International Journal of Recent Technology and Engineering
ISSN : 2277 - 3878 Website: www.ijrte.org Volume-9 Issue-3, SEPTEMBER 2020 Published by: Blue Eyes Intelligence Engineering and Sciences Publication
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www.ijrte.org Exploring Innovation Editor-In-Chief 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 (BEIESP), Bhopal (MP), India
Associate Editor-In-Chief Dr. Takialddin Al Smadi Professor, Department of Communication and Electronics, Jerash Universtiy, Jerash, Jordan
Dr. Vo Quang Minh Senior Lecturer and Head, Department of Land Resources, College of Environment and Natural Resources (CENRes), Can Tho City, Vietnam.
Dr. Stamatis Papadakis Lecturer, Department of Preschool Education, University of Crete, Greece.
Dr. Ali OTHMAN Al Janaby Lecturer, Department of Communications Engineering, College of Electronics Engineering University of Ninevah, Iraq.
Dr. Rabiul Ahasan Professor, Department of Industrial Engineering, King Saud University, Saudi Arabia.
Dr. Hakimjon Zaynidinov Professor and Head, Department of Computer Science, Tashkent University of Information Technologies, Uzbekistan.
Prof. MPS Chawla Ex-Chairman, IEEE MP Sub-Section, India, Professor-Incharge (head)-Library, Associate Professor in Electrical Engineering, G.S. Institute of Technology & Science Indore, Madhya Pradesh, India.
Associate Editor-In-Chief Members Dr. Anil Kumar Yadav Ph.D(ME), ME(ME), BE(ME) Professor, Department of Mechanical Engineering, LNCT Group of Colleges, Bhopal (M.P.), India
Dr. Gamal Abd El-Nasser Ahmed Mohamed Said Ph.D(CSE), MS(CSE), BSc(EE) Department of Computer and Information Technology , Port Training Institute, Arab Academy for Science, Technology and Maritime Transport, Egypt.
Dr. Ahmed Daabo Lecturer and Researcher, Department of Mining Engineering, University of Mosul, Iraq
Dr. Carlo H. Godoy Jr Professor, Department of Support, Human Edge Software Philippines, Philippines
Dr. Morteza Pakdaman Assistant Professor, Department of CRI, Climatology of Atmospheric Disasters Research Group, Climatological Research Institute (CRI), Mashhad, Iran.
Scientific Editors Dr. Fadiya Samson Oluwaseun Assistant Professor, Girne American University, as a Lecturer & International Admission Officer (African Region) Girne, Northern Cyprus, Turkey.
Dr. Robert Brian Smith International Development Assistance Consultant, Department of AEC Consultants Pty Ltd, AEC Consultants Pty Ltd, Macquarie Centre, North Ryde, New South Wales, Australia
Dr. Durgesh Mishra Professor (CSE) and Director, Microsoft Innovation Centre, Sri Aurobindo Institute of Technology, Indore, Madhya Pradesh India
Dr. Vinod Kumar Singh Associate Professor and Head, Department of Electrical Engineering, S.R.Group of Institutions, Jhansi (U.P.), India
Dr. Rachana Dubey Ph.D.(CSE), MTech(CSE), B.E(CSE) Professor, Department of Computer Science & Engineering, Lakshmi Narain College of Technology Excellence (LNCTE), Bhopal (M.P.), India
Executive Editor Chair Dr. Deepak Garg Professor, Department of Computer Science And Engineering, Bennett University, Times Group, Greater Noida (UP), India
Members of Executive Editor Chair Dr. Vahid Nourani Professor, Faculty of Civil Engineering, University of Tabriz, Iran.
Dr. Saber Mohamed Abd-Allah Associate Professor, Department of Biochemistry, Shanghai Institute of Biochemistry and Cell Biology, Shanghai, China.
Dr. Xiaoguang Yue Associate Professor, Department of Computer and Information, Southwest Forestry University, Kunming (Yunnan), China.
Dr. Sunandan Bhunia Associate Professor & Head, Department of Electronics & Communication Engineering, Haldia Institute of Technology, Haldia (Bengal), India.
Dr. Awatif Mohammed Ali Elsiddieg Assistant Professor, Department of Mathematics, Faculty of Science and Humatarian Studies, Elnielain University, Khartoum Sudan, Saudi Arabia.
Technical Program Committee Dr. Mohd. Nazri Ismail Associate Professor, Department of System and Networking, University of Kuala (UniKL), Kuala Lumpur, Malaysia.
Technical Program Committee Members Dr. Wilson Udo Udofia Associate Professor, Department of Technical Education, State College of Education, Afaha Nsit, Akwa Ibom, Nigeria.
Dr. Ch. Ravi Kumar Dean and Professor, Department of Electronics and Communication Engineering, Prakasam Engineering College, Kandukur (Andhra Pradesh), India.
Dr. Sanjay Pande MB FIE Dip. CSE., B.E, CSE., M.Tech.(BMI), Ph.D.,MBA (HR) Professor, Department of Computer Science and Engineering, G M Institute of Technology, Visvesvaraya Technological University Belgaum (Karnataka), India.
Dr. Hany Elazab Assistant Professor and Program Director, Faculty of Engineering, Department of Chemical Engineering, British University, Egypt.
Dr. M.Varatha Vijayan Principal, Department of Mechanical Engineering, Mother Terasa College of Engineering and Technology, Pudukkottai (Tamil Nadu) India.
Dr. S. Balamurugan Director, Research and Development, Intelligent Research Consultancy Services (IRCS), Coimbatore (Tamil Nadu), India.
Dr. Rajalakshmi Rahul FIE Dip. CSE., B.E, CSE., M.Tech.(BMI), Ph.D.,MBA (HR) Founder and CEO Talaash Research Consultants, Chennai (Tamil Nadu), India.
Reviewer Chair Dr. Arun Murlidhar Ingle Director, Padmashree Dr. Vithalrao Vikhe Patil Foundation’s Institute of Business Management and Rural Development, Ahmednagar (Maharashtra) India.
Members of Reviewer Chair Dr. S. A. Mohan Krishna Associate Professor, Department of Information Science and Engineering, Vidyavardhaka College of Engineering, Mysuru (Karnataka), India
Dr. Ashok Koujalagi Assistant Professor & Postdoctoral Researcher, Department of Computer Science, Basaveshwar Science College, Bagalkot (Karnataka), India
Dr. A. Baradeswaran Principal, Department of Electronics & Communication Engineering, Madha Engineering College, Chennai (Tamil Nadu), India
Dr. R. Padma Priya Professor, Department of Electronics & Communication Engineering, Madha Engineering College, Chennai (Tamil Nadu), India
Dr. Raghunath Satpathy Assistant Professor, Department of Biotechnology, Majhighariani Institute of Technology and Science, Odisha, India
Dr. Hema Chandran K. Professor & HOD, Department of Electronics & Communication Engineering, Ashoka Institute of Engineering and Technology, Hyderabad (Telangana), India
Dr. M. Rakesh Assistant Professor, Department of Electronics and Communication Engineering, Vignan’s Institute of Management and Technology for Women, Ghatkeser (Hyderabad), India
Dr. Parul Mishra Assistant Professor, Department of English, GD Goenka University Gurugram, Gurgaon (Haryana), India
Dr. Sunil Kumar Mishra Associate Professor, Department of Communication Skills (English), Amity University, Gurgaon (Haryana), India
Dr. Kaushik Mukherjee Faculty of Management Studies, Assistant Professor, AKS University, Satna(MP), India
Dr. Siva Reddy Sheri Associate Professor, Department of Mathematics, School of Technology Hyderabad Campus, GITAM University, Visakhapatnam (Andhra Pradesh), India.
Dr. Nitasha Soni Assistant Professor, Department of Computer Science, Manav Rachna International Institute of Research and Studies, Faridabad (Haryana), India.
Dr. Pushpender Sarao Professor, Department of Computer Science & Engineering, Hyderabad Institute of Technology and Management, Hyderabad (Telangana), India.
Dr. K. Priya Professor & Head, Department of Commerce, Vivekanandha College of Arts & Sciences for Women (Autonomous, Elayampalayam, Namakkal (Tamil Nadu), India.
Dr. Sunil Tekale Professor, Dean Academics, Department of Computer Science & Engineering, Malla Reddy College of Engineering, Secunderabad (Telangana), India.
Dr. Brijesh Singh Senior Yoga Expert and Head, Department of Yoga, Samutakarsha Academy of Yoga, Music & Holistic Living, Prahladnagar, Ahmedabad (Gujarat), India.
Dr. Vinita Kumar Department of Physics, Dr. D. Ram D A V Public School, Danapur, Patna(Bihar), India
Dr. Rama Shanker Professor & Head, Department of Statistics, Eritrea Institute of Technology, Asmara, Eritrea
Dr. Subhash Laxman Gadhave Professor, Department of Mechanical Engineering, Shri Jagdishprasad Jhabarmal Tibrewala University, (Rajasthan) India.
Dr. Raja Mohammad Latif Professor, Department of Mathematics and Natural Sciences, University of Alberta Edmonton, Canada.
Dr. S.N. Ramaswamy Professor, Department of Civil Engineering, Kalasalingam University, (Krishnankoil) India.
Volume-9 Issue-3, September 2020, ISSN: 2277–3878 (Online) S. No Published By: Lattice Science Publication Page No.
Authors: Dattatray Kisan Rajmane, Milind Laxman Waikar Projection of Temperature and Precipitation using Multiple Linear Regression and Artificial Neural Paper Title: Network as a Downscaling Methodology for Upper Bhima Basin Abstract: Study of Climate change effect on water resources is very important for its effective management. Projection of temperature and precipitation can be performed by using General Circulation Model (GCM) outputs. GCM can make the projections of climate parameters with different emission scenarios at coarser scale. However hydrological models require climate parameters at smaller scale Downscaling technique is used for obtaining small scale climate variables from large scale variables of GCM outputs. In this study downscaling has been carried out by using Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) techniques. Performance of MLR and ANN models has been evaluated considering Coefficient of determination value (R2). It has been observed that ANN performs better against MLR Model, showed the results that rainfall distribution pattern is varied, in monsoon season rainfall decreases while it increases in post monsoon period. Due to its good evaluation performance such techniques can be applicable for downscaling purpose
Keywords: Artificial neural network, General Circulation model, Multiple linear regression, Upper Bhima Basin
1. References: 1. Climate Change 2007: Synthesis Report (IPCC) 2. IPCC Report 2014. 1-8 3. B. C. Hewitson and R.G. Crane, ―Climate downscaling: techniques and application‖, Clim. Res., vol.7, (1996), 85–95. 4. R.L. Wilby, M.L. Wigley, D. Conway, P.D. Jones, B.C. Hewitson, J. Main and D.S. Wilks, ―Statistical downscaling of general circulation model output: a comparison of methods‖, Water Resour. Res., 34,(1998), 2995–3008. 5. A. J. Askew, ―The influence of climate change and climate variability on the hydrologic regime and water resources‖, IAHS Publication,Vol.168, (1987), pp. 421-430. 6. E.P. Salathe, Jr., P.W. Mote and M.W. Wiley, ―Review of scenario selection and downscaling methods for the assessment of climate change impacts on hydrology in the United States Pacific Northwest‖, Int. J. Climatol., Vol. 27, (2007), 1611–1621. 7. Rao S. Govindaraju, ―Artificial neural network in hydrology I: Preliminary concepts‖, Journal of Hydrologic Engineering, Vol. 5, April 2000, 115-123 8. Ahmed Amara Konate Heping Pan, Nasir Khan and Jie Huai Yang, ― Generalized regression and feed forward back propagation neuralnetwork in modelling porosity from Geophysical well logs‖, Journal of Petroleum exploration and production Technology, Vol.5, (2015), 157-166. 9. David E. Rumelhart, Geoffrey E. Hintont & Ronald J. WilliamsJ. Wang, ―Learning representations by back-propagating errors‖, Nature Publishing Group, Vol.323 (9), 1986, pp 533-536. 10. Government of Maharashtra Hydrology Project II, Water Resources Department, ―Real Time Streamflow Forecasting and Reservoir Operation System for Krishna and Bhima River Basins in Maharashtra‖ (RTSF & ROS), Inception Report, December 2011, 11. Subimal Ghosh, P.P. Mujumdar, ―Statistical Downscaling of GCM simulations to streamflow using relevance vector machine‖, Advances in Water Resources, Vol 31, July 2007, pp. 132-146 12. K. Pearson, ―Mathematical contributions to the theory of evolution III regression heredity and panmixia. Philos‖. Trans. R Soc. Lond. Ser., 187, (1896), 253–318. Anicia Coleen S. Reyes, Gem Ryan C. Milan, James Marvin M. Quilaton, Bryant Exel G. Sigue, Authors: Steven Valentino E. Arellano, Kenneth C. Karamihan SBC-Based Diabetic Retinopathy and Diabetic Macular Edema Classification System using Deep Paper Title: Convolutional Neural Network Abstract: This Raspberry Pi Single-Board Computer-Based Diabetic Retinopathy (DR) and Diabetic Macular Edema (DME) Classification System using Deep Convolutional Neural Network through Inception v3 Transfer Learning and MATLAB digital image processing paradigm based on International Clinical DR and DME Disease Severity Scale with Python application, which would capture the image of the retina of diabetic patients to classify the grade, severity, and types of DR; and the grade of DME without using dilating drops. It would also display, save, search and print the partial diagnosis that can be done to the patients. Diabetic patients, endocrinologists and ophthalmologists of one of the medical centers in City of San Pedro, Laguna, Philippines tested the system. Obtained results indicated that the classification of DR and DME, and its characteristics using the system were accurate and reliable, which could be an assistive device for endocrinologists and ophthalmologists. 2. Keywords: Diabetic retinopathy, diabetic macular edema, deep convolutional neural network, digital image 9-16 processing, transfer learning
References: 1. Z. Punthakee, R. Goldenberg, and P. Katz. (2018). Definition, Classification and Diagnosis of Diabetes, Prediabetes and Metabolic Syndrome. Canadian Journal of Diabetes [Online]. 42(1), pp. S10-S15. Available: https://guidelines.diabetes.ca/docs/cpg/Ch3- Definition-Classification-and-Diagnosis-of-Diabetes-Prediabetes-and-Metabolic-Syndrome.pdf 2. World Health Organization (2016). Diabetes Country Profiles- Philippines [Online]. Available: https://www.who.int/diabetes/country - profiles/phl_en.pdf?ua=1 3. R. Cheloni, S. A. Gandolfi, C. Signorelli, and A. Odone. (2019). Global prevalence of diabetic retinopathy: protocol for a systematic review and meta-analysis. BMJ Open [Online]. 9(3), e022188. Available: http://dx.doi.org/10.1136/bmjopen-2018-022188 4. Mayo Clinic (2018). Diabetic Retinopathy [Online]. Available: https://www.mayoclinic.org/diseases-conditions/diabetic- retinopathy/diagnosis-treatment/drc-20371617 5. B. Curry. (2018, July 27). An Introduction to Transfer Learning in Machine Learning [Online]. Available: https://medium.com/kansas- city -machine-learning-artificial-intelligen/an-introduction-to-transfer-learning-in-machine-learning-7efd104b6026 6. Mindfire Solutions. (2017, October 3). Python: 7 important Reason Why You Should Use Python [Online]. Available: https://medium.com/@mindfiresolutions.usa/python-7-important-reasons-why-you-should-use-python-5801a98a0d0b 7. A. M. Azab, J. Toth, L. S. Mihaylova, and M. Arvaneh, ―Signal Processing and Machine Learning for Brain-Machine Interfaces‖, 1st ed. vol. 114, The Institution of Engineering and Technology, London, United Kingdom, 2018, pp. 81-101. 8. M. D. Abràmoff, Y. Lou, A. Erginay, W. Clarida, R. Amelon, J. C. Folk, and M. Niemeijer. (2016). Improved Automated Detection of Diabetic Retinopathy on a Publicly Available Dataset Through Integration of Deep Learning. Investigative Ophthalmology & Visual Science [Online]. 57(13), pp. 5200-5206. Available: https://doi.org/10.1167/iovs.16-19964 9. R. Gargeya, and T. Leng. (2017). Automated Identification of Diabetic Retinopathy Using Deep Learning. Ophthalmology [Online]. 124(7), pp. 962-969. Available: https://doi.org/10.1016/j.ophtha.2017.02.008 10. D. K. Prasad, L. Vibha, and K. P. Venugopal. (2015, December). Early detection of diabetic retinopathy from digital retinal fundus images. Recent Advances in Intelligent Computational Systems (RAICS) IIEE [Online]. 1(1) pp. 240-245. Available: https://doi.org/10.1109/RAICS.2015.7488421 11. R. Pires, S. Avila, J. Wainer, E. Valle, M. D. Abramoff, and A. Rocha. (2019). A data-driven approach to referable diabetic retinopathy detection. Artificial Intelligence in Medicine [Online]. 96(1), pp. 93-106. Available: https://doi.org/10.1016/j.artmed.2019.03.009 12. S. Wan, Y. Liang, and Y. Zhang. (2018). Deep convolutional neural networks for diabetic retinopathy detection by image classification. Computers & Electrical Engineering [Online]. 72(1), pp. 274-282. Available: https://doi.org/10.1016/j.compeleceng.2018.07.042 Authors: Sunay Dharwadkar, Sarvesh Harmalkar, Chinmay Nagzarkar, Vivkanand Tendulkar
Paper Title: Surveillance FPV Drone with Obstacle Avoidance System Abstract: The unmanned air vehicle (UAV) is mostly used in inspection and surveillance operations recent. The UAV is also termed as vertical take-off landing (VTOL), since it is capable of vertical take-off and landing without need of a runway. The big tunnels, infrastructure and large bridges are inserted using UAV by photographic inspection. UAVs are also used for surveillance purposes by the military and by the security guards. Since this is a new technology of the last decade, deep research has to be done. UAVS have the cross structure arrangement to which the rotor blades are attached at the end points of cross beams. These rotors are driven by the DC brush motors and motors are powered by the lithium ion rechargeable battery. The working principle of quad- rotors is the same as the chopper by controlling the rpm of each rotor blade, due to which the gyroscopic torque will act and the vehicle will move in the desired direction. To hold the quad- rotor at a stationary position at some height constant rpm of all rotors has to be maintained. This signal is given by the controller from some distance, which is received by the, and then it processes the signal and drives the motor via the flight controller and drives the rotors. A quad- rotor is equipped with a high quality camera for 3. photography and video shooting during surveillance. Since a quad rotor is manoeuvring in air, the wind may exert a drag force and take it along with it and the quad rotor may hit any obstacle or inspection objects. To avoid this, it is equipped with the ultrasonic sensors which is capable of sensing the obstacle and realize collision 17-20 avoidance between wall or any object.
Keywords: About four key words or phrases in alphabetical order, separated by commas.
References: 1. Prathamesh Salaskar , Saee Paranjpe , Jagdish Reddy , Arish Shah, ―Quadcopter – Obstacle Detection and Collision Avoidance,‖ in International Journal of Engineering Trends and Technology (IJETT) – Volume 17 Number 2 – Nov 2014 2. Joseph Azeta1, , Christian Bolu1 , Daniel Hinvi1 and Abiodun A Abioye1, ―Obstacle detection using ultrasonic sensor for a mobile robot,‖ in IOP Conf. Series: Materials Science and Engineering 707 (2019) 012012 IOP Publishing doi:10.1088/1757- 899X/707/1/0120124. 3. Muhammad Fazlur Rahman, Rianto Adhy Sasongko, ―Obstacle Avoidance for Quadcopter using Ultrasonic Sensor,‖ in IOP Conf. Series: Journal of Physics: Conf. Series 1005 (2018) 012037 doi :10.1088/1742-6596/1005/1/012037 4. Marco Claudio De Simone * ID , Zandra Betzabe Rivera and Domenico Guida ―Obstacle Avoidance System for Unmanned Ground Vehicles by Using Ultrasonic Sensors in machines. 5. Kenjiro Niwa, Keigo Watanabe, and Isaku Nagai ―A Detection Method Using Ultrasonic Sensors for Avoiding a Wall Collision of Quadrotors,‖ in International conference of mechatronics and Aotpmation August 6-9, Takamatsu, japan. Authors: Swetharani K, Vara Prasad
Paper Title: Design and Implementation of an Efficient Rose Leaf Disease Detection using K-Nearest Neighbours Abstract: Plants are prone to different diseases caused by multiple reasons like environmental conditions, light, bacteria, and fungus. These diseases always have some physical characteristics on the leaves, stems, and fruit, such as changes in natural appearance, spot, size, etc. Due to similar patterns, distinguishing and identifying category of plant disease is the most challenging task. Therefore, efficient and flawless mechanisms should be discovered earlier so that accurate identification and prevention can be performed to avoid several losses of the entire plant. Therefore, an automated identification system can be a key factor in preventing loss in the cultivation and maintaining high quality of agriculture products. This paper introduces modeling of rose plant 4. leaf disease classification technique using feature extraction process and supervised learning mechanism. The outcome of the proposed study justifies the scope of the proposed system in terms of accuracy towards the 21-27 classification of different kind of rose plant disease.
Keywords: Plant Disease, Rose, Machine Learning, KNN, Classification.
References: 1. DeLind, Laura B. "Are local food and the local food movement taking us where we want to go? Or are we hitching our wagons to the wrong stars?." Agriculture and human values, 28, no. 2 (2011): 273-283. 2. Arora, Naveen Kumar. "Agricultural sustainability and food security." (2018): 217-219. 3. Pingali, Prabhu, Anaka Aiyar, Mathew Abraham, and Andaleeb Rahman. "Economic Growth, Agriculture, and Food Systems: Explaining Regional Diversity." In Transforming Food Systems for a Rising India, pp. 15-45. Palgrave Macmillan, Cham, 2019. 4. Rey, Dolores, Ian P. Holman, and Jerry W. Knox. "Developing drought resilience in irrigated agriculture in the face of increasing water scarcity." Regional Environmental Change 17, no. 5 (2017): 1527-1540. 5. J. K. Kamble, "Plant Disease Detector," 2018 International Conference On Advances in Communication and Computing Technology (ICACCT), Sangamner, 2018, pp. 97-101. 6. Rishabh Sinha ―Study on Rose Diseases: Identification, Detection and Cure‖ International Journal of Engineering Development and Research, 2017 7. Minaee, Saeed & Jafari, Mehrnoosh&Safaie, Naser. (2018). Design and development of a rose plant disease-detection and site-specific spraying system based on a combination of infrared and visible images. Journal of Agricultural Science and Technology. 20. 23-36. 8. Song, Chorong, Miho Igarashi, Harumi Ikei, and Yoshifumi Miyazaki. "Physiological effects of viewing fresh red roses." Complementary therapies in medicine 35 (2017): 78-84. 9. https://www.apsnet.org/edcenter/apsnetfeatures/Pages/Roses.aspx 10. Gavhale, Ms& Gawande, Ujwalla. (2014). An Overview of the Research on Plant Leaves Disease detection using Image Processing Techniques. IOSR Journal of Computer Engineering. 16. 10-16. 10.9790/0661-16151016. 11. Sandhu, Gurleen Kaur, and Rajbir Kaur. "Plant Disease Detection Techniques: A Review." In 2019 International Conference on Automation, Computational and Technology Management (ICACTM), pp. 34-38. IEEE, 2019. 12. Patel, Arpita & Joshi, Barkha. (2017). A Survey on the Plant Leaf Disease Detection Techniques. IJARCCE. 6. 229-231. 10.17148/IJARCCE.2017.6143. 13. Wheeler, Bryan Edward John. "An introduction to plant diseases." An introduction to plant diseases. (1969). 14. Akhtar, Asma, Aasia Khanum, Shoab A. Khan, and Arslan Shaukat. "Automated plant disease analysis (APDA): performance comparison of machine learning techniques." In 2013 11th International Conference on Frontiers of Information Technology, pp. 60- 65. IEEE, 2013. 15. C. G. Dhaware and K. H. Wanjale, "A modern approach for plant leaf disease classification which depends on leaf image processing," 2017 International Conference on Computer Communication and Informatics (ICCCI), Coimbatore, 2017, pp. 1-4. 16. J. N. Reddy, K. Vinod and A. S. RemyaAjai, "Analysis of Classification Algorithms for Plant Leaf Disease Detection," 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT), Coimbatore, India, 2019, pp. 1-6. 17. U. Shruthi, V. Nagaveni and B. K. Raghavendra, "A Review on Machine Learning Classification Techniques for Plant Disease Detection," 2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS), Coimbatore, India, 2019, pp. 281-284. 18. Kaur, Manpreet, and Rekha Bhatia. "Leaf Disease Detection and Classification: A Comprehensive Survey." In Proceedings of International Conference on IoT Inclusive Life (ICIIL 2019), NITTTR Chandigarh, India, pp. 291-304. Springer, Singapore, 2020. 19. Lu, Yang, Shujuan Yi, Nianyin Zeng, Yurong Liu, and Yong Zhang. "Identification of rice diseases using deep convolutional neural networks." Neurocomputing 267 (2017): 378-384. 20. R. M. Prakash, G. Saraswathy, G. Ramalakshmi, K. Mangaleswari, and T. Kaviya, ―Detection of leaf diseases and classification using digital image processing,‖ in Innovations in Information, Embedded and Communication Systems (ICIIECS), 2017 International Conference on. IEEE, 2017, pp. 1–4. 21. Dhingra, Gittaly, Vinay Kumar, and Hem Dutt Joshi. "A novel computer vision based neutrosophic approach for leaf disease identification and classification." Measurement 135 (2019): 782-794. 22. V. Pooja, R. Das, and V. Kanchana, ―Identification of plant leaf diseases using image processing techniques,‖ in Technological Innovations in ICT for Agriculture and Rural Development (TIAR), 2017 IEEE. IEEE, 2017, pp. 130–133. 23. Nidhis, A. D., Chandrapati Naga Venkata Pardhu, K. Charishma Reddy, and K. Deepa. "Cluster Based Paddy Leaf Disease Detection, Classification and Diagnosis in Crop Health Monitoring Unit." In Computer Aided Intervention and Diagnostics in Clinical and Medical Images, pp. 281-291. Springer, Cham, 2019. 24. P. B. Padol and A. A. Yadav, ―Svm classifier based grape leaf disease detection,‖ in Advances in Signal Processing (CASP), Conference on. IEEE, 2016, pp. 175–179. 25. Pooja Kulinavar, Vidya I. Hadimani, ―Classification of leaf diseases based on multiclass SVM classifier,‖ International Journal of Advance Research, Ideas and Innovations in Technology, Vol.3, Issue.4, pp321-325, 2017 26. Islam, Taohidul, Manish Sah, SudiptoBaral, and Rudra RoyChoudhury. "A Faster Technique on Rice Disease Detectionusing Image Processing of Affected Area in Agro-Field." In 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT), pp. 62-66. IEEE, 2018. 27. Santos Ferreira, Alessandro, Daniel Matte Freitas, Gercina Gonçalves da Silva, HemersonPistori, and Marcelo TheophiloFolhes. "Weed detection in soybean crops using ConvNets." Computers and Electronics in Agriculture 143 (2017): 314-324. 28. Prashar, K., Dr. Rajneesh Talwar, Dr.Chander Kant, ―Robust Automatic Cotton Crop Disease Recognition (ACDR) Method using the Hybrid Feature Descriptor with SVM‖. 4th International Conference on Computing on sustainable Global Development, March, 2017. Authors: Wageda Alsobky, Hala Saeed, Ali N.Elwakeil
Paper Title: Different Types of Attacks on Block Ciphers Abstract: Cryptanalysis is a very important challenge that faces cryptographers. It has several types that should be well studied by cryptographers to be able to design cryptosystem more secure and able to resist any type of attacks. This paper introduces six types of attacks: Linear, Differential , Linear-Differential, Truncated differential Impossible differential attack and Algebraic attacks. In this paper, algebraic attack is used to formulate the substitution box(S-box) of a block cipher to system of nonlinear equations and solve this system by using a classical method called Bases . By Solving these equations, we made algebraic attack on S-box.
Keywords: Linear attack; Differential attack; Algebraic attack; S-box; Bases 5. References: 1. Christof Paar · Jan Pelzl2009Understanding Cryptography: A Textbook for Students and PractitionersSpringer Heidelberg Dordrecht 28-31 London New York. 2. William Stallings2011Cryptography and network security : principles and practiceFifth edition. Boston Prentice Hall. 3. Blondeau, C., & Gérard, B. (2009, May). On the data complexity of statistical attacks against block ciphers. In Workshop on coding and cryptography-wcc (Vol. 2009, pp. 469-488). 4. Weinmann, R. P. (2009). Algebraic methods in block cipher cryptanalysis (Doctoral dissertation, Technische Universität). 5. The Laws of Cryptography with Java Code2003Available online at Neal Wagner's home page1-334 6. Analysis of Development of Dynamic S-Box Generation2017Computer Science and Information Technology154-163 7. Cryptographic analysis of all 4 × 4-bit S-boxes2012Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)118-133 8. Abomhara, M. (2015). Cyber security and the internet of things: vulnerabilities, threats, intruders and attacks. Journal of Cyber Security and Mobility, 4(1), 65-88. 9. Lai, X., Massey, J. L., & Murphy, S. (1991, April). Markov ciphers and differential cryptanalysis. In Workshop on the Theory and Application of of Cryptographic Techniques (pp. 17-38). Springer, Berlin, Heidelberg. 10. Matsui, M. (1993, May). Linear cryptanalysis method for DES cipher. In Workshop on the Theory and Application of Cryptographic Techniques (pp. 386-397). Springer, Berlin, Heidelberg. 11. Langford, S. K., & Hellman, M. E. (1994, August). Differential-linear cryptanalysis. In Annual International Cryptology Conference (pp. 17-25). Springer, Berlin, Heidelberg. 12. Lee, S., Hong, S., Lee, S., Lim, J., & Yoon, S. (2001, December). Truncated differential cryptanalysis of Camellia. In International Conference on Information Security and Cryptology (pp. 32-38). Springer, Berlin, Heidelberg. 13. Kim, J., Hong, S., Sung, J., Lee, S., Lim, J., & Sung, S. (2003, December). Impossible differential cryptanalysis for block cipher structures. In International Conference on Cryptology in India (pp. 82-96). Springer, Berlin, Heidelberg. 14. Bard, G. (2009). Algebraic cryptanalysis. Springer Science & Business Media. 15. Improved rijndael-like S-box and its transform domain analysis2006Lecture Notes in Computer Science including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)153-167 16. Evaluating algebraic attacks on the AES2003Diplom thesis, Technische 17. Generating S-Box Multivariate Quadratic Equation Systems And Estimating Algebraic Attack Resistance Aided By SageMath20151- 21 18. Resistance of S-boxes against algebraic attacks2004Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)83-93 19. Gröbner bases, Gaussian elimination and resolution of systems of algebraic equations1983Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)146-156 20. An introduction to Gröbner bases1994American Mathematical Soc. 21. ―Algebraic Construction of Powerful Substitution‖ International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277 3878, Volume-8 Issue-6, March 2020. 22. -―Performance Analysis of Advanced Encryption Standard (AES) S-boxes ―International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-9 Issue-1, May 2020. 23. ‖A Review of Advanced Encryption Standard (AES) Performance‖ Benha Journal of Engineering Science and Technology (BJEST) ISSN: 2357-0105, Volume-1 Issue-1, July 2018 Authors: Mukhitdinova Firyuza Abdurashidovna, Mukhamedov Хaidarali Mеlievich
Paper Title: Improving the Legal Framework and Social Policy in the Context of a Pandemic Abstract: The article examines the essence of social policy in the field of support in the context of a pandemic. The analysis of the situation in the social sphere and its structural components in the Republic of Uzbekistan in the context of the pandemic is given. It is proposed to improve the legislative framework for improving social protection and living standards of the country's population, as well as to modernize the system of state guarantees in the social sphere.
Keywords: law. the right to the protection of social sphere, social policy, situation, employment, strategy, development) 6. References: 1. Constitution Of The Republic Of Uzbekistan. T. 2018, Uzbekistan, C 32-35 2. 2. European Convention for the protection of rights and fundamental freedoms (1950)\ 3. 3. Decree of the President of the Republic of Uzbekistan no. PP-3839 dated July 5, 2018 "on measures to further improve labor migration abroad» 4. 4. Decree of the President of the Republic of Uzbekistan dated December 10, 2018 no. PP-4056 "on improving the activities of the National center for human rights of the Republic of Uzbekistan". 5. 5. Decree of the President of the Republic of Uzbekistan on January 9, 2019 "on radical improvement of the system of raising legal awareness and legal culture in society» 6. 6. Resolution of the President of the Republic of Uzbekistan" on additional measures to strengthen the social protection of orphans and children left without parental care " dated February 11, 2019 7. 7. Decree of the President of the Republic of Uzbekistan dated April 23, 2019 "on additional measures to further strengthen guarantees of the rights of the child» 8. 8. Labor Code of Uzbekistan. Leх. UzM. Young, The Techincal Writers Handbook. Mill Valley, CA: University Science, 1989. Jolan Baccay Sy, Marlon Gan Rojo, Eunelfa Regie Calibara, Alain Vincent Comendador, Wubishet Authors: Degife Paper Title: Multi-Station Automated Hand Washing System (MSAHWS) Abstract: The paper presents a design and development of a multi-station automated hand-washing system (MSAHWS) that could be integrated into overall solution strategies for combating the threat of SARS-Cov-2 infections and minimizing the health and economic devastation the virus spread can inflict. The researchers seek to create a system that uses a single micro-controller and caters to several users, each of them being served independently of each other. The MSAHWS development follows a four-part methodology: formulation of the sanitary, operational, manufacturing and economic requirements; design, modeling, and simulation of the micro- controller-based control system; MSAHWS hardware prototype development; and system test and data 7. collection. The MSAHWS design and development focuses on a double-station system that uses a single Arduino Uno, an ultrasonic sensor for each station, 4 FET‘s, 4 liquid pumps, a water tank, a soap reservoir, a power supply and a frame to house the system. The non-contact system eliminates possible viral 36-43 transmission from one person to another via the hand washing machine yet ensures the required cleanliness of the hands. The system is first simulated in PROTEUS to test its functionality and responses based on the demanded or required criteria. A prototype is then built to test and verify the system‘s actual operation and responses and thence to make the necessary adjustment of parameters to realize an acceptable performance level. Tests show that all the requirements are met. Photos of the built and tested prototype, a diagram of the initial system design concept, a screen capture of the control system software model, a schematic diagram of the control system, a sketch with dimensions of the hand washing machine frame or housing, and the flowchart on which the Arduino script is developed. The operation and user-interaction of the actual system is also described. The control system program is written such that the resulting hand washing activity complies with the WHO standard on hand washing duration and makes entirely possible a complete and hygienic hand washing activity with soap and water. The system is envisioned for strategic deployment in public and private areas like public markets, banks, hospitals, schools, offices, residences, and many others. The paper has shown that it is possible to control multiple hand washing stations, each acting independently of each other, using a single micro- controller and a proper control system programming.
Keywords: Arduino Automated System, Covid19, Handwashing, Hygiene, Proteus.
References: 1. worldometer 2020, COVID-19 CORONAVIRUS PANDEMIC, worldometer, 22 July 2020,
Keywords: Artificial Neural Network (ANN), Grid Computing, SARS-CoV-2, Reverse Transcription 8. Polymerase Chain Reaction (RT-PCR) test. 44-50 References: 1. Bansal, M.: Cardiovascular disease and COVID-19. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 14(3), pp.247- 250. (2020). https://doi.org/10.1016/j.dsx.2020.03.013. 2. Chan, K., Zheng, J., Mok, Y., LI, Y., Liu, Y., Chu, C. and Ip, M.: SARS: prognosis, outcome and sequelae. Respirology, 8(s1), pp.S36- S40. (2003). https://doi.org/10.1046/j.1440-1843.2003.00522.x 3. Choi, B., Madusanka, N., et.al: Convolutional Neural Network-based MR Image Analysis for Alzheimer‘s Disease Classification. Current Medical Imaging Formerly Current Medical Imaging Reviews, 16(1), pp.27-35. (2020). DOI: 10.2174/1573405615666191021123854. 4. Czajkowski, K., Fitzgerald, S., Foster, I. and Kesselman, C., n.d. Grid information services for distributed resource sharing. Proceedings 10th IEEE International Symposium on High Performance Distributed Computing. 5. Das, A., Rad, P., Choo, K., Nouhi, B., Lish, J. and Martel, J.: Distributed machine learning cloud teleophthalmology IoT for predicting AMD disease progression. Future Generation Computer Systems, 93, pp.486-498, (2019). 6. Driggin, E., Madhavan, M., Bikdeli, B., et.al: Cardiovascular Considerations for Patients, Health Care Workers, and Health Systems During the COVID-19 Pandemic. Journal of the American College of Cardiology, 75(18), pp.2352-2371, (2020). 7. Gentile, S., Strollo, F. and Ceriello, A.: COVID-19 infection in Italian people with diabetes: Lessons learned for our future (an experience to be used). Diabetes Research and Clinical Practice, 162, (2020). https://doi.org/10.1016/j.diabres.2020.108137. 8. Guan, W., Liang, W., Zhao, Y., et.al: Comorbidity and its impact on 1590 patients with COVID-19 in China: a nationwide analysis. European Respiratory Journal, 55(5), (2020). https://doi.org/10.1183/13993003.00547-2020. 9. Guan, W., Ni, Z., Hu, Y., et.al: Clinical characteristics of 2019 novel coronavirus infection in China. The New England Journal of Medicine. (2020). https://doi.org/10.1101/2020.02.06.20020974. 10. Han, C., Youn, C. and Jung, W.: Web-Based System for Advanced Heart Disease Identification Using Grid Computing Technology. 2008 21st IEEE International Symposium on Computer-Based Medical Systems. (2008). 11. Huang, C. and Wang, Y., et.al: Clinical Features of Patients Infected with 2019 Novel Coronavirus in Wuhan, China. (2020). [online] Available at: https://doi.org/10.1016/ S0140-6736(20)30183-5. Accessed 27 May 2020. 12. Indian Council of Medical Research, New Delhi (2020). [online] Icmr.gov.in. Available at: https://www.icmr.gov.in/. Accessed 27 May 2020. 13. Landman, A., Feetham, L. and Stuckey, D., (2020). Cancer patients in SARS-CoV-2 infection: a nationwide analysis in China. The Lancet Oncology, [online] 21(3). Available at: https://doi.org/10.1016/ S1470-2045(20)30096-6. Accessed 27 May 2020. 14. Li, B., Yang, J., Zhao, F. et al: Prevalence and impact of cardiovascular metabolic diseases on COVID-19 in China. Clinical Research in Cardiology, 109, 531–538. (2020). https://doi.org/10.1007/s00392-020-01626-9. 15. Liu K., fang Y.Y., et.al: Clinical characteristics of novel coronavirus cases in tertiary hospitals in Hubei Province. Chinese Medical Journal. (2020). https://doi.org/10.1097/CM9.0000000000000744. 16. Meng J., Guohui X., et.al: Renin-angiotensin system inhibitors improve the clinical outcomes of COVID 19 patients with hypertension. Emerging Microbes and Infections, 9(1), 757-760. (2020). 17. Nasser I.M., Al-Shawwa M.O, Abu-Naser S.S: Artificial Neural Network for Diagnose Autism Spectrum Disorder. International Journal of Academic Information Systems Research (IJAISR), 3(2), 27-32. (2019). 18. National Foundation for Infectious Diseases. 2020. Coronaviruses. [online] Available at: https://www.nfid.org/infectious- diseases/coronaviruses/. Accessed 27 May 2020. 19. Park S.C., Tan J., et.al: Computer-aided detection of early interstitial lung diseases using low-dose CT images. Physics in Medicine and Biology, 56(4), 1139–1153. (2011). 20. Shah D., Dixit R., Shah A., Shah P., Shah M.: A Comprehensive Analysis Regarding Several Breakthroughs Based on Computer Intelligence Targeting Various syndromes. Augmented Human Research, 5(14), (2020). https://doi.org/10.1007/s41133-020-00033-z. 21. Singh. A. K., Gupta. R., Misra. A.: Co-morbidities in Covid-19: Outcomes in hypertensive cohort and controversies with angiotensin system blockers. Diabetes & Metabolic Syndrome: Clinical Research & Review, 14(4), 283-287. (2020). https://doi.org/10.1016/j.dsx.2020.03.016 22. Sirois S., Wei D.Q., Du Q., Chou K.C.: Virtual Screening for SARS-CoV Protease Based on KZ7088 Pharmacophore points. J.Chem.Inf.Comput.Sci, 44, 1111-1122. (2004). https://doi.org/10.1021/ci034270n. 23. South. A. M., Diz. D. I., Chappell. M. C.: COVID-19, ACE2, and the cardiovascular consequences. American journal of Physiology, 318(5). H1084-H1090. (2020). https://doi.org/10.1152/ajpheart.00217.2020. 24. Wang D., Hu B., et.al: Clinical Characteristics of 138 Hospitalized patients With 2019 Novel Coronavirus-Infected Pneumonia in Wuhan, China. JAMA, 323(11). (2020). 1061-1069. doi:10.1001/jama.2020.1585. 25. Zhang P., Zhu L., et.al: Association of Inpatients Use of Angiotensin Converting Enzyme Inhibitors and Angiotensin II Receptor Blockers with Mortality among Patients with Hypertension Hospitalized with COVID-19. Circulation Research. (2020). https://doi.org/10.1161/CIRCRESAHA.120.317134. 26. Zhu. N., Zhang. D., Wang. W., et.al: A Novel Coronavirus from Patients with Pneumonia in China, 2019. The New England Journal of Medicine, (2020). DOI: 10.1056/NEJMoa2001017. Authors: Ajijur Rahman, Shanowaj Choudury
Paper Title: Abstraction, the Big Idea, and it’s Significance in Science and Technology Education Abstract: this paper discusses the role of abstraction in science and technology education. It starts with a humble introduction of abstraction in general, while discussing the first few encounters of a learner with this idea. Significance of abstraction and the required motivation level of learner are also discussed. An expected change in the attitude of a learner at transition to higher studies is proposed. Thereafter the contribution of abstraction in the evolution of Computer Science and Engineering is discussed in some detail. Moreover a deduction of the Computer Science Curriculum is also shown along the same line as its evolution. Finally the paper concludes with emphasizing the importance of understanding links between different layers of 9. abstractions.
Keywords: abstraction, computer science, learner, link 51-54
References: 1. B. P. Lathi, ―Signal Processing and Linear System,‖ 2nd Ed, Oxford university Press. 2. H. S. Hall, S. R. Knight, ―Elementary Algebra for Schools‖, Cambridge University Press. 3. P. J. Nahin, ―An Imaginary Tale: The story of i,‖ Princeton University Press. 4. Harry R. Lewis, Christos H. Papadimitriou, ―Elements of the Theory of Computation‖, Second Edition, Prentice Hall. 5. Morris Mano, ―Digital Design‖, PHI. 6. R. C Gonzalez & Woods, ―Digital Image Processing‖, Prentice Hall. 7. Davis Martin, ―Engines of Logic-mathematicians and origin of computers‖. 8. Anant Agarwal, Jeffrey H. Lang, ―Foundation of Analog and Digital Electronics,‖ Elsevier. 9. H. S. Hornby, ―Oxford Advanced learner Dictionary‖. Authors: P. Ramakrishna, T. Vamshika, M. Swathi
Paper Title: Fpga Implementation of Memory Bists using Single Interface Abstract: The development of IC integration technologies leads to an extensive use of memories and buffers in different memory intensive applications. Therefore, probability of occurrence of fault in every single read and writes operation is increased in Memory BIST (MBIST). There were many testing approaches that were developed for efficient testing and diagnosis of fault. However, all algorithms are not strengthened enough to 10. detect all possible faults that may be present due to fabrication errors or environmental disturbance. Keeping this in mind and taking the possibility of development of efficient algorithm a hybrid memory testing algorithm is presented. To overcome those drawbacks, pipelining based MBIST designed to detect the all the types of 55-58 memory faults by utilizing March-C testing algorithm. By introducing the Pipelining approach, majorly path delays are reducing. The proposed architecture designed and verified using Xilinx ISE environment under various testing methods with respect to the different category of memories. The simulation and synthesis results shows that the proposed method shows the enhanced performance with the hardware resource utilization and delay consumption compared to the conventional approaches.
Keywords: DFT, MBIST, PRPG, RAM, SOC, VLSI
References: 1. Ogasahara, Yasuhiro, et al. "Implementation of pseudo-linear feedback shift register-based physical unclonable functions on silicon and sufficient Challenge–Response pair acquisition using Built-In Self-Test before shipping." Integration 71 (2020): 144-153. 2. LIANG, Huaguo, et al. "A novel BIST scheme for circuit aging measurement of aerospace chips." Chinese Journal of Aeronautics 31.7 (2018): 1594-1601. 3. DeMara, R. F., N. Imran, and R. A. Ashraf. "Emerging Resilience Techniques for Embedded Devices." Rugged Embedded Systems: Computing in Harsh Environments, Elsevier Publishing. 4. Jamal, K., K. Manjunatha Chari, and P. Srihari. "Test pattern generation using thermometer code counter in TPC technique for BIST implementation." Microprocessors and Microsystems 71 (2019): 102890. 5. Kumar, Mahesh. "An Efficient Fault Detection of FPGA and Memory Using Built-in Self Test [BIST]." American Journal of Electrical and Computer Engineering 3.1 (2019): 38-45. 6. Harutyunyan, Gurgen, Samvel Shoukourian, and Yervant Zorian. "Fault Awareness for Memory BIST Architecture Shaped by Multidimensional Prediction Mechanism." IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 38.3 (2018): 562-575. 7. Lee, Kuen-Jong, Bo-Ren Chen, and Michael Andreas Kochte. "On-chip self-test methodology with all deterministic compressed test patterns recorded in scan chains." IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 38.2 (2018): 309- 321. 8. Lee, Sanghoon, et al. "A built-in self-test andin situanalog circuit optimization platform." IEEE Transactions on Circuits and Systems I: Regular Papers 65.10 (2018): 3445-3458. 9. Moghaddam, Elham, et al. "Logic BIST with capture-per-clock hybrid test points." IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 38.6 (2018): 1028-1041. 10. Efanov, Dmitry V., et al. "Synthesis of Built-in Self-Test Control Circuits Based on the Method of Boolean Complement to Constant- Weight 1-out-of-n Codes." Automatic Control and Computer Sciences 53.6 (2019): 481-491. 11. Balaji, G. Naveen, and S. Chenthur Pandian. "Design of test pattern generator (TPG) by an optimized low power design for testability (DFT) for scan BIST circuits using transmission gates." Cluster Computing 22.6 (2019): 15231-15244. 12. Moghaddam, Elham, et al. "Logic BIST with capture-per-clock hybrid test points." IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 38.6 (2018): 1028-1041. 13. Cui, Xiaole, et al. "Design and Test of the In-Array Build-In Self-Test Scheme for the Embedded RRAM Array." IEEE Journal of the Electron Devices Society 7 (2019): 1007-1012. 14. Silveira, Reinaldo, Qadeer Qureshi, and Rodrigo Zeli. "Flexible architecture of memory BISTs." 2018 IEE 19th Latin-American Test Symposium. IEEE, 2018. Authors: V. V. Mandhare, D. R Pede, P. S. Vikhe
Paper Title: Network Intrusion Detection using a Deep Learning Approach Abstract: At present situation network communication is at high risk for external and internal attacks due to large number of applications in various fields. The network traffic can be monitored to determine abnormality for software or hardware security mechanism in the network using Intrusion Detection System (IDS). As attackers always change their techniques of attack and find alternative attack methods, IDS must also evolve in response by adopting more sophisticated methods of detection .The huge growth in the data and the significant advances in computer hardware technologies resulted in the new studies existence in the deep learning field, including ID. Deep Learning (DL) is a subgroup of Machine Learning (ML) which is hinged on data description. The new model based on deep learning is presented in this research work to activate operation of IDS from modern networks. Model depicts combination of deep learning and machine learning, having capacity of wide range accurate analysis of traffic network. The new approach proposes non-symmetric deep auto encoder (NDAE) for learning the features in unsupervised manner. Furthermore, classification model is constructed using stacked NDAEs for classification. The performance is evaluated using a network intrusion detection analysis dataset, particularly the WSN Trace dataset. The contribution work is to implement advanced deep learning algorithm consists IDS use, which are efficient in taking instant measures in order to stop or minimize the malicious actions.
11. Keywords: Intrusion Detection System (IDS), Non- Symmetric Deep Auto-Encoder (NDAE), Deep Learning (DL), WSN Trace, Machine Learning (ML). 59-64 References: 1. R. Bace and P. Mell, ―NIST special publication on intrusion detection systems,‖ BOOZ-ALLEN AND HAMILTON INC MCLEAN VA, 2001. 2. A. Lazarevic, V. Kumar, and J. Srivastava, ―Intrusion detection: A survey,‖ in Managing Cyber Threats, Springer, 2005, pp. 19–78. 3. W. Stallings, ―Cryptography and network security principles and practices,‖ USA: Prentice Hall, 2006. 4. M. H. Aghdam and P. Kabiri, ―Feature Selection for Intrusion Detection System Using Ant Colony Optimization,‖ IJ Netw. Secur, vol. 18, no. 3, pp. 420–432, 2016. 5. C.-F. Tsai, Y.-F. Hsu, C.-Y. Lin, and W.-Y. Lin, ―Intrusion detection by machine learning: A review,‖ Expert Syst. Appl., vol. 36, no. 10, pp. 11994–12000, 2009. 6. B. Dong and X. Wang, ―Comparison deep learning method to traditional methods using for network intrusion detection,‖ in Proc. 8th IEEE Int. Conf. Commun. Softw. Netw. Beijing, China, Jun. 2016, pp. 581–585. 7. R. Zhao, R. Yan, Z. Chen, K. Mao, P. Wang, and R. X. Gao, ―Deep learning and its applications to machine health monitoring: A survey,‖ Submitted to IEEE Trans. Neural Netw. Learn. Syst., 2016. [Online]. Available: http://arxiv.org/abs/1612.07640. 8. S. Pouyanfar et al., ―A Survey on Deep Learning: Algorithms, Techniques, and Applications,‖ ACM Comput. Surv. vol. 51, no. 5, p. 92, 2018. 9. Y. LeCun, Y. Bengio, and G. Hinton, ―Deep learning. nature 521 (7553): 436,‖ Google Sch., 2015. 10. N. Shone, T. N. Ngoc, V. D. Phai and Q. Shi, "A Deep Learning Approach to Network Intrusion Detection," in IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 2, no. 1, pp. 41-50, Feb. 2018. 11. F. Farahnakian and J. Heikkonen, ―A deep auto-encoder based approach for intrusion detection system,‖ in Advanced Communication Technology (ICACT), 2018 20th International Conference on, 2018, pp. 178–183. 12. N. Gao, L. Gao, Q. Gao, and H. Wang, ―An intrusion detection model based on deep belief networks,‖ in Advanced Cloud and Big Data (CBD), 2014 Second International Conference on, 2014, pp. 247– 252. 13. S. Seo, S. Park, and J. Kim, ―Improvement of Network Intrusion Detection Accuracy by Using Restricted Boltzmann Machine,‖ in Computational Intelligence and Communication Networks (CICN), 2016 8th International Conference on, 2016, pp. 413–417 14. M. A. Salama, H. F. Eid, R. A. Ramadan, A. Darwish, and A.E. Hassanien, ―Hybrid intelligent intrusion detection scheme,‖ in Soft computing in industrial applications, Springer, 2011, pp. 293–303. 15. Y. Li, R. Ma, and R. Jiao, ―A hybrid malicious code detection method based on deep learning,‖ methods, vol. 9, no. 5, 2015. 16. K. Alrawashdeh and C. Purdy, ―Toward an online anomaly intrusion detection system based on deep learning,‖ in Machine Learning and Applications (ICMLA), 2016 15th IEEE International Conference on, 2016, pp. 195–200. 17. J. Kim, J. Kim, H. L. T. Thu, and H. Kim, ―Long short term memory recurrent neural network classifier for intrusion detection,‖ in Platform Technology and Service (PlatCon), 2016 International Conference on, 2016, pp. 1–5. 18. C. Yin, Y. Zhu, J. Fei, and X. He, ―A deep learning approach for intrusion detection using recurrent neural networks,‖ IEEE Access, vol. 5, pp. 21954–21961, 2017. 19. S. Althubiti, W. Nick, J. Mason, X. Yuan, and A. Esterline, ―Applying Long Short-Term Memory Recurrent Neural Network for Intrusion Detection,‖ in SoutheastCon 2018, 2018, pp. 1–5. 20. T. A. Tang, S. Ali, R. Zaidi, D. Mclernon, L. Mhamdi, and M. Ghogho, ―Deep Recurrent Neural Network for Intrusion Detection in SDN-based Networks,‖ in 2018 4th IEEE Conference on Network Softwarization and Workshops (NetSoft), 2018, pp. 25–29. 21. Y. Yao, Y. Wei, F. Gao, and G. Yu, ―Anomaly intrusion detection approach using hybrid MLP/CNN neural network,‖ in Intelligent Systems Design and Applications, 2006. ISDA‘06. Sixth International Conference on, 2006, vol. 2, pp. 1095–1102. 22. K. Wu, Z. Chen, and W. Li, ―A Novel Intrusion Detection Model for a Massive Network Using Convolutional Neural Networks,‖ IEEE Access, vol. 6, pp. 50850–50859, 2018. 23. J. Kim, N. Shin, S. Y. Jo, and S. H. Kim, ―Method of intrusion detection using deep neural network,‖ in Big Data and Smart Computing (BigComp), 2017 IEEE International Conference on, 2017, pp. 313–316. 24. T. A. Tang, L. Mhamdi, D. McLernon, S. A. R. Zaidi, and M. Ghogho, ―Deep learning approach for network intrusion detection in software defined networking,‖ in Wireless Networks and Mobile Communications (WINCOM), 2016 International Conference on, 2016, pp. 258–263. 25. Revathi, S & Malathi, A. (2013). A Detailed Analysis on NSL-KDD Dataset Using Various Machine Learning Techniques for Intrusion Detection. International Journal of Engineering Research & Technology (IJERT). 2. 1848-1853. 26. Claude Turner*, Rolston Jeremiah, Dwight Richards, Anthony Joseph, ―A Rule Status Monitoring Algorithm for Rule-Based Intrusion Detection and Detection Systems‖, Procedia Computer Science, ISSN: 1877-0509, Vol: 95, Page: 361-368, 2016 Authors: Anjani K. Upadhyay, Debasmita Chatterjee, Madhuri Swain, Lopamudra Ray Evaluation of a Potential Antibacterial, Produced by Streptomyces Cinereoruber Sp. Isolated from Paper Title: Chlika lake. Abstract: Streptomyces, isolated from marine and estuarine habitat have been widely recognized as a potential source of antifungal, anti-tumour, anti-bacterial compounds. In the present study, the antimicrobial agent production potential of a Streptomyces cinereoruber sp was evaluated. The selective isolation of the strain was carried out on starch casein agar. The primary screening of the Streptomyces isolate was done by cross streak method against pathogenic test strains Escherichia.coli MTCC 82, Staphylococcus aureus MTCC 96, Bacillus cereus IP406 and Salmonella typhi MTCC 734 and Micrococcus leuteus and the antimicrobial property against Micrococcus leuteus was confirmed. The secondary screening was carried out by using the culture supernatant against the test strain by agar well diffusion method. The growth and antimicrobial production ability of the strain against Micrococcus leuteus was studied. The antimicrobial agent production was also observed till pH 11 and NaCl concentration 3% (w/v). The partially purified compound showed a peak similar to streptomycin in HPLC. The culture condition for the production of the compound was optimised.
Keywords: Streptomyces, Antibacterial, Optimization
References: 1. Andrews, J. M. 2001. Determination of minimum inhibitory concentrations. J. Antimicrob. Chemother. 48(Suppl. 1):5–16 2. Betina V.1983. The chemistry and biology of antibiotics. Amsterdam: Elsevier Scientific Pub. Co; p. 190. 3. Costanza R, Kemp WM, Boynton WR.1993. Predictability, scale, and biodiversity in coastal and estuarine ecosystems: implications for management. Ambio.1:88-96. 12. 4. de lima procopio, R. E; da silva, I. R; Martins, M.K; de azevedo, J.L & de Araujo, J. M. (2012) Antibitics produced by Streptomyces. Braz J Infect Dis. 16: 466-471. 5. Goodfellow, M. (1989). The Actinomycetes I. Supragenericclassifcation of actinomycetes. In Bergey's Manual of SystematicBacteriology, vol. 4, pp. 2333±2339. Edited by S. T. Williams, M. E. Sharpe & J. G. Holt. Baltimore: Williams &Wilkins. 65-69 6. Goodfellow, M., Kumar, Y., Labeda, D. P. & Sembiring, L. 2007. The Streptomyces violaceusniger clade: a home for streptomycetes with rugose ornamented spores. Antonie Van Leeuwenhoek 92,173-99. 7. Gordon, R. E., Barnett, D. A., Handerhan, J. E. and Pang, C. H.N. 1974. Nocardia coeliaca, Nocardia autotrophica, and the nocardin strain. Int J Bacteriol 24, 54-63. 8. Holder, I.A., and Boyce, S. T. 1994. Agar well diffusion assay testing of bacteriak susceptibilitu to various antimicrobials in concentration non-toxic for human cell in culture. Burns, 20 426-429. 9. Jayapal KP, Lian W, Glod F, Sherman DH, Hu WS. 2007. Comparative genomic hybridizations reveal absence of large Streptomyces coelicolor genomic islands in Streptomyces lividans. BMC Genomics, 8:229. 10. Kagan IA, Flythe MD. 2014. Thin-layer chromatographic (TLC) separations and bioassays of plant extracts to identify antimicrobial compounds. JoVE. (85). 11. Kampfer, P. & Labeda, D. P. 2006. International Committee on Systematics of Prokaryotes; Subcommittee on the taxonomy of the Streptomycetaceae: Minutes of the meeting, 25 July 2005, San Francisco, CA, USA. Int J Syst Evol Microbiol 56, 495. 12. Kelly, K.I. 1964. Inter-society color council-national bureau of standard color-name charts illustrated with centroid colors. US Government Printing Office, Washington. 13. Shirling, E. B. & Gottlieb, D. 1966. Methods for characterization of Str8eptomyces species. Int J Syst Bacteriol 16, 313–340. 14. Srinivas TN, Kumar PA, Sucharitha K, Sasikala C, Ramana CV.2009. Allochromatium phaeobacterium sp. nov. Int J Syst Evol Microbiol. 59:750-3. 15. Sucharita K, Sasikala C, Park SC, Baik KS, Seong CN, Ramana CV.2009. Shewanella chilikensis sp. nov., a moderately alkaliphilic gammaproteobacterium isolated from a lagoon. International journal of systematic and evolutionary microbiology.59:3111-5. 16. Taddi, A; Rodriguez, H. J.; Marquez-Vilchez; Castelli, C. 2006. Isolation and identification of streptomyces sp. From venezuelan site: Morphological and biochemical studies. Microbiological Research 161:222-231. 17. Tindall, B. J., Sikorski, J., Smibert, R. M. & Kreig, N. R. 2007. Phenotypic characterization and the principles of comparative systematics. In Methods for General and Molecular Microbiology, 3rd edn, pp. 330–393. Edited by C. A. Reddy, T. J. Beveridge, J. A. Breznak, G. A. Marzluf, T. M. Schmidt & L. R. Snyder. Washington, DC: American Society for Microbiology. 18. Williams, S.T., Goodfellow, M., Alderson, G., Wellington, E. M. H., Sneath, P. H. A., Sackin, M. J. 1983. Numerical classification of Streptomyces and related genera. J. Gen Microbiol 129, 1743-1813. 19. Williams, S.T., Goodfellow, M., & Alderson, G. 1989. Genus Streptomyces Waksman and Henrici 1943. 339 AL. In Bergey’s manual of Sytematic Bacteriology, vol. 4, pp. 2452-2492. Edited by S.T. Williams, M. E. Sharpe & J. G. Holt. Baltimore: Williama &Wikins. Authors: P.Pitchaipandi, C.Baskaran Use of Web 2.0 Social Networking Sites for Collaborative Sharing Research Information by the Paper Title: Social Science Research Scholars at Alagappa University, Karaikudi. Abstract: This study attempts to the Web 2.0 Social Networking Sites for Collaborative Sharing Research Information by the Social Science Research Scholars at Alagappa University, Karaikudi. A sample size 97 Scholars was selected by random sampling method. The data required for the study were collected through a questionnaire. The findings of the study: 30.9% of the respondents using Facebook/ WhatsApp along with most highly used in the popular web browser used for Google chrome 72.2% Google chrome. 48.5% of respondents‘ preference of ―Very Strongly Agree‖ Collaborate with Research projects and Teams. Whereas 46.4% ―Research Collaboration ―Strongly agree‖ of the respondents respectively. 30.9% purpose of Web 2.0 for Collaborations of Research Communication while 19.6% Opportunities and Learning for Web 2.0 tools support social interaction in the learning process of the respondents respectively.
Keywords: Web 2.0 tools, Collaborative learning, Sharing Research Information, Web 2.0 Opportunities, Blog/Wiki articles. 13. References: 1. Seema Sood, S. K. (2015). Web 2.0 Technology, Social Online Networking And Its Implications For Academic Libraries. International 70-74 Journal of Digital Library Services , 5 (1), 38-47. 2. Gopal, K. (2001). Intellectual freedom in Digital Libraries , Scholarly Books, 1-287. 3. 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. 4. 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. 5. 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. 6. 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. 7. 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. 8. Tautkevičienė, G., & Dubosas, M. (2014). The Purposes of Students‘ Use of Web 2.0 Tools for Learning at the University. Journal of Emerging Trends in Computing and Information Sciences, 5(12). 9. Williams, J. B., & Jacobs, J. (2004). Exploring the use of blogs as learning spaces in the higher education sector. Australasian journal of educational technology, 20(2). 10. Chang, A. (2011). Web 2.0 Social Network Sites And Facebook Marketing. Binus Business Review, 2(2), 708-717. Authors: Pankaj Agarwal, Sapna Yadav, Juhi Chaudhary
Paper Title: How India and its Neighbors are doing during Covid-19 Pandemics- a Critical Analysis Abstract: The prime objective of this work is to understand how India & its neighbors are doing during the ongoing period of Covid-19 pandemics. We have used the web crawlers to find specific data of India from official website www.mohfw.gov.in. We also referred to a dataset of global cases from Gitub for our work. We have analyzed the covid19 cases from 22/1/2020 till 1/5/2020. We applied a time series prediction model to forecast the possible deaths for next five days. We have taken into account six of our neighbors excluding China to understand how India is doing in comparison to our neighbors. We observed that considering the size of India 14. population India has done fairly well. However the number of increasing cases in India particularly in the month of May needs a serious call from Indian Govt. We have presented the outcomes of our work through different kinds of comparisons & analysis. He have presented the prediction of next ten days for India & its neighbors for 75-79 the duration 4/5/2020 to 13/5/2020
Keywords: Covid-19, SARIMA model, Prediction Analysis, Time Series, Indian Neighbors
References: 1. www.mohfw.gov.in 2. World Health Organization (WHO): https://www.who.int/ 3. Johns Hopkins CSSE: https://github.com/CSSEGISandData/COVID-19 4. WorldoMeters: https://www.worldometers.info/coronavirus/ Authors: Carlos I. Poclin Meza, Kevin L. Monteza Corrales, Lenis R. Wong Portillo
Paper Title: Techniques for Malignant Melanoma Diagnosis: A Systematic Literature Review Abstract: Malignant melanoma is the deadliest type of skin cancer. If melanoma detection and diagnosis is performed in its early stages, the probabilities of recovery and survival are higher. Dermoscopy is a manual 15. method which is applied by doctors to diagnose this disease, but it strongly depends on the experience of the specialist who performs this skin assessment. Although, many proposals have been made for automated 80-86 detection and diagnosis of malignant melanoma based on images processing, there are still improvement opportunities for melanoma diagnosis. This paper aims to identify the current status of the latest researches related to techniques for malignant melanoma diagnosis based on images analysis, considering the three research questions that have been elaborated for the systematic literature review: Q1) Which are the latest methods for malignant melanoma detection? Q2) Which systems for malignant melanoma diagnosis have been implemented in the last 5 years? And Q3) Which CAD systems for malignant melanoma detection have been developed? Furthermore, a cross-analysis of the outcome was performed. The results propose the implementation of systems using Inception V3 and the classifier Support Vector Machine, which achieved high accuracies in malignant melanoma diagnosis based on images processing.
Keywords: CAD Systems for Melanoma Diagnosis, CNN for Melanoma Detection, Dermoscopic Images Processing, Melanoma Detection, Support Vector Machine.
References: 1. I. Márquez Rodas, J. A. Avilés, E. Mercader, J. L. Escat, V. Parra, and A. Álvarez. 2020. ―Melanoma‖. In Sociedad Española de Oncología Médica. Available: https://seom.org/info-sobre-el-cancer/melanoma?showall=1. 2. N. E. Herrera Gonzáles and A. Y. Aco Flores, ―El melanoma en México‖, Revista de Especialidades Médico-Quirúrgicas, vol. 15, no. 3, pp. 161-164, julio–septiembre 2010. 3. World Health Organization (WHO). 2005. ―La Organización Mundial de la Salud desaconseja el uso de camas solares a las personas menores de 18 años‖. Available: https://www.who.int/mediacentre/news/notes/2005/np07/es/. 4. L. Wong, D. Mauricio, and G. Rodriguez. ―A Systematic Literature Review About Software Requirements Elicitation‖. In Journal of Engineering Science and Technology (JESTEC), pp. 296 – 317, Feb. 2017. 5. B. A. Kitchenham and S. Charters. ―Guidelines for performing systematic literature reviews in software engineering version 2.3‖. Retrieved January 9, 2014. Available: http://www.elsevier.com/__data/promis_misc/ 525444systematicreviewsguide.pdf. 6. L. Rosado, M. João, M. Vasconcelos, and M. Ferreira. 2015. ―Pigmented skin lesion computerized analysis via mobile devices‖. In Proceedings of the 31st Spring Conference on Computer Graphics (SCCG ‘15). Association for Computing Machinery, New York, NY, USA, 105–108. DOI: https://doi.org/10.1145/2788539.2788553. 7. A. Sáez, J. Sánchez-Monedero, P. A. Gutiérrez, and C. Hervás-Martínez, "Machine Learning Methods for Binary and Multiclass Classification of Melanoma Thickness From Dermoscopic Images," in IEEE Transactions on Medical Imaging, vol. 35, no. 4, pp. 1036-1045, April 2016, doi: 10.1109/TMI.2015.2506270. 8. Z. Waheed, A. Waheed, M. Zafar, and F. Riaz, "An efficient machine learning approach for the detection of melanoma using dermoscopic images," 2017 International Conference on Communication, Computing and Digital Systems (C-CODE), Islamabad, 2017, pp. 316-319, doi: 10.1109/C-CODE.2017.7918949. 9. A. A. A. Al-abayechi, H. A. Jalab, and R. W. Ibrahim. 2017. ―A classification of skin lesion using fractional poisson for texture feature extraction‖. In Proceedings of the Second International Conference on Internet of things, Data and Cloud Computing (ICC ‘17). Association for Computing Machinery, New York, NY, USA, Article 136, 1–7. DOI: https://doi.org/10.1145/3018896.3036379. 10. L. Yu, H. Chen, Q. Dou, J. Qin, and P. Heng, "Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks," in IEEE Transactions on Medical Imaging, vol. 36, no. 4, pp. 994-1004, April 2017, doi: 10.1109/TMI.2016.2642839. 11. A. H. Shahin, A. Kamal, and M. A. Elattar, "Deep Ensemble Learning for Skin Lesion Classification from Dermoscopic Images," 2018 9th Cairo International Biomedical Engineering Conference (CIBEC), Cairo, Egypt, 2018, pp. 150-153, doi: 10.1109/CIBEC.2018.8641815. 12. L. B. Maia, A. Lima, R. M. Pinheiro Pereira, G. B. Junior, J. Dallyson Sousa de Almeida, and A. C. de Paiva, "Evaluation of Melanoma Diagnosis using Deep Features," 2018 25th International Conference on Systems, Signals and Image Processing (IWSSIP), Maribor, 2018, pp. 1-4, doi: 10.1109/IWSSIP.2018.8439373. 13. A. Gupta, M. Bhatnagar, A. Issac, M. K. Dutta, and C. M. Travieso. 2019. ―Imaging method for noise removal and segmentation of skin lesions from dermoscopic images‖. In Proceedings of the 2nd International Conference on Applications of Intelligent Systems (APPIS ‘19). Association for Computing Machinery, New York, NY, USA, Article 16, 1–5. DOI: https://doi.org/10.1145/3309772.3309788. 14. J. Jaworek-Korjakowska, P. Kleczek, and M. Gorgon, "Melanoma Thickness Prediction Based on Convolutional Neural Network With VGG-19 Model Transfer Learning," 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Long Beach, CA, USA, 2019, pp. 2748-2756, doi: 10.1109/CVPRW.2019.00333. 15. R. Ali, R. C. Hardie, B. Narayanan Narayanan, and S. De Silva, "Deep Learning Ensemble Methods for Skin Lesion Analysis towards Melanoma Detection," 2019 IEEE National Aerospace and Electronics Conference (NAECON), Dayton, OH, USA, 2019, pp. 311-316, doi: 10.1109/NAECON46414.2019.9058245. 16. O. Abuzaghleh, B. D. Barkana, and M. Faezipour, "Noninvasive Real-Time Automated Skin Lesion Analysis System for Melanoma Early Detection and Prevention," in IEEE Journal of Translational Engineering in Health and Medicine, vol. 3, pp. 1-12, 2015, Art no. 4300212, doi: 10.1109/JTEHM.2015.2419612. 17. N. C. F. Codella, Q.-B. Nguyen, S. Pankanti, D. A. Gutman, B. Helba, A. C. Halpern, and J. R. Smith. 2017. ―Deep learning ensembles for melanoma recognition in dermoscopy images‖. IBM J. Res. Dev. 61, 4–5 (July/September 2017), 5:1–5:15. DOI: https://doi.org/10.1147/JRD.2017.2708299. 18. Y. Hasija, N. Garg, and S. Sourav, "Automated detection of dermatological disorders through image-processing and machine learning," 2017 International Conference on Intelligent Sustainable Systems (ICISS), Palladam, 2017, pp. 1047-1051, doi: 10.1109/ISS1.2017.8389340. 19. S. Mustafa and A. Kimura, "A SVM-based diagnosis of melanoma using only useful image features," 2018 International Workshop on Advanced Image Technology (IWAIT), Chiang Mai, 2018, pp. 1-4, doi: 10.1109/IWAIT.2018.8369646. 20. S. M. Alizadeh and A. Mahloojifar, "A Mobile Application for Early Detection of Melanoma by Image Processing Algorithms," 2018 25th National and 3rd International Iranian Conference on Biomedical Engineering (ICBME), Qom, Iran, 2018, pp. 1-5, doi: 10.1109/ICBME.2018.8703575. 21. R. Moussa, F. Gerges, C. Salem, R. Akiki, O. Falou, and D. Azar, "Computer-aided detection of Melanoma using geometric features," 2016 3rd Middle East Conference on Biomedical Engineering (MECBME), Beirut, 2016, pp. 125-128, doi: 10.1109/MECBME.2016.7745423. 22. Y. Ge, B. Li, Y. Zhao, E. Guan, and W. Yan. 2018. ―Melanoma Segmentation and Classification in Clinical Images Using Deep Learning‖. In Proceedings of the 2018 10th International Conference on Machine Learning and Computing (ICMLC 2018). Association for Computing Machinery, New York, NY, USA, 252–256. DOI: https://doi.org/10.1145/3195106.3195164. 23. N. Hameed, A. Shabut, and M. A. Hossain, "A Computer-aided diagnosis system for classifying prominent skin lesions using machine learning," 2018 10th Computer Science and Electronic Engineering (CEEC), Colchester, United Kingdom, 2018, pp. 186-191, doi: 10.1109/CEEC.2018.8674183. 24. Z. Yu et al., "Melanoma Recognition in Dermoscopy Images via Aggregated Deep Convolutional Features," in IEEE Transactions on Biomedical Engineering, vol. 66, no. 4, pp. 1006-1016, April 2019, doi: 10.1109/TBME.2018.2866166. 25. L. Rosado and M. J. Vasconcelos. ―Automatic segmentation methodology for dermatological images acquired via mobile devices‖, in 8th International Conference on Health Informatics, 2015, pp. 246-250. 26. M. J. M. Vasconcelos, L. Rosado, and M. Ferreira. ―Border abruptness assessment methodology for skin lesion image analysis‖, in 20th Edition of the Portuguese Conference on Pattern Recognition, 2014. 27. M. J. M. Vasconcelos, L. Rosado, and M. Ferreira. ―Principal axes-based asymmetry assessment methodology for skin lesion image analysis‖, in Advances in Visual Computing-Springer, 2014, pp. 21-31. 28. M. J. M. Vasconcelos, L. Rosado, and M. Ferreira. ―A new color assessment methodology using cluster-based features for skin lesion analysis‖, in 38th International ICT Convention MIPRO, 2015. 29. M. A. Hall and G. Holmes. ―Benchmarking attribute selection techniques for discrete class data mining‖, Knowledge and Data Engineering IEEE Transactions on 15, 2003, pp. 1437-1447. 30. H. Liu and L. Yu. ―Toward integrating feature selection algorithms for classification and clustering‖, Knowledge and Data Engineering IEEE Transactions on 17, 2005, pp. 491-502. 31. R. B. Oliveira, A. S. Pereira, and J. M. R. S. Tavares. 2018. ―Computational diagnosis of skin lesions from dermoscopic images using combined features‖. Neural Computing and Applications, 8. 32. N. Otsu, ―Threshold selection method from gray-level histograms.‖ IEEE Trans Syst Man Cybern, vol. SMC-9, no. 1, pp. 62–66, 1979. 33. M. Celebi, H. Kingravi, B. Uddin, H. Iyatomi, Y. Aslandogan, W. Stoecker, and R. Moss, ―A methodological approach to the classification of dermoscopy images,‖ Computerized Medical Imaging and Graphics, vol. 31, no. 6, pp. 362–373, 2007. 34. A. Sáez, C. Serrano, and B. Acha, ―Model-based classification methods of global patterns in dermoscopic images,‖ IEEE Transactions on Medical Imaging, vol. 33, no. 5, pp. 1137–1147, 2014. 35. T. Ojala, M. Pietikinen, and T. Menp, ―Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,‖ IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 971–987, 2002. 36. C. Hervás-Martínez and F. Martínez-Estudillo, ―Logistic regression using covariates obtained by product-unit neural network models,‖ Pattern Recognition, vol. 40, no. 1, pp. 52–64, 2007. 37. C. Hervás-Martínez, F. J. Martínez-Estudillo, and M. Carbonero-Ruz, ―Multilogistic regression by means of evolutionary product-unit neural networks,‖ Neural Networks, vol. 21, no. 7, pp. 951–961, 2008. 38. A. A. Abbas and W.-H. Tan. 2013. ―An improved Automatic Segmentation Skin Lesion from Dermoscopic Images Using Optimal RGB Channel‖. In Conference on Computer Science & Computational Mathematics (ICCSCM 2013), 39. 39. A. A. A. Al-Abayechi, R. Logeswaran, X. Guo, and W.-H. Tan. 2013. ―Lesion border detection in dermoscopy images using bilateral filter‖. In Signal and Image Processing Applications (ICSIPA), 2013 IEEE International Conference on IEEE, 365-368. 40. K. Sikka, N. Sinha, P. K. Singh, and A. K. Mishra. 2009. ―A fully automated algorithm under modified FCM framework for improved brain MR image segmentation‖. Magnetic Resonance Imaging 27, 7, 994-1004. 41. N. Otsu. 1975. ―A threshold selection method from gray-level histograms‖. Automatica 11, 285-296, 23-27. 42. N. Laskin. 2003. ―Fractional poisson process‖. Communications in Nonlinear Science and Numerical Simulation 8, 3, 201-213. 43. N. Razmjooy, B. S. Mousavi, F. Soleymani, and M. H. Khotbesara. 2013. ―A computer-aided diagnosis system for malignant melanomas‖. Neural Computing and Applications 23, 7-8, 2059-2071. 44. T. G. Dietterich. 2000. ―Ensemble methods in machine learning‖. In International workshop on multiple classifier systems Springer, 1- 15. 45. J. M. D. C. Viana. 2009. ―Classification of skin tumours through the analysis of unconstrained images‖. 46. P. Li, Gennady. Samorodnitsk, and John. Hopcroft, "Sign cauchy projections and chi-square kernel," presented at the Proc. Adv. Neural Inform. Process. Syst., 2013. 47. K. He, X. Zhang, S. Ren, and J. Sun, ―Deep Residual Learning for Image Recognition,‖ in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770–778. 48. A. Vedaldi and A. Zisserman, "Efficient Additive Kernels via Explicit Feature Maps". In IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, pp. 480-492, 2012. 49. M. Faraki, M. Harandi, A. Wiliem, and B. Lovell, "Fisher tensors for classifying human epithelial cells". In Pattern Recognit., vol. 47, pp. 2348-2359, 2014. 50. C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, ―Rethinking the Inception Architecture for Computer Vision‖. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 2818–2826 51. B. Lei, W. Li, Y. Yao, X. Jiang, E. Tan, J. Qin, et al., "Multi-modal and multi-layout discriminative learning for placental maturity staging," in Pattern Recognit., ed, 2016. 52. K. Simonyan and A. Zisserman, ―Very deep convolutional networks for large-scale image recognition‖, 2014. 53. C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, ―Going deeper with convolutions‖, in Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 1–9. 54. B. Efron, ―The efficiency of logistic regression compared to normal discriminant analysis,‖ Journal of the American Statistical Association, vol. 70, no. 352, pp. 892–898, 1975. 55. M. A. Hearst, S. T. Dumais, E. Osuna, J. Platt, and B. Scholkopf, ―Support vector machines,‖ IEEE Intelligent Systems and their applications, vol. 13, no. 4, pp. 18–28, 1998. 56. H. Zhang, ―The optimality of naive bayes,‖ AA, vol. 1, no. 2, p. 3, 2004. 57. Y. Freund and R. E. Schapire, ―A decision-theoretic generalization of on-line learning and an application to boosting,‖ Journal of computer and system sciences, vol. 55, no. 1, pp. 119–139, 1997. 58. T. K. Ho, ―Random decision forests,‖ in Document analysis and recognition, 1995., proceedings of the third international conference on, vol. 1. IEEE, 1995, pp. 278–282. 59. N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. Philip Kegelmeyer. ―Smote: Synthetic minority oversampling technique‖. J. Artif. Intell. Res., 16:321–357, 2002. 60. G. Huang, Z. Liu, L. van der Maaten, and K. Q. Weinberger. ―Densely connected convolutional networks‖. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2261–2269, 2017. 61. G. D. Finlayson, and E. Trezzi. 2004. ―Shades of gray and colour constancy‖. In Color and Imaging Conference (Vol. 2004, No. 1, pp. 37-41). Society for Imaging Science and Technology. 62. D. Whiteman and A. Green, ―Melanoma and sunburn‖, Cancer Causes Control, vol. 5, no. 6, pp. 564-572, 1994. 63. C. Cortes and V. Vapnik, ―Support-vector networks‖, Mach. Learn., vol. 20, no. 3, pp. 273-297, 1995. 64. G. H. John and P. Langley, "Estimating continuous distributions in Bayesian classifiers," in Proceedings of the Eleventh conference on Uncertainty in artificial intelligence, 1995, pp. 338-345: Morgan Kaufmann Publishers Inc. 65. J. Long, E. Shelhamer, and T. Darrell, ―Fully convolutional networks for semantic segmentation‖, 2015. [Online]. Available: https://arxiv.org/abs/1411.4038. 66. A. Krizhevsky, I. Sutskever, and G. E. Hinton, ―ImageNet classification with deep convolutional neural networks,‖ in Proc. Adv. Neural Inf. Process. Syst., 2012, pp. 1106–1114. [Online]. Available: https://papers.nips.cc/paper/4824-imagenetclassification-with- deep-convolutional-neural-networks.pdf. 67. O. Ronneberger, P. Fischer, and T. Brox, ―U-net: Convolutional networks for biomedical image segmentation,‖ in Medical Image Computing and Computer-Assisted Intervention (MICCAI) (Lecture Notes in Computer Science, vol. 9351). New York, NY, USA: Springer, 2015, pp. 234–241. [Online]. Available: http://arxiv.org/abs/1505.04597. 68. R. Carsten, K. Vladimir, and B. Andrew, "Grabcut: Interactive foreground extraction using iterated graph cuts.," ACM Transactions on Graphics (SIGGRAPH), vol. 23, no. 3, pp. 309-314, 2004. 69. American Academy of Dermatology, "What to look for: ABCDEs of melanoma," October 2017. [Online]. Available: https:/Iwww.aad.org/public/spot-skin-cancer/learn-about-skin-cancer/detect/what-to-look-for. 70. C. Hsu and C. Chang, ―A practical guide to support vector classification‖, Department of Computer Science, National Taiwan University, Tech. Rep., 2003. 71. S. Jain, N. Pise, and others, ―Computer aided Melanoma skin cancer detection using Image Processing,‖ Procedia Comput. Sci., vol. 48, pp. 735–740, 2015.
