International Journal of Innovative Technology and Exploring Engineering
ISSN : 2278 - 3075 Website: www.ijitee.org Volume-9 Issue-10, AUGUST 2020 Published by: Blue Eyes Intelligence Engineering and Sciences Publication
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www.ijitee.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. 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.
Dr. Mayank Singh PDF (Purs), Ph.D(CSE), ME(Software Engineering), BE(CSE), SMACM, MIEEE, LMCSI, SMIACSIT Department of Electrical, Electronic and Computer Engineering, School of Engineering, Howard College, University of KwaZulu- Natal, Durban, South Africa.
Dr. Hitesh Kumar Ph.D.(ME), M.E.(ME), B.E. (ME) Professor and Head, Department of Mechanical Engineering, Technocrats Institute of Technology, Bhopal (MP), India
Scientific Editors Prof. (Dr.) Hamid Saremi Vice Chancellor of Islamic Azad University of Iran, Quchan Branch, Quchan-Iran
Dr. Moinuddin Sarker Vice President of Research & Development, Head of Science Team, Natural State Research, Inc., 37 Brown House Road (2nd Floor) Stamford, USA.
Prof. (Dr.) Nishakant Ojha Principal Advisor (Information &Technology) His Excellency Ambassador Republic of Sudan& Head of Mission in New Delhi, India
Dr. Shanmugha Priya. Pon Principal, Department of Commerce and Management, St. Joseph College of Management and Finance, Makambako, Tanzania, East Africa, Tanzania
Dr. Veronica Mc Gowan Associate Professor, Department of Computer and Business Information Systems,Delaware Valley College, Doylestown, PA, Allman, China.
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. Labib Francis Gergis Rofaiel Associate Professor, Department of Digital Communications and Electronics, Misr Academy for Engineering and Technology, Mansoura, Egypt.
Dr. Hugo A.F.A. Santos ICES, Institute for Computational Engineering and Sciences, The University of Texas, Austin, USA.
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. Haw Su Cheng Faculty of Information Technology, Multimedia University (MMU), Jalan Multimedia (Cyberjaya), Malaysia.
Dr. Hasan. A. M Al Dabbas Chairperson, Vice Dean Faculty of Engineering, Department of Mechanical Engineering, Philadelphia University, Amman, Jordan.
Dr. Gabil Adilov Professor, Department of Mathematics, Akdeniz University, Konyaaltı/Antalya, Turkey.
Dr.Ch.V. Raghavendran Professor, Department of Computer Science & Engineering, Ideal College of Arts and Sciences Kakinada (Andhra Pradesh), India.
Dr. Thanhtrung Dang Associate Professor & Vice-Dean, Department of Vehicle and Energy Engineering, HCMC University of Technology and Education, Hochiminh, Vietnam.
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. Karthikeyan Parthasarathy Assistant Professor, School of Management Studies, Kongu Engineering College Erode (Tamil Nadu), India.
Dr. Ramani Kannan Senior Lecturer, Department of Electrical and Electronics Engineering, Center for Smart Grid Energy Research, Institute of Autonomous system. Universiti Teknologi PETRONAS (UTP), Malaysia.
Dr. Somnath Thigale Assistant Professor, Department of Computer Science and Engineering, SKN Sinhgad COE Korti-Pandharpur (Maharashtra), India.
Dr. Giriraj Kumar Prajapati Professor, Department of Electronics Communication Engineering, Sree Chaitanya Institute of Technological Sciences, Karimnagar (Telangana), India.
Dr. M. Madhiarasan Assistant Professor, Department of Electrical and Electronics Engineering, Bharat Institute of Engineering and Technology, Hyderabad, (Telangana), India.
Dr. R. K. Bathla Professor, Department of Computer Science & Engineering, Madhav University, Rajasthan, India.
Dr. G. Venkata Rama Subbarao Associate Professor, Department of Civil Engineering, Velagapudi Ramakrishna Siddhartha Engineering College, Kanuru, Vijayawada (Andhra Pradesh), India.
Dr. Vijay Kumar Sinha Associate Professor, Department of Computer Science & Engineering, Chandigarh Engineering College, Landran, Mohal (Punjab), India.
Dr. T. Muthumanickam Professor and Head, Department of Electronics and Communication Engineering, Vinayaka Mission’s Kirupananda Variyar Engineering College, Periyaseeragapadi (Tamil Nadu), India.
Dr. Suneel Kumar Singh Associate Professor & Controller of Examination & Nodal Officer, Red Ribbon Club, Department of Science Modern Institute of Technology Dhalwala, Rishikesh (Uttarakhand), India.
