Kumar, M., An Improved Energy Efficient Quality of Service Routing for Border Gateway Protocol, Computer and Electrical Engineering, Elsevier, Vol. 67, 2018 Authors: Ponlakshmi P, Vidhya Saraswathi P

Paper Title: Precision Farming and Smart Irrigation using IoT Abstract: Agriculture is the spine of Indian Economy. It mainly depends on Irrigation. The Internet Of Things is used to farmers are easier to monitor and control water possessions. This paper proposed, IoT architecture customized for smart irrigation application. Arduino board is used to communicate a variety of sensors like ultrasonic, soil moistur and light sensors. This work managed to decrease the expenditure, diminish devastate water and diminish substantial individual edge. Relay is developed to organize the switching of solenoid. Also, the scheme preserved to measure the soil moisture. It controls the solenoid valve according to human. Graphical User Interface (GUI) connected withAndroid application to motivate watering movement. SMS alert moreover sent to the home user in critical situations. 219. Keywords: IoT, Soil moisture sensor, ultrasonic sensor, light sensor, Arduino board 1226- 1229 References:

1. S. Rajeswari,K. Suthendran,K. Rajakumar,A Smart Agricultural Model by Integrating IoT, Mobile andCloud-based Big Data Analytics,International Journal of Pure and Applied Mathematics, 2018. 2. Dr.N.Suma, Sandra Rhea Samson, S.Saranya, G.Shanmugapriya, R.Subhashri,IOT Based Smart Agriculture Monitoring System,International Journal on Recent and Innovation Trends in Computing and Communication ,2017. 3. Karan Kanasura, Vishal Zaveri,SENSOR BASED AUTOMATED IRRIGATION SYSTEM WITH IOT: A TECHNICAL REVIEW ,Babu Madhav Institute of Technology, Uka Tasadia University, Bardoli, Gujarat, India. 4. Yashika Mahajan, Mrunalini Pachpande, Ruchita Sonje, Swati Yelapure, Manisha Navale,AUTOMATION BY IOT & MACHINE LEARNING IN IRRIGATION,IJARIIE,2018. 5. Prakhar Srivastava, Mohit Bajaj, Ankur Singh Rana,Overview of ESP8266 Wi-Fi module based Smart Irrigation System using IOT,International Conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics2018. Authors: Rasha Rokan Ismail, Taha Mohammed Hasan

Paper Title: Cloud Computing Security Management using CSP Abstract: The cloud was defined by lots of experts, yet the NIST (National Institute of Standards and Technology) has presented the definition: “a model for enabling comfortable, on-demand network access to a shared pool of configurable computing resources The aim of this paper is a model for safe data sharing on cloud computing with intension to provide data confidentiality and access control over shared data, it also removes the burden of key management and files by users. The system also supports dynamic changes of membership and enables clients to reach the data they require even when the owner does not exist in the system. In the proposed system, a new security system is introduced, it provides a mechanism through which communication is safely achieved as well as it protects users and their hidden information from unauthorized users. The Entities in Proposed System consist of three parts : CSP, Users (owner ,clients ) and TPA , in this paper the focus will be on the CSP and the users. The proposed system are provides data confidentiality, access control of share data, removes the burden of key management and file encryption/decryption byusers,support dynamically of users membership . The use of a digital signature ensures the integrity and confidentiality of sharing data sent by users 220. so that it cannot be read by the recipient TPA as it encrypts, sends a new encrypted signature and sends it to the CSP so that it cannot read its content CSP proved to be effective in the security of cloud computing 1230- 1236 Keywords: The Entities in Proposed System consist of three parts : CSP,

References: 1. Peter, M. & Tim, G.,"The NIST definition of Cloud Computing", Information Technology Laboratory, 2009. 2. Furht B.&Escalante, A," Handbook of Cloud Computing", New York: Springer, 2010. 3. Richard Mayo, Charles Perng, “An explanation of where the ROIcomes from”, IBM, November 2009 4. P. Mell, T. Grance, The NIST definition of cloud computing (draft), NIST Special Publ. 800 (145) (2011) 7. 5. C. Wang, Q. Wang, K. Ren, N. Cao, W. Lou, Toward secure and dependable storage services in cloud computing, IEEE Trans. Services Comput. 5 (2)(2012) 220–232. 6. R.D. Dhungana, A. Mohammad, A. Sharma, I. Schoen, Identity management framework for cloud networking infrastructure, in: IEEE International Conference on Innovations in Information Technology (IIT), 2013, pp. 13–17. 7. M. B. Ghadge and A. S. A. Ubale, “A Survey on Block Design-based Key Agreement for Group Data Sharing in Cloud Background ”, IEEE Transactions on Dependable and Secure Computingpp, pp. 1189–1194,ISSN : 234-610 2018. 8. Y. Tang, P.P. Lee, J.C.S. Lui, R. Perlman, Secure overlay cloud storage with access control and assured deletion, IEEE Trans. Dependable Secure Comput.9 (6) (2012) 903–916. 9. R.D. Dhungana, A. Mohammad, A. Sharma, I. Schoen, Identity management framework for cloud networking infrastructure, in: IEEE International Conference on Innovations in Information Technology (IIT), 2013, pp. 13–17. Authors: Bipasha Rajkhowa, Aniket Das

221. Paper Title: Impact of Artificial Intelligence on Customer Experience Abstract: With the increasing competition in the market today, the customers have a variety of options to 1237- choose from. The biggest challenge faced today by any business is to understand and deliver the exact 1242 requirements of the customers to retain the existing customer base and acquire new customers. The key to this is meeting and surpassing customer expectations leading to the goal of customer satisfaction through enhanced customer experiences. Upcoming technologies like Artificial Intelligence provide widespread opportunities to understand dynamic customer behavior and trends. However, not much exploration is done in academic research as to what factors primarily impact customer experience and the emerging significance of AI in this domain. Hence, the objective of this study is to understand the impact of artificial intelligence on customer experience. The paper adopts a quantitative approach wherein a survey was conducted across 207 participants to understand the impact of AI-enabled chatbots to deliver better personalization, quality of service, and hassle-free service to achieve better customer experience. The study provides implications to academicians as it contributes to the literature on the impact of AI on customer experience, helps practitioners in exploring and analyzing the various useful aspects of such emerging technologies in reshaping market trends, the companies and the society at large by providing a better experience to the customers, thus enabling a healthier customer relationship.

Keywords: Artificial Intelligence, Customer Experience, Chatbots, Hassle free service, Personalization, Quality of Service.

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