16. Authors: Isaac Ñuflo, Franco Mecca, Lenis Wong Machine Learning and Deep Learning Techniques, Features and Obstacles in the Cataract Paper Title: Diagnosis Abstract: Cataract is a degenerative condition that, according to estimations, will rise globally. Even though there are various proposals about its diagnosis, there are remaining problems to be solved. This paper aims to identify the current situation of the recent investigations on cataract diagnosis using a framework to conduct the literature review with the intention of answering the following research questions: RQ1) Which are the existing methods for cataract diagnosis? RQ2) Which are the features considered for the diagnosis of cataracts? RQ3) Which is the existing classification when diagnosing cataracts? RQ4) And Which obstacles arise when diagnosing cataracts? Additionally, a cross-analysis of the results was made. The results showed that new research is required in: (1) the classification of ―congenital cataract‖ and, (2) portable solutions, which are necessary to make cataract diagnoses easily and at a low cost.
Keywords: Cataract Diagnosis, Image Processing, Ophthalmology, Machine Learning Techniques, Deep Learning Techniques.
References: 1. J. Jamison, cataracts, clinical guide to nutrition & dietary supplements in disease management, 2003, pp. 259-264. 2. W. Sodeman and t. Sodeman, cataracts: patient and caregiver‘s guide, instructions for geriatic patients, 2005, pp. 70. 3. R. Abel, cataracts, integrative medicine, vol. 4, 2018, pp. 830-837. 4. L. Wong, d. Mauricio and g. Rodriguez, a systematic literature review about software requirements elicitation, journal of engineering science and technology, v. 12, n. 2, 2017, pp. 296-317. 5. B.a. Kitchenham and s. Charters, guidelines for performing systematic literature reviews in software engineering version 2.3, 2007 6. M. Kugamourthy and o. Goonetileke, ―eophthalmologist -- intelligent eye disease diagnosis system‖, 5th conference on intelligent systems, modelling and simulation, 2014, pp. 339-344. 7. J. Zheng, l. Guo and l. Peng, ―fundus image based cataract classification‖, ieee international conference on imaging systems and techniques (ist) proceedings, 2014, pp. 1-6. 8. X. Gao, s. Lin and t. Y. Wong, ―automatic feature learning to grade nuclear cataracts based on deep learning‖, ieee transactions on biomedical engineering, vol. 62, 2015, pp. 2693-2701. 9. S. Patange and a. Jagadale, ―framework for detection of cataract and gradation according to its severity‖, international conference on pervasive computing (icpc), 2015, pp. 1-3. 10. M. Kaur, j. Kaur and r. Kaur, ―low cost cataract detection system using smart phone‖, international conference on green computing and internet of things (icgciot), 2015, pp. 1607-1609. 11. Y. N. Fuadah, a. W. Setiawan, t. L. R. Mengko and budiman, ―mobile cataract detection using optimal combination of statistical texture analysis‖, 4th international conference on instrumentation, communications, information technology, and biomedical engineering (icici-bme), 2015, pp. 232-236. 12. L. Guo, j. Yang, l. Peng, j. Li and q. Liang, ―a computer-aided healthcare system for cataract classification and grading based on fundus image analysis‖, computers in industry, vol. 69, 2015, pp. 72-80. 13. V. Harini and v. Bhanumathi, ―automatic cataract classification system‖, international conference on communication and signal processing (iccsp), 2016, pp. 0815-0819. 14. D. Patil, a. Nair, n. Bhat, r. Chavan and d. Jadhav, ―analysis and study of cataract detection techniques‖, international conference on 87-93 global trends in signal processing, information computing and communication (icgtspicc), 2016, pp. 516-519. 15. i. Shaheen and m. U. Akram, ―an integrated framework for clinical grading of cataract‖, 1st international conference on next generation computing applications (nextcomp), 2017, pp. 92-97. 16. Z. Qiao, q. Zhang, y. Dong and j. Yang, ―application of svm based on genetic algorithm in classification of cataract fundus images‖, ieee international conference on imaging systems and techniques (ist), 2017, pp. 1-5. 17. J. Rana and s. M. Galib, ―cataract detection using smartphone‖, 3rd international conference on electrical information and communication technology (eict), 2017, pp. 1-4. 18. J. Li, q. Hu, a. Imran, l. Zhang, j. Yang and q. Wang, ―vessel recognition of retinal fundus images based on fully convolutional network‖, ieee 42nd annual computer software and applications conference (compsac), 2018, pp. 413-418. 19. J. Cheng, ―sparse range-constrained learning and its application for medical image grading‖, ieee transactions on medical imaging, vol. 37, 2018, pp. 2729-2738. 20. A. Dixit, s. Pathak, r. Raj, c. Naveen and v. R. Satpute, ―an efficient fuzzy based edge estimation for iris localization and pupil detection in human eye for automated cataract detection system‖, international conference on computing, communication and networking technologies (icccnt), 2018, pp. 1-6. 21. Y. Xiong, z. He, k. Niu, h. Zhang and h. Song, ―automatic cataract classification based on multi-feature fusion and svm‖, ieee 4th international conference on computer and communications (iccc), 2018, pp. 1557-1561. 22. T. Pratap and p. Kokil, ―computer-aided diagnosis of cataract using deep transfer learning, biomedical signal processing and control‖, vol. 53, 2019. 23. W. Song, y. Cao, x. Qiao, q. Wang and j. Yang, ―an improved semi-supervised learning method on cataract fundus image classification‖, ieee 43rd annual computer software and applications conference (compsac), 2019, pp. 362-367. 24. H. I. Morales lopez, j. C. Sanchez garcia and j. A. Diaz mendez, ―cataract detection techniques: a review‖, ieee latin america transactions, vol. 14, 2016, pp. 3074-3079. 25. H. Lin, r. Li, z. Liu, j. Chen, y. Yang, h. Chen, z. Lin, w. Lai, e, long, x. Wu, d. Lin, y. Zhu, c. Chen, d. Wu, t. Yu, q. Cao, x. Li, j. Li, w. Li, j. Wang, m. Yang, h. Hu, l. Zhang, y. Yu, x. Chen, j. Hu, k. Zhu, s. Jian, y. Huang, g. Tan, j. Huang, x. Lin, x. Zhang, l. Luo , x. Liu, b. Cheng, d. Zheng, m. Wu, w. Chen and y. Liu, ―diagnostic efficacy and therapeutic decision-making capacity of an artificial intelligence platform for childhood cataracts‖, eye clinics: a multicentre randomized controlled trial, vol. 9, 2019, pp. 1-8. 26. M. Yusuf, s. Theophilous, j. Adejoke, a. Hassan, ―web-based cataract detection system using deep convolutional neural network‖, ieee 2nd international conference of the ieee nigeria computer chapter (nigeriacomputconf), 2019, pp. 1-7. 27. R. Sigit, m. Kom, m. Bayu, d. Kurnia. And s. Si, ―classification of cataract slit-lamp image based on machine learning‖, international seminar on application for technology of information and communication, 2018, pp 1-6. 28. J. Erichsen, a. Mensah and l. Kessel, ―non-invasive tryptophan fluorescence measurements as a novel method of grading cataract‖, experimental eye research, vol. 165, 2017, pp. 1-6. 29. Y. Zhou, g. Li and h. Li, ―automatic cataract classification using deep neural network with discrete state transition‖, ieee transactions on medical imaging, vol. 39, 2019, pp. 1-11. 30. J. Li, x. Xu and y. Guan, ―automatic cataract diagnosis by image-based interpretability‖, ieee international conference on systems, man, and cybernetics, 2018, pp. 1-6. 31. H. Zhang and he, ―automatic cataract grading methods based on deep learning‖, computer methods and programs in biomedicine, vol. 182, 2019, pp. 1-19. 32. C. Palomo and m. Puell, ―capacity of straylight and disk halo size to diagnose cataract‖, journal of cataract & refractive surgery, vol. 41, 2015, pp. 2069-2074 33. J. Yang, j. Li, r. Shen and y. Zeng, ―exploiting ensemble learning for automatic cataract detection and grading‖, computer methods and programs in biomedicine, vol. 124, 2016, pp. 1-24. 34. P. Chen, p. Tsai, c. Lee and c. Chang, ―congenital cataracts diagnosed by prenatal ultrasound‖, taiwanese journal of obstetrics and gynecology, vol. 54, 2015, pp. 1-2. Authors: Ahmad Farhad Farahmand, Bhartesh, Sayed Shuaib Qammer
Paper Title: Displacement Analysis of RC Frames and Its Seismic Performance Appraisal Abstract: In India when structure engineer‘s analysis and design a structure like buildings, they are checking it for displacement because of safety and control of damages; so in this paper a set of frames with different height of reinforced moment resisting frames were analyzed by two popular methods of performance-based plastic design method and direct displacement-based design method. For calculation of base shear, the IS code has been used in both methods and ETABS software used for seismic performance evaluation by nonlinear static pushover analysis. The results of analysis with different methods compared by suitable parameters and graphs, such as: (a) story lateral force, (b) beam seismic moment, (c) displacement profile and (d) capacity curve. Results shows acceptable performance in 2 methods in terms of capacity and deformation.
Keywords: Performance-based plastic design, displacement profile, Seismic performance, Direct displacement- based design, beam seismic moment, capacity curve.
References: 1. Subhash C. Goel and Shih-Ho Chao, (2008), Performance based plastic design: Earthquake Resistant Steel Structure, ICC publication, USA. 2. Shibata A, sozen m. (1976), ―Substitute structure method for seismic design in reinforced concrete ―. J struct Div, ASCE, Vol .102, No. 1, PP 1-18. 3. Moechle JP. (1996), ―displacement-based seismic design criteria‖, In: proceedings of 11th world conference on Earthquake Engineering, Acapulco, Mexico. Paper no. 2125. Oxford: pergamon. 4. SEAOC. (Vision 200), ―performance based seismic design of buildings‖, vol. I and II: conceptual framework Sacramento (CA): Structure Engineering Association of California. 5. ATC 40(1996), ―Seismic evaluation and retrofit of existing concrete buildings‖, Redwood city (CA): Applied Technology Council. 6. FEMA 273 (1996),‖ NEHRP guidelines for the seismic rehabilitation building‖ FEMA 274 commentary. Washington (DC): Federal Emergency management Agency. 7. R> O> Hamburger, C. Rojahn and J. A. Heintz , and m. G. Mahoney, (2012) ― FEMA p58: Next-Generation Building Seismic performance Assessment Methodology‖, WCEE. 8. GULKAN, P. and Sozen, M.A, (1974). Inelastic response of reinforced concrete structures to earthquake motions. ACI journal. 71(12): p. 604-610 17. 9. Shibata, A. and Sozen, M.A, (1976). Substitute-structure method for seismic design in R/C journal structure division. 102(1): P. 1-18 10. Moehli, J.P, (1992). Displacement-based design of building structures subjected to earthquakes. Earthquake spectra. 8(3): P 11. Kowalsky, M.J, Preistly, M.J.N, and MacRae, G.A, (1995). Displacement-Based Design of RC Bridge columns in seismic Rejoins. 94-101 Earthquake Engineering and Structural Dynamics. 24(12): P. 1623-1643. 12. Calvi, G.M. and kingsly, G.R, (1995). Displacement-based seismic Design of Multi-Degree-of-freedom, Bridge Structurea. Earthquake Engineering and Structure Dynamics. 24(9): p. 1247-1266. 13. Chopra, A.K. and Goel, R.K, (2001). Direct Displacement-based Design: Use of Inelastic Design Spectra Versus Elastic Design Spectra. Earthquake Spectra. EERI 17(1): p. 47-64. 14. Prestley M.J.N., Calvi, M.C., and Kowalsky, M.J. 2007., Direct Displacement-Based Seismic Design of concrete building. Bulletin of the New Zealand Society for Earthquake Engineering. 33(4), 421-444. 15. Sullivan, T.J., Priestley, M.J.N., and Calvi, G.M., (2006). Direct Displacement-Based design of Frame-Wall Structures. Journal of Earthquake Engineering. 10(sup001): p. 91-124. 16. Priestley, M.J.N., and Calvi, M.C., and Kowalsy, (2007). Direct Displacement-Based design of Structures, NZSEE Conference. 17. Sullivan, T.J. and Lago, A., (2012). Towards a simplified Direct DBD Procedure for the seismic design of moment resisting frames with viscous dampers. Engineering Structures, 35: P. 140-148. 18. Malekpour, S., GHaffarzadeh, H., and Dashti, F., (2013). Direct displacement-based design of steel-braced reinforced concrete frames. Structural Design Tall and Special Buildings. 22(18): P. 1422-1438. 19. Chao, S.-H, Goel, S. C., and Lee, S.-S. (2007)., ―A seismic design of lateral force distribution based on inelastic state of structures,‖ Earthquake Spectra, Earthquake Engineering Research Institute, Vol. 23, NO. 3, pp. 547-569. 20. Leelataviwat, S. 1999 ―Drift and Yield Mechanism based Seismic Design and Upgrading of Steel Moment Frames.‖ Ph.D. Thesis, Department of Civ. & Env. Engrg., University Michigan, Ann Arbor, MI, USA, 21. Lee, Soon-Sik and Goel, S. C. 2001, ―Performance-Based Design of Steel Moment Frames Using Target Drift and Yield Mechanism‖ Report No. UMCEE 01-17, Department of Civ, & Env. Engrg., University of Michigan, Ann Arbor, MI, USA. 22. Goel, S. C. and Chao, S.-H. (2009)., Performance-Based Plastic Design: Earthquake-resistant Steel Structures, ICC, USA. 23. Wen-Cheng Liaol and Subbash C. Goel. (2012)., ―Performance-Based Plastic Design and Evaluation of Seismic Resistant RC Moment Frame‖, Journal of Marine Science and Technology. 24. Priestley, M.J.N., and Calvi, G.M., and Kowalsy, M.J., ―Displacement-Based design of Structures‖, IUSS Press, Press, pavia, Italy, 2007. 25. Alok Madan, Arshad K. Hashmi., ―Performance based design of masonry infilled reinforced concrete frames for near-field earthquakes Using energy methods‖ International Journal of Civil, Environmental, Structural, construction and Architectural Engineering Vol:8, No:6, 2014. 26. IS 1893 (part 1): 2002, ―Criteria for Earthquake Resistant Design of structures‖, Part-1 General Provisions and Buildings (Fifth Revision), Bureau of Indian Standard, New Delhi. 27. IS 456:2000, ―Plain and Reinforced code of Practice (fourth Revision)‖, bureau of Indian standard, New Delhi. 28. IS 13920:1993, ―Ductile Detailing of Reinforced Concrete Structures Subjected to Seismic Forces‖, bureau of Indian standard, New Delhi. Authors: Anshu Parashar, Anand Kumar Pandey, Ritesh Kumar Rai
Paper Title: Placement of PV Units Considering Uncertainties of Generation and Load in Distribution Systems 18. Abstract: In conventional power system the transmission and distribution (T&D) losses is a major concern. Renewable energy resources placed at load centers can reduce the T&D losses. For power system planners and 102-106 researchers it is essential to find the optimal size and position of renewable energy resources to be place in distribution networks. Renewable energy source such as solar energy is abundantly present in the environment. With the help of solar photovoltaic (SPV) system solar energy can be converted to electrical energy. Placement of SPV in distribution system is an interesting area for researchers and planners, the random placement of SPV in distribution system leads to more power losses and poor voltage profile. In this article mathematical modelling of time varying nature of SPV and variable load has been explained and particle swarm optimization (PSO) method is proposed to find the best size and location of the SPV system. This method is tested on IEEE 33 bus system. For the validation of result existing technique based on analytical expression is selected. It is found that PSO gives better result in compare to analytical method.
Keywords: Solar photovoltaic system, Multi-objective index, Time varying solar irradiance, Power system optimization, Particle swarm optimization.
References: 1. Duong Quoc Hung, N.M., and and K.Y. Lee, Determining PV penetration for Distribution systems with Time-Varying load Models. IEEE TRANSACTIONS ON POWER SYSTEMS, 2014. 29 NO. 6. 2. Niknam T, Taheri SI, Aghaei J, Tabatabaei S, Nayeripour M. A modified honey bee mating optimization algorithm for multiobjective placement of renewable energy resources. Appl Energy 2011;88(12):4817–30. 3. Taher N. A new HBMO algorithm for multiobjective daily Volt/Var control in distribution systems considering distributed generators. Appl Energy 2011;88(3):778–88. 4. Martinez-Rojas M, Sumper A, Gomis-Bellmunt O, Sudrià-Andreu A. Reactive power dispatch in wind farms using particle swarm optimization technique and feasible solutions search. Appl Energy 2011;88(12):4678–86. 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. 6. L. F. Ochoa, A. Padilha-Feltrin, and G. P. Harrison, ―Evaluating distributed generation impacts with a multiobjective index,‖ IEEE Trans Power Del., vol. 21, no. 3, pp. 1452–1458, Jul. 2006. 7. D. Singh and K. S. Verma, ―Multiobjective optimization for DG planning with load models,‖ IEEE Trans. Power Syst., vol. 24, no. 1, pp. 427–436, Jan. 2009. 8. A.M. El-Zonkoly, ―Optimal placement of multi-distributed generation units including different load models using particle swarm optimisation,‖ IETGener. Transm. Distrib., vol. 5, no. 7, pp. 760–771, Jul. 2011. 9. Bakos GC. Distributed power generation: a case study of small scale PV power plant in Greece. Appl Energy 2009; 86(9):1757–66. 10. Wang C, Nehrir MH. Analytical approaches for optimal placement of distributed generation sources in power systems. IEEE Trans Power Syst 2004;19(4):2068–76. Acharya N, Mahat P, Mithulananthan N. An analytical approach for DG allocation in primary distribution network. Int J Elect Power Energy Syst 2006;28(10):669–78. 11. Hung DQ, Mithulananthan N, Bansal RC. Analytical expressions for DG allocation in primary distribution networks. IEEE Trans Energy Convers 2010;25(3):814–20. 12. Singh D, Verma KS. Multiobjective optimization for DG planning with load models. IEEE Trans Power Syst 2009;24(1):427–36. 13. Khatod DK, Pant V, Sharma J. Evolutionary programming based optimal placement of renewable distributed generators. 14. IEEE Trans Power Syst 2013;28(2):683–95. Ochoa LF, Harrison GP. Minimizing energy losses: optimal accommodation and smart operation of renewable distributed generation. IEEE Trans Power Syst 2011;26(1):198–205. 15. Hung DQ, Mithulananthan N, Bansal RC. Analytical strategies for renewable distributed generation integration considering energy loss minimization. Appl Energy 2013; 105:75–85. 16. 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. 17. C. T. Ioan, ―The particle swarm optimization algorithm: Convergence analysis and parameter selection,‖ Inf. Process. Lett. vol. 85, pp. 317–325, 2003. 18. Lee S-H, Park J-W. Selection of optimal location and size of multiple distributed generations by using kalman filter algorithm. Power Syst IEEE Trans 2009; 24:1393–400. 19. Singh D, Singh D, Verma K. GA based optimal sizing and placement of distributed generation for loss minimization. Int J Electr Comput Eng 2007; 2:556–62. 20. Nara K, Hayashi Y, Ikeda K, Ashizawa T. Application of tabu search to optimal placement of distributed generators. in: IEEE Power Engineering Society Winter Meeting, vol. 2; 2001, p. 918–923. Authors: P.Sivasankaran B.Radjaram, K.Karthigayan
Paper Title: Maximizing Machine Capacity by Improving Efficiency using Linear Programming Model Abstract: In the global manufacturing system, machine performance is considered to be one of the vital role in organization wellbeing. In specific analysing the capacity utilization of machines in each shift is a big challenging job in industrial organization. The primary importance is keeping the machines in uptime condition at the same time loading the jobs in machines decides the capacity usage of machines to do the useful jobs. In this paper focus is made on the capacity planning of machines in production shift. capacity utilization measures the actual capacity of machine with respect to the potential output within a specific period. In real situations if the demand for the product increases the production capacity also increases but at the same time if the demand falls capacity will also become very low. Hence in this work attempt has been made to develop a mathematical 19. model for machine capacity planning using linear programming model solved by using LINDO software.
Keywords: Capacity utilization,uptime,LINDO, Linear Programming 107-114
References: 1. Arabacı, Ö. ve Arabacı, R. (2008). Kapasite Kullanım Oranları ve Enflasyon İlişkisi:Türkiye Örneği. Uludağ Üniversitesi. İktisadi ve İdari Bilimler Fakültesi Dergisi. Cilt:27,Sayı:2, 93-109. 2. Bircan, H. ve Kartal, Z. (2003). Doğrusal Programlama Tekniği ile Kapasite PlanlamasıYaklaşımı ve Çimento İşletmesinde Bir Uygulaması. Cumhuriyet Üniversitesi. İktisadi veİdari Bilimler Dergisi. Cilt:5, Sayı:1, 131-149. 3. Bulut, Z.A. (2004). İşletmeler Açısından Kapasite Planlaması ve Kapasite PlanlamasınaEtki Eden Faktörler. Mevzuat Dergisi. Yıl:7, Sayı:80. Erişim Tarihi: 14 Aralık 2013,http://www.mevzuatdergisi.com/2004/08a/06.htm 4. Büyükkeklik, M. (2007). Üretim Planlama Problemlerinde Doğrusal Programlama 5. Modellerinin Kullanımı: Bir Üretim şletmesinde Uygulama. Yüksek Lisans Tezi, NÜ Sosyal 6. Bilimler Enstitüsü, Niğde. 7. Candes, E. J., & Tao, T. (2005). Decoding by linear programming. Information Theory,IEEE Transactions on, 51(12), 4203-4215. 8. Charnes, A., & Cooper, W. W. (1961). Management models and industrial applications oflinear programming [by] A. Charnes [and] WW Cooper (Vol. 1). John Wiley & Sons. 9. Dantzig, G. B. (1965). Linear programming and extensions. Princeton university press. 10. Ekodialog.com.(2009). Erişim Tarihi: 25 Aralık 2013. http://www.ekodialog.com/isletme_ekonomisi/isletme_kapasite_planlamasi_2.html 11. Ergülen, A., & Kazan, H. (2012). Taşımacılık Sektörünün İşleyiş Süreci, Bulanık DağıtımProbleminin Tamsayılı Doğrusal Programlama Model Denemesi. Uluslararası Yönetimİktisat ve İşletme Dergisi, 3(6), 109-126. 12. Esin, A. (1998). Yöneylem Araştırmasında Yararlanılan Kantitatif Yöntemler. Ankara:Gazi Üniversitesi. Yayın Nu:128, S.24-298. 13. Ferrier, G. D., & Lovell, C. K. (1990). Measuring cost efficiency in banking:econometric and linear programming evidence. Journal of econometrics, 46(1), 229-245. Authors: Jahidul Haque Chaudhuri, Rohan Deb, Jhinuk De
Paper Title: Innovative Way to Decrease the Water Consumption of Direct Evaporative Air-Cooler Abstract: In the present study the existing direct evaporative coolers (DEC) is modified in such a way that DEC consume less amount of water and provide better cooling effect. In desert area, water consumption by air cooler is a serious problem. Therefore, the present study addressed this issue and primary objective of the study is to minimize the consumption of water. For this purpose, the property of the endothermic reaction is utilized. There are few salts that produce endothermic reaction if it is diluted in water. Those salt crystals absorb heat from the surrounding environment (water) and ultimately the temperature of the overall solution gets reduced. This cold solution is then passed through honeycomb cooling pad, as a result more amount of air can be cooled using the same volume of water as compared to the traditional air-cooler. Ammonium Chloride (NH4Cl), Ammonium Nitrite (NH4NO3) salts satisfy the basic criteria for the endothermic reaction but NH4Cl will be more useful to use in the air-coolers, as Ammonium Nitrite is costlier and also hazardous. A salt water separator arrangement also attached with modified air-cooler which will help to regenerate Ammonium Chloride crystal from solution with the help of solar energy. In this study, firstly discussed about proposed design of an air-cooler system, which is able to nicely handle chemical solution. Then compared the study with experimental outcome which have been carried out with and without using salt. From the result it has been observed that modified design of air cooler has great potential to improve the traditional air cooler in terms of cooling effect and water consumption.
Keywords: direct evaporative cooler; NH4Cl; honeycomb cooling pad; modified air-cooler design.
20. References: 1. Bishoyi, D., & Sudhakar, K. (2017). Experimental performance of a direct evaporative cooler in composite climate of India. Energy and Buildings, 153, 190-200. 115-121 2. Kovačević, I., & Sourbron, M. (2017). The numerical model for direct evaporative cooler. Applied Thermal Engineering, 113, 8-19. 3. Franco, A., Valera, D. L., & Peña, A. (2014). Energy efficiency in greenhouse evaporative cooling techniques: cooling boxes versus cellulose pads. Energies, 7(3), 1427-1447. 4. Sellami, K., Feddaoui, M., Labsi, N., Najim, M., Oubella, M., & Benkahla, Y. K. (2019). Direct evaporative cooling performance of ambient air using a ceramic wet porous layer. Chemical Engineering Research and Design, 142, 225-236. 5. Heidarinejad, G., & Bozorgmehr, M. (2008). Heat and mass transfer modeling of two stage indirect/direct evaporative air coolers. ASHRAE journal Thailand. 6. Dai, Y. J., & Sumathy, K. (2002). Theoretical study on a cross-flow direct evaporative cooler using honeycomb paper as packing material. Applied thermal engineering, 22(13), 1417-1430. 7. Al-Badri, A. R., & Al-Waaly, A. A. (2017). The influence of chilled water on the performance of direct evaporative cooling. Energy and Buildings, 155, 143-150. 8. Dhamneya, A. K., Rajput, S. P. S., & Singh, A. (2018). Thermodynamic performance analysis of direct evaporative cooling system for increased heat and mass transfer area. Ain Shams Engineering Journal, 9(4), 2951-2960. 9. Guan, L., Bennett, M., & Bell, J. (2015). Evaluating the potential use of direct evaporative cooling in Australia. Energy and Buildings, 108, 185-194. 10. Kavaklioglu, K., Koseoglu, M. F., & Caliskan, O. (2018). Experimental investigation and radial basis function network modeling of direct evaporative cooling systems. International Journal of Heat and Mass Transfer, 126, 139-150. 11. Kim, M. H., & Jeong, J. W. (2013). Cooling performance of a 100% outdoor air system integrated with indirect and direct evaporative coolers. Energy, 52, 245-257. 12. Wu, J. M., Huang, X., & Zhang, H. (2009). Theoretical analysis on heat and mass transfer in a direct evaporative cooler. Applied Thermal Engineering, 29(5-6), 980-984. 13. Roux, A., Musbally, G. M., Perron, G., Desnoyers, J. E., Singh, P. P., Woolley, E. M., & Hepler, L. G. (1978). Apparent molal heat capacities and volumes of aqueous electrolytes at 25° C: NaClO3, NaClO4, NaNO3, NaBrO3, NaIO3, KClO3, KBrO3, KIO3, NH4NO3, NH4Cl, and NH4ClO4. Canadian Journal of Chemistry, 56(1), 24-28. 14. Kabeel, A. E., & Bassuoni, M. M. (2017). A simplified experimentally tested theoretical model to reduce water consumption of a direct evaporative cooler for dry climates. International Journal of Refrigeration, 82, 487-494. Authors: Mohan Rawat, R N Singh Thermal Performance of Composite Roof Structures with Insulating Layers in Non-Conditioned Paper Title: Buildings for Hot-Dry Climate Abstract: The roof configurations with an insulating layer and their impact on hourly floating temperature analyzed in a hot-dry climate context. A predefined computer program using a modified Fourier admittance 21. method utilized as the primary research. The thermal performance of ten composite roof structures evaluated to obtain optimal roof structure for hot-dry climate, Jodhpur. Nine composite roof structures with an insulation layer and one without insulation layer as the base case were analyzed for the summer months (April-September). 122-127 The utilization of roof thermal insulation showed a significant influence on the overall thermal performance of roofs. It also revealed that minimum temperature variation found about 8.8 0C for the composite roof structure of Reinforced Cement Concrete (RCC) with foam concrete insulation (i.e., RF-5) with thicknesses 150 mm and 140 mm respectively. The analysis assessed that composite roof structure with an insulating layer is a useful technique to reduced indoor temperature in non-conditioned buildings of hot-dry climate.
Keywords: Fourier admittance method, Heat Gain, Hot-dry climate, Thermal comfort, Simulation.
References: 1. Lee, S. W., Lim, C. H., Chan, S. A., & Von, K. L. ―Techno-economic evaluation of roof thermal insulation for a hypermarket in equatorial climate‖: Malaysia. Sustainable cities and society,2017, 35, 209-223. 2. Al-Homoud, M. S. ―The effectiveness of thermal insulation in different types of buildings in hot climates‖. Journal of Thermal Envelope and Building Science, 2004, 27(3), 235-247. 3. Schiavoni, S., Bianchi, F., & Asdrubali, F. ―Insulation materials for the building sector: A review and comparative analysis‖. Renewable and Sustainable Energy Reviews,2016, 62, 988-1011. 4. Yew, M. C., Sulong, N. R., Chong, W. T., Poh, S. C., Ang, B. C., & Tan, K. H. ―Integration of thermal insulation coating and moving- air-cavity in a cool roof system for attic temperature reduction‖. Energy Conversion and Management, 2013,75, 241-248. 5. Sansaniwal, S. K., Mathur, J., Garg, V., & Gupta, R. ―Review of studies on thermal comfort in Indian residential buildings‖. Science and Technology for the Built Environment, 2020 1-22. 6. Arumugam, R. S., Garg, V., Ram, V. V., & Bhatia, A. ―Optimizing roof insulation for roofs with high albedo coating and radiant barriers in India‖. Journal of Building Engineering, 2015, 2, 52-58. 7. Kaynakli, O. ―A review of the economical and optimum thermal insulation thickness for building applications‖. Renewable and Sustainable Energy Reviews, 2012, 16(1), 415-425. 8. Sodha, M. S., Kaur, B., Kumar, A., & Bansal, N. K.‖Comparison of the admittance and Fourier methods for predicting heating/cooling loads‖. Solar energy, 36(2), 1986, 125-128. 9. Bansal, N. K., Garg, S. N., & Kothari, S. ―Effect of exterior surface colour on the thermal performance of buildings‖. Building and environment, 1992, 27(1), 31-37. 10. Sodha, M. S., Singh, S. P., & Sawhney, R. L. ―Evaluation of discomfort in a room with desert cooler‖. International Journal of Energy Research, 1990, 14(7), 745-756. 11. Kumar, A., & Suman, B. M. ―Experimental evaluation of insulation materials for walls and roofs and their impact on indoor thermal comfort under composite climate‖. Building and Environment, 2013, 59, 635-643. 12. Yumrutaş, R., Kaşka, Ö.,& Yıldırım, E. ―Estimation of total equivalent temperature difference values for multilayer walls and flat roofs by using periodic solution‖. Building and Environment, 2007, 42(5), 1878-1885. 13. Shi, D., Zhuang, C., Lin, C., Zhao, X., Chen, D., Gao, Y., & Levinson, R. ―Effects of natural soiling and weathering on cool roof energy savings for dormitory buildings in Chinese cities with hot summers‖. Solar Energy Materials and Solar Cells,2019, 200, 110016 Authors: Alfonso Alexander Ruesta Sedano, Jeanette Giuliana Gamarra Herrera, Lenis Rossi Wong Portillo
Paper Title: Techniques for Images Processing, Factors and Results of Colposcopy to Diagnose Cervical Cancer Abstract: The colposcopy is a test that is performed if you have relationed symptoms with cancer or if the result of Pap smears test gives an abnormal cells; however, it has a continue problem because there are few doctors who know about colposcopy and it leads to misinterpretation. Therefore, in the last years various proposals have emerged to solve this problem. The present study aims to identify the current state of the latest research related to the detection of cervical cancer during the colposcopy test using the image evaluation. A framework is proposed based on 3 research questions: (1) What techniques are used for image processing with colposcopy to diagnose cervical cancer? (2) What are the factors that help diagnose cervical cancer during colposcopy? And (3) What results corroborate or provide the diagnosis produced by the colposcopy test in the detection of cervical cancer? One of the results proposes that the use of Convolution Neural Network (CNN) improves the sensitivity of the diagnosis of cervical cancer, since it achieved greater precision in colposcopy image processing. Furthermore, the diagnosis can be corroborated with the ―results‖ of the ―Biopsy‖ and ―Expert Judgment‖.
Keywords: Colposcopy, Colposcopy techniques, Colposcopy image, Convolutional Neuronal Network.
References: 1. National Cancer Institute, ―Cervical Cancer Treatment (PDQ®)–Patient Version‖ 27 september 2019. [Online]. Available: 22. https://www.cancer.gov/types/cervical/patient/cervical-treatment-pdq 2. American Cancer Society, ―Test for Cevical Cancer‖ 5 december 2016. [Online]. Available: https://www.cancer.org/cancer/cervical- cancer/detection-diagnosis-staging/how-diagnosed.html#written_by [Last access: october 2019]. 3. J. Ruiz, ―Pasado, presente y futuro de la colposcopia‖, Medigraphic, vol. 2, nº 2, april 2010. 128-133 4. E. Alvarez, ―Manual de la Clinica de Detección Temprana Colposcopia‖ 27 november 2010. [Online]. Available: https://www.paho.org/gut/index.php?option=com_docman&view=download&alias=225-manual-de-la-clinica-de-deteccion-temprana- colposcopia&category_slug=temas-de-salud&Itemid=518 5. International Agency for Research on Cancer, ―Cancer Today‖ 2019. [Online]. Available: https://gco.iarc.fr/today/online-analysis- multi- bars?v=2018&mode=cancer&mode_population=countries&population=900&populations=900&key=total&sex=2&cancer=39&type=0 &statistic=5&prevalence=0&population_group=0&ages_group%5B%5D=0&ages_group%5B%5D=17&nb_items=10&group_cancer= 1&include_nmsc=1&include_nmsc_other=1&type_multiple=%257B%2522inc%2522%253Atrue%252C%2522mort%2522%253Afals e%252C%2522prev%2522%253Afalse%257D&orientation=horizontal&type_sort=0&type_nb_items=%257B%2522top%2522%253 Atrue%252C%2522bottom%2522%253Afalse%257D&population_group_globocan_id= [Last access: october 2019]. 6. International Agency for Research on Cancer, ―Cancer Today‖ 2019. [Online]. Available: https://gco.iarc.fr/today/online-analysis- multi- bars?v=2018&mode=cancer&mode_population=countries&population=900&populations=604&key=total&sex=2&cancer=39&type=0 &statistic=5&prevalence=0&population_group=0&ages_group%5B%5D=0&ages_group%5B%5D=17&nb_items=1&group_cancer=1 &include_nmsc=1&include_nmsc_other=1&type_multiple=%257B%2522inc%2522%253Atrue%252C%2522mort%2522%253Afalse %252C%2522prev%2522%253Afalse%257D&orientation=horizontal&type_sort=0&type_nb_items=%257B%2522top%2522%253At rue%252C%2522bottom%2522%253Afalse%257D&population_group_globocan_id= [Last access: octubre 2019]. 7. L. Wong, D. Mauricio y G. Rodriguez, ―A systematic literature review about software requirements elicitation‖, Journal of Engineering Science and Technology, vol. 12, nº 2, february 2017. 8. S. Boonlikit, ―Performance of the abbreviated Reid colposcopic index in prediction of high-grade lesions‖, International Journal of Gynecology & Obstetrics, vol. 134, 2016. 9. S. Kushwah y B. Kushwah, ―Correlation of two colposcopic indices for predicting premalignant lesions of cervix‖, Journal of Mid-Life Health, vol. 8, 2017. 10. J. Louwers, A. Zaal, M. Kocken, J. Berkhof, E. Papagiannakis, P. Snijders, C. Meijer y R. Verheijen, ―The performance of dynamic spectral imaging colposcopy depends on indication for referrals‖, Gynecologic Oncology, vol. 139, 2015. 11. H. Aydoğmuş y S. Aydoğmuş, ―Comparison of colposcopic biopsy results of patients who have cytomorphological normal but HPV 16-18 or other high-risk HPV subtypes positive‖, Asian Pacific Journal of Cancer Prevention, vol. 20, 2019. 12. P. J. Coronado y M. Fasero, ―Colposcopy combined with dynamic spectral imaging. A prospective clinical study‖, European Journal of Obstetrics & Gynecology and Reproductive Biology, vol. 196, 2016. 13. A. Kaufmann, C. Founta, E. Papagiannakis, R. Naik y A. Fisher, ―Standardized digital colposcopy with dynamic spectral imaging for conservative patient management‖, Case Rep Obstet Gynecol, 2017. 14. M. T. Roensbo, A. Hammer y J. Blaakær, ―Can dynamic spectral imaging system colposcopy replace conventional colposcopy in the detection of high‐grade cervical lesions?‖, Acta Obstetricia et Gynecologica Scandinavica, vol. 94, 2015. 15. S. G. Parra, A. M. Rodriguez, K. D. Cherry, R. A. Schwarz, R. M. Gowen, L. B. Guerra, A. M. Milbourne, P. A. Toscano, S. P. Fisher- Hoch, K. M. Schemeler y R. R. Richards-Kortum, ―Low-cost, high-resolution imaging for detecting cervical precancer in medically- underserved areas of Texas‖, Gynecologic Oncology, vol. 154, 2019. 16. E. De Castro Hillmann, O. Moreira Bacha, M. Roy, G. Paris, D. Berbiche, V. Nizard y J. G. Lopes Ramos, ―Cervical digital photography: An alternative method to colposcopy‖ Gynecologic Oncology, vol. 14, nº 8, agosto 2019. 17. Y. Tanaka, Y. Ueda, A. Okazawa, M. Kakuda, S. Matsuzaki, E. Kobayashi, K. Yoshino y T. Kimura, ―'Smartscopy' as an alternative device for cervical cancer screening: a pilot study‖, BMJ Innov, vol. 3, 2017. 18. Y. Tanaka, Y. Ueda, R. Kakubare, M. Kakuda, S. Kubota, S. Matsuzaki, A. Okazawa, T. Egawa-Takata, S. Matsuzaki, E. Kobayashi y T. Kimura, ―Histologic correlation between smartphone and coloposcopic findings in patients with abnormal cervical cytology: experiences in a tertiary referral hospital‖, American Journal of Obstetrics and Gynecology, vol. 221, 2019. 19. C. Gallay, A. Girardet, M. Viviano, R. Catarino, A. C. Benski, P. L. Tran, C. Ecabert, J. P. Thiran, P. Vassilakos y P. Petignat, ―Cervical cancer screening in low-resource settings: a smartphone image application as an alternative to colposcopy‖, Int J Womens Health, vol. 9, 2017. 20. S. K. Saini, V. Bansal, R. Kaur y M. Juneja, ―ColpoNet for automated cervical cancer screening using colposcopy images‖ Machine Vision and Applications, vol. 31, nº 15, 25 march 2020. 21. C. Buiu, V.-R. Dânâilâ y C. N. Râdutâ, ―MobileNetV2 ensemble for cervical precancerous lesions classification‖ Processes, vol. 8, 16 may 2020. 22. Z. Yue, D. Shuai, Z. Weidong, W. Hao, M. Jie, Y. Zhang y Y. Zhang, ―Automatic CIN grades prediction of sequential cervigram image using LSTM with multistate CNN features‖ IEEE Journal of Biomedical and Health Informatics, vol. PP, pp. 1-1, 13 june 2019. 23. T. Zhang, Y.-m. Luo, P. Li, Y.-z. Du, P. Sun, B. Dong y H. Xue, ―Cervical precancerous lesions classification using pre-trained densely connected convolutional networks with colposcopy images‖ Biomedical Signal Processing and Control, vol. 55, january 2020. 24. M. A. Godfrey, M. Nikilopoulos, N. Povolotskaya, R. Chenoy y R. Wuntakal, ―Post-coital bleeding: What is the incidence of significant gynaecological pathology in women referred for colposcopy?‖, Sexual and Reproductive Healthcare, vol. 22, 2019. 25. A. T. Marujo, L. Correia, M. Brito, T. Paula y J. Borrego, ―ASC-H cytological result: clinical relevance and accuracy of colposcopy in predicting high-grade histological lesions—a 7-year experience of a single institution in Portugal‖, Journal of the American Society of Cytopathology, vol. 6, 2017. 26. X. Zhang, Y. Dou, M. Wang, Y. Li, F. Wang, X. Xie y X. Wang, ―A retrospective analysis on 1901 women with high grade cervical intraepithelial neoplasia by colposcopic biopsy‖, vol. 217, 2017. 27. V. J. M. Pop, T. Wouters, R. L. M. Bekkers, V. R. M. Spek y J. M. J. Piek, ―Development of the patient‘s experience and attitude colposcopy eindhoven 1uestionnaire (PEACE-q)‖, BMC Health Serv Res, vol. 19, 2019. 28. D. Beyer, A. Rody, C. Cirkel, N. Schmidt y K. Neumann, ―Mandatory colposcopic findings of severe cervical dysplasia. Are there key- signs that need our special attention?‖, Journal of Gynecology Obstetrics and Human Reproduction, vol. 46, 2017. 29. A. Ciavattini, M. Serri, J. Di Giuseppe, C. A. Liverani, M. G. Fallani, D. Tsiroglou, M. Papiccio, G. Delli Carpini, A. Pieralli, N. Clemente y F. Sopracordevole, ―Reliability of colposcopy during pregnancy‖, European Journal of Obsterics & Gynecology and Reproductive Biology, vol. 229, 2018. 30. I. Baasland, B. Haguen, C. Vogt, M. Valla y P. R. Romundstad, ―Colposcopy and additive diagnostic value of biopsies from colposcopy‐negative areas to detect cervical dysplasia‖, Acta Obstetricia et Gynecologica Scandinavica, vol. 95, 2016. 31. B. Abolafia-Cañe, J. Á. Monserrat-Jordán, J. Cuevas-Cruces y J. E. Arjona-Berral, ―Diagnóstico precoz del cáncer de cérvix: correlación entre citología, colposcopia y biopsia‖ Revista Española de Patología, vol. 51, nº 3, july - september 2018. Authors: Mohanapria M K, Rajambal K Switched-Capacitor based Quadruple Boost 9-Level Inverter Topology with Multicarrier PWM Paper Title: Technique Abstract: This paper presents Switched-Capacitor based Quadruple Boost 9-Level Inverter topology which possesses several advantages over conventional MLI types, SCMLI topologies. The self-voltage balancing capability of switched capacitors which reduces complexity in control is compared with existing SCMLI topology. The simulation study of the SCQB9LI topology is carried out. Switched capacitors are designed for self-voltage balancing nature. The MLS-PWM strategy is employed for generating gate pulses. The performance of the chosen inverter topology is investigated for different modulation indices and its results are presented. A comparative study with conventional SCMLI topologies proves the effectiveness of SCQB9LI topology.
Keywords: Multicarrier Level Shifted Pulse Width Modulation (MLS-PWM) technique, Quadruple Boost, Self-Voltage balance, Switched-Capacitor based Quadruple Boost 9-Level Inverter (SCQB9LI). 23. References: 1. L. Tolbert, F.-Z. Peng, and T. Habetler, ―Multilevel converters for large electric drives,‖ IEEE Trans. Ind. Applicat., vol. 35, pp. 36–44, 134-139 Jan./Feb. 1999. 2. J. S. Lai and F. Z. Peng, ―Multilevel converters–A new breed of power converters,‖ IEEE Trans. Ind. Applicat., vol. 32, pp. 509–517, May/June 1996. 3. Rodriguez, J.; Bernet, S.; Wu, B.; Pontt, J.O.; Kouro, S.; "Multilevel Voltage-Source-Converter Topologies for Industrial Medium- Voltage Drives," Industrial Electronics, IEEE Transactions on, vol.54, no.6, pp.2930-2945, Dec. 2007. 4. P. P. Rodriguez, M. M. D. Bellar, R. R. S. Munoz-Aguilar, S. S. Busquets- ˜ Monge, and F. F. Blaabjerg, ―Multilevel clamped multilevel converters (MLC),‖ IEEE Trans. Power Electron., vol. 27, no. 3, pp. 1055–1060, Mar. 2012. 5. K. Ilves, A. Antonopoulos, S. Norrga, and H.-.P. Nee, ―A new modulation method for the modular multilevel converter allowing fundamental switching frequency,‖ IEEE Trans. Power Electron., vol. 27, no. 8, pp. 3482–3494, Aug. 2012. 6. M. F. Kangarlu and E. Babaei, "A generalized cascaded multilevel inverter using the series connection of sub multilevel inverters," IEEE Trans. Power Electron., vol. 28, no. 2, pp. 625–636, Feb. 2013. 7. Y. Hinago and H. Koizumi, "A switched-capacitor inverter using series/parallel conversion with an inductive load," IEEE Trans. Ind. Electron., vol. 59, no. 2, pp. 878–887, 2012. 8. J. Liu, K. Cheng, and Y. Ye, "A cascaded multilevel inverter based on switched-capacitor for high-frequency ac power distribution system," IEEE Tran. Power Electron., vol. 29, no. 8, pp. 4219–4230, 2014. 9. J. Liu, W. Lin, J. Wu, and J. Zeng, ―A novel nine-level quadruple boost inverter with inductive-load ability,‖ IEEE Tran. Power Electron., pp. 1–1, 2018. 10. N. Sandeep, Jagabar Sathik Mohammed Ali, Udaykumar R. Yaragatti, and Krishnasamy Vijayakumar, "Switched-Capacitor Based Quadruple Boost Nine-Level Inverter", IEEE Trans. Power Electron., Volume: 34, Issue: 8, Aug. 2019. 11. Taghvaie, J. Adabi, and M. Rezanejad, ―A self-balanced step-up multilevel inverter based on switched-capacitor structure,‖ IEEE Trans. Power Electron., vol. 33, no. 1, pp. 199–209, 2018. 12. J. Zeng, J. Wu, J. Liu, and H. Guo, "A quasi-resonant switched-capacitor multilevel inverter with self-voltage balancing for single-phase high-frequency ac microgrids," IEEE Trans. Ind. Inform., vol. 13, no. 5, pp. 2669–2679, 2017. 13. Z. Zheng, K. Wang, L. Xu, and Y. Li, ―A Hybrid Cascaded Multilevel Converter for Battery Energy Management Applied in Electric Vehicles,‖ IEEE Trans. Power Electron., vol. 29, no. 7, pp. 3537–3546, 2014. 14. J. S. M. Ali and V. Kumar, ―Compact switched capacitor multilevel inverter (cscmli) with self voltage balancing and boosting ability,‖ IEEE Trans. Power Electron., pp. 1–1, 2018. 15. S. S. Lee, M. Sidorov, C. S. Lim, N. R. N. Idris, and Y. E. Heng, ―Hybrid cascaded multilevel inverter (hcmli) with improved symmetrical 4-level submodule,‖ IEEE Trans. Power Electron., vol. 33, no. 2, pp. 932–935,2018. Authors: Veena Malik, S. C. Dharmadhikari
Paper Title: Enriching E-Commerce Fraud Detection by using Machine Learning Abstract: As there has been a proliferation of the internet platform, it has been increasingly getting affordable for a lot of individuals. The rise has been instrumental in achieving several services including the E-commerce platform. This has led to an unprecedented increase in the amount of fraud that is being committed on this platform. The fraud that is being committed on the E-commerce platforms is very different from the frauds committed on other platforms online. Numerous researches have been performed to combat the evils of credit card frauds and money laundering rings. But there is a severe lack of research on the fraud that is committed on the E-commerce platform. Therefore, this research paper defines an innovative approach for the identification of fraud on E-commerce platforms through the implementation of machine learning approaches. The presented technique utilizes Linear Clustering, Entropy Estimation and Frequent itemset mining in addition to the inclusion of Artificial Neural Networks, Hypergraph formation and Fuzzy classification. The implementation of this system will give more security for E-commerce platform-based transactions by identifying fraudulent activities with better efficiency. The methodology has been tested extensively through rigorous experimentation to evaluate the performance metrics which yielded significantly positive results.
Keywords: Linear Clustering, Entropy Estimation, Frequent Itemset, Hyper graph, Artificial Neural Network, Fuzzy Classification.
References: 1. Elina Bumbiere, ―The basic of Ecommerce Fraud-What it is and how to manage,‖ [Online] Available:http//www.printful.com/blog/the- basic-of- ecommerce-fraud-what-is-it-and-how-to-manage-it/. 2. V. Malik and Dr. S. C. Dharmadhikari, ―Analysis of fraudulent Transaction Detection Techniques based on Customers Behavioural Patterns,‖ CIIT International Journal of Artificial Intelligent System and Machine Learning, Vol. 11, No. 12, pp. 219-224, December2019. 3. W.H. Ju and Y. Vardi, ―A hybrid high-order Markov chain model for computer intrusion detection,‖ Journal of Computational and Graphical Statistics, vol.10, pp.277-295, 2004. 4. T. Guo, and G. Li, ―Neural data mining for credit card fraud detection‖, International Conference on Machine Learning and 24. Cybernetics, pp.3630-3634, July 2008. 5. R. Chen, S. Luo, X. Liang and V.C.S. Lee, ―Personalized Approach Based on SVM and ANN for Detection Credit Card Fraud‖, International Conference on Neural Networks and brain, pp. 810-815, April 2006. 140-146 6. G. Mota, J. Fernandes and O. Belo, ―Usage signature and applications analysis an alternative method for preventing fraud in ecommerce applications,‖ Proc. IEEE International Conference on data science and Advance Analytics, pp.203-208, October 2014. 7. N. Kimoto and Y. Endo, ―On Linear Clustering with Constraints on Cluster Size,‖ Joint International Conference on Soft Computing and Intelligent Systems and International Symposium on Advanced Intelligent Systems, pp. 832-836, December 2018. 8. G. Ciuperca, V. Girardin and L. Lhote, ―Computation and Estimation of Generalized Entropy rates for Denumerable Markov Chains,‖ IEEE Transactions on Information theory vol. 57, July 2011. 9. T. Murata and H. Ishibuchi, ―Adjusting Membership Functions of Fuzzy Classification Rules by Genetics Algorithms,‖ Proc. of IEEE International Conference on Fuzzy Systems, 1995. 10. S. Surbhi and Sanjeev Kumar, ―Fraud Detection during Money Transaction and Prevention,‖ International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT 2019). 11. R. Rambola, P. Varshney and P. Vishwakarma, ―Data Mining Techniques for Fraud Detection in Banking Sector,‖ International Conference on Computing Communication and Automation (ICCCA 2018). 12. J. Kingston, ―Representing, Reasoning and Predicting Fraud using Fraud Plans,‖ International Conference on Research Challenges in Information Science (RCIS 2017). 13. O. Elrajubi, A. Elshawesh and M. Abuzaraida, ―Detection of Bypass Fraud based on Speaker Recognition‖, International Conference on Information Technology (ICIT 2017). 14. V. Mareeswari and G. Gunasekaran, ―Prevention of Credit Card Fraud Detection based on HSVM,‖ International Conference on Information Communication and Embedded System (ICICES 2016). 15. E. Tarmazakov and D. Silnov, ―Modern Approaches to Prevent Fraud in Mobile Communications Networks,‖ IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus 2018). 16. X. Min and R Lin, ―K-Means Algorithm: Fraud Detection Based on Signaling Data,‖ IEEE World Congress on Service, 2018. 17. S. Delecourt and Li Guo, ―Building a robust mobile payment fraud detection system with adversarial examples,‖ IEEE Second International Conference on Artificial Intelligence and Knowledge Engineering (AIKE 2019). 18. A. Kasgari, M. Taghavifard, and S. Kharazi ―Price manipulation fraud detection by Intelligent Visual Fraud surveillance system,‖ International Conference on Control, Decision and Information Technologies, 2019. 19. M. Zamini and G Montazer, ―Credit Card Fraud Detection using autoencoder based clustering,‖ International Symposium on Telecommunications, IST, 2018. 20. K. Yang, ―A Memory-Enhanced Framework for Financial Fraud Detection,‖ IEEE International Conference on Machine Learning and 21. Applications, 2018. 22. B. Omair and A. Alturki, ―Taxonomy of Fraud Detection Metrics for Business Processes,‖ IEEE Access, 2020. 23. N. Malini and M. Pushpa, ―Analysis on Credit Card Fraud Identification Techniques based on KNN and Outlier Detection,‖ International Conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics (AEEEICB 2017). 24. I. Benchaji, S. Douzi and B. Ouahidi, ―Using Genetic Algorithm to Improve Classification of Imbalanced Datasets for credit card fraud detection,‖ Cyber Security in Networking Conference (CSNet), 2018. 25. Y. Chen and C. Wu, ―On Big Data-based Fraud Detection Method for Financial Statements of Business Groups,‖ IIAI International Congress on Advanced Applied Informatics, 2017. 26. H. Weng, Z. Li, S. Ji, C. Chu, H. Lu, T. Du, and Q. He, ―Online E-Commerce Fraud:A Large-scale Detection and Analysis,‖ International Conference on Data Engineering, IEEE ,2018. Authors: Suspended Paper Title: Abstract: 25. Keywords: 147-152
References:
Authors: S.Rajeswari, S.A.Arunmozhi, Y.Venkataramani Q- Learning Algorithm with Network Coding in Multi-Path Transfer Protocol for Wireless Mesh Paper Title: Network Abstract: In a wireless mesh network, the network coding algorithm used to improve network efficiency. In this paper, we have implemented the Q-learning algorithm with CSMA/CA as in distributed co-ordination function along with multi-path transfer protocol (MPTP). The functioning of CSMA/CA is based on physical carrier sensing. Q-learning algorithm, along with network coding, is implemented to achieve better throughput. Our proposed method has used to reduce the packet loss and to minimize the end to end delay of the network communication. Also, it will improve the possibility of receiver buffer blocking.
Keywords: Network Coding, Q-Learning Algorithm, Mptp, Ieee802.11 Dcf.
References: 1. Amerimehr.M and Ashtiani.F, ―Delay and Throughput Analysis of a Two-Way Opportunistic Network Coding-Based Relay Network,‖ IEEE Transactions on Wireless Communications, vol. 13, no. 5, (2014), pp. 2863–2873 2. Ahlswede.R, Cai.N and Yeung.R, ―Network Information Flow,‖ IEEE Transactions on Information Theory, vol. 46, no. 4, (2014),pp. 1204–1216. 3. Ashtiani.F, Amerimehr.M.H and Iraj.M.B, ―An Analytical Approach for Throughput Evaluation of Wireless Network Coding,‖ in Proceedings of the IEEE International Conference on Communications (ICC‘09), (2009) pp. 1696–1700. 26. 4. Chiu.M Le.J and Lui.J , ―On the Performance Bounds of Practical Wireless Network Coding,‖ IEEE Transactions on Mobile Computing, vol. 9, no. 8, (2010), pp. 1134–1146 5. Denno.S, Morikura.M, Hirano.T, Sugiyama.T, and Umehara.D, ―Wireless Network Coding in Slotted ALOHA with Two-hop Unbalanced Traffic,‖ IEEE Journal on Selected Areas in Communications, vol. 27, no. 5 (2013), pp. 647–661. 153-157 6. Denno.S, Morikura.M, Sugiyama.T, and Umehara.D, ―Performance Analysis of Slotted ALOHA and Network Coding for Single- Relay Multi-User Wireless Networks,‖ Ad Hoc Networks, vol. 9, no. 2, (2011), pp. 164 – 179. 7. Ephremides.A and Sagduyu.Y, ―On Broadcast Stability Region in Random Access through Network Coding,‖ in Proceedings of the 44th Annual Allerton Conference on Communication, Control, and Computing, vol. 15, no. 1, (2006),pp. 298–313. 8. Fu.L, Lin.S, Wang.X and Xie.J, ―Hybrid Network Coding for Unbalanced Slotted ALOHA Relay Networks,‖ IEEE Transactions on Wireless Communications, vol. 15, no. 1, (2016), pp. 298–313. 9. Fu.L and Lin.S,―Unsaturated Throughput Analysis of Physical Layer Network Coding Based on IEEE 802.11 Distributed Coordination Function,‖ IEEE Transactions on Wireless Communications, vol. 12, no. 11, (2013),pp. 5544–5556. 10. Fragiadakis.C, Georgiadis.L, Paschos.G and Tassiulas.L, ―Wireless Network Coding with Partial Overhearing Information,‖ in Proceedings of IEEE INFOCOM, (2013), pp. 2337–2345. 11. Geordiadis.L and Xiang.Y, ―A New Wireless Multicast Queuing Design Using Network Coding and Data-Flow Model,‖ IEEE Communications Letters, vol. 20, no. 8, (2016), pp. 1603–1606. 12. Guo.S, Ji.H, Xiang.Y and Zeng.D, ―On the Throughput of Two Way Relay Networks Using Network Coding,‖ IEEE Transactions on Parallel and Distributed Systems, vol. 25, no. 1, (2014), pp. 191–199. 13. Jamali.V, Schober.R and Zlatanov.N, ―Bidirectional Buffer-Aided Relay Networks With Fixed Rate Transmission-Part II: Delay Constrained Case,‖ IEEE Transactions on Wireless Communications, vol. 14, no. 3, (2015), pp. 1339–1355. 14. Li.H and Moghadam.N, ―Queue Stability Analysis in Network Coded Wireless Multicast Network,‖ IEEE Communications Letters, vol. 20, no. 5, (2016), pp. 950–953. 15. Sagduyu.Y and Ephremides.A, ―Cross-Layer Optimization of MAC and Network Coding in Wireless Queueing Tandem Networks,‖ IEEE Transactions on Information Theory, vol. 54, no. 2, (2008), pp. 554–571 Authors: Elmer Diaz, Andres Ccopa, Lenis Wong Cervical Cancer: Machine Learning Techniques for Detection, Risk Factors and Prevention Paper Title: Measures Abstract: Cervical Cancer is considered the fourth most common female malignancy worldwide and represents a major global health challenge. As a result, in recent years, various proposals and researches have been conducted. This study aims to analyze the data presented in current researches regarding cervical cancer and 27. contribute to future research, all through the framework of literature review, based on 3 research questions: Q1: What are the risk factors that cause cervical cancer? Q2: What preventive measures are currently established for 158-163 cervical cancer? and, Q3: What are the techniques to detect cervical cancer? Findings show that detection techniques are complementary since they are categorized under machine learning. Therefore, we recommend that further study be promoted in these techniques as they are helpful in the detection process. In addition, risk factors can be considered for a greater scope in detection, such as HPV infection, since it is the most relevant factor for the development of cervical cancer. Finally, we suggest to conduct further research on preventive measures for cervical cancer.
Keywords: Cervical cancer, Cervical cancer diagnosis, Machine learning.
References: 1. Zur Hausen, H, (2002), ―Papillomaviruses and cancer: from basic studies to clinical application‖. Nature Reviews Cancer, 2(5), 342– 350. doi:10.1038/nrc798. 2. MINSA, (2016) ―Guía de práctica clínica para la prevención y manejo del cancer de cuello uterino‖. Available: ftp://ftp2.minsa.gob.pe/descargas/Prevencion_salud/guia_tecnica_cancer_cuello_utero.pdf 3. P. Cohen et al., ―Cervical Cancer‖ in The Lancent, vol. 393, no. 10167, pp. 169-182, Jan 2019, doi: 10.1016/S0140-6736(18)32470-X. 4. L. Wong, D. Mauricio and G. Rodriguez, ―A systematic literature review about software requirements elicitation‖ in Journal of Engineering Science and Technology, vol. 12, no 2, pp. 296-317, Feb. 2017. 5. B.A Kitchenham and S. Charters, ―Guidelines for performing systematic literature reviews in software engineering version 2.3‖, 2014, from http://www.elsevier.com/__data/promis_misc/ 525444sy stematicreviewsguide.pdf 6. T. Stewart, J. Moodley and F. Walter, ―Population risk factors for late-stage presentation of cervical cancer in sub-Saharan Africa‖, in Cancer Epidemiology, vol. 53, pp. 81-92, 2018, doi: 10.17863/CAM.17958. 7. M. Fani et al., ―Correlation of human papillomavirus 16 and 18 with cervical cancer and their diagnosis methods in Iranian women: A systematic review and meta-analysis‖ Current Problems in Cancer, vol. 44, no. 1, 2019, doi: 10.1016/j.currproblcancer.2019.06.008. 8. P. Sharma and S. Pattanshetty ―A study on risk factors of cervical cancer among patients attending a tertiary care hospital: A case- control study‖, Clinical Epidemiology and Global Health, vol. 6, no. 2, pp. 83–87, 2017, doi. 10.1016/j.cegh.2017.10.001. 9. H. Xu et al., ―Hormonal contraceptive use and smoking as risk factors for high-grade cervical intraepithelial neoplasia in unvaccinated women aged 30–44 years: A case-control study in New South Wales, Australia‖, in Cancer Epidemiology, vol. 55, pp. 162–169, 2018, doi: 10.1016/j.canep.2018.05.013. 10. D. Zahras and Z. Rustam, "Cervical Cancer Risk Classification Based on Deep Convolutional Neural Network," 2018 International Conference on Applied Information Technology and Innovation (ICAITI), Padang, Indonesia, 2018, pp. 149-153, doi: 10.1109/ICAITI.2018.8686767. 11. K. Okunade et al., ―Comparative analysis of serum trace element levels in women with invasive cervical cancer in Lagos, Nigeria‖ in Pan African Medical Journal, vol. 31, no 194, 2018, doi: 10.11604/pamj.2018.31.194.14425. 12. M. Smith et al., ―Potential for HPV vaccination and primary HPV screening to reduce cervical cancer disparities: Example from New Zealand‖ in Vaccine, vol. 36, no. 42, pp. 6314-6324, 2018, doi: 10.1016/j.vaccine.2018.08.063. 13. S. Almazrou, B. Saddik and H. Jradi, ―Knowledge, attitudes, and practices of Saudi physicians regarding cervical cancer and the human papilloma virus vaccine‖ in Journal of Infection and Public Health, vol. 13, no. 4, pp. 584-590, 2019, doi: 10.1016/j.jiph.2019.09.002. 14. R. Sankaranarayanan, ―HPV vaccination: The most pragmatic cervical cancer primary prevention strategy‖, in International Journal of Gynecology & Obstetrics, vol. 131, pp. 33–35, 2015, doi: 10.1016/j.ijgo.2015.02.014. 15. J. Firmino-Machado et al., ―A 3-step intervention to improve adherence to cervical cancer screening: The SCAN randomized controlled trial‖ in Preventive Medicine, vol. 123, pp. 250-261, 2019, doi: 10.1016/j.ypmed.2019.03.025. 16. S. Mustafa, S. Adeshina, M. Dauda and W. Soboyejo, "Classification of cervical cancer tissues using a novel low cost methodology for effective screening in rural settings," 2014 11th International Conference on Electronics, Computer and Computation (ICECCO), Abuja, 2014, pp. 1-4, doi: 10.1109/ICECCO.2014.6997552. 17. S. Kaaviya, V. Saranyadevi and M. Nirmala, "PAP smear image analysis for cervical cancer detection," 2015 IEEE International Conference on Engineering and Technology (ICETECH), Coimbatore, 2015, pp. 1-4, doi: 10.1109/ICETECH.2015.7275029. 18. M. Kuko and M. Pourhomayoun, "An Ensemble Machine Learning Method for Single and Clustered Cervical Cell Classification," 2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI), Los Angeles, CA, USA, 2019, pp. 216-222, doi: 10.1109/IRI.2019.00043. 19. K. V. Bhuvaneshwari, and B. Poornima ―Cervical Cancer Cell Identification & Detection Using Fuzzy C Mean and K nearest Neighbor Techniques‖ in International Journal of Innovative Technology and Exploring Engineering (IJITEE), vol. 8, no. 10, pp. 1080-1084, 2019, doi: 10.35940/ijitee.I7892.0881019. 20. S. Nehra, J. L. Raheja, K. Butte and A. Zope, "Detection of Cervical Cancer using GLCM and Support Vector Machines," 2018 6th Edition of International Conference on Wireless Networks & Embedded Systems (WECON), Rajpura (near Chandigarh), India, 2018, pp. 49-53, doi: 10.1109/WECON.2018.8782065. 21. D. Kashyap et al., "Cervical cancer detection and classification using Independent Level sets and multi SVMs," 2016 39th International Conference on Telecommunications and Signal Processing (TSP), Vienna, 2016, pp. 523-528, doi: 10.1109/TSP.2016.7760935. 22. Han Yeong Oh, Seong Hyun Kim and Dong Wook Kim, "A study on the development of diagnosis algorithm and application program for early diagnosis of cervical cancer using cervix cell," Fourth edition of the International Conference on the Innovative Computing Technology (INTECH 2014), Luton, 2014, pp. 37-40, doi: 10.1109/INTECH.2014.6927749. 23. M. Rohmatillah, S. H. Pramono, Rahmadwati, H. Suyono and S. A. Sena, "Automatic Cervical Cell Classification Using Features Extracted by Convolutional Neural Network," 2018 Electrical Power, Electronics, Communications, Controls and Informatics Seminar (EECCIS), Batu, East Java, Indonesia, 2018, pp. 382-386, doi: 10.1109/EECCIS.2018.8692888. 24. D. Sharma, A. Bhan and A. Goyal, "Cervical Cancer Screening in Pap Smear Images Using Improved Distance Regularized Level Sets," 2018 2nd International Conference on Trends in Electronics and Informatics (ICOEI), Tirunelveli, 2018, pp. 1445-1448, doi: 10.1109/ICOEI.2018.8553943. 25. A. Ghoneim, G. Muhammad and M. S. Hossain, ―Cervical cancer classification using convolutional neural networks and extreme learning machines‖ in Future Generation Computer Systems, vol. 102, pp. 643-649, 2019, doi: 10.1016/j.future.2019.09.015. 26. M. Devi et al., ―Classification of Cervical Cancer Using Artificial Neural Networks‖, in Procedia Computer Science, vol. 89, pp. 465- 472, 2016, doi: 10.1016/j.procs.2016.06.105. 27. P. Makkonen et al., ―Impact of organized and opportunistic Pap testing on the risk of cervical cancer in young women – A case-control study from Finland‖ in Gynecologic Oncology, vol. 147, no. 3, pp. 601–606, 2017, doi: 10.1016/j.ygyno.2017.09.010. 28. N. Wentzensen et al., ―A prospective study of risk-based colposcopy demonstrates improved detection of cervical precancers‖, in American Journal of Obstetrics and Gynecology, vol. 218, no. 6, pp. 604.e1–604.e8, 2018, doi: 10.1016/j.ajog.2018.02.009. 29. J. Jagtap et al., "Effective Screening and Classification of Cervical Precancer Biopsy Imagery," in IEEE Transactions on NanoBioscience, vol. 16, no. 8, pp. 687-693, Dec. 2017, doi: 10.1109/TNB.2017.2728321. 30. Y. Song et al., ―Random biopsy in colposcopy-negative quadrant is not effective in women with positive colposcopy in practice‖ in Cancer Epidemiology, vol. 39, no. 2, pp. 237–241, 2015, doi: 10.1016/j.canep.2015.01.008. Authors: Rasheed Saleem Abed
Paper Title: Al-Hadba Minaret in the Last 100 Years 28. Abstract: Al-Hadba minaret is located in a central location on the right side of the river Tigris within the city of Mosul North of Iraq. It is one of the important heritage landmarks in Iraq, Mostly characterized by its curvature 164-166 and height. It was built before more than 800 years. Later, It was destroyed due to military actions in the summer of 2017. Recently plans for reconstruction are going on. Accordingly, many different data has to be collected and organized to help form a model closer to the original shape. Prior to its destruction, careful surveying was performed to record the shape and movement of the minaret. This research provides a description of the results of this work. For a century, the minaret leaning has been slowly growing closer to the danger point. Modern construction techniques can be used to rebuild a more stable structure and avoid that movement.
Keywords: AlHadba minaret, Mosul heritage, leaning, deformation surveying.
References: 1. https://en.wikipedia.org/wiki/Great_Mosque_of_al-Nuri_(Mosul) retrieved on 20/7/2020. 2. M. T. H. Al-Layla, "Al-Hadbaa Minaret-problems and solutions," (Proceedings of symposium, 2011). in Arabic. 3. Al-Omari, A., Khalil, A. A., & Khattab, S. (2020). From in situ investigation to FEM analysis: Application of Al-Hadba minaret foundation. In AIP Conference Proceedings (Vol. 2213 4. Al-Shaikh K.,(1979). Leaning Minarets of Mosul. J. Summer. No.31. 5. Lizzi, F., and G. Carnervale,(1981). The Static Restoration of the Leaning AL-Hadba Minaret in Mosul (Iraq), 3rd International Symposium on Babylon, Ashor, and Haditha, Baghdad, Iraq. 6. Mahmood, M. N., and Sarsam, H.H., (1989). Leaning of Old Minarets in MOSUL City, International Conference on Case Histories in Structural Failures, CHSF89, Singapore, pp G28-G39. 7. Abed, Rasheed. S. and Ghassan N. (2013). Measurements in Alhadba Minaret using Robotic Total Station. Eng.& Tech Journal, Vol.31, Part (A), No,5. Authors: R.Sravani, P.Deepak Reddy
Paper Title: V/C Digital Controlled DC-DC Converter Abstract: In this paper, the switching of dc-dc converter using voltage/current digital control is proposed. It is the combination of existed digital average voltage and digital average current controls. The stability analysis of V/C digital controlled dc-dc converter is derived by using sampled data model. The transient analysis of V/C digital controlled dc-dc converter is also derived by using z-domain small signal model. The proposed V/C digital controlled dc-dc converter has over current protection, fast load transient response, no sub-harmonic oscillations at any value of duty cycle, and wider stability range. The proposed system is analysed with a simple buck converter. The output voltage and inductor current weighting factors influence the stability boundary and transient performances of V/C digital controlled dc-dc converter. The stability analysis and transient analysis is investigated and verified by circuit simulations.