Dr. M. Vamshi Krishna Associate Dean SOET & H.O.D, Department of Electronics & Communication Engineering, Centurion University of Technology & Management Paralakhemundi, Odisha, India.
Dr. R. Padmavathi Assistant Professor, Department of Chemistry, Sree Sastha Institute of Engineering and Technology, Chennai (Tamil Nadu), India.
Dr. B. Kavitha Rani Professor, Department of Computer Science & Engineering, CMR Technical Campus Kandlakoya (V), Medchal (M), Hyderabad (Telengana), India.
Dr. Srinivas Konda Professor, Department of Computer Science & Engineering, CMR Technical Campus Kandlakoya (V), Medchal (M), Hyderabad (Telengana), India.
Dr. Keyur D. Bhatt Professor & Head, Department of Chemistry, Mehsana Urban Institute of Sciences, Ganpat University, Kherva (Gujarat), India
Dr. Appala Srinuvasu Mutipati Associate Professor, Department of Computer Science and Engineering, Raghu Institute of Technology, Dakamarri, Visakhapatnam (A.P.), India
Dr. Froilan D. Mobo Associate Professor, Designated Assistant Unit Head for General Education Unit, Philippine Merchant Marine Academy, San Narciso, Zambales
Dr. Jaya Christiyan K.G Professor, Department of Mechanical Engineering, Ramaiah Institute of Technology, Bangalore (Karnataka), India
Dr. Dipti Ranjan Malik Assistant Professor, Department of Sociology, NIMS University, Jaipur (Rajasthan), India
Dr. Vijaya Lakshmi Pothuraju Associate Professor, Department of MBA, CMR College of Engineering & Technology, (Autonomous) Kandlakoya, Hyderabad, India
Dr. Ahmmed S. Ibrehem Assistant Professor, Department of Chemical Engineering, Dhofar University, Salalah, Oman
Dr. K. G. Durga Prasad Professor & Head, Department of Mechanical Engineering, Technical Campus, GVP College for Degree & P.G.Courses, Rushikonda, Visakahapatnam (Andhra Pradesh), India
Dr. Sucheta Ghorai Giri Assistant Professor, Department of Microbiology, Parul Institute of Applied Sciences, Parul University, Limda, Vadodara (Gujarat), India
Dr. K. Narendra Kumar Associate Professor, Department of Computer Science & Engineering, Chalapathi Institute of Engineering and Technology, Lam, (Andhra Pradesh), India
Dr. B. Suresh Babu Professor, Department of Electrical & Electronics Engineering, Shri Vishnu Engineering College for Women Vishnupur, Bhimavaram (Andhra Pradesh), India
Dr. A. Akila Assistant Professor, Department of Computer Engineering, Vels Institute of Science, Technology & Advanced Sciences, Pallavaram, Chennai (Tamil Nadu), India
Dr. R. Parameswari Associate Professor, Department of Computer Engineering, Vels Institute of Science, Technology & Advanced Sciences, Pallavaram, Chennai (Tamil Nadu), India
Dr. Dipak Gade Program Manager, Department of Computer Engineering, IGATE Solutions Ltd., Pune (Maharashtra), India
Dr. K. Mohan Das Professor & Head, Department of Civil Engineering, Ashoka Institute of Engineering and Technology, Hyderabad (Telangana), India
Dr. Hitesh V. Paghadar Associate Professor, Department of Electrical Engineering, OM Engineering College, Chokli (Gujarat), India
Dr. P. Prathap Professor, Department of Mechanical Engineering, Hindusthan Institute of Technology, Malumichampatty (Tamil Nadu), India
Dr. A. V. Senthil Kumar Director, Department of Masters in Computer Application, Hindusthan College of Arts and Science, Coimbatore (Tamil Nadu), India
Dr. Rishi Kumar Assistant Professor, Department of Science, Guru Nanak College, Budhlada (Punjab), India
Dr. Surya Nagendra Pentakota Associate Professor, Department of Mechanical Engineering, Nadimpalli Satyanarayana Raju Institute of Technology, VIsakhapatna, (Andhra Pradesh), India
Dr. Santanu Koley Associate Professor, Department of Computer Science and Engineering, Budge Budge Institute of Technology, Nischinta Pur, (West Bengal), India
Dr. Madhuri C. Hingane Assistant Professor, Department of Computer Science, Pune District Education Association’s College of Engineering, Pune (Maharashtra), India