Keywords: sampled data modelling, stability analysis, transfer function, transient analysis, z-domain small signal modelling.
References: 1. K.-H. Cheng, C.-W. Su, and H.-H. Ko, ―A high-accuracy and highefficiency on-chip current sensing for current-mode control CMOS DCDC buck converter,‖ in Proc. IEEE 15th Int. Conf. Electron. Circuits Syst., Aug./Sep. 2008, pp. 458–461. 2. Y.-C. Lin, C.-J. Chen, D. Chen, and B. Wang, ―A ripple-based constant on-time control with virtual inductor current and offset cancellation for DC power converters,‖ IEEE Trans. Power Electron., vol. 27, no. 10, pp. 4301–4310, Oct. 2012. 3. K. Yao, Y. Ren, and F. C. Lee, ―Critical bandwidth for the load transient response of voltage regulator modules,‖ IEEE Trans. Power Electron., vol. 19, no. 6, pp. 1454–1461, Nov. 2004. 4. Y. Chi, X.-Q. Lai, and H.-X. Du, ―Fast transient response high-accuracy current-sensing technique for step-up DC–DC converter,‖ Electron. Lett., vol. 51, no. 7, pp. 577–579, Apr. 2015. 5. G. Zhou, J. Xu, and J. Wang, ―Constant-frequency peak-ripple-based control of buck converter in CCM: Review, unification, and 29. duality,‖ IEEE Trans. Ind. Electron., vol. 61, no. 3, pp. 1280–1291, Mar. 2013. 6. R. Mammano, ―Switching power supply topology voltage mode vs. current mode,‖ Unitrode Corp., Merrimack, NH, USA, Unitrode Design Note DN-62, 1994. 167-175 7. C.-S. Huang, C.-Y. Wang, J.-H. Wang, and C.-H. Tsai, ―A fast-transient quasi-V2 switching buck regulator using AOT control,‖ in Proc. IEEE Asian Solid-State Circuits Conf., Nov. 2011, pp. 53–56. 8. C.-C. Fang and C.-J. Chen, ―Subharmonic instability limits for V2controlled buck converter with outer loop closed/open,‖ IEEE Trans. Power Electron., vol. 31, no. 2, pp. 1657–1664, Feb. 2016. 9. G. Zhou, S. He, X. Zhang, and S. Zhong, ―Critical output-capacitor ESR for stability of V2 controlled buck converter in CCM and DCM,‖ Electron. Lett., vol. 50, no. 12, pp. 884–886, Jun. 2014. 10. D. M. Mitchell, ―An analytical investigation of current-injected control for constant-frequency switching regulators,‖ IEEE Trans. Power Electron., vol. PE-1, no. 3, pp. 167–174, Jul. 1986. 11. W. Tang, F. C. Lee, and R. B. Ridley, ―Small-signal modeling of average current-mode control,‖ IEEE Trans. Power Electron., vol. 8, no. 2, pp. 112–119, Apr. 1993. 12. C. Restrepo, J. Calvente, A. Romero, E. Vidal-Idiarte, and R. Giral, ―Current-mode control of a coupled-inductor buck–boost DC–DC switching converter,‖ IEEE Trans. Power Electron., vol. 27, no. 5, pp. 2536–2549, May 2012. 13. F. Wang, J. Xu, and B. Wang, ―Comparison study of switching DC-DC converter control techniques,‖ in Proc. IEEE Int. Conf. Commun. Circuits Syst., Jun. 2006, pp. 2713–2717. 14. C. Mi, J. Xu, G. Zhou, and Y. Jin, ―On the stability of V2C controlled boost converter in continuous conduction mode,‖ in Proc. IEEE 6th Int. Power Electron. Motion Control Conf., May 2009, pp. 1300–1304. 15. W. Huang, ―A new control for multi-phase buck converter with fast transient response,‖ in Proc. IEEE 16th Annu. Appl. Power Electron. Conf. Expo., Mar. 2001, pp. 273–279. 16. P. Cortés, M. P. Kazmierkowski, R. M. Kennel, D. E. Quevedo, and J. Rodríguez, ―Predictive control in power electronics and drives,‖ IEEE Trans. Ind. Electron., vol. 55, no. 12, pp. 4312–4324, Dec. 2008. 17. Z. Zhang et al., ―Predictive control with novel virtual-flux estimation for back-to-back power converters,‖ IEEE Trans. Ind. Electron., vol. 62, no. 5, pp. 2823–2834, May 2015. 18. J. Chen, A. Prodic, R. W. Erickson, and D. Maksimovic, ―Predictive digital current programmed control,‖ IEEE Trans. Power Electron., vol. 18, no. 1, pp. 411–419, Jan. 2003. 19. G. Zhou, J. P. Xu, and Y. Y. Jin, ―Improved digital peak voltage predictive control for switching DC–DC converters,‖ IET Power Electron., vol. 4, no. 2, pp. 227–234, Feb. 2011. 20. G. Zhou, J. Xu, and Y. Jin, ―Elimination of subharmonic oscillation of digital-average-current-controlled switching DC–DC converters,‖ IEEE Trans. Ind. Electron., vol. 57, no. 8, pp. 2904–2907, Aug. 2010. 21. X. Liu, G. Zhou, M. Leng, and S. Zhou, ―Digital average voltage control for switching DC-DC converters,‖ in Proc. IEEE8th Int. Power Electron. Motion Control Conf., May 2016, pp. 1156–1160. 22. J. Xu, G. Zhou, and M. He, ―Improved digital peak voltage predictive control for switching DC–DC converters,‖ IEEE Trans. Ind. Electron., vol. 56, no. 8, pp. 3222–3229, Aug. 2009. 23. G. Zhou, J. Xu, J. Wang, and Y. Jin, ―Comparison study on digital peak current, digital peak voltage, and digital peak voltage/peak current controlled buck converter,‖ in Proc. IEEE 4th Conf. Ind. Electron. Appl., May 2009, pp. 799–804. 24. G. Feng, E. Meyer, and Y.-F. Liu, ―A new digital control algorithm to achieve optimal dynamic performance in DC-to-DC converters,‖ IEEE Trans. Power Electron., vol. 22, no. 4, pp. 1489–1498, Jul. 2007. 25. P. Karamanakos, T. Geyer, and S. Manias, ―Direct voltage control of DC-DC boost converters using model predictive control based on enumeration,‖ in Proc. IEEE 15th Int. Power Electron. Motion Control Conf., Sep. 2013, pp. DS2c.10-1–DS2c.10-8. 26. L. Cheng, P. Acuna, R. P. Aguilera, M. Ciobotaru, and J. Jiang, ―Model predictive control for DC-DC boost converters with constant switching frequency,‖ in Proc. IEEE 2nd Annu. Southern Power Electron. Conf., Auckland, New Zealand, Dec. 2016, pp. 1–6. 27. R. Redl and J. Sun, ―Ripple-based control of switching regulators— An overview,‖ IEEE Trans. Power Electron., vol. 24, no. 12, pp. 2669–2680, Dec. 2009. 28. G. F. Franklin, J. D. Powell, and M. L. Workman, Digital Control of Dynamic Systems, 3rd ed. Reading, MA, USA: Addison-Wesley, 1998. 29. K.-H. Cheng, C.-W. Su, and H.-H. Ko, ―A high-accuracy and highefficiency on-chip current sensing for current-mode control CMOS DCDC buck converter,‖ in Proc. IEEE 15th Int. Conf. Electron. Circuits Syst., Aug./Sep. 2008, pp. 458–461. 30. Guohua Zhou, hongbo zhao, W.Z, S.Xu ―Digital average voltage/ digital average current predictive control for switching dc-dc converters,‖ IEEE Journal of emerging and selected topics in power electronics, vol.6, no.4, December 2018. Authors: Ponraj A, Aswin Kumar M, Balasubramaniam AS, Giridhar K
Paper Title: Smart Warehouse Governance using AI and Raspberry Pi Abstract: Sorting is the process of systematic selection and arrangement. Sorting involves intense labor work. The use of Artificial Intelligence in recognizing the objects by their color makes the process of sorting completely autonomous. Modern Industries require modern solutions for the problems encountered during the process of sorting. With the advent of Artificial Intelligence, the machines that can recognize an object by their color proves to be a primary solution that can completely automate the process of sorting. This paper presents a five-axis arm mounted on a robotic model that makes use of a color sorting technique. It performs pick and place operations in real-time. The color sorting technique detects the color of the object in the frame captured by the camera. The frame size is used to detect the position of the object in the real world. The robot model moves according to the frame size of the object. Raspberry Pi microcontroller drives the servo motor and dc motor to move the five-axis arm and the robotic model to sort and perform pick and place operation based on their color. 30. The color sorting algorithm is based on the Hue-Saturation-Value model. This model finds its application in places where sorting is done based on color and not the object itself. For example, it is used to sort objects like different colored clothes, food items, etc. It also finds its application in very large scale warehouses such as 176-179 Amazon, Flipkart, etc which focusses on smart automated warehouses that reduce the labor requirements.
Keywords: Microcontroller, Gripper, Servo motors, Webcam, DC motors, Robotic arm, Image processing
References: 1. Goldy Katal, Saahil Gupta, Shitij Kakkar, ―Design and Operation of Synchronized Robotic Arm‖, IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308, Volume: 02 Issue: 08 | Aug-2013. 2. Minu Mariya Thomas, Resmi Jose, Sumeesh V.R, Tony George,‖ Objecto-Sortometer‖ International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 p-ISSN: 2395-0072 Volume: 03 Issue: 03 | Mar-2016 3. Lim jie shen*, Irda hassan ‖Design and Development of Color Sorting Robot‖ EURECA 2014 Special Issue January (2015). 4. Peng An, (2016), ―Obstacle avoidance strategy of mobile robot based on wireless sensor networks‖, International Journal of Online and Biomedical Engineering, Vol 12, No11. 5. Rodica Holonec, (2008), ―An Automated Sorting System Based on Virtual Instrumentation Techniques‖, International Journal of Online and Biomedical Engineering, Vol 4. José Luis Hernández Corona, Ernesto Mendoza Vázquez, Alejandra Ortiz Castro, Amador Arroyo Authors: López, Moisés Martínez Aguirre Implementation of an Automated System in the Measurement of Temperature in Broken Dynamic Paper Title: Equipment Abstract: This article presents a titration project describing the implementation in the rotodynamic equipment of an economical automated temperature module, as a preventive solution for future failures caused by the lack of analysis in the increase or decrease in temperature. The project is currently contextualized in the area of industry, first, providing background to frame the importance of temperature control and measurement and also know what its evolution has been like. Immediately focuses on explaining the theoretical basis for giving context to the reader. For the purpose of detecting the increase or decrease of heat in machinery by implementing a monitoring system. The development of the project is based on the use of an LM35 transistor that connected to 31. an Arduino Uno through various cables, will display the temperature measurement and make interface of the obtained results that will be reflected in a 2x16 LCD screen. The project is applied in a prototype bench in three key parts of the pulley, and in the two bearings to make the simulations, then perform corresponding tests and 180-183 check that theory. A simple and lower cost system, but above all efficient that meets the expectations of the problem presented.
Keywords: Control, automation, electromechanical failures, Signals, Simulation.
References: 1. Albañil, HH, & Espejo Mora, E. (2002). Fracture Mechanics And Failure Analysis. Bogotá: National University of Colombia. 2. Coltters, R. (August 20, 2013). Thermal fatigue - effect of temperature. Obtained from Metfsusion:https://metfusion.wordpress.com/2013/08/20/fatiga-termica/ .Consult Friday, June 12. 3. Granda, G. (April 24, 2020). The reason. Main Models of Thermal graphic cameras. 4. How to prevent overheating in heavy machinery? (January 10, 2018). Obtained from Bardhal, industry:https://www.bardahlindustria.com/blog/page/57/ .Retrieved Friday, June 12. 5. "Protection systems and safety devices in machinery and equipment used in workplaces" Official Mexican Standard, NOM-004-STPS- 199, Official Gazette of the Federation, May 31, 1999. 6. Enriquez Harper G. [Ed.1]. (2004). Fundamentals of electric motor control in industry. Mexico City. Mexico: Limusa p. (125,150). 7. Villena, R. The protector of Tools. Extremadura The Newspaper obtained fromhttps://www.elperiodicoextremadura.com/noticias/almendralejo/protector-herTools_1236008.html, accessed June 5, 2020. 8. CDMX Electronics, (2020). LM35 temperature sensor obtained from:https://www.cdmxelectronica.com/producto/sensor-de- temperatura-lm35/ 9. Montero, Á. G. (2015). Electronics I An innovative, simple, easy-to-use system that can revolutionize data acquisition and process control: fast, simple, intuitive, easy. Research and Science. obtained from:https://www.investigacionyciencia.es/blogs/fisica-y- quimica/9/posts/electrnica-libre-i-12878 10. Educate for change. (2020). Schematic of the complete assembly of the Prototype. Obtained from:https://educarparaelcambio.com/arduino/reto-4-liquid-crystal-display-pantalla-lcd/ Authors: Alex R Mathew
Paper Title: Threats and Protection on E-Sim Abstract: Threats involve various risks and threats are associated with the embedded SIM technology, for instance, the Internet of things (IoT) identity. IoT refers to the working capabilities enabling the allocation of unique identifiers (UID) to effectively connect with the related devices thus enhancing communication. An e- SIM application cannot produce reliable and actual data used to obtain the subscriber‘s anticipated outcome. The SIM technology does not provide some reliable data that can be employed by the user to formulate some serious productive outcomes. Failure by the technology to process and automatically provide the user with the notification suppose of any infringement or hacking. SIM-jacking is the other notable threats facing the embedded universal integrated connectivity card (e-UICC). Incompetent Log Rhythm Al Engine influences the fraudster hacking experience due to failure protections within the operational surrounding. The e-SIM technology system lacks timely threat, risk, and other various vital operations predictability to react to the experienced unbearable operations challenges induced by the fraudsters. Similarly, the embedded SIM incurs the insider threats whereby the service providers fail to secure the much-needed privacy concerning an individual‘s vital information. The situations of personal data leakage are witnessed within the system operations.The e-SIM hijacking enables the fraudsters to secretly obtain the victim‘s vital data of the subscriber, hijack, and receive the information intended to the individual to his/her personal phone. The process results to complete mobile account operations by the hacker resulting to further access to the victim‘s bank information and transfer of cash. The other threat experienced by e-SIM users is the provision of false information. The SIM subscribers normally fall into traps of the fraudsters by receiving short messages (SMS) citing assistance kind of news from the service providers, thus drawing the victim‘s bank amount. Identity fraud and device poisoning are other additional threats encountered in the application of e-SIM. Generally, the entire process of fraud invasion and victimization influence the victim‘s business decisions of the affected individuals. Protections focuses on the embedded SIM provides greater security in addition to a re-programmable technological system, unlike the physical SIM card. The subscriber's personal information is not contained within the e-SIM but with the service providers, thus enhancing its effectiveness. An e-SIM enables the consumers to effectively shift carriers between the T-Mobile 32. and Sprint without physical movement, thus supportive of security systems. Despite the security measures put into place, e-SIM like any other SIM card experiences information theft. Therefore, the service providers should encounter the emerging fraudster effects by proper monitoring of the network system to enable security 184-186 restrictions. The system should induce strict conditions that enable the evaluation and differentiation between the IoT and the non-IoT devices during their operation.
Keywords: Cellular network, Charging, IoT and non-IoT devices, and Security.
References: 1. M. M. Danziger, L. M. Shekhtman, A. Bashan, Y. Berezin, and S. Havlin, 2016. Vulnerability of interdependent networks and networks of networks. In Interconnected networks (pp. 79-99). Springer, Cham. 2. J. Franklin, C. Brown, S. Dog, N. McNab, S. Voss-Northrop, M. Peck, and B. Stidham, 2016. Assessing Threats to Mobile Devices & Infrastructure: the Mobile Threat Catalogue (No. NIST Internal or Interagency Report (NISTIR) 8144 (Draft)). National Institute of Standards and Technology. 3. J. M. Heikkilä, 2017. Information security risk analysis and mitigation methods for industrial internet in industrial vendor segment (Master's thesis). 4. Y. Huang, and P. Mishra, 2020, March. Vulnerability-aware Dynamic Reconfiguration of Partially Protected Caches. In 2020 21st International Symposium on Quality Electronic Design (ISQED) (pp. 255-260). IEEE. 5. S. R. Hussain, M. Echeverria, A. Singla, O. Chowdhury, and E. Bertino, 2019, May. Insecure connection bootstrapping in cellular networks: the root of all evil. In Proceedings of the 12th Conference on Security and Privacy in Wireless and Mobile Networks (pp. 1- 11). 6. M. Jakobsson, 2020. Social Engineering Resistant 2FA. arXiv preprint arXiv:2001.06075. 7. Y. S. Jang, 2020. Detection of SQL Injection Vulnerability in Embedded SQL. IEICE Transactions on Information and Systems, 103(5), pp.1173-1176. 8. M. Khan, P. Ginzboorg, K. Järvinen, and V. Niemi, 2018, November. Defeating the downgrade attack on identity privacy in 5G. In International Conference on Research in Security Standardisation (pp. 95-119). Springer, Cham. 9. P. Pathak, N. Vyas, and S. Joshi, 2017. Security Challenges for Communications on IOT & Big Data. International Journal of Advanced Research in Computer Science, 8(3). 10. K. Sung, B. Levine, and M. Zheleva, 2020, July. Protecting location privacy from untrusted wireless service providers. In Proceedings of the 13th ACM Conference on Security and Privacy in Wireless and Mobile Networks (pp. 266-277). 11. Y. Wang, J. Shen, J. Lin, and R. Lou, 2019. Staged method of code similarity analysis for firmware vulnerability detection. IEEE Access, 7, pp.14171-14185. 12. T. Xie, G. H. Tu, C. Y. Li, and C. Peng, 2020. How Can IoT Services Pose New Security Threats In Operational Cellular Networks? IEEE Transactions on Mobile Computing. Authors: Anjani K. Upadhyay, Debasmita Chatterjee, Madhuri Swain, Lopamudra Ray Evaluation of a Potential Antibacterial, Produced by Streptomyces Cinereoruber Sp. Isolated from Paper Title: Chlika lake. Abstract: Streptomyces, isolated from marine and estuarine habitat have been widely recognized as a potential source of antifungal, anti-tumour, anti-bacterial compounds. In the present study, the antimicrobial agent production potential of a Streptomyces cinereoruber sp was evaluated. The selective isolation of the strain was carried out on starch casein agar. The primary screening of the Streptomyces isolate was done by cross streak method against pathogenic test strains Escherichia.coli MTCC 82, Staphylococcus aureus MTCC 96, Bacillus cereus IP406 and Salmonella typhi MTCC 734 and Micrococcus leuteus and the antimicrobial property against Micrococcus leuteus was confirmed. The secondary screening was carried out by using the culture supernatant against the test strain by agar well diffusion method. The growth and antimicrobial production ability of the strain against Micrococcus leuteus was studied. The antimicrobial agent production was also observed till pH 11 and NaCl concentration 3% (w/v). The partially purified compound showed a peak similar to streptomycin in HPLC. The culture condition for the production of the compound was optimised.
Keywords: Streptomyces, Antibacterial, Optimization
References: 1. Andrews, J. M. 2001. Determination of minimum inhibitory concentrations. J. Antimicrob. Chemother. 48(Suppl. 1):5–16 2. Betina V.1983. The chemistry and biology of antibiotics. Amsterdam: Elsevier Scientific Pub. Co; p. 190. 3. Costanza R, Kemp WM, Boynton WR.1993. Predictability, scale, and biodiversity in coastal and estuarine ecosystems: implications for management. Ambio.1:88-96. 4. de lima procopio, R. E; da silva, I. R; Martins, M.K; de azevedo, J.L & de Araujo, J. M. (2012) Antibitics produced by Streptomyces. Braz J Infect Dis. 16: 466-471. 33. 5. Goodfellow, M. (1989). The Actinomycetes I. Supragenericclassifcation of actinomycetes. In Bergey's Manual of SystematicBacteriology, vol. 4, pp. 2333±2339. Edited by S. T. Williams, M. E. Sharpe & J. G. Holt. Baltimore: Williams &Wilkins. 6. Goodfellow, M., Kumar, Y., Labeda, D. P. & Sembiring, L. 2007. The Streptomyces violaceusniger clade: a home for streptomycetes 187-197 with rugose ornamented spores. Antonie Van Leeuwenhoek 92,173-99. 7. Gordon, R. E., Barnett, D. A., Handerhan, J. E. and Pang, C. H.N. 1974. Nocardia coeliaca, Nocardia autotrophica, and the nocardin strain. Int J Bacteriol 24, 54-63. 8. Holder, I.A., and Boyce, S. T. 1994. Agar well diffusion assay testing of bacteriak susceptibilitu to various antimicrobials in concentration non-toxic for human cell in culture. Burns, 20 426-429. 9. Jayapal KP, Lian W, Glod F, Sherman DH, Hu WS. 2007. Comparative genomic hybridizations reveal absence of large Streptomyces coelicolor genomic islands in Streptomyces lividans. BMC Genomics, 8:229. 10. Kagan IA, Flythe MD. 2014. Thin-layer chromatographic (TLC) separations and bioassays of plant extracts to identify antimicrobial compounds. JoVE. (85). 11. Kampfer, P. & Labeda, D. P. 2006. International Committee on Systematics of Prokaryotes; Subcommittee on the taxonomy of the Streptomycetaceae: Minutes of the meeting, 25 July 2005, San Francisco, CA, USA. Int J Syst Evol Microbiol 56, 495. 12. Kelly, K.I. 1964. Inter-society color council-national bureau of standard color-name charts illustrated with centroid colors. US Government Printing Office, Washington. 13. Shirling, E. B. & Gottlieb, D. 1966. Methods for characterization of Str8eptomyces species. Int J Syst Bacteriol 16, 313–340. 14. Srinivas TN, Kumar PA, Sucharitha K, Sasikala C, Ramana CV.2009. Allochromatium phaeobacterium sp. nov. Int J Syst Evol Microbiol. 59:750-3. 15. Sucharita K, Sasikala C, Park SC, Baik KS, Seong CN, Ramana CV.2009. Shewanella chilikensis sp. nov., a moderately alkaliphilic gammaproteobacterium isolated from a lagoon. International journal of systematic and evolutionary microbiology.59:3111-5. 16. Taddi, A; Rodriguez, H. J.; Marquez-Vilchez; Castelli, C. 2006. Isolation and identification of streptomyces sp. From venezuelan site: Morphological and biochemical studies. Microbiological Research 161:222-231. 17. Tindall, B. J., Sikorski, J., Smibert, R. M. & Kreig, N. R. 2007. Phenotypic characterization and the principles of comparative systematics. In Methods for General and Molecular Microbiology, 3rd edn, pp. 330–393. Edited by C. A. Reddy, T. J. Beveridge, J. A. Breznak, G. A. Marzluf, T. M. Schmidt & L. R. Snyder. Washington, DC: American Society for Microbiology. 18. Williams, S.T., Goodfellow, M., Alderson, G., Wellington, E. M. H., Sneath, P. H. A., Sackin, M. J. 1983. Numerical classification of Streptomyces and related genera. J. Gen Microbiol 129, 1743-1813. 19. Williams, S.T., Goodfellow, M., & Alderson, G. 1989. Genus Streptomyces Waksman and Henrici 1943. 339 AL. In Bergey’s manual of Sytematic Bacteriology, vol. 4, pp. 2452-2492. Edited by S.T. Williams, M. E. Sharpe & J. G. Holt. Baltimore: Williama &Wikins. Authors: Nurul Syahira Binti Nordin, Woo Ying Yee, Md Saeed Hasan Joarder, Badrul Hisham Bin Ahmad.
Paper Title: Compression Studies on LoRa Antenna Design for IoT Applications Abstract: In this paper, a review on the LoRa antenna design for IoT application is studied. The expansion of the Internet of Things ( IoT) has led the industry to develop new communication solutions, as current protocols are inadequate in terms of scope and energy efficiency to satisfy IoT requirements. Before studying antenna design, some background LoRa and IoT were discussed at beginning of the paper. LoRaWAN is an open LPWAN standard developed by LoRa Alliance and has main characteristics such as low energy consumption, 34. long-range communication, builtin protection and GPS-free positioning. Besides, a comparison according to the method, resonance frequency, material, size of the antenna and the output is shown in the form of table. In addition, the strength and the weakness of each of the antenna design were discussed before the end of the paper. 198-201
Keywords: LoRa, IoT, antenna design, resonance frequency.
References: 1. H. Karjaluoto, ―An Investigation of Third Generation ( 3G ) Mobile Technologies and Services", May, 2014. 2. V. T. Conference, 2000 IEEE 51 st Vehicular Technology Conference. 2000. 3. N. Todtenberg and R. Kraemer, ―Ad Hoc Networks A survey on Bluetooth multi-hop networks‖, Ad Hoc Networks, vol. 93, 2019. 4. B. Hammi, R. Khatoun, A. Fayad, and L. Khoukhi, ―Internet of Things (IoT) Technologies for Smart Cities Internet of Things (IoT) Technologies for Smart Cities‖, 2017. 5. R. G. P. V. K, ―Agriculture Monitoring with Lora Based Wireless Sensor Networks‖, vol. 7, no. 02, 2019, pp. 650–655. 6. M. Usmonov and F. Gregoretti, ―Design and implementation of a LoRa based wireless control for drip irrigation systems Design and Implementation of a LoRa Based Wireless Control for Drip Irrigation Systems‖, 2019. 7. D. Bankov, E. Khorov, and A. Lyakhov, ―On the Limits of LoRaWAN Channel Access‖, November, 2016. 8. J. Buckley et al., ―Compact 433 MHz antenna for wireless smart system applications Compact 433 MHz antenna for wireless smart system applications,‖ April, 2014, pp. 2013–2015. 9. L. H. Trinh et al., ―Miniature Antenna for IoT Devices Using LoRa Technology‖, 2017, pp. 170–173. 10. F. Ferrero and S. Antipolis, ―Dual-band LoRa Antenna : Design and Experiments‖, pp. 243–246. 11. Q. Zhang and Y. Gao, ―Embedded Antenna Design on LoRa Radio for IoT Applications‖, pp. 2–4. 12. L. H. Trinh, T. Q. K. Nguyen, H. L. Tran, P. C. Nguyen, N. V Truong, and F. Ferrero, ―Low-profile horizontal omni-directional antenna for LoRa wearable devices‖, 2017, pp. 136–139. 13. H. T. Chattha, M. K. Ishfaq, Y. Saleem, Y. Huang, and S. J. Boyes, ―Band-Notched Ultrawide Band Planar Inverted-F Antenna‖, 2012.
Authors: Susamma Mathew, Garima Saini, S.S Gill
Paper Title: Design of Compact MIMO Antenna for 5G Mobile Terminal Abstract: This report gives the research work carried out for design and analysis of MIMO antenna using two identical Microstrip slot radiators having enhanced isolation. The slot radiators offer compact size in order to accommodate other electronic components for reduction of volume of the wireless communication system. The defected ground structure is formed on the ground plane in between the antenna elements and feed lines to improve the isolation between them. The substrate used for constructing the antenna is FR-4 having the measurements of 26mm x 22mm x 0.8mm and it has the relative permittivity of 4.4. The printed microfilm strip etched on the opposite side of the substratum is used to couple the signal to each antenna. The HFSS software is used in this paper for designing the antenna and for checking the performance of the antenna. The -10dB bandwidth is 1.1GHz in the frequency range of 3.1 GHZ to 4.2GHz. The maximum isolation obtained after simulation is -23.1dB at 3.13GHz. The maximum gain of 2.26dB is obtained. Simulated radiation diagram of the designed antenna indicates that it is a good radiator for 5G applications in the sub 6GHz frequency band.
Keywords: MIMO, Microstrip Slot radiators, Reflection coefficient, Isolation, 5G
References: 1. S.Sharawi, Muhammad Ikram and Atif Shamim, ―A Two Concentric Slot Loop Based Connected Array MIMO Antenna System for 4G/5G Terminals‖, IEEE Transactions on Antennas and Propagation, Vol. 65, Issue: 12, Page(s): 6679 – 6686, December 2017 35. 2. Haitham Alsaif, Muhammad Usman, Muhammad T. Chughtai, and Jamal Nasir, ―Cross Polarized 2×2 UWB-MIMO Antenna System for 5G Wireless Applications‖, Progress In Electromagnetics Research , Vol. 76, pp.157–166,2018(Received 11 October 2018, Accepted 21 November 2018, Scheduled 7 December 2018) 202-206 3. Mujeeb Abdullah, Yong-Ling Ban, Kai Kang, Ming-Yang Li, and Muhammad Amin, ―Eight-Element Antenna Array at 3.5GHz for MIMO Wireless Application‖, Progress In Electromagnetics Research , Vol. 78, pp:209–216, 2017(Received 23 August 2017, Accepted 29 September 2017, Scheduled 17 October 2017) 4. He Huang, Xiaoping Li and Yanming Liu ―A Low Profile, Dual-polarized Patch Antenna for 5G MIMO Application‖, IEEE Transactions on Antennas and Propagation, Volume: 67, Issue: 2, pp.1275-1279, Feb. 2019 5. Huiqing Zhai, Lei Xi, Yiping Zang and Long Li ―A Low Profile Dual-polarized High Isolation MIMO Antenna Arrays for Wideband Base Station Applications‖, IEEE Transactions on Antennas and Propagation, Volume: 66,Issue:1,pp:191-202, Jan. 2018 6. Ming-Yang Li, Yong-Ling Ban, Zi-Qiang Xu and Gang Wu, ―Eight-Port Orthogonally Dual- Polarized Antenna Array for 5G Smartphone Applications‖, IEEE Transactions on Antennas and Propagation, Volume: 64, Issue: 9, pp.3820-3830, September 2016 7. Muhammad Ikram, Rifaqat Hussain and Mohammad S. Sharawi, ―4G/5G antenna system with dual function planar connected array‖, IET Microwaves, Antennas & Propagation, Vol.11,Issue 12, pp. 1760-1764, October 2017 8. Manish Kumar Soni and Garima Saini, ―A Compact Dual Element PIFA Array for Wireless MIMO Advanced TDD LTE Applications‖, International Journal of Advancement in Engineering Technology, Management and Applied Science, Volume: 04, Issue: 09, PP. 85-93, September 2017 9. R. Anita, P. V. Vinesh, K. C. Prakash, P. Mohanan, and K. Vasudevan, ―A Compact Quad Element Slotted Ground Wideband Antenna for MIMO Applications‖, IEEE Transactions on Antennas and Propagation,Vol:64,Issue:10,pp:4550-4553,October2016, 10. Vincent Tseng and Cheng‐Yuan Chang ―Linear Tapered Slot Antenna for Ultra‐Wideband Radar Sensor: Design Consideration and Recommendation‖, MDPI Sensors, pp: 1-24, Received: 6 January 2019;; Accepted:5 March 2019;Published 9 March 2019 5 March 2019; www.mdpi.com/journal/sensors. 11. Nurhayati, Eko Setijadi, and Gamantyo Hendrantoro, ―Radiation Pattern Analysis and Modeling of Coplanar Vivaldi Antenna Element for Linear Array Pattern Evaluation‖, Progress In Electromagnetics Research B, Vol. 84, pp: 79-96, 2019, Received 5 April 2019, Accepted 22 May 2019, Scheduled 14 June 2019 Authors: Aditi
Paper Title: Health and Housing for Urban Poor in India Post Covid-19 Abstract: The COVID-19 pandemic has built a troublesome new standard for everybody through shelter-in- place systems and physical and social distancing guidelines. Yet for billions of urban underprivileged, certain guidelines aren‘t merely troublesome; they‘re radically impracticable. Social and physical distancing is a severely significant acknowledgement to the pandemic COVID-19 however, it additionally implies that 36. occupants must have sufficient space, services and social security nets to sustain such an order. It is candidly not the fact over cities in Asia, Latin America and Africa. Health facilities and services are deficient in terms of the 208-216 transition from state to local level causing negligence of slum areas at global to micro-level. These dwellers of slums area accustomed to unhygienic and un-sanitized environment much on a regular basis. Majority of slums are vastly located near urban centers i.e. in and around in economically less developed countries, experiencing urbanization at a greater rate compared to more developed countries. Many countries often lack the ability to provide infrastructure like roads, affordable housing, basic services like water, sanitation etc., sufficiently for in- fluxing people in the cities due to urbanization creating a big concern for the country. Health policies need to consider equity and social justice for urban poor in order to equally uplift them in the society. The paper deals with the issues faced by the urban poor in India and the programs and policies that had been issued over time during the past which could not suffice to positively impact the downfalls of these people. The paper also highlights the health conditions of these urban poor and the areas where it has been lacking behind. The pandemic has caused the nation to come to a halt but the urban poor having no such privilege to comply with the situation are forced to thrive in degrading conditions. The research paper will help figure out trigger areas for downfall of these inhabitants of the nation and formulate strategies to counteract the same in post COVID-19 situation.
Keywords: Health conditions, Housing, Slums, Urban and rural India urbanization, Urban poor
References: 1. Agarwal S, Sangar K. Need for dedicated focus on urban health within national rural health mission. Indian J Public Health. 2005;49(3):141-51. 2. Challenges of Slums-Global Report on Human Settlements 2003 available on www.unchs.org 3. Govt. of India, Ministry of Housing and Urban Poverty Alleviation.2001.Draft National Slum Policy:2001. Nirman Bhawan, New Delhi 4. Ramachandran R. Urbanization and urban system in India. New Delhi: Oxford University Press. 2001. 5. Report of the committee on slum statistics/census, Government of India, 2001, pp. 6-7. 6. Report of the 11th Five Year Plan(2007-2012).Working Group on Urban Housing with Focus on Urban Slums. Ministry of Housing and Urban Poverty Alleviation, Govt. of India 7. Women‘s medical service. Summary of the findings of investigations into the causes of maternal mortality in India. New Delhi, 1947. 8. Urban Health Resource Centre, ―Health of the urban poor in India; key results from the NFHS, 2005–2006‖. 2007. 9. http://pdf.usaid.gov/pdf_docs/Pnadk385.pdf 10. http://nbo.nic.in/images/pdf/report_of_slum_committee.pdf 11. https://shodhganga.inflibnet.ac.in/bitstream/10603/115771/15/15_chapter%208.pdf Authors: Sachchidanand Shukla, Pratima Soni, Naresh Kumar Chaudhary, Geetika Srivastava
Paper Title: Development of Low Frequency Small Signal Amplifier using BJT-JFET in Sziklai Pair Topology Abstract: A new PSpice Model of BJT and JFET is proposed and its hybrid combination is used in Sziklai pair topology to design small signal amplifier. The proposed amplifier with maximum voltage gain 30.41, maximum current gain 43.05 and THD 2.44% is capable of amplifying low magnitude signals in a frequency range distributed from 3.035Hz to 93.808Hz. This feature explores the possibility to use proposed amplifier circuit in EEG, seismographs and underwater communication circuits. Three different circuit/device combinations are also exposed during the exploration of proposed amplifier and therefore mentioned with primary details. Qualitative behaviour, e.g. temperature dependency, noise behaviour, effect of the variation of biasing resistances and capacitors, small signal AC analysis etc., of the proposed circuit, is also studied to observe its performance under different environment
Keywords: Sziklai pair, Circuit Simulation, Small signal Amplifier.
References: 1. Rod Elliott, ―Compound pair Vs Darlington pair‖, http://sound.westhost.com/articles/cmpd-vs-darl.htm, January 6, 2011 2. David A. Hodges, ―Darlington‘s Contribution to Transistor Circuit Design‖, IEEE Transactions on Circuits and Systems-I, Vol.46, No.-1, January 1999, pp 102-104 37. 3. George C. Sziklai, ―Push-pull complementary type transistor amplifier‖, U.S. Patent 2,762,870, September 11, 1956 4. B. Pandey, S. Srivastava, S. N. Tiwari, J. Singh and S. N. Shukla, ―Qualitative Analysis of Small Signal Modified Sziklai Pair Amplifier‖, Indian Journal of Pure and Applied Physics, 50, 2012, pp 272-276 217-223 5. S.N.Shukla and S.Srivastrava, ―A New Circuit Model of Small-signal Sziklai pair amplifier‖, International Journal of Applied Physics and Mathematics, Vol.3, No.4, 2013, pp 231-236 6. S.N. Shukla, G. Srivastava, P. Soni, R. Mishra, ―Development of a Small Signal Amplifier with Modelling of BJT-JFET Unit in Sziklai Pair Topology‖, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, Vol.8, Issue 5, 2019, pp 1530-1538 7. M. H. Rashid, ―Introduction to PSpice Using OrCAD for Circuits and Electronics‖, Pearson Education, 3rd Ed., 2004, pp 255-256 8. R. Green, G. R. Nicol, ―An amplifier and recording system for seismic crustal refraction work‖, New Zealand Journal of Geology and Geophysics, Vol.12, 1969, pp 771-783 9. R.S. Khandpur, ―Hand Book of Bio-medical Instrumentation‖, Tata McGraw Hill, 2nd ed. (Sixth reprint), 2005, pp 170-175 10. Y.Q. Tan, F. Ibrahim and M. Moghavvemi, ―Two Electrodes Low Voltage Input EEG Amplifier for Brain Computer Interface‖, IEEE International Conference on Intelligent and Advanced Systems, 2007, pp 315-320 11. M. H. Sayed ElAhl, M. M. E. Fahmi, S. N. Mohammad, ―Qualitative analysis of high frequency performance of modified Darlington pair‖, Solid State Electronics, Vol. 46, 2002, pp 593-595 12. M.H. Ali, Aliyu Sisa Aminu, ―Analysis of Darlington pair in Distributed Amplifier Circuit‖, IOSR Journal of Electrical and Electronics Engineering, Vol.10, Issue 2, Ver.I, 2015, pp 77-80 13. S. N. Shukla, ―Small-signal amplifier with JFETs in triple Darlington topology‖, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, Vol.3, Issue 9, 2014, pp 18812-18819 14. A. Mottershead, ―Electronic Devices and Circuits‖, Printice-Hall of India, 16th reprint, 1993, pp 360 15. Bridget Benson, Ying Li, Brian Faunce, Kenneth Domond, Don Kimball, Curt Schurgers, Ryan Kastner, ―Design of a Low-Cost Underwater Acoustic Modem‖, IEEE Embedded Systems Letters, VOL. 2, NO. 3, 2010, pp 58-61 16. R. Zimmerman, G. L. D'Spain and C. D. Chadwell, "Decreasing the radiated acoustic and vibration noise of a mid-size AUV," in IEEE Journal of Oceanic Engineering, vol. 30, no. 1, 2005, pp. 179-187 Authors: T. Tritva Jyothi Kiran
38. Paper Title: Deep Transform Learning Vision Accuracy Analysis on GPU using Tensor Flow Abstract: Transfer learning is one of the most amazing concepts in machine learning and A.I. Transfer learning 224-227 is completely unsupervised model. Transfer learning is a machine learning technique in which a network that has been trained to perform a specific task is being reused or repurposed as a starting point to perform another similar task. For this work I used ImageNet Dataset and MobileNet model to analyse Accuracy performance of my Deep Transform learning model on GPU of Intel® Core™ i3-7100U CPU using TensorFlow 2.0 Hub and Keras. ImageNet is an open source Large-Scale dataset of images consisting of 1000 classes and over 1.5 million images. And my overall idea is to analyse accuracy of Vision performance on the very poor network configuration. This work reached an Accuracy almost near to 100% on GPU of Intel® Core™ i3-7100U CPU which is great result with datasets used in this work are not easy to deal and having a lot of classes. That‘s why it‘s impacting the performance of the network. To classify and predict from tons of images from more classes on low configured network is really challenging one, it‘s a great thing the computer vision accuracy showed an excellent vision nearly 100% on GPU in my work.
Keywords: Accuracy, Vision, TensorFlow, Transform Learning, Deep Learning, GPU, Dense layer, ImageNet Database.
References: 1. Jyothi Maggu, Emilie Chouzenoux, Giovanni Chiercha and Angshul Majumdar, Convolutional Transform learning, ICONIP, pp.162-174, 2018. 2. Shruti Nagpal, Maneet Singh, Richa Singh, Mayank Vatsa, Face Sketch Matching via Coupled Deep Transform Learning, ICCV, 2017. 3. Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, ImageNet Large Scale Visual Recognition Challenge, cs.CV, 2015. 4. Mark Sandler, Andrew Howard, Menglong Zhu, Andey Zhmoginov, MobileNetV2: Inverted Residuals and Linear Bottlenecks, arXiv, 2019. 5. T. Chugh, M. Singh, S. Nagpal, Transfer Learning based evolutionary algorithm for composite face sketch recognition. In IEEE Conference, 2017. 6. Emilio Soria Olivas, Jose David Martin Guerreo, Marcelino Martinez Sober, Hand Book of Research on Machine Learning Applications and Trends. 7. Mobilenetv2 source code. Available from 8. https://github.com/tensorflow/models/tree/master/research/slim/ 9. nets/mobilenet. 10. ImageNet dataset open source available from: http://www.image-net.org/ Authors: Md. K.M.Farooqui, K.V.L Somasekhar, D.V.Seshagirirao, P. Nagavsrinivas, S. Durga prasad
Paper Title: Reducing Defects on Cam Shaft by Six Sigma Methodology Abstract: The efficiency of an IC engine is mainly depends on opening and closing of inlet and outlet valves. The valves are operated by cam shaft. So cam shaft must be free from defects to maintain proper combustion of a internal combustion engine. Thus we are focusing to reduce the defects and improving quality in the manufacturing process and operations of a cam shaft in manufacturing industry for different automotives. In camshaft manufacturing the considerable defects are material selection, changing their mechanical properties while machining, temperature defects, casting defects, tolerances and surface roughness. So manufactured cam shaft has to overcome above defects for safe operating of valves. Six Sigma is a business technique and a methodical philosophy utilization of which prompts achievement in benefit through quantum gain in item quality, consumer loyalty and efficiency. The goal was to diminish the quantity of deformities to as low as 3.4 parts per million chances. Methodology used: DMAIC is one of the tool of six sigma used to improve the process and also to find root causes for any problem to reduce the defects occuring in any industry. we are using 39. DMAIC tool to reduce defects and improve quality. DMAIC stands for define, measure, analyze, improve, control. 228-235 Keywords: Six sigma, DMAIC, defects per million opportunities, control charts, xbar chart.
References: 1. Hsiang-Chin Hung and Ming-Hsien Sung, (2011), ―Applying Six Sigma to Manufacturing Processes in the Food Industry to Reduce Quality‖ Scientific Research. 2. S.Pimsakul,N.Somsuk,W.Junboon,T.Laosirihongthong,(2013),- Production Process Improvement Using the Six Sigma DMAIC Methodology: A Case Study of a Laser Computer Mouse Production Process‖ The 19th International Conference on Industrial Engineering and Engineering Management. 3. Mohit Taneja1, Arpan Manchanda,(2013), ―Six Sigma an Approach to Improve Productivity in Manufacturing Industry‖ International Journal of Engineering Trends and Technology (IJETT). 4. Adan Valles et. Al (2009), ―Implementation of Six Sigma in a Manufacturing Process: A case study‖, International Journal of Industrial Engineering. 5. Nilesh V Fursule Dr Satish V Bansode Swati N Fursule (2012), Understanding the Benefits and Limitations of Six Sigma Methodology‖, International Journal Of Scientific And Research Publications. 6. Shewhart, W. A. (1931, 1980). Economic Control of Quality of Manufacturing, Milwaukee, WI: ASQ Quality Press. Authors: Roopa KV, Sanjeev Kumar K.M
Paper Title: “One Tap Shopping”: Impulsive Fashion And Apparel Buying Behaviour Abstract: Digitalization has transformed brick and motor fashion-oriented business to one tap convenient 40. business through smart phones via mobile applications. The digital age is more inclined towards fashion and apparel due to ample exposure of current trends in fashion industry through internet, social media, travelling, 236-242 cultural exchange and others. The study emphasized on discovering the online fashion and apparel buying behavior, satisfaction level, and exploring the most influential factors towards the digital consumers for online fashion and apparel shopping by analyzing 256 respondents through convenient and judgmental sampling. Data is analyzed through Factor analysis and multiple regression. The study reveals that Price sensitivity factors has significant weightage towards online fashion and apparel shopping like flash sales, loyalty programs and points, spike sales- exciting offers on all categories for limited period, cashback offers, Discounts and offers.
Keywords: Spike sales, ―YAMI‖- young – aspirational- mobile native - impulsive action, social engagement, website artistry.
References: 1. Internet usage in India, statistics and facts. July 7 2020. Statista from https://www.statista.com/topics/2157/internet-usage-in-india/ 2. Global E-commerce sales to reach nearly $ 3.46 trillion in 2019. Nov13, 2019. Digital commerce 360 from https://www.digitalcommerce360.com/article/global-ecommerce-sales/ 3. E-commerce sales by country in 2019, Oberlo from https://www.oberlo.com/statistics/ecommerce-sales-by-country. 4. E-commerce in India- statistics and facts, Aug 4, 2020, Statista from https://www.statista.com/topics/2454/e-commerce-in-india/. 5. The Era of the connected consumers, Jan 22,2020. Economic times, Brand Equity from https://brandequity.economictimes.indiatimes.com/news/digital/the-era-of-the-connected-consumer/73478274 6. Online fashion market in India, Nov, 2019.Redseers from https://redseer.com/newsletters/online-fashion-market-in-india/ 7. Amazon and flip kart are taking fashion beyond metros, Sep 30, 2019. Financial express from https://www.financialexpress.com/industry/amazon-and-flipkart-are-making-fashion-sense-beyond-metros/1721343/. 8. Pani, A., & Sharma, M. (2012). Emerging trends in fashion marketing: a case study of apparel retailing in Authors: Rubina Jahangir Khan, Raj Kulkarni , Jagannath Jadhav
Paper Title: A Machine Learning Approach for Ecg Analysis for Emotions Abstract: Emotions are feelings which one can feel and are hard to be put in a form by a person .However they reflect the mental state of a person. Emotions like joy and sadness can be somehow detected from the facial expressions or through the body language. But these emotions do have an impact upon our system. An individual‘s electrocardiogram is a way through which one can know the impact of different parameters such as stress, joy, sadness, anger on the mechanism of our body. The emotions such as anger, sadness have an adverse effect on the cardio system and is seen in the form of abnormal ECG which can be a good pointer to a counselor when finding out the reasons and diagnosis. The decomposition technique along with the Hilbert transform can be used for feature retrieval. The different emotions are detected through the binary classification technique
Keywords: denoised, mean frequency, fission, fusion, decomposition, classifier
References: 41. 1. A.N PAithane,Dr. Bormane,Sneha Dinde ,‖Human Emotion Recognition using Electrocardiogram Signals.‖,International Journal on Recent and Innovation Trends in Computing and Communication 2. Volume: 2 Issue: 2 194 – 197 243-246 3. 1Amjad M.R. AlzeerAlhouseini,Imad Fakhri Al-Shaikhli,Abdul Wahab bin Abdul Rahman, International Journal of Advancements in Computing Technology(IJACT) Volume8, Number3, June 2016 4. WANG Bin ,LIU Guang-Yuan, An Experimental Study on Electrocardiography toward Emotion Recognition in 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery. 5. 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. 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. Foteini Agrafioti, Adam K.Anderson―ECG Pattern Analysis for Emotion Detection‖, IEEE Transactions on Affectiv Computing, 2012, Vol. 3, No.1, pp.102-115. 9. N.E. Huang, Z. Shen, R.R. Long, M.L. Wu, Q. Zheng, N.C. Yen, and C.C. Tung, ―The Empirical Mode Decomposition and Hilbert Spectrum for Nonlinear and Nonstationary Time Series Analysis,‖ Proc. Royal Soc. London, vol. 454, pp. 903-995, 1998. 10. S.M.A. Bhuiyan, R.R. Adhami, and J.F. Khan, ―A Novel Approachof Fast and Adaptive Bidimensional Empirical Mode Decomposition,‖
Authors: G Chandhini, B Chithra, P Kiruthikadevi, Bhagya sasi, V. Kamal Kumar
Paper Title: IoT Based Underground Drainage Monitoring System Abstract: Underground drainage monitoring system plays an important role in keeping the cities clean and healthy. Compared to other countries, India consists of highest number of sewage workers. Exposure of sewage workers to poisonous gases like hydrogen sulphide, sulphur dioxide, carbon monoxide, methane, ammonia, nitrogen oxide increases the death of the sewage workers. The main aim of this project is to design a network system which helps in monitoring poisonous gases present in sewage. Whenever the gas level crosses the 42. threshold value, the information with different gas ppm values is displayed in the smart phone through the app. It also indicates whether it is safe for the manual scavengers to work in the environment or not. 247-249 Keywords: Smart phone, IOT, Alarm, Threshold value, Sensors, Application, LED, Sewage system.
References: 1. Prof S. A. Shaikhl, Suvarna A. Sonawane2, ―Monitoring Smart City Application Using Node MCU on IOT‖ International Journal of Innovative science, Engineering and Technology, Vol 5Issue VIL,July 2017. 2. Prof Muragesh SK1, Santhosha Rao2,‖Automated Internet of Things for Underground Drainage and Manhole Monitoring systems for Metropolitan Cities.‖International Journal of Innovative Science, Engineering & Technology, Vol. 2 june 4, june 2015. 3. Lazarescu, M. T., ―Design of a WSN Platform for Long-Term Environment Monitoring for IoT Applications, ―Emerging and Selected Topics in Circuits and system, IEEE journal on, vol 3, no.1,pp.45,54, March 2013. 4. T.Leppanen, Harjula, E., Ylianttila, M., Ojala, T.,,,and Yang, L. T. (2013).‖Cloud things: A common architecture for integrating the internet of things with cloud computing.‖ Proceeding of the 2013 IEEE 17th International conference on computer supported cooperative work in design (CSCWD), 651-657(june). 5. Manna, s., Bhunia, S. S., and Mukherjee, N. (2014).‖Vehicular pollution monitoring using iot‖ International Conference on Recent Advances and Innovation in Engineering(ICRAIE-2014), 1-5(May). Authors: Jenifer Mahilraj Trajectory Based Location Prediction and Enriched Ontological User Profiles for Efficient Paper Title: Website Recommendation Abstract: The spread over of huge amount of information in the vast area of internet makes difficult for the users to obtain the search items that are relevant to them. The adoption of web usage mining helps to discover the accurate search results that satisfy their requirements. To fulfill their need, it is necessary to know their preferences of search at various contexts. In general, the user profiles are used to determine the taste of the users. The traditional method of user profiling does not provide a complete detail regarding their search. In addition, the search preference of the individuals varies in accordance with time and location. The user profiles do not update the dynamic location changes of the users. The traditional location based recommendation systems suggest the search results based on their location to compensate the dynamic preferences of the users. The drawbacks of the conventional systems are resolved by the Location and User Profile (LUP) based recommendation system. To attain a higher user satisfaction by providing accurate search results, a trajectory based location prediction and enriched ontological user profiles to recommend the appropriate websites to the users is proposed in this paper. In this article, we suggest a novel method for predicting the location of a user's profile using Semantic Trajectory Pattern (STP), based on both the place and semantic features of user trajectories. Our prediction model 's central concept is based on a novel cluster-based prediction approach that evaluates the location of user search data based on the regular activities of related users in the same cluster, calculated by evaluating the typical behavior of users in semantic trajectories. The combination of location information along with enriched ontological user profiles improves the efficiency of the proposed web recommendation system. The experimental results are evaluated using recall, precision and F- measure metrics.
Keywords: Geographic mining , Ontological user profiles, semantic mining, Trajectory pattern mining, Web usage mining.
References: 1. Bao, J., Zheng, Y., Wilkie, D., & Mokbel, M.‖Recommendations in location- based social networks: a survey‖,Geoinformatica,2015, vol 19, pp 525-565. 2. Cheng, C., Yang, H., King, I., & Lyu, M. R. ―Fused Matrix Factorization with Geographical and Social Influence in Location- Based Social Networks‖, Paper presented at the Aaai conference,2012. 43. 3. Eyharabide, V., & Amandi, A. (2012). ―Ontology-based user profile learning‖ AppliedIntelligence, vol 36, page 857-869. 4. Gao, H., Tang, J., & Liu, H. ―Mobile location prediction in spatio-temporal context‖. Paper presented at the Nokia mobile data challenge workshop,2012. 5. Jagan, S., & Rajagopalan, S.‖ A Survey on Web Personalization of Web UsageMining‖. International Research Journal of Engineering 250-257 and Technology,2015,vol 2 issue 1, pp 6-12. 6. Levandoski, J. J., Sarwat, M., Eldawy, A., & Mokbel, M. F.‖ LARS: A Location-Aware Recommender System‖,Paper presented at the 2012 IEEE 28th International Conference on Data Engineering, 1-5 April 2012. 7. Liu, X., & Aberer, K. ―SoCo: a social network aided context-aware recommender system‖. Paper presented at the Proceedings of the 22nd international conference on World Wide Web,2013. 8. Liu, Y., Zhao, Y., Chen, L., Pei, J., & Han, J. ―Mining frequent trajectory patterns for activity monitoring using radio frequency tag arrays‖, IEEE Transactions on Parallel and Distributed Systems, 2012,vol 23, pp2138-2149. 9. Majid, A., Chen, L., Chen, G., Mirza, H. T., Hussain, I., & Woodward, J. ―A context-aware personalized travel recommendation system based on geotagged social media data mining‖. International Journal of Geographical Information Science,2013,vol 27,pp 662-684. 10. Makvana, K., Shah, P., & Shah, P. ,‖A novel approach to personalize web search through user profiling and query reformulation‖, paper presented at the International Conference on Data Mining and Intelligent Computing (ICDMIC), 2014 11. Mathew, W., Raposo, R., & Martins, B.‖Predicting future locations with hidden Markov models‖,paper presented at the Proceedings of the 2012 ACM Conference on Ubiquitous Computing. 12. Meehan, K., Lunney, T., Curran, K., & McCaughey, A.‖Context-aware intelligent recommendation system for tourism‖ paper presented at the 2013 IEEE International Conference on PervasiveComputing and Communications Workshops (PERCOM Workshops). 13. Mehtaa, P., Parekh, B., Modi, K., & Solanki, P,‖Web personalization using web mining: concept and research issue‖, International Journal of Information and Education Technology, 2012,vol 2, pp 510. 14. Noulas, A., Scellato, S., Lathia, N., & Mascolo, C,‖A random walk around the city: New venue recommendation in location-based social networks‖,Paper presented at the International Conference on Privacy, Security, Risk and Trust (PASSAT), 2012 and International Confernece on Social Computing (SocialCom),2012 15. Savage, N. S., Baranski, M., Chavez, N. E., & Höllerer, T. (2012).‖loco: A location based context aware recommendation system Advances in Location-Based Services ―Springer,2012,pp 37-54. 16. Wang, X., Rosenblum, D., & Wang, Y. ―Context-aware mobile music recommendation for daily activities.‖,paper presented at the Proceedings of the 20th ACM international conference on Multimedia,2012. 17. Yin, H., Sun, Y., Cui, B., Hu, Z., & Chen, L‖LCARS: a location-content- aware recommender system‖,paper presented at the Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining,2013. 18. Ying, J.-C., Chen, H.-S., Lin, K. W., Lu, E. H.-C., Tseng, V. S., Tsai, H.-W., . . . Lin,S.-C..‖Semantic trajectory-based high utility item recommendation system. Expert Systems with Applications‖, 2014 ,vol 41, pp 4762-4776. 19. M.jenifer,Dr.Thabasu kannan‖ LACFAC-Location-Aware Collaborative Filtering and Association-based Clustering Approach for Web Service Recommendation‖ Int. J. Web Engineering and Technology, Vol. 13, No. 3, 2018,pp 203-222. 20. David Martin et.al,‖OWL-S: Semantic Markup for Web Services‖, W3C Member Submission 22 November 2004. 21. Josh Jia-Ching Ying et al.,‖ Semantic trajectory mining for location prediction‖, Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information SystemsNovember 2011,Pages 34–43.
Authors: Shreerang J. More, Pranav S. Patil, Jitendra M. More, Prayag S. Patil, Satish S. Marathe
Paper Title: IoT based Patient Health Care for COVID 19 Centre Abstract: In this paper, COVID 19 centre monitoring and management system has been proposed and integration of different sensor network with Internet of Things (IoT). The sensors implemented can communicate with data collection and processing unit. The data collection done by that unit can directly transferred to cloud using internet connectivity at COVID 19 centre. Therefore work aimed to propose COVID 19 centre management with IoT based approach to handle medical services and patient monitoring and treatment work flow. In the experimented model, Node MCU ESP8266 controller and temperature sensor (DHT11) are integrated. A system has capability to monitor and control COVID 19 centre services and patient monitoring via remote connection. It is evaluated with three temperature sensors connected to measure temperature of patients. Mobile based blynk has been utilized for the cloud based IoT implementation. Sensor sends data over blynk server and then can be seen anywhere using smart phone application. In addition, when patient get fever more than regular value, an alert was sent to authority in a quick time. After results, it is indicated that the developed system has effective potential to work in pandemic situation and has technological feasibility. The benefits of implemented research methods are useful in digital health management in pandemic scenario. Even hospitals, COVID centers, intensive care unit (ICU) can be operated effectively and patient diagnosis application based on online database has wide scope in the area of internet of things and patient health management. 44.
Keywords: Blynk , COVID 19, Health Care, IoT, Node MCU, Sensor Network. 258-263
References: 1. Singh, Ravi Pratap, et al. "Internet of things (IoT) applications to fight against COVID-19 pandemic." Diabetes & Metabolic Syndrome: Clinical Research & Reviews (2020). 2. Durani, Homera, et al. "Smart automated home application using IoT with Blynk app." 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT). IEEE, 2018. 3. Krishnan, D. Shiva Rama, Subhash Chand Gupta, and Tanupriya Choudhury. "An IoT based patient health monitoring system." 2018 International Conference on Advances in Computing and Communication Engineering (ICACCE). IEEE, 2018. 4. Serikul, Peerasak, Nuttapun Nakpong, and Nitigan Nakjuatong. "Smart farm monitoring via the Blynk IoT platform: case study: humidity monitoring and data recording." 2018 16th International Conference on ICT and Knowledge Engineering (ICT&KE). IEEE, 2018. 5. Chamola, Vinay, et al. "A Comprehensive Review of the COVID-19 Pandemic and the Role of IoT, Drones, AI, Blockchain, and 5G in Managing its Impact." IEEE Access 8 (2020): 90225-90265. 6. Cacovean, Dan, Irina Ioana, and Gabriela Nitulescu. "IoT System in Diagnosis of Covid-19 Patients." Informatica Economica 24.2 (2020): 75-89. 7. Senthamilarasi, C., et al. "A smart patient health monitoring system using IoT." International Journal of Pure and Applied Mathematics 119.6 (2018): 59-70. 8. Rahman, Ruhani Ab, et al. "IoT-based personal health care monitoring device for diabetic patients." 2017 IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE). IEEE, 2017. 9. https://docs.blynk.cc/ Authors: Chaarumathi P, Divya S.R, Divyajothi R, Mehareethaa K.V, Kamalkumar V
Paper Title: Voice Controlled Fire Fighting Robot Abstract: Even though there are a lot of advancements in technology, there have been an increased number of devastating losses in the field of fire-fighting. Fire accidents that occur in industries like atomic power plants, petroleum refineries, chemical factories and other large-scale fire industries end in quite serious consequences which can cause injuries or even death of individuals. Therefore, this paper is enhanced to develop an automated fire extinguishing robotic vehicle that saves the lives of firefighters and other persons in those areas. The proposed robotic vehicle is controlled using specified speech commands. The language input is more familiar which makes interaction with the robotic vehicle much easier. The advantages of voice-controlled robots are hands-free and rapid data input operations. The speech recognition process is done in such a way that it recognizes specified commands from the user and the designed robot navigates based on the instructions via the speech commands. The fire can be extinguished using a water tank that is fitted along with the robotic vehicle. Consequently, the site of fire is live monitored using ESP 32 and the status of the fire zone is updated to the user 45. through message.
Keywords: Arduino, ESP32, Fire extinguishing, Live monitoring. 264-267
References: 1. Ratnesh Malik, ―Fire Fighting Robot: An Approach‖, Indian Streams Research Journal Vol.2, Issue.II/March; 12pp.1-4 2. Swati A. Deshmukh, Karishma A. Matte and Rashmi A. Pandhare, ―Wireless Fire Fighting Robot‖, International Journal For Research In Emerging Science and Technology. 3. Lakshay Arora, Prof.Amol Joglekar, ―Cell Phone Controlled Robot with Fire Detection Sensors‖, (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 6 (3), 2015, 2954-2958. 4. Arpit Sharma, Reetesh Verma, Saurabh Gupta and Sukhdeep Kaur Bhatia, ―Android Phone Controlled Robot Using Bluetooth‖, International Journal of Electronic and Electrical Engineering. ISSN 0974-2174, Volume 7, Number 5 (2014), pp. 443-448 5. Saravanan P, ―Design and Development of Integrated Semi- Autonomous Fire Fighting Mobile Robot‖, International Journal of Engineering Science and Innovative Technology (IJESIT) Volume 4, Issue 2, March 2015. 6. Guiseppe Riccardi (2015), ‗Active Learning: Theory and Applications to Automatic Speech Recognition‘, IEEE Transaction of Speech and Audio Processing, Vol. 13, No. 4, pp 1-8. 7. Shantanu Chakrabarthy ―Robust Speech Feature Extraction by Growth Transformation in reproducing kernel Hillber space‖, IEEE Transactions on Audio Speech and Language Processing, Vol.15, No 6, June2007. 8. T. Aprille and T. Trick, ―Steady-State Analysis of Nonlinear Circuits with Periodic Inputs‖, Proceedings of the IEEE, vol. 60, no.1, pp.108-114, January 2015. 9. Rakib, T. and Sarkar, M.R., ―Design and fabrication of an autonomous fire fighting robot with multisensor fire detection using PID controller‖, 2016 5th IEEE International Conference on Informatics, Electronics and Vision (ICIEV), May 13-14, 2016, Bangladesh, pp. 909-914. 10. Suresh, J., ―Fire-fighting robot‖, 2017 IEEE International Conference on Computational Intelligence in Data Science (ICCIDS), June 2- 3, 2017, India, pp. 1-4. Authors: Aidy Ali, M. F. Abdullah, M. K. Faidzi, Kannan Rassiah
Paper Title: Fatigue Lifetime Prediction of Laminated Composites, A Review Abstract: It is evident that laminated reinforced composite are successfully prolonging the life of composites compared to particle and fiber type of reinforced composites method. The question on how this laminated composites take up the fatigue loading is crucial in order to give sound confident to industry replacing their design from metal to composites based. The lack of confident and uncertainties‘ life of composites components become an issue to designer to shift from metal based to composites based especially when the design required to be done in short time. This review gives a clear picture the state of fatigue life modelling and the life prediction of laminated composites structures. The types of model are favorable when it is accurate, simple and required less input parameters. In the end, this review gives clear pictures on mechanism that involve and the fundamental of formula that available at present.
Keywords: Fatigue of Composite, Laminated Composites, Modelling of Fatigue Composite, Life Prediction.
References: 1. R.G. Budynas, and J.K. Nisbett, Shigley's Mechanical Engineering Design, 10th Edition New York. McGraw-Hill, 2015. 2. S. Kalpakjian, S.R. Schmid, and K.S. Sekar, Manufacturing engineering and technology, Pearson Education South Asia Pte Ltd, 2014. 3. W.D. Callister, and D.G. Rethwisch, Materials science and engineer an introduction, New York: John Wiley & Sons, 2007. 4. J.F. Shackelford, Y.H. Han, S. Kim, and S.H. Kwon, CRC materialsscience and engineering handbook. CRC press, 2016. 5. K. Rassiah, and M. M. H. Megat Ahmad, ―A review on mechanicalproperties of bamboo fiber reinforced polymer composite‖ Australian Journal of Basic and Applied Sciences 7, 2013, pp. 247-253. 6. M.R. Sanjay, G.R. Arpitha, and B. Yogesha, ―Study on mechanicalproperties of natural-glass fibre reinforced polymer hybrid composites: A review‖, Materials today: proceedings, 2(4-5), 2015, pp. 2959-2967. 7. K.G. Satish, B. Siddeswarappa, and K.M. Kaleemulla, ―Characterization of in-plane mechanical properties of laminated hybridcomposites‖, Journal of Minerals and Materials Characterization and Engineering, Vol. 9 (2), 2010, pp. 105-114. 8. R. Agarwal, N.S. Saxena, K.B. Sharma, S. Thomas, and L.A. Pothan, ―Thermal conduction and diffusion through glass-banana fibe polyester composites‖, Ind. J. Pure Appl. Phys., Vol.41 (6), 2003, pp 448-452. 9. P.N.B. Reis, J.A.M Ferreira, F.V. Antunes, and J.D.M. Costa, ―Flexural 10. behaviour of hybrid laminated composites‖, Composites Part A: Applied Science and Manufacturing, Vol. 38, Issue 6, 2007, pp. 1612- 1620. 11. Y. Liu, M. Farnsworth, and A. Tiwari, A review of optimisation 12. techniques used in the composite recycling area: State-of-the-art and steps towards a research agenda. DOI : 46. 10.1016/j.jclepro.2016.08.038 13. J.N. Reddy, Mechanics of laminated composite plates and shells: theoryand analysis. CRC press, 2004. 14. S.S. Morye and R. P. Wool, ―Mechanical properties of glass/flax hybrid 268-278 15. composites based on a novel modified soybean oil matrix material‖, Polymer Composites Vol 26, no. 4, 2005, pp. 407 - 416. DOI: 10.1002/pc.20099 16. S. Suresh, Fatigue of Materials, 2nd edition Massachusetts Institute of 17. Technology, Cambridge University Press, 1998. Online ISBN:9780511806575 https://doi.org/10.1017/CBO9780511806575 18. Aidy Ali, Mike W. Brown, and C. A Rodopoulous, ―Modelling of crack 19. coalescence in 2024-T351 Al alloy friction stir welded joints‖, International Journal of Fatigue, Vol 30, 2008, pp. 2030-2043. 20. Aidy Ali, X. An, C. A. Rodopoulos, M. W. Brown, P. O'Hara, A. Levers 21. d and S. Gardiner, ―The Effect of Controlled Shot Peening on the Fatigue Behaviour of 2024-T3 Aluminium Friction Stir Welds‖, International Journal of Fatigue, Vol 29, 2007, pp. 1531-1545. 22. Laird Nan M and James H. Ware, ―Random-Eff ects Models for Longitudinal Data‖, Biometrics Vol 38, 1982, pp. 963-974. 23. Aidy Ali, Ng Wei Kuan, Faiz Arifin, Kannan Rassiah, Faiz Othman, Shauqi Hazin, and Megat Hamdan Megat Ahmad, "Fracture properties of hybrid woven bamboo/woven e-glass fiber composites", International Journal of Structural Integrity, Emerald, Vol 9, No 4, 2018, pp. 491-519. https://doi.org/10.1108/IJSI-09-2017-0051 24. Aidy Ali, Rabiatun Adawiyah, Kannan Rassiah, Wei. Kuan Ng, Faiz Arifin, Faiz Othman, Muhammad Shauqi Hazin, M.K Faidzi, M. Abdullah, and M.M.H. Megat Ahmad, "Ballistic Impact Properties of Woven Bamboo-Woven E Glass- Unsaturated Polyester Hybrid Composites", Defense Technology. Vol 9 No 4, pp. 491-519, 2018. https://doi.org/10.1108/IJSI-09-017-0051 25. M. Kaminski, F. Laurin, J.F. Maire, C. Rakotoarisoa, and E. Hémon. Fatigue damage modeling of composite structures: the onera viewpoint. AerospaceLab, 2015, pp. 1-12. Available: https://hal.archives-ouvertes.fr/hal-01193150 26. W. Huang, J. Zhao, A. Xing, G. Wang, and H, Tao. ―Influence of tool path strategies on fatigue performance of high-speed ball-end- milled AISI H13 steel‖, The International Journal of Advanced Manufacturing Technology, Vol. 94, no. 1-4, 2018, pp. 371-380. 27. J.W. Weeton, K.L. Thomas, and D.M. Peter, Engineer‘ guide to composited material: American Society for Metal International, 1987. 28. Clemence Rubiella, Cyrus A. Hessabi, and Arash Soleiman Fallah, ―State of the art in fatigue modelling of composite wind turbine blades‖, International Journal of Fatigue 117, 2018, pp. 230-245. 29. F.H. Bhuiyan, and R.S Fertig III, ―Predicting matrix and delamination fatigue in fiber-reinforced polymer composites using kinetic theory of fracture‖, International Journal of Fatigue, 117, 2018, pp. 327-339. 30. T. Suzuki, H. Mahfuz, and M. Takanashi, ―A new stiffness degradation model for fatigue life prediction of GFRPs under random loading‖, International Journal of Fatigue, 119, 2019, pp. 220-228. 31. C. Ganesan, P.S. Joanna, and G. Srilochan, ―Modeling the damage of composite materials subjected to fatigue loading‖, In IOP Conference Series: Materials Science and Engineering, Vol. 377, No. 1, 2018, (p. 012093), IOP Publishing, DOI: 10.1088/1757- 899X/377/1/012093. 32. A. Tabiei, and W. Zhang, ―Composite laminate delamination simulation and experiment: a review of recent development‖, Applied Mechanics Reviews, 70(3), 2018, 030801. DOI: 10.1115/1.4040448. 33. J. Llobet, P. Maimí, Y. Essa, andF.M. de la Escalera, ―Progressive matrix cracking in carbon/epoxy cross-ply laminates under static and fatigue loading‖, International Journal of Fatigue, 119, 2019, pp. 330-337. 34. C. Tao, S. Mukhopadhyay, B. Zhang, L.F. Kawashita, J. Qiu, and S.R. Hallett, ―An improved delamination fatigue cohesive interface model for complex three-dimensional multi-interface cases‖, Composites Part A: Applied Science and Manufacturing, 107, 2018 pp. 633-646. 35. H. Ijaz, W. Saleem, M. Zain-ul-Abdein, A.A. Taimoor, and A.S.B. Mahfouz, ―Fatigue Delamination Crack Growth in GFRP Composite Laminates: Mathematical Modelling and FE Simulation‖, International Journal of Aerospace Engineering, vol. 2018, Article ID 2081785, 8 pages, 2018. https://doi.org/10.1155/2018/2081785. 36. G. Allegri, ―Modelling fatigue delamination growth in fibre-reinforced composites: Power-law equations or artificial neural networks‖, Materials & Design, 155, 2018, pp. 59-70. 37. N. Jagannathan, S. Gururaja, and C.M. Manjunatha, ―Probabilistic strength based matrix crack evolution model in multi-directional composite laminates under fatigue loading‖, International Journal of Fatigue, 117, 2018, pp. 135-147. 38. M. K. Faidzi, A. K. Hamizi, M. F Abdullah, M. A Aliimran, K. Z Ku 39. Ahmad, Raja Nor Othman, Aidy Ali, ―Fatigue Crack Growth Behaviour of Sandwiched Metal Panel of Aluminium and Mild Steel under Constant Amplitude Loading‖, International Journal of Engineering and Technology, 7 (4.33), 2018, pp. 362-366. 40. Mohd Khairul Faidzi Muhamad Paudzi, Mohamad Faizal Abdullah, 41. Aidy Ali. Fatigue Analysis of Hybrid Composites of Kenaf/Kevlar Fibre Reinforced Epoxy Composites, Jurnal Kejuruteraan (Journal of Engineering), Online First http://dx.doi.org/10.17576/jkukm-2018-SI-1(7),2018, ISSN:0128-0198, E-ISSN:2289-7526 Authors: Durga Pathrikar, V. N. Jirafe
Paper Title: Implementation of Iterative bilateral filtering for removal of Rician noise in MR images using FPGA Abstract: Magnetic resonance image noise reduction is important to process further and visual analysis. Bilateral filter is denoises image and also preserves edge. It proposes Iterative bilateral filter which reduces Rician noise in the magnitude magnetic resonance images and retains the fine structures, edges and it also reduces the bias caused by Rician noise. The visual and diagnostic quality of the image is retained. The quantitative analysis is based on analysis of standard quality metrics parameters like peak signal-to-noise ratio and mean structural similarity index matrix reveals that these methods yields better results than the other proposed denoising methods for MRI. Problem associated with the method is that it is computationally complex hence time consuming. It is not recommended for real time applications. To use in real time application a 47. parallel implantation of the same using FPGA is proposed.
Keywords: Iterative Bilateral Filtering, MRI, Rician Noise, FPGA 279-284
References: 1. Wright, G.: Magnetic resonance imaging. IEEE Signal Process. Mag. 1, 56–66 (1997) 2. Nishimura, D.G.: Principles of Magnetic Resonance Imaging. Stanford University, Stanford, CA (2010) 3. Manjón, J.V., Carbonell-Caballero, J., Lull, J.J., Garciá-Martí, G., Martí-Bonmatí, L., Robles, M.: MRI denoising using non-local means. Med. Image Anal. 12, 514–523 (2008) 4. Wiest-Daesslé, N., Prima, S., Coupé, P., Morrissey, S. P., Barillo, C.: Rician noise removal by non-local means filtering for low signal- to-noise ratio MRI: applications to DT-MRI. In: Proceedings of MICCAI, pp. 171-179 (2008) 5. Manjón, J.V., Coupé, P., Martí-Bonmatí, L., Collins, D.L., Robles, M.: Adaptive non local means denoising of MR images with spatially varying noise levels. J. Magn. Reson. Imaging 31, 192–203(2010) 6. R. Riji, Jeny Rajan, Jan Sijbers, Madhu S. Nair. "Iterative bilateral filter for Rician noise reduction in MR images", Signal, Image and Video Processing, 2014 Authors: Baratov Mirodilzhon, Tukhtashev Khikmatilla Trends in Development Private Ownership of land and land Parcels in Uzbekistan: Scientific and Paper Title: Theoretical Analysis Abstract: the article analyzes the stages of privatization of non-agricultural land and the stages of their development. It also analyzed the theoretical aspects and studied the peculiarities of developed countries Peru, Poland, France and the United States in the area of land privatization. In the article recommendations and conclusions on the development of existing legislation were developed based on foreign and domestic experience in land privatization.
Keywords: land plots, privatization of land plots, land of industrial, commercial and service points, auction sale, sale of private property.
References: 1. G. O. Young, ―Synthetic structure of industrial plastics (Book style with paper title and editor),‖ in Plastics, 2nd ed. vol. 3, J. Peters, Ed. New York: McGraw-Hill, 1964, pp. 15–64. 48. 2. W.-K. Chen, Linear Networks and Systems (Book style). Belmont, CA: Wadsworth, 1993, pp. 123–135. 3. H. Poor, An Introduction to Signal Detection and Estimation. New York: Springer-Verlag, 1985, ch. 4. 4. B. Smith, ―An approach to graphs of linear forms (Unpublished work style),‖ unpublished. 285-289 5. E. H. Miller, ―A note on reflector arrays (Periodical style—Accepted for publication),‖ IEEE Trans. Antennas Propagat., to be published. 6. J. Wang, ―Fundamentals of erbium-doped fiber amplifiers arrays (Periodical style—Submitted for publication),‖ IEEE J. Quantum Electron., submitted for publication. 7. C. J. Kaufman, Rocky Mountain Research Lab., Boulder, CO, private communication, May 1995. 8. Y. Yorozu, M. Hirano, K. Oka, and Y. Tagawa, ―Electron spectroscopy studies on magneto-optical media and plastic substrate interfaces(Translation Journals style),‖ IEEE Transl. J. Magn.Jpn., vol. 2, Aug. 1987, pp. 740–741 [Dig. 9th Annu. Conf. Magnetics Japan, 1982, p. 301]. 9. M. Young, The Techincal Writers Handbook. Mill Valley, CA: University Science, 1989. 10. (Basic Book/Monograph Online Sources) J. K. Author. (year, month, day). Title (edition) [Type of medium]. Volume(issue). Available: http://www.(URL) 11. J. Jones. (1991, May 10). Networks (2nd ed.) [Online]. Available: http://www.atm.com 12. Shagayda N.I. The turnover of agricultural land in Russia: the transformation of institutions and practice // Scientific works № 142 p. – M., In-t. Gaidara, 2010. – 332 P .; Sitkova O.N. Private property right to agricultural land plots and problems of its protection: dissertation ofcand. legal sciences. –Krasnodar: 2007. – 28 p. 13. Issues of development and improvement of land legislation of the Republic of Uzbekistan: a set of issues of the Republican scientific- practical conference. –Tashkent: Tashkent State University of Law, 2019. – 373 p. 14. Amelina N.E. Free privatization of land plots from the lands of settlements and problems of recognition of ownership of them by citizens: Abstract diss. ... cand. legal sciences. –Krasnodar: 2007. – 27 P.; Tyapkina E.A. Legal problems of privatization of state enterprises: Author. diss. ... cand. legal sciences. –M., 2003. – 27 p. 15. Bezbach V.V. Private land ownership in Latin America (legal regulation): Doctor of Law dissertation 12.00.03. –M., 1997. –17 p. 16. Kholmuminov J.T. Natural resource law in foreign countries: comparative analysis and improvement of legislation. Monograph. – Tashkent: Tashkent State University of Law, 2012 –P.44-45. 17. Kresnikova N. Regulation of land use in foreign countries and Russia // International Agricultural Journal. 2005. –№2. –P.43-46. 18. National Database of Legislation (LexUZ), dated 13.08.2019, № 03/19/552/3541. 19. Usmonov M.B. Renting in agriculture. –Tashkent: Tashkent State University of Law 2005. – 76 p. 20. Jalilov I.D. The emergence and development of Soviet land law in Uzbekistan. –Tashkent: Sci., 1970. – 29 p. 21. According to Article 17 of the Land Code, legal entities must have land plots on the basis of permanent possession, permanent use, term (temporary) use, lease and property rights, and individuals (citizens) must have the right for land plots to inherit, lifelong possession, permanent use, temporary (temporary) use, lease and ownership. 22. Usmonov M.B., Jo'raev Y.O., Rustamboev M.X. and others. Land law. –Tashkent: Tashkent State University of Law, 2002. – 58 p. (The author of this chapter is U.Bozorov). 23. Collection of Legislation of the Republic of Uzbekistan, 2005, № 28-29, Article 204. 24. National Database of Legislation (LexUz), 04.05.2018, № 06/18/5428/1158. 25. http://lex.uz/docs/767394. 26. This resolution repealed according to the Resolution of the Cabinet of Ministers of the Republic of Uzbekistan from April 11, 1995 of № 126 "About the procedure for sale of objects of commercial and service pointss with the lands where they are located and on private property // Collection of Resolutions of the Government of the Republic of Uzbekistan, 1995, № 4, Article 15. 27. Bulletin of the Supreme Council of the Republic of Uzbekistan, 1994, № 11-12, Article 301. 28. Collection of Resolutions of the Government of the Republic of Uzbekistan, 1995, № 4, Article 15. 29. National Database of Legislation, 05.01.2018, № 03/18/456/0512. 30. Collection of Legislation of the Republic of Uzbekistan, 2006, № 30, Article 288. 31. Collection of Legislation of the Republic of Uzbekistan, 2012, № 39, Article 446. Authors: Aditya Kaduskar, Omkar Vengurlekar, Varunraj Shinde
Paper Title: RSSI Filtering Methods Applied to Localization using Bluetooth Low Energy Abstract: Bluetooth Low Energy or BLE is a technology targeting mostly small-scale IoT applications including wearables and broadcasting beacons that require devices to send small amounts of data using minimal power. This paper focuses on our implementation, which is a system, designed to filter RSSI (Received Signal Strength Indicator), calculate the co-ordinates of a BLE device that is programmed as a Beacon and display the coordinates. Since RSSI is susceptible to noise and a downgrade in its reliability is unavoidable, several filtration methods have been used. The ‗Kalman – Histogram‘ method, which incorporates the usage of a histogram of the RSSI readings along with the Kalman filter, is our own approach to tackle issues regarding noisy RSSI readings. The localization of stationary ‗Assets‘, has been evaluated using the Trilateration algorithm: a result in mathematics which is used to locate a single point using its distance from three or more other points. The purpose of this research work is to provide a comparative result analysis of the results obtained using the aforementioned filters, indicating the effect of these filters on our localization system. As our research suggests, the ‗Kalman – Histogram‘ filter performs better as compared to other filters and can be used in localization applications for better accuracy.
Keywords: Bluetooth Low Energy (BLE), Raspberry Pi, Localization
References: 1. O. Silver, ―An Indoor Localization System Based on BLE Mesh Network‖, 2016. 2. R. Heydon, ―Detailed Bluetooth Low Energy: The Developer's Handbook‖, 2013. 49. 3. F. Zafari, A. Gkelias, and K. K. Leung, A Survey of Indoor Localization Systems and Technologies,‖ IEEE Communications Surveys & Tutorials, vol. 21, no. 3, pp. 2568-2599, 2019. 4. A. Pal, ―Localization Algorithms in Wireless Sensor Networks: Current Approaches and Future Challenges.‖, Network Protocols & 290-299 Algorithms, vol. 2, no. 1, pp. 45-73, 2010. 5. P. Kriz, F. Maly, and T. Kozel, ―Improving Indoor Localization Using Bluetooth Low Energy Beacons‖, Mobile Information Systems, vol. 2016, 2016. 6. C. M. El Amine, O. Mohamed, and B. Boualam, ―The Implementation of Indoor Localization Based On An Experimental Study of RSSI Using a Wireless Sensor Network‖, Peer-to-Peer Networking and Applications, vol. 9, no. 4, pp. 795-808, 2016. 7. J. Du, J.-F. Diouris, and Y. Wang, ―A RSSI-Based Parameter Tracking Strategy for Constrained Position Localization‖, EURASIP Journal on Advances in Signal Processing,‖ vol. 2017, no. 1, pp. 1-10, 2017. 8. P. Askari and F. Barekat, ―Trilateration and Bilateration in 3D and 2D Space Using Active Tags,‖ 10 2017. 9. V. Canton Paterna, A. Calveras Auge, J. Paradells Aspas, and M. A. Perez Bullones, ―A Bluetooth Low Energy Indoor Positioning System With Channel Diversity, Weighted Trilateration And Kalman Filtering,‖ Sensors, vol. 17, no. 12, p. 2927, 2017. 10. S. Subedi, G.-R. Kwon, S. Shin, S.-s. Hwang, and J.-Y. Pyun, ―Beacon Based Indoor Posi-tioning System Using Weighted Centroid Localization Approach‖, in 2016 Eighth International Conference on Ubiquitous and Future Networks (ICUFN), pp. 1016-1019, IEEE, 2016. 11. K. Curran, E. Furey, T. Lunney, J. Santos, D. Woods, and A. McCaughey, ―An Evaluation of Indoor Location Determination Technologies‖, Journal of Location Based Services, vol. 5, no. 2, pp. 61-78, 2011. 12. J. Talvitie, M. Renfors, and E. S. Lohan, ―A Comparison Of Received Signal Strength Statistics Between 2.4 GHz and 5 GHz Bands For Wl-Based Indoor Positioning‖, in 2015 IEEE globecom workshops (GC Wkshps), pp. 1-6, IEEE, 2015. 13. R. Faragher and R. Harle, ―Location Fingerprinting With Bluetooth Low Energy Beacons‖, IEEE journal on Selected Areas in Communications, vol. 33, no. 11, pp. 2418-2428, 2015. 14. H. Park, J. Noh, and S. Cho, ―Three-Dimensional Positioning System Using Bluetooth Low-Energy Beacons‖, International Journal of Distributed Sensor Networks, vol. 12, no. 10, p. 1550147716671720, 2016. 15. M. WENGER, J. CARRERA, and Z. ZHAO, Indoor Positioning Using Raspberry Pi With uwb‖, Bachelor Thesis, University of Bern, Institute of Computer Science, 2019. 16. X. Zhu, Y. Feng, et al., ―RSSI-Based Algorithm For Indoor Localization‖, Communications and Network, vol. 5, no. 02, p. 37, 2013. 17. Google, Bluetooth SIG, ―https://www.bluetooth.com/blog/proximity-and-rssi/‖, 18. (accessed September 21, 2015). 19. Google, Bluetooth SIG, ―https://www.bluetooth.com/blog/introducing-bluetooth-mesh-networ king/‖, (accessed July 18, 2017). Authors: Palwinder Kaur Mangat, Kamaljit Singh Saini
Paper Title: Predictive Analytics for Students’ Performance Prediction Abstract: Personalized learning is being popular due to digitizations that enable a large number of technologies to support it. To predict students‘ learning abilities, it is necessary to estimate their behavior to know about their weaknesses and strengths. If it is possible for teachers to predict in advance at-risk and dropout students, they can plan more effectively to handle them. We are describing in this paper various intelligent tutoring systems with Educational Data Mining, Predictive Learning Analytics, prediction of at-risk students at an earlier basis, how this prediction task is done. We are describing various prediction models that can be used to predict students‘ behavior and how portable these predictive models are and the various risk prediction systems that are being used.
Keywords: Predictive Learning Analytics, Intelligent Tutoring Systems, Student Risk Prediction, Risk Prediction Systems, EDM, Early Warning Systems (EWS).
References: 1. R. Conijn, C. Snijders, A. Kleingeld, and U. Matzat, ―Predicting Student Performance from LMS Data : A Comparison of 17 Blended Courses Using Moodle LMS,‖ vol. 10, no. 1, pp. 17–29, 2017. 2. U. States, ―On early prediction of risks in academic performance for students,‖ vol. 59, no. 6, pp. 1–14, 2015. 3. D. Baneres, M. E. Rodríguez, and M. Serra, ―An Early Feedback Prediction System for Learners At-risk within a First-year Higher Education Course,‖ vol. 1382, no. c, 2019. 4. W. Chen, C. G. Brinton, D. Cao, A. Mason-singh, C. Lu, and M. Chiang, ―Early Detection Prediction of Learning Outcomes in Online Short-Courses via Learning Behaviors,‖ vol. 1382, no. c, pp. 1–14, 2018. 5. C. Ramos, C. Frasson, S. Ramachandran, and C. Ramos, ―Introduction to the Special Issue on Real World Applications of Intelligent Tutoring Systems,‖ IEEE Trans. Learn. Technol., vol. 2, no. 2, pp. 62–63, 2009. 6. S. U. N. Yu and X. U. Tianwei, ―Intelligent Search Agents for Web-based Intelligent Tutoring Systems,‖ pp. 1148–1151, 2008. 7. D. Riofr, J. Ram, and M. Berrocal-lobo, ―Predicting Student Actions in a Procedural Training Environment,‖ vol. X, no. c, pp. 1–13, 50. 2017. 8. [8] L. I. U. L. Wu, M. W. Hua, I. E. College, and I. Technology, ―Design an Applied Student Model for Intelligent Tutoring System,‖ pp. 2–6. 300-305 9. B. I. Mohammad, S. I. Shaheen, and S. A. Mokhtar, ―Novel Online Tutor Modeling For Intelligent Tutoring Systems,‖ 2001. 10. G. Gutjahr, K. Menon, and P. Nedungadi, ―Using an intelligent tutoring system to predict mathematics and English assessments,‖ Proc. - 5th IEEE Int. Conf. MOOCs, Innov. Technol. Educ. MITE 2017, pp. 135–140, 2018. 11. S. Ke and X. Lu, ―Study on intelligent tutoring system based on multi-agents,‖ Proc. - 2010 6th Int. Conf. Nat. Comput. ICNC 2010, vol. 6, no. Icnc, pp. 2948–2952, 2010. 12. S. U. N. Yu, ―A Multi-Agent Intelligent Tutoring System,‖ pp. 1724–1728, 2009. 13. R. Rollande and J. Grundspenkis, ―Representation of study program as a part of graph based framework for tutoring module of intelligent tutoring system,‖ 2012 2nd Int. Conf. Digit. Inf. Process. Commun. ICDIPC 2012, pp. 108–113, 2012. 14. T. A. O. Ren and J. Xiao, ―The Influence of Student Abilities and High School on Student Growth : A Case Study of Chinese National College Entrance Exam,‖ vol. 7, 2019. 15. S. P. M. Choi et al., ―International Forum of Educational Technology & Society Learning Analytics at Low Cost : At-risk Student Prediction with Clicker Data and Systematic Proactive Interventions Published by : International Forum of Educational Technology & Society Linked refe,‖ vol. 21, no. 2, 2018. 16. R. M. M. F. Luis, M. Llamas-Nistal, and M. J. F. Iglesias, ―Enhancing learners‘ experience in e-learning based scenarios using Intelligent tutoring systems and learning analytics: First results from a perception survey,‖ Iber. Conf. Inf. Syst. Technol. Cist., pp. 1–4, 2017. 17. M. Srilekshmi, ―Learning Analytics to Identify Students at-risk in MOOCs,‖ pp. 194–199, 2016. 18. R. Umer, T. Susnjak, A. Mathrani, and S. Suriadi, ―A learning analytics approach : Using online weekly student engagement data to make predictions on student performance .,‖ 2018 Int. Conf. Comput. Electron. Electr. Eng. (ICE Cube), pp. 1–5, 2018. 19. J. Jonathan, S. Sohail, F. Kotob, and G. Salter, ―The Role of Learning Analytics in Performance Measurement in a Higher Education Institution,‖ 2018 IEEE Int. Conf. Teaching, Assessment, Learn. Eng., no. December, pp. 1201–1203, 2018. 20. E. Seidel, ―Using predictive analytics to target and improve first year student attrition,‖ no. 2011, 2017. 21. L. W. Santoso, ―Early Warning System for Academic using Data Mining,‖ 2018 Fourth Int. Conf. Adv. Comput. Commun. Autom., pp. 1–4, 2018. 22. S. Milinkovi and V. Vujovi, ―Students ‘ Success Predictive Models Based on Selected Input Parameters Set,‖ 2019 18th Int. Symp. INFOTEH-JAHORINA, no. March, pp. 1–6, 2019. 23. A. Wolff, Z. Zdrahal, and M. Pantucek, ―Improving retention : predicting at-risk students by analysing clicking behaviour in a virtual learning environment,‖ pp. 145–149, 2013.
Authors: Aboli V. Chavhan, Arif Khan
Paper Title: Water Pollution – Sources, Effects and Control Abstract: Water could be a basic asset inside the lives of people World Health Organization each enjoys its utilization and World Health Organization square measure harmed by its abuse and flightiness (flooding, dry spells, saltiness, causticity, and debased quality). Water could be a limited and weak asset. Thus, utilization of 51. polluted water places lives and jobs in peril because of water have no substitute. There square measure numerous ways during which water implied for human utilization will get debased. These grasp squanders from businesses like mining and development, food process, hot squanders from power creating enterprises, household and 306-310 agrarian squanders and by shifted microbiological operators. These days, water is being refined by differed ways anyway examination is being led to appear for a great deal of dependable and less expensive ways that may cleanse water at a sensible worth.
Keywords: water contamination, impact, spillover, control measures, composts
References: 1. Adetunde, L.A. what's more, Glover, R.L.K. (2010). Bacteriological Quality of Borehole Water utilized by Students' of University for Development Studies, Navrongo Campus in Upper-East Region of 2. Ghana. Ebb and flow Research Journal of Biological Sciences. 2(6):361-364. Akio, M. (1992). Severe Sea: The Human Cost of Minamata sickness. First Edition. Kosei Publishing, Japan. 3. 3.Baig, J.A., Kazi, T. G., Arain,M. B., Afridi, H. I., Kandhro, G.A., Sarfraz, R. A., Jamali, M. K. What's more, Shah, A. Q. (2009)? Assessment of arsenic and other physico-compound boundaries of surface and spring water of Jamshoro, Pakistan. Diary of Hazardous Materials. 166, 662–669. 4. Bu, H., Tan, X., Li, S. What's more, Zhang, Q. (2010)? Water quality appraisal of the Jinshui River (China) utilizing multivariate measurable procedures. Environ Earth Sci. 60, 1631–1639. 5. Craftsman, S.R., Caraco, N.F., Correll, D.L., Howarth, R.W., Sharpley, A.N. what's more, Smith, V.H. (1998). Non point contamination of surface waters with phosphorus and nitrogen. Natural Applications. 8: 559-568. 6. CPCB Report. (2013). Status of Water Quality in India 2011. Observing of Indian National Aquatic Resources, Series: MINARS/35/2013-14. Pp. 1-212. 7. Friberg, L., Piscator, M., Nordberg, G.F and Kjellstrom, T. (1974). Cadmium inside nature, second version, Chemical Rubber Company Press, Cleveland, Ohio, 248 pp. 8. Goolsby, D.A. what's more, Battaglin, W.A. (2001). Long haul changes in focuses and transition of nitrogen inside the Mississippi Basin, U.S.A. Hydrological Processes. 15: 1209-1226. 9. 9.Howarth, R. W., Billen,G., Swaney, D., Townsend, A., Jaworski, N., Lajtha, K., Downing, J.A., Elmgren, R., 10. Murdoch, P. Also, Zhu, Z. (1996). Local nitrogen financial plans and riverine N and P motions for the wastes toward the North Atlantic Ocean: Natural and human impacts. Biogeochemistry.35:181–226. 11. Kumar, R., Singh, R.D. also, Sharma, K.D. (2005). Water assets of India. Current Science. 85(5): 794-811. 12. Larry, W. (2006). World Water Day. A Billion people Worldwide Lack Safe Drinking http://environment.about.com/od/environmentalevents/a/waterdayqa.htm, March 22nd, 2006. 13. Mian, I. A., Begum, S., Riaz, M., Ridealgh, M., mcclean, C. J. Also, Cresser, M. S. (2010). Authors: Ankush S, Vinayprasad M S
Paper Title: Enhance Security for Authentication Abstract: An enhanced security for authentication is defined because it is vital that authentication is an extremely important crucial robust process for each user to access any of the applications. Magnificent growth and usage of the internet raise agitate about the way to communicate, protect data and sensitive information safely. In today's world hackers use differing types of attacks in order to acquire valuable information. Many of the attacks are primarily used to get into an application to steal the credentials followed by internal information of the users. The first thing of security is defined in three terms. i.e., confidentiality, integrity and availability. Confidentiality can protect information from unauthorized access and exploiting of sensitive data. Integrity measures protect information from unauthorized alteration. Whereas availability so as for a data system to be useful it must be available to authorized users. The most objective of this paper is to supply information about confidentiality in terms of multifactor authentication. Confidentiality plays a serious role in terms of authentication. Authentication is the process of proving or showing to be true. This includes confidentiality and integrity. The improved security for authentication is additionally known for multifactor authentication for the users. This multifactor authentication is implemented for an android application using a visual-picture login technique to access the an application.
52. Keywords: Multifactor authentication (MFA), One-time-password(OTP), Visual-picture login, Confidentiality, Integrity, Click-points, Coordinates. 311-315
References: 1. ABBAS ACAR, ―A PRIVACY-PRESERVING MFA‖, IEEE INTERNATIONAL JOURNAL, 2019. 2. XINYI HUANG, YANG XIANG, ―ROBUST MULTI-FACTOR AUTHENTICATION FOR FRAGILE COMMUNICATIONS‖, IEEE INTERNATIONAL JOURNAL, 2014. 3. NAPA SAE-BAE, ―BIOMETRIC-RICH GESTURE: BASED ON MULTI-TOUCH‖, INTERNATIONAL JOURNAL, 2012.K. ELISSA, ―TITLE OF PAPER IF KNOWN,‖ UNPUBLISHED. 4. ALIREZA PIRAYESH, ANGELOS STAVROU, ―UNIVERSAL MULTI-FACTOR AUTHENTICATION USING GRAPHICAL PASSWORDS‖, IEEE INTERNATIONAL JOURNAL 2008. 5. ABBAS ACAR, WENYI LIU, ―A PRIVACY-PRESERVING MULTIFACTOR AUTHENTICATION SYSTEM‖, WILEY JOURNAL, 2019M. YOUNG, THE TECHNICAL WRITER‘S HANDBOOK. MILL VALLEY, CA: UNIVERSITY SCIENCE, 1989. 6. B. MADHURAVANI, DR. P. BHASKARA REDDY, ―A COMPREHENSIVE STUDY ON DIFFERENT AUTHENTICATION FACTORS‖, IJERT 2013. 7. ALZAHRAA J. MOHAMMED AND ALI A. YASSIN, ―EFFICIENT AND FLEXIBLE MULTI-FACTOR AUTHENTICATION PROTOCOL BASED ON FUZZY EXTRACTOR OF ADMINISTRATOR‘S FINGERPRINT AND SMART MOBILE DEVICE‖, MDPI ARTICLE, 2019. 8. NEENU ANN SHAJI, SUMITHA SOMAN, ―MULTI-FACTOR AUTHENTICATION FOR NET BANKING‖, INTERNATIONAL JOURNAL OF SYSTEM AND SOFTWARE ENGINEERING, VOL 5, 2017. 9. DIVYA JAMES, MINTU PHILIP, ―A NOVEL ANTI PHISHING FRAMEWORK BASED ON VISUAL CRYPTOGRAPHY‖, IEEE 2012 10. SUGATA SANYAL, AYU TIWARI, AND SUDIP SANYAL, ―A MULTIFACTOR SECURE AUTHENTICATION SYSTEM FOR WIRELESS PAYMENT‖, SPRINGER-VERLAG, 2010 11. https://www.loginradius.com/blog/2019/06/what-is-multi-factor-authentication/ https://developer.android.com/docs Authors: Klodian Gumeni
Paper Title: Analyzing and Solving Stability Problems during the Commissioning of the Steam Turbine
53. Abstract: The commissioning of the steam turbine in the CCPP of Vlore (Albania) was carried out after a shutdown of about one year. During previous operation of the unit, in particular in a couple of shutdowns, were observed high vibration at bearing MAD 21. Before the restart, the oil deflector of the MAD21 bearing (the 316-320 bearing located in the front standard, on the inlet side of the steam turbine and adjacent to the clutch) was modified increasing the radial clearance on the part of the oil deflector acting as a thermal shield with the aim of eliminating / preventing the risk of rubbings. Rubs at the location of the above mention oil deflector were considered as the very likely cause of high vibration at bearing MAD 21. A lot of tests were carried out during the recommissioning phase and the data received were analyzed. This paper details the discovery of the problems, initial attempts to address them and the use of the rotor dynamics tools to find a solution of the problem by the optimization of the bearings. The bearings were not optimized as per rotordynamics analysis (RDA) findings, because it was a too expensive solution. The solution was found making some modification on control system of the ST, without affecting the mechanical integrity of the machine.
Keywords: bearing vibration, critical speed, shut down and start up trends, trip limit.
References: 1. J.S.Rao, ―Vibratory Condition Monitoring of Machines‖, Narosa Publishing House, 2000. 2. Ansaldo Energia, ST – Start UP Curves, pp 2 – 3, 5 – 6. 3. Ansaldo Energia, Steam Turbine General Description, pp. 2 – 3, 5 – 7. 4. Ansaldo Energia, ST – Start UP Procedures, pp. 9 – 10. 5. ISO 7919-2, Mechanical Vibration – Evaluation of machine vibration by measurements on rotating shafts, pp.8. 6. Ansaldo Energia, ST Commissioning Procedure, pp. 80 – 85. 7. C.Scheffer, P.Girdhar, ―Practical Machinery Vibration Analysis and Predictive Maintenance‖, Newnes, An Imprint of Elsevier, 2004, pp. 134 – 145. 8. Maurice L. Adams, Jr. ―Rotating Machinery Vibration‖, Marcel Dekker, 2000. Authors: J.Karpagam, P.Bavithra, I.Infranta Merlin, J.Kousalya
Paper Title: IoT Based Smart Farming Abstract: Agriculture is the key factor to satisfy the economy of our country and it is one of the basic needs of the human resources. Watering the plants is very important in agriculture. By using recent and current technologies irrigation system can be upgraded with the help of sensors and Microcontrollers. Thus, this irrigation system can be groomed and upgraded into an automated process.
Keywords: Irrigation, Water level sensor, GSM Module, Arduino-UNO.
54. References: 1. Aman Bafna, Anish Jain, Nisrag Shah and Rishab Parekh, ―IoT Based Irrigation using Arduino and Android on the bases of Weather Prediction‖, International Research Journal of Engineering and Technology, Vol 05 Issue 05, 2018, pp. 433-437 321-324 2. Kim. Y, Evans. R. G, Iversen. W.M, 2008 ‗Remote Sensing and Control of an Irrigation System using a Distributed Wireless Sensor Network‘, IEEE Transactions on Instrumentation and Measurement, Vol. 57, No.:8, pp 1-6. 3. Dhivya A, Infanta J, Chakrapani K, 2012 ‗Automated Agricultural Process using PLC and Zigbee‘, Journal of Artificial Intelligence, Vol.5, No.:4, pp 123-130. 4. Satyanarayana G. V, Mazaruddin S. D, 2013 ‗Wireless Sensor Based Remote Monitoring System for Agriculture using Zigbee and GPS‘, in Proceedings of Conference on Advances in Communication and Control Systems, pp. 110-114. 5. DewesgvreeRane, ‗Review Paper based on Automatic Irrigation System based on RF Module‘, International Journal of AICT, Vol.1, No. 9, January 2015. 6. A Goap, D Sharma, AK Shukla, CR Krishna ,2018, ‗Smart Irrigation System‘, Computers and Electronics in agriculture 155, 41-49, 2018 Authors: Kehdinga George Fomunyam
Paper Title: Theory or Practice? the Search for Value for Money in Engineering Education Abstract: Engineering education was predicated on two sources. One on trade apprenticeship where people that are trained locally under the tutelage of someone are engaged in further studies to broaden their theoretical and practical knowledge. The other source of engineering education was within the four walls of the educational institution which has in its core natural sciences and it emphasizes specialization in a specific aspect of engineering. This study seeks to understand if value for money in engineering education is in theory or practice. Value for money is one of the measures of quality of education. Value for money as a concept that has been defined by various authors and the World Bank defined value for money as the effective, efficient, and economic use of resources, which requires the evaluation of relevant costs and benefits with the assessment of risks and of non-priced items and/or cost of life cycle. The objective of this research is to determine if the search for value 55. for money in engineering education is a theory or practice. Findings from the study revealed that engineering education is one of foundation for the development of the society. By engineering education, the dynamics of life has been influenced and also human culture giving more substance to civilization and politics. It was also 325-330 found out that value for money is not only a financial marker but it has with it various economic, social, physical dimension. In engineering education costs are expended and this this necessitates the drive for value for money. This study recommends that there is a need for better measures of value for money in engineering education and there is a need to advance knowledge on the theories of engineering to ensure relevance in this changing era.
Keywords: engineering, engineering education, value for money, search for value for money, theory, practice.
References: 1. Barnett C, Barr J, Christie A, et al. 2010. Measuring the impact and value for money of governance and conflict programmes. ITAD: Hive, East Sussex, http://assets.publishing.service.gov.uk/media/57a08b1eed915d3cfd000b44/60797_ITAD-VFM-Report-Dec10.pdf 2. Blandin, B. 2012. ―The Competence of an Engineer and How It Is Built Through an Apprenticeship Program: A Tentative Model.‖ International Journal of Engineering Education 28 (1): 57–71. doi:0949-149X/91. 3. Booth, S. (2004). Engineering Education and the Pedagogy of Awareness. In Brown, S.(Ed) Effective Learning and Teaching in Higher Education (PP 9-23). London: Taylor & Francis E-Library. 4. Booth, S. (2004). Engineering Education and the Pedagogy of Awareness. In Brown, S.(Ed) Effective Learning and Teaching in Higher Education (PP 9-23). London: Taylor & Francis E-Library. 5. Continental AG. ―In Search of Global Engineering Excellence: Educating the Next Generation of Engineers for the Global Workplace‖. Hanover, Germany, Continental AG, 2006. (Available at http://www.conti-online.com). 6. Davidson A, Miskelly J, Kelly A. (2008). Audit commission: Value for money in Schools-Literature and Data Review final report. 7. Department for international development (DFID)DFIDs approach to value for money(VFM)London. 2011. http://www.gov.uk/government/uploads/system/uploads/attachment_data/file/67479/DFID-approach-value-money.pdf 8. Erlendsson J. 2002. Value for money studies in higher education. http://hi.is/~joner/eaps/wh_vfmhe.htm 9. Fomunyam, K. G. (2016a). Student teachers negotiating teachers‘ professional identity. Journal of Educational Science, 13(2), 186- 193. 10. Fomunyam, K. G. (2017a). Education as politics in the context of Operation Ghost Town in Southern Cameroon. Knowledge for Transformation, 1(1), 1-4. 11. Grasso, Dominico; Burkins, Melody B. (Eds); Holistic Engineering Education, Beyond Technology, Springer Verlag, New York, 2010. 12. Harvey L. 2006. Understanding quality. Section B 4 1-1 of ―Introducing Bologna objectives and tools in Purser, L(Ed) EUA Bologna Handbook: Making Bologna Work, Brussels European University Association and Berlin, Raabe 13. Holz-Clause, M., Guntuku, D., Koundinya, V., Clause, R., & Singh, K. (2015). Current and Future Trends in Higher Education Learning: Implications for Curriculum. In N. Ololube, P. Kpolovie, & L. Makewa (Eds.), Handbook of Research on Enhancing Teacher Education with Advanced Instructional Technologies (pp. 277-292). Hershey, PA: IGI Global. 14. http://www.whitehouse.gov/issues/education/educate-innovate (accessed on 26 June 2011). 15. https://www.raeng.org.uk/international/activities/pdf/RAEng_ 16. Itabashi-Campbell, R., and J. Gluesing. 2013. ―Engineering Problem-Solving in Social Contexts: ‗Collective Wisdom‘ and ‗Ba‘.‖ In Engineering Practice in a Global Context: Understanding the Technical and the Social, edited by B. Williams, J. D. Figueiredo, and J. P. Trevelyan, 129–158. Leiden, Netherlands: CRC/ Balkema 17. Jackson, P. 2012. Value for money and international development.: deconstructing myths to promote a more constructive discussion. Paris: OECD. http://oecd.org/development/effectiveness/49652541.pdf 18. Obama‘s Campaign to Improve the Participation and Performance of America‘s Students in Science, Technology, Engineering, and Mathematics (STEM). ―Educate to Innovate‖. 2009. Available online: http://www.whitehouse.gov/issues/education/educate-innovate (accessed on 26 June 2011). 19. Royal Academy of Engineering. (2012). Engineers for Africa: Identifying Engineering Capacity Needs in Sub-Saharan Africa. Summary Report. Retrieved on December 13, 2013, from https://www.raeng.org.uk/international/activities/pdf/RAEng_Africa_Summary_Report.pdf 20. Treasury HM. 2004. Regularity, propriety and value for money:London:HM Treasury. http://webarchive.nationalarchives.gov.uk/20130129110402/ http://www.hm-treasury.gov.uk/d/reg_prop_and_VfM-November04.pdf 21. World Bank (2016). Value for money: Achieving VFM in investments projects financed by the World Bank. http://www.worldbank.org. Authors: Kehdinga George Fomunyam Social Capital in the Context of Fitness for Purpose: Do Engineering Students in Africa Possesses the Paper Title: Right Capital? Abstract: Social capital is important as it becomes an imperative as key indices for development and growth of students who opt for engineering education. Engineering educators have important role to play in motivating engineering students with untapped potentials to possess the right capital by creating productive teaching platforms. This paper explored the relationship of social capital on engineering education in addition to students possessing the right capital in their respective course of study. This paper argued that engineering educators should develop students‘ social capital within the context of social networks and norms by promoting knowledge-based social capital and its productivity among engineering students. This paper was guided by Social Capital Theory, which lay emphases on the views that student learning should be centred on education invested on human capital and social capital. Specifically, we explore engineering students having the right capital in their study and social capital is a quality criterion that enhances students in possessing the right capital towards EE in Africa. Thus, to address the social capital gaps in engineering education, it suggested that engineering educational curriculum as well as staff development and capacity building should be designed in developing engineering student to possess the right capital in their field of study. A number of educational- oriented recommendations for social capital in engineering education investment were made.
56. Keywords: Africa, Curriculum, Development, Engineering students, Social capital
331-338 References: 1. Abdullah I., Malek A., Manaf A. (2019). Formulating Strategic Management in Social Capital within Gated and Guarded Community to Achieve Social Well-Being. Academy of Strategic Management Journal; 18(3). 2. Ahlborg M.G., Svedberg P., Nyholm M., Morgan A., Nygren J.M. (2019). Into the realm of social capital for adolescents: A latent profile analysis. PLOS ONE; 14(2): e0212564. 3. Akintimehin O.O., Eniola A.A., Alabi O.J., Eluyela D.F., Okere W., Ozordi E. (2019). Social capital and its effect on business performance in the Nigeria informal sector. Heliyon; 5 (7). 4. Almohamed A., Vyas D. (2019). Rebuilding Social Capital in Refugees and Asylum Seekers. ACM Transactions on Computer-Human Interaction; 26 (6): 1–30. 5. Amaewhule, C. E., Abraham, N. M. (2019). Social Capital Investment as Determinant of Teachers Emotional Intelligence in Public Secondary Schools in Rivers State. International Journal of Innovative Social & Science Education Research, 7(2), 133–142. 6. Babalola O.K., Ahmed S., Onuh U.A.L. (2020). Engineering education (EE): a veritable tool for job creation and sustainable development in Nigeria. International Journal of Electrical and Electronics Engineering (IJEEE); 9 (1): 1–8. 7. Batistic S., Tymon A. (2017). Networking Behaviour, Graduate Employability: A Social Capital Perspective. Education + Training; 59 (4): 374–388. 8. Benbow R.J., Lee C. (2019). Teaching-focused social networks among college faculty: exploring conditions for the development of social capital. Higher Education; 78 (1): 67–89. 9. Berthelsen H., Westerlund H., Pejtersen J.H., Hadzibajramovic E. (2019). Construct validity of a global scale for Workplace Social Capital based on COPSOQ III. PLoS ONE; 14 (8). 10. Bosbach M., Maietta O.W. (2019). The Implicit Price for Fair Trade Coffee: Does Social Capital Matter? Ecological Economics; 158: 34–41. 11. Bourdieu P. (1986). The Forms of Capital. In J. G. Richardson (Ed.), Handbook of theory and research for the sociology of education; New York: Greenwood Press. Pp. 241–258. 12. Bourdieu P., Wacquant L.P.D. (1992). An invitation to Reflexive sociology. Chicago: University of Chicago Press. 13. Brown S., Flick L., Williamson K. (2005). Social Capital in Engineering Education. 35th ASEE/IEEE Frontiers in Education Conference, S3D-10, October 19-22, 2005, Indianapolis, IN. 14. Buck-Mcfadyen E., Isaacs S., Strachan P., Akhtar-Danesh N., Valaitis R. (2019). How the Rural Context Influences Social Capital: Experiences in Two Ontario Communities. Journal of Rural and Community Development; 14 (1): 1–18. 15. Carreras M., Bowler S. (2019). Community Size, Social Capital, and Political Participation in Latin America. Political Behavior; 41(3): 723–745. 16. Ceryn E, Rees G, Taylor C, Wright C. (2017). Widening Access‘ to Higher Education: the Reproduction of University Hierarchies Through Policy Enactment. Journal of Education Policy; 34 (1): 101–116. 17. Chen Y., Starobin S.S. (2019). Formation of Social Capital for Community College Students: A Second-Order Confirmatory Factor Analysis. Community College Review; 47 (1): 3–30. 18. Coleman J.S. (1988). Social Capital in the Creation of Human Capital. American Journal of Sociology, 94 (supplement): S95–S120. 19. Coleman J.S. (1990). Foundations of social theory. Cambridge: Harvard University Press. 20. Coleman J.S. (1993). Rational reconstruction of society. American Sociological Review, 58(1), 1–15. 21. Coleman J.S. (2000). Social capital in the creation of human capital. In Knowledge and social capital; Elsevier: Pp. 17–41. 22. Cooke P., Clifton N., Oleaga M. (2005). Social capital, firm embeddedness and regional development. Regional Studies; 39 (8): 1065– 1077. 23. Danko A. I (2016), The Role of Engineering Education in National Development Entrepreneurship Education for Vocational and Technical Students (2nd Edition) Abuja Benchmark Publishers Ltd.Ch.3, pp 23-27. 24. Erinne J.N. (2015). Engineering Education for 21st Century Africa: The Chemical Engineering and Nigerian Perspectives, the Nigeria Institution of Mechanical Engineering Annual Symposium. 25. Gilani D. (2019): Creating connections: the role of universities in enhancing graduates‘ social capital and challenging nepotism, Perspectives: Policy and Practice in Higher Education. Pp. 1-6. 26. Harris C.M., Wright P.M., McMahan G.C. (2019). The emergence of human capital: Roles of social capital and coordination that drive unit performance. Human Resource Management Journal; 29 (2); 162–180. 27. Juma, C.O. (2015) ―Unleash the Power of Universities‖, New African Magazine. Accessed on June 30th, 2020 from http://newafricanmagazine.com/calestous-juma-unleash-the-power-of-universities. 28. Kang, D.S., Gold J., Kim J., Kim I. (2019). Social capital and career growth. International Journal of Manpower. 29. Magana A.J., Falk M.L., Vieira C., Reese M.J. (2016). A case study of undergraduate engineering students‘ computational literacy and self-beliefs about computing in the context of authentic practices. Computers in Human Behavior; 61: 427– 442. 30. Magana A.J., Ortega-Alvarez J.D., Lovan R., Gomez D., Marulanda J., Dyke S. (2017). Virtual, local and remote laboratories for conceptual understanding of dynamic systems. International Journal of Engineering Education; 33(1–A): 91–105. 31. Magana A.J., Fennell H., Vieira C., Falk M.L. (2019). Characterizing the interplay of cognitive and metacognitive knowledge in computational modeling and Simulation practices. Journal of Engineering Education; 7(2). 32. Marilyn C. (2018). Rethinking Graduate Employability: The Role of Capital, Individual Attributes and Context. Studies in Higher Education; 43 (11): 1923–1937. 33. Martin J.P., Simmons D.R., Yu S.L. (2013). The Role of Social Capital in the Experiences of Hispanic Women Engineering Majors. Journal of Engineering Education; 102 (2): 227–243. 34. Mejia J.A., Revelo R.A., Villanueva I., Mejia J. (2018). Critical Theoretical Frameworks in Engineering Education: An Anti-Deficit and Liberative Approach. Educ. Sci.; 8, 158. 35. Putnam, R.D. (2001). Social Capital: Measurement and Consequences. Isuma: Canadian Journal of Policy Research 2 (Spring 2001). 36. Rogošić S., Baranovic B. (2016). Social Capital and Educational Achievements: Coleman vs. Bourdieu. CEPS Journal; 6 (2): Pp. 81- 100. 37. Saukani N., Ismail N.A. (2019). Identifying the Components of Social Capital by Categorical Principal Component Analysis (CATPCA). Soc Indic Res; 141:631–655. 38. Skvoretz J., Kersaint G., Campbell-Montalvo R., Ware J.D., Smith C.A.S., Puccia E., Martin J.P., Lee R., MacDonald G., Wao H. (2019). Pursuing an engineering major: social capital of women and underrepresented minorities, Studies in Higher Education; Pp. 1- 17. 39. Thompson JJ, Jensen-Ryan D. (2018). Becoming a ―Science Person‖: Faculty Recognition and the Development of Cultural Capital in the Context of Undergraduate Biology Research. CBE—Life Sciences Education, Winter ; 17:ar62: 1–17. 40. World Economic Forum, (2016a). Africa Skills Initiative: Stakeholders Consultation Workshop, 20 July 2016, Johannesburg, South Africa. 41. World Economic Forum, (2016b). The Future of Jobs: Employment, Skills and Workforce Strategy for the Fourth Industrial Revolution. Executive Briefing, World Economic Forum: Geneva Switzerland. Pp. 1-68. 42. World Economic Forum (2016c). The Human Capital Report 2016. Executive Briefing, World Economic Forum: Geneva Switzerland. Pp. 1-10. 43. World Economic Forum, (2017a). Realizing Human Potential in the Fourth Industrial Revolution: An Agenda for Leaders to Shape the Future of Education, Gender and Work. Executive Briefing, World Economic Forum: Geneva Switzerland. Pp. 1-53. 44. World Economic Forum (2017b). The Future of Jobs and Skills in Africa: Preparing the Region for the Fourth Industrial Revolution. Executive Briefing, World Economic Forum: Geneva Switzerland. Pp. 1-28. Authors: Kehdinga George Fomunyam
Paper Title: Engineering Education and soft skills in the Era of the Fourth Industrial Revolution in Africa Abstract: The Fourth Industrial Revolution (4IR) is impacting engineering education (EE) in diverse with several changes from the effects of the previous three industrial revolutions. Remarkable industrialization has been recorded in the fourth industrial revolution. However, certain skill gaps has been identified missing in engineering courses and curriculum as employers seek skills development aligned with the fourth industrial revolution (4IR). This paper was guided by Lifelong Learning Theory which explain that the paradigm shift 57. from the first three industrial revolutions to 4IR has led to EE transformation of acquiring not only technical skills but also soft skills. This has led to critical EE curriculum review to extrapolate its impacts of soft skills on 339-345 4IR emerging workforce. This paper takes a broad look at the EE and soft skills in the era of 4IR in Africa, while examining the EE in previous revolutions and, exploring the impacts and implications of soft skills on EE. The possibilities of adequate investments in EE and soft skills programmes becomes an imperative to address skill gaps and prepare engineering graduate students for future work. The importance of soft skills, values, and improvement of soft skills in engineering education in 4IR era are discussed among others. Thus, to address soft skills gaps in EE, industrial cooperation and educational partnership is significant to centre on EE curriculum future-oriented skills development to consolidate with 4IR workforce demands. A number of policy recommendations for 4IR compatibility with EE polices are made.
Keywords: Africa, Curriculum, Engineering education, Soft skills, Lifelong
References: 1. Akokuwebe M.E. & Okunola R.A. (2015). Demographic transition and rural development in Nigeria. Journal of Developing Country Studies (USA), 5 (6), 1-13. 2. Akokuwebe M.E. (2017). Youth unemployment and electoral malpractices in Nigeria. The Nigerian Journal of Sociology and Anthropology (Nigeria) 2017; 15(3):35-52. 3. Akokuwebe M.E., Ukpabi D., Ejeh S.O. (2017). Mass media and effective socialization amongst in-school adolescents: a perceptive study. Centrepoint Journal (Humanities Edition), University of Ilorin (Ilorin: Nigeria), 20 (2), 1-18. 4. Andrews, J. & Higson, H (2008). Graduate employability. ‖soft skill ‖verses hard‖ business knowledge: A European study. Higher Education in Europe, 33: 411-422. 5. Atman C.J., Sheppard S.D., Turns J., Adams R.S., Fleming L.N., Stevens R., Streveler R.A., Smith K.A., Miller R.L., Leifer L.J., Yasuhara K., Lund D. (2010). Enabling Engineering Student Success: The Final Report for the Center for the Advancement of Engineering Education. San Rafael, CA: Morgan & Claypool Publishers. 6. Brunhaver S. (2015). Early career outcomes of engineering alumni: Exploring their connection to the undergraduate experience (dissertation). Stanford, CA: Stanford University. 7. Brunhaver S., Korte R., Lande M., Sheppard S. (2010). Supports and barriers that recent engineering graduates experience in the workplace. Proceedings of the American Society for Engineering Education Annual Conference and Exposition, June 20–23, Louisville, KY. 8. Business-Higher Education Forum (BHEF). 2011. Creating the Workforce of the Future: The STEM Interest and Proficiency Challenge. Washington. 9. Carnevale AP, Smith N, Melton M. 2011. STEM. Washington: Georgetown University Center on Education and the Workforce. 10. Carrico C.A., Winters K.E., Brunhaver S., Matusovich H.M. (2012). The pathways taken by early career professionals and the factors that contribute to pathway choices. Proceedings of the American Society for Engineering Education Annual Conference and Exposition, June 10–13, San Antonio. 11. Dameron S. & Durand T. (2013). Strategies for business school in a multi-polar word. Education and training 55,323-335. 12. Fomunyam KG (2019). Education and the Fourth Industrial Revolution: Challenges and Possibilities for Engineering Education. International Journal of Mechanical Engineering and Technology (IJMET), 10 (8), 271-284. 13. Forbes MH, Bielefeldt AR, Sullivan JF. 2015. The choice opportunity disparity: Exploring curricular choice opportunities for engineering vs. non-engineering majors. Proceedings of the ASEE Annual Conference and Exposition, June 14–17, Seattle. 14. Gibb S. (2014). Soft skills assessment: theory development and the research agenda. International Journal of Lifelong Learning Education, 33 (4), 455-471. 15. Gray A. (2016). The 10 skills you need to thrive in the Fourth Industrial Revolution. Accessed on 27th May, 2020 from https://www.weforum.org/agenda/2016/01/the-10-skills-you-need-to-thrive-in-the-fourth-industrial-revolution/. 16. Hoit R., Sawicki S. & Sloan J. (2010). A theoretical review of skill shortages and skill need: Evidence report 20. Accessed on 26th of May, 2020 from http://www.ukces.org.uk/asset/bispartn ers/docs/publication/evidence.report.20.a.theoretical-review-of-skill- shortageand.skill-need.pdf. 17. Illeris, K.(2011). The fundament of workplace learning. Understanding how people learn in working life. London: Routledge. 18. Illeris, K.(2011). The fundamentals of workplace learning: understand how people learn in working life. London: Routledge. 19. Julia Gross, 2012. Lifelong learning and your career, in Building Your Library Career with Web 2.0, 2012. 20. Kechagias, K. (ED.) (2011). Teaching and assessing soft skills MASS project report school Thessaloniki, Neapoli. 21. Kirk D. (2010). Causes of unemployment in South Africa. Accessed on May 29th, 2020 from https://twentythirdfloor.co.za/2010/12/01/causes-of-unemployment-in-south-africa/. 22. Knobbs C.G. & Grayson D.J. (2012). An approach to developing independent learning and non-technical skills amongst final year mining engineering students. European Journal of Engineering Education, 37 (3), 5-10. 23. Litchfield K., Javernick-Will A., Maul A. (2016). Technical and professional skills of engineers involved and not involved in engineering service. Journal of Engineering Education, 105 (1), 70–92. 24. Marco Kalz, 2015. Lifelong Learning and Its Support with New Technologies in International Encyclopaedia of the Social & Behavioural Sciences (Second Edition). 25. Martins J., Duarte M., Cunha S., Almada-Lobo B., Marques A.T., Magalhaes B. (2007). The role of hard and soft skills on engineering education. International Conference on Engineering Education ─ ICEE 2007, Coimbra, Portugal, September 3-7, 2007, 1-6. 26. Mejia JA, Revelo RA, Villanueva I, Mejia J (2018). Critical Theoretical Frameworks in Engineering Education: An Anti-Deficit and Liberative Approach. Educ. Sci., 8 (158), 1-13. 27. Mourtos NJ (2015). Preparing Engineers for the 21st Century: How to teach engineering students process skills. International Journal of Quality Assurance in Engineering and Technology Education, 4 (4), 1-26. 28. Naji K.K., Ebead U., Al-Ali A.K., Du X. (2020). Comparing Models of Problem and Project-Based Learning (PBL) Courses and Student Engagement in Civil Engineering in Qatar. Eurasia Journal of Mathematics, Science and Technology Education, 16 (8), 1867. 29. National Academy of Engineering (NAE) (2004). The Engineer of 2020: Visions of Engineering in the New Century, Washington. D.C.: The National Academies Press. 30. Ongbali SO, Afolalu SA, Udo MO. (2019). Factors causing youth unemployment problem in Nigeria: A review. International Journal of Mechanical Engineering and Technology, 10 (1), 1874-1879. 31. Peters MA (2017). Technological unemployment: educating for the fourth industrial revolution. Educational Philosophy and Theory, 49 (1), 1-6. 32. Peters, Michael A. (2017). Technological unemployment: educating for the Fourth Industrial Revolution. Journal of Self-governance and Management economics 5, No. 1, 25-33. 33. Pradhan A., Agwa-Ejon J. (2018). Opportunities and challenges of embracing smart factory in South Africa. In 2018 Portland International Conference on Management of Engineering and Technology (PICMET). Honolulu, HI, USA, IEEE. 34. Ra S., Shrestha U., Khatiwada S., Yoon S.W. & Kwon K. (2019). The rise of technology and impact on skills. International Journal of Training Research, 17 (1), 26-40. 35. Reaves J. (2019). 21st-Century Skills and the Fourth Industrial Revolution: A Critical Future Role for Online Education. International Journal on Innovations in Online Education, 3 (1), 1-21. 36. Sackey SM, Bester A. (2016). Industrial engineering curriculum in Industry 4.0 in a South African context. South African Journal of Industrial Engineering, 27 (4), 101-114. 37. Schwab K. (2016). The Fourth industrial Revolution: What it means, how to respond. Accessed on 22nd May, 2020 from https://www.weforum.org/agenda/2016/01/the-fourth-industrial-revolution-what-it-means-and-how-to-respond/ 38. Shvetsova OA, Kuzmina AD. (2018). Development of engineering personnel in the era of the Fourth Industrial Revolution. In 2018 Third International Conference on Human Factors in Complex Technical Systems and Environment (ERGO) and Environments (ERGO). St. Petersburg, Russia, IEEE, 45-48. 39. Tonso K.L. (2014). Engineering identity. In: Cambridge Handbook of Engineering Education Research, eds Johri A, Olds BM. New York: Cambridge University Press. 40. Tracey TJG, Sodano SM. 2013. Structure of interests and competence perceptions. In: Handbook of Vocational Psychology, 4th ed., eds Walsh WB, Savickas ML, Hartung PJ. New York: Taylor & Francis. 41. World Economic Forum (WEF) (2015a). The Human Capital Report 2015. World Economic Forum Global Agenda Council White Paper, Geneva: World Economic Forum, 2015 42. World Economic Forum (WEF) (2015b). Global Agenda Council on the Future of Software and Society, Deep Shift: Technology Tipping Points and Societal Impact, World Economic Forum Global Agenda Council White Paper, 2015. 43. World Economic Forum (WEF) (2016). The Future of Jobs: Employment, Skills and Workforce Strategy for the Fourth Industrial Revolution. Geneva: World Economic Forum, 2016. Accessed on 23rd May, 2020 from http://www3.weforum.org/dos/WEF_Future_of_Jobs.pdf. 44. World Economic Forum (WEF) (2017a). Realizing human potential in the Fourth Industrial Revolution. An Agenda for Leaders to Shape the Future of Education, Gender and Work. Paper Presented at World Economic Forum, Geneva, 2017. 45. World Economic Forum (WEF) (2017b). Accelerating Workforce Reskilling for the Fourth Industrial Revolution an agenda for leaders to shape the future of education, gender and work. Paper Presented at World Economic Forum, Geneva, 2017. Authors: Kehdinga George Fomunyam
Paper Title: Engineering for Survival in Rural Africa in the Era of Covid-19 Abstract: Engineering has been reputed as a discipline that makes things work better. By the ingenuity of engineering, there is the potential to deploy creativity to solve some of the problems of the world and help in shaping the future. Shortly after the novel coronavirus, SARS-COV-2 (2019-NCov) was initially identified in the Chinese city of Wuhan in a group of patients diagnosed with pneumonia on December 31, 2019, it resulted to fast paced human to human transmission which has generated lots of media stirs and hype concerning issues of public health globally. Corona virus disease 2019 (COVID-19) is a ribonucleic acid virus (RNA) which has the physical appearance of a crown when viewed under the microscope which is as a result of glycoprotein spikes on its envelope. Findings from the study revealed that engineering has great impact on health conditions in rural Africa and the era of COVID-19 brought with it various consequences on the health systems of people. Understanding that there is no known cure for COVID-19 is key and various countries of the world depended on their knowledge and expertise to deal with the disease. This study therefore recommends that there is a need for intensified effort on engineering in rural Africa.
Keywords: engineering, survival, COVID-19, corona virus, rural Africa
References: 1. African Union: Impact of the Coronavirus COVID-19 on the African Economy. African Union. 2020; Accessed on April 22, 2020. 2. Corman VM, Landt O, Kaiser M, et al. Detection of 2019 novel coronavirus (2019- nCoV) by real-time RT-PCR. Euro Surveill 2020;25(January (3)), doi:http://dx. doi.org/10.2807/1560-7917.ES.2020.25.3.2000045. 3. Haider N, Yavlinsky A, Simons D, Osman AY, Ntoumi F, Zumla A, et al. Passengers‘ destinations from China: low risk of novel coronavirus (2019-nCoV) transmis-sion into Africa and South America. Epidemiol Infect 2020a;148:e41, doi:http:// dx.doi.org/10.1017/S0950268820000424 Published 2020 Feburary 26. 4. Hoffman SJ, Silverberg SL. Delays in global disease outbreak responses: lessons from H1N1, Ebola, and Zika. Am J Public Health 2018;108(3):329–33, doi:http://dx. doi.org/10.2105/AJPH.2017.304245. 5. http://www.africacdc.org/Africa centers for disease control and prevention. . [Accessed 17 February 2020]. 6. http://www.merriam-webster.com/dictionary/survival#h1 7. http://www.raeng.org.uk/publications/reports/assessing-the-economic-returns-ofengineering-rese 8. Huang, C.; Wang, Y.; Li, Z.; Ren, L.; Zhao, J.; Hu, Y.; Zhang, L.; Fan, G.; Xu, J.; Gu, X.; et al. Clinical features of patients infected 58. with 2019 novel coronavirus in Wuhan, China. Lancet 2020, 395, 497–506. 9. Ippolito G, Hui DS, Ntoumi F, MAeurer M, Zumla A. 2020. Toning down the 2019-nCov hype-and restoring hope. Lancet Resp Med. 8(3):230-1, doi: http//dx.doi.org/10.1016/S2213-2600(20)30070-9 346-350 10. Largent EA. EBOLA and FDA: reviewing the response to the 2014 outbreak, to find lessons for the future. J Law Biosci 2016;3(3)489–537, doi:http://dx.doi.org/ 10.1093/jlb/lsw046 [published 2016 September 16]. 11. Li Q, Guan X, Wu P, et al. 2020. Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia. N Engl J Med. Doi:http//dx.doi.org/10.1056/NEJMoa2001316 12. Lu, H.; Stratton, C.W.; Tang, Y.W. Outbreak of pneumonia of unknown etiology in Wuhan, China: The mystery and the miracle. J. Med. Virol. 2020, 92, 401–402. 13. Lu, R.; Zhao, X.; Li, J.; Niu, P.; Yang, B.; Wu, H.; Wang, W.; Song, H.; Huang, B.; Zhu, N.; et al. Genomic characterization and epidemiology of 2019 novel coronavirus: Implications for virus origins and receptor binding. Lancet 2020, 395, 565–574. 14. Matthews, P., Ryan-Collins, L., Wells, J., Sillem, H. and Wright, H. (2012). Engineers for Africa: Identifying engineering capacity needs in sub-Saharan Africa. Royal Academy of Engineering, Africa-United Kingdom Engineering for Development Partnership. 15. ncov.pdf (accessed on 25 March 2020). 16. Nduka Orjinmo. 2020. Coronavirus: Nigeria's mega churches adjust to empty auditoriums https://www.bbc.com/news/world-africa-52189785 17. Nigeria CDC 2020. https://ncdc.gov.ng/ncdc. [Accessed 18 February 2020]. 18. Perlman, S.; Netland, J. Coronaviruses post-SARS: Update on replication and pathogenesis. Nat. Rev. Microbiol. 2009, 7, 439–450. 19. Phan, T. Novel coronavirus: From discovery to clinical diagnostics. Infect. Genet. Evol. 2020, 79. 20. Respirology 2018,23, 130–137 21. Royal Academy of Engineering. 2020. Project CARE (COVID-19 AFRICAN Rapid Entrepreneurs) https://www.raeng.org.uk/policy/engineering-response-COVID-19-coronavirus/project-care 22. Talisuna AO, Okiro EA, Yahaya AA, et al. Spatial and temporal distribution of infectious disease epidemics, disasters and other potential public health emergencies in the World Health Organisation Africa region, 2016-2018. Global Health 2020;16(1)9, doi:http://dx.doi.org/10.1186/s12992-019-0540-4 [published 2020 January 15 23. WHO. Ebola virus disease. 2020. https://www.afro.who.int/health-topics/ebola- virus-disease. 24. WHO. WHO African Region: JEE mission reports. 2020. https://www.who.int/ihr/ procedures/mission-reports-africa/en/. 25. World Bank. 2020. How countries are using edtech (including online learning, radio, television, texting) to support access to remote learning during the COVID-19 pandemic. https://www.worldbank.org/en/topic/edutech/brief/how-countries-are-using-edtech-to- support-remote-learning-during-the-COVID-19-pandemic 26. World Economic Forum. 2020. Here‘s how Africans are using tech to combat the coronavirus pandemic. https://www.weforum.org/agenda/2020/04/africa-technology-coronavirus-covid19-innovation-mobile-tech-pandemic 27. World Health Organization Director-General‘s Opening Remarks at the Media Briefing on COVID-19–11 March 2020. Available online: https://www.who.int/dg/speeches/detail/who-director-general-s-openingremarks- 28. at-the-media-briefing-on-COVID-19---11-march-2020 29. World Health Organization Novel Coronavirus (2019-nCoV), Situation Report 1. 21 January 2020. Available online: https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200121-sitrep-1-2019- 30. Yin, Y.;Wunderink, R.G. MERS, SARS and other coronaviruses as causes of pneumonia. Authors: Anushree H R, Sowmya B P
Paper Title: Ensembled Machine Learning Model for Aviation Incident Risk Prediction Abstract: With the fabulous development of air traffic request expected throughout the following two decades, the security of the air transportation framework is of expanding concern. In this paper, we encourage the "proactive security" worldview to expand framework wellbeing with an emphasis on anticipating the seriousness of strange flight occasions as far as their hazard levels. To achieve this objective, a prescient model should be created to look at a wide assortment of potential cases and measure the hazard related with the conceivable result. By using the episode reports accessible in the Aviation Safety Reporting System (ASRS), we construct a half breed model comprising of help vector machine and K-closest neighbor calculation to evaluate the hazard related with the result of each perilous reason. The proposed system is created in four stages. Initially, we classify all the occasions, in view of the degree of hazard related with the occasion result, into five gatherings: high hazard, decently high hazard, medium hazard, respectably medium hazard, and okay. Furthermore, a help 59. vector machine model is utilized to find the connections between the occasion outline in text configuration and occasion result. In this application K-closest neighbors (KNN) and bolster vector machines (SVM) are applied to group the everyday nearby climate types In equal, knn calculation is utilized to highlights and occasion results 351-353 subsequently improving the forecast. At long last, the forecast on hazard level order is stretched out to occasion level results through a probabilistic choice tree.
Keywords: ASRS, KNN, SVM, Decision Tree.
References: 1. Choubin, B., et al., Snow avalanche hazard prediction using machine learning methods. Journal of Hydrology,2019. 577. 2. Dehghani, M., et al., Prediction of hydropower generation using Grey wolf optimization adaptive neuro-fuzzyinference system. Energies, 2019. 12(2). 3. Mosavi, A., Y. Bathla, and A. Varkonyi-Koczy, Predicting the future using web knowledge:,D. Luca, L. Sirghi, and C. Costin, Editors. 2018, Springer Verlag. p. 341-349. 4. Maxwell, A.E., T.A. Warner, and F. Fang, Implementation of machine-learning classification in remote sensing: International Journal of Remote Sensing, 2018. 39(9): p. 2784-2817. Authors: Abdul Awwal, Aarish Khan
Paper Title: Performance Analysis of a Roundabout and a 3-leg Intersection Under Heterogeneous Traffic Abstract: This paper addresses the analysis of the operational performance of a roundabout and a 3-legged intersection located in quite a busy area of the Aligarh city. The city has an urban population of around 0.9 million people. The roundabout and 3-legged intersection are located in the close proximity of busy commercial areas and schools. Roundabout that has been taken under consideration is un-signalized and 3-legged intersection is priority controlled. The Current study has been undertaken analyze the operational execution of the two intersections and to pave the way for forthcoming investigations related to improvement of the intersections in the Aligarh District region. Traffic data was accumulated on weekdays during peak periods (5:30 pm to 6:30 pm). Video recording was taken in consideration to accomplish this task. The traffic was categorized in 3 classes; light vehicles, heavy vehicles and bicycles. To execute the evaluation of functioning performance of both intersections, SIDRA INTERSECTION software has been used. Results have shown that both the roundabout and 3-legged intersection are operating nearly at their maximum capacities and Level of Service (LOS) is not adequate for such amount of traffic influx into both the intersections. Volume to capacity (v/c) ratio has revealed that both the roundabout and 3-legged intersection are in an unstable state and roundabout condition is worse than the 3-legged intersection as the heavy vehicle volume influx is quite higher for the roundabout.
60. Keywords: Capacity, Level of Service, SIDRA Intersection, Heterogenous Traffic
References: 354-359 1. Falcocchio, John C., and Herbert S. Levinson. Road traffic congestion: a concise guide. Vol. 7. Cham: Springer, 2015. 2. Olagunju, K. (2015, March). Evaluating traffic congestion in developing countries–a case study of Nigeria. In A paper presented at the. 3. Jain, V., Sharma, A., & Subramanian, L. (2012, March). Road traffic congestion in the developing world. In Proceedings of the 2nd ACM Symposium on Computing for Development (pp. 1-10). 4. Acharjee, A., Sarkar, P. P., & Pal, J. (2018). Effect of socioeconomic and latent variables in vehicle ownership: A case study of Agartala city, India. International Journal of Engineering & Technology, 7(1.1), 472-476. 5. Surbakti, M., & Iswahyudi, F. (2018, February). Roundabout performance analysis in the city of Medan. In IOP Conference Series: Materials Science and Engineering (Vol. 309, No. 1, p. 012115). IOP Publishing. 6. Dargay, J., Gately, D., & Sommer, M. (2007). Vehicle ownership and income growth, worldwide: 1960-2030. The energy journal, 28(4). 7. Rao, B. S., Rambabu, T., & Rao, G. V. (2017). ANALYSIS OF CAPACIT SERVICE AT UNC INTERSECTIONS UNDER TRAFFIC CON. International Journal of Civil Engineering, 8(8). 8. Ranjitkar, P., Shahin, A., & Shirwali, F. (2014). Evaluating operational performance of intersections using SIDRA. The Open Transportation Journal, 8(1). 9. Mukhtyarali, S. S., Zala, L. B., & Amin, A. A. (2017). Capacities and LOS Measures of Intersections. Kalpa Publications in Civil Engineering, 1, 209-218. 10. Naik, M. R., Umapathi, M., & Vishwas, R. K. M. J. (2017). A STUDY ON DESIRABLE IMPROVEMENTS AT SELECTED 11. UNSIGNALISED INTERSECTION ON NH-206 AND NH-13. International Journal of Advance Engineering and Research Development, 4(5). 12. Marfani, S., Shihora, D., Kanthariya, C., & Kansara, H. (2018). Traffic Improvement for Urban Road Intersection, Surat. International Research Journal of Engineering and Technology, 5(03). 13. Chandramouli, C., & General, R. (2011). Census of india 2011. Provisional Population Totals. New Delhi: Government of India, 409- 413. 14. Zawawa, G., & Naghawi, H. (2017). Evaluation of the Operational Performance of Continuous Green T-Intersection under Different Levels of Congestion. Periodica Polytechnica Transportation Engineering. 15. Naghawi, H., Jadaan, K., Al-Louzi, R., & Hadidi, T. (2018). Analysis of the Operational Performance of Three Unconventional Arterial Intersection Designs: Median U-Turn, Superstreet and Single Quadrant. International Journal of Architectural, Civil and Construction Sciences, 12(3), 387-395. 16. Ragab, M., & El-Naga, I. A. (2019). MEASURES TO IMPROVE TRAFFIC OPERATIONS AT SIGNALIZED INTERSECTIONS IN URBAN AREAS. International Journal for Traffic and Transport Engineering, 9(4). 17. Mukhtyarali, S. S., Zala, L. B., & Amin, A. A. (2017). Capacities and LOS Measures of Intersections. Kalpa Publications in Civil Engineering, 1, 209-218. 18. Zainuddin, N. I., Shah, S. M. R., Hashim, M. Z., Roslam, M. S., & Tey, L. S. (2018, October). Comparison of operational performance before and after improvement: Case study at Pengkalan Weld, Pulau Pinang. In AIP Conference Proceedings (Vol. 2020, No. 1, p. 020027). AIP Publishing LLC. 19. Ragab, M., & El-Naga, I. A. (2019). MEASURES TO IMPROVE TRAFFIC OPERATIONS AT SIGNALIZED INTERSECTIONS IN URBAN AREAS. International Journal for Traffic and Transport Engineering, 9(4). 20. Nguyen, Q. V. (2017). Operational performance of roundabout intersection at Sheldon and Waterman Road. 21. Zainuddin, N. I., Shah, S. M. R., Hashim, M. Z., Roslam, M. S., & Tey, L. S. (2018, October). Comparison of operational performance before and after improvement: Case study at Pengkalan Weld, Pulau Pinang. In AIP Conference Proceedings (Vol. 2020, No. 1, p. 020027). AIP Publishing LLC. 22. Manual, H. C. (2000). Highway capacity manual. Washington, DC, 2. 23. Muley, D., & Al-Mandhari, H. S. (2014). Performance evaluation of Al Jame‘Roundabout using SIDRA. World Academy of Science, Engineering and Technology, International Journal of Civil, Environmental, Structural, Construction and Architectural Engineering, 8(12), 1296-1301. 24. Said, M. S. Determination of Peak Hours And Level Of Service At An Unsignalised Intersection Using aa SIDRA 2.0. 25. Kang, N., & Nakamura, H. (2016). An analysis of heavy vehicle impact on roundabout entry capacity in Japan. Transportation research procedia, 15, 308-318. 26. Li, J., Yue, Z. Q., & Wong, S. C. (2004). Performance evaluation of signalized urban intersections under mixed traffic conditions by gray system theory. Journal of Transportation Engineering, 130(1), 113-121. 27. Akçelik, R., & Troutbeck, R. (1991, July). Implementation of the Australian roundabout analysis method in SIDRA. In Highway Capacity and Level of Service-Proceddings of the International Symposium on Highway Capacity (pp. 17-34). U. Brannolte). AA Balkema, Rotterdam. Authors: Kenneth Gitonga Ntonja, Geoffrey Muchiri Muketha, Gabriel Ndungu Kamau Cloud Data Privacy Preserving Model for Health Information Systems Based on Multi Factor Paper Title: Authentication Abstract: With cloud computing (CC) becoming popular in recent years, variety of institutions, organizations, businesses and individual users are creating interest. They are adopting the technology in order to take advantage of shared web applications, low infrastructure cost, utility and distributed computing, cluster computing as well as reliable IT architecture. In the area of health, Cloud Health Information Systems (CHIS) play a key role not only on the healthcare businesses but patients as well. On the patient side, CHIS aid in sharing of medical data and health information, timely access of critical patient information and coordination of clinical services. Patients, who continue to demand for instantaneous and quality healthcare services are now able to access the services from experts even when they are not necessarily in the same physical location. This is being aided by proliferation of telemedicine through hosted cloud architecture. From the business perspective, CC has helped to cut down operational expenses by way of cost-effective clinical information system infrastructure through the implementation of a distributed platform. The platform has therefore saved businesses millions of dollars that would have gone to infrastructural and human resource investment. Even with these immense opportunities, cloud computing uptake has been serious inhibited by the privacy and security concerns. Due to the sensitivity of personal health information, businesses and individuals are apprehensive when it comes to adopting the technology or releasing the data to the cloud. This study is a results discussion of an enhanced model for attainment of data privacy on the cloud through use of multi factor authentication.
61. Keywords: Cloud Computing, Health Management Systems, Multi-factor Authentication, One Time Password. 360-367 References: 1. Bajwa, M. S., Himani, & Sandeep, S. K. H. (2015). An Enhanced Data OwnerCentric Model for Ensuring Data Security in Cloud. 2015 Second InternationalConference on Advances in Computing and Communication Engineering 2. Avizienis, J. Laprie, B. Randell, and C. Landwehr, ―Basic concepts and taxonomy of dependable and secure computing,‖ IEEE Transactions on Dependable and Secure Computing, vol. 1,no. 1, pp. 11–33, 2004. 3. Hyokyung Chang and Euiin Choi, ―User Authentication in Cloud Computing‖, Springer-Verlag Berlin Heidelberg 2011, UCMA 2011, Part II, CCIS 151, pp. 338–342. 4. Baliga, P. Kamat, and L. Iftode, ―Lurking in the Shadows: Identifying Systemic Threats to Kernel Data (Short Paper),‖ in 2007 IEEE Symposium on Security and Privacy, May 2007. 5. Jiangshan Yu, Guilin Wang, Yi Mu, and Wei Gao, "An Efficient Generic Framework for Three-Factor Authentication with Provably Secure Instantiation", IEEE, 2013 6. Behl, ―Emerging security challenges in cloud computing: an insight to cloud security challenges and their mitigation,‖ in Proceedings of the World Congress on Information and Communication Technologies (WICT ‘11), pp. 217–222, IEEE, December 2011. 7. Pandey, R. M. Tugnayat, and A. K. Tiwari, ―Data Security Framework for Cloud Computing Networks,‖ International Journal of Computer Engineering & Technology, vol. 4, no. 1, pp. 178–181, 2013. 8. A.Ibrahim, B.Mahmood and M.Singhal, A secure framework for sharing electronic health records over clouds, in Proc. of IEEE International Conference on Serious Games and Applications for Health (SeGAH), pp. 1-8, 2016. 9. Amir, M. T., Rodziah, T., Rusli, A. & Masrah, A. A. M. (2012). Security Frameworkof Cloud Data Storage Based on Multi Agent System Architecture – A Pilot Study. 10. Shirly Lee, Tae Yong Kim and Hoon-Jae Lee, "Mutual Authentication Scheme for Cloud Computing", Future Information Communication Technology and Applications, Springer, chapter 17, 2013, pp 149-157. 11. PrachiSoni and MonaliSahoo, "Multi-factor Authentication Security Framework in Cloud Computing", International Journal of Advanced Research in Computer Science and Software Engineering, Volume 5, Issue 1, ISSN: 2277 128X, January 2015 12. Arockiam L. and Monikandan S., Data security and privacy in cloud storage using hybrid symmetric encryption algorithm. International Journal of Advanced Research in Computer and Communication Engineering, 2(8), pp.3064-3070, 2013. 13. Bartock, M. et al. (2015). Trusted Geolocation in the Cloud: Proof of ConceptImplementation.National Institute of Standards and Technology.Beal, V. (2015).Data. http://www.webopedia.com/TERM/D/data.html. Accessed 20.09.2018. 14. E. Keller, J. Szefer, J. Rexford, and R. B. Lee, ―NoHype: virtualized cloud infrastructure without the virtualization,‖ in Proc. 37th annual international symposium on Computer architecture, New York, NY, USA, 2010, pp. 350-361. 15. F. Monrose, P. Wycko, and A. D. Rubin, ―Distributed execution with remote audit,‖ In NDSS, 1999. 16. Keramidas, A. Antonopoulos, D. N. Serpanos, and S. Kaxiras, ―Non deterministic caches: A simple and effective defense against side channel attacks,‖ Design Automation for Embedded Systems, 12(3):221-230, 2008. 17. G. Kumaresan, N. Veeraragavan, Dr. L. Arockiam, "A Study of User Authentication Techniques in Cloud Computing", Journal of Emerging Technologies and Innovative Research (JETIR) (ISSN-2349-5162), Volume 2, Issue 8, August 2015, pp. 3309-3314. 18. HyosikAhn, Hyokyung Chang, Changbok Jang, and Euiin Choi, ―User Authentication Platform Using Provisioning in Cloud Computing Environment‖, Springer-Verlag Berlin Heidelberg 2011, ACN 2011, CCIS 199, pp. 132–138. 19. Kong, O. Aciicmez, J.-P. Seifert, and H. Zhou, ―Deconstructing new cache designs for thwarting software cache-based side channel attacks, In 2nd ACM Workshop on Computer Security Architectures, pages 25-34, October 2008. 20. Szefer, E. Keller, R. B. Lee, and J. Rexford, ―Eliminating the hypervisor attack surface for a more secure cloud,‖ in Proc. 18th ACM conference on Computer and communications security, New York, NY, USA, 2011, pp. 401-412.. 21. Benson, R. Dowsley, and H. Shacham, ―Do you know where your cloud files are?,‖ in Proc. 3rd ACM workshop on Cloud computing security workshop, New York, NY, USA, 2011, pp. 73-82. 22. Suzaki, K. Iijima, T. Yagi, and C. Artho, ―Memory deduplication as a threat to the guest OS,‖ in Proc. Fourth European Workshop on System Security, New York, NY, USA, 2011, p. 1:1-1:6. 23. Blanton, Y. Zhang, and K. B. Frikken, ―Secure and Verifiable Outsourcing of Large-Scale Biometric Computations,‖ in Privacy, Security, Risk and Trust (PASSAT), IEEE Third International Conference on Social Computing (SocialCom), 2011, pp. 1185-1191. 24. J. Atallah and K. B. Frikken, ―Securely outsourcing linear algebra computations,‖ In ASIACCS, 2010. 25. M. Y. A. Younis and K. Kifayat, ―Secure cloud computing for critical infrastructure: a survey,‖ Tech. Rep., Liverpool John Moores University, Liverpool, UK, 2013. 26. Michael E.Whitman In defense of the realm: Understanding the threats to information security International Journal of Information Management, 24, 2004, pp.43-57. 27. Nan Chen and Rui Jiang, "Security Analysis and Improvement of User Authentication Framework for Cloud Computing", Journal of Networks, Vol. 9, No. 1, January 2014, Pp 198-203. 28. Rohitash Kumar Banyal, Pragya Jain and Vijendra Kumar Jain, "Multi-factor Authentication Framework for Cloud Computing", IEEE Computer Society, Fifth International Conference on Computational Intelligence, Modelling and Simulation, pp 105-110. Authors: Abdulrazag Y. Zekri, Li Yang, Gamal Alusta, Mamdouh Ghannam Sequential Injection of Carbonated Water: A Possible Process for Coupling CO2 Enhanced Oil Paper Title: Recovery and Storage Abstract: Low salinity and carbonated water flooding have been investigated as possible techniques of improved/enhanced oil recovery. Carbonated water injection consists of dissolving carbon dioxide CO2 in water prior to injection and could be considered as a way to store greenhouse gas safely. Low salinity water flooding is a process of diluting high salinity injection water to a very low level of salinity. In this project, the effect of combining the two techniques in a sequential flooding was studied. The primary aim of this study is to optimize the oil recovery and evaluate CO2 storage during this process, employing low permeability carbonate cores and different sequential carbonated and non-carbonated brines flooding. Formation brine, seawater, low salinity carbonated and non-carbonated were used in this work. Core samples grouped as composite cores with similar over all reservoir permeability. Different sequences of brines were employed to determine the optimum system. The experiment's result showed that carbonated water performs better than the noncarbonated brines. A new technique for estimate CO2 retention based on the displacement efficiency of the carbonated water flooding system is presented. The interfacial tension, contact angle measurements results indicated that wettability is the dominant mechanism of the studied systems. A sequential composite core flooding consists of carbonated low salinity followed by low salinity and seawater injection (CLSW- LSW-SW) is the optimum flooding system among the studied systems. Technically, CLSW flooding displayed an excellent incremental displacement efficiency ∆DE of 21.4% and CSW exhibited the best CO2 retention per incremental ∆Np.