Dr. M. M. Bagali Professor and Head, Department of MBA, Acharya Institute of Technology, Bangalore (Karnataka), India
Dr. Mayank Jain Professor, Department of Electronics and Communication Engineering, Sri Indu College of Engineering & Technology, Sheriguda (Telangana), India
Dr. Sharanappa Chapi Assistant Professor, Department of Physics, K.L.E. Society’s J.T. College, Gadag-Betgeri, (Karnataka), India
Dr. A. Vamshi Krishna Reddy Lecturer, Centre for Environment, Institute of Science & Technology, Jawaharlal Nehru Technological University, Hyderabad (Telangana), India
Dr. Amit Bindaj Karpurapu Professor, Department of Electronics and Communication Engineering, Swarna Bharathi Institute of Science and Technology (SBIT), Khammam (Telangana), India
Dr. Atul Ramdas Kolhe Associate Professor, Department of Civil Engineering, Dr. D Y Patil Institute of Engineering Management & Research, Pimpri- Chinchwad (Maharashtra), India
Dr. B. Bazeer Ahamed Associate Professor, Department of Computer Science and Engineering, Balaji Institute of Technology & Science, Laknepally (Telangana), India
Dr. C. Arunabala Professor, Department of Electronics and Communication Engineering, KITS (KKR & KSR Institute of Technology & Sciences) Vinjanampadu, Vatticherukuru Mandal, Guntur (Andhra Pradesh), India
Dr. A. Swarupa Rani Associate Professor, Department of Computer Science, Siddharth institute of Engineering & Technology, Narayanavanam (Andhra Pradesh), India Volume-9 Issue-10, August 2020, ISSN: 2278-3075 (Online) S. No Published By: Blue Eyes Intelligence Engineering and Sciences Publication Page No.
Authors: Soumitra De, Jaydev Mishra
Paper Title: Normalization of Inconsistent Neutrosophic Data in a Model View of Relational Database Abstract: Database of neutrosophic relational model simplify the relational classical database model by accepting inaccurate data in the neutrosophic form. Here, authors are focused on extensive view of different normalizing neutrosophic forms of classical relational model using neutrosophic set. Initially, in this work authors have introduced the concept of neutrosophic closure of attribute set and neutrosophic key which are essential to develop the normalization concepts of neutrosophic relational database. An algorithm has been developed by the authors for neutrosophic closure based on attributes .These attributes are used to locate the neutrosophic key easily. Then, we have used the -nfd , partial -nfd concepts and neutrosophic key as focused in [1] different forms of normalization for the database of neutrosophic relational. Finally, this neutrosophic normalization technique is demonstrated on some real life neutrosophic relation.
Keywords: Neutrosophic set, similarity measure of neutrosophic data, -nfd, partial -nfd, Neutrosophic key, neutrosophic attribute closure, forms of different neutrosophic normalization.
References: 1. S. De, J. Mishra, “A New Approach of Functional Dependency in a Neutrosophic Relational Database Model”, Asian Journal of Computer Science and Technology , Vol. 8, No. 2, 2019, pp.44-48. 2. E. Codd, “ A Relational Model for Large Shared Data Banks”. Comm. of ACM 13, 1970, pp 377–387. 3. J. Mishra, S. Ghosh, “A Multivalued Integrity Constraint in Fuzzy Relational Database”, International Journal of Computational Cognition 9, 2011, pp. 72–78. 4. G. Chen,E. E. Kerre, J. Vandenbulcke,” Normalization Based on ffd in a Fuzzy Relational Data Model”, Information Systems 21, 1996, pp. 299–310. 1. 5. K.V.S.V.N. Raju, A.K. Majumdar, “Fuzzy functional dependencies and lossless join decomposition of fuzzy relational database system”, ACM Transactions on Database Systems 13, 1988, pp.129–166. 6. A. Yazici, M.I. Sozat, “The Integrity Constraints for Similarity-Based Fuzzy Relational Databases”, International Journal of 1-6 Intelligent Systems 13, 1998, pp. 641–659. 7. O. Bahar, A. Yazici, “Normalization and Lossless Join Decomposition of Similarity-Based Fuzzy Relational Databases”, International Journal of Intelligent Systems 19, 2004, pp. 885–917. 