62. Keywords: CO2 storage, Sequential Flooding, Carbonated Water, CO2-EOR, Low Salinity, Low Permeability.
References: 368-376 1. Ahmed, T., 2018. Reservoir Engineering Handbook. Gulf Professional Publishing, 5th Edition, 1282-1283. 2. Alizadeh A, Ioannidis M and Piri M (eds), 2011. CO2-saturated brine flooding: An effective process for mobilization and recovery of water flood residual oil. Proceeding of International Symposium of the Society of Core Analysts, Austin, TX, USA, 19–21. 3. Bisweswar, G., Al‑Hamairi, A., Jin, S., 2019. Carbonated water injection: an efficient EOR approach. A review of fundamentals and prospects. Journal of Petroleum Exploration and Production Technology (2020) 10:673–685. https://doi.org/10.1007/s13202-019- 0738-2. 4. Burton, M., Bryant, SL, (2009). Eliminating buoyant migration of sequestered CO2 through surface dissolution: implementation costs and technical challenges. SPE Reservoir Eval. Eng. 12(03):399–407. 5. Chang, Y.B., Coats, B.K., Nolen, J.S., 1998. A compositional model for CO2 floods including CO2 solubility in water. SPE Reservoir Evaluation. Eng. 1 (2) (1998) 155–160. 6. Chung, F. H., Jones, R.A., Nguyen, H.T., 1988. Measurements and correlations of the physical properties of properties of CO2/heavy crude oil mixtures. SPE Reservoir Eng. 3 (3) (1988) 822–828. 7. Dong, Y., Dindoruk, B., Ishizawa, C., and Lewis, E.J., 2011. An experimental investigation of carbonated water flooding. SPE Annual Technical Conference and Exhibition; 2011: Society of Petroleum Engineers. Denver, Colorado, USA, 30 October–2 November (2011). https://doi.org/10.2118/145380-MS. 8. Enick, R.M., Klara, S.M., 1990. CO2 solubility in water and brine under reservoir conditions. Chem. Eng. Commun. 90(1):23–33. 9. Herzog, H., 2000. The economics of CO2 separation and capture. Open source. 10. Kechut, N. I., Sohrabi, M., Jamiolahmady, M., 2011. Experimental and numerical evaluation of carbonated water injection (CWI) for improved oil recovery and CO2 storage. SPE-143005. 10. 11. McGlade, C., 2019). Can CO2-EOR really provide carbon-negative oil? https://www.iea.org/commentaries/can-CO2-eor-really- provide-carbon-negative-oil 12. Metz, B., 2007. Special report on carbon dioxide capture and storage https://www.ipcc.ch/pub/repor%20ts.htm 13. Mohammadkhania, S., Shahverdia, H., Nielsenb, S., Esfahanya, M., Shapiroc, A., 2019. Bicarbonate flooding of homogeneous and heterogeneous cores from a carbonaceous petroleum reservoir, Journal of Petroleum Science and Engineering, 178, 251-26. 14. Riazi, M., 2011. Pore Scale Mechanisms of Carbonated Water Injection in Oil Reservoirs. Heriot-Watt University, Edinburgh, Scotland. http://hdl.handle.net/10399/2454. 15. Mosavat, N., and Torabi, F., 2016. Micro-optical analysis of carbonated water injection in irregular and heterogeneous pore geometry. Fuel 175:191–201. 16. Ruidiaz, E. M., Winter, A., Trevisan, O. V., 2018. Oil recovery and wettability alteration in carbonates due to carbonate water injection. Journal of Petroleum Exploration and Production Technology volume 8, 249–258B. 17. Shu, G., Dong, M., Chen, S., Hassanzadeh, H., 2016. Mass transfer of CO2 in a carbonated water–oil system at high pressures. Indus Eng Chem Res 56(1):404–416. 18. Seyyedi, M., Mahzari, P., Sohrabi, M., 2017. An integrated study of the dominant Mechanism leading to improved oil recovery by carbonated water injection. J Indus Eng. Chem. 45:22–32. 19. Sohrabi, M., Riazi, M., Jamiolahmady, M., Ireland, S., Brown, C., 2008. Carbonated water injection for oil recovery and CO2 storage. Symposium of the Sustainable energy UK conference: Meeting the Science and Engineering challenge, Oxford, UK. 20. Sohrabi, M., Emadi, A., Farzaneh, S.A., Ireland, S., 2015. A thorough investigation of mechanisms of enhanced oil recovery by carbonated water injection. Proceedings of the SPE Annual Technical Conference and Exhibition; Houston, Texas, USA, 28–30. 21. Soleimania, P., Shadizadehb, S., Kharratc, R., 2020. Experimental assessment of hybrid smart carbonated water flooding for carbonate reservoirs, Petroleum, in press https://doi.org/10.1016/j.petlm.2020.03.006 22. Steffens, A., 2010. Modeling and laboratory study of carbonated water flooding. Delft University of Technology, Delft. http://resolver.tudelft.nl/uuid:ff0d9ac4-b77d-4546-b18a-194e69919475 23. Welker, J.R., Dunlop, D.D., 1963. Physical properties of carbonated oils, J. Petrol. Technol. 15 (1963) 873–876 08. 24. Zekri, A., Al-Attar, H., Al-Farisi, O., Almehaideb , R., Lwisa, E., 2015. Experimental investigation of the effect of injection water salinity on the displacement efficiency of miscible carbon dioxide WAG flooding in a selected carbonate reservoir. Journal of Petroleum Exploration and Production Technology, 2015, DOI 10.1007/s13202-015-0155-0. Authors: G Ajay Bhaskar Naidu, Y Avinash Reddy, CH Naveen Chowdary
Paper Title: Design of 5g Mimo Antenna with Enhanced Isolation Abstract: In this paper, MIMO 2-port, 2-element antenna for 5G applications is presented. This is monopole antenna structure consists of two-rectangular patch of same shapes. Each antenna has a feeding plate connect at the centre of the patch antenna for enhancing isolation the etching of rectangular slots on the ground plane in between the two patches along with thick and sheet of the substrate just below at the centre of the patch. Maximum isolation achieved among the ports is less than -30db. envelope correlation coefficient is below 0.10 in bands of interest. The minimum frequency range covered by the four ports of this antenna is from around 3.0 to 4.0 GHz, thus covering expected future 5G band (3300–3700 MHz).
Keywords: Maximum isolation achieved among the ports is less than -30db.
References: 1. J.G. Andrews, S. Buzzi, W. Choi, S.V. Hanley, A. Lozano, A.C.K. Soong, and J.C. Zhang, ‗‗Whatwill5Gbe?‘‘IEEEJ.Sel. Areas Common., vol.32, no. 6, pp. 1065–1082, Jun. 2014. 2. G. J. Foschini and M. J. Gans, ‗‗On limits of wireless communications in a fading environment when using multiple antennas,‘‘ Wireless Pers. Commun., vol. 6, no. 3, pp. 311–335, Mar. 1998. 3. P.-S. Kildal and K. Rosengren, ‗‗Correlation and capacity of MIMO systems and mutual coupling, radiation efficiency, and diversity gain of their antennas: Simulations and measurements in a reverberation chamber,‘‘ IEEE Commun. Mag., vol. 42, no. 12, pp. 104– 112, Dec. 2004. 4. R. G. Vaughan and J. B. Andersen, ‗‗Antenna diversity in mobile communications,‘‘ IEEE Trans. Veh. Technol., vol. VT-36, no. 4, pp. 147–172, Nov. 1987. 5. Mattheijssen, M.H.A.J. Herben, G. Dolmans, and L.Leyten, ‗‗Antennapatterndiversityversusspacediversityforuseathandhelds,‘‘IEEE 63. Trans. Veh. Technol., vol. 53, no. 4, pp. 1035–1042, Jul. 2004. 6. S. D. Targonski, R. B. Waterhouse, and D. M. Pozar. v "Wideband aperture coupled stacked patch antenna using thick substrates," Electronics Letters, vol. 32, no. 21, pp. 1941–1942, 1996. 377-380 7. J. A. Ansari and R. B. Ram, "Broadband stacked U-slot micro strip patch antenna," Progress in Electromagnetics Research Letters, vol. 4, pp. 17–24, 2008. 8. D. Uzer, S. S. Gultekin, and O. Dundar, "Estimation and design of U-slot physical patch parameters with artificial neural networks," in Proceedings of Progress in Electro-Magnetic Research Symposium, Kuala Lumpur, Malaysia, 2012, pp. 27– 30. 9. Michael D. Foegelle ―MIMO device performance measurements in a wireless environment simulator‖ IEEE Electromagnetic Compatibility Magazine (Volume: 1, 123 - 130, Fourth Quarter 2012). 10. S. Zhang, Z. Ying, J. Xiong, and S. He, ―Ultrawideband MIMO/Diversity Antennas With a Tree-Like Structure to Enhance WidebandIsolation‖, IEEE Antennas Wireless Propag Lett 8 (2009),1279–1282. 11. A. Diallo, C. Luxey, P. L. Thuc, R. Staraj, and G. Kossiavas, ‗‗Study and reduction of the mutual coupling between two mobile phone PIFAs operating in the DCS 1800 and UMTS bands,‘‘ IEEE Trans. Antennas Propag., vol. 54, no. 11, pp. 3063–3074, Nov. 2006. 12. H. Li, J. Xiong, and S. L. He, ‗‗Extremely compact dual-band PIFAs for MIMO application,‘‘ Electron. Lett., vol. 45, no. 17, pp. 869– 870, Aug. 2009. 13. M. K. Meshram, R. K. Animeh, A. T. Pimpale, and N. K. Nikolova, ‗‗A novel quad-band diversity antenna for LTE and Wi-Fi applications with high isolation,‘‘ IEEE Trans. Antennas Propag., vol. 60, no. 9, pp. 4360–4371, Sep. 2012. 14. A. Ramachandran, S. V. Pushpakaran, M. Pezholil, and V. Kesavath, ‗‗A four-port MIMO antenna using concentric square-ring patches loaded with CSRR for high isolation,‘‘ IEEE Antennas Wireless Propag. Lett., vol. 15, pp. 1196–1199, 2016. 15. C.-Y. Chiu and R. D. Murch, ‗‗Compact four-port antenna suitable for portable MIMO devices,‘‘ IEEE Antennas Wireless Propag. Lett., vol. 7, pp. 142–144, 2008. 16. Arun, H., A. K. Sarma, M. Kanagasabai, S. Velan, C. Raviteja, and M. Alsath, ―Deployment of modified serpentine structure for mutual coupling reduction in MIMO antennas,‖ IEEE Antennas Wireless Propag. Lett., Vol. 13, 277–280, 2014. 17. Tang, T. C. and K. H. Lin, ―An ultrawideband MIMO antenna with dua lband-notched function,‖ IEEE Antennas Wireless Propag. Lett., Vol. 13, 1076–1079, 2014 18. T. Taga, ‗‗Analysis for mean effective gain of mobile antennas in land mobile radio environments,‘‘ IEEE Trans. Veh. Technol., vol. 39, no. 2, pp. 117–131, May 1990. 19. J. Yang, S. Pivnenko, T. Laitinen, J. Carlsson, and X. Chen, ‗‗Measurements of diversity gain and radiation efficiency of the Eleven antenna by using different measurement techniques,‘‘inProc. 4thEur.Conf. Antennas Propag., Apr. 2010, pp. 1–5.
64. Authors: Deepti Varshney, Mamta Bansal, Birendra Kumar Sharma Robust Watermarking Technique for Sharing Family Photos on Social Media using Aadhar Number Paper Title: and DCT Abstract: The mind setup of persons has been changed in today‘s environment due to the easily available of internet and smart phone on very low-price cost. Smart phone and internet are two main resources which are being used by persons most of the time in his/her daily routine specially in lockdown due to COVID-19. In this lockdown, persons are doing some creative activity, making fun, etc and recording all his/her this personal information in the form of multimedia contents like text, images, audio and video. This created multimedia content is shared by persons frequently on globe through internet in the daily routine life and some other persons are watching this daily routine activity and making huge business with these data by sometimes with original content or sometimes with modified content without concerns/information/permission of the originator. In this process if everything is going in right way then no issues but if something going wrong then require legal issues and for this, we need to protect our data legally through some methodology. So this paper proposed secure watermarking technique for protecting multimedia content like images using Aadhar number and Discrete Cosine Transform (DCT) technique. In this proposed methodology individual can share the information‘s with watermarked information which is hidden in shared images and on demand at the time of legal issue originator will show the actuality and its ownership. This paper explained details concepts of the embedding and reverse of embedding ( i.e. extracting) process for authentication of the images and its protection from the misuse or fraud. The experimental result of the proposed methodology is shown on different family photos shared on globe and found robust results.
Keywords: Discrete Cosine Transform (DCT), Document Based (DB), Working Domain Based (WDB), Human Perception Based (HPB) and Application Based (AB), Discrete Wavelet Transform (DWT), Intellectual Property Right (IPR), Similarity Ratio (SR)
References: 1. Himanshu Rastogi and B. K. Sharma, ―A Study on Intellectual Property Right and Digital Watermarking‖, International Journal of Advanced Research in Computer Science, Volume 8, No. 7, July – August 2017, ISSN ISSN No. 0976-5697 2. Nishith Desai Associates, Intellectual Property Law in India, July (2015) 3. http://eprints.uthm.edu.my/6936/1/MOHAMED_ABDISALAN_SAID.pdf 381-387 4. http://eprints.rclis.org/28939/1/Intellectual%20Property%20Rights%20in%20Digital%20Environment_ISI.pdf 5. Arathi Chitla, M. Chandra Mohan, ―Authentication of Images through Lossless Watermarking (LWM) Technique with the aid of Elliptic Curve Cryptography‖, International Journal of Computer Applications (0975 – 8887) Volume 57– No.6, November 2012 6. Peyman Rahmati, and Andy Adler, and Thomas Tran. ―Watermarking in E-commerce‖, International Journal of Advanced Computer Science and Applications,Vol. 4, No. 6, 2013 7. Rania A. Ghazy, Alaa M. Abbas ―Block-based SVD image watermarking in spatial and transform domains‖ , International Journal of Electronics, 2015 Vol. 102, No. 7, 1091–1113 8. Malli B, Lagishetty Mounica, Nandhitha.N.M ,Balamurugan.V, ―Development of efficient Quality Preserving Invisible Watermarking Technique to embed both Images and Data in an Image‖ IEEE Online International Conference on Green Engineering and Technologies (IC-GET), 2016, 9. https://www.wipo.int/edocs/pubdocs/en/intproperty/450/wipo_pub_450.pdf 10. https://gss.bsa.org/wp-content/uploads/2018/05/2018_BSA_GSS_Report_en.pdf 11. Lalit Kumar Saini & Vishal Shrivastava, ―A Survey of Digital Watermarking Techniques and its Applications‖, International Journal of Computer Science Trends and Technology (IJCST), Volume 2 Issue 3, PP- 70-73, May-Jun 2014. 12. Prabhishek Singh, R S Chadha ,―A Survey of Digital Watermarking Techniques, Applications and Attacks‖,International Journal of Engineering and Innovative Technology (IJEIT), Volume 2, Issue 9, March 2013 13. Monika Patel,Priti Srinivas Sajja and Ravi K. Sheth, ―Analysis and Survey of Digital Watermarking Techniques‖, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 10, PP 203-210, October 2013 14. M.Hariharalakshmi, Dr. M.Sivajothi, Dr.M.Mohamed SathikInternational ―Survey of Digital Watermarking techniques for Data security‖ Journal of Innovative Research in Computer and Communication Engineering, Vol. 5, Issue 3, March 2017 15. B.K.Sharma ―Watermarking for copyright protection of software codes‖ A Ph.D. Thesis, 2012 16. Sunil Kumar Vishwakarma, B. K. Sharma, Syed Qamar Abbas, ―Digital Watermarking for Image authentication using Spatial-Scale Domain based Techniques‖ IJRTE, ISSN: 2277-3878, Volume-8 Issue-4, November 2019 17. Himanshu Rastogi and Birendra Kumar Sharma, ―Methodology implementation for IPR protection of Mobile Application Code using Digital Watermarking‖, International Journal of Scientific & Technology Research (IJSTR), Volume.-8, Issue- 10, October, 2019, , ISSN ISSN No. 2277-8616 18. Himanshu Rastogi and Birendra Kumar Sharma, ―Implementation of Digital Watermarking Technique to secure IPR of Web Application code‖, International Journal of Innovative Technology and Exploring Engineering (IJITEE),Volume.-8, Issue-11, September 2019 , ISSN ISSN No. 2278- 3075 19. Deepti Varshney, Mamta Bansal, Birendra Kumar Sharma ―Watermarking for Images using Alphanumeric Technique‖, International Journal of Recent Technology and Engineering (IJRTE), Volume.-8, Issue-6, March 2020, ISSN ISSN No. 2277-3878 20. www.csitweb.com Authors: Kittimeth Wattananaphakasem, Ubolrat Wangrakdiskul, Jakawat Deeying
Paper Title: The Effect of Laser Energy and Nitrogen Flow in Solder Joints Properties of Head Gimbal Assembly Abstract: The objective of this research is to study the effect of laser energy and Nitrogen flow on the solder joints of the Head Gimbal Assembly (HGA). The soldering of the HGA components isn't the same as general semiconductors. Since the soldering figure perpendicular to each other so that, it was used the laser solder jet 65. bonding system. The solder jet bonding system uses a solder ball consisting of Sn-2.0Ag-0.7Cu (SAC207) is used for connection of the HGA pad made from a Cu trace coated with Au. The growth of intermetallic 388-394 compounds (IMCs) and shear strength will be analyzed to investigate the effects of laser energy and Nitrogen flow on solder joint reliability. In this research, laser energy levels since 2, 2.5, 3, 3.5, 4, and 4.5 mJ and keep the Nitrogen flow value at 90 mbar. As for the Nitrogen flow effect analysis, the Nitrogen flow level was used at 80, 100, 120, and 140 mbar and keep the laser energy value 3.5 mJ. The results of the study show that the increased levels of laser energy can inhibit the growth of intermetallic compounds as well as the AuSn4 phase that can present benefit to solder joints with results showing within the shear strength to increase significantly. The increase in Nitrogen flow levels has the same effect as the increase in laser energy levels, which can decreases the growth of intermetallic compounds and AuSn4 phase also including increased shear strength. The difference between laser energy and Nitrogen flow increasing shows the level of laser energy can clearly distinct the effect on each level. But the increase in Nitrogen flow level is statistically insignificant from each level.
Keywords: Intermetallic compound, Laser solder jet bonding, SAC solder, solder joints
References: 1. Wood, E. P., and K. L. Nimmo, ―In search of new lead-free electronic solders,‖ Journal of Electronic Materials, vol. 23.8, pp. 709-713, 1994. 2. Hu, Yu-hua, et al, ―Reliability studies of Sn–9Zn/Cu and Sn–9Zn–0.06 Nd/Cu joints with aging treatment,‖ Materials & Design, vol. 34, pp. 768-775, 2012. 3. Jiang, Hongjin, Kyoung-sik Moon, and C. P. Wong, ―Recent advances of nanolead-free solder material for low processing temperature interconnect applications,‖ Microelectronics Reliability, vol. 53.12, pp. 1968-1978, 2013. 4. Canyook, Rungsinee, and Kittichai Fakpan, ―Effect of Cu and Ni Addition on Microstructure and Wettability of Sn-Zn Solders,‖ Key Engineering Materials, vol. 728, pp. 9-14, 2017. 5. Sona, Mrunali, and K. Narayan Prabhu, ―Wetting kinetics and joint strength of Sn-0.3 Ag-0.7 Cu lead-free solder alloy on copper substrate as a function of reflow time,‖ Materials Science Forum, vol. 830, pp. 286-289, 2015. 6. Liu, Xiaoying, et al, ―The adsorption of Ag3Sn nano-particles on Cu–Sn intermetallic compounds of Sn–3Ag–0.5 Cu/Cu during soldering,‖ Journal of alloys and compounds, vol. 492.1-2, pp. 433-438, 2010. 7. Li, Xiao Yan, et al, ―Isothermal aging effects on the microstructure, IMC and strength of SnAgCu/Cu solder joint,‖ Key Engineering Materials, vol. 353, pp. 2928-2931, 2007. 8. Mayappan, Ramani, and Zainal Arifin Ahmad, ―Effect of Bi addition on the activation energy for the growth of Cu5Zn8 intermetallic in the Sn–Zn lead-free solder,‖ Intermetallics, vol. 18.4, pp. 730-735, 2010. 9. Luo, Z-B., et al, ―Revisiting mechanisms to inhibit Ag3Sn plates in Sn–Ag–Cu solders with 1 wt.% Zn addition,‖ Journal of alloys and compounds, vol. 500.1, pp. 39-45, 2010. 10. Kamarudin, Maslinda, Abu Seman Anasyida, and Nurulakmal Mohd Sharif, ―Effect of Aluminium and Silicon to IMC Formation in Low Ag-SAC Solder,‖ Materials Science Forum, vol. 819, pp. 63-67, 2015. 11. Yahya, Iziana, et al, ―Intermetallic evolution of Sn-3.5 Ag-1.0 Cu-0.1 Zn/Cu interface under thermal aging,‖ Advanced Materials Research, vol. 620, pp. 142-146, 2013. 12. Tsao, L. C., et al, ―Effects of nano-Al2O3 particles on microstructure and mechanical properties of Sn3. 5Ag0. 5Cu composite solder ball grid array joints on Sn/Cu pads,‖ Materials & Design, vol. 50, pp. 774-781, 2013. 13. Hanim, Azmah, et al, ―Interfacial Reaction Analysis of Sn-Ag-Cu Solder Reinforced with 0.01 wt% CNTs with Isothermal Aging,‖ Materials Science Forum, vol. 864, pp. 175-179, 2016. 14. Chellvarajoo, Srivalli, M. Z. Abdullah, and C. Y. Khor, ―Effects of diamond nanoparticles reinforcement into lead-free Sn–3.0 Ag–0.5 Cu solder pastes on microstructure and mechanical properties after reflow soldering process,‖ Materials & Design, vol. 82, pp. 206-215, 2015. 15. Li, Hui, ―Effects of small amount addition of rare earth Y on microstructure and property of Sn3. 0Ag0. 5Cu solder,‖ Key Engineering Materials, vol. 584. Pp. 3-8, 2014. 16. Fakpan, Kittichai, and Rungsinee Canyook, ―Effects of Sb and Zn Addition on Mechanical Properties and Corrosion Resistance of Sn– Ag–Cu Solders,‖ Key Engineering Materials, vol. 728, pp. 129-134, 2017. 17. Fu, Shen Li, et al, ―Reliability and Bondability Study on Interfacial Behavior between SnAgCu Solder and Cu-Ni-Au OSP Pads,‖ Key Engineering Materials, vol. 573, pp. 1-7, 2014. 18. Sungkhaphaitoon, Phairote, and Thawatchai Plookphol, ―Effect of Cooling Rate on the Microstructure and Mechanical Properties of Sn- 0.7 wt.% Cu Solder Alloy,‖ Key Engineering Materials, vol. 675, pp. 513-516, 2016. 19. Tian, Yanhong, et al, ―Intermetallic compounds formation at interface between PBGA solder ball and Au/Ni/Cu/BT PCB substrate after laser reflow processes,‖ Materials Science and Engineering: B, vol. 95.3, pp. 254-262, 2002. 20. WANG, Jian-xin, et al, ―Effect of diode-laser parameters on shear force of micro-joints soldered with Sn-Ag-Cu lead-free solder on Au/Ni/Cu pad,‖ Transactions of nonferrous metals society of China, vol. 16.6, pp. 1374-1379, 2006. 21. Park, Yong-Sung, et al, ―Effects of fine size lead-free solder ball on the interfacial reactions and joint reliability,‖ 2010 Proceedings 60th Electronic Components and Technology Conference (ECTC). IEEE, Las Vegas, NV, USA, June 1-4, 2010, pp. 1436-1441. 22. Pan, Jianbiao, et al, ―Effect of gold content on the reliability of SnAgCu solder joints,‖ IEEE Transactions on Components, Packaging and Manufacturing Technology, vol. 1.10, pp. 1662-1669 2011. 23. Ji, Hongjun, Jiao Wang, and Mingyu Li, ―Microstructure and reliability of hybrid interconnects by Au stud bump with Sn-0.7 Cu solder for flip chip power device packaging.‖ Microelectronics Reliability, vol. 66, pp. 134-142, 2016. 24. Huang, M. L., Yuemei Liu, and J. X. Gao, ―Interfacial reaction between Au and Sn films electroplated for LED bumps,‖ Journal of Materials Science: Materials in Electronics, vol. 22.2, 2011. Authors: Pollypriya Buragohain
Paper Title: Use of Information and Communication Technology and Product Promotion Abstract: E-agriculture, i.e., Information and communication technology (ICT) in agriculture enriches the agriculture and brings rural development. Due to this upbringing of information and communication technology, agricultural production has increased and also enhancing the market which indicates a complete change of makeover. Indian farming is revolutionized and all farmers including small landholders are benefited through the use of ICT in agriculture. ICT helps a lot to increase the demand for new perspectives in agricultural field. The present study was basically conducted in Jorhat district of Assam during the time of 2018 and data was collected 66. from 40 farmers through a well structured questionnaire. The main mathematical or statistical tools that have been used in this study are percentages, Likert Scale and measurement of central tendency. The present study 395-398 tries to analyse the socio-economic characteristics of the farmers which consider or use the information and communication technology in agriculture. This study also focuses on the farmer‘s attitude towards using ICT in agriculture and also the frequency of using ICT in agriculture. In this study, it is obtained that there are highest 37.50 percent of farmers are small farmers with a land holding of 1-2 ha and highest 40 percent of farmers have 16 to 20 years of farming experience. From the analysis, it is also noticed that majority(4.68) of farmers strongly agree with the statement that ICT helps in community based planning by providing timely information regarding agricultural field which is followed by the concept of ICTs helpful for reducing the distance in Digital Divide or Technological gap(4.63). With some statements the farmers are strongly disagree or disagree such as ICT helps to exchange the opinion, knowledge, experience and also the assets(2.94) and by improving rural livelihoods ICT fill up the social segregation gap(2.15). Again it is observed that to gaining knowledge and information all 40 farmers are using mobile phones as ICT very frequently.
Keywords: ICT, Agriculture, Development, Digital Divide, Farmer.
References: 1. Kafura, A. R. (2016), Use of ICT as Extension Tool by The Farmers of Gazipur District in Bangladesh, M.S. Thesis, Department of Agricultural Extension and Rural Development, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur-1706, Bangladesh. 2. Lokeswari K. (2016), A Study of the use of ICT among rural farmers, International Journal of Communication Research, Vol. 6(3): 232-238. 3. Mittal, S.(2012), Modern ICT for Agricultural Development and Risk Management in Small holder Agriculture in India, Socio- Economic Working paper 3. 4. Raghuprasad K.P., Devaraja S.C. and Gopala Y.M. (2013), An analysis of knowledge level of farmers on utilization of ICT tools for farm communication, Journal of rural development Nird, Hyderabad, Vol. 32, no. (3) pp. 301-310. 5. Syiem R., Saravann R. (2015), Access and Usage of ICTs for Agriculture and Rural Development by the tribal farmers in Meghalaya State of North-East India. Journal of Agricultural Informatics. Vol. 6(3): 24-41. Authors: P. Oliver jayaprakash, A K.Gunasekaran
Paper Title: Theoretical Framework for the Freight Movements Through a Multicommodity Port Abstract: Port based freight movement planning is a complicated task that could be carried out efficiently to handle the cargo, optimally utilize the infrastructure and plan the future infrastructure requirements. The nature of activities at the port is dynamic with uncertainties,since the operationsare time bound, scholastic and probabilistic.As the huge capital is involved in port infrastructure, the inter-relationship between port activities need to be understood and a system model enveloping the relationship among the variables is much needed for optimal utilization of existing facilities and to predict the future infrastructure requirements. The conventional four step model approach for modeling the person trips would not effectively reflect the commercial scheduling constraintsand requirements of freight trips. This research workattemptsto model the port operations, to assess the level of service of roads and gate operations as subsystems to understand the interdependencies between the variables and the impact on the port operations as a whole.
Keywords: System, Modeling, Multicommodity port, dynamic commodity flow, Turnaround time, Vessel arrival.
References: 1. Kek, C.C. ―Port performance indicators:transportation, water and urban development department‖, World Bank, Transport Report No. PS-6, 1993. 2. Wadhwa, L. C. ―Capacity and performance of bulk handling ports‖, Proceeding of Australian transportation research forum. Vol. 15, Part I, 1990. 3. Wadhwa, L.C., Whelan, G. A., Morel, D.L. and Schneider, M. ―Establishing relationships between port capacity, throughput and performance by simulation studies‖, International congress on modelling and simulation proceedings, IMACS, 1997. 4. Peter Marlow, B. and Ana Paixão, C.―Measuring lean ports performance‖, International journal of transport management, Vol. 1, No. 4, pp. 189-202, 2003. 5. Jose Tongzon, L. ―Determinants of port performance and efficiency‖, Transportation Research Part A: Policy and Practice, Vol. 29, 67. No. 3, pp. 245-252, 1995. 6. Ng Siew,M. and Muhammad, Z.―Petroleum Terminal‘s operation processes on vessel turnaround time‖, Proceedings of EASTS- International Symposium on Sustainable Transportation incorporating Malaysian Universities, Transport Research Forum Conference, 399-410 2008. 7. Asperen Van, E., Borgman, B., and Dekker, R. ―Evaluating container stacking rules using simulation‖, International journal of Hugan& E. Yücesan (Eds.),proceedings of the 2010 Winter Simulation Conference Proceedings of the Winter Simulation Conference, Baltimore.pp. 1924-1933, 2010 8. Daniela, A., and Elena, T. ―A discrete event simulation model for the analysis of critical factors in the expansion plan of a marine container terminal‖, Proceedings 23rd European conference on Modelling and Simulation, 2009 . 9. Borgman, B., Asperen Van, E., and Dekker, R. ―Evaluating container stacking rules using simulation‖, International journal of Hugan& E. Yücesan (Eds.),proceedings of the 2010 Winter Simulation Conference Proceedings of the Winter Simulation Conference, Baltimore.pp. 1924-1933, 2010. 10. Petering, M. E. H."Decision support for yard capacity, fleet composition, truck substitutability, and scalability issues at seaport container terminals via discrete event simulation, "Transportation Research E, 47, pp.85-103,2011. 11. Su Min, J., Kap Hwan, K., Herbert, K.―Routing automated guided vehicles in container terminals through the Q-learning technique‖, Journal of Logistics Research , Vol. 3, No. 1, pp. 19-27, 2011. 12. YounJu, W., Kap Hwan, K.― Estimating the space requirement for outbound container inventories in port container terminals‖, International Journal of Production Economics, Vol. 133, No. 1, pp. 293-301, 2011. 13. Byung Kwon, L. and Kap Hwan, K. ―Comparison and evaluation of various cycle-time models for yard cranes in container terminals‖, International Journal of Production Economics, Vol. 126, No. 2, pp. 350-360, 2010. 14. Dhingra, S.L., Mujumdar, P. and Gajjar R. ―Time Series Modelling of Urban Attracted Truck Movements‖, Journal of Advanced Transportation, Vol.27-3 (Special issue), Winter, 1993. 15. Haitham Al-Deek, M. ―Which method is better for developing freight planning models at seaports-neural networks or multiple regressions?‖, TRR,TRB, Washington, D.C., pp. 90-97, 2007. 16. Nathan, H., Michael, W. and Jeff, D.―Finding the number of yard cranes needed to achieve desired truck turn time at marine container terminals‖, TRR,TRB,Washington,D.C. pp.98-108, 2007. 17. Rodrigo G. and Felipe A.―Modeling the internal transport system in a container port‖, TRR,TRB,No.1782,Washington, D.C., pp. 84- 91, 2002. 18. Khalid Bichou, ―Review of port performance approaches and a supply chain framework to port performance benchmarking‖, Research in Transportation Economics, Vol. 17, No. 6, pp. 567-598, 2006. 19. Wisinee, W. ―Commodity Distribution Model Incorporating Spatial Interactions for Urban Freight Movement‖, 85th Annual Meeting of the TRB, Washington D.C., 2006. 20. Amelia Regan, C., and Rodrigo Corido, A. ―Modelling freight demand and shippers behaviours State of the art future directions‖, ITE, USA, Pergamon press, 2002. 21. Daniel, P.―Forecasting growth with time series models‖, Journal of Forecasting, Vol. 14, No. 2, pp. 97-105, 1995. 22. Amaury, L., Michel, V., Eric de Bodt, Marie, C. and Philippe, G.―Forecasting time-series by kohonen classification‖, ESANN' Proceedings, pp. 221-226, 1998. 23. Siem Jan, K. and Marius, O.―Forecasting daily time series using periodic unobserved components time series models‖, Computational Statistics & Data Analysis, Vol. 51, No. 2, pp.885-903, 2006. 24. Vedat,Y.―The analysis of forecasting performance by using time series data for two mediterranean islands‖, Review of Social, Economic and Business Studies, Vol. 2, pp. 175-196, 2003. 25. Kalekar and Prajakta, S. ―Time series forecasting using Holt-Winters Exponential Smoothing‖, Kanwalrekhi school of information technology, 2004. 26. Nghiep, N. and Cripps,Al. ―Predicting housing value: a comparison of multiple regression analysis and artificial neural networks‖, Journal of Real Estate Research, Vol. 22, No. 3, pp. 313-336, 2001. 27. Ram Pandyala, M., ―Urban highway freight modeling including intermodal connectors for florida‖, BC208,Research Center. Florida Department of Transportation, Final Report DOT-T-97-10, 2002. 28. Roselina, S., Siti, M., Shamsuddin, H., Siti, Z.,Mohammed, H., and Ajith, A. ―Forecasting time series data using hybrid grey Relational artificial neural network and Auto regressive integrated Moving average model‖, Neural Network World, Vol. 6, pp. 573-605, 2007 29. Taylor,A.J. ―System dynamics in shipping‖, Operational research quarterly, Pergamon press, Vol. 27, No. 1, pp .41-56, 1976. 30. Krishnamurthy. ―Data mining- statistics applications: a key to managerial decision making‖, Socio-economic Voices, pp. 1-11, 2010. 31. Kia, M., Shayan, E. and Ghotb, F.―Investigation of port capacity under the new approach by computer simulation‖, Journal of Computers and Industrial Engineering, Vol. 42, pp. 533-540, 2002. 32. Dimitris P., and Anne Kremidjian, S.―Ship traffic modeling methodology for ports‖, Vol. 129, Vol. 5, pp. 163-202, 2003. 33. Hanne-Lovise, S., Katherine, G., and MagnhildViste, ―Visualized system dynamics models as information and planning tools‖, Proceedings of Informing Science Conference, pp.1113-1123, 2003. 34. Dahal, K., Galloway, Stuart, Burt, Graeme, McDonald, Jim,Hopkins, and Ian, ―A port system simulation facility with an optimisation capability‖, International Journal of Computational Intelligence and Applications, Vol. 3, No. 4. pp. 395-410, 2003. 35. Sampsa, R. ―National sea transport demand and capacity forecasting with system dynamics‖, Master‘s thesis, Systems analysis laboratory, Helsinki University of Technology, Helsinki, 2008. 36. Carlucci, F., and Cira, A.―Modeling a plan for seaport investments‖, Pomorstvo‖, GOD, Vol. 23, pp.405-425, 2009. 37. Shabayek, A. A., and Yeung, W. W. ―A simulation model for the kwaichung container terminals in Hong Kong‖, European Journal of Operational Research, Vol. 140, pp. 1-11, 2002. 38. Naggar, El. ―Application of queuing theory to the container terminal at Alexandria seaport‖, Journal of Soil Science and Environmental Management, Vol. 1, No. 4, pp. 77-85, 2010. Authors: Swamy H.C.M, G. Prince Arul Raj
Paper Title: Rheological Behavior of Ordinary Concrete, SCC with and without Glass and Steel Fibers Abstract: Rheology indicates its flowability and deformation. These two parameters indicate directly workability. It measures the normal and shearing forces in fresh concrete state. In this article the flowability and its measurement are discussed for ordinary and SCC with Glass and steel fibers are demonstrated. The strength parameter for a particular concrete mix is demonstrated with sampling and acceptance criteria. The new draft code on design of concrete mix (IS-10262) verified by compliance with specifications. The different parameters like percentage of Glass and Steel fibers, different percentages of silica fume, and different dosages of superplasticizer are tested and reported. A comparative analysis for, with and without glass fibers on ordinary and SCC predicted.
Keywords: Rheology of ordinary concrete, Flowability of SCC, Workability of SCC with Glass fibers, 68. Rheology of SCC with steel fibers, Sampling and acceptance criteria for SCC, Compliance with specification for SCC. 411-417 References: 1. L.D‘Aloia Schwartzentruber, etl .(2006) ―Rehological behavior of fresh cement paste formulated from a SCC‖ ,Cement and concrete research361203-1213 2. Mette R. Geiker, etl.(2002) ―The effect of measuring procedure on the apparent rheological properties of SCC , Cement and concrete research32(2002)1791-1795 3. Tomasz Ponikiewski, etl. ―The Rheological and Mechanical Properties of SCC with High Calcium Fly Ash ―, http://www.claisse.info/Proceedings.html 4. Oladipupo.S , etl.(2015) ―Evaluation of Fresh and Hardened Properties of SCC, open journal of CE,2015,5-7 5. M. Benacha ,etl.(2013) ―Rehological Charactrization of SCC: V-Funnal and Horizontal Plexiglass Channal, IJESIT, ISSN:2319-5967 Vol2, Issu1, January 2013 6. A Vennila, etl.(2016) ―Study on Mechanical Properties of SCC with mineral admixture and Glass fibers‖ Journal of Advances in CE. Vol 2( 1) 2016. Pp.13-20 7. A.M. Neville,etl.‖Concrete technology‖ ELBS 8. M.L.Gambhir, ―Concrete technology‖ Fourth EditionMc Graw-Hill Higher Education. Authors: Usmanova Muborak Akmalzhanovna, Burkhanhadzhaeva Khurshida Vahdatovna Social Assistance and Social Services for Citizens during the Quarantine Period from a Pandemic Paper Title: (On the Example of Uzbekistan and International Experience Abstract: The article considers the state policy of social protection of the population in the Republic of Uzbekistan. Methods of legal regulation of social security law. The system of social security law is analyzed. 69. The history of formation and development of social security in the Republic of Uzbekistan is studied. Attention is paid to the rights of social security during the period of quarantine from a pandemic, and international legislation and experience are comparatively analyzed . The article deals with themain characteristics of the legal 418-424 regulation of remote workers ' labor; theconcept and features of remote laboras a subject of labor law. The authors analyzed therelationship of an employment contract with a remote employee with other labor contracts. Legal acts in the field of regulating the work of remote workersin the context of a pandemic have been studied. Features of concluding an employment contract with a remote employee. Electronic interaction during the pandemic period, whichis under the control of the employer; - interaction between the employer and the employee is carried outusing public information and telecommunications networks.
Keywords: principle. law, social security, citizens, law, experience, quarantine, need, conditions, law, labor, remotely, law, pandemic, Internet
References: 1. Constitution Of The Republic Of Uzbekistan (1992). Tashkent.Uzbekistan. 2019. - from 40 2. Regulatory legal acts, international documents 3. The universal Declaration of human rights (1948). 4. The international Covenant on civil and political rights (1966). 5. The international Covenant on economic, social and cultural rights (1966). 6. . European Convention for the protection of human rights and fundamental freedoms (1950). 7. European social Charter (1988). 8. ILO Convention No. 48 establishing a system of international co-operation for the maintenance of rights arising from disability, old age and survivors ' insurance (1935). 9. ILO social security minimum standards Convention No. 102 (1952). 10. ILO Convention No. 117 on basic aims and standards of social policy (1962). 11. ILO Convention No. 128 on disability, old age and survivors ' benefits (1967). 12. ILO Convention No. 130 on medical assistance and sickness benefits (1969). 13. the Law of the Republic of Uzbekistan ―on state pension provision for citizens". www.lex.uz 14. Law of the Republic of Uzbekistan no. ZRK-272 of 22.12.2011 "On amendments and additions to the Labor code of the Republic of Uzbekistan and the law of the Republic of Uzbekistan "on state pension provision for citizens"". www.lex.uz Law of the Republic of Uzbekistan dated December 23, 2010 № ZRU-272 "on amendments and additions to the Labor code of the Republic of Uzbekistan and the law of the Republic of Uzbekistan "on state pension carefor citizens" " www.lex.uz 15. Law of the Republic of Uzbekistan from 02.12.2004 N 702-II "on accumulative pension provision of citizens" "Collection of legislation of the Republic of Uzbekistan", 2004, N 51, article 512 16. .DecreeOf the President of the Republic of Uzbekistan "on the state program for implementing the strategy of action on five priority areas of development of the Republic of Uzbekistan in 2017-2021 in the" year of active investment and social development» dated January 17, 2019 no. up-5635 17. Tursunov Y., Usmanova M., Sattarova G. the Law of social security. Textbook for Universities. Vol.: Aloqachi, 2008. 498c. 18. Machulskaya E. E. Pravo sotsial'nogo obepecheniya [the Law of social welfare]. Moscow: yurait, 2011, 315 p Authors: W.F.Tang, S.L.Mak, C.H.Li
Paper Title: Additive Manufacturing Technology in Orthodontic Devices Development Abstract: Traditional wires and brackets has been widely used as orthodontic devices for long time. The metal wires and brackets help to correct the position of teeth as well as fix the cavity. However, metal brace wires have quite a lot limitations. Patients wearing metal brace have many food restrictions and feel not comfortable. Brushing and flossing are required to remove the food debris frequently. Hence, clear plastic aligners have popped up recently. Since the metal brace fabrication process has associated with prolonged process time as a result of a long workflow process starting from brace mold presentation to the prosthesis execution. The growing of additive manufacturing technology make it possible to develop complex structures and shapes of dental brace. By combining 3D oral scanning, it is possible to shorten the lead time of orthodontic treatment process. This review, therefore, investigates the use of Digital Light Processing (DLP) Additive Manufacturing Technology for plastic dental brace development as a remedy to the problems associated with the traditional methods. The study reveals that it is feasible to fabricate these plastic braces utilising the DLP technology. DLP technology is affordable and arguably able to produce dental models with high levels of assurance and accuracy.
Keywords: dental brace, clear aligners, orthodontic device, additive manufacturing, stereolithography, fused deposition modelling
70. References: 1. , A., 2020. Safe braces. British Dental Journal, 228(10), pp.739-739. 2. Bártolo, P.J. ed., 2011. Stereolithography: materials, processes and applications. Springer Science & Business Media. 425-432 3. Benson, P.E., Da'as, T., Johal, A., Mandall, N.A., Williams, A.C., Baker, SR and Marshman, Z., 2015. Relationships between dental appearance, self-esteem, socio-economic status, and oral health-related quality of life in U.K schoolchildren: A 3-year cohort study. European journal of orthodontics, 37(5), pp.481-490. 4. Bowman, S.J., 2017, March. Improving the predictability of clear aligners. In Seminars in orthodontics (Vol. 23, No. 1, pp. 65-75). WB Saunders. 5. Castiaux, A.D., Pinger, C.W., Hayter, E.A., Bunn, M.E., Martin, R.S. and Spence, D.M., 2019. PolyJet 3D-printed enclosed microfluidic channels without photocurable supports. Analytical Chemistry, 91(10), pp.6910-6917. 6. Charalambis, A., Davoudinejad, A., Tosello, G. and Pedersen, D.B., 2017. Cost estimation of a specially designed direct light processing (DLP) additive manufacturing machine for precision printing. In euspen’s 17th International Conference & Exhibition. The European Society for Precision Engineering and Nanotechnology. 7. Choi, J.W., Kim, H.C. and Wicker, R., 2011. Multi-material stereolithography. Journal of Materials Processing Technology, 211(3), pp.318-328. 8. Comba, B., Parrini, SIMONE, Rossini, G.A.B.R.I.E.L.E., Castroflorio, T. and Deregibus, A., 2017. Three-dimensional finite element analysis of upper-canine destabilisation with clear aligners, composite attachments, and class II elastics. J Clin Orthod, 51(1), pp.24-8. 9. Davoudnejad, A., Pedersen, D.B. and Tosello, G., 2017. Characterization of additive manufacturing processes for polymer micro parts productions using direct light processing (DLP) method. In the 33rd International Conference of the Polymer Processing Society (PPS- 33). 10. Dawood, A., Marti, B.M., Sauret-Jackson, V. and Darwood, A., 2015. 3D printing in dentistry. British dental journal, 219(11), pp.521- 529. 11. Dul, S., Fambri, L. and Pegoretti, A., 2016. Fused deposition modelling with ABS–graphene nanocomposites. Composites Part A: Applied Science and Manufacturing, 85, pp.181-191. 12. Francois, M.M., Sun, A., King, W.E., Henson, N.J., Tourret, D., Bronkhorst, C.A., Carlson, N.N., Newman, C.K., Haut, T.S., Bakosi, J. and Gibbs, J.W., 2017. Modelling of additive manufacturing processes for metals: Challenges and opportunities. Current Opinion in Solid State and Materials Science, 21(LA-UR-16-24513; SAND-2017-6832J). 13. Garino, F., Castroflorio, T., Daher, S., Ravera, S., Rossini, G., Cugliari, G. and Deregibus, A., 2016. Effectiveness of composite attachments in controlling upper-molar movement with aligners. J Clin Orthod, 50(6), pp.341-7. 14. Gomez, J.P., Peña, F.M., Martínez, V., Giraldo, D.C. and Cardona, C.I., 2015. Initial force systems during bodily tooth movement with plastic aligners and composite attachments: A three-dimensional finite element analysis. The Angle Orthodontist, 85(3), pp.454-460. 15. Gokuldoss, P.K., Kolla, S. and Eckert, J., 2017. Additive manufacturing processes: Selective laser melting, electron beam melting and binder jetting—Selection guidelines. Materials, 10(6), p.672. 16. Harun, W.S.W., Kamariah, MSIN, Muhamad, N., Ghani, S.A.C., Ahmad, F. and Mohamed, Z., 2018. A review of powder additive manufacturing processes for metallic biomaterials. Powder Technology, 327, pp.128-151 17. Hilliard, J.K., 2008. System and method for fabricating orthodontic aligners. U.S. Patent Application 11/842,411. 18. Javaid, M. and Haleem, A., 2019. Current applications and status of additive manufacturing in dentistry: A literature-based review. Journal of craniofacial and oral biology research, 9(3), pp.179-185. 19. Jasiuk, I., Abueidda, D.W., Kozuch, C., Pang, S., Su, F.Y. and McKittrick, J., 2018. An overview of additive manufacturing of polymers. Jom, 70(3), pp.275-283. 20. Jesson, J., Matheson, L. and Lacey, F.M., 2011. Doing your literature review: Traditional and systematic techniques. Sage. 21. Jindal, P., Juneja, M., Bajaj, D., Siena, F.L. and Breedon, P., 2020. Effects of post-curing conditions on mechanical properties of 3D printed clear dental aligners. Rapid Prototyping Journal. 22. Jiang, J., 2019. Support Optimisation for Additive Manufacturing. 23. Kaluđerović, M.R., Schreckenbach, J.P. and Graf, H.L., 2016. Titanium dental implant surfaces obtained by anodic spark deposition– from the past to the future. Materials Science and Engineering: C, 69, pp.1429-1441. 24. Kazemi, M. and Rahimi, A., 2019. Improving the efficiency of fabrication of AM parts by segmentation design in the DLP process. Rapid Prototyping Journal. 25. Kiviat, J. and Fleming, Y., 2018. Orthodontic Appliance Intolerance Due to Dental Adhesive Allergy. Dermatitis, 29(6), pp.349-350. 26. Lee, H., Lim, C.H.J., Low, M.J., Tham, N., Murukeshan, V.M. and Kim, Y.J., 2017. Lasers in additive manufacturing: A review. International Journal of Precision Engineering and Manufacturing-Green Technology, 4(3), pp.307-322. 27. Marotti, J., Heger, S., Tinschert, J., Tortamano, P., Chuembou, F., Radermacher, K. and Wolfart, S., 2013. Recent advances of ultrasound imaging in dentistry–a review of the literature. Oral surgery, oral medicine, oral pathology and oral radiology, 115(6), pp.819-832. 28. Melchels, F.P., Feijen, J. and Grijpma, D.W., 2010. A review on stereolithography and its applications in biomedical engineering. Biomaterials, 31(24), pp.6121-6130. 29. Millett, D.T., Mandall, N.A., Mattick, R.C., Hickman, J. and Glenny, A.M., 2017. Adhesives for bonded molar tubes during fixed brace treatment. Cochrane Database of Systematic Reviews, (2). 30. Moser, N., Santander, P. and Quast, A., 2018. From 3D imaging to 3D printing in the dentistry-a practical guide. International journal of computerised dentistry, 21(4), pp.345-356. 31. Mu, Q., Wang, L., Dunn, C.K., Kuang, X., Duan, F., Zhang, Z., Qi, HJ and Wang, T., 2017. Digital light processing 3D printing of complex conductive structures. Additive Manufacturing, 18, pp.74-83. 32. Nayar, S.K. and Boult, T.E., Columbia University of New York, 2014. Adaptive imaging using digital light processing. US Patent 8,675,119. 33. Oliveira, G., 2019. Accuracy And Precision Of 3-dimensional Printed Dental Models Produced By Different Additive Manufacturing Technologies. 34. Resnik, D.B., 2015. What is ethics in research & why is it essential—National Institute of Environmental health sciences? 35. Sherman, S.L., Kadioglu, O., Currier, G.F., Kierl, J.P. and Li, J., 2020. Accuracy of digital light processing printing of 3-dimensional dental models. American Journal of Orthodontics and Dentofacial Orthopedics, 157(3), pp.422-428. 36. Shivapuja, P.K., Shah, D., Shah, N. and Shah, S., Real 3d Polymers Group LLC, 2019. Direct 3D-printed orthodontic aligners with torque, rotation, and full control anchors. U.S. Patent 10,179,035. 37. Shkarin, V.V., Davydov, B.N., Domenyuk, D.A. and Dmitrienko, S.V., 2018. Non-removable arch orthodontic appliances for treating children with congenital maxillofacial pathologies–efficiency evaluation. Archiv EuroMedica, 8(1), pp.97-98. 38. Singh, R., 2011. Process capability study of polyjet printing for plastic components. Journal of mechanical science and technology, 25(4), pp.1011-1015. 39. Singamneni, S., Diegel, O., Huang, B., Gibson, I. and Chowdhury, R., 2010. Curved-layer fused deposition modelling. Journal for New Generation Sciences, 8(2), pp.95-107. 40. Sood, A.K., Ohdar, R.K. and Mahapatra, SS, 2010. Parametric appraisal of mechanical property of fused deposition modelling processed parts. Materials & Design, 31(1), pp.287-295. 41. Szuhanek, C., Grigore, A., Schiller, E., Bratu, D.C., Onisei, D. and Onisei, D., 2015. The role of digital setup in the orthodontic treatment with plastic aligners. Mat. Plast, 52, p.522. 42. Szuhanek, C., Fleser, T. and Grigore, A., 2015. Applications of Thermoplastic Materials in the Fabrication of Orthodontic Aligners. Mat. Plast, 52, p.385. 43. Sorooshian, S. and Kamarozaman, A.A., 2018. Fashion braces: an alarming trend. Sao Paulo Medical Journal, 136(5), pp.497-498. 44. Thrasher, C.J., Schwartz, J.J. and Boydston, A.J., 2017. Modular elastomer photo resins for digital light processing additive manufacturing. ACS applied materials & interfaces, 9(45), pp.39708-39716. 45. Uno, M., Yokokawa, Y., Kawaki, H., Oka, T., Tamaki, Y. and Ishigami, H., 2019. Investigating the effectiveness of ceramic materials, particularly zirconium oxide, and the advantages the white metal holds over traditional materials used in dentistry: impact, 2019(2), pp.68-70. 46. Vandenberghe, B., 2018. The digital patient–Imaging science in dentistry. Journal of dentistry, 74, pp.S21-S26. 47. Wang, X., Gao, W.Y., Luan, J., Wojtas, L. and Ma, S., 2016. A most effective strategy to boost the robustness of metal-organic frameworks via the introduction of size-matching ligand braces. Chemical Communications, 52(9), pp.1971-1974. 48. Weir, T., 2017. Clear aligners in orthodontic treatment. Australian dental journal, 62, pp.58-62. 49. Ziemian, C., Sharma, M. and Ziemian, S., 2012. Anisotropic mechanical properties of ABS parts fabricated by fused deposition modelling. Mechanical engineering, 23. 50. Zuzak, K.J., Cadeddu, J.A., Ufret-vincenty, R., Francis, R.P. and Livingston, E., University of Texas System, 2013. Digital light processing hyperspectral imaging apparatus. US Patent Application 13/776,105. Authors: Preeti Tripathi, Imran Khan Computational Simulation - Design & Analysis Functionality of Grid Connected (GC) Photo-Voltaic Paper Title: (PV) System Abstract: The electrical power produced via photo-voltaic (PV) array relies largely on weather conditions. In 71. this paper, we presented a continuous state functionality of the PV grid-connected (GC) unit at distinct solar irradiances. The presented model is developed on MATLAB environment, which includes the PV array using an improved perturb and observe (MP&O) tracking system interconnected to DC to DC boosting 433-438 conversion application, the 3-phase 3 level electric power inverter which usually associated to the utility grid using low pass filter, coupled transformer and synchronous control mechanism of PV inverter. The presented model is lab-created within day-by- day climatic conditions to estimate its working mechanism. The simulation results of the proposed system satisfy requirements grid performance with high power quality. In the proposed work number of cell modules used 90, number of parallel strings 60, maximum PV output voltage1000wb/m2 at 274 V, minimum voltage at 600 wb/m2 at 250V, maximum power at 1000 wb/m2-100 kw, and minimum power at 600 wb/m2-57 kw.
Keywords: PV, MP&O, boost converter, UG
References: 1. Physical Progress (Achievements), Ministry of New andRenewable Energy, Govt. of India. 31 January 2014. Retrieved 21February 2014. 2. State wise installed solar power capacity (PDF), Ministry of New andRenewable Energy, Govt. of India. 1 March 2016. Retrieved24 March 2016. 3. D. M. Tobnaghi, ―A Review on Impacts of Grid-Connected PV System on Distribution Network,‖ International Journal of Electrical, Computer, Energetic, Electronic and Communication Engg. , Vol. 10, No.1, 2016. 4. ―Grid Integration of Distributed Solar Photovoltaics (PV) in India,‖A Prayas (Energy Group) Report, July 2014. 5. IEEE Standard 519-1992 – Recommended Practices and Requirements for Harmonic Control in Electrical Power Systems. Revision to IEEE 519-2014. 6. B. K. Perera, P. Ciufo, S. Perera, ―Point of Common Coupling (PCC) voltage control of a grid-connected solar Photovoltaic (PV) system,‖ 39th Annual Conference of the IEEE Industrial Electronics Society (IECON 2013), 2013, pp.7475-7480. 7. H. Ko, S. R. Lee, H. Dehbonei, and C. V. Nayar, "Applicationof Voltage-and Current-Controlled Voltage Source Inverters forDistributed Generation Systems," IEEE Transactions on EnergyConversion, vol. 21, pp. 782-792, 2006. 8. M. P. Kazmierkowski and L. Malesani, "Current ControlTechniques for Three-Phase Voltage-Source PWM Converters:A Survey," IEEE Transactions on Industrial Electronics, vol. 45, no. 5, pp. 691-703, 1998.E. 9. Adel A. Elbaset, Hamdi Ali and Montaser Abd-El Sattar ―NovelSeven-Parameter Model for Photovoltaic Modules‖ Solar Energy Materials & Solar Cells, vol. 130, pp. 442-455, 2014.Polycarpou, M.M.: Stable Adaptive Neural Control Scheme for Nonlinear Systems. IEEE Trans. Automat. Contr., 41 (1996) 447-450. 10. Brigitte Hauke–―Basic Calculation of a Boost Converter's Power Stage‖ Application Report SLVA372B – November 2009 – Revised July 2010-Texas Instrument.Ge, S.S., et al.: A Direct Method for Robust Adaptive Nonlinear Control with Guaranteed Transient Performance. System Control Lett., 37 (1999) 275-284 11. SoerenBaekhoejKjaer et al ―A Review of Single-Phase Grid- Connected Inverters for Photovoltaic Modules‖ IEEE Transactions on Industry Applications, vol. 41, no. 5, September/October 2005. 12. M. Prodanović and T. C. Green, "Control and filter design of three- phase inverters for high power quality grid connection," IEEE Transactions on Power Electronics, vol. 18, no. 1, pp. 373-380, January 2003. 13. T. C. Wang, Z. Ye, G. Sinha, and X. Yuan, "Output FilterDesign for a Grid-Interconnected Three-Phase Inverter," in IEEE 34th Annual Power Electronics Specialist Conference (PESC'03), pp. 779-784, Acapulco, Mexico, June 15-19,2003. 14. Gabriel OoiHeo Peng, ―Investigation and Implementation of Multilevel Power Converters for Low/Medium/High Power Applications‖, Ph.D Thesis, School of Electrical and Electronic Engineering, Nanyang Technological University, 2015. 15. J. Rodriguez, J.-S. Lai, and F. Z. Peng, "Multilevel Inverters: A Survey of Topologies, Controls, and Applications," IEEE Transactions on Industrial Electronics, vol. 49, no. 4, pp. 724- 738, August 2002. 16. J. S. Lai and F. Z. Peng, "Multilevel Converters-A New Breed of Power Converters," IEEE Transactions on Industry Applications, vol. 32, no. 3, pp. 509-517, May/June 1996. 17. C. Hochgraf, R. Lasseter, D. Divan, and T. Lipo"Comparison of multilevel inverters for static var compensation," in IEEE Conference Record of Industry Applications Society Annual Meeting, pp. 921-928, Denver, Colorado, October 2-6, 1994. 18. E.D. Mehleri, H. Sarimveis, N.C. Markatos, and L.G.Papageorgiou, ―Optimal Design and Operation of DistributedEnergy Systems: Application to Greek Residential Sector,‖Renewable Energy, vol. 51, pp. 331-342, March 2013. Authors: Gowtham Kishor Kumar B, P. Palson,
Paper Title: Factors Influencing Cost Overrun and Delay with Their Risks in Construction Management. Abstract: Cost overrun and delay are the most important factors which affect the rate of progress in construction industries. There are numerous Risks are involved in cost and schedule overrun which leads to unprofitable situation or dropping the project. Previous literature studies are mainly focused only on cost overrun and delay but they do not deal with their risks which is important to study. This study is to assess the factors influencing time and cost overruns on construction projects and their risks also. The objectives of the study were achieved through valid questionnaire. The questionnaires are collected over 40 construction companies. From this survey, identify and ranking the various elements which are responsible for the inflation of cost and schedule overrun using analytical software like SPSS. And discuss about the significant values obtained from the collecting data and recommendation and mitigation ideas from the ranking of overrun factors. The significant value should be more than 0.05 and from our analysis most of the factors are above that value. The study clarified that incorrect estimates and low productivity level of labors highly contributes to overrun in 72. construction management. It will leads to unprofitable situation, so proper scheduling and better management will rectify these problems. 439-443 Keywords: Elements, Cost overrun, Schedule overrun, Construction projects.
References: 1. Emmanuel Bentil and Edward Nana and Alfred Fokuo, 2016, Existence and Impact of Overruns in construction projects in GHANA. 2. Adam Abderisak, per-Erik Josephson and Goran Lindal,2015, Implication of Overruns in Major Public construction projects 3. Fouzi A. HOSSEN (2010), ―Project cost risk assessment: an application of project risk management process in Libyan construction projects‖ Faculty of Engineering, University of Omar Elmukhtar, Libya 4. Sai Murali Krishna Reddy. Raya and S.S Bhanu Prakash (2016), ―Cost and Time Overrun in Indian Construction Industry‖ Industrial Science Research Journal,2(4),1-9. 5. Paul Terna Gbahabo and Oluseye Samuel Ajuwon, Effects of cost overrun and schedule delay in Sub Saharan Africa, efficient and effective monitoring and evaluation performance. 6. Aftab Hameed Memon, et al (2014), ―Significant Factors Causing Time Overrun in Construction Projects of Peninsular Malaysia‖ Modern Applied Science;8(4), 16-28 7. Alirezarezaei and saeedjalal,2016 Investigation on Causes of Delay and Cost overrun in Construction Industry 8. N.Hamezah, et al (2011) ―Causes of construction Delay Theoretical framework‖ 9. Raj kapur shah,2016, Exploration of Causes for Delay and Cost Overruns in Construction Projects: Case Study of Australia, Malaysia &Ghana, 10. Adriana Gomez and Jose Luiz poniz tienda (2017), INTI journal. Authors: Veena Devi Shastrimath V., Ashwini, Andrea Olivero, Deepa Bhat
Paper Title: Railway Access Control System using Face Recognition Abstract: Nowadays booking tickets and getting inside a railway station is adifficult task. Manual checking becomes a burden and time consuming. Also as everything is getting digitized in this modern world introduce face recognition and Quick Response (QR) code system for entry helps in passenger convenience.Face recognition is a method of identifying or verifying the identity of an individual using their face. Face recognition systems can be used to identify people in photos, video, or in real-time.So this system focuses on passengers‘ convenience through allowing them to book tickets online and by introducing face recognition system and QR code system for entry to a railway station.This system helps inidentifying people who try to travel without buying tickets and also helps toapprehend the blacklisted person which increases security in the railway station. Online booking is one of the convenient ways tobook the ticket. This system also provides the convenience to passenger by issuing the digital ticket in the form of QR code thus avoiding any fuss due to the loss of the physical ticket.
Keywords: Entry Control System, Face Detection, Face Recognition, User Interface. 73. References: 1. M. Nakada, H. Wang, and D. Terzopoulos, ―Acfr: Active facerecognition using convolutional neural networks,‖ in 2017 IEEE 444-448 Conference on Computer Visionand Pattern Recognition Workshops (CVPRW), 2017, pp. 35–40. 2. W.-K. CF. Guzzi, L. D. Bortoli, S. Marsi, S. Carrato, and G. Ramponi, ―Distillation of a cnn for a high accuracy mobile face recognition system,‖ in 2019 42nd International Convention on Information and Communication Technology, Electronics andMicroelectronics (MIPRO), 2019, pp. 989–994.. 3. Q. Li, ―An improved face detection method based on face recognition application,‖in 2019 4th Asia-Pacific Conference on Intelligent Robot Systems (ACIRS),2019, pp. 260–264E. 4. X. Zhang, M. Peng, and T. Chen, ―Face recognition from near-infrared images with convolutional neural network,‖ in 2016 8th International Conference onWireless Communications Signal Processing (WCSP), 2016, pp. 1–5. 5. C. Ding and D. Tao, ―Trunk-branch ensemble convolutional neural networks for video-based face recognition,‖ IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, no. 4, pp. 1002–1014, 2018.C. 6. Hou A-Lin, Feng Yuan, and Geng Ying, ―Qr code image detection using runlength coding,‖ in Proceedings of 2011 International Conference on Computer Science and Network Technology, vol. 4, 2011, pp. 2130–2134. 7. Lihong Wan, Na Liu, Hong Huo, and Tao Fang, ―Face recognition with convolutional neural networks and subspace learning,‖ in 2017 2nd InternationalConference on Image, Vision and Computing (ICIVC), 2017, pp. 228–233 8. A. Jamnik, M. Shahare, S. Kamble, N. Kale, M. Bhadade, and S. V. Sonekar, ―Digital ticket booking and checking using aadhaar card or fingerprint and android application,‖ in 2019 3rd International Conference on Recent Developments in Control, Automation Power Engineering (RDCAPE), 2019, pp. 503–507., 9. K. He, X. Zhang, S. Ren, and J. Sun, ―Deep residual learning for image recognition,‖ in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770–778. Authors: G.Sundari, J.Shanmugapriyan Design and Implementation of PID, GA and Fuzzy logic Controllers for an Electrical Drive with Paper Title: Various Noise Disturbances Abstract: It is a great challenge for human being to keep up the constant speed in drive when external Noise disturbances occur due to fluctuations of power supply. In order to avoid these issues, PID controllers are intended using predictable method such as Ziegler Nichols method. But finest level is not obtained in transient and steady state. During the MATLAB Simulation, the error is present transient and steady state behavior in conventional PID controllers. Hence it is necessary to design a PID controller with Novel intelligent technique for speed control of drive like fuzzy and Genetic Algorithm. It considers error as fitness function which is to be minimized using various GA operators such as mutation etc. The Drive will be operated with different external noises like sinusoidal noise, Saw tooth noise and Ramp noise and comparison between PID, GA and Fuzzy PID will be presented and their performances are studied.
Keywords: Noise disturbances PID controller, Genetic controller, fuzzy controller. 74. References: 1. Younis S. Dawood , Ali K. Mahmood and Muhammed ―Comparison of PID, GA and Fuzzy Logic Controllers for Cruise Control 449-453 System ―, A. International Journal of Computing and Digital Systems ISSN (2210-142X) Int. J. Com. Dig. Sys. 7, No.5 (Sep-2018) 2. Ramesh Chandra Chourasia, Mukesh Kumar ,―Speed Control of S.E.D.C. Motor by Using Pi and Fuzzy Logic Controller‖, International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-3, Issue-2, May 2013 3. Umesh Kumar Bansal and Rakesh Narvey, ―Speed Control of DC Motor Using Fuzzy PID Controller‖. ISSN 2231-1297,Volume 3, Number 9 (2013), pp. 1209-1220 © Research India Publications 4. Sharad Chandra Rajpoot1, Prashant Singh, Rajpoot2, Premlata3, Durga Sharma4―Design and Simulation of Neuro Fuzzy Controller for Speed Control of a Separately Excited dc Motor‖, IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE) e-ISSN: 2278-1676,p-ISSN: 2320-3331, Volume 11, Issue 6 Ver. IV (Nov. – Dec. 2016), PP 56-67 5. Hybrid control technique for minimizing the torque ripple of brushless direct current motor‖Measurement and Control 2018, Vol. 51(7- 8) 321–335 © The Author(s) 2018 Article reuse guidelines: sagepub.com/journals-permission 6. Akbarzadeh M.R , Kim T, Y.T. &B.Feerouzbakhah, ‗Evolutionary Fuzzy speed regulation for a DC motor‘],‖ ‗ NASA Center for Autonomous control Engineering, 7. Chin- Hung wang, Tzung- Pei Hong, and Shian- Shyong Teng‖ Integrating Fuzzy knowledge by Genetic Algorithms‖, (1998), ,IEEE Transactions on Evolutionary computation, VOL.2, No.4. 8. Abdulla Ismail and Sharaf A.M (2002),‖An Efficient Neuro Fuzzy speed controller for Large Industrial Motor Drives,‖ IEEE International conference on Control Applications, September 18-20,2002,Glasgow, Scotland,U.K. 9. Chin- Hung wang, Tzung- Pei Hong, and Shian- Shyong Teng (1998)]‗Evolutionary Fuzzy speed regulation for a DC motor‘, NASA Center for Autonomous control Engineering, Authors: Putta. Rama Krishna Veni, C Aruna Bala
Paper Title: The Multi Stage U-net Design for Brain Tumor Segmentation using Deep Learning Architecture. Abstract: Now a day‘s diagnosis and accurate segmentation of brain tumors are critical conditions for successful treatment. The manual segmentation takes time consuming process, more cost and inaccurate. In this paper implementation of cascaded U-net segmentation Architecture are divided into substructures of brain tumor segmentation. The neural network is competent of end to end multi modal brain tumor segmentations.The Brain tumor segments are divided three categories. The tumor core (TC),the enhancing tumor(ET),the whole tumor (WT).The distinct data enhancement steps are better achievement. The proposed method can test result conclude average counter scores of 0.83268, 0.88797 and 0.83698, as well as Hausdorff distances 95%) of 2.65056, 4.61809 and 4.13071, for the enhancing tumor(ET), whole tumor (WT) and tumor core (TC) respectively. In this method validating with BraTS 2019 dataset and identify the test time enhancement improves the Brain tumor segmentation accurate images.
Keywords: Deep learning • Brain tumor segmentation • U-Net
References: 1. Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4 28 75. 2. Roth, H.R., et al.: A multi-scale pyramid of 3D fully convolutional networks for abdominal multi-organ segmentation. In: Frangi, A.F., Schnabel, J.A., Davatzikos, 3. C., Alberola-L´opez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11073, pp. 417–425. Springer, Cham (2018). 454-460 https://doi.org/10.1007/978-3-030-00937-3 48 20. Shen, H., Wang, R., Zhang, J., McKenna, S.J.: Boundary-aware fully convolutional network for brain tumor segmentation. In: Descoteaux, M., Maier-Hein, L., 4. Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10434, pp. 433–441. Springer, Cham (2017). https://doi.org/10.1007/978-3-319- 66185-8 49 5. Siskind, J.M., Pearlmutter, B.A.: Divide-and-conquer checkpointing for arbitrary programs with no user annotation. Optim. Methods Softw. 33(4–6), 1288–1330 (2018) 6. Tu, Z., Bai, X.: Auto-context and its application to high-level vision tasks and 3D brain image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 32(10), 1744–1757 (2009) 7. Wu, Y., He, K.: Group normalization. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11217, pp. 3–19. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01261-8 1 8. Zhao, X., Wu, Y., Song, G., Li, Z., Zhang, Y., Fan, Y.: A deep learning model integrating FCNNS and CRFS for brain tumor segmentation. Med. Image Anal. 43, 98–111 (2018) 9. Zhou, C., Chen, S., Ding, C., Tao, D.: Learning contextual and attentive information for brain tumor segmentation. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., 10. Reyes, M., van Walsum, T. (eds.) BrainLes 2018. LNCS, vol. 11384, pp. 497–507. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11726-9 44 11. Zhou, C., Ding, C., Lu, Z., Wang, X., Tao, D.: One-pass multi-task convolutional neural networks for efficient brain tumor segmentation. In: Frangi, A.F., Schnabel, 12. J.A., Davatzikos, C., Alberola-L´opez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11072, pp. 637–645. Springer, Cham (2018). https://doi.org/10.1007/ 978-3-030-00931-1 73 13. Zhou, C., Ding, C., Wang, X., Lu, Z., Tao, D.: One-pass multi-task networks with cross-task guided attention for brain tumor segmentation. arXiv preprint arXiv:1906.01796 (2019) Authors: Divya Sathya Sree.I, Pangedaiah.B
Paper Title: A New Islanding Detection Technique using Ensemble Empirical Mode Decomposition Abstract: Penetration of distributed generation (DG) is rapidly increasing but their main issue is islanding. Advanced signal processing methods needs a renewed focus in detecting islanding. The proposed scheme is based on Ensemble Empirical Mode Decomposition (EEMD) in which Gaussian white noise is added to original signal which solves the mode mixing problem of Empirical mode decomposition (EMD) and Hilbert transform is applied to obtained Intrinsic mode functions(IMF). The proposed method reliably and accurately detects disturbances at different events.
Keywords: Distributed Generation (DG), Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD), Intrinsic Mode Function (IMF). 76.
References: 461-466 1. P. Mahat, Z. Chen, and B. Jensen, ―Review of islanding detection methods for distributed generation,‖ in Proc. 3rd Int. Conf. Electric Utility Deregulation Restructuring Power Technol., Nanjing, China, 2008, pp. 2743– 2748. 2. H. H. Zeineldin and S. Kennedy, ―Sandia frequency-shift parameter selection to eliminate nondetection zones,‖ IEEE Trans. Power Del., vol. 24, no. 1, pp. 486–487, Jan. 2009. 3. H. H. Zeineldin and M. M. A. Salama, ―Impact of load frequency dependence on the NDZ and performance of the SFS islanding detection method,‖ IEEE Trans. Ind. Electron., vol. 58, no. 1, pp. 139–146, Jan. 2011. 4. A. Samui and S. R. Samantaray, ―Assessment of ROCPAD relay for islanding detection in distributed generation,‖ IEEE Trans. Smart Grid, vol. 2, no. 2, pp. 391–398, Jun. 2011. 5. S. Jang and K. Kim, ―An islanding detection method for distributed generation algorithm using voltage unbalance and total harmonic distortion of current,‖ IEEE Trans. Power Del., vol. 19, no. 2, pp. 745–752, Apr. 2004. 6. V. Menon and M. H. Nehrir, ―A hybrid islanding detection technique using voltage unbalance and frequency set point,‖ IEEE Trans. Power Syst., vol. 22, no. 1, pp. 442–448, Feb. 2007. 7. Y. H. Gu and M. H. J. Bollen, ―Time-frequency and time-scale domain analysis of voltage disturbances,‖ IEEE Trans. Power Del., vol. 15, no. 4, pp. 1279–1284, Oct. 2000. 8. S. R. Samantaray, A. Samui, and B. C. Babu, ―Time-frequency transform based islanding detection in distributed generation,‖ IET Renew. Power Gener., vol. 5, no. 6, pp. 431–438, Nov. 2011. 9. A. Khamis and H. Shareef, ―Pattern recognition of islanding detection using TT-transform,‖ J. Asian Sci. Res., vol. 2, pp. 607–613, Nov. 2012. 10. S. Santoso, W. M. Grady, E. J. Powers, J. Lamoree, and S. C. Bhatt, ―Characterization of distribution power quality events with Fourier and wavelet transforms,‖ IEEE Trans. Power Del., vol. 15, no. 1, pp. 247–254, Jan. 2000. 11. P. K. Ray, N. Kishor, and S. R. Mohanty, ―Islanding and power quality disturbance detection in grid-connected hybrid power system using wavelet and S-transform,‖ IEEE Trans. Smart Grid, vol. 3, no. 3, pp. 1082–1094, Sep. 2012. 12. C. Tao Hsieh, J. Min Lin, and S. J Huang, ―Enhancement of islandingdetection of distributed generation systems via wavelet transform-based approaches,‖ Electr. Power Energy Syst., vol. 30, no. 10, pp. 575–580, 2008. 13. O. Poisson, P. Rioual, and M. Meunier, ―Detection and measurement of power quality disturbances using wavelet transform,‖ IEEE Trans. Power Del., vol. 15, no. 3, pp. 1039–1044, Jul. 2000. 14. K. T. Sweeney, S. F. McLoone, and T. E. Ward, ―The use of ensemble empirical mode decomposition with canonical correlation analysis as a novel artifact removal technique,‖ IEEE Trans. Biomed. Eng., vol. 60, no. 1, pp. 97–105, 2013. 15. Dan Chen, Duan Li, Muzhou Xiong, Hong Bao, and Xiaoli Li, ―GPGPU-Aided Ensemble Empirical-Mode Decomposition for EEG Analysis During Anesthesia,‖ IEEE Trans. Inf. Technol. Biomed., vol. 14, no. 6, pp. 1417–1427, 2010. 16. S.Biswal, M.Biswal, and O.P.Malik, ―Hilbert Huang Transform Based Online Differential Relay Algorithm for Shunt Compensated Transmission Line,‖ IEEE Trans.Power Deliv., vol. 33, no. 6, pp. 2803–2811, 2018. Authors: Arpita Dey, Buddhadeb Sau
Paper Title: Locating Targets in RFID System in a Sensing Covered Anchor-Free Network Abstract: In an RFID system, the RFID readers consume huge energy and are considerably expensive in practical applications. To minimize the total number of readers with guaranteed surveillance such that the position of each tag can be uniquely determined is a challenge. This paper considers a simple but practically useful model of anchor-free network of RFID readers where each tag falls within the sensing zone of at least two readers. To maintain the quality of service in the real applications, a practical condition, the communication range is at least twice its sensing range, is considered. Under this condition, a characterization of a network is proved. An efficient algorithm for recognizing such a network is then developed without any initial position information of the readers. Using these readers as the references, an algorithm is designed for finding the exact positions of the tags in distributed manner. Unlike the existing techniques, it requires no external references for tag tracking. The proposed technique finds at most two possible positions (in some cases, unique position), out of which one is correct, for each tag.
Keywords: Targets tracking, exact positions of RFID read-ers, 2-sensing covered network, locating targets with no anchors, locating RFID tags with RFID readers.
References: 1. Zoe¨ Abrams, Ashish Goel, and Serge Plotkin. Set k-cover algorithms for energy efficient monitoring in wireless sensor networks. In Information Processing in Sensor Networks, 2004. IPSN 2004. Third International Symposium on, pages 424–432. IEEE, 2004 2. A. Bekkali, H. Sanson, and M. Matsumoto. Rfid indoor positioning based on probabilistic rfid map and kalman filtering. In Wireless and Mobile Computing, Networking and Communications, 2007. WiMOB 2007. Third IEEE International Conference on, pages 21–21, Oct 2007. 3. Pratik Biswas, Kim-Chuan Toh, and Yinyu Ye. A distributed sdp approach for large-scale noisy anchor-free graph realization with applications to molecular conformation. SIAM Journal on Scientific Computing, 30(3):1251–1277, 2008. 77. 4. Mathieu Bouet and Aldri L Dos Santos. Rfid tags: Positioning principles and localization techniques. In Wireless Days, 2008. WD‘08.1st IFIP, pages 1–5. IEEE, 2008. 5. K. Chawla, G. Robins, and Liuyi Zhang. Object localization using rfid.In Wireless Pervasive Computing (ISWPC), 2010 5th IEEE 467-475 International Symposium on, pages 301–306, May 2010. 6. A. Dey and B. Sau. Tracking of rfid tags in a sensing covered anchorfree network of rfid readers. In 2016 IEEE 6th International Conference on Advanced Computing (IACC), pages 689–694, 2016. 7. Lance Doherty, Kristofer SJ Pister, and Laurent El Ghaoui. Convex position estimation in wireless sensor networks. In INFOCOM 2001. Twentieth Annual Joint Conference of the IEEE Computer and Com munications Societies. Proceedings. IEEE, volume 3, pages 1655 1663.EEE, 2001. 8. Robert C Francis, James P McGee, Robert A Sainati, Richard L Sheehan Jr, and Sai-Kit K Tong. Object tracking and management system and method using radio-frequency identification tags, jul 29 2003. US Patent 6,600,418. 9. Daniel Hahnel, Wolfram Burgard, Dieter Fox, Ken Fishkin, and MatthaiPhilipose. Mapping and localization with rfid technology. In Roboticsand Automation, 2004. Proceedings. ICRA‘04. 2004 IEEE InternationalConference on, volume 1, pages 1015–1020. IEEE, 2004. 10. C. Hekimian-Williams, B. Grant, Xiuwen Liu, Zhenghao Zhang, and P. Kumar. Accurate localization of rfid tags using phase difference. In RFID, 2010 IEEE International Conference on, pages 89–96, April 2010. 11. Bruce Hendrickson. Conditions for unique graph realizations. 21(1):65– 84, 1992. 12. Bill Jackson and Tibor Jordan´. Connected rigidity matroids and unique realizations of graphs. Journal of Combinatorial Theory, Series B, 94(1):1–29, 2005. 13. Hai Liu, Miodrag Bolic, Amiya Nayak, and Ivan Stojmenovic´. Tax-onomy and challenges of the integration of rfid and wireless sensor networks. Network, IEEE, 22(6):26–35, 2008. 14. R. Olfati-Saber. Distributed kalman filtering for sensor networks. In 2007 46th IEEE Conference on Decision and Control, pages 5492–5498, Dec 2007. 15. Himani S Parekh, Darshak G Thakore, and Udesang K Jaliya. A survey on object detection and tracking methods. International Journal of Innovative Research in Computer and Communication Engineering, 2(2):2970–2979, 2014. 16. A. Ribeiro, I. D. Schizas, S. I. Roumeliotis, and G. Giannakis. Kalman filtering in wireless sensor networks. IEEE Control Systems, 30(2):66– 86, April 2010. 17. B. Sau and K. Mukhopadhyaya. Length-based anchor-free distributed localization in a covered sensor network. pages 77–82, Dec 2008. 18. Buddhadeb Sau and Krishnendu Mukhopadhyaya. Length-based anchor-free localization in a fully covered sensor network. In Communication Systems and Networks and Workshops, 2009. COMSNETS 2009. First International, pages 1–10. IEEE, 2009. 19. Buddhadeb Sau and Krishnendu Mukhopadhyaya. Localizability of wireless sensor networks: Beyond wheel extension. In Stabilization, Safety, and Security of Distributed Systems, pages 326–340. Springer, 2013. 20. Anthony Man-Cho So and Yinyu Ye. Theory of semidefinite pro-gramming for sensor network localization. Mathematical Programming,109(2-3):367–384, 2007. 21. Dong-Liang Wu, W.W.Y. Ng, D.S. Yeung, and Hai-Lan Ding. A brief survey on current rfid applications. In Machine Learning and Cybernetics, 2009 International Conference on, volume 4, pages 2330– 2335, July 2009. 22. Alper Yilmaz, Omar Javed, and Mubarak Shah. Object tracking: A survey. Acm computing surveys (CSUR), 38(4):13, 2006. 23. Yimin Zhang and Moeness G Amin. Localization and tracking of passive rfid tags. In Defense and Security Symposium, pages 624809–624809. International Society for Optics and Photonics, 2006. 24. Yimin Zhang, Moeness G Amin, and Shashank Kaushik. Localization and tracking of passive rfid tags based on direction estimation. Interna-tional Journal of Antennas and Propagation, 2007, 2007. Authors: A.Asyraf, S. Syafiie, M. Halim Shah Ismail
Paper Title: Type 1 Diabetes Mellitus Mobile Application with Blood Glucose Simulation Abstract: There are many mobile applications for diabetes currently in the market which try to help people with diabetes better manage their condition. Common features are the ability to log in user meal intake, amount of carbohydrates, insulin, physical activity and etc. and present the data back to them in a more organize manner such as in charts so that they can learn their blood glucose trend. However, few are trying to simulate their blood glucose level which might help them understand better the effect of these input to their blood glucose. In this paper, a mobile application is presented which can predict the trend of glucose from the meal and insulin intake of diabetes patient. The application used a glucose-insulin dynamics mathematical model to simulate the changes of blood glucose level over time for the user. Data of a clinical patient was used as input to the developed application to study its performance. It was found out that the accuracy of the application made the application to not be 100% reliable as predictor of blood glucose but a good educational tool for diabetes patient as it can simulate the glucose response from carbohydrate and insulin intake. A more accurate and complex mathematical model needs to be use for future development as the current linear and relatively simple model may not be accurate enough for the application.
Keywords: Application, Diabetes, Glucose, Insulin, Mobile, Simulation, T1DM,
References: 1. C. for D. C. and Prevention, ―National diabetes fact sheet: national estimates and general information on diabetes and prediabetes in the United States, 2011,‖ Atlanta, GA US Dep. Heal. Hum. Serv. centers Dis. Control Prev., vol. 201, no. 1, pp. 2568–2569, 2011. 2. A. D. Association, ―Diagnosis and classification of diabetes mellitus,‖ Diabetes Care, vol. 37, no. Supplement 1, pp. S81–S90, 2014. 3. WebMD, ―What Are Autoimmune Disorders?,‖ WebMD, 2018. https://www.webmd.com/a-to-z-guides/autoimmune-diseases. 78. 4. World Health Organization, ―Diabetes,‖ 2019. https://www.who.int/en/news-room/fact-sheets/detail/diabetes. 5. O. El-Gayar, P. Timsina, N. Nawar, and W. Eid, ―Mobile applications for diabetes self-management: Status and potential,‖ J. Diabetes Sci. Technol., vol. 7, no. 1, pp. 247–262, 2013, doi: 10.1177/193229681300700130. 476-480 6. M. Arnhold, M. Quade, and W. Kirch, ―Mobile applications for diabetics: a systematic review and expert-based usability evaluation considering the special requirements of diabetes patients age 50 years or older,‖ J. Med. Internet Res., vol. 16, no. 4, p. e104, 2014. 7. T. Chomutare, N. Tatara, E. Årsand, and G. Hartvigsen, ―Designing a diabetes mobile application with social network support.,‖ in HIC, 2013, pp. 58–64. 8. C. Hou, B. Carter, J. Hewitt, T. Francisa, and S. Mayor, ―Do mobile phone applications improve glycemic control (HbA1c) in the self- management of diabetes? A systematic review, meta-analysis, and GRADE of 14 randomized trials,‖ Diabetes Care, vol. 39, no. 11, pp. 2089–2095, 2016. 9. S. Kumar, H. Moseson, J. Uppal, and J. L. Juusola, ―A diabetes mobile app with in-app coaching from a certified diabetes educator reduces A1C for individuals with type 2 diabetes,‖ Diabetes Educ., vol. 44, no. 3, pp. 226–236, 2018. 10. S. G. Mougiakakou, I. Kouris, D. Iliopoulou, A. Vazeou, and D. Koutsouris, ―Mobile technology to empower people with Diabetes Mellitus: Design and development of a mobile application,‖ in 2009 9th International Conference on Information Technology and Applications in Biomedicine, 2009, pp. 1–4. 11. E. Årsand, N. Tatara, G. Østengen, and G. Hartvigsen, ―Mobile phone-based self-management tools for type 2 diabetes: the few touch application,‖ J. Diabetes Sci. Technol., vol. 4, no. 2, pp. 328–336, 2010. 12. L. T. Harris et al., ―Designing mobile support for glycemic control in patients with diabetes,‖ J. Biomed. Inform., vol. 43, no. 5, pp. S37–S40, 2010. 13. E. D. Lehmann and T. Deutsch, ―A physiological model of glucose-insulin interaction in type 1 diabetes mellitus,‖ J. Biomed. Eng., vol. 14, no. 3, pp. 235–242, 1992, doi: 10.1016/0141-5425(92)90058-S. 14. M. Berger and D. Rodbard, ―Computer simulation of plasma insulin and glucose dynamics after subcutaneous insulin injection,‖ Diabetes Care, vol. 12, no. 10, pp. 725–736, 1989, doi: 10.2337/diacare.12.10.725. 15. J. D. Lambert, Numerical methods for ordinary differential systems: the initial value problem. John Wiley & Sons, Inc., 1991. 16. Y. A. Çengel, Differential Equations for Engineers and Scientists. McGraw Hill, 2013. 17. G. Krishnan, ―Using the power of familiarity in design,‖ UX Collective, 2019. https://uxdesign.cc/familiarity-in-design-70df1979f80. 18. I. B. Hirsch, ―Type 1 diabetes mellitus and the use of flexible insulin regimens,‖ Am. Fam. Physician, vol. 60, no. 8, p. 2343, 1999. 19. Am Fam Physician, ―Diabetes: How to Use Insulin,‖ American Academy of Family Physicians, 1999. https://www.aafp.org/afp/1999/0801/p649.html (accessed Jul. 13, 2020).
Authors: Anusha Nellutla, Gnana Sai Ganesh Chittajallu, Shaik Feroz
Paper Title: Leaf Disease Detection using Labview Imaq Vision Abstract: The intension of our project is to design a system which can identify the good leaves from the diseased ones. Image processing is a powerful tool capable of many applications. Image processing combined 79. with Machine Vision can simulate and execute real time projects. In this project we have used LabVIEW along with IMAQ Vision to acquire real time images and process them. LabVIEW IMAQ Vision is potentially useful 481-492 for agricultural products since it combines the merits of both LabVIEW and IMAQ Vision, which have graphical programming environment and rich image processing functions. The project aims to provide a brief introduction into the IMAQ vision components like Image Acquisition, Calibration, Defect detection. Major leaf diseases‘ symptoms include spots or discolouration of leaves. The presence or absence of macro and micro nutrients, bug infestation and other diseases can be identified through leaves. In this project we have obtained the images through LabVIEW IMAQ vision pallet. Further on two procedures were followed – one based on colour of the leaves and other is based on spots and patterns present on the leaves. For the discolouration we first split the image into its constituent planes- RGB and CMYK, here we used Green, Cyan and Yellow planes. Then on we decided a threshold based on sample data using Linear Regression based prediction model of Machine Learning to classify the data into three states – safe, risk and high risk.The second method was detecting spots. First, we split the images into its constituent planes to convert the RGB image to Greyscale and increase the contrast using the Colour Plane Extraction tool then use the Look up table tool to further enhance the contrast. Then on locate the bright objects and then using dilation from the Morphology tool box we increase the size of the spots to increase detection rate. Using Advanced Morphology tool box we removed the boundary objects to isolate the spots. Then using the shape detection or circle detection algorithm we can detect the spots. Several samples were obtained and are successfully classified. Finally, current limitations and likely future development trends are discussed. Combining LabVIEW along with different programming algorithms can help in raising the accuracy of the system.
Keywords: Image acquisition, colour plane extraction, Gray morphological operation, Edge detection, Real time Colour matching.
References: 1. Malti K. Singh, Subrat Chetia2, ―Detection and Classification of Plant Leaf Diseases in Image Processing using MATLAB‖, International Journal of Life Sciences Research, Vol. 5, Issue 4, pp: (120-124), Month: October - December 2017. 2. Z. Husin, A.Y.M. Shakaff, A.H.A. Aziz, R.S.M. Farook, ―Plant Chili Disease Detection using the RGB Color Model‖, 3. https://www.google.com/search?q=diseased+leaves&rlz=1C1CHBF_enIN883IN883&oq=diseased+leaves&aqs=chrome..69i57j0l7.91 36j1j7&sourceid=chrome&ie=UTF-8 4. Sandesh Raut, Kartik Ingole, ―Review On Leaf Disease Detection Using Image Processing Techniques‖, International Research Journal of Engineering and Technology, Volume: 04 Issue: 04 | Apr -2017. 5. https://www.ni.com/pdf/manuals/320999c.pdf 6. https://www.ni.com/pdf/manuals/371077a.pdf 7. https://www.geeksforgeeks.org/linear-regression-python-implementation/ 8. https://knowledge.ni.com/KnowledgeArticleDetails?id=kA00Z0000019UFmSAM&l=en-IN Authors: Ankit Balvanshi, H.L. Tiwari, Mayank Gupta, Akhilesh Sharma
Paper Title: Statistical Downscaling of Maximum Temperature in Hoshangabad District of India Abstract: The Global Climate ModelsCanESM2 and CGCM3 were utilised to downscale the maximum temperature for Hoshangabad district of Madhya Pradesh, India. The area of study comprises to be of 6704 km2. The predictors employed for CanESM2 were ncepmslpgl, ncepp500gl, ncepp850gl and ncepmslpas, ncepp500gl, ncepp850gl were the predictors fixed for CGCM3. The total duration of the study was from the years 1979 – 2001. The two GCMs, CGCM3 and CanESM2 were checked for their capability in downscaling the maximum temperature climatic parameter. The GCM outputs were evaluated on Nash Sutcliffe Efficiency (NSE) and coefficient of determination (r2) criterias. The period of calibration was taken to be 1979-1995 and 1996-2001 was chosen as the period of validation. GCM CanESM2 obtained NSE of 0.77, 0.75 and r2 of 0.79, 0.79 during the periods of calibration and validation respectively. It was concluded that CanESM2 model is found comparatively more suitable for downscaling of maximum temperature for Hoshangabad region. The GCM can be further employed to generate the future scenario of maximum temperature in the region.
Keywords: Global Climate Model, CGCM3, CanESM2, NSE, r2.
80. References: 1. IPCC (2007), Summary for Policymakers. In: Climate Change 2007: The Physical Science Basis, Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, United 493-496 Kingdom and New York, NY, USA. 2. Parekh, F. and Prajapati, K.P. (2013), Climate Change Impacts on Crop Water Requirement for Sukhi Reservoir Project, International Journal of Innovative Research in Science, Engineering and Technology, 2(9), 4285-4292. 3. Shukla, R., Khare, D. and Deo, R. (2015), Statistical Downscaling of Climate Change Scenarios of Rainfall and Temperature over Indira Sagar Canal Command area in Madhya Pradesh, India, IEEE 14th International Conference on Machine Learning and Applications, 313-317. 4. Kundu, S., Khare, D., and Mondal, A. (2017), Future Changes in Rainfall, Temperature and Reference Evapotranspiration in the Central India by Least Square Support Vector Machine, Geoscience Frontiers, 8(3),583-596. 5. Jaiswal, R.K., Tiwari, H.L., Lohani, A.K. and Yadava, R.N. (2018), Statistical Downscaling of Minimum Temperature of Raipur (C.G.) India, Climate Change Impacts. Water Science and Technology Library, 82, 35-45. 6. STARDEX, (2005), Downscaling climate extremes, Final Report: 1-21. 7. Fowler, H. J., Blenkinsop, S., and Tebaldi, C. (2007), Linking Climate Change Modelling to Impacts Studies: Recent Advances in Downscaling Techniques for Hydrological Modelling, International Journal of Climatology 27(12), 1547-1578. 8. Wilby, R. L., Troni, J., Biot, Y., Tedd, L., Hewitson, B. C., Smith, D. M., and Sutton, R. T. (2009), A Review of Climate Risk Information for Adaptation and Development Planning, International Journal of Climatology, 29(9), 1193-1215. 9. Daniels, A. E., Morrison, J. F., Joyce, L. A., Crookston, N. L., Chen, S. C., and McNully, S. G. (2012), Climate Projections General Technical Report, Fort Collins, CO, U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station: 1-32. 10. Wilby, R.L. and Dawson, C.W. (2007), A Decision Support Tool for the Assessment of Regional Climate Change Impacts, User Manual, UK. Authors: Raamesh. ASP, Balasundaram. N., Karthik. V. 81. An Investigation on the Impact of Industrial Wastes as A Replacement for Sand In Fiber-Reinforced Paper Title: M20 Grade Concrete Abstract: Waste disposal in environment due to rapid urbanization and industrialization is increasing day by day. Disposal of wastes in the environment is more difficult in the construction industry. Marble powder and quarry dust are the waste materials obtained from the dressing and processing unit of marble production and quarries respectively. These waste materials are dumped in the environment as a landfill, and they can be used as a viable substitute material to the ingredients of concrete to a great extent. This will result in the production of economically green concrete; this happens because of less usage of river sand, leading to reduction in damage to to the environment. In this paper, natural sand used in the fiber-reinforced concrete (FRC) of grade 20 was replaced by varying proportions (0%, 25%, and 50%) and combinations of quarry dust (QD) and marble powder (MP) with 0.5% of basalt fiber added to the mix in order to explore the impact of QD and MP on the mechanical properties of concrete. The strength properties were assessed at 3rd, 7th, 14th and 28th day and the obtained results are tabulated. It is observed that a particular proportion of QD and MP enhances the strength of FRC.
Keywords: Marble powder, Quarry Dust, Fiber Reinforced Concrete, Basalt Fiber.
References: 1. P. Sivakumar, Dr. N. Balasundaram, K. Vivek, ―Study on the performance of White Crystal Stone with Fine Aggregate Flyash based SCC of Grade M30‖, International Journal of Civil Engineering and Technology (IJCIET), Volume 9, Issue 7, July 2018, pp. 1678– 1684, ISSN Print: 0976-6308 and ISSN Online: 0976-6316. 2. K. Tamilselvan, N. Balasundaram, V. Karthik, S. Suryarakash, ―An Experimental Investigation on the Strength Characteristics of Hybrid Fiber Reinforced Self Compacting Concrete‖, Pakistan Journal of Biotechnology, Vol 15 (4) 957-960 (2018). 3. Karthik V, G.Baskar., ―Investigation on strength properties of self compacting concrete with copper slag as fine aggregate‖, International Journal of Civil Engineering Research and Development (IJCERD), ISSN 2248- 9428, Volume 5, Number 1, January - April (2015) Volume 10, pg: 18-25 497-501 4. V.Karthik, G. Baskar., ―Study on durability properties of self-compacting concrete with copper slag partially replaced for fine aggregate‖, International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976-6308 and 0976-6316 Volume 6, Issue 9, Sep 2015, pp. 20-30. 5. Sounthararajan V. M. and Sivakumar A. (2013), Effect of the lime content in marble Powder for producing high strength concrete, ARPN Journal of Engineering and Applied Sciences Vol. 8, No. 4. 6. Sakalkale A. D., Dhawale G.D., Kedar R. S. (2014), Experimental Study on Use of Waste Marble Dust in Concrete, International Journal of Engineering Research and Applications, Vol. 4, No. 10 , pp. 44-50. 7. Pathan V. G. and Pathan M. G. (2014) Feasibility and Need of use of Waste Marble Powder in Concrete Production. IOSR Journal of Mechanical and Civil Engineering (IOSR-JMCE) e-ISSN: 2278-1684, p-ISSN: 2320-334X PP 23-26 8. laidi A.S.E. , Azzouz L , Kadri E. and Kenai S.( 2012), Effect of natural pozzolana and marble powder on the properties of self- compacting concrete, Construction and Building Materials, Vol. 31, pp. 251–257. 9. Rai B., Khan N. H., Kr A., Tabin R. S. and Duggal S.K. (2011), Influence of Marble powder/granules in Concrete mix, International Journal of Civil and Structural Engineering, Vol. 1, No. 4. 10. Aruntas H.Y.,Gürü M., Dayı M and Tekin I. (2010), Utilization of waste marble dust as an additive in cement production, Materials and Design, Vol. 31, pp. 4039–4042. 11. Ilangovan R, Mahendran N and Nagamani K (2008), "Strength and durability properties of concrete containing quarry rock dust as fine aggregates", ARPN Journal of Engineering and Applied Science, Vol.3(5), pp.20-26. 12. Ilangovan R. and Nagamani K. 2006. Application of quarry Rock dust as fine aggregate in concrete construction. National Journal on construction Management: NICMR. Pune. December. pp. 5-13. 13. M. Sahul Hameed and A.S.S. Sekar 2009. Properties of Green concrete containing Quarry dust and Marble sludge Powder as a aggregate. APRN Journal pp. 83-89. 14. Tumadhir M , ―Thermal and Mechanical Properties of Basalt Fiber Reinforced Concrete‖ , World Academy of Science, Engineering and Technology ,Vol:7 2013-04-26. 15. Kunal Singha, “A Short Review on Basalt Fiber”, International Journal of Textile Science 2012, 1(4): 19-28 DOI:10.5923/j.textile.20120104.02. Authors: Himanshi Koli, M.P.S. Chawla
Paper Title: Design Analysis of PV-Wind Energy System with Pumped Hydro Storage using HOMER Pro Abstract: Renewable energy in the recent era world-widely has proven to be a major shift for clean energy generation. It is a great opportunity or solutions to address increasing clean energy demand especially in a developing country such as India. As wind energy and solar energy are most commonly used renewable resources, gives their abundance in the region. Focusing aim of the analysis is to present the reliability of pumped hydro storage (PHS) system with respect to battery banks on the basis of operation and maintenance (O&M) cost with minimum loss. Thus, this system will have feasibility and practical capability to provide persistent supply operation to remote areas. The Hybrid Optimization Model for Electric Renewable (HOMER) software also known as HOMER Prois used to conduct simulation of the system.
82. Keywords: Renewable energy system, PV-wind energy system, PHS, HOMER Pro.
References: 502-505 1. https://las493energy.wordpress.com/2018/10/01/an-exploration-of-hybrid-pumped-hydro-storage-systems/ 2. A. K. Raji and D. N. Luta, ―Modeling and Optimization of a Community Microgrid Components,‖Energy Procedia, vol. 156, pp. 406– 411, Jan. 2019. 3. Shashikant Golande and Manohar Kalgunde, ―Utilization Of Solar Energy With Pumped Hydro Storage Based On Standalone Photovoltaic Power Generation,‖ 2017 International Conference on Computer Electrical & Communication Engineering(ICCECE), Kolkata,pp. 1-4. 4. Afshin Izadin, Arash Pourtaherian and Sarasadat Motahari, ― Basic Model and Governing Equation of Solar Cells used in Power and Control Applications,‖ Sept 2011 5. D.P.Kothari, K.C.Singhal, ―Renewable energy sources and emerging technologies‖, PHI Learning Private limited, second edition, August 2017 6. Motupalli Priyanka, Niranchana R, S Selvakumar, Nithya Venkatesan, ―Feasibility Study of Replacement of Hybrid Renewable Energy – A Case Study,‖IJITEE Trans BEIESP, Volume-8, Issue-6S3, April 2019. 7. S.P. Koko, K. Kusakana, and H.J. Vermaak, ―Grid-interactive micro-hydrokinetic with pumped-hydro storage: The case study of three South African demand sectors,‖2017 International Conference on the Domestic Use of Energy(DUE) 8. Bruno P. Pougoue Tchintchui, Atanda K. Raji, ―Techno-economic analysis of a renewable energy solution for an off-grid residence,‖ Cape Peninsula University of Technology, Cape Town, South Africa, 2019 Proceedings of the 27th Domestic use of Energy Conference. Authors: Arun Kumar Chaudhary, Vijay Kumar
Paper Title: Half Logistic Exponential Extension Distribution with Properties and Applications Abstract: In this article, we have introduced a new distribution based on type I half logistic-G family and exponential extension as a base distribution known as Half Logistic Exponential Extension (HLEE) distribution. The statistical properties of this model are also explored, such as the behavior of probability density, hazard rate, and quantile functions are investigated. The Maximum likelihood estimation (MLE) method is used to estimate model parameters. For the potentiality of the proposed model we have compared the goodness of fit with some others models. We have proven the importance and flexibility of the new distribution in modeling with real data applications empirically.
Keywords: Estimation, Exponential extension, Half-logistic exponential extension distribution, MLE.
References: 1. Abdulkabir, M., & Ipinyomi, R. A. (2020). Type II half logistic exponentiated exponential distribution: properties and applications. Pakistan Journal of Statistics, 36(1). 2. Afify, A.Z., Cordeiro, G.M., Yousof, H.M., Alzaatreh, A. & Nofal, Z.M. (2016). The Kumaraswamy transmuted-G family of distributions: Properties and applications. Journal of Data Science, 14(2), 245-270. 3. Almarashi, A. M., Elgarhy, M., Elsehetry, M. M., Kibria, B. G., & Algarni, A. (2019). A new extension of exponential distribution with statistical properties and applications. Journal of Nonlinear Sciences and Applications, 12, 135-145. 4. Ashour, S. K., & Eltehiwy, M. A. (2015). Exponentiated Power Lindley distribution. Journal of advanced research, 6(6), 895-905. 5. Balakrishnan, N. (1985). Order statistics from the half logistic distribution. Journal of Statistical Computation and Simulation, 20(4), 287-309. 6. Barreto-Souza, W., Santos, A.H.S. & Cordeiro, G.M. (2010). The beta generalized exponential distribution. Journal of Statistical Computation and Simulation, 80(2), 159-172. 7. Cordeiro, G.M. & de Castro, M. (2011). A new family of generalized distributions. Journal of Statistical Computation and Simulation, 81, 883-898. 83. 8. Cordeiro, G.M., Alizadeh, M. & Diniz Marinho, P.R. (2015). The type I half-logistic family of distributions, Journal of Statistical Computation and Simulation, 86(4), 707-728. 9. Ghitany, M.E.,Atieh, B. & ,Nadarajah, S. (2008). Lindley distribution and its application. Mathematics and Computers in Simulation, 506-512 78, 493-506. 10. Gomez, Y.M., Bolfarine, H. & Gomez, H.W. (2014). A new extension of the exponential distribution. Revista Colombiana de Estadistica, 37(1), 25-34. 11. Gupta, R.D. & Kundu, D. (2001). Exponentiated exponential family; an alternative to gamma and Weibull. Biometrical Journal, 43, 117-130. 12. Hassan, A. S., Mohamd, R. E., Elgarhy, M., & Fayomi, A. (2018). Alpha power transformed extended exponential distribution: properties and applications. Journal of Nonlinear Sciences and Applications, 12(4), 62-67. 13. Kumar, V. (2010). Bayesian analysis of exponential extension model. J. Nat. Acad. Math, 24, 109-128. 14. Kumar, V. & Ligges, U. (2011). reliaR : A package for some probability distributions. http://cran.r- project.org/web/packages/reliaR/index.html 15. Kundu, D., & Raqab, M.Z. (2005). Generalized Rayleigh Distribution: Different Methods of Estimation, Computational Statistics and Data Analysis, 49, 187-200. 16. Lemonte, A. J. (2013). A new exponential-type distribution with constant, decreasing, increasing, upside-down bathtub and bathtub- shaped failure rate function. Computational Statistics & Data Analysis, 62, 149-170. 17. Louzada, F., Marchi, V. & Roman, M. (2014). The exponentiated exponential geometric distribution: a distribution with decreasing, increasing and unimodal failure rate. Statistics: A Journal of Theoretical and Applied Statistics, 48(1), 167-181. 18. Merovci, F. (2013). Transmuted exponentiated exponential distribution. Mathematical Sciences And Applications E-Notes, 1(2), 112- 122. 19. Moors, J. J. A. (1988). A quantile alternative for kurtosis. Journal of the Royal Statistical Society: Series D (The Statistician), 37(1), 25-32. 20. Murthy, D.N.P., Xie, M. & Jiang, R. (2003). Weibull Models, Wiley, New York 21. Nadarajah, S., & Haghighi, F. (2011). An extension of the exponential distribution. Statistics, 45(6), 543-558. 22. Nadarajah, S. & Kotz, S. (2006), The beta exponential distribution. Reliability Engineering and System Safety, 91(6), 689-697. 23. Ristic, M.M. & Balakrishnan, N. (2012), The gamma-exponentiated exponential distribution. Journal of Statistical Computation and Simulation, 82(8), 1191-1206. 24. Schmuller, J. (2017). Statistical Analysis with R For Dummies, John Wiley & Sons, Inc., New Jerse 25. Venables, W. N., Smith, D. M. & R Development Core Team (2020). An Introduction to R, R Foundation for Statistical Computing, Vienna,Austria. ISBN 3-900051-12-7. URL http://www.r-project.org. Authors: Kenji Sakoma, Makoto Sakamoto
Paper Title: A Consideration on "Sweetness" by 3D CG with Fruits as an Example Abstract: Today, there are a lot of images and videos drawn by 3D computer graphics (hereinafter referred to as 3DCG) around us, and 3DCG is permeating our lives [1]. Recently, research and development of 3DCG-related technologies such as 3D printers, AR, and VR have been actively carried out, and further progress in 3DCG can 84. be expected in the future. 3DCG is a technology that creates images and videos by creating objects in a virtual three-dimensional space. CAD, VR, AR, simulators, 3D printers, etc. have been developed as technologies that 513-516 apply this. One of the reasons why it was applied to such technology is the high expressiveness of 3DCG. It is possible to express various substances such as wood, metal, plastic, and glass, and it is becoming possible to reproduce things that do not have a specific shape, such as flames, smoke, and fluids. One of the researches on such 3DCG technology is digital food, and research is being conducted with the aim of putting it into practical use in the future. Digital food is expected to solve problems related to food freshness management, disposal, new product development simulation, etc., but even with the expressive power of 3DCG, meat and fish are still difficult. I am not good at expressing fresh foods such as vegetables and fruits, and "freshness" and "organic coloring" such as "fresh flowers". This is one of the issues that cannot be avoided even in the research and development of digital foods and must be solved. In this technology, while understanding the principle of 3DCG, I learned using some software in order to explore what technology is necessary to create a digital food. Also, in learning, we set the goal of "expressing fresh fruits", which is one of the challenges of digital food, and the gloss of the skin peculiar to fruits, the slight unevenness of the surface, especially the freshness of the cut surface of fruits we focused on reproducing the expression of freshness.
Keywords: Computer graphics, Ambient light, Specular reflection light, Diffuse reflected light, Subsurface scattering, Fresnel formula
References: 1. Haruka Tsuboi. ―Fundamental Study on Tourism Support Using 3DCG‖, Proceedings of the International Conference on Artificial Life and Robotics, OS1-4（D-ROM) pp.272-275(2018). 2. Hdeki Komuro, ―POV-Ray de manabu jisshu konpyu-ta gurafikkusu‖ [Practical computer graphics to learn with POV-Ray], ASCII MEDIA WORKS Inc., (2000). 3. Japan Sensor Corporation, Unavailable: https://www.japansensor.co.jp/faq/968/index.html, Ltd., (2020-01-29). 4. Hiroshi Harada, ―Blender nyumon‖ [Introduction to Blender], Available: https://www.blender3d.biz.html. (2020 -01-29). 5. Noriko Kurachi, ―CG Magic: rendaring‖ [CG Magic: Render], Available: https://www.mext.go.jp/content/1407196_21_1_1_2.pdf. 6. Max Born and Emil Wolf, Principles of Optics,Cambridge University Press, Cambridge, (1997). 7. H. W. Jensen et all.‖ Rendering of Wet Materials,‖Proceedings of the Seventh Eurographics Workshop on Rendering, pp. 273-282, (1999). 8. Warren J. Smith. Modern Optical Engineering.McGraw-Hill. pp. 228-256.3). Authors: Kenji Sakoma, Makoto Sakamoto
Paper Title: For Colorization using Template Matching Basic Research on Abstract: Colorization, also known as colorization, is a term introduced by Wilson Markle in 1970, and is a method of coloring black-and-white images and videos using a computer. Coloring is important. Imagine coloring a picture as an example. The painting before painting only gives information of existence, such as trees, flowers, and clouds. Some things can be identified by color. This does not give us enough information from the picture. But what about coloring? If you paint the sky red, it will be a sunset, and if you paint the ground green, it will be a meadow. In other words, it is possible to express not only the background but also the background. This makes it possible to read information that cannot be understood only in black and white. With the development of digital devices such as smartphones these days, the chances of seeing black-and-white images are decreasing, but in modern times, black-and-white images are used for X-ray images, MRI images, aerial photographs, fixed-point observations, etc. There are many opportunities to be lost. The development of color photography began in the world in the 1800s, and the development of color photography began in Japan in 1940. In other words, the photographs before that were black and white, and colorization was used to colorize them. Currently, many researchers are studying colorization methods and processes, and the processing time and the burden on users are being reduced. However, software that can perform highly complete colorization is 85. expensive, and some are complicated to operate. Therefore, in this research, as basic research for the development of a fully automatic colorization program for free software, color images (template images) lacking 517-519 information by template matching using ZNCC and black-and-white images with similar brightness patterns are colored. We made a prototype of a colorization program that restores images.
Keywords: Computer graphics, Colorization, Zero-means Normalized Cross-Correlation, BMP image, HSV color space, Color Propagation
References: 1. Hiroaki Kinumatsu, ―Kararize-shon ni kansuru kisokenkyu‖ [Basic research on colorization], Master Thesis, University of Miyazaki (2012). 2. ―Bitmappu gazo‖ [Bitmap image], Available: http://ja.wikipedia.org/wiki/. 3. RGB, Available: http://ja.wikipedia.org/wiki/RGB. 4. ―HSV kukan‖ [HSV space], Available: http://ja.wikipedia.org/wiki/. 5. ―Tenpure-to macchingu ni tuite rikaisuru‖ [Understand template matching], Available: https://www.yukisako.xyz/entry/template- matching. 6. Seiki Inoue, Nobuyuki Yagi, Masaki Hayashi, Eisuke Nakasu, Koji Mitani, Makoto Okui, ―C gengo de manabu jissen gazo shori‖ [Practical image processing to learn in C language], Ohmsha, Ltd., (1999). 7. Uruma Kazunori, Seiichiro Hangai, ―Kararize-shon: shiro kuro gazo / eizo wo jido de kara-ka suru‖ [Colorization: Automatic colorization of black and white images/videos], IEICE Fundamentls Review Vol.11 No.3 2017. Authors: Hamdy Amin Morsy
Paper Title: Developing a New CCN Technique for Arabic Handwritten Digits Recognition Abstract: Convolutional Neural Networks (CNN) have many applications in object recognition such as 86. character and digit recognition. Few researches are performed on Arabic handwritten digits recognition. In this research, we will develop a new algorithm to utilize the convolutional neural networks with sigmoid function (ζ- 520-524 CNN) to recognize Arabic handwritten digits recognition. The performance of this method provides minimum cost functions with maximum testing accuracy results in compared to other existing techniques.
Keywords: Machine Learning, Neural Networks, Image Processing, Natural Language Processing
References: 1. A.Charu, "Neural Networks and Deep Learning: A Textbook", Springer, 1st ed. 2018 Edition. 2. Oludare I. Abiodun et al, "Comprehensive Review of Artificial Neural Network Applications to Pattern Recognition", DOI 10.1109/ACCESS.2019.2945545, IEEE Access. 3. S. Al-Ma‘adeed, D. Elliman, C. A. Higgins, "A data base for Arabic handwritten text recognition research", In Proceedings eighth international workshop on frontiers in handwriting recognition, IEEE 2002, pp 485–489. 4. H. A. Morsy, "Comparison of Commonly Used Non-Adaptive Image Scaling Techniques",CiiT Digital Image Processing 10 (9), 177- 180. 5. H. A. Morsy, H, "Performance Analysis of the Effects of Non-Adaptive Image Scaling on Image Edges", IJRTE 7 (6), 1692 – 1696. 6. D. C. Ciresan, U. Meier, L. M. Gambardella, J. Schmidhuber, "Convolutional neural network committees for handwritten character classification", In 2011 International conference on document analysis and recognition, pp 1135–1139. IEEE. 7. A. El-Sawy, M. Loey, E. Hazem, "Convolutional neural network for Arabic handwritten characters dataset classification" , https://www.kaggle.com/mloey1/cnn-for-arabic-handwritten-characters/comments. Accessed: 30 May 2019. 8. I. Goodfellow, Y. Bengio, A. Courville,"Deep learning. MIT Press", 2016,http://www.deeplearningbook.org. 9. S. Impedovo, F. M. Mangini, D. Barbuzzi, "A novel prototype generation technique for handwriting digit recognition", Pattern Recognit, 2014, 47(3):1002–1010. 10. G. Latif, J. Alghazo, L. Alzubaidi, M. M. Naseer, Y. Alghazo, "Deep convolutional neural network for recognition of unified multi- language handwritten numerals" In 2018 IEEE 2nd International workshop on Arabic and derived script analysis and recognition (ASAR), pp 90–95. 11. Y. LeCun, L. Bottou, Y. Bengio, P. Haffner, et al, "Gradient based learning applied to document recognition", Proc IEEE, 1998, 86 (11):2278–2324. 12. A. Mars, G. Antoniadis, "Arabic online handwriting recognition using neural network", Int J ArtifIntell Appl(IJAIA), 2016,7(5). 13. M. A. Mudhsh, Almodfer R, "Arabic handwritten alphanumeric character recognition using very deep neural network", 2017, Information 8(3). 14. M. Ramzan, H. U. Khan, S. M. Awan, W. Akhtar, M. Ilyas, A. Mahmood, A. Zamir, "A survey on using neural network based algorithms for hand written digit recognition", 2018, Int J Adv Comput Sci Appl. https://doi.org/10.14569/IJACSA.2018.090965. 15. N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, R. Salakhutdinov, "Dropout: a simple way to prevent neural networks from overfitting", 2005, J Mach Learn Res 15(1):1929–1958. 16. J. Wu, "Introduction to convolutional neural networks. National Key Lab for Novel Software Technology", 2017, vol 5, Nanjing University, China, p 23. 17. K. Younis, "Arabic handwritten characters recognition based on deep convolutional neural networks", 2018, Jordan J Comput Inform Technol (JJCIT) 3. 18. U. R. Babu, Y. Venkateswarlu, A. K. Chintha, "Handwritten digit recognition using k-nearest neighbor classifier", In Proceedings of the 2014 World Congress on Computing and Communication Technologies, (WCCCT 2014), Trichirappalli, India, 27 February–1 March 2014; pp. 60–65. 19. X. X. Niu, C. Y. Suen, "A novel hybrid CNN-SVM classifier for recognizing handwritten digits", Pattern Recognit. 2012, 45, 1318– 1325. [CrossRef]. 20. F. A. Al-omari, O. Al-jarrah, "Handwritten Indian numerals recognition system using probabilistic neural networks", Adv. Eng. Inform. 2004, 18, 9–16. 21. M. Takruri, R. Al-Hmouz, A. Al-Hmouz, "A three-level classifier: fuzzy C means, support vector machine and unique pixels for Arabic handwritten digits", In: World Symposium on Proceedings of Computer Applications & Research (WSCAR), pp. 1—5 (2014). 22. J. H. Alkhateeb, M. Alseid, "DBN – based learning for Arabic handwritten digit recognition using DCT features", In: 2014 6thInternational Conference on Computer Science and Information Technology (CSIT), pp. 222—226 (2014). 23. M. Salameh, "Arabic digits recognition using statistical analysis for end/conjunction points and fuzzy logic for pattern recognition techniques", World Comput. Sci. Inf. Technol. J. 4(4), 50—56 (2014). 24. O. E. Melhaoui, M. E. Hitmy, F. Lekhal, "Arabic numerals recognition based on an improved version of the loci characteristic", Int. J. Comput. Appl. 24(1), 36—41 (2011). 25. P. P. Selvi, T. Meyyappan, "Recognition of Arabic numerals with grouping and ungrouping using back propagation neural network", In: International Conference on Proceedings of Pattern Recognition, Informatics and Mobile Engineering (PRIME), pp. 322—327 (2013). 26. S. A. Mahmoud, "Arabic (Indian) handwritten digits recognition using Gabor-based features", In: International Conference on Proceedings of Innovations in Information Technology, IIT 2008, pp. 683—687 (2008). 27. A. El-Sawy, H. El-Bakery, M. Loey, "CNN for Handwritten Arabic digits recognition based on LeNet-5", Springer International Publishing AG 2017. Authors: Mahima Chandane, Ankita Chavan, Renuka Kamath, Dipali Madane, Madhuri Badole
Paper Title: Intelligent Music Player Based on Emotions Abstract: This project presents a system to automatically detect emotional dichotomy and mixed emotional experience using a Linux based system. Facial expressions, head movements and facial gestures were captured from pictorial input in order to create attributes such as distance, coordinates and movement of tracked points. Web camera is used to extract spectral attributes. Features are calculated using Fisherface algorithm. Emotion detected by cascade classifier and feature level fusion was used to create a combined feature vector. Live actions of user are to be used for recording emotions. As per calculated result system will play songs and display books list. 87. Keywords: Smart Emotion, Face Detection, Face Recognition, Emotion Prediction, OpenCV. 525-528 References: 1. Amol S Patwardhan. "Multimodal mixed emotion detection", 2017 2nd International Conference on Communication and Electronics Systems (ICCES), 2017 2. Eman Alajrami, Hani Tabash, Yassir Singer, M.T. El Astal. "On using AI-Based Human Identification in Improving Surveillance System Efficiency", 2019 International Conference on Promising Electronic Technologies (ICPET), 2019 3. A. M. Jagtap, Vrushabh Kangake, Kushal Unune, Prathmesh Gosavi. "A Study of LBPH, Eigenface, Fisherface and Haar-like attributes for Face recognition using OpenCV", 2019 International Conference on Intelligent Sustainable Systems (ICISS), 2019 4. van der Zwaag, Marjolein D., Joris H. Janssen, and Joyce HDM Westerink. "Directing Physiology and Mood through Music: Validation of an Affective Music Player", IEEE Transactions on Affective Computing, 2012. 5. "Machine Learning Based Malware Detection: a Boosting Methodology", International Journal of Innovative Technology and Exploring Engineering, 2020 6. Marjolein D. van der Zwaag, Joris H. Janssen, Joyce H.D.M. Westerink. "Directing Physiology and Mood through Music: Validation of an Affective Music Player", IEEE Transactions on Affective Computing, 2013. Authors: Shruti Bhavsar, Sanjana Khairnar, Pauravi Nagarkar, Sonali Raina, Amol Dumbare On Time Document Retrieval using Speech Conversation and Diverse Keyword Clustering During Paper Title: Presentations Abstract: In this paper we present the idea of extracting keywords from discussions, with the point of using these words to recuperate, for each small piece of conversation and generating reports to individuals. Regardless, even a smaller piece contains a blend of words, which can be effortlessly interrelated to a couple of subjects; additionally, using a customized talk affirmation (ASR) system presents slips among them. Thus it is hard to sum up effectively the data needs of the conversation individuals. We initially propose a count to kill significant words from the yield of an ASR system which makes usage of topic showing strategies and of a sub particular prize limit which supports varying characteristics in the word set, to organize the potential contrasting characteristics of subjects and diminish ASR disturbance. By then, we set forward a strategy to surmise different topically detached requests from this definitive word set, remembering the ultimate objective is to build the potential outcomes of making at any rate one appropriate proposition while using these inquiries to investigate the English Wikipedia. The readings depict that our pronouncement continue ahead over past procedures that watch simply word recurrence or idea commonality, and states the good response for a report recommended framework to be used as a piece of conversations.
Keywords: Document Recommendation, Information retrieval keyword extraction, Meeting analysis, Local database, Extraction, Keyword, Clustering
88. References: 1. A. Csomai and R. Mihalcea, ―Linking educational materials to encyclopedic knowledge,‖ in Proc. Conf. Artif. Intell.Educat.: BuildingTechnol. Rich Learn. Contexts That Work, 2007, pp. 557–559. 529-532 2. Habibi, M., Popescu-Belis, A. (2015), Keyword Extraction and Clustering for Document Recommendation in Conversations, IEEE/ACM Transactions on Audio, Speech, and Language Processing, 23(4), 746–759. doi:10.1109/taslp.2015.2405482 3. Hunyadi, L. - Keyword extraction: aims and ways today and tomorrow. - In: Proceedings of the Keyword Project: Unlocking Content through Computational Linguistics. 2001. 4. Pere R. Comas and Jordi Turmo ― Spoken Document Retrieval Based on Approximated Sequence Alignment‖ 5. Scott Deerwester ,Susan T. Dumais , ―Indexing by latent semantic analysis‖ 6. Melville and Vikas Sindhwani, IBM T.J. Watson Research Center,Yorktown Heights, NY 105, Recommender System Prem hw}@us.ibm.com 7. Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schütze, An Introduction to Information Retrieval. Cambridge University Press, 2008 8. Khalid Al-Kofahi, Peter Jackson, Mike Dahn*, Charles Elberti, William Keenan, John Duprey.A "Document Recommendation System Blending Retrieval and Categorization Technologies". 9. Michael J. Pazzani and "Daniel Billsus, Content-based Recommendation Systems ―. 10. Sangeetha. J 1, Kavitha R ," An Improved Privacy Policy Inference over the Socially Shared Images with Automated Annotation Process ", / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 6 (3) , 2015, 3166-3169. 11. Aishwarya Singh, Bhavesh Mandalkar, Sushmita Singh , Prof. yogesh Pawar, "A Survey on User-Uploaded Images Privacy Policy Prediction Using Classification and Policy Mining",International Journal of Innovative Research in Computer and Communication Engineering .Vol. 3, Issue 9, September 2015. 12. X. Liu, X. Zhou, Z. Fu, F. Wei, and M. Zhou, ―Exacting social events for tweets using a factor graph,‖ in Proc. AAAI Conf. Artif. Intell., 2012, pp. 1692–1698. 13. A. Cui, M. Zhang, Y. Liu, S. Ma, and K. Zhang, ―Discover breaking events with popular hashtags in twitter,‖ in Proc. 21st ACM Int. Conf. Inf. Knowl. Manage., 2012, pp. 1794–1798. 14. A. Ritter, Mausam, O. Etzioni, and S. Clark, ―Open domain event extraction from twitter,‖ in Proc. 18th ACM SIGKDD Int. Conf. Knowledge Discovery Data Mining, 2012, pp. 1104–1112. Authors: Abarna M, Jane Lourde Teresha A, Devisri R, Maithreyini M, V. Kumar Chinnaiyan
Paper Title: Helping Hand for Unsighted People-Acousticsight Abstract: Technology is best when it brings people together. Today technology plays a vital role in humanity. Also applied science can make the impossible possible. The proposed project aims to show equality in the safe navigation of visually impaired people just like a normal person. The project aims to help the secure guidance of humans with bad eyesight. This system support the sole in attaining the landing place, leading them across the way and alert them about the barrier that are expected in their path through the vibration and generate simulated speech output through headset. Therefore, this technology hold back them from striking the barrier. It add on value to conventional canes with barrier predicting, preventing human from accident and reducing difficulties in 89. navigation. An ultrasonic sensor is execute to determine the distant of obstacles from the person. It is a Raspberry Pi based platform that is used to alert the person of impending obstacles. Also can create the place for all other components and it has functioning code. Here, a vibration motor is used to warn the person from the 534-536 collision. Combined with the role of guiding, it also has aid preventing plan in case of emergency. The GPS is included to find the location of person and the location is sent to the person‘s family through the notification by means of Blynk app. Accordingly, The project convince the visually impaired people can travel alone without getting fear or accidents at the moment.
Keywords: Visually impaired, sensing, vibration motor, barrier prediction.
References: 1. Alessio Carulllo and Marco Parvis (2001) ‗An Ultrasonic Sensor for Distane Measurement in Automotive Applications‘, IEEE Sensor Journal, Vol. 1. 2. K.Gopala Krishnan, C.M.Porkodi, and K.kanimozhi (2013) ‗Image Recognition for Visually Impaired people By Sound‘, International conference on Communication and Signal Processing, Vol. 39, pp. 943-946. 3. Shashank Chaurasis and K.V.N.Kavitha (2014) ‗An Electronic Walking Stick for Blinds‘, International Conference on Information Communication and Embedded Systems(ICICES). 4. Sung Jae Kang, Young Ho, Kim, In Hyuk Moon (2001) ‗Development of an Intelligent Guide-stick for the Blind, Seoul of Korea, IEEE International Conference on Robotics & Automation Vol. 4. 5. N.Mahumad, R.K. Saha, R.B. Zafar , M.B.H. Bhuian and S.S.Sarwar (2014) ‗Vibration and Voice Operated System for Visually Impaired Person‘, International Conference on Informatics Electronics & Vision (ICIEV) IEEE,pp. 1-5. Authors: Kurshid Madina, Saksham Mansotra
Paper Title: Genderpredictions using Convolution Neural Networks Abstract: Nowadays Deep learning was advanced so much in our daily life. From 2014, there is massive growth in this technology as there is a vast amount of data present. We are even getting better results from whatever we may do. In my work, I have used Convolution Neural Networks as my project depends on image classification. So what I‘m trying to do is I‘m using two classes in which one class is male and one class is female. I‘m classifying both the classes and trying to predict who is male and who is female. For this, I have been using layers like Sequential, Convolution2D, Max-pooling, Flattening, and finally Dense. So, I connect all of these layers. I have been using two more extra layers which are Convolution2D and max-pooling connected as one layer for better classifications. In my model, I‘m using Adam optimizer as I‘m having only two classes and in my experiments, I found Adam as a good optimizer and I use binary cross entropy as my loss function as I‘m using only two classes if we have more than two classes we can use categorical loss function and the images which I use for predictions will be converted into 64*64 matrix form. In the end, I will be getting predictions as 1 for male and 0 for female. 90. Keywords: Computer Vision, Gender Classification, Human-computer interaction, Convolution Neural Network (CNN). 537-540
References: 1. M. K. Bhuyan., et al. ―A novel set of features for continuous hand gesture recognition,‖ Journal on Multimodal User Interfaces., vol. 8, no. 4, pp. 333–343, 2014. 2. Kasthurirangan Gopalakrishnan, et al. – Deep Convolution Neural Networks with transfer learning for computer vision-based data- driven pavement distress detection,|| Journal on Elsevier, vol 157, pp 322-330,2017. 3. Deegan J Atha, et al. – Evaluation of deep learning approaches based on convolution neural networks for corrosion detection, || Journal on sage, 2017. 4. Gil Levi, Taj Hassner. – Age and Gender Classification using Convolution Neural Networks.|| predeedings of IEEE Conference on Computer vision and pattern recognition (CVPR) Workshops, pp.34-42 ,2015. 5. Yann LeCun, et al. – Gradient-Based Learning Applied to Document Recognition.|| Proc. Of the IEEE, 1998. 6. Jianxin Wu – Introduction to Convolution Neural Networks, 2017. 7. C.-C. Jay Kuo – Understanding Convolution Neural Networks with a Mathematical Model, arXiv, 2016. 8. Dominik Scherer et al. – Evaluation of pooling Operations in Convolution Architectures for Object Recognition, ICANN,pp 92- 101,2010. 9. JonghoonJin, et al. – Flattened Convolution Neural Networks for FeedForward Acceleration, arXiv, 2015. 10. Zixian Zeng, et al. – CNN Model Design of Gesture Recognition Based on Tensorflow Framework, IEEE, 2019. Najihah Mohd Noor, Yumi Zuhanis Has-Yun Hashim, Muhammad Shirwan Abdullah Sani, Wan Authors: Wardatul Amani Wan Salim Characterization, Antibacterial and Anti-Inflammatory Activities of Electrospun poly (vinyl alcohol) Paper Title: (PVA) Containing Aquilaria Malaccencis Leaf Extract (ALEX) Nanofibers Abstract: Plant-based electrospun nanofibers are widely fabricated as wound dressing in recent years primarily due to the presence of bioactive compounds which can facilitate the wound healing effects. In this study, poly(vinyl alcohol) (PVA) fibre mats containing Aquilaria malaccensis leaf extract (ALEX) [5, 10 and 15 %(w/w)] were fabricated by electrospinning method as potential wound dressing material. The nanofibers were uniform, beadless and randomly oriented with average diameters ranged between 195.27 – 281.20 nm. The presence of ALEX in the PVA nanofibers were evaluated by Attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) and differential scanning calorimetry (DSC). The mechanical properties, swelling degree and weight loss of nanofiber mats were also determined. ALEX was rapidly released from the ALEX-loaded PVA nanofibers in the first 12 hours and increased gradually afterwards. The released rate of ALEX was dependent on the ALEX content in the PVA nanofibers. This result is also contributed by the 91. swelling degree and porosity of the nanofibers where the results were found to be between 241.66 – 305.86% and 64.53 – 30.81%, respectively. Meanwhile, the tensile stress and maximum elongation at break for all 541-549 electrospun nanofiber mats were in the range of 8.56 – 2.68 MPa and 205.94 – 166.31%, respectively. The nanofiber mats inhibited growth of Escherichia coli, Vibrio vulnificus, Bacillus subtilis and Staphylococcus aureus with zone of inhibition of 7.5 - 15.0 mm for gram positive bacteria and 6.1 - 11.7 mm for gram negative bacteria. ALEX-loaded PVA nanofibers also showed potent anti-inflammatory activity against lipoxygenase with percentage of inhibition between 80.887 – 86.977%. Taken together, the results of this study suggest that ALEX-loaded PVA nanofibers have the desired properties of bioactive wound dressing.
Keywords: Electrospinning, agarwood, polyvinyl alcohol, wound dressing
References: 1. Pilehvar-Soltanahmadi, Y., Dadashpour, M., Mohajeri, A., Fattahi, A., Sheervalilou, R., & Zarghami, N. (2017). An Overview on Application of Natural Substances Incorporated with Electrospun Nanofibrous Scaffolds to Development of Innovative Wound Dressings. Mini-Reviews in Medicinal Chemistry, 18(5), 414–427. https://doi.org/10.2174/1389557517666170308112147 2. Yousefi, I., Pakravan, M., Rahimi, H., Bahador, A., Farshadzadeh, Z., & Haririan, I. (2017). An investigation of electrospun Henna leaves extract-loaded chitosan based nanofibrous mats for skin tissue engineering. Materials Science and Engineering C, 75, 433–444. https://doi.org/10.1016/j.msec.2017.02.076 3. Liu, Y., Wei, J., Gao, Z., Zhang, Z., & Lyu, J. (2017). A Review of Quality Assessment and Grading for Agarwood. Chinese Herbal Medicines, 9(1), 22–30. https://doi.org/10.1016/S1674-6384(17)60072-8 4. Elias, M. F., Ibrahim, H., & Mahamod, W. R. W. (2017). A Review on the Malaysian Aquilaria species in Karas Plantation and Agarwood Production. International Journal of Academic Research in Business and Social Sciences, 7(4), 1021–1029. https://doi.org/10.6007/ijarbss/v7-i4/2911 5. Eissa, M., Hashim, Y. Z. H. Y., & Zainurin, N. A. A. (2018). Aquilaria malaccensis Leaf as an Alternative Source of Anti-inflammatory Compounds. International Journal on Advanced Science, Engineering and Information Technology, 8(4–2), 1625. https://doi.org/10.18517/ijaseit.8.4-2.7054 6. Komakech, R., Matsabisa, M. G., & Kang, Y. (2019). The Wound Healing Potential of Aspilia africana (Pers.) C. D. Adams (Asteraceae) . Evidence-Based Complementary and Alternative Medicine, 2019, 1–12. https://doi.org/10.1155/2019/7957860 7. Dev, S. K., Choudhury, P. K., Srivastava, R., & Sharma, M. (2019). Antimicrobial, anti-inflammatory and wound healing activity of polyherbal formulation. Biomedicine and Pharmacotherapy, 111(September 2018), 555–567. https://doi.org/10.1016/j.biopha.2018.12.075 8. Zainurin, N. A. A., Hashim, Y. Z. H. Y., Mohamed Azmin, N. F., & Al-Khatib, M. F. R. (2020). Understanding the effects of different parameters of soxhlet extraction on bioactive compounds from aquilaria malaccensis leaf through GCMS-based profiling. Food Research, 4, 63–73. https://doi.org/10.26656/fr.2017.4(S1).S09 9. Vatankhah, E. (2018). Rosmarinic acid-loaded electrospun nanofibers: In vitro release kinetic study and bioactivity assessment. Engineering in Life Sciences, 18(10), 732–742. https://doi.org/10.1002/elsc.201800046 10. Suwantong, O., Pankongadisak, P., Deachathai, S., & Supaphol, P. (2013). The potential of electrospun poly(L-lactic acid) fiber mats containing a crude garcinia dulcis extract for use as wound dressings. Chiang Mai Journal of Science, 40(3), 517–533. https://doi.org/10.1007/s00289-014-1102-9 11. Li, C. W., Fu, R. Q., Yu, C. P., Li, Z. H., Guan, H. Y., Hu, D. Q., … Lu, L. C. (2013). Silver nanoparticle/chitosan oligosaccharide/poly(vinyl alcohol) nanofibers as wound dressings: A preclinical study. International Journal of Nanomedicine, 8, 4131–4145. https://doi.org/10.2147/IJN.S51679 12. Shalaby, T., Hamad, H., Ibrahim, E., Mahmoud, O., & Al-Oufy, A. (2018). Electrospun nanofibers hybrid composites membranes for highly efficient antibacterial activity. Ecotoxicology and Environmental Safety, 162(July), 354–364. https://doi.org/10.1016/j.ecoenv.2018.07.016 13. Azhar, U. haq, Malik, A., Anis, I., Khan, S. B., Ahmed, E., Ahmed, Z., Nawaz, S. A., & Choudhary, M. I. (2004). Enzymes Inhibiting Lignans from Vitex negundo. Chem. Pharm. Bull, 52(11), 1269–1272. 14. Tort, S., Acartürk, F., & Beşikci, A. (2017). Evaluation of three-layered doxycycline-collagen loaded nanofiber wound dressing. In International Journal of Pharmaceutics (Vol. 529). https://doi.org/10.1016/j.ijpharm.2017.07.027 15. Adeli, H., Khorasani, M. T., & Parvazinia, M. (2018). Wound dressing based on electrospun PVA/chitosan/starch nanofibrous mats: Fabrication, antibacterial and cytocompatibility evaluation and in vitro healing assay. International Journal of Biological Macromolecules, 122, 238–254. https://doi.org/10.1016/j.ijbiomac.2018.10.115 16. Tobin, D. J. (2017). Introduction to skin aging. Journal of Tissue Viability, 26(1), 37–46. https://doi.org/10.1016/j.jtv.2016.03.002 17. Ramalingam, R., Dhand, C., Leung, C. M., Ezhilarasu, H., Prasannan, P., Ong, S. T., … Arunachalam, K. D. (2019). Poly-ε- caprolactone/gelatin hybrid electrospun composite nanofibrous mats containing ultrasound assisted herbal extract: Antimicrobial and cell proliferation study. Nanomaterials, 9(3). https://doi.org/10.3390/nano9030462 18. Bayat, S., Amiri, N., Pishavar, E., Kalalinia, F., Movaffagh, J., & Hahsemi, M. (2019). Bromelain-loaded chitosan nanofibers prepared by electrospinning method for burn wound healing in animal models. Life Sciences, 229, 57–66. https://doi.org/10.1016/j.lfs.2019.05.028 19. Vakilian, S., Norouzi, M., Soufi-Zomorrod, M., Shabani, I., Hosseinzadeh, S., & Soleimani, M. (2018). L. inermis-loaded nanofibrous scaffolds for wound dressing applications. Tissue and Cell, 51(February), 32–38. https://doi.org/10.1016/j.tice.2018.02.004 20. Garcia-Orue, I., Gainza, G., Gutierrez, F. B., Aguirre, J. J., Evora, C., Pedraz, J. L., … Igartua, M. (2017). Novel nanofibrous dressings containing rhEGF and Aloe vera for wound healing applications. International Journal of Pharmaceutics, 523(2), 556–566. https://doi.org/10.1016/j.ijpharm.2016.11.006 21. Ramalingam, R., Dhand, C., Leung, C. M., Ong, S. T., Annamalai, S. K., Kamruddin, M., … Arunachalam, K. D. (2019). Antimicrobial properties and biocompatibility of electrospun poly-ε-caprolactone fibrous mats containing Gymnema sylvestre leaf extract. Materials Science and Engineering C, 98(August 2018), 503–514. https://doi.org/10.1016/j.msec.2018.12.135 22. Manotham, S., Pengpat, K., Eitssayeam, S., Rujijanagul, G., Sweatman, D. R., & Tunkasiri, T. (2015). Fabrication of Polycaprolactone/Centella asiatica Extract Biopolymer Nanofiber by Electrospinning. Applied Mechanics and Materials, 804, 151–154. https://doi.org/10.4028/www.scientific.net/amm.804.151 23. Suganya, S., Senthil Ram, T., Lakshmi, B. S., & Giridev, V. R. (2011). Herbal Drug Incorporated Antibacterial Nanofibrous Mat Fabricated by Electrospinning: An Excellent Matrix For Wound Dressings. Journal of Applied Polymer Science, 121(5), 2893–2899. https://doi.org/10.1002/app.33915 24. Wisastra, R., Ghizzoni, M., Boltjes, A., Haisma, H. J., & Dekker, F. J. (2012). Anacardic acid derived salicylates are inhibitors or activators of lipoxygenases. Bioorganic and Medicinal Chemistry, 20(16), 5027–5032. https://doi.org/10.1016/j.bmc.2012.06.019 25. Whitman, S., Gezginci, M., Timmermann, B. N., & Holman, T. R. (2002). Structure-activity relationship studies of nordihydroguaiaretic acid inhibitors toward soybean, 12-human, and 15-human lipoxygenase. Journal of Medicinal Chemistry, 45(12), 2659–2661. https://doi.org/10.1021/jm0201262 26. Leelaprakash, G., & Mohan Dass, S. (2011). Invitro anti-inflammatory activity of methanol extract of enicostemma axillare. International Journal of Drug Development and Research, 3(3), 189–196. Authors: Pallavi S Biradar, Anand Jatti Face and Thumb Based Multimodal Bio-Metric Authentication using Harris Feature Extraction and Paper Title: Stenography Abstract: In the past recent, identification of a person in an effective manner is a foremost concern for any security authentication in numerous applications such as, banking, e-commerce, communications etc. One of the best identification technology for person identification and authentication compared with the existing password 92. based authentication is the multimodal biometric technology. Multimodal can be defined as, a system which uses two or more biometrics for identification of person. In the paper we propose a multimodal bio-metric system with a unique methodology and features extraction method incorporated in system for a secure authentication. 550-555 The two modalities used in the system are face and thumb. We use Harris based image feature extraction for both and choose the best unique features from both and fused using concatenation. The extracted unique features are embedded in a cover image using modulo operator based steganography technique. This encrypted data is shared as an image file to the receiver for authentication. At the receiver end the hidden features are decrypted and separated into face and thumb features. These decrypted features are compared with the pre-trained authorized person feature, based on the multi-svm classifier result the person is decided as authorized or unauthorized. The accuracy of the system is been calculated and was resulted in a good accuracy. The system can be made much more secure by adding an additional secret key for encryption and decryption.
Keywords: Bio-metric, Harris, modulo operator, multi-modal, multi-svm. Stenography.
References: 1. X. Zhang, D. Cheng, P. Jia, Y. Dai and X. Xu, "An Efficient Android-Based Multimodal Biometric Authentication System With Face and Voice," in IEEE Access, vol. 8, pp. 102757-102772, 2020, doi: 10.1109/ACCESS.2020.2999115. 2. A. Herbadji, N. Guermat, L. Ziet and M. Cheniti, "Multimodal Biometric Verification using the Iris and Major Finger Knuckles," 2019 International Conference on Advanced Electrical Engineering (ICAEE), Algiers, Algeria, 2019, pp. 1-5, doi: 10.1109/ICAEE47123.2019.9014704. 3. B. Ammour, T. Bouden and L. Boubchir, "Face-Iris Multimodal Biometric System Based on Hybrid Level Fusion," 2018 41st International Conference on Telecommunications and Signal Processing (TSP), Athens, 2018, pp. 1-5, doi: 10.1109/TSP.2018.8441279. 4. K. Meng, Z. Huang, X. Wang and K. Wang, "Multimodal Biometric Verification Using Sparse Representation Based Classification," 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC), Chongqing, 2018, pp. 26-31, doi: 10.1109/ICIVC.2018.8492886. 5. P. Jayapriya and R. Manimegalai, "Finger Knuckle Biometric Authentication using Texture-Based Statistical Approach," 2018 International Conference on Intelligent Computing and Communication for Smart World (I2C2SW), Erode, India, 2018, pp. 170-174, doi: 10.1109/I2C2SW45816.2018.8997143. 6. M. G. Gurubasavanna, S. Ulla Shariff, R. Mamatha and N. Sathisha, "Multimode authentication based Electronic voting Kiosk using Raspberry Pi," 2018 2nd International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), 2018 2nd International Conference on, Palladam, India, 2018, pp. 528-535, doi: 10.1109/I-SMAC.2018.8653726. 7. K. Ramesh, M. V. Prasad and K. Hemachandran, "Design and implementation of advanced ARM7 based biometric security system using wireless communication," 2018 2nd International Conference on Inventive Systems and Control (ICISC), Coimbatore, 2018, pp. 543-546, doi: 10.1109/ICISC.2018.8398859. 8. M. Hammad, Y. Liu, and K. Wang, ―Multimodal Biometric Authentication Systems Using Convolution Neural Network based on Different Level Fusion of ECG and Fingerprint,‖ IEEE Access, vol. 7, pp. 26527-26542, 2018. 9. S. Nakagawa, L. Wang, and S. Ohtsuka, ―Speaker identification and verification by combining MFCC and phase information,‖ IEEE Trans. Audio Spe., vol. 20, no. 4, pp.1085-1095, 2012. 10. W. Yang, Z. Wang, and B. Zhang, ―Face recognition using adaptive local ternary patterns method,‖ Neurocomputing, vol. 213, pp.183- 190, 2016. Authors: Shrugal Varde, M.S.Panse
Paper Title: Stereo Vision-based Path Finder for Visually Impaired Abstract: This paper introduces a novel electronic mobility aid for visually impaired users that helps them navigate in any given environment and avoid knee level to head height obstacles. The mobility aid uses stereo imaging system to capture the images of the area in front of the user. The processing unit generates a disparity map and a segmentation algorithm extracts information about the relative distance of obstacles from the user. This information is relayed to the user in simplified vibration pattern feedback to inform the user of the path to be taken to avoid collision with the obstacle. Special hardware was designed to make the system portable and cost effective.The mobility aid was validated on 55 visually impaired users. The subjects walked in a controlled test environment with a varying number of obstacles placed in their path. The accuracy of the device to help the user avoid obstacles and the average speed of walking of the user were determined. The results obtained were satisfactory and the device has the potential for use in standalone mode as well as in conjunction with a white cane and thus help visually impaired people counter mobility problems.
Keywords: visually impaired, stereo, mobility, disparity, navigation
References: 93. 1. Global data on visually impairement. World Health Organization, 2010. 2. C. Jackson, ―Correspondence with Carroll L. Jackson, Executive Director of the Upshaw Institute for the Blind,‖, Available ftp.eecs.umich.edu/people/johannb/Carroll_Jackson_Letter.pdf, Aug.1995 556-561 3. I. Ulrich and J. Borenstein, "The GuideCane-applying mobile robot technologies to assist the visually impaired," in IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, vol. 31, no. 2, pp. 131-136, March 2001. 4. K. Ito, M. Okamoto, J. Akita, T. Ono, I. Gyobu, T. Tagaki, T. Hoshi, and Y. Mishima, ―CyARM: An alternative aid device for blind persons,‖ CHI '05 Extended Abstracts on Human Factors in Computing Systems, Portland, OR, Apr. 2–7, 2005, pp. 1483–1488. 5. S. Shoval, J. Borenstein and Y. Koren, "Mobile robot obstacle avoidance in a computerized travel aid for the blind," Proceedings of the 1994 IEEE International Conference on Robotics and Automation, San Diego, CA, USA, 1994, pp. 2023-2028 vol.3. 6. D. Aguerrevere, M. Choudhury, and A. Barreto, ―Portable 3D sound / sonar navigation system for blind individuals,‖ presented at the 2nd LACCEI Int. Latin Amer. Caribbean Conf. Eng. Technol. Miami, FL, Jun. 2–4 2004. 7. S. Meers and K. Ward, ―A substitute vision system for providing 3D perception and GPS navigation via electro-tactile stimulation,‖ presented at the 1st Int. Conf. Senor. Technology, Palmerston North, New Zealand, Nov. 21–23, 2005 8. L. A. Johnson and C. M. Higgins, "A Navigation Aid for the Blind Using Tactile-Visual Sensory Substitution," 2006 International Conference of the IEEE Engineering in Medicine and Biology Society, New York, NY, 2006, pp. 6289-6292. 9. P. B. L. Meijer, "An experimental system for auditory image representations," in IEEE Transactions on Biomedical Engineering, vol. 39, no. 2, pp. 112-121, Feb. 1992. 10. J. L. Gonzalez-Mora, A. F. Rodriguez-Hernaindez, E. Burunat, F. Martin and M. A. Castellano, "Seeing the world by hearing: Virtual Acoustic Space (VAS) a new space perception system for blind people.," 2006 2nd International Conference on Information & Communication Technologies, Damascus, 2006, pp. 837-842. 11. S. Kammoun, G. Parseihian, O. Gutierrez, A. Brilhault, A. Serpa, M. Raynal, B. Oriola, M.J.-M. Macé, M. Auvray, M. Denis, S.J. Thorpe, P. Truillet, B.F.G. Katz, C. Jouffrais, Navigation and space perception assistance for the visually impaired: The NAVIG project,IRBM,Volume 33, Issue 2,2012,Pages 182-189.
94. Authors: Rohit B Kaliwal, Santosh L Deshpande Paper Title: Efficiency of Probabilistic Network Model for Assessment in E-Learning System Abstract: The knowledge acquirement by the learner is a major assignment of an E-Learning framework. Evaluation is required in order to adapt knowledge resources and task to learner ability. Assessment provides learner‘s an approach to evaluate the skills gained through the e-learning domain they are accessing. A dissimilar method can be used to assess the information acquirement, such as probabilistic Bayesian Network model. A Bayesian Network is a graphical representation of the probabilistic relationships of a complex system. This network can be used for reasoning with uncertainty. Bayesian Network is the most challenging task in e- learning system as learner evaluation model are an element of uncertainty. In this paper the current proposed scheme is constructed on Bayesian Network to deduce the stage of knowledge possessed by the learner. It also proposes type of assessment to identify the knowledge whatever the learner identifies. Throughout the assessment, it can be performed by two approaches namely Sequential and Random. In Sequential approach, questions can be displayed on the learner machine in sequential order. In Random approach, questions can be displayed on the learner machine in random order. However, both have their inherent limitations. Questions that are considered to be answered easily by the learner may also be presented to the learner who is not desirable. This system determined on the illustration of Bayesian Network model and algorithm for inference about learner‘s knowledge. The Bayesian Network model was efficiently implemented for three levels of learner called Higher Learners (HL), Regular Learners (RL) and Irregular Learners (IL) for learner‘s assessment and was successfully implemented with 81.1% of probabilities for learner‘s assessment.