8. L.A. Zadeh, “Fuzzy Sets. Information and Controls 8”, 1965, pp. 338–353. 9. W.L. Gau, D.J. Buehrer, “Vague Sets”, IEEE Trans. Syst. Man, Cybernetics 23, 1993, pp. 610–614. 10. F. Zhao, Z. M. Ma and L. Yan, “A Vague Relational Model and Algebra”, Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007), Vol. 1, 2007, pp: 81-85. 11. F. Zhao and Z. M. Ma, “Vague Query Based on Vague Relational Model”, Springer- Verlag Berlin Heidelberg, AISC 61, 2009, pp: 229-238. 12. J. Mishra , S. Ghosh, “A Vague Multivalued Data Dependency”, International Journal of Fuzzy Information and Engineering, Springer, Vol. 5, No. 4, 2013, pp: 459-473. 13. J. Mishra , S. Ghosh,” Uncertain Query Processing using Vague Sets or Fuzzy Set: Which one isBetter?”, International Journal of Computers, Communications and Control , Vol. 9, No. 6, 2014, pp. 730-740. 14. F. Smarandache, “First International Conference on Neutrosophy, Neutrosophic Probability, Set, and Logic”, University of New Mexico, Vol. 1, No. 3, 2001. 15. M. Arora, R. Biswas, U.S. Pandy, “Neutrosophic Relational Database Decomposition”, International Journal of Advanced Computer Science and Applications,Vol. 2, No. 8, 2011, pp.121-125. 16. S. De, J. Mishra, “Query Processing of Inconsistent Data using Neutrosophic Set”, IEEE International Conference on Computing Communication and Automation, Vol.10, No. 6, 2016, pp. 120-124. 17. S. De, J. Mishra, “Compare Different Similarity Measure Formula Based Imprecise Query on Neutrosophic Data”, International Conference on Advanced Computing and Intelligent Engineering, Vol.5, No.12, 2016, pp:330-334. 18. S. De, J. Mishra, “Inconsistent Data Processing Using Vague Set and Neutrosophic Set for Justifying Better Outcome”, IEEE International Conference on Inventive Communication and Computational Technologies,Vol.7,No.4, 2017, pp. 26-30. 19. S. De, J. Mishra, “Processing of Inconsistent Neutrosophic Data and Selecting Primary Key from the Relation”, International Conference on Inventive Computing and Informatics, Vol.6, No. 7, 2017, pp. 245-250. 20. C.J. Date, “An introduction to Data Base Systems 8/E”, Addison Wesley (2004). 2. Authors: Jeeva. R, Muthukumaran. N
Paper Title: Identification of Fictitious Messages in Social Network using E-Hits and Newsapi Abstract: Social network has become a primary resource for users to send and receive the foremost up-to-date 7-11 data and trend the present events. Currently, most of the social network contains the fictional content that was created by the influential spreaders wherever the message originality and therefore the spreader identity cannot be found which affects the end users. The proposed models to discover fictitious messages are verifying the contextual integrity with the trained classifier using large datasets. But the problem lies in updating of datasets with the recent or trending events from trusted sources in a regular interval. In the existing model, Hypertext- Induced Topic Search (HITS) method has been used for rating posts based on hub score and authority score. The hub score is calculated based on how many posts are posted or liked or tagged by the user and authority score is calculated based on how many users liked or tagged a post. If the user who ranks high in hub score tries to trend the low ranked post in authority score, the user will be marked as spreader. But the problem lies in the identification and verification of the posts that ranks in authority score. In our proposed system, we have enhanced the HITS algorithm by adding a third mechanism called top score which assigns weightage for every post based on the time they have posted. The time and content of the post has been verified by the integrated new model NewsAPI. Based on the three scores, the posts are filtered and matched with the news collected from NewsAPI. The news or posts that have not been matched either with the context or with the time will be marked as fictitious.