Keywords: Assessment, Knowledge design, Bayesian Network (BN), Evaluation, E-Learning, Intelligent Tutoring System (ITS)
References: 1. Carbonell, J.R, ―AI in CAI: an artificial intelligence approach to computer assisted instruction‖, IEEE transaction on Man. Machine System, pp.190–202, 1970. 562-566 2. R. Santhi, B. Priya, J.M. Nandhini, ―Review of intelligent tutoring systems using bayesian approach‖, 2013. 3. Radenkovic, B, ―Web portal for adaptive e-learning. Telecommunication in Modern Satellite Cable and Broadcasting Services (TELSIKS)‖, 10th International Conference, pp. 365 – 368, 2011. 4. Joseph Psotka, Sharon A. Mutter, ―Intelligent Tutoring Systems: Lessons Learned‖, Lawrence Erlbaum Associates, ISBN 0-8058- 0192–8, 1988. 5. Ramırez-Noriega, A. Juarez-Ramırez, R. Huertas, C. Martınez-Ramırez, Y, ―A Methodology for building Bayesian Networks for Knowledge Representation in Intelligent Tutoring Systems‖, In: Congreso Internacional de Investigacion e Innovacion en Ingenierıa de Software, pp. 124–133, 2015. 6. Millan E, Descal co, L. Castillo, G. Oliveira, P. Diogo S, ―Using Bayesian networks to improve knowledge assessment‖, Computers & Education, pp.436–447, 2013. 7. Kammerdiner, ―A.: Bayesian networks Bayesian Networks‖, In: Floudas, C.A., Pardalos, P.M. (eds.) Encyclopedia of Optimization SE - 32, pp. 187–196. Springer US 2009. 8. Liu, Z., Wang H, ―A Modeling Method Based on Bayesian Networks in Intelligent Tutoring System Structure‖, pp. 967–972, 2007. 9. Srinivas. R. M, Dr. D.H. Rao, ―Application of Bayesian Networks for Learner Assessment in E-Learning Systems‖, International Journal of Computer Applications Volume 4 – No.4, pp. 0975 – 8887, July 2010. 10. Goguadze, G. Sosnovsky, S. Isotani, S., McLaren, B.M, ―Evaluating a Bayesian Student Model of Decimal Misconceptions‖, In: Proceedings of the 4th International Conference on Educational Data Mining, 2011. 11. Torabi, R., Moradi, P., Khantaimoori, A.R, ―Predict Student Scores Using Bayesian Networks‖, Procedia - Social and Behavioral Sciences, pp.4476–4480, 2012. 12. Mahbobe Bani Asad Askari, Mostafa Ghazizadeh Ahsaee, ―Bayesian network structure learning based on cuckoo search algorithm‖, 6th Iranian Joint Congress on Fuzzy and Intelligent Systems, 2018. 13. De Bruyn, E. Mostert, E. Van Schoor, ―Computer-based testing - The ideal tool to assess on the different levels of Bloom‘s taxonomy‖, 2011 14th International Conference on Interactive Collaborative Learning, ICL 2011 - 11th International Conference Virtual University, VU‘11,September, pp.444–449, 2011. 14. Oktariani Nurul Pratiwi, Yenie Syukriyah, ―Question Classification for e-Learning Using Machine Learning Approach‖, International Conference on ICT for Smart Society (ICISS), 2020. 15. J Pearl, ―Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference‖, Morgan Kaufmann, 1988. 16. Nazeeh Ghatasheh, ―Knowledge Level Assessment in e-Learning Systems Using Machine Learning and User Activity Analysis‖, International Journal of Advanced Computer Science and Applications, Vol. 6, No. 4, 2015. Authors: Sk. Faruque Ahmed, Mohibul Khan, Nillohit Mukherjee
Paper Title: Synthesis and Optical Characterization of Carbon Nanofibers Abstract: Radio frequency plasma enhanced chemical vapor deposition technique has been used to synthesized graphitic carbon nanofibers thin films. Ni catalyst in thin film form used for the synthesis of carbon nanofibers. The deposition temperature of the substrate has been varied from 500 - 600 0C. The morphology of the CNF thin films changed with the variation of substrate temperature. The graphitic phase of the synthesized carbon nanofibers has confirmed by X-ray diffraction patterns analyses. Field emission scanning electron microscopic studies showed fibrous structure in the films. The length of the carbon nanofibers few micrometers and the 95. diameter range 300-400 nm. The different vibrational modes of carbon nanofibers analyzed using Fourier transformed infrared spectroscopy measurements. Photoluminescence of the carbon nanofibers have also been 567-570 studied which showed a strong emission peak at 468 nm.
Keywords: Carbon nanofibers; RF-PECVD; XRD; FESEM; FTIR, Photoluminescence.
References: 1. H. W. Kroto, J. R. Heath, S. C. O‘Brien, R.F. Curl, R. E. Smalley, ―C60: Buckminsterfullerene‖ Nature, 354 (1985) 162-163 2. S Ijima, ―Helical microtubules of graphitic carbon‖, Nature, 354 (1991) 56-58. 3. A. G. Rinzler, J.H. Hafner, P. Nikolaev, L. Lou, S. G. Kim, D. Tomanek, P. Nordlander, D. T. Colbert, R. E. Smalley, ―Unraveling Nanotubes: Field Emission from an Atomic Wire‖, Science, 269 (1995) 1550-1553. 4. S. Tatsuura, M. Furuki, Y. Sato, I. Iwasa, M. Tian, H. Mitsu, ―Semiconductor Carbon Nanotubes as Ultrafast Switching Materials for Optical Telecommunications‖, Adv. Mater., 15 (2003) 534-537. 5. K. Liu, Ph Avouris, R. Martel, W.K. Hsu, ―Electrical transport in doped multiwalled carbon nanotubes‖, Phys. Rev. B, 63 (2001) 161404 (R). 6. V. V. Simonyan, J. K. Johnson, ―Hydrogen storage in carbon nanotubes and graphitic nanofibers‖, J. Alloys and Comp., 330-332 (2002) 659-665. 7. Y. Y. Wei, G. Eres, ―Directed assembly of carbon nanotube electronic circuits,‖ Appl. Phys. Lett., 76 (2000) 3759. 8. N. Choi, T. Uchihashi, H. Nishijima, T. Ishida, W. Mizutani, S. Akita, Y. Nakayama, M. Ishikawa, H. Tokumoto, ―Atomic Force Microscopy of Single-Walled Carbon Nanotubes Using Carbon Nanotube Tip‖, Jpn. J. Appl. Phys., 39 Part1 (2000) 3707. 9. N. Gupta, S. M. Gupta, S. K. Sharma, ―Carbon nanotubes: synthesis, properties and engineering applications‖, Carbon Letters, 29 (2019) 419-447. 10. Y. Ando, X. Zhao, ―Synthesis of Carbon Nanotubes by Arc Discharge method, New Diamond and frontier carbon technology‖, New Diamond and Frontier Carbon Technology, 16 (2006) 123-137. 11. C. M. Chena, Y. M. Dai, J. G. Huan, J. M. Jehng, ―Intermetallic catalyst for carbon nanotubes (CNTs) growth by thermal chemical vapor deposition method‖, Carbon, 44 (2006) 1808-1820. 12. Y. Matsumoto, M. T. Oe, M. Nakao, K. Kamimura, Y. Onuma, H. Matsushima, ―Preparation of carbon nanofibers by hot filament- assisted sputtering‖, Mater. Science and Eng. B, 74 (2000) 218-221. 13. S. Hondaa, K. Y. Leea , K. Fujimotoa , K. Tsujia , S. Ohkuraa , M. Katayamaa , T. Hiraob , K. Ouraa, ―Formation of carbon nanofiber films by RF magnetron sputtering method‖, Physica B, 323 (2002) 347-349. 14. T. Ikunoa, J. T. Ryub, T. Oyamaa, S. Ohkuraa, Y. G. Baeka, S. Hondaa, M. Katayamaa, T. Hiraoc, K. Ouraa, ―Characterization of low temperature growth carbon nanofibers synthesized by using plasma enhanced chemical vapor deposition‖, Vacuum, 66 (2002) 341- 345. 15. Sk. F. Ahmed, M. Khan, N. Mukherjee, ―Enhancement of Electrical Property of Carbon Nanotube by Silicon Incorporation‖ International Journal of Engineering and Advanced Technology, 10 (2020) 62-65. 16. T. Yanase, T. Miura, T. Shiratori, M. Weng, T. Nagahama, T. Shimada, ―Synthesis of Carbon Nanotubes by Plasma-Enhanced Chemical Vapor Deposition Using Fe1−xMnxO Nanoparticles as Catalysts: How Does the Catalytic Activity of Graphitization Affect the Yields and Morphology‖, Journal of Carbon Research, 5 (2019) 46. 17. R. Seshadri, A. Govindaraj, N. H. Aiyer, R. Sen, G. N. Subbanna, A. R. Raju and C. N. R. Rao "Investigations of Carbon Nanotubes", Current Science, 66 (1994) 839-847. 18. JCPDS data file no. 00-013-0148. 19. C. Balazsi, Z. Konya, F. Weber, L. P. Biro, P. Arato, ―Preparation and characterization of carbon nanotube reinforced silicon nitride composites‖ Materials Science and Engineering C, 23 (2003) 1133-1167. 20. J. Jang, J. Bae, S. H. Yoon, ―A study on the effect of surface treatment of carbon nanotubes for liquid crystalline epoxide–carbon nanotube composites‖ J. Mater. Chem. 13 (2003) 676-681. 21. C. Deeney, S. Wang, S. A. Belhout, A. Gowen, B. J. Rodriguez, G. Redmond, S. J. Quinn, ―Templated microwave synthesis of luminescent carbon nanofibers‖ RSC Adv., 8 (2018) 12907-12917. 22. C. Li, G Shi, ―Carbon nanotube-based fluorescence sensors‖ J. of Photochemistry and Photobiology C: Photochemistry Reviews, 19 (2014) 20-34. Authors: Victor Alcántara Alza
Paper Title: Cyanide in salt bath Applied to ASTM A-517 Steel: Effects on Hardness, Wear and Microstructure Abstract: The effects of the cyanide treatment (CN) in a salt bath at elevated temperatures on the hardness; adhesive and abrasive wear; of ASTM A-517 steel, were investigated. For abrasive wear, 1‖ x 3‖ x 5/16" samples were prepared according to ASTM G-65 standard. For adhesive wear, specimens wit ring-shaped: φ ext. = 40 mm, φ int = 20 mm and 10 mm thick, according to ASTM G -77. The CN treatment was carried out, at high temperatures: 800 – 850 – 900 – 950 °C, immersing the samples in a salt bath: 6% NaCN + 80% BaCl2 + 14% NaCl before entering the muffle furnace, with soaking time of 3 hr. Hardness tests were performed on a Rockwell Durometer taking measurements on the HRC scale. The adhesive wear tests were carried out on a parallel lathe coupling the Amsler device, following the ASTM G-77 standard. The abrasive wear tests were performed according to the ASTM G65 standard. Microscopy was done at the optical level. A maximum hardness of 63.5 HRC was found in all samples, representing an increase of 11.3% with respect to the state of supply (T&R). In abrasive wear, its value increased to 66%, compared to supply samples. The most suitable microstructure is presented by cyanide samples at 850ºC, with a layer of compounds (hard layer) formed by: massive cementite; tempered martensite and carbide. It is concluded that when applying cyanide to ASTM A- 517 steel, the hardness and wear properties are increased to optimal values, if the cyanide treatment (CN) is 96. carried out at 850 °C.
Keywords: Cyanide, adhesive wear, abrasive wear, wear steels, surface hardening 571-580
References: 1. M. Ferreira, ―Desgaste de Materiales – Introducción‖ [online]. Available: 2. https://docplayer.es/9344147-Desgaste-de-materiales.html 3. K. Hock, ―Wear Resistance of pernitrided hardcoated steels for tools and machine components‖. Elsevier Science Publishers, Amsterdam, 1996 4. T. Kumar, J. Ambulingam, M. Gopal, A. Rajadurai., ―Surface hardening of AISI 304, 316, 304l and 316l SS using cyanide free salt bath nitriding process‖. International Symposium of Research Students on Materials Science and Engineering. December 2002-04 Chennai India. 5. B. Selcuk, R. Ipek, and B.M. Karami M., ―A study on friction and wear behaviour of carburized, carbonitrided and borided AISI 1020 and 5115 steels‖. Journal of Materials processing Technology. Vol 141, pp 189-196. 6. G.Wahl, ―Effect of carbonitriding schedules and subsequent oxidation on the properties of articles‖. VII International Congress on Heat Treatment of Materials. Vol 7. 33, No 7, pp 487-490. 8. A. P Guliaev, ―Metalografía‖. 2º Edición. Editorial Mir, Moscú, 1983 9. G. J. Li, Q. Peng, C. Li, Y. Wang, J. Gao, S. Chenc, J. Wang and B. Shen (2008), ―Effect of DC plasma nitriding temperature on microstructure and dry-sliding wear properties of 316L stainless steel‖. Journal of Surface Coatings. 202(12), pp: 2749-2754. 10. J. Suchanek and V. Kuklik,. (2009), ―Influence of heat and thermochemical on abrasion resistance of structural and tool steels‖. Wear, vol 267, pp 2100-2108 11. S.P Ayodeji, T.E Abioye and S.O Olanrewaju; ―Investigation of Surface Hardness of Steels in Cyanide Salt Bath Heat Treatment Process" IMECS 2011, March 16-18, 2011, Hong Kong, Vol II, ISBN: 978-988-19251-2-1 12. I. Hilerio, E. López, H. Jiménez, M.A. Barrón. (2006) ―Estudio del comportamiento del acero AISI 8620 en abrasion seca‖ Memorias del XII congreso internacional anual de la SOMIM, México. 13. C. Bohórquez, (2007), Nitrocarburación austenítica de los aceros AISI/SAE 1020 y 8620 mediante el empleo de alcoholes y nitrurantes líquidos, Universidad libre, Bogotá, colombia.‖8° congreso iberoamericano de Ing. Mecánica‖. 14. Z. Zhou, M. Dai, Z. Shen, J. Hu; A novel rapid D.C. salt bath nitrocarburizing technology, Vacuum 109 (2014) 144-147 15. DOI: http://dx.doi.org/10.1016/j.vacuum.2014.07.016 16. 13. A.P. García, G. Vargas, J. López; Evolución microestructural de la superficie del acero inoxidable AISI 304 L oxinitrocarburizado en un baño de sal libre de cianuro y su potencial aplicación en colectores insolares, Surface & coatings Technology 353 (2018) 190-198 17. 14. Propiedades del Acero Antidesgaste CHRONIT T-1, Aceros Bohler del Perú, S.A. (2007). Available: 18. https://dokumen.tips/documents/propiedades-acero-chronit-t-1-400-y-chronit-t-1-500-plancha-antidesgaste.html (URL) 19. 15. Friction, Lubrication, and Wear Technology, ASM Handbook 1992, Vol. 18, International. USA 20. 16. K.H. Zum Gahr., Microstructure and wear of materials, Elsevier, Amsterdam, 80 – 350, 1987. 21. 17. M.A.L. Márquez, Diseño de una máquina tribológica para pruebas de desgaste abrasivo severo, Tesis de Maestría, SEPI ESIME IPN, México, Febrero de 2002. 22. 18. N. P. Suh, Tribophysics, Edit. Prentice Hall, USA. 1986 23. 19. R. G. Bayer, Mechanical wear prediction and prevention, Edi. Marcel Dekker, 1994. USA 24. 20. I. Hutchings, P. Shipway, Tribology: Friction and wear of engineering materials, Second Edition, 2017. Ed. Elsevier 25. eBook ISBN: 9780081009512 Authors: Edy Budiman, Andi Tejawati, Ummul Hairah
Paper Title: Bioinformatics Database Query Performance and Optimization Abstract: Borneo bioinformatics portal test is a critical element of SQA and represents a comprehensive review of specifications, design and coding. The test represents an abnormality in the development of the portal. A series of tests systematically reveals several different types of errors. This study aims to evaluate the performance and optimization of Borneo's Bioinformatics portal with a series test activities using the Web Performance Optimization methodology. Testing query performance with measuring the response time and page loading timings from the object relationship mapping (ORM) model Laravel PHP framework in offline and online. For optimization, we set a pre-test and post-test scenario to evaluate the efficiency performance test results. The results study found that the query relation model, parsing script (javaScript and CSS), service scale and dimension images in the interaction process to the database are the dominant resources affecting the performance of the Bioinformatics portal. Performance optimization through determining the appropriate query relation model, minify and defer parsing script or combine images using CSS sprites to reduce scala image.
Keywords: Bioinformatics, query, database relationship, ORM.
References: 1. J. Y. Chen, J. V. Carlis, and N. Gao, ―A complex biological databases querying method,‖ Proceedings of the ACM Symposium on Applied Computing, vol. 1, no. March 2005, pp. 110–114, 2005, doi: 10.1145/1066677.1066708. 2. A. Messina, A. Fiannaca, L. La Paglia, M. La Rosa, and A. Urso, ―BioGraph: A web application and a graph database for querying and analyzing bioinformatics resources,‖ BMC Systems Biology, 2018, doi: 10.1186/s12918-018-0616-4. 3. H. Haviluddin, E. Budiman, and N. F. Hidayat, ―A database integrated system based on SOAP web service,‖ TEM Journal, 2019, doi: 10.18421/TEM83-12. 4. A. Kalderimis et al., ―InterMine: Extensive web services for modern biology,‖ Nucleic Acids Research, 2014, doi: 10.1093/nar/gku301. 5. E. Budiman, N. Puspitasari, Haerullah, M. Jamil, M. Wati, and A. Saudek, ―Evaluation of the bioinformatics resource portal,‖ in Proceedings - 2018 3rd International Conference on Information Technology, Information Systems and Electrical Engineering, 97. ICITISEE 2018, 2018, pp. 54–59, doi: 10.1109/ICITISEE.2018.8720973. 6. J. Chomicki and D. Toman, ―Temporal Databases,‖ in Foundations of Artificial Intelligence, Volume 1., L. V. M. Fisher, D. Gabbay, Ed. Elsevier B.V., 2005, pp. 429–467. 581-588 7. P. Cybula, H. Kozankiewicz, K. Stencel, and K. Subieta, ―Optimization of distributed queries in grid via caching,‖ 2005, doi: 10.1007/11575863_58. 8. E. Budiman, N. Puspitasari, S. N. Alam, T. M. A. Akbar, Haeruddin, and D. Indra, ―Performance analysis of the resource loading time for borneo biodiversity information system,‖ 2018, doi: 10.1109/IAC.2018.8780515. 9. S. Wu, F. Li, S. Mehrotra, and B. C. Ooi, ―Query optimization for massively parallel data processing,‖ 2011, doi: 10.1145/2038916.2038928. 10. E. Budiman, N. Puspitasari, M. Wati, J. A. Widians, and Haviluddin, ―Web Performance Optimization Techniques for Biodiversity Resource Portal,‖ Journal of Physics: Conference Series, vol. 1230, no. 1, 2019, doi: 10.1088/1742-6596/1230/1/012011. 11. L. Zamboulis, N. Martin, and A. Poulovassilis, ―Query performance evaluation of an architecture for fine-grained integration of heterogeneous grid data sources,‖ Future Generation Computer Systems, 2010, doi: 10.1016/j.future.2010.05.008. 12. E. Budiman and S. N. Alam, ―Database: Taxonomy of plants Nomenclature for borneo biodiversity information system,‖ 2018, doi: 10.1109/IAC.2017.8280642. 13. L. Caroprese, E. Zumpano, and E. Vocaturo, ―No SQL Database Management Systems for Big Data,‖ International Journal of Engineering and Advanced Technology, vol. 9, no. 5, pp. 21–26, 2020, doi: 10.35940/ijeat.D9145.069520. 14. N. Puspitasari and E. Budiman, ―Evaluation of Borneo‘s Biodiversity Information System,‖ 2018 Electrical Power, Electronics, Communications, Controls and Informatics Seminar, EECCIS 2018, pp. 434–439, 2019, doi: 10.1109/EECCIS.2018.8692955. 15. N. K. Gundla and Z. Chen, ―Creating NoSQL Biological Databases with Ontologies for Query Relaxation,‖ 2016, doi: 10.1016/j.procs.2016.07.120. 16. N. Dengen, E. Budiman, J. A. Widians, M. Wati, U. Hairah, and M. Ugiarto, ―Biodiversity information system: Tropical rainforest borneo and traditional knowledge ethnic of dayak,‖ Journal of Telecommunication, Electronic and Computer Engineering, vol. 10, no. 1–9, 2018. 17. E. Budiman, M. Jamil, U. Hairah, H. Jati, and Rosmasari, ―Eloquent object relational mapping models for biodiversity information system,‖ in 2017 4th International Conference on Computer Applications and Information Processing Technology (CAIPT), Aug. 2017, vol. 2018-Janua, pp. 1–5, doi: 10.1109/CAIPT.2017.8320662. 18. U. Hairah, A. Tejawati, E. Budiman, and F. Agus, ―Borneo biodiversity: Exploring endemic tree species and wood characteristics,‖ in Proceeding - 2017 3rd International Conference on Science in Information Technology: Theory and Application of IT for Education, Industry and Society in Big Data Era, ICSITech 2017, 2017, vol. 2018-Janua, pp. 435–440, doi: 10.1109/ICSITech.2017.8257152. 19. L. A. Bultet et al., ―The SIB Swiss Institute of bioinformatics‘ resources: Focus on curated databases,‖ Nucleic Acids Research, vol. 44, no. D1, pp. D27–D37, 2016, doi: 10.1093/nar/gkv1310. 20. M. Cannataro and P. Veltri, ―Bioinformatics web portals,‖ in Selected Readings on Database Technologies and Applications, 2008. 21. P. Artimo et al., ―ExPASy: SIB bioinformatics resource portal,‖ Nucleic Acids Research, 2012, doi: 10.1093/nar/gks400. 22. Haeruddin, H. Johan, U. Hairah, and E. Budiman, ―Ethnobotany database: Exploring diversity medicinal plants of Dayak Tribe Borneo,‖ in International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), 2017, vol. 2017-Decem, doi: 10.1109/EECSI.2017.8239094. 23. J.-S. Varré, B. Schmidt, S. Janot, and M. Giraud, ―Manycore High-Performance Computing in Bioinformatics,‖ 2011. 24. P. D. Karp et al., ―A comparison of microbial genome web portals,‖ Frontiers in Microbiology. 2019, doi: 10.3389/fmicb.2019.00208. 25. W. W. Li et al., ―Building cyberinfrastructure for bioinformatics using service oriented architecture,‖ 2006, doi: 10.1109/ccgrid.2006.1630932. 26. E. Budiman and S. N. Alam, ―User perceptions of mobile internet services performance in borneo,‖ in 2017 Second International Conference on Informatics and Computing (ICIC), Nov. 2017, vol. 2018-Janua, pp. 1–6, doi: 10.1109/IAC.2017.8280643. 27. S. Kaur, K. Kaur, and P. Kaur, ―An Empirical Performance Evaluation of Universities Website,‖ International Journal of Computer Applications, 2016, doi: 10.5120/ijca2016910922. 28. D. E. Luna, L. F. Luna-Reyes, J. R. Gil-Garcia, and R. Sandoval-Almazán, ―Government web portals performance evaluation using data envelopment analysis,‖ 2011, doi: 10.1145/2037556.2037617. 29. D. google, ―About PageSpeed Insights,‖ developers.google.com. https://developers.google.com/speed/docs/insights/v5/. 30. Marcelduran, ―Web Performance Best Practices and Rules,‖ yslow.org. http://yslow.org/. 31. Carbon60, ―Recommendations,‖ gtmetrix.com. https://gtmetrix.com/recommendations.html (accessed Jun. 06, 2020). 32. marcelduran, ―YSlow Ruleset Matrix,‖ yslow.org. http://yslow.org/ruleset-matrix/. 33. Widjaja, Kekinian Keanekaragaman Hayati Indonesia 2014. 2014. 34. E. Budiman, N. Puspitasari, M. Wati, Haviluddin, and R. Rahim, ―Model Framework for Development of Biodiversity Information Systems,‖ Journal of Physics: Conference Series, vol. 1230, no. 1, 2019, doi: 10.1088/1742-6596/1230/1/012012. 35. D. F. R. A. Cleary and L. DeVantier, ―Indonesia: Threats to the Country‘s Biodiversity,‖ Encyclopedia of Environmental Health, no. November 2017, pp. 622–632, 2011, doi: 10.1016/B978-0-444-52272-6.00504-3.
Authors: D.B.Shanmugam, J.Dhilipan, A.Vignesh, T.Prabhu
Paper Title: Challenges in Data Quality and Complexity of Managing Data Quality Assessment in Big Data Abstract: High Quality Data are the precondition for examining and making use of enormous facts and for making sure the estimation of the facts. As of now, far reaching exam and research of price gauges and satisfactory appraisal strategies for massive records are inadequate. To begin with, this paper abridges audits of Data excellent studies. Second, this paper examines the records attributes of the enormous records condition, presents high-quality difficulties appeared by large data, and defines a progressive facts exceptional shape from the point of view of records clients. This system accommodates of big records best measurements, best attributes, and best files. At long last, primarily based on this system, this paper builds a dynamic appraisal technique for records exceptional. This technique has excellent expansibility and versatility and can address the troubles of enormous facts fine appraisal. A few explores have verified that preserving up the character of statistics is regularly recognized as hazardous, however at the equal time is considered as simple to effective basic leadership in building aid the executives. Enormous data sources are exceptionally wide and statistics structures are thoughts boggling. The facts got may additionally have satisfactory troubles, for example, facts mistakes, lacking data, irregularities, commotion, and so forth. The motivation behind facts cleansing (facts scouring) is to pick out and expel mistakes and irregularities from facts so as to decorate their quality. Information cleansing may be separated into 4 examples dependent on usage techniques and degrees manual execution, composing of splendid software programs, records cleaning inconsequential to specific software fields, and taking care of the difficulty of a kind of explicit software area. In these 4 methodologies, the 1/3 has terrific down to earth esteem and may be connected effectively. 98. Keywords: Total Data Quality Management, Data Quality Metrics, Data Quality Assessment, 589-593 References: 1. Almutiry, O., Wills, G., &Alwabel, A. (2013, June 24–26). Toward a framework for data quality in cloudbased health information system. Paper presented at the International Conference on Information Society (i-Society 2013). 2. Batini, C., &Scannapieco, M. (2016). Data and information quality: Dimensions, principles and techniques. Cham: Springer. 3. Eppler, M. J. (2006). Managing information quality: Increasing the value of information in knowledge intensive products and processes (2nd ed.). Berlin: Springer. 4. Katal, A., Wazid, M., &Goudar, R. (2013) Big Data: Issues, Challenges, Tools and Good Practices. Procedures of the 2013 Sixth International Conference on Contemporary Computing, Noida: IEEE, pp 404–409. 5. McGilvray, D. (2008) Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information, California: Morgan Kaufmann. 6. McGilvray, D. (2010) Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information, Beijing: Publishing House of Electronics Industry. 7. Meng, X. F., & Ci, X. (2013) Big Data Management: Concepts, Techniques and Challenges. Journal of Computer Research and Development 50(1), pp 146–169. 8. Nature (2008) Big Data. Retrieved November 5, 2013 from the www.http://www.nature.com/ news/specials/bigdata/index.html. 9. Song, M., & Qin, Z. (2007) Reviews of Foreign Studies on Data Quality Management. Journal of Information2, pp 7–9. 10. Wang, H., & Zhu, W. M. (2007) Quality of Audit Data: A Perspective of Evidence. Journal of Nanjing University(Natural Sciences) 43(1), pp 29–34. 11. Wang, J. L., Li, H., & Wang, Q. (2010) Research on ISO 8000 Series Standards for Data Quality. Standard Science12, pp 44–46. 12. LavanyaBandla,Rajkumar Rajasekaran,JollyMasih,Twitter sentimental Analysis using Hadoop Eco System (2020), Xi'an JianzhuKejiDaxueXuebao/Journal of Xi'an University of Architecture & Technology, 10.37896/JXAT12.08/2747 13. Rajkumar Rajasekaran, Govinda k, Ashrith Reddy, Uday Sai Reddy, Yashwanth Reddy: Visual Analysis of Temperature Time Series and Rainfall Using Big Data. DOI:10.36872/LEPI/V50I3/201023 14. Rajasekaran Rajkumar, Jolly Masih, K.Govinda: An analysis of mobile pass-codes in case of criminal investigations through social network data. International Journal of Computers and Applications 09/2019, DOI:10.1080/1206212X.2019.1662169
99. Authors: Velmurugan. PS, Jyoti Ranjan Sahoo Paper Title: Public Debt, Current Account Deficit and Economic Growth: A Study on Indian Context Abstract: External debt and internal debt form main components of the public debt structure in India. India‘s debt profile shows increasing external debt and simultaneously increasing the deficit in current account which have impact on economic growth of India. Our study assesses the impact of India‘s Gross External Debt (GED), Internal Debt (IND) and Current Account Deficit (CAD) on economic growth (GDP) by using time series data from 1998-99 to 2018-19. We intend to find long-run as well as short run relationship between the variables with the help of Eviews software. Stationarity of data is tested by considering Augmented Dickey-Fuller (ADF) test statistics and used Johansen Co-integration test and Vector Error Correction Model (VECM). The result shows co-integration among the variables with one equation. The result of VECM shows existence of long-run relationship among the variables. But the study fails to find the short-run causality among the variables. The results show external debt (GED), internal debt (IND), and Current Account Deficit (CAD) have negative and statistically insignificant relationship with GDP. It shows increase in public debt and deficit in current account results in decrease in GDP growth.
Keywords: External Debt, Internal Debt, GDP, Current Account, CAD, VECM, India
References: 1. Abubakar, A.B., Alagiriswamy, J. and Ahmad, S.I., International Journal of Research in Commerce, Economics and Management. 2. ALOYSIUS, C., 2016. AN EMPIRICAL STUDY OF THE IMPACT OF PUBLIC DEBT ON ECONOMIC GROWTH OF INDIA (Doctoral dissertation, University of Madras Chennai). 3. Bal, D.P. and Rath, B.N., 2014. Public debt and economic growth in India: A reassessment. Economic Analysis and Policy, 44(3), pp.292-300. 4. Calderon, C.A., Chong, A. and Loayza, N.V., 2002. Determinants of current account deficits in developing countries. The BE Journal of Macroeconomics, 2(1). 5. Chowdhury, A., 2001. External debt and growth in developing countries: a sensitivity and causal analysis. WIDER-Discussion Papers. 6. Coskun, A., 2010. Current account sustainability: post-2001 application of Turkey. (Unpublished master‘s thesis). Istanbul University, Turkey. 594-601 7. Dhir, Sonia., 2017. Economic Growth, Public Debt and Public Expenditure in India- An Empirical Analysis. Aussie Sino Studies. v(3), p.104. 8. Edo, S., Osadolor, N.E. and Dading, I.F., 2019. Growing external debt and declining export: The concurrent impediments in economic growth of Sub-Saharan African countries. International Economics. 9. Gomez-Gonzalez, P., 2019. Inflation-linked public debt in emerging economies. Journal of International Money and Finance, 93, pp.313-334. 10. Ibhagui, O.W., 2018. External debt and current account adjustments: The role of trade openness. Cogent Economics & Finance, 6(1), p.1446247. 11. Kaur, K., 2014. Time Series Analysis of Indian External Debt. Total Reserves and Economic Growth Rates: In International journal of scientific research, 3(4). 12. Mehta, B.M. and Kayumi, H.F., 2014. Effect of India‘s current account deficit on external debts and foreign exchange rates. IOSR Journal Of Economics And Finance, 1(7), pp.54-65. 13. Murat, S., Hobikoğlu, E.H. and Dalyancı, L., 2014. Structure and sustainability of current account deficit in Turkish economy. Procedia-Social and Behavioral Sciences, 150, pp.977-984. 14. Nyoni, T., Musisinyani, B. and Nyoni, M., 2017. The impact of current account deficits on economic growth in Zimbabwe. International journal for innovative research in multidisciplinary field, 3(8). 15. Putunoi, G.K. and Mutuku, C.M., 2013. Domestic debt and economic growth nexus in Kenya. Current Research Journal of Economic Theory, 5(1), pp.1-10. 16. Qureshi, I. and Liaqat, Z., 2020. The long-term consequences of external debt: Revisiting the evidence and inspecting the mechanism using panel VARs. Journal of Macroeconomics, 63, p.103184. 17. Rathnayake, A.S.K., 2020. Sustainability of the fiscal imbalance and public debt under fiscal policy asymmetries in Sri Lanka. Journal of Asian Economics, 66, p.101161. 18. Sahin, I.E. and Mucuk, M., 2014, July. The effect of current account deficit on economic growth: The case of Turkey. In Proceedings of International Academic Conferences (No. 0301828). International Institute of Social and Economic Sciences. 19. Yurdakul, F. and Ucar, B., 2015. The relationship between current deficit and economic growth: An empirical study on Turkey. Procedia Economics and Finance, 26, pp.101-108. 20. Yusuf, S. and Said, A.O., 2018. Public Debt and Economic Growth: Evidence from Tanzania. Journal of Economics, Management and Trade, pp.1-12. Authors: Yendluri Lohith JayaSurya , Yendluri Priya Yasaswini , Somepalli Saranya
Paper Title: Image Steganography Abstract: Steganography is the practice of concealing a file, message, image, or video within another file, message, image, or video. The advantage of steganography is that the intended secret message does not attract attention to itself as an object of scrutiny. Steganography is concerned both with concealing the fact that a secret message is being sent and its contents. The change is so subtle that someone who is not specially looking for it is unlikely to notice the change. We intend to perform image steganography by designing a neural network that prepares the secret jpg image and hides the prepared jpg image in a cover jpg image. 100. Keywords: steganography , neural network , jpg image 602-605
References: 1. Shumeet Baluja,Hiding Images in Plain Sight: Deep Steganography, 31st Conference on Neural Information Processing Systems(NIPS 2017), Long Beach, CA, USA. 2. Jessica Fridrich,Miroslav Goljan,and RuiDu.Detecting lsb steganography in color,and gray-scale images.IEEE multimedia, 8(4):2228, 2001. 3. Yinlong Qian, Jing Dong, Wei Wang, and Tieniu Tan. Deep learning for steganalysis via convolutional neuralnetworks.In SPIE/IST Electronic Imaging ,International Society for Optics and Photonics, 2015H. 4. Sabah Husien and Haitham Badi.Articial neural network for steganography.Neural Computing and Applications, 26(1):111116, 2015. 5. Robert Jaruek,Eva Volna,and Martin Kotyrba. Neural network approach to image steganography techniques.In Mendel 2015, pages 317327.Springer, 20 6. Georey E Hinton and Ruslan R Salakhutdinov.Reducing the dimensionality of data with neural networks.Science, 313(5786):504507, 2006. Authors: C Ankita, Supriya A, Bhagya R Design and Simulation of millimeter wave Mylar based flexible Antenna for 5G wireless Paper Title: Applications Abstract: The millimeter wave (mm-wave) is expected to play a crucial role in providing broad frequency bandwidth for large data transmission. The restrictions of wave propagation are anticipated to get eliminated in mm-wave propagation through the assistance of antenna technologies. The higher frequency spectrum prevalence of the 5G applications are likely to be dependent on a small advanced antenna technology. This paper presents an antenna design which uses Mylar as substrate for the 5G wireless applications. The structure of the antenna adopted here is of a T-shaped patch designed with ideal symmetrical slot structures. To increase the bandwidth the idea of defective ground structure (DGS) is used. The antenna model discussed here shows a high impedance bandwidth and a fair radiation pattern in the required direction with a maximum gain of 8.35dB at 28 GHz frequency. The proposed antenna is compared with the basic microstrip patch antenna which is designed at low frequency to prove that the bandwidth is enhanced and so other parameters in the proposed antenna such that it is suitable for mm-wave 5G wireless applications. 101. Keywords: Directivity, HFSS, microstrip patch antenna, millimeter waves, radiation pattern, return loss. 606-610 References: 1. Y.R.Li, B.Gao, X.Zhang and K.Huang, "Beam Management in Millimeter-Wave Communications for 5G and Beyond," in IEEE Access, vol. 8, pp. 13282-13293, 2020. 2. M. Faisal, A. Gafur, S. Z. Rashid, M. O. Shawon, K. I. Hasan and M. B. Billah, "Return Loss and Gain Improvement for 5G Wireless Communication Based on Single Band Microstrip Square Patch Antenna," 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), Dhaka, Bangladesh, 2019, pp. 1-5. 3. S. F. Jilani and A. Alomainy, "Millimetre-wave T-shaped MIMO antenna with defected ground structures for 5G cellular networks," in IET Microwaves, Antennas & Propagation, vol. 12, no. 5, pp. 672-677, 18 4 2018. 4. F. Al-Ogaili and R. M. Shubair, "Millimeter-wave mobile communications for 5G: Challenges and opportunities," 2016 IEEE International Symposium on Antennas and Propagation (APSURSI), Fajardo, 2016, pp. 1003-1004. 5. M. M. Amir Faisal, M. Nabil and M. Kamruzzaman, "Design and Simulation of a Single Element High Gain Microstrip Patch Antenna for 5G Wireless Communication," 2018 International Conference on Innovations in Science, Engineering and Technology (ICISET), Chittagong, Bangladesh, 2018, pp. 290-293. 6. U.Farooq et al., "Design of a 1×4 CPW Microstrip Antenna Array on PET substrate for Biomedical Applications," 2019 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, Atlanta, GA, USA, 2019, pp. 1345- 1346 Authors: Joan Hazel V. Tiongson, Marifel Grace C. Kummer
Paper Title: Rural Health Unit Decision Support System with Mapping Abstract: In this highly challenging and demanding world, presence of data and technology are overwhelming. But at present, some institutions still engage in manual-type of operations like the Rural Health Unit of the Municipality of Solano, Nueva Vizcaya. Problems, issues and challenges encountered by the unitin the delivery of its medical servicesand the extent of compliance in ISO/IEC 25010 Software Quality Standards were identified. And with the uncontrollable availability of data, these can be handled and treated using data mining techniques to predict disease occurrences. In this study, the clustering and classification data mining techniques were utilized in order to predict disease occurrences of every barangay of the municipality at a given time. An efficient record management system along with a decision support system was developed to meet the challenges of the unit. It mainly features the disease-occurrence mapping to assist physicians and other health professionals in the unit in their decision-making tasks particularly in diagnosis, treatment and recommendations.In terms of ISO/IEC 25010 Software Quality Standards, the system gained a ―very great extent‖ qualitative rating.
Keywords: Challenges, Clinical Decision Support System (CDSS), Data Mining, Electronic Medical Record 102. (EMR), Health Information Technology (HIT) 611-616 References: 1. Aguilar, Remo-tito. (nd). Disruptions in Health: Healthcare information technology in a limited resource community. Retrieved from http://remomd.com/technology/disruptions-in-health-healthcare-information-technology-in-a-limited-resource-community.html 2. Allianz Care. (nd). Healthcare in the Philippines. Retrieved from https://www.allianzworldwidecare.com/en/support/view/national- healthcare-systems/healthcare-in-philippines/ 3. Chapple, Mike. (2020). The Use of Classification in Data Mining. Retrieved from https://www.lifewire.com/classification-1019653 4. Dey, Monali and Siddharth Swarup Rautaray. (2014). Study and Analysis of Data Mining Algorithms for Healthcare Decision Support System. Retrieved from https://pdfs.semanticscholar.org/d32e/c14e005d9907603d2e46daa1b68a9b63d95b.pdf 5. Estdale, John and Elli Georgiadou. (2018). Applying the ISO/IEC 25010 Quality Models to Software Product. Retrieved from https://www.researchgate.net/publication/326896024_Applying_the_ISOIEC_25010_Quality_Models_to_Software_Product_25th_Eur opean_Conference_EuroSPI_2018_Bilbao_Spain_September_5-7_2018_Proceedings 6. Estinar, Aaron O. et. al. (2018). Pampanga‘s Barangay Health Information System (PBHIS): A Decision Support & Health Information System for Rural Health Unit 1. Retrieved from https://www.dlsu.edu.ph/wp-content/uploads/pdf/conferences/research-congress- proceedings/2018/fnh-12.pdf 7. Garcia, Gabriel et. al. (2015). DiabeSys. Retrieved on from https://www.dlsu.edu.ph/wp-content/uploads/pdf/conferences/research- congress-proceedings/2015/FNH/018FNH_Tangkeko_MS.pdf 8. Hicks, Joy. (2018). Benefits of Integrating an Electronic Health Record System. Retrieved on from https://www.verywellhealth.com/benefits-of-integrating-electronic-health-records-2317142 9. Jones, Mila. (2018). HealthCare: How Technology Impacts The Healthcare Industry. Retrieved from https://healthcareinamerica.us/healthcare-how-technology-impacts-the-healthcare-industry-b2ba6271c4b4 10. Kalyani, P. (2012). Approaches to Partition Medical Data using Clustering Algorithms. Retrieved from https://pdfs.semanticscholar.org/82e1/22314dcbe9170e3f8fe9863737cc6f2237ec.pdf 11. Manca, Donna P. (2015). Do electronic medical records improve quality of care? Yes. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4607324/ 12. Manila Times. (2018). At a glance: The Philippine health care system. Retrieved from https://www.manilatimes.net/at-a-glance-the- philippine-health-care-system/395117/ 13. Ogbuabor and Ugwoke. (2018). Clustering algorithm for a Healthcare dataset Using Silhouette Score Value. Retrieved from https://aircconline.com/ijcsit/V10N2/10218ijcsit03.pdf 14. Peth, Linda. (2017). Why technology integration is essential in home health care. Retrieved from https://ndocsoftware.com/2017/07/technology-integration-essential-home-health-care/ 15. Silitonga, Parasian. (2018). Clustering of Patient Disease Data by Using K-Means Clustering. Retrieved from https://www.researchgate.net/publication/323772077_Clustering_of_Patient_Disease_Data_by_Using_K- Means_Clustering#fullTextFileContent 16. Triotree.com. (2019). Significance of IT in Healthcare. Retrieved from http://triotree.com/blog/significance-of-it-in-healthcare/ 17. Venkatesan, E. and T. Velmurugan. (2015). Role of Classification Algorithms in Medical domain: A Survey. Retrieved from https://www.researchgate.net/publication/280858435_Role_of_Classification_Algorithms_in_Medical_domain_A_Survey Authors: Telesphore Tiendrebeogo, Cheick Yacouba Rachid Coulibaly, Maliki Badolo
Paper Title: Robust Formal Watermarking Model Based on the Hyperbolic Geometry for Image Security Abstract: The digital revolution has led to an increase in the production and exchange of valuable digitized documents across institutions, companies and the general public alike. Ensuring the authenticity, integrity and ownership of these official or high-value documents is essential if they are to be considered useful. Digital watermarking is a possible solution to this challenge as it has already been used for copyright protection, source tracking, and video authentication to name just a few applications of its use. It also enables integrity protection, which is of value for numerous documents types (e.g., official documents, medical images). In this paper, we propose a new watermarking solution that is applicable to image watermarking and is based on hyperbolic geometry. Our new solution builds upon existing work in geometrical watermarking.
Keywords: Watermarking Hyperbolic Geometry, Poincaré Disk Model, Hypercatadioptric Projection, Cryptography, Image Processing
References: 1. B. Macq, P. R. Alface, M. Montanola, Applicability of watermarking for intellectual property rights protection in a 3d printing scenario, in: Proceedings of the 20th International Conference on 3D Web Technology, 2015, pp. 89–95. doi:10.1145/2775292.2775313. 2. Y. Z., L. L., Digital image watermarking algorithms based on dual transform domain and self-recovery, International Journal on Smart Sensing and Intelligence Systems 8 (1) (2015) 199–219. 3. D. X., W. F., W. Y., F. Duan, C. L., W. H., Self-calibration of hybrid central catadioptric and perspective cameras, Computer Vision and Image Understanding 116 (6) (2012) 715–729. 4. T. T., M. D., Virtual and consistent hyperbolic tree: A new structure for distributed database management, in: 3rd International Conference on Networked Systems, Lecture Notes in Computer Science vol. 9466, 2015. 5. M. M., T. K., A cryptographic method for secure watermark detection, in: Information Hiding, Lecture Notes in Computer Science vol. 4473, 2006, pp. 26–41. 6. K. M., K. V., A survey on digital image watermarking and its techniques, International Journal of Signal Processing, Image Processing 103. and Pattern Recognition 8 (5) (2015) 145–150. 7. C. G., S. R., Y. K. R., Classification of watermarking based upon various parameters, International Journal of Computer Applications & Information Technology 1 (2) (2012) 16–19. 617-627 8. Y. U., S. J.P., S. D., S. P.K., Different watermarking techniques & its applications: A review, International Journal of Scientific & Engineering Research 5 (4) (2014) 1288–1294. 9. D. P., K. K., A study on spatial and transform domain watermarking techniques, International Journal of Computer Applications 71 (14) (2013) 38–41. 10. N. K.D., D. D.S., Performance comparison of two hybrid techniques for image steganography in frequency domain, International Journal of Inno- vative Research in Computer and Communication Engineering 3 (6) (2015) 68–74. 11. G. M., H. E.M., M. M., An improved image watermarking method in frequency domain, Journal of Applied Security Research 12 (2) (2017) 260–275. 12. A. Y.B., T. I., D. N., B. M.S., Euclidean distance distortion based robust and blind mesh watermarking, International Journal of Interactive Multi-media and Artificial Intelligence 4 (2) (2016) 46–51. 13. H. O., S. W.M., Y. B. A. B., A. M.A., Public watermarking scheme for 3d laser scanned archeological models, in: IEEE Symposium on Computers and Communications, 2012, pp. 382–389. 14. G.-J. L., K.-Y. Y., An improved double image digital watermarking scheme using the position property, Journal Multimedia Tools and Applications 74 (17) (2015) 7261–7283. 15. S. B., S. L., Efficient descriptor for full and partial shape matching, Journal of Multimedia Tools and Applications 75 (6) (2016) 2989– 3011. 16. K. Wang, G. Lavoué, F. Denis, A. Baskurt, Hierarchical watermarking ofsemiregular meshes based on wavelet transform, IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 3 (4) (2008) 620–634. 17. S. S.S., Blind wavelet based watermarking technique for image authentication, International Journal of Advanced Research in Computer Science 6(1) (2015) 127–131. 18. W. Y.H., M. S.D., Data hiding technique using audio watermarking, International Journal Of Engineering And Computer Science 4 (3) (2015) 11109–11112. 19. A. N., T. H., Pde based scheme for multi-modal medical image watermarking, International Journal BioMedical Engineering 14 (2015) 108–126. 20. B. L., S. H.I., A. M.B., Enhanced watermarking scheme for 3d mesh models, in: 7th International Conference on Information Technology, 2015, pp. 612–619. 21. P. W., X. Hongling, W. L., S. Wenlong, Harris scale invariant corner detection algorithm based on the significant region, International Journal of Signal Processing, Image Processing and Pattern Recognition 9 (3) (2016) 413–420. 22. H.D., ―Foundations of geometry,thesis in mathematics‖, in: The Open Court Publishing Company, Univerty of G¨ottingen, 1950, p. 105. 23. M. P., Premiers principes de la métagéométrie ou géométrie générale, Revue néo-scolastique. 3ème année 1896 (10) (1896) 143–170. 24. T. S., Hyperbolic geometry: history, models, and axioms, U.U.D.M. Project Report (2004). 25. B. Y., Hyperbolic geometry tiling (08 2006). 26. T. T., A. D., M. D., Reliable and scalable distributed hash tables harnessing hyperbolic coordinates, in: IFIP International Conference on New Technologies, Mobility and Security, 2012, pp. 1–6. 27. M. A., Un afficheur générique d‘arbres à l‘aide de la géométrie hyperbolique, Journées francophones des langages applicatifs (JFLA) (2000). 28. B. A. F., M. D., The hyperbolic metric and geometric function theory, in: International Workshop on Quasiconformal Mappings And Their Applications, 2006. 29. S. L. A catadioptric sensor withmultiple viewpoints, In Robotics and Autonomous Systems 51 (2005) 667–674. 30. B. S., N. S. K., A theory of single-viewpoint catadioptric image formation, International Journal of Computer Vision 35 (2) (1999) 1– 22. 31. B. D. A., Geometry (06 2002). 32. M. J., Vision stéréoscopique par ordinateur pour la détection et le suivi de cibles pour une application automobile, Ph.D. thesis, Institut National Polytechnique de Grenoble (2008). 33. T. R., Suivi d‘objet pour la television interactive, Ph.D. thesis, Ecole TELECOM ParisTech (2008). 34. P. P., De l‘importance des points d‘int´erˆet et du maillage 2d, Ph.D. thesis, Institut National Polytechnique de Toulouse (2009). 35. B. J.P., General central projection systems : Modeling, calibration and visual servoing, Ph.D. thesis, University of Coimbra (2003). 36. C. F., D. K. C. C., S. O., étalonnage de caméras catadioptriques hyper-boloïdes, Traitement du signal:Vision omnidirectionnelle 22 (5) (2005). 37. D. W., H. M.E., New directions in cryptography, IEEE Transactions on Information Theory 26 (6) (1976) 644–654. 38. J. D., P. K., The self-synchronizing stream cipher, Springer Lecture Notes in Computer Science 4986 (2008) 210–223. 39. D. R. L., An introduction to chaotic dynamical systems (04 1989). 40. B. I., V. P., G. J.-P., Continuous wavelet transform on the hyperboloid, Applied and Computational Harmonic Analysis 59 (2) (1975) 375–382. 41. S. L., Analyse: Topologie générale et analyse fonctionnelle, in: Hermann, 1970, p. 432. 42. J. Bahi, C. Guyeux, Hash functions using chaotic iterations, Journal of Algorithms and Computational Technology 4 (2) (2010) 167– 181. Authors: Mohammed Salahuddin, R G Nauman Khan, Mohammed Moiz, Mohammed Tosif Ahmed
Paper Title: Seismic Assessment of R.C.C Frame Building using Pushover Analysis Abstract: India is a making country with an arrangement of structure practices and social and money related structure, which needs to build up its own special strategies for seismic danger appraisal. The latest decade has shown our lack in peril decline programs, during the couple of hurting seismic quakes. In view of this quake alone in India there was massive loss of life and property. After this troublesome adversity thought is by and by being given to the appraisal of the adequacy of solidarity in structures to contradict strong ground developments. After Bhuj seismic quake IS-1893 was revised and appropriated in the year 2002, going before this scene it was refreshed in 1984. The code was first conveyed in 1962 as 'Recommendations for Earthquake Resistant Design of Structure'. The central reason behind the loss of life and property was inadequacy of learning of direct of structures during ground developments. The frailty of the structures against seismic development must be fundamentally inspected. The most preferred strategy for seismic evaluation is Inelastic static assessment or Pushover examination in view of its straightforwardness. Inelastic static examination frameworks join Capacity 104. Spectrum Method, Displacement Coefficient Method and the Secant Method. In this examination we are looking over seismic execution of G+17 standard RCC structure. The structure has been surveyed using Pushover Analysis. 628-634
Keywords: Pushover Analysis, Nonlinear Static investigation, Performance point, Capacity bend, Displacement, Drift of stories, seismic zone, Etabs programming
References: 1. FEMA. NEHRP guidelines for the seismic rehabilitation of buildings (FEMA 273).Washington (DC): Building Seismic Safety Council; 1997. 2. FEMA 356 NEHRP Pre standard and commentary for the seismic rehabilitation of buildings. (2000). 3. Chopra AK. Dynamics of structures: theory and applications to earthquake engineering. Englewood Cliffs, NJ. 1995. 4. R.Hasan (2002), ―Push-over analysis for performance-based seismic design‖ Computers and Structures 80 (2002) 2483-2493. 5. Erolkalkan (2007), ―Assessment of current nonlinear static procedures for seismic evaluation of buildings‖ , Engineering Structures 29 (2007) 305-316. 6. Dynamics of structures – Third edition - Anil k Chopra, Pearson Press. Earthquake resistance design of structure – Second Edition – S.K Duggal, Oxford University press , New Delhi Authors: Najmu Nissa, Sanjay Jamwal, Shahid Mohammad Early Detection of Cardiovascular Disease using Machine learning Techniques an Experimental Paper Title: Study Abstract: Human body prioritizes the heart as the second most important organ after the brain. Any disruption in the heart ultimately leads to disruption of the entire body. Being the members of modern era, enormous changes are happening to us on a daily basis that impact our lives in one way or the other. A major disease among top five fatal diseases includes the heart disease which has been consuming lives worldwide. Therefore, 105. the prediction of this disease is of prime importance as it will enable one to take a proper and needful approach at a proper time. Data mining and machine learning are taking out and refining of useful information from a 635-641 massive amount of data. It is a basic and primary process in defining and discovering useful information and hidden patterns from databases. The flexibility and adaptability of optimization algorithms find its use in dealing with complex non -linear problems. Machine Learning techniques find its use in medical sciences in solving real health-related issues by early prediction and treatment of various diseases. In this paper, six machine learning algorithms are used and then compared accordingly based on the evaluation of performance. Among all classifiers, decision tree outperforms over the other algorithms with a testing accuracy of 97.29%.
Keywords: Heart Disease, Machine Learning Models, Python, Spyder.
References: 1. Babu, Sarath, ―Heart disease diagnosing using data mining technique.‖ Electronics Communication and Aerospace Technology (ICECA),2017 International conference of vol.1.IEEE,2017 2. MissChaitrali S. Dangare, Dr.Mrs. SulabhaS.Apte, A data mining approach for prediction of heart disease using neural networks, international journal of computer engineering and technology, 2012. 3. https://www.statista.com/statistics/1108824/cardiovascular-as-cause-of-death-estimate-and-actual-worldwide/ 4. Jayumin Patel, Prof.TejalUpadhyay, Dr.SamirPatel,‖Heart Disease Prediction Using Machine Learning and Data Mining Techniques ‖, IJCSC, vol 7.No 1-sept 2015-Mar 2016. 5. SonamNikahr, A. M.Karandikhar, "Prediction Of Heart Disease Using Machine Learning Algorithms", International Journal Of Advanced Engineering, Management and Science, vol-2, June-2016. 6. SeyedaminPouriyeh, Sara Vahid, Giovanna Sannino,‖A Comprehensive Investigation and Comparison Of Machine Learning Techniques In The Domain Of Heart Disease‖, 22nd IEEE symposium on computers and communication(ISCC 2017):workshop- ICTS4EHealth 2017. 7. YounessKhourdifi, Mohamed Bahaj, ― Heart Disease Prediction and Classification Using Machine Learning Algorithms Optimized By Particle Swarm Optimization and Ant Colony Optimization‖, international journal of intelligent engineering and systems, vol-12, No- 1,2019. 8. Mamta Alex P and Shaicy P Shaji,‖Prediction and diagnosis of heart disease patients using data mining techniques‖, International Conference on Communication and Signal Processing, April 4-6,2019. 9. Anaconda Inc., ―Anaconda Distribution,‖ Anaconda, 2019, [Online]. Available: https://www.anaconda.com/distribution/ 10. Mythili T., Dev Mukherji, Nikita Padalia, and Abhiram Naidu,‖ A Heart Disease Prediction Model using SVM-Decision Trees-Logistic Regression (SDL)‖, International Journal of Computer Applications (0975 – 8887) Volume 68– No.16, April 2013. 11. Larose,D.," Discovering knowledge in data: an introduction to data mining‘‘. New jersey: john wiley&sons, inc,2005. 12. Umair Shafique, Faiz Majeed, Haseeb Qaiser,and Irfan Ul Mustafa,‖Data Mining in Healthcare for Heart Disease‖, internationaljournal of innovation and applied sciences, vol.10 No.4 Mar,2015,pp.1312-1322 13. https://encryptedtbn0.gstatic.com/images?q=tbn%3AANd9GcRb0oOWXHEXhRAdMH_YVV8nP6_HJEIda-gwYQ&usqp=CAU] 14. Umair Shafique, Faiz Majeed, Haseeb Qaiser, and Irfan Ul Mustafa,‖Data Mining in Healthcare for Heart Disease", internationaljornal of innovation and applied sciences, vol.10 No.4 Mar 2015,pp.1312-1322. 15. https://www.analyticsvidhya.com/blog/wp-content/uploads/2014/10/ANN.png 16. GeorgeJonh and Pat Langley. ―Estimating continuousdistributions in Bayesian classifiers‖ in proceedings of the Eleventh Conference On Uncertainty in Artificial Intelligence, Morgan Kaufman, pages 338-345,1995. 17. Umair Shafique, Faiz Majeed, Haseeb Qaiser, and Irfan Ul Mustafa,‖Data Mining in Healthcare for Heart Disease", internationaljornal of innovation and applied sciences, vol.10 No.4 Mar 2015,pp.1312-1322. 18. Sonam Nikhar and A.M. Karandikar,‖Prediction Of Heart Disease Using Machine Learning Algorithms‖, International Journal Of Advanced Engineering, Management and Science, vol-2,issue-6, June-2016. 19. H.Benjamin Fedrick David and S. Antony Belcy, ―Heart Disease Prediction Using Data Mining Techniques‖ 20. https://miro.medium.com/max/1170/1*58f1CZ8M4il0OZYg2oRN4w.png 21. D.K. Srivastava and L. Bhambhu, ―Data classification using support vector machine,‖ J.Theor.Appl.Info.Technol.,2009 22. A. A. Abdillah and Suwarno, ―Diagnosis of diabetes using support vector machines with radial basis function kernels,‖ Int. J. Technol., vol. 7, no. 5, pp. 849–858, 2016, DOI: 10.14716/ijtech.v7i5.1370. Authors: R.Sundaramoorthy, J.Justin Maria Hillary, S.R.Raja Balayanan, K.Kalidas, R.V.Rangarajan
Paper Title: Composite Wear Actions of Glass Fiber Reinforced Titanates Filled Epoxy Resin Abstract: Glass fibre-reinforced polymer composites find numerous applications in today 's aggressive world because of their different benefits such as high wear resistance, strength to weight ratio and low cost. Particle fillers can be further enhanced with the added composite efficiency. Titanates are successfully used as polymer filler to achieve this. A number of these short-glass epoxy composites and the study of their wear behavior are included in current work. They are manufactured and characterized. It also outlines a technique for parametric analysis of the sliding wear behavior, based on Taguchi‘s test-design approach.
Keywords: Polymer composites; Titanates; Epoxy; Wear test; Taguchi.
References: 1. Anisha Christy, Rajesh Purohit, R. S. Rana, (2017) ―Development and Analysis of Epoxy/nano SiO2 Polymer Matrix Composite fabricated by Ultrasonic Vibration assisted Processing‖, Materials Today Proceedings, Volume 4, pp. 2748–275. 2. Enqiu He, Shijie Wang, Yunlong Li, Quan Wang, (2017) ―Enhanced tribological properties of polymer composites by incorporation of 106. nano-SiO2 particles: A molecular dynamics simulation study‖ Computational Materials Science, Volume 134, pp. 93–99. 3. Prashanthakuamr H D, Bhanuprakash N, (2017) ―Friction and wear behaviour of polymer matrix composites – a review‖ International Research Journal of Engineering and Technology (IRJET), Volume 04, pp. 988-991. 642-647 4. Zhongnan Wang, Derek G. Chetwynd, Ken Mao, (2018) ―Friction characteristics of polymers applicable to small-scale devices‖, Tribology International, S0301-679X (17)30550-9. 5. Yuanliang Zhao, XiaowenQi, YuDong, JianMaa, Qinglong Zhang, Laizhou Song, YulinYang, Qingxiang Yang, (2016) ―Mechanical, thermal and tribologicl properties of polyimide/nano- SiO2 composites synthesized using an in-situ polymerization‖, Tribology International, Volume 103, pp. 599–608. 6. T.Madhsudhan, M. Senthilkumar, Athith D.(2016) ―Mechanical characteristics and tribological behaviour study on natural - glass fiber reinforced polymer hybrid composites: a review‖, International Research Journal of Engineering and Technology (IRJET), Volume 03, Issue 04, pp. 2243-2246. 7. Andrey I. Dmitrieva Anton Yu nikonov, Wener Osterle, (2016) ― Multiscale modeling of low friction sliding behavior of a hybrid epoxy matrix nanocomposite‖, Procedia Structural Integrity , Volume 2, pp. 2347-2354. 8. S. Basavarajappa , S. Ellangovan , K.V. Arun (2009) ―Study on dry sliding wear behaviour of Graphite filled glass–epoxy composites‖ Materials and Design, Volume30, pp. 2670–2675. 9. Peerapan Dittanet, Raymond A. Pearson (2012) ―Effect of silica nano particle size on toughening mechanisms of filled epoxy‖, Polymer, Volume 53, pp. 1890-1905. 10. M. Conradi, M. Zorko, (2013) ―Mechanical properties of epoxy composites reinforced with a low volume fraction of nanosilica fillers‖, Materials Chemistry and Physics, Volume 137, pp. 910-915. 11. Sundaramoorthy R., Ravindran R.,(2018), ―Amalgamation of aluminium-Mg2Si alloys in various weight percentage and study of their tensile and wear behaviors‖. Int. J. Additive and Subtractive Materials Manufacturing, vol.2, No.1,pp.45-60. 12. J Justin Maria Hillary , R Ramamoorthi, J Dixon Jim Joseph and C Samson Jerold Samuel,(2020), ―A study on microstructural effect and mechanical behaviour of Al6061–5%SiC–TiB2 particulates reinforced hybrid metal matrix composites, Vol. 54(17) 2327–2337. 13. Sundaramoorthy R, Ravindran R, (2017), ‗Synthesis of Aluminum-Magnesium Silicide(Al-Mg2Si) Alloys and study of their mechanical properties‘, Journal of Material Science and Surface Engineering, vol. 5, no. 2, pp. 533-536. 14. Mainak Sen, Pujan Sarkar, Nipu Modak, Prasanta Sahoo (2015) ―Woven E-glass Fiber Reinforced Epoxy Composite – Preparation and Tribological Characterization‖, International Journal of Materials Chemistry and Physics, Vol. 1, pp. 189-197. 15. Kishore , P. Sampathkumaran , S. Seetharamu , S. Vynatheya , A. Murali , R.K. Kumar (2000),―SEM observations of the effects of velocity and load on the sliding wear characteristics of glass fabric–epoxy composites with different fillers‖ Wear, Volume 237, pp. 20–27. 16. Umanath K, 2011, ‗Friction and Wear Behavior of Al-6061 Alloy (SiCp + Al2O3) Hybrid Composites‘, International Journal of Engineering Science and Technology, vol. 3, pp. 5441-5451. 17. A.P. Harsha, Sanjeev Kumar Jha (2008) ―Erosive wear studies of epoxy-based composites at normal incidence‖ Wear, Volume 265, pp. 1129–1135. 18. B. Suresha, G. Chandramohan, P. Samapthkumaran, S. Seetharamu and S. Vynatheya (2006), ―Friction and Wear Characteristics of Carbon-epoxy and Glass-epoxy Woven Roving Fiber Composites‖. Journal of Reinforced Plastics and Composite, Volume 25, pp. 771-782. 19. N.Mohan, C.R.Mahesha, B.M.Rajaprakash (2013) ―Erosive wear behaviour of WC filled glass epoxy composites‖ Procedia Engineering, Volume 68, pp. 694 – 702. 20. Sundaramoorthy R., Ravindran R.,(2019), ―Tool wear optimization in CNC milling operation of Al-Mg2Si alloys by Taguchi Method‖. SN Applied Sciences, vol.1, No.9, pp.1093. 21. S.S. Mahapatra, and A. Patnaik, (2006), Parametric Analysis and Optimization of Drilling of Metal Matrix Composites based on the Taguchi Method, The International Journal for Manufacturing Science and Technology, vol. 8(1), pp. 5-12. 22. B. D, Agarwal L. J. Broutman (1990), Analysis and performance of fiber composites: Second Edition. John Wiley and Sons, Inc. 23. S.P. Glen, (1993). Taguchi methods: A hands on approach. Addison Wesley, New York. Authors: Romy Jun A. Sunico, Elwin S. Argana, Mark Anthony T. Golo, Maribel A. Aniňon
Paper Title: Vulca Loc: A Mobile Application for Finding Vulcanizing Shops embedding GPS Abstract: This paper discusses the ideas and process of developing a mobile locator application for Vulcanizing Shops in Siargao Island with Global Positioning System (GPS) and Google Map Application Programming Interface (API). This mobile application is an innovation tool to show the location, availability and services of the vehicle services shops available in the island to ease the hassle of the tourists with vehicle errors. It also provides shortest possible route method that includes relevant information about the services of the shops. The study adopts the Rapid Application Development model and used ISO 9126 to evaluate the application in terms of usability (4.37), functionality (4.13) and Maintainability (4.20). Therefore, the application is certain to provide a significant support to the local and foreign tourists; therefore, providing an accurate and hassle time- free locating a vulcanizing shops 107. Keywords: API, GPS, RAD Model, Siargao, Vehicle Services Shop 648-652
References: 1. CNN Philippines, Best Asia Islands 2018, Available: http://cnnphilippines.com/lifestyle/2018/10/11/siargao-boracay-palawan-best- asia-islands-2018.html 2. Beermann, Mattias, et al. "Locating position within enclosure." U.S. Patent No. 9,612,121. 4 Apr. 2017. 3. Leng, Limyen. ATM Locator Mobile Application. Diss. University Malaysia Pahang, 2012. 4. Ramos, Anna Liza A., et al. "E-Vision: A Campus Locator Map Mobile Application using A* Algorithm." International Journal of Computer Science and Software Engineering 7.1 (2018): 6-11. 5. Basal, Ahmet, et al. "Effectiveness of mobile applications in vocabulary teaching." Contemporary Educational Technology 7.1 (2016): 47-59. 6. Shuib, Liyana, Shahaboddin Shamshirband, and Mohammad Hafiz Ismail. "A review of mobile pervasive learning: Applications and issues." Computers in Human Behavior 46 (2015): 239-244. 7. David G. Go Live! Mobile for the Nation’s Largest Telephone Locator Platform. 2017 Authors: Anjali Dadhich, Blessy Thankachan
Paper Title: Design of NLP technique fore-customer review Abstract: With the passage of time and the growth of ecommercea new web world needs to be built their users can share their ideas and opinions differently domains.There are thousands of websites that sell these various products. The quick growth in the number of reviews and their availability and the arrival of rich reviews for rich products for sale online, the right choice for many products has been difficult for users. Consumers will soon be able to verify the authenticity and quality of the products. What better way is there to ask people who have already bought the product? That‘s where customer reviews come from. What‘s worse is the popular products with thousands of updates — we don‘t have the time or the patience to read all of them thousands. 108. Therefore, our app simplifies this task by analysing and summarizing all the reviews that will help the user determine what other consumers have experienced in purchasing this product. This function focuses on mining 653-655 updates from websites like Amazon, allowing the user to write freely to view. Automatically removes updates from websites. It also uses algorithms such as the Naïve Bayes classifier, Logistic Regression and SentiWordNet algorithm to classify reviews as good and bad reviews. Finally, we used quality metric parameters to measure the performance of each algo.
Keywords: Sentiment Analysis, Naïve Bayes classifier, Logistic Regression, Senti Word Net, Opinion Mining.
References: 1. G.Angulakshmi, Dr.R.ManickaChezian, ―An Analysis on Opinion Mining: Techniques and Tools.‖ International Journal of Advanced Research in Computer and Communication Engineering, Vol. 3, Issue 7, July 2014. 2. Callen Rain, ―Sentiment Analysis in Amazon Reviews Using Probabilistic Machine Learning‖, Swarthmore College Compter Society, November 2013 3. Weishu Hu, Zhiguo Gong, JingzhiGuo, ―Mining Product Features from Online Reviews‖IEEE International Conference on E-Business Engineering, 2010. 4. PoojaKherwa, ArjitSachdeva, Dhruv Mahajan, ―An approach towards comprehensive sentimental data analysis and opinion mining‖. IEEE International Advance Computing Conference (IACC), 2014. 5. Toqir Ahmad Rana, Yu-N Cheah, ―Hybrid Rule-Based Approach for Aspect Extraction and Categorization from Customer Reviews‖, 9th International Conference on IT in Asia (CITA'15), At Kuching, Sarawak, Malaysia, August 2015 6. Prashast Kumar, ArjitSachdeva, ―An approach towards feature specific opinion mining and sentimental analysis across e-commerce websites‖. 5th International Conference- Confluence the Next Generation Information Technology Summit (Confluence), 2014. a. Hui Song, Jianfeng Chu, Yun Hu, Xiaoqiang Liu, ―Semantic Analysis and Implicit Target Extraction of Comments from E-commerce Websites‖. Fourth World Congress on Software Engineering, 2013. b. R. Piryani, D. Madhavi, V.K. Singh, ―Analytical mapping of opinion mining and sentiment analysis research during 2000-2015‖, Information Processing and Management, ScienceDirect, Elsevier 2016 c. Vidisha M. Pradhan, Jay Vala, Prem Balani, ―A Survey on Sentiment AnalysisAlgorithms for Opinion Mining‖, International Journal of Computer Applications, Volume 133, No.9, January 2016 7. G. Sneka, CT. Vidhya, ―Algorithms for Opinion Mining and Sentiment Analysis: An Overview‖, International Journal of Advanced Research in Computer Scinece and Software Engineering, Volume 6, Issue 2, February 2016 8. Neetu, ―Hierarchical classification of web content using Naïve Bayes approach‖,International Jouranal on Computer Science and Engineering (IJCSE), Vol. 5, No. 05, May 2013 a. Daniel Jurafsky, James H Martin, Chapter on ‖Logistic Regression – Speech and Language Processing‖, Standford University, November 2016 b. Andrea Esuli, Fabrizio Sebastiani, ―SentiWordNet: A Publicly Available Lexial Resource for Opinion Mining‖, 5th Conference on Lanuage Resources and Evaluation (LREC 2006), Genova, IT, 2006 c. Stefano Baccianella, Andrea Esuli, Fabrizio Sebastiani, ‖SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining‖ Proceedings of the 7th Conference on Language Resources and Evaluation (LREC 2010), Valletta, MT, 2010 Authors: Noor Faisal Abas, Omosebi. Taiwo.O
Paper Title: Utilization of Pet Wastes Aggregate in Building Construction - A Review Abstract: The rapid increase of plastics waste produced worldwide today poses a danger to human health because of the pollution caused by the unsafe disposal and non-biodegradability of this waste combined with toxic gas emissions during incineration. Globally, PET (polyethylene terephalate) is commonly used for bottling water and other plastic containers. Recycling the waste would be an additional benefit. This study focuses some researchers on the forms, methods of recycling and various literature applications of PET wastes. Recycled PET can of course be used when combined with the sand aggregate to manufacture of various construction materials, such as tiles, bricks, paving stones etc. This research focuses on its application as it attracts substantial building materials such as the manufacture of various PET waste tiles and their unique mechanical , physical and chemical properties; There are some important studies discussed in relation to PET waste, recycling methods , and results from the study. Even various applications are described here. Its usefulness is further defined as roofing Composite concrete, floor tiling and other applications.
Keywords: polyethylene terephthalate; waste; recycling; aggregate; tile; etc.
References: 1. Suganthy, P., Chandrasekar, D., and Kumar, S.P.K. ―Utilization of pulverized plastic in cement concrete as fine aggregate" in International Journal of Research in Engineering and Technology, (2013) 2(6), pp. 1015{1018}. 2. Foti, D. ―Use of recycled waste pet bottles fibers for the reinforcement of concrete" in Composite Structures, . (2013) 96, pp. 396{404}. 3. Ruiz-Herrero, J.L., Nieto, D.V., Lopez-Gil, A., Arranz, A., Fernandez, A., Lorenzana, A., and RodrguezPerez, M.A. ―Mechanical and 109. thermal performance of concrete and mortar cellular materials containing plastic waste" in Construction and Building Materials, (2016) 104, pp. 298{310}. 4. Sadiq, M.M. and Khattak, M.R. ―Literature review on differentplasticwastematerialsuseinconcrete" in Journal of Emerging 656-663 Technologies and Innovative Research (JETIR), (2015) 2(6), pp. 1800{1803}. 5. Malak, K.R. ―Use of waste plastic in concrete mixture as aggregate replacement" in International Journal of Engineering, Education and Technology (ARDIJEET), (2015) 3(2), pp. 1{7}. 6. Patil, P.S., Mali, J.R., Tapkire, G.V., and Kumavat, H.R. (2014) ―Innovative techniques of waste plastic used in concrete mixture" in International Journal of Research in Engineering and Technology, (2014 3(9), pp.29{31}. 7. G. O. Bamigboye, B.U. Ngene, D. Ademola. J. k. Jolayemi ―Experimental Study on the Use of Waste Polyethylene Terephthalate (PET) and River Sand in Roof Tile Production‖ in International Conference on Engineering for Sustainable World, Journal of Physics: Conference Series (2019) 1378 (042105). 8. T.M. Coelho, R. Castro 1, J.A. Gobbo Jr ―PET containers in Brazil: Opportunities and challenges of a logistics model for post- consumer waste recycling‖ in Resources, Conservation and Recycling (2011) 55, 291–299. 9. Ghernouti, Y., Rabehi, B., Bouziani, T., Ghezraoui, H., and Makhlou, A. ―Fresh and hardened properties of self-compacting concrete containing plastic bag waste bers (WFSCC)" in Construction and Building Materials, (2015) 82, pp. 89{100}. 10. Virginija JANKAUSKAITĖ, Gintaras MACIJAUSKAS, Ramūnas LYGAITIS―Polyethylene Terephthalate Waste Recycling and Application Possibilities: a Review in ISSN (2008) 1392–1320 MATERIALS SCIENCE (MEDŽIAGOTYRA). Vol. 14, No. 2. 11. 11. Nikles, D. E., Farahat, M. S.―New Motivation for the Depolymerization Products Derived from Poly(ethylene Terephthalate) (PET) Waste‖: a Review Macromolecular Materials and Engineering (2005) 290: pp. 13 – 30. 12. FindLaw ―Points to Consider for the Use of Recycled Plastics in Food Packaging: Chemistry Considerations‖: U.S. Food and Drug Administration, December 1992. Available from http://corporate.findlaw.com/(date of access May 2008). 13. US Patent 5876644. Food Quality Polyester Recycling, 1999. 14. Fisher, M. M.―Plastics Recycling. In: Plastics and Environment. Ed. by A. L. Andrady, John Wiley & Sons, 2003: pp. 563 – 627. 15. Gupta, V. B., Bashir, Z. ―PET Fibres Films, and Bottles‖ Handbook of Thermoplastic Polyesters. Ed. By Fakirov S. Vol. 1, Weinheim, Germany: Wiley-VCH,(2002) pp. 317 – 388. 16. Oldenburg, K. U. ―Changing the way, we think: cleaner production is simply being more efficient‖. Our planet. (1993) 5(3), 8-9. 17. Zhao, R., Torley, P., and Halley, P.J. ―Emerging biodegradable materials: starch-and protein-based bio Nano composites" in Journal of Materials Science, (2008) 43(9), pp. 3058{3071}. 18. 18 .http:/www.plastiseurope.org. 19. 19. Ruiz-Herrero, J.L., Nieto, D.V., Lopez-Gil, A., Arranz, A., Fernandez, A., Lorenzana, A., and RodrguezPerez, M.A.―Mechanical and thermal performance of concrete and mortar cellular materials containing plastic waste" in Construction and Building Materials, (2016) 104, pp. 298{310}. 20. 20. Usman, M., Javaid, A., and Panchal, S. ―Feasibility of waste polythene bags in concrete" in International Journal of Engineering Trends and Technology (IJETT), (2008) 23(6), pp. 317{319}. 21. 21. Marzouk OY, Dheilly RM, Queneudec M. ―Valorization of post-consumer waste plastic in cementations concrete composites‖ in Waste Manage; (2007), 27(2):310–8. [15]. 22. 22. Reis JML, Chianelli-Junior R, Cardoso JL, Marinho FJV. ―Effect of recycled PET in the fracture mechanics of polymer mortar‖. In Constr Build Mater; (2011) 25(6):2799–804. [16]. 23. 23. Afshoon, I.,Sharif, Y., ―Ground Copper Slag as a supplementary Cementry material and its influence on the fresh properties of selt-consolidating concrete IESJ.part A Civ. Struct. Eng. (2014) 7(4),229-242. 24. 24. Mohammed Jalaluddin J ―Use of plastic waste in civil constructions and innovative decorative material (eco-friendly)‖ in MOJ Civil Eng. 2017;3(5):359‒368. DOI: 10.15406/mojce.2017.03.00082. 25. 25. EPA, ―Report on Plastics‖ (2003), USA. 26. 26. T.M. Coelho, R. Castro 1, J.A. Gobbo Jr ―PET containers in Brazil: Opportunities and challenges of a logistics model for post- consumer waste recycling‖ in Resources Conservation and Recycling (2011) 55; 291–299. 27. 27. Foolmaun RK, Ramjeawon T. ―Life cycle assessment (LCA) of PET bottles and comparative LCA of three disposal options in Mauritius‖ in Journal Environment and Waste Management; (2008) 2(1/2):125–38. 28. 28. Diptikar Behera, Yirgalem Damtew, Aman Mola ―EXPERIMENTAL INVESTIGATION ON RECYCLING OF PLASTIC WASTES AND BROKEN GLASS IN TO CONSTRUCTION MATERIAL‖ in IJCRT | Volume 6, Issue 1 January 2018 | ISSN: 2320- 2882. 29. 29. Hannawi K, Kamali-Bernard S, Prince W. ―Physical and mechanical properties of mortars containing PET and PC waste aggregates‖ in Waste Manage; 2019; 30(11):2312–20. [17]. 30. 30. Amano, M. ―PET bottle system in Sweden and Japan‖ an integrated analysis from a life-cycle perspective (2004), Lund, Sweden. 31. 31. Albano C, Camacho N, Hernandez M, Matheus A, Gutiérrez A. ―Influence of content and particle size of waste pet bottles on concrete behavior at different w/c ratios‖ in Waste Manage; 2009: 29:2707–16. 32. 32. E. Rahmani , M. Dehestani , M.H.A. Beygi , H. Allahyari , I.M. Nikbin) ―On the mechanical properties of concrete containing waste PET particles‖ in Construction and Building Materials (2013) 47: 1302–1308. 33. 33. Ayalon O, Avnimelech Y, Shechter M. ―Application of a comparative multidimensional LCA in solid waste management policy: the case of soft drink containers‖ in Journal of Environmental Science and Policy;(2000) 3(2–3):135–44 34. 34. Boustead I. ―Eco-profiles of the European plastics industry. Report 8: polyethylene terephthalate (PET)‖. Brussels: Association of Plastic Manufacturers in Europe Technical and environmental Centre (1995). 35. 35. National association for PET container resources ―Report on post-consumer PET container recycling activity‖ 2000. 36. 36. American Society for Testing and Materials (ASTM C128). ―Standard Test Method for Density, Relative Density (Specific Gravity), and Absorption of Fine Aggregate‖ in West Conshohocken, (2001). PA, USA. 37. 37. British Standard (BS EN ISO 604). Plastics-Determination of compressive properties. (1999), British Standard, United Kingdom. 38. 38. American Society for Testing and Materials (ASTM C29/C29M). Standard Test Method for Bulk Density (―Unit Weight‖) and Voids in Aggregate. West Conshohocken, (2009) PA, USA. 39. 39. British Standard (BS EN ISO 62). ―Plastics-determination of water absorption‖ (1999) in British Standard, United Kingdom. 40. 40. Dinesh.S, Dinesh.A, Kirubakaran.K (2016) ―UTILISATION OF WASTE PLASTIC IN MANUFACTURING OF BRICKS AND PAVER BLOCKS‖ International Journal of Applied Engineering Research, ISSN 0973-4562 Vol. 11 No.3 © Research India Publications; http/www.ripublication.com/ijaer.htm 41. 41. Mendes MR, Amaraki T, Hanaki K. ―Comparison of the environmental impact of incineration and landfilling in Sao Paulo city as determined by LCA. Journal Resources, Conservation and Recycling; (2004) 41(4):47–63. 42. 42. V. Mohan, S. Gayathri―Effective Utilization of Plastic Wastes in Tile Manufacturing: A step Towards Sustainability‖ in International Journal of Science Research and Engineering Development—Volume 2issue 3, May-June 2019. 43. 43. Pagar S. R.,Panchamiya P. B., Bagul. K. P., Kale A. B.,―Effect of Plastic Waste on Tile By Using Thermosetting Method‖ in 6th International Conference on Recent Trends in Engineering & Technology (ICRTET-2018). 44. 44. Ridham Dhawan, Brij Mohan Singh Bisht, Rajeev Kumar, Saroj Kumar, S.K.Dhawan,―Recycling of Plastic Waste into Tiles with reduced Flammability and Improved Tensile Strength‖ in Process Safety and Environmental Protection 124 (2019) 299-307. 45. 45. R. Saxena, T. Gupta, R.K. Shama, S. Chaudhary, and A. Jain, ―Assessment of mechanical and durability properties of concrete containing PET waste‖ in Scientra Iranica .2020; 27 (1), 1-9. 46. 46. G. Siva Kumar Reddt, G. Javid Hussain, A. Muni Teja, P. Madhan Kumar, B. Hemanth Kumar, (2019)―Effect of Plastic Waste on Tile by Using Thermosetting Method‖ in International Journal of Resaerch in Enginering, Science and Management Volume-2,Issue- 11, November-2019. 47. 47. Prof. T.S. VandaLI,Prof. M.R. Ingalagi, Mr Raghavendr Paste, Mr Akansh Patil, Mr. Prajwal G V.‖Design and Fabrication of Composite Tile Maker Machine by Recycling of Plastics‖ in a Journal of Composition Theory Volume XII Issue VI JUNE 2019. 48. 48. P. O. Awoyera, A. Adesina,―Plastic Waste to c Construction Products: Status, Limitations and Future Perspective‖ in case studies in construction materials 2020; 12 e00330. 49. 49. Agunwamba, J.Waste: engineering and management tools, 2001;Immaculate publications, enugu 50. 50. Williams PT. ―Waste treatment and disposal‖ in Chisterter: (1998) Wiley. 51. 51. Pereira de Oliveira LA, Castro-Gomes JP. ―Physical and mechanical behavior of recycled PET fiber reinforced mortar‖ in Constr Build Mater;2011; 25(4):1712–7. 52. 52. Frigione M. ‖Recycling of PET bottles as fine aggregate in concrete. Waste Manag;30(6):1101–6. [11] (2010). 53. 53. Choi YW, Moon DJ, Kim YJ, Lachemi M.―Characteristics of mortar and concrete containing fine aggregate manufactured from recycled waste polyethylene terephthalate bottles‖ in Constr Build Mater; (2009)23(8):2829–35. [12] 54. 54. Choi YW, Moon DJ, Seung CJ, Cho SK. ‖Effects of waste PET bottles aggregate on the properties of concrete‖ in Cem Concr Res 2005;35(4):776–81. [13] 55. 55. Akçaozoglu S, Atis CD, Akçaozoglu K.―An investigation on the use of shredded waste PET bottles as aggregate in lightweight concrete‖ in Waste Manage;2010; 30(2):285–90. [14] 56. 56. Denison R. A. ―Environmental lifecycle comparisons of recycling, landfilling, and incineration‖ a review of recent studies. Annual Review of Energy and the Environment;1996; 21:191–237. 57. 57. Grant, T, James, KL, Lundie, S, Sonneveld, K. ―Life cycle assessment for paper and packaging‖ in waste management scenarios in Victoria; (2001) p. 2 58. 58. Grimberg E, Blauth P. Coleta Seletiva reciclando materiais, reciclando valores. São Paulo: Polis; 1998. 59. 59. Manzini E, Vezzoli C. O (2005)―desenvolvimento de produtos sustentáveis‖. São Paulo: Edusp; 2005 60. 60. Molgaard C. Environmental impacts by disposal of plastic from municipal solid waste. Journal Resources, Conservation and Recycling 1995;15(1):51–63. 61. 61. Person, L, et al. Life cycle assessment of packaging systems for beer and soft drinks. Technical report 6: disposable PET bottles. Ministry of Environment and Energy, Denmark, Danish Environmental Protection Agency, Miljprojekt no. 405; 1998. p. 198. 62. 62. Perugini F, Mastellone ML, Umberto A. Environmental aspects of mechanical recycling of PE and PET: a life cycle assessment study. Progress in Rubber, Plastics and Recycling Technology 2004;20(1):69–84 63. 63. Molgaard C. Environmental impacts by disposal of plastic from municipal solid waste. Journal Resources, Conservation and Recycling 1995;15(1):51–63. 64. 64. Perugini F, Mastellone ML, Umberto A. Environmental aspects of mechanical recycling of PE and PET: a life cycle assessment study. Progress in Rubber, Plastics and Recycling Technology 2004;20(1):69–84 65. 65. Diptikar Behera, Yirgalem Damtew, Aman Mola ―EXPERIMENTAL INVESTIGATION ON RECYCLING OF PLASTIC WASTES AND BROKEN GLASS IN TO CONSTRUCTION MATERIAL‖ in © 2018 IJCRT | Volume 6, Issue 1 January 2018 | ISSN: 2320-2882. 66. 66. Prashant Kumar, Ranjit Kumar Yadav , Ravi Kumar , Shivangi Maury, Subodh Chand, Sudhir Yadav, Prof. (Dr.) V.K. Saini ―RECYLING OF WASTE PLASTIC USING EXTRUSION PROCESS‖ in IJARIIE-ISSN(O)-2395-4396 67. 67. Scheirs, J., 1998. Polymer Recycling: Science, Technology, and Applications. Wiley. 68. 68.. Hopewell, J., Dvorak, R., Kosior, E.,. Plastics recycling: challenges and opportunities. Philos. Trans. R. Soc. B-Biol. Sci. 2009; 364, pp 2115–2126. 69. 69. George, N., Kurian, T., Recent developments in the chemical recycling of postconsumer poly(ethylene terephthalate) waste. Ind. Eng. Chem. Res.2014, 53, 14185–14198. 70. 70. Awaja, F., Pavel, D., 2005. Recycling of PET. Eur. Polymer J. 41, 1453–1477. 71. 71. Aguado, J., S, D.P. ―Feedstock Recycling of Plastic Waste., 1999. 72. 72. Al-Salem, S., Lettieri, P., Baeyens, J.,. Recycling and recovery routes of plastic solid waste (PSW): a review. Waste Manage. 2009a, 29, 2625–2643. 73. 73. Al-Salem, S., Lettieri, P., Baeyens, J.. Thermal treatment of different grades and types of Polyethylene (PE) wasted articles. In: Proceedings of 8th World Congress of Chemical Engineering Montreal (Quebec), 2009b ,pp. 1–4. 74. 74. Al-Salem, S., Lettieri, P., Baeyens, J., 2010. The valorization of plastic solid waste (PSW) by primary to quaternary routes: from re-use to energy and chemicals. Prog. Energy Combust. Sci. 36, 103–129. 75. 75. Scheirs, J., Kaminsky, W., 2006. Feedstock Recycling and Pyrolysis into Diesel and Other Fuels. 76. 76. Scheirs, J., Kaminsky, W., 2006b. Feedstock Recycling and Pyrolysis of Waste Plastics. John Wiley & Sons. 77. 77. R. Miandad, M.A. Barakat, Asad S. Aburiazaiza, M. Rehan b , I.M.I. Ismail b , A.S. Nizami ―Effect of plastic waste types on pyrolysis liquid oil‖ in International Biodeterioration & Biodegradation 119 (2017) 239-252. 78. 78. American Society for Testing and Materials (ASTM C29/C29M). Standard Test Method for Bulk Density (―Unit Weight‖) and Voids in Aggregate. West Conshohocken, 1997; PA, USA ] 79. 79. Alyamac, K. E, Tugrul, E ―A durable eco friend and aesthetic concrete work; marble concrete, in 11th International Congress on Advance in Civil Engineering (ACE 2014), Vol. 50 pp 21-25. 80. 80. Afshoon, I.,Sharif, Y., ―Ground Copper Slag as a supplementary Cementry material and its influence on the fresh properties of selt- consolidating concrete IESJ.part A Civ. Struct. Eng. 2014; 7(4),229-242. 81. 81. Al-jabri, K.S.,Al-Saidu, A.H. Taha, R. ―Effect of Copper Slag as a fine aggregate on the properties of cement mortars and concrete. Const. Build. Mater. 2011, 25(2), 933-938. 82. 82. Ghafoori, N, Bucholc, J., ―Investigation of lignite based botton ash for structural concrete, J. Mater. Civ. Eng. .1996, 8(3), 128-137. 83. 83. Ghosi, A. Ghosi, A., Neogi, S., ―Reuse of fly ash and bottom ash in mortars with improved thermal conductivity performance for buildings. Helijon 2018), 4(11), 00934. 84. 84. Gorai, B., Jana, R.K, (2003 ―Characteristic and utilization of copper slag- a review – Resourc. Conserv. Recycl. 2003, 39(4), 299- 932. 85. 85. Guney, Y., Sari. Y. D., Yaldin, M., Tuncan, A. Donmez, S., ―Re-usage of waste foundry sand in high-strengh concrete waste manag. 2010, 30(8-9), 1705-1713. 86. 86. Kurama, H. Kaya. M. ―Usage of coal combustion bottom ash in concrete mixture. Const. Build. Mater. 2008, 22(9: 1650-1663. 87. 87. Kim. H. K., Ha, K.A., Lee, HG.K., ―Internal Curing for high- strength mortar Const. Build. Mater.2016, 126, 1-8. 88. 88. Khanduri, A. Siddique, R. G―Properties of mortar incorporating waste foundry sand. Maters Dissertation, 2010, pp. 1-63. 89. 89. Khyaliya, R.K., Kabeer, K.S.A, Vyas. A.K., ―Evaluation of strength and durability of lean mortar mixes containing marble waste. Constr. Build. Mater.2017, 147. 598-607. 90. 90. Eren. O., Marar. K.―Effects of limestone crusher dust and steel fibres on concrete. Constr. Build. Mater. 2009, 23(2), 981-988. 91. 91. Madheswaran, C.K. Ambily, P.S., Daratreya, J.K. Rajamane, N.P., ―Studies on use of copper slag as replacement material for river sand in building constructions. J. Inst. Eng.: Series A 2014, 95(3), 169-177. 92. 92. Mailar. G., Sujay Raghavendra, N. Hiremath, D., Sreedhara, B.M., Manu, D.S., ―Sustainable Utilization of discarded foundry sand and crushed brick masonry aggregate in the production of lightweight concrete Eng. Struct. Technol.2017, 9(1), 52-61. 93. 93. Mandal. A.K. Paramkusam, B.R. Sinha, O. P.―Fluidized bed combustion bottom ash: a better and alternative geo-material resource for construction waste manag. Res. 2018, 36(4), 351-360. 94. 94. Monosi. S., Sani, D., Tittarelli, F., ―Used foundary sand in cement mortars and concrete production. Open waste Manag. J. 2010, 3(1). 95. 95. Moon. H.Y., Choi. Y.W., Song. Y.K. Jeon. J. K.(2005) ―Fundamental properties of mortar and concrete using waste foundry sand . J. Korea concr. Inst. 17(1), 141-147. 96. 96. Mu, O., Du, H., Zhoux, He. K., Lin, Z. Yan, F., Guo, R ―Performance of copper slag contained mortars after exposure to elevated temps. Constr. Build. Mater. 2018, 172, 378-386. 97. 97. Mubiayi. M. P., ―Characterisation of sand stones: mineralogy and physical properties in: 2014, proceedings of the world congress on Engineering. 98. 98. Naik, T.R. Singh S. S., Ramme. B. W ―Performance and leading assessment of foldable slurry: J. Environ. Eng.2001, 127(4), 359- 368 99. 99. Oruji, S. Bake.,N. A. Gudru, R. K. Nalluri, L. Gunay-din- Sen. O., Khard, K. Ingran, E. Mitigation of ASR expansion in concrete using ultra-fine coal bottom ash, Constr. Build. Mater. 2019, 203. 814-824. 100. 100. OZ, D. Koca. S. Koca. H. ―Recycling of coal combustion waste, Watse Mabag. Res. 2009, 27(3), 267-273. 101. 101. Prabhu. G. G. Hyun. J. H. Kim. Y. Y. ―Effects of foundary sand as a fine aggregate in concrete production. Constr. Build. Mater, 2014, 70, 514-521 102. 102. Ratielzonooz, M., Mirza, J., Sahim, M. R. Husin, M.W., Khankhje, E. ―Investigation of coal bottom ash and fly ash in concrete as replacement for sand and cement. Constr. Build. Mater 2016, 116. 15-24. 103. 103. Rajaasekar, A. Amnachalam. K. Kottaisany. M. ―Assessment of strength and durability characteristics of the fine aggregate in mansonry mortar. Arabian. J Sci. Eng. 2019, 39 (2), 737-745. 104. 104. Ramzi, N. I.R., Shahidan, S., Maarof. M. Z., Ali. N ―Physical and chemical properties of coal bottom ash (CBA) from Tanjuma Bin Power Plant. In IOD Conference series: Materials Science and Engineering Vol. 160. IOP Publishing p. 2016, 12056.1. 105. 105. Ramodoss . P. Sundrarajar, T., ―Utilization of Ignite based bottom ash as partial replacement of fine aggregate in mansory mortar, Arabian J. Sci. Eng. 2014, 39 (2), 737-745. 106. 106. Siddique, R. ―Compressive strength, water absorption, sorptivity, abrasion resiustance and permeability of self-compacting concrete containing coal bottom ash, Constr. Build. Mater. 2013, 47, 1444-1450 107. 107. Singh, G., SI=iddique, R ―Effect of waste foundary sand (wfs) as partial replacement of sand on the strength, ultrasonic pulse velocity and permeability of concrete. Constr. Build. Mater. 2012, 26 (1), 416-422. 108. 108. Zain, M. F.M., Islam, M.N. Raduri. S.S. Yap. S. G. ―Cement- based solidification for the safe disposal of blasted copper slag, cement Concr. Compos. 2004, 26(7), 845-851. 109. 109. Yoon, J. Y., Lee J. Y. Kim. J. H. ―Use of raw- state bottom ash for aggregates in construction materials. J. Mater. Cycle waste Manag.2019, 1-12. 110. 110. Jarusiripot. C., ―Removal of reactive dye by absorption over chemical pre-treatment coal based bottom ash . Procedia Chem. 2014, 9. 121-130. 111. 111. Cevik. S. Mutuk. T, Oktay. B. M. Demirbas. A.k. Mechanical and Micro structural characterization of cement mortars prepared by waste foundry sand (wfs). J. Aust. Ceram. Soc. 2017, 53(2), 829-837. 112. Mohammed Jalaluddin J ―Use of plastic waste in civil constructions and innovative decorative material (eco-friendly)‖ in MOJ Civil Eng. 2017;3(5):359‒368. DOI: 10.15406/mojce.2017.03.00082. Authors: M Laxmidevi Ramanaiah Multi-Objective Grey Wolf Optimization for Optimal Allocation of Distributed Generators in Paper Title: Distribution Networks Abstract: The power loss in the radial distribution network is appreciable as compared to transmission network. To reduce the power loss in distribution network which is radial in nature, the solution methodology adopted in this paper is optimal placement of distributed generators (DG). The optimization incorporated is Multi-objective Grey Wolf Optimization (MOGWO). The optimization is accomplished for three different cases. In each case two objective functions are simultaneously optimized to obtain non-dominated solutions using Multi-objective Grey Wolf Optimization. Case (1): To minimize the real power loss and maximize the savings obtained due to DG installation. Case (2): To minimize real power loss and maximum voltage deviation in the network. Case (3): To minimize real power loss and rating of DG installed. MOGWO method maintains an archive which contains pareto-optimal solutions. The archive mimics the behaviour of grey wolves. MOGWO method is verified on radial distribution networks. The effectiveness of the optimization method is proven by comparing the results with other optimization methods available in the literature.
Keywords: Distributed Generators, Multi-objective Grey Wolf Optimization, Real Power Loss, Savings, Voltage deviation.
References: 1. W. L. Theo, J. S. Lim, W. S. Ho, H. Hashim, and C. T. Lee, ―Review of distributed generation (DG) system planning and optimisation techniques: Comparison of numerical and mathematical modelling methods,‖ Renewable and Sustainable Energy Reviews, vol. 67, pp. 531–573, 2017. A. M. El-Zonkoly, ―Optimal placement of multi-distributed generation units including different load models using particle swarm optimization,‖ Swarm and Evolutionary Computation, vol. 1, no. 1, pp. 50–59, 2011. 2. F. S. Abu-Mouti and M. E. El-Hawary, ―Optimal distributed generation allocation and sizing in distribution systems via artificial bee 110. colony algorithm,‖ IEEE Transactions on Power Delivery, vol. 26, no. 4, pp. 2090–2101, 2011. 3. R. Varikuti and D. M. D. Reddy, ―Optimal Placement of DG Units Using Fuzzy and Real Coded Genetic Algorithm,‖ Journal of Theoretical and Applied Information Technology, pp. 145–151, 2009. 4. D. Q. Hung and N. Mithulananthan, ―Multiple distributed generator placement in primary distribution networks for loss reduction,‖ 664-670 IEEE Transactions on Industrial Electronics, vol. 60, no. 4, pp. 1700–1708, 2013. 5. B. Mohanty and S. Tripathy, ―A teaching learning based optimization technique for optimal location and size of DG in distribution network,‖ Journal of Electrical Systems and Information Technology, vol. 3, no. 1, pp. 33–44, 2016. 6. K. Nekooei, M. M. Farsangi, H. Nezamabadi-Pour, and K. Y. Lee, ―An improved multi-objective harmony search for optimal placement of DGs in distribution systems,‖ IEEE Transactions on Smart Grid, vol. 4, no. 1, pp. 557–567, 2013. 7. P. S. Georgilakis and N. D. Hatziargyriou, ―A review of power distribution planning in the modern power systems era: Models, methods and future research,‖ Electric Power Systems Research, vol. 121, pp. 89–100, 2015. 8. D. Rama Prabha and T. Jayabarathi, ―Optimal placement and sizing of multiple distributed generating units in distribution networks by invasive weed optimization algorithm,‖ Ain Shams Engineering Journal, vol. 7, no. 2, pp. 683–694, 2016. 9. S.Kansal, V. Kumar, and B. Tyagi, ―Hybrid approach for optimal placement of multiple DGs of multiple types in distribution networks,‖ International Journal of Electrical Power and Energy Systems, vol. 75, no. October, pp. 226–235, 2016. 10. S. Ganguly and D. Samajpati, ―Distributed generation allocation on radial distribution networks under uncertainties of load and generation using genetic algorithm,‖ IEEE Transactions on Sustainable Energy, vol. 6, no. 3, pp. 688–697, 2015. 11. S.Kansal, B. Tyagi, and V. Kumar, ―Cost–benefit analysis for optimal distributed generation placement in distribution systems,‖ International Journal of Ambient Energy, vol. 38, no. 1, pp. 45–54, 2017. 12. M. Laxmidevi Ramanaiah and M. D. Reddy, ―Optimal Unified Power Quality Conditioner Allocation in Distribution Systems for Loss Minimization using Grey Wolf Optimization,‖ International Journal of Engineering Research and Applications, vol. 7, no. 11, pp. 48– 53, 2017. 13. S. Mirjalili, S. M. Mirjalili, and A. Lewis, ―Grey Wolf Optimizer,‖ Advances in Engineering Software, vol. 69, pp. 46–61, 2014. 14. S. Mirjalili, S. Saremi, S. M. Mirjalili, and L. D. S. Coelho, ―Multi-objective grey wolf optimizer: A novel algorithm for multi-criterion optimization,‖ Expert Systems with Applications, vol. 47, pp. 106–119, 2016. 15. M. E. Baran and F. F. Wu, ―Network reconfiguration in distribution systems for loss reduction and load balancing,‖ IEEE Transactions on Power Delivery, vol. 4, no. 2, pp. 1401–1407, 1989. 16. M. E. Baran and F. F. Wu, ―Optimal capacitor placement on radial distribution systems,‖ IEEE Transactions on Power Delivery, vol. 4, no. 1, pp. 725–734, 1989. 17. M.Laxmidevi Ramanaiah and Dr. M. Damodar Reddy, ―Performance of Unified Power Quality Conditioner in radial distribution networks using Particle Swarm Optimization Method,‖ International Journal of Applied Engineering Research, vol. 12, no. 24, pp. 14718-14726, 2017. Authors: Deepthi Chamkur V, C R Byrareddy
Paper Title: Heptagonal Shaped UWB Antenna with DGS for Wireless LTE with Enhanced Bandwidth Abstract: The paper discusses about the implementation of Heptagonal shaped compact ultra-wideband planar Microstrip patch antenna with and without defected ground plane structure (DGS) with analysis of various parameters like return loss, VSWR bandwidth etc. A substrate made up of dielectric constant FR4 epoxy is utilized and the 2D and 3D radiation pattern are also discussed. DGS has helped to fine tune and increase the 111. bandwidth & its effects have been studied. A volume of 28x32x1.7 (1523.2 mm^3) is occupied by the size of antenna with dielectric constant of εr = 4.4, tanδ= 0.02. In order to provide fine tuning in the return loss graph, a 671-677 50Ω line with width of W=3mm direct line feeding method has been used for the micro-strip line and slots have been introduced in the ground plane structure, for achieving the good bandwidth coupling between the slots plays an important role. The antenna parameters including VSWR, Gain and return losses v/s frequency effects for the antenna with variation of slots and dimensions has been studied in this paper along with the analysis of important parameters such as return loss (dB), bandwidth, VSWR (Voltage Standing Wave Ratio) of patch antenna which has been performed using Ansoft HFSS v15 tool. The proposed design of the heptagonal shaped antenna operates as an ultra-wide band antenna ranging from 3.20 GHz to 10 GHz and beyond covering most of applications from LTE, Wimax (3.5/5.55GHz), Radio altimeter, RFID and ISM WLAN 5.2/5.8GHz etc.
Keywords: Microstrip Patch, heptagonal, defected ground structure, VSWR, 2D and 3D patterns, HFSS, Wireless
References: 1. Deepthi Chamkur V, C R Byrareddy, Saleem Ulla Shariff, ―Split-ground Compact Ultra-wide Band Patch Antenna for Wireless LTE/UMTS Operations", International Journal of Recent Technology and Engineering, 2019. DOI: 10.35940/ijrte.C5497.098319 2. T Kshitija, S Ramakrishna, Suhas B Shirol, Priyatam Kumar. "Micro - Strip Patch Antenna Using Various Types of Feeding Techniques: An Implementation", 2019 International Conference on Intelligent Sustainable Systems (ICISS), 2019 3. Mst. Dilshad Jahan, Chinmoy Das, Nayan Sarker. "Design & Analysis of a Micro-strip Patch Antenna for RFID, WiMAX and X- band Applications", 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), 2019 4. Chen Jing Zhang,Mei Song Tong,"A Multiband LTE Antenna with Resonant Rings" 5. W. T. Li, Y. Q. Hei, W. Feng and X. W. Shi, "Planar Antenna for 3G/Bluetooth/WiMAX and UWB Applications With Dual Band- Notched Characteristics," in IEEE Antennas and Wireless Propagation Letters, vol. 11, pp. 61-64, 2012. 6. L. Dang, Z. Y. Lei, Y. J. Xie, G. L. Ning and J. Fan, "A Compact Microstrip Slot Triple-Band Antenna for WLAN/WiMAX Applications," in IEEE Antennas and Wireless Propagation Letters, vol. 9, pp. 1178-1181, 2010. 7. C. A. Balanis, ―Antenna Theory: Analysis and Design‖,1997 by John Wiley & Sons, Inc. 8. R. Garg, P. Bhartia, I. Bahl, A. Ittipiboon, ―Microstrip Antenna Design Handbook‖, ARTECH HOUSE, Boston 2001. 9. Chao-Ming Wu, Yung-Lun Chen, and Wen-Chung Liu, ―A Compact Ultrawideband Slotted Patch Antenna for Wireless USB Dongle Application‖, IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS, VOL. 11, 2012. DOI: https://doi.org/10.1109/LAWP.2012.2202366 10. Farnaz Mirza mohammadi, Javad Nourinia and Changiz Ghobadi, "A Novel Dual-Wideband Monopole-Like Microstrip Antenna with Controllable Frequency Response ", 'IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS, VOL. 11, 2012 289. DOI: https://doi.org/10.1109/LAWP.2012.2190130 11. Help Share Ideas website, http;//www.helpshareideas.com 12. Intelligent Data Communication Technologies and Internet of Things", Springer Science and Business Media LLC, 2020 13. Ansoft HFSS (High Frequency Structure Simulation) V-15 tool used, HFSS user manual, Ansoft Corporation, USA. 14. Pozar, David M., Microwave Engineering, Second Edition, Wiley, New York 1998. 15. Sigmaplot and Microsoft excel Software. 16. Biao Li, Ying-Zeng Yin, Wei Hu, YangDing and Yang Zhao, ―Wideband Dual-Polarized Patch Antenna With Low Cross Polarization and High Isolation‖, IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS, VOL. 11, 2012 427. DOI: https://doi.org/10.1109/LAWP.2012.2195149 Authors: Heet Savla, Vruddhi Mehta, Ramchandra Mangrulkar
Paper Title: Prediction and Diagnosis of COVID-19 using Machine Learning Algorithms Abstract: The world is reworking in a digital era. However, the field of medicine was quite repulsive to technology. Recently, the advent of newer technologies like machine learning has catalyzed its adoption into healthcare. The blending of technology and medicine is facilitating a wealth of innovation that continues to improve lives. With the realm of possibility, machine learning is discovering various trends in a dataset and it is globally practiced in various medical conditions to predict the results, diagnose, analyze, treat, and recover. Machine Learning is aiding a lot to fight the battle against Covid-19. For instance, a face scanner that uses ML is used to detect whether a person has a fever or not. Similarly, the data from wearable technology like Apple Watch and Fitbit can be used to detect the changes in resting heart rate patterns which help in detecting coronavirus. According to a study by the Hindustan Times, the number of cases is rapidly increasing. Careful risk assessments should identify hotspots and clusters, and continued efforts should be made to further strengthen capacities to respond, especially at sub-national levels. The core public health measures for the Covid-19 response remain, rapidly detect, test, isolate, treat, and trace all contacts. The work presented in this paper represents the system that predicts the number of coronavirus cases in the upcoming days as well as the possibility of the infection in a particular person based on the symptoms. The work focuses on Linear Regression and SVM models for predicting the curve of active cases. SVM is least affected by noisy data, and it is not prone 112. to overfitting. To diagnose a person our application has a certain question that needs to be answered. Based on this, the KNN model provides the maximum likelihood result of a person being infected or not. Tracking and monitoring in the course of such pandemic help us to be prepared. 678-683
Keywords: Healthcare, K-Nearest Neighbor, Linear Regression, Machine Learning Support Vector Machine.
References: 1. Krati Saxena, Dr. Zubair Khan, Shefali Singh," Diagnosis of Diabetes Mellitus using K Nearest Neighbor Algorithm," International Journal of Computer Science Trends and Technology (IJCST) – Volume 2 Issue 4, July-Aug 2014, Page 36, ISSN: 2347-8578. 2. M. Nirmala Devi, S. A. alias Balamurugan and U. V. Swathi, "An amalgam KNN to predict diabetes mellitus," 2013 IEEE International Conference ON Emerging Trends in Computing, Communication and Nanotechnology (ICECCN), Tirunelveli, 2013, pp. 691-695, DOI: 10.1109/ICE-CCN.2013.6528591. 3. V. Chamola, V. Hassija, V. Gupta and M. Guizani, "A Comprehensive Review of the COVID-19 Pandemic and the Role of IoT, Drones, AI, Blockchain, and 5G in Managing its Impact," in IEEE Access, vol. 8, pp. 90225-90265, 2020, doi: 10.1109/ACCESS.2020.2992341. 4. Ramesh, D., Katheria, Y.S. Ensemble method based predictive model for analyzing disease datasets: a predictive analysis approach. Health Techno. 9, 533–545(2019).https://doi.org/10.1007/s12553-019-00299-3 5. S. S. Arun and G. Neelakanta Iyer, "On the Analysis of COVID19 - Novel Corona Viral Disease Pandemic Spread Data Using Machine Learning Techniques," 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India, 2020, pp. 1222-1227, doi: 10.1109/ICICCS48265.2020.9121027. 6. F. Rustam et al., "COVID-19 Future Forecasting Using Supervised Machine Learning Models," in IEEE Access, vol. 8, pp. 101489- 101499, 2020, DOI: 10.1109/ACCESS.2020.2997311. 7. Ahmad S, Hafeez A, Siddqui SA, Ahmad M, Mishra S. A Review of COVID-19 (Coronavirus Disease-2019) Diagnosis, Treatments and Prevention. EJMO 2020;4(2):116–125. 8. M.K., Arti. (2020). Modeling and Predictions for COVID 19 Spread in India. 10.13140/RG.2.2.11427.81444. 9. Ghosal S., Sengupta S., Majumder M., Sinha B. Linear Regression Analysis to predict the number of deaths in India due to SARS- CoV-2 at 6 weeks from day 0 (100 cases - March 14th 2020) Diabetes Metab Syndr. 2020;14:311–315. doi: 10.1016/j.dsx.2020.03.017. 10. Lixiang Li, Zihang Yang, Zhongkai Dang, Cui Meng, Jingze Huang, Haotian Meng, Deyu Wang, Guanhua Chen Jiaxuan Zhang, Haipeng Peng, Yiming Shao, Propagation Analysis and Prediction of the COVID-19, Infectious Disease Modelling, Volume 5, 2020, Pages 282-292. Authors: Junhwan Moon
Paper Title: Research Method of Clustering of COVID-19 with Text-mining Abstract: A suspected patient with symptoms similar to coronavirus infection-19 was identified for the first time on January 8, 2020 in Korea. After that, the world was dominated by COVID-19. People all around the world must face a new society that will be changed by COVID-19. To prepare for such future, this study collected related words around the keyword COVID-19 and has predicted what risk factors and opportunity factors occur. As a result of SNA analysis by collecting news data from January to May, 2020, when COVID-19 was rapidly spreading, the key words "Prevention of epidemics", "Inspection", "Quarantine", "Infection", "Government", Keywords such as "Patient", "Addition", "Diffusion", "Judgment" and "Prohibition" have had important influences. Furthermore, COVID-19 has been affecting the daily lives of individual citizens, and their interest in the government response process increased. Therefore, the response to the new infectious disease must be quarantine based on science and technology and data, and it is imperative to establish a legal basis for using social facilities as treatment facilities.
Keywords: Coronavirus, COVID-19, Big data, Social Network Analysis, Cluster Analysis, Future Prediction, South Korea.
References: 1. C. Batini, C. Cappiello, C. Francalanci, A. Maurino, and G. Viscusi. (2011, August). ―A capacity and value based model for data architectures adopting integration technologies,‖ In AMCIS. 2. Central Accident Remediation Headquarters. Central Defense Response Headquarters, 2020. [Online]. Available: http://ncov.mohw.go.kr/tcmBoardView.do?brdId=3&brdGubun=31&dataGubun=&ncvContSeq=583&contSeq=583&board_id=311&g ubun=ALL 3. Central Accident Remediation Headquarters. Central Defense Response Headquarters, 2020. [Online]. Available: http://ncov.mohw.go.kr/tcmBoardView.do?brdId=3&brdGubun=31&dataGubun=&ncvContSeq=1241&contSeq=1241&board_id=311 &gubun=ALL 4. Central Accident Remediation Headquarters. Central Defense Response Headquarters, 2020. [Online]. Available: http://ncov.mohw.go.kr/tcmBoardView.do?brdId=&brdGubun=&dataGubun=&ncvContSeq=353682&contSeq=353682&board_id=&g ubun=ALL 113. 5. Ministry of Education, 2020. [Online]. Available: https://www.moe.go.kr/sub/info.do?m=020102&s=moe 6. Ministry of Employment and Labor, 2020. [Online]. Available: http://www.moel.go.kr/news/enews/report/enewsView.do?news_seq=10754 7. National Trauma Center, 2020. [Online]. Available: 684-690 https://nct.go.kr/serviceCenter/pressDetail.do?currentPageNo=2&refnceSeq=339&searchKeyword1= 8. Ying Liu, Albert A Gayle, Annelies Wilder-Smith and Joacim Rocklöv, ―The reproductive number of COVID-19 is higher compared to SARS coronavirus‖, Journal of travel medicine, 2020. 9. Yan Bai, Lingsheng Yao, Tao Wei, Fei Tian, Dong-Yan Jin, Lijuan Chen and Meiyun Wang, ―Presumed asymptomatic carrier transmission of COVID-19‖, Jama, 323(14), 1406-1407, 2020. 10. Freeman, Linton C. (Ed.)., Research methods in social network analysis. Routledge, 2017. 11. Milgram, Stanley, ―The small world problem‖, Psychology today, Vol. 2, no. 1, pp.60-67, 1967. 12. Freeman, Linton, ―The development of social network analysis‖, A Study in the Sociology of Science, 1, 687, 2004. 13. Marin, Alexandra and Barry Wellman, "Social network analysis: An introduction", The SAGE handbook of social network analysis, 11, 2011. 14. Otte, Evelien, and Ronald Rousseau, ―Social network analysis: a powerful strategy, also for the information sciences‖, Journal of information Science, Vol. 28, no. 6, pp.441-453, 2002. 15. Kim, Jooho, and Makarand Hastak, ―Social network analysis: Characteristics of online social networks after a disaster‖, International Journal of Information Management, Vol. 38, no. 1, pp.86-96, 2018. 16. Wasserman, Stanley, and Katherine Faust, Social network analysis: Methods and applications, Vol. 8, Cambridge university press, 1994. 17. Lee, Min-Joong, Sunghee Choi, and Chin-Wan Chung, ―Efficient algorithms for updating betweenness centrality in fully dynamic graphs‖, Information Sciences, 326, pp.278-296, 2016. 18. Scott, Robert A, The Making of Blind Men, Deviance: A Symbolic Interactionist Approach, 236, 1995. 19. De Nooy, Wouter, Andrej Mrvar, and Vladimir Batagelj, ―Exploratory social network analysis with Pajek: Revised and expanded edition for updated software‖, Vol. 46, Cambridge University Press, 2018. 20. Rowley, Timothy J, ―Moving beyond dyadic ties: A network theory of stakeholder influences‖, Academy of management Review, Vol. 22, no. 4, pp.887-910, 1997. 21. Ng, Andrew Y., Michael I. Jordan, and Yair Weiss, ―On spectral clustering: Analysis and an algorithm‖, In Advances in neural information processing systems, pp. 849-856, 2002. 22. Xie, Junyuan, Ross Girshick, and Ali Farhadi, ―Unsupervised deep embedding for clustering analysis‖, In International conference on machine learning, pp. 478-487, 2016. 23. Lee, Jae Yun, Heejung Kim, and Pan Jun Kim, ―Domain analysis with text mining: Analysis of digital library research trends using profiling methods‖, Journal of Information Science, Vol. 36, no. 2, pp.144-161, 2010. 24. Kim, Heejung, and Jae Lee, ―Archiving research trends in LIS domain using profiling analysis‖, Scientometrics, Vol. 80, no. 1, pp75- 90, 2009. 25. Huang, Wei, and Sung-Kwun Oh, ―Optimized polynomial neural network classifier designed with the aid of space search simultaneous tuning strategy and data preprocessing techniques‖, Journal of Electrical Engineering & Technology, Vol. 12, no. 2, pp.911-917, 2017.
114. Authors: Islahuddin Jalal, Hashmatullah Rasekh, Qudrattullah Omerkhel, Qamaruddin Shamsi Paper Title: Risk Analysis of BYOD in Afghanistan’s Organization Abstract: Improving ICT management strategies is an ongoing need for almost all organizations. At the same time, the challenges that BYOD brings to the organization need to be carefully considered. BYOD terminology can refer to related concepts, technologies, and strategies that enable employees to use organizational resources. The use of different databases, applications, and personal devices such as smartphones, laptops, tablets, and any other mobile device such as memory chips and external hard drives can provide examples of these resources. The implementation of BYOD has brought a clear advantage to the organization. However, the use of BYOD in organizations can pose some risks and threats. The main purpose of this study was to analyze BYOD risks in Afghan organizations through cross-sectional quantitative research methods. An online survey of 24 questions was conducted on various aspects of BYOD risk from various organizations in Afghanistan. Through the use of raw data, survey results related to BYOD implementation in Afghanistan have been collected. Thus, the researchers found out that IT staffs have a low level of awareness of the risks and challenges of BYOD security and the latest technologies used by Afghan organizations. Finally, recommendations have been made.
Keywords: Risk Analysis, Bring Your Own Device (BYOD), Cyber Security, Information Security.
References: 691-698 1. G. O. Young, ―Synthetic structure of industrial plastics (Book style with paper title and editor),‖ in Plastics, 2nd ed. vol. 3, J. Peters, Ed. New York: McGraw-Hill, 1964, pp. 15–64. 2. W.-K. Chen, Linear Networks and Systems (Book style). Belmont, CA: Wadsworth, 1993, pp. 123–135. 3. H. Poor, An Introduction to Signal Detection and Estimation. New York: Springer-Verlag, 1985, ch. 4. 4. B. Smith, ―An approach to graphs of linear forms (Unpublished work style),‖ unpublished. 5. E. H. Miller, ―A note on reflector arrays (Periodical style—Accepted for publication),‖ IEEE Trans. Antennas Propagat., to be published. 6. J. Wang, ―Fundamentals of erbium-doped fiber amplifiers arrays (Periodical style—Submitted for publication),‖ IEEE J. Quantum Electron., submitted for publication. 7. C. J. Kaufman, Rocky Mountain Research Lab., Boulder, CO, private communication, May 1995. 8. Y. Yorozu, M. Hirano, K. Oka, and Y. Tagawa, ―Electron spectroscopy studies on magneto-optical media and plastic substrate interfaces(Translation Journals style),‖ IEEE Transl. J. Magn.Jpn., vol. 2, Aug. 1987, pp. 740–741 [Dig. 9th Annu. Conf. Magnetics Japan, 1982, p. 301]. 9. M. Young, The Techincal Writers Handbook. Mill Valley, CA: University Science, 1989. 10. (Basic Book/Monograph Online Sources) J. K. Author. (year, month, day). Title (edition) [Type of medium]. Volume(issue). Available: http://www.(URL) 11. J. Jones. (1991, May 10). Networks (2nd ed.) [Online]. Available: http://www.atm.com 12. (Journal Online Sources style) K. Author. (year, month). Title. Journal [Type of medium]. Volume(issue), paging if given. Available: http://www.(URL) research work, membership, achievements, with photo that will be maximum 200-400 words. Authors: Rohan Yashraj Gupta, Satya Sai Mudigonda, Pallav Kumar Baruah, Phani Krishna Kandala Implementation of Correlation and Regression Models for Health Insurance Fraud in Covid-19 Paper Title: Environment using Actuarial and Data Science Techniques Abstract: Fraud acts as a major deterrent to a company‘s growth if uncontrolled. It challenges the fundamental value of ―Trust‖ in the Insurance business. COVID-19 brought additional challenges of increased potential fraud to health insurance business. This work describes implementation of existing and enhanced fraud detection methods in the pre-COVID-19 and COVID-19 environments. For this purpose, we have developed an innovative enhanced fraud detection framework using actuarial and data science techniques. Triggers specific to COVID-19 are identified in addition to the existing triggers. We have also explored the relationship between insurance fraud and COVID-19. To determine this we calculated Pearson correlation coefficient and fitted logarithmic regression model between fraud in health insurance and COVID-19 cases. This work uses two datasets: health insurance dataset and Kaggle dataset on COVID-19 cases for the same select geographical location in India. Our experimental results shows Pearson correlation coefficient of 0.86, which implies that the month on month rate of fraudulent cases is highly correlated with month on month rate of COVID-19 cases. The logarithmic regression performed on the data gave the r-squared value of 0.91 which indicates that the model is a good fit. This work aims to provide much needed tools and techniques for health insurance business to counter the fraud. 115. Keywords: Fraud detection framework, Pearson correlation, Logarithmic regression, COVID-19, actuarial techniques, data science techniques, fraud detection, fraud prevention, fraud triggers. 699-706
References: 1. R. A. Bauder and T. M. Khoshgoftaar, ―Medicare Fraud Detection Using Machine Learning Methods,‖ in 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), 2017, vol. 2018-Janua, pp. 858–865, doi: 10.1109/ICMLA.2017.00-48. 2. L. V. . M. R. M. Ekin Tahir; Fulton, T. Ekin, R. M. Musal, and L. V. Fulton, ―Overpayment models for medical audits: multiple scenarios,‖ J. Appl. Stat., vol. 42, no. 11, pp. 2391–2405, Nov. 2015, doi: 10.1080/02664763.2015.1034659. 3. A. Gangopadhyaya and A. B. Garrett, ―Unemployment, Health Insurance, and the COVID-19 Recession,‖ SSRN Electron. J., Apr. 2020, doi: 10.2139/ssrn.3568489. 4. D. Thornton et al., ―Predicting Healthcare Fraud in Medicaid: A Multidimensional Data Model and Analysis Techniques for Fraud Detection,‖ Procedia Technol., vol. 9, pp. 1252–1264, 2013, doi: 10.1016/j.protcy.2013.12.140. 5. ―Insurance Fraud in Times of Crisis - FRISS.‖ [Online]. Available: https://www.friss.com/blog/insurance-fraud-in-times-of-crisis/. [Accessed: 05-Jun-2020]. 6. ―Coalition Against Insurance Fraud: Rapid National Response Urged To Head Off Coming Wave of COVID-19 Insurance Scams - InsuranceNewsNet.‖ [Online]. Available: https://insurancenewsnet.com/oarticle/coalition-against-insurance-fraud-rapid-national- response-urged-to-head-off-coming-wave-of-covid-19-insurance-scams#.Xts8DzozbIX. [Accessed: 06-Jun-2020]. 7. ―Indian general insurance market grows 14.5% | Insurance Business.‖ [Online]. Available: https://www.insurancebusinessmag.com/asia/news/breaking-news/indian-general-insurance-market-grows-14-5-213662.aspx. [Accessed: 20-Sep-2020]. 8. S.-H. Li, D. C. Yen, W.-H. Lu, and C. Wang, ―Identifying the signs of fraudulent accounts using data mining techniques,‖ Comput. Human Behav., vol. 28, no. 3, pp. 1002–1013, May 2012, doi: 10.1016/j.chb.2012.01.002. 9. N. Pillay, A. P. Engelbrecht, A. Abraham, M. C. Du Plessis, V. Snášel, and A. K. Muda, Advances in Nature and Biologically Inspired Computing, vol. 419. Cham: Springer International Publishing, 2016. 10. ―How European Insurers Can Manage The Impact Of Covid-19.‖ [Online]. Available: https://www.oliverwyman.com/our- expertise/insights/2020/mar/covid-19-european-insurance.html. [Accessed: 05-Jun-2020]. 11. ―COVID-19 Impact on Global Insurance Fraud Detection Industry 2020: Industry Trends, Size, Share, Growth Applications, SWOT Analysis by Top Key Players and Forecast Report to 2026 – Jewish Market Reports.‖ [Online]. Available: https://jewishlifenews.com/uncategorized/covid-19-impact-on-global-insurance-fraud-detection-industry-2020-industry-trends-size- share-growth-applications-swot-analysis-by-top-key-players-and-forecast-report-to-2026/. [Accessed: 05-Jun-2020]. 12. ―Impact of COVID-19 on Life And Health Insurance | TCS.‖ [Online]. Available: https://www.tcs.com/blogs/how-technology-can-aid- insurers-combat-covid-19. [Accessed: 27-May-2020]. 13. ―COVID-19 Impact on Fraud Detection and Prevention (FDP) Market | Coronavirus Outbreak & FDP Industry | MarketsandMarkets.‖ [Online]. Available: https://www.marketsandmarkets.com/Market-Reports/covid-19-impact-on-fraud-detection- and-prevention-market-23997778.html. [Accessed: 05-Jun-2020]. 14. ―COVID-19 online fraud trends: Industries, schemes and targets - Help Net Security.‖ [Online]. Available: https://www.helpnetsecurity.com/2020/05/15/covid-19-online-fraud/. [Accessed: 05-Jun-2020]. 15. ―COVID-19 (C-19) and Fraudulent Claims.‖ 16. ―COVID-19 – A backdoor to increased fraud risk?‖ [Online]. Available: https://www2.deloitte.com/ch/en/pages/financial- advisory/articles/covid-19-operating-in-the-new-normal-fraud-risk.html. [Accessed: 05-Jun-2020]. 17. N. Dragano, C. J. Rupprecht, O. Dortmann, M. Scheider, and M. Wahrendorf, ―Higher risk of COVID-19 hospitalization for unemployed: an analysis of 1,298,416 health insured individuals in Germany,‖ medRxiv, p. 2020.06.17.20133918, Jun. 2020, doi: 10.1101/2020.06.17.20133918. 18. J. W. Goodell, ―COVID-19 and finance: Agendas for future research,‖ Financ. Res. Lett., vol. 35, Jul. 2020, doi: 10.1016/j.frl.2020.101512. 19. ―Evaluating the impact of COVID-19 on insurance sector technology - Omdia.‖ [Online]. Available: https://technology.informa.com/623267/evaluating-the-impact-of-covid-19-on-insurance-sector-technology. [Accessed: 05-Jun-2020]. 20. ―How Will COVID-19 Impact Personal Injury Cases?‖ [Online]. Available: https://www.atltriallaw.com/how-will-covid-19-impact- personal-injury-cases/. [Accessed: 05-Jun-2020]. 21. R. Y. Gupta, S. Sai Mudigonda, P. K. Kandala, and P. K. Baruah, ―Implementation of a Predictive Model for Fraud Detection in Motor Insurance using Gradient Boosting Method and Validation with Actuarial Models,‖ in 2019 IEEE International Conference on Clean Energy and Energy Efficient Electronics Circuit for Sustainable Development (INCCES), 2019, pp. 1–6, doi: 10.1109/INCCES47820.2019.9167733. 22. V. Chandola, ―Anomaly Detection : A Survey,‖ 2009. 23. D. Cutler, ―How Will COVID-19 Affect the Health Care Economy?,‖ JAMA - Journal of the American Medical Association, vol. 323, no. 22. American Medical Association, pp. 2237–2238, 09-Jun-2020, doi: 10.1001/jama.2020.7308. 24. ―Insurance sector in India: Overview, IRDAI, Companies, Stats & Trends.‖ [Online]. Available: https://www.acko.com/articles/general-info/insurance-sector-india/. [Accessed: 08-Jun-2020]. 25. ―Insurance Sector in India: Industry Overview, Market Size & Trends | IBEF.‖ [Online]. Available: https://www.ibef.org/industry/insurance-sector-india.aspx. [Accessed: 08-Jun-2020]. 26. ―Potential Impact Of COVID-19 On Insurance Fraud Detection Market | Leading Companies Analysis 2027 – Cole Reports.‖ [Online]. Available: https://coleofduty.com/news/2020/05/23/potential-impact-of-covid-19-on-insurance-fraud-detection-market-leading- companies-analysis-2027/. [Accessed: 05-Jun-2020]. 27. P. Babuna, X. Yang, A. Gyilbag, D. A. Awudi, D. Ngmenbelle, and D. Bian, ―The Impact of COVID-19 on the Insurance Industry,‖ Int. J. Environ. Res. Public Health, vol. 17, no. 16, p. 5766, Aug. 2020, doi: 10.3390/ijerph17165766. 28. E. Summary, T. Nadu, I. A. I. Office, and B. T. Results, ―COVID-19 A Study and Projections for India - An Update.‖ 29. R. Y. Gupta, S. S. Mudigonda, P. K. Kandala, and P. K. Baruah, ―A Framework for Comprehensive Fraud Management using Actuarial Techniques,‖ vol. 10, no. 3, pp. 780–791, 2019. 30. N. Rai, P. K. Baruah, S. S. Mudigonda, and P. K. Kandala, ―Fraud Detection Supervised Machine Learning Models for an Automobile Insurance,‖ Int. J. Sci. Eng. Res., vol. 9, no. 11, pp. 473–479, 2018. 31. ―Rise in searches for ‗How to set fire‘ a sign insurance fraud beckons as economy crashes.‖ [Online]. Available: https://www.washingtonexaminer.com/news/rise-in-searches-for-how-to-set-fire-a-sign-insurance-fraud-beckons-as-economy-crashes. [Accessed: 06-Jun-2020]. 32. ―COVID-19 in India | Kaggle.‖ [Online]. Available: https://www.kaggle.com/sudalairajkumar/covid19-in-india. [Accessed: 19-Sep- 2020]. Authors: Amena Begum, Md. Zahidur Rahman, Nurunnahar Nancy, Md. Enamul Haq Comparative Performance Evaluation of Mobile Ad-hoc Network Routing Protocols using NS2 Paper Title: Simulator Abstract: A Mobile Ad-hoc Network (MANET) is an independent assortment of mobile users that communicate over moderately bandwidth constrained wireless links. MANET‘s topology is dynamic that can change rapidly because the nodes move freely and can organize themselves randomly; has the advantage of being quickly deployable. Although numerous routing protocols have been proposed for mobile ad hoc networks, there is no universal scheme that works well in scenarios with different network sizes, traffic loads and node mobility patterns, so mobile ad hoc routing protocol election presents a great challenge. In this paper, an attempt has been made to compare the performance of three routing protocols in Mobile Ad-hoc Networks – Ad-Hoc On- demand Distance Vector (AODV), Dynamic Source Routing (DSR) and Destination Sequenced Distance Vector 116. (DSDV). We have evaluated the performance of these routing protocols with varying the number of mobile nodes and packet sizes on the basis of four important metrics such as packet delivery ratio, average end to end delay, normalized routing overhead and throughput. Network Simulator version 2.35 (NS-2.35) is used 707-713 as the simulation tool for evaluating these performance metrics. The outcome of this research shows that AODV protocol outperforms DSDV and DSR protocols.
Keywords: MANET, AODV, DSR, DSDV, NS2, Performance Metrics, Analysis
References: 1. Network Simulator version 2 (NS-2). [Online]. Available at: http://www.isi.edu/nsnam/ns. 2. Magnus Frodigh, Per Johansson and Peter Larsson, ―Wireless ad hoc networking—The art of networking without a network,‖ 2010. 3. Ayush Pandeyand Anuj Srivastava, ―Performance Evaluation of MANET through NS2 Simulation,‖ International Journal of Electronic and Electrical Engineering, volume 7, issue 1, pp. 25-30, 2014. 4. C. Perkins and P. Bhagwat, ―Highly Dynamic Destination-Sequenced Distance-Vector Routing (DSDV)‖ for Mobile Computers, ACM SIGCOMM, October 1994. 5. Shree Murthy, J.J. Garcia-Luna-Aveces, ―A Routing Protocol for Packet Radio Networks,‖ ACM International Conference on Mobile Computing and Networking, pp. 86-95, November, 1995. 6. C.-C. Chiang, ―Routing in Clustered Multihop, Mobile Wireless Networks with Fading Channel,‖ IEEE SICON ‘97, pp. 197– 211, 1997. 7. C.E. Perkins, E. Royer, and S.R. Das, ―Ad hoc on demand distance vector (AODV) routing,‖ Internet Draft, March 2000. 8. D B. Johnson, D A. Maltz, and Y. Hu, ―The dynamic source routing protocol for mobile ad hoc network,‖ Internet-Draft, April 2003. 9. Haas Z.J, ―A new routing protocol for the reconfigurable wireless network,‖ IEEE 6th International Conference on Universal Personal Communications, ICUPC ‗97, San Diego, CA, pp. 562 - 566, 1997. 10. Marc Greis tutorial on ns2. [Online]. Available at: www.isi.edu/nsnam/ns/tutorial. 11. K. Fall and K. Varadhan, ―The NS Manual,‖ The VINT Project, UC Berkeley, January 2002. Authors: Rutuja Gugale, Pratiksha Sonar, Anagha Mandekar, Sonali Ubale, Vaishali Latke
Paper Title: Brain Tumor Detection using Deep Learning Abstract: Nowadays the leading techniques for diagnosing and revealing the different diseases are image processing. And there is an increase in the cases of cancer these days. The unrestricted development of cells cause‘s lumps which leads to brain tumor also called glioblastoma. There are mainly two types of tumor benign which has covering over the tumor and malignant is the one which spreads throughout the places. Earlier the development of unrestricted cells used to be diagnosed by doctors physically through monitoring the image by which the results were not used to be precise sometimes. But time along boarding of medical fields lead to different medical facilities by which the results could be precise. The broadly approach method of imaging that scrutinizes the internal structure of the human race is Magnetic resonance Imaging. This approach of imaging techniques is also used for detecting brain tumors. The detection of glioblastoma processes has machine vision methods such as Image pre-processing, Segmentation in Image, Feature extraction and classification. Several image segmentation and image classification techniques are available for detecting tumor of the brain. 117. Convolution neural networks (CNN) based classifiers are proposed to prevail the limitations. This CNN is such a classifier which is used to differentiate between the competent data and the trail data, from which the results could be obtained. 714-717
Keywords: Brain tumor Detection, Watershed Algorithm,Capsule Network, Convolutional Neural Network, MRI Images, Tumor Boundary.
References: 1. iNilesh Bhaskarrao Bahadur, Arun Kumar Ray and Har Pal Thethi,‖Image Analysis for MRI Based Brain Tumor Detection and Feature Extraction Using Biologically Inspired BWT and SVM‖, Hindawi International Journal of Biomedical Imaging volume 2017 2. iAndras Jakab, Stefan Bauer et al.,―TheMultimodal Brain Tumor ImageSegmentation Benchmark (BRATS) ―IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 34, NO. 10, 2015. 3. iIsrael D. Gebru, Xavier Alameda-Pineda, Florence Forbes and RaduHoraud, ―EM Algorithms for WeightedData Clustering with Application to Audio-Visual Scene Analysis ― IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. XX, NO. Y, 2016. 4. Prateek Katiyar, Mathew R. Divine et al., ―A Novel Unsupervised Segmentation Approach Quantifies Tumor Tissue Populations Using Multiparametric MRI: First Results with Histological Validation‖ Mol Imaging Biol 19:391Y397 DOI: 10.1007/s11307-016-1009-y , 2016. Authors: Mayu S.Bankar, Vidya P. Kodgirwar
Paper Title: Electrical Pole Climbing Robot for Fault Detection using Wi-Fi Abstract: This project is to create electrical pole climbing robot which can be used to reduce risk of electrician to connect the distribution lines for supplying purposes. Pole climbing robot, nowadays, is very common and interesting idea, which mainly works by connecting the distribution lines according to the directions given to it. In this modern era robots are being developed for various purposes to accomplish many tasks which seem to be complex and life endangering for humans. Benefits of using robots have been immense in terms of risk-free, speed and efficiency of doing required tasks compared to that of humans. The main objective of this work is to save human lives. Considering on that issue, a pole climbing robot has been designed. However, further modifications of this work might be able to perform the wiring and repairing tasks instead of an electrician. The developed robot works on the principle of linear motor, which is partially autonomous. With the installation of 118. this project, risk of human injuries and death can be minimized while working in the distribution lines which is the main consideration of this project. 718-722 Keywords: Wi-Fi, Microcontroller, Robot Arms, DC motors, Power supply, Gripper.
References: 1. BAKHTIAR KHALID, MD. HASIBULLAH, RAZU AHMED, ZAKIR HASAN CHOUDHURY, IMRULKAISH AND MD. KHALILUR RHAMAN, “ELECTRICAL POLE CLIMBING ROBOTS FOR WIRING AND REPAIRING DISTRIBUTION LINES”, 18TH INTERNATIONAL CONFERENCE ON COMPUTER & INFORMATION TECHNOLOGY, 21-23 DECEMBER 2015 2. Salataren, R., Aracil, R., Sabater, J.M., Reinoso, O. and Jimenez,L.M. (1999), ―Modeling, simulation and conception of parallel climbing robots for construction and service‖, paper presented at the 2nd International Conference on Climbing and Walking Robots, pp. 253-5. 3. J. C., M. Prieto, M. Armada, and P. G. de Santos, ―A six-legged climbing robot for high payloads‖, in IEEE Int. Conf. on Cont. App, Trieste, Italy, Sept. 1998, pp. 446–450 4. M. NiliAhmadabadi, Senior Member, IEEE , H. Moradi, Member, IEEE , A. Sadeghi, A. Madani, andM. Farahnak, ―The Evolution of UT Pole Climbing Robots‖ 5. M. Saidur Rahman, F. Rahman, A. Rahman. B. Kamran, A. Biswas and J. Hossain, ―Burn Injury in Bangladesh: Electrical Injury a Major Contribution‖, Int J Burns Trauma.2011;(1): 62–67, PMCID: PMC3415945, Published online 2011 Sep 3 6. R. Azizur & U. Kutub, 9-10 January, 2010, ―Ensuring Safety: A Great Challenge for Electricity Distribution System‖, proceeding of the 2010 International Conference on Industrial Engineering and Operation Management, Dhaka, Bangladesh 7. M. Tavakoli, M.R. Zakerzadeh, G.R. Vossoughi, S. Bagheri, (2005),"A hybrid pole climbing and manipulating robot with minimum DOFs for construction and service applications", Industrial Robot: An International Journal, Vol. 32 Iss: 2 pp. 171 – 178 8. T. Mahmoud, ―Design, Implementation, Pathplanning, and Control of Pole Climbing Robot‖, University of Coimbra, Faculty of Science and Technology, Department of Electrical and Computer Engineering, Coimbrca, July 2010 Authors: J.R.Arunkumar, M.Sundar Rajan, Anusuya Ramasamy, Bhupesh Kumar Singh
Paper Title: Efficient Cognitive Skill Based Learning System using Augmented Reality Abstract: Augmented Reality provides an interactive experience by imposing virtual objects over real world environment and used in different field in learning, entertainment, or edutainment by developing higher order cognitive and practical learning skills. With the infusion of digital technology, nowadays all the educational institutions adapted the online mode learning environment like smart classroom for content delivery, Webcast Lecture by using AR. AR attracts research attention for its ability to allow students to be immersed in realistic experiences. AR will allow learners too deep about real time and cognitive skill development experiences. Recent scenario in education and academic sectors needs emerging technologies for learning system. In that scenario AR technology will be used to create new type of self-learning and automated application in academic. This technology is used to enhance the teaching and learning for students in effective way and efficient too. Even this technology will attract the students to learn fast and improve the cognitive skill also. This is a new standard, merging features from ubiquitous computing, tangible computing, and social computing. The benefits of this proposed component include inspiring deep and thoughtful education, in real world problems and challenges can be refining the creative problem solving abilities while also as long as exposure/ new perception. This proposed research paper goals to improve present educational system using Augmented Reality.
Keywords: Augmented Reality, Learning, Education, Social Computing.
References: 1. Bujak, K.R.; Radu, I.; Catrambone, R.; MacIntyre, B.; Zheng, R.; Golubski, G. A Psychological Perspective on Augmented Reality in the Mathematics Classroom. Comput. Educ. 2013, 68, 536–544. 2. Pacheco, D.; Wierenga, S.; Omedas, P.; Oliva, L.S.; Wilbricht, S.; Billib, S.; Knoch, H.; Verschure, P.F.M.J. A Location-Based Augmented Reality System for the Spatial Interaction with Historical Datasets. In Proceedings of the 2015 Digital Heritage, Granada, Spain, 28 September–2 October 2015; IEEE: Washington, DC, USA, 2016; pp. 393–396. 3. A High Level Framework for Interacive, Animated 3D Graphics Applications,‖ proceedings of ACM SiGGRAPH 9 4, pp.421-434 (1994) Azuma, 1997; Zhou, Duh, &Billinghurst, 2008. 4. Broll et al., 2008; Johnson et al., 2010b; Liu, 2009 5. Arvinitis et al, 2007; Dunleavy, Dede & Mitchell, 2008 119. 6. AIR-EDUTECH, Lamees Mahmoud Mohd Said Al Qassem, Hessa Al Hawai, M. Jamal Zemerly, 2016M. Billinghurst, ―Augmented Reality in Education‖, New horizons for learning IX , October 2003. 7. Hsin-Yi Chang, Hsin-Kai Wu and Ying-Shao Hsu; ―Integrating a mobile augmented reality activity to contextualize student learning of 723-727 a socioscientific issue, 2013. 8. Kangdon Lee, ―Augmented Reality in Education and Training‖, 2012. 9. Freitas, R., & Campos, P. (2008). SMART: a System of augmented reality for teaching 2nd grade students. Proceedings of the 22nd British Computer Society Conference on Human-Computer Interaction (HCI 2008), 27-30. Liverpool John Moores University, UK. 10. M.Billinghurst, H. Kato and I. Poupyrev, "The Magicbook: Moving Seamlessly Between Reality and Virtuality", IEEE Computer Graphics and Applications, vol. 21, no. 3, pp. 6-8, May/June 2001. 11. Billinghurst, M. (2002). Augmented Reality in Education. Seattle WA: New Horizons for Learning - Technology in Education. 12. Kiyokawa, K., Billinghurst, M., Hayes, S., Gupta, A., Sannohe, Y., & Kato, H. (2002). Communication Behaviors of Co-Located Users in Collaborative AR Interfaces. IEEE and ACM International Symposium on Mixed and Augmented Reality (ISMAR 2002) (pp. 139- 148). Darmstadt, Germany: IEEE Press. 13. Liarokapis, F., & Anderson, E. F. (2010). Using Augmented Reality as a Medium to Assist Teaching in Higher Education. (October 2016). https://doi.org/10.2312/eged.20101010. 14. Paper,C., &Baldiris, S. (2017). Augmented Reality Applications for Education : Five Directions for Future Augmented Reality Applications for Education : Five Directions for Future Research. (June). https://doi.org/10.1007/978-3-319-60922-5. 15. Trondsen, E., Member, B., & Vikings, S. (2019). Augmented / Virtual Reality in Nordic / Baltic Education, Learning and Training. (April), 1–73. 16. Burton, E.P., Frazier, W., Annetta, L., Lamb, R., Cheng, R. and Chmiel, M. 2011. Modeling Augmented Reality Games with Preservice Elementary and Secondary Science Teachers. Journal of Technology and Teacher Education 19, 303-329. 17. Coffin, C., Bostandjiev, S., Ford, J. And Hollerer, T. 2010. Enhancing classroom and distance learning through augmented reality. In World Conference on Educational Multimedia, Hypermedia and Telecommunications, 1140-1147. 18. Kesim, M. and Ozarslan, Y. 2012. Augmented Reality in Education: Current Technologies and the Potential for Education. Procedia - Social and Behavioral Sciences 47, 297-302. https://doi.org/10.1016/j.sbspro.2012.06.654 19. Abdullah Mubarak Ghare, Mohammed Akhtar Khan, Mustafa Rangwala, Shadab Kazi ,Prof. Sameer Panwala, Prof. Abdus Salam‖Augmented Reality for Educational Enhancement‖International Journal of Advanced Research in Computer and Communication Engineering Vol. 6, Issue 3, March 2017. 20. MonaAlkhattabi .‖Augmented Reality as E-learning Tool in Primary Schools‘ Education: Barriers to Teachers‘ Adoption‖ iJET ‒ Vol. 12, No. 2, 2017 https://doi.org/10.3991/ijet.v12i02.6158. 21. AnuroopKatiyar, Karan Kalra and Chetan Garg.‖Marker Based Augmented Reality ―Advances in Computer Science and Information Technology (ACSIT) Volume 2, Number 5; April-June, 2015 pp. 441-445. 22. MARE: Mobile Augmented Reality Based Experiments in Science, Technology and Engineering C. Onime, J. Uhomoibhi and S. Radicella, Online Experimentation: Emerging Technologies and IoT,The International journal of analytical and experimental modal analysis Volume XII, Issue IV, April/2020 ISSN NO:0886-9367 Page No:622. 23. A Review of Augmented Reality Applications for History Education and Heritage Visualisation Jennifer Challenor * and Minhua Ma, Multimodal Technol. Interact. 2019, 3, 39; doi:10.3390/mti3020039. S.R.Pranav Sai*, Ajay Singh Pawar, Satya Sai Mudigonda, Phani Krishna Kandala, Pallav Kumar Authors: 120. Baruah Assessing the sustainability of General Insurance Business through Real Time Monitoring of KPIs Paper Title: using Recurrent Neural Network Abstract: A company‘s sustainability is driven significantly by its operational efficiency. Operational efficiency plays a significant role in the growth and the profitability of a company. Thus, operational efficiency of a company forms the basis for the metrics known as the Key Performance Indicators(KPIs). These KPIs bridge the concept of performance an operation and a means to measure the same quantitatively. In this work, we used Recurrent Neural Network (RNN) with the Long Short Term Memory(LSTM) cells for projecting the public disclosure data of select General Insurance(GI) companies operating in India to the future. We use this data to calculate the KPIs pertaining to the operations of general insurance companies and calculate how the operations of the GI company affect its performance at various levels. Since this analysis is done for the projected data, we get a framework to assess the sustainability of the GI companies by monitoring these KPIs in real-time. The complex RNN and LSTM algorithms were implemented with the help of the Google Colaboratory platform by using the GPUs of the Google Hardware with the help of the Cloud Computing framework.
Keywords: Actuarial Analysis, General Insurance, Public Disclosure, RNN, LSTM, Google Colaboratory
References: 1. Sai, Pranav & Kandala, Phani & Mudigonda, Satya & Baruah, Pallav Kumar. (2019). Assessing Sustainability of General Insurance Business through Real Time KPI using GPUs and Neural Networks.2277-3878.10.35940/ijrte.D1129.1284S219. 2. Pradyumna M, Pranav Sai S.R.- A Framework for assessing performance sensitivity of select KPIs for General Insurance companies in India using Risk Management Dashboard Approach, IJSER, Volume 10, Issue 3, March 2019Edition 3. Shiu, Y. (2004). Determinants of United Kingdom General Insurance Company Performance. British Actuarial Journal. 1010.1017/S1357321700002968. 4. www.irdai.gov.in. (n.d.). Retrieved from https://www.irdai.gov.in/ADMINCMS/cms/NormalData_Layout.aspx?page=PageNo765&mid=31.2 5. Shiu,Y.,2004.DeterminantsofUnitedKingdomGeneral Insurance Company Performance. British ActuarialJournal. 6. Booth, P., Chadburn, R., Cooper, D., Haberman, S. & James, D., Modern actuarial theory and practice, second edition, September 2004, ISBN-13: 978-1584883685, Chapman & Hall, U.K.McGraw-Hill,U.K. 7. Browne, Mark J., and Robert E. Hoyt. ―Economic and Market Predictors of Insolvencies in the Property-Liability Insurance Industry.‖ The Journal of Risk and Insurance, vol. 62, no. 2, 1995, pp. 309–327.JSTOR, www.jstor.org/stable/253794. 8. Canadian Institute of Actuaries (1998). Standard of practice on dynamic capital adequacy testing (in effect January 1, 1999). This document is available at http://www.actuaries.ca/publications/sope.html 9. Blum, Peter, and Michel Dacorogna. "DFA‐Dynamic Financial Analysis." Wiley StatsRef: Statistics Reference Online(2014). 10. Enz, R. & Karl, K. (2001). The profitability of the non-lifeinsuranceindustry:it'sback-to-basicstime.SwissRe. Sigma, 5, 1-37. 11. Greene, William H. Econometric Analysis.2003, ISBN 13: 9780130132970. Pearson EducationIndia 12. Gujarati, Damodar N., Basic econometrics, third edition, 1995, ISBN 0‐07‐025214‐9, New York: McGraw-Hill. 13. Neter, J., Wasserman, W. and Kutner, M.H. (1989) Applied Linear Regression Models. 2nd Edition, RichardD.Irwin, Inc., Homewood 14. Pesaran,H.,Smith,R.&Im,K.(1996).Dynamiclinear models for heterogeneous panels. In the econometrics of panel data. Edited by Ma¨ tya¨ s, L. & Sevestre, P. (second revised edition). Kluwer Academic Publishers, The Netherlands. 15. https://www.irdai.gov.in/ADMINCMS/cms/NormalData_La yout.aspx?page=PageNo129&mid=3.1.9 728-738 16. Gonzalez, R. (2018, July). A work in progress. The Actuary, The magazine of the Institute and Faculty of Actuaries, pp.23-25. 17. https://www.irdai.gov.in/ADMINCMS/cms/NormalData_La yout.aspx?page=PageNo264&mid=3.2.10 18. Schmidhuber, Jürgen. "Deep learning in neural networks:Anoverview."Neuralnetworks61(2015):85-117. 19. Glorot, Xavier, and Yoshua Bengio. "Understandingthe difficulty of training deep feedforward neural networks." Proceedings of the thirteenth international conference on artificial intelligence and statistics.2010. 20. Ilya Sutskever, Oriol Vinyals, Quoc V. Le, Sequence to Sequence Learning with Neural Networks, (Submitted on 10 Sep 2014 (v1), last revised 14 Dec 2014 (this version,v3)) 21. Bergstra,James,etal."Theano:DeeplearningonGPUs with python." NIPS 2011, BigLearning Workshop, Granada, Spain. Vol. 3. Microtome Publishing.,2011. 22. W. Keckler, Stephen & Dally, William & Khailany, Brucek & Garland, Michael & Glasco, David. (2011). GPUs and the Future of Parallel Computing. Micro, IEEE. 31. 7 - 17. 10.1109/MM.2011.89. 23. Warburton, Kevin. "Deep learning and education for sustainability." International Journal of Sustainability in Higher Education 4.1 (2003):44-56. 24. Seiya Tokui, Kenta Oono, Shohei Hido, Justin Clayton. Chainer: a Next-Generation Open Source Framework for Deep Learning. In Workshop on Machine Learning Systems at Neural InformationProcessingSystems (NIPS), 2015. 25. Alec Radford & Luke Metz indico Research Boston, MA {alec,luke}@indico.io, Soumith Chintala Facebook AI Research New York, NY [email protected] UNSUPERVISED REPRESENTATION LEARNING WITH DEEP CONVOLUTIONAL GENERATIVE ADVERSARIALNETWORKS 26. Ade Ibiwoye, O. O. E. A. A. B. S., 2012. Artificial Neural Network Model for Predicting Insurance Insolvency. International Journal of management and business research, pp. 59-68. 27. Akhter Mohiuddin Rather, A. A. V., 2015. Recurrent neural network and a hybrid model for prediction of stock returns. Expert Systems with Applications, 42(6), pp. 3234-3241. 28. AlevDilekAydın,S.Ç.C.,2015.PredictionofFinancial CrisiswithArtificialNeuralNetwork:AnEmpiricalAnalysis onTurkey. 29. Chakraborty, S., 2007. Prediction of corporate financial health by an Artificial Neural Network. International Journal of ElectronicFinance. 30. Constantin, D., 2016. A NEW MODEL FOR ESTIMATING THE RISK OF BANKRUPTCY OF THE INSURANCE COMPANIES BASED ON THE ARTIFICIAL NEURAL NETWORKS.Romania,International Multidisciplinary Scientific Geo Conference. 31. Isseveroglu Gulsun, G. U., 2010. Early warning model with statistical analysis procedures in Turkish insurance companies. 32. Patrick L. Brockett, L. L. G. J. J. C. Y., 2006. A Comparison of Neural Network, Statistical Methods, and Variable Choice for Life Insurers' Financial Distress Prediction. The journal of risk andinsurance. 33. SanchoSalcedo-Sanz, 2005. Genetic programming for thepredictionofinsolvencyinnon-lifeinsurancecompanies. In: Computers & Operations Research. s.l.:s.n., pp.749-765. 34. PeterDEnglandandRichardJVerrall,Stochasticclaims reserving in general insurance, British Actuarial Journal,vol. 8, no. 3, pp. 443– 5182002 35. Segovia-Vargas, M. J., 2004. PREDICTION OF INSOLVENCY INNON-LIFE INSURANCE COMPANIES USING SUPPORT VECTOR MACHINES, GENETIC ALGORITHMS, AND SIMULATED ANNEALING.FuzzyEconomic Review, January, pp. 79-94. 36. Badi H. Baltagi, Econometric Analysis of Panel Data, Fifth Edition, September 2013, ISBN: 978-1-118-67232-7, John Wiley &. Sons, Ltd Copyright © 2005 John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex P019 8SQ,England 37. TadaakiHosaka, 2019. Bankruptcy prediction using imaged financial ratios and convolutional neural networks. ExpertSystemswithApplications,Volume117,pp.287-299. 38. https://www.ibef.org. (n.d.). Retrieved from https://www.ibef.org/industry/insurancesector-india.aspx