Keywords: Authority score, HITS, Hub score, NewsAPI, Spreader, Top score
References: 1. Juan Martinez, Lourdes Araujo “Detecting malicious tweets in trending topics using a statistical analysis of language”, Vol. 40, No. 8, 2013. 2. Daniel Moise, Anca Francisca Cruceru “An empirical study of promoting different kinds of events through various social media networks websites”, Procedia - Social and Behavioral Sciences 109, 2014. 3. Natalia Criado, Jose M. Such “Implicit Contextual Integrity in Online Social Networks”, 2015 4. Xiaoming Fu, Jar-Der Luo, Margarete Boos, “Social Network Analysis: Interdisciplinary Approaches and Case Studies”, 2016. 5. Aleksei Romanov, Alexander Semenov, Oleksiy Mazhelis and Jari Veijalainen “Detection of Fake Profiles in Social Media”, 13th International Conference on Web Information Systems and Technologies, 2017. 6. Ermelinda Oro, Clara Pizzuti, Nicola Procopio, Massimo Ruffolo, “Detecting Topic Authoritative Social Network Users: a Multilayer Network Approach”, IEEE Transactions On Multinetwork, Vol. 20, Issue 5, May 2018. 7. A. Aldhaheri and J. Lee, “Event Detection on large social media using temporal analysis”, in Proc.7thAnnu. computing and communication workshop and conf., Las Vegas, NV, USA, pp.1-6, 2017. 8. Leilei shi, yan wu, lu liu, xiang sun, and liang jiang, “Event Detection and Identification of Influential Spreaders in Social Network Data Streams,” Big Data Mining And Analytics, pp.34–46, vol 1, no.1, 2018. 9. Meet Rajdev, “Fake and Spam Messages: Detecting Misinformation during Natural Disasters on Social Network”, All Graduate Theses and Dissertations - 4462, 2015. 10. Sandeep Sirsat, Dr. Vinay Chavan, “Pattern Matching for Extraction of Core Contents from News Web Pages”, in 2016 Second International Conference on Web Research (ICWR). 11. Stuart E. Middleton, Symeon Papadopoulos and Yiannis Kompatsiaris, “Social Computing for Verifying Social Network Content in Breaking News”, IEEE Internet Computing, DOI 10.1109/MIC.2018.112102235, 2018. 12. Wenhao Zhu, Song Dai, Yang Song and Zhiguo Lu, “Extracting News Content with Visual Unit of Web Pages”, IEEE Communication Surveys & Tutorials, Vol. 16, No. 4, 2014. 13. Zhen Tan, Chunhui He, Yang Fang, Bin Ge and Weidong Xiao1, “ Title-based Extraction Of News Contents For Text Mining”, IEEE Access, Volume 6, October 2018. 3. Authors: Ranjith N, Lincy Mathews.
Paper Title: Genetic Analysis with Feature Reduction to Predict the Onset of Parkinson’s disease Abstract: Parkinson is a disease which directly affects the brain cells and certain movement, voice and other 12-16 disabilities. Hence curable medication is not available in market. The best solution is the early diagnosis to relieve the symptoms of Parkinson’s disease affected people. One major concern effecting public is Parkinson's disease (PD). This paper studies the bias of various traditional algorithms on the voice-based data that has various parameters recorded from Parkinson patients and healthy patients. A brief survey of techniques are mentioned for the prediction of Parkinson's diseases is presented. To accomplish this task, identifying the best feature reduction approach was the primary focus. This paper further applies feature reduction techniques using a genetic algorithm for efficient prediction of Parkinson's disease along with machine learning-based approaches. The proposed method also presents higher accuracy in prediction by using this optimal feature reduction technique.
Keywords: Parkinson’s disease, Genetic algorithm, Feature reduction, Support Vector Machine, K-Nearest Neighbor, Naïve Bayes, Speech data.
References: 1. Jifeng Guo,Hongxu Pan,,Li Shu,,Xinxiang Yan, ,Qiying Sun,,Beisha Tang,, Dongxiao Liang,Qian Xu,,, “Genetic Impact on Clinical Features in Parkinson's Disease: A Study on SNCA-rs11931074,,” pp. 2-4, 2018. 2. K.R, Chaudhuri,,C.Marrasand,,, “Nonmotor features of Parkinson's disease subtypes,,” vol. 31, pp. 1095-1102, 2016. 3. Y.Wang et al,, Z.Liu,, J.Guo,, “Lack of association between IL-10 and IL-18 gene promoter polymorphisms and Parkinson's disease with cognitive impairement in a Chinese population,,,” p. 6, 2016. 4. R.Sindhu,, M.Hariharan,, K.Polat,,, “A new hybrid intelligent system for accurate detection of Parkinson's deisease,,,” vol. 113, pp. 904-913, 2014. 5. Najmeh-Samadiani,, Fayyazifar, Najmeh,, “Parkinson's disease detection using Ensemble techniques and Genetic Algorithm,,,” Artificial Intelligence and Signal Processing Conference,,, pp. 162-165, 2017. 6. Jinse Park,Ki-Won Choi,, Mangal Sain,, Satyabrata Aich,, Hee-Cheol Kim,,, “A Mixed Classification Approach for the Prediction of Parkinson's disease using Nonlinear Feature Selection Technique based on the Voice Recording,,” pp. 959-962, 2017. 7. Z.H. Liu et al,, K.Li, B.S. Tang,,, “LRRK2 A419V variant is a rsk factor for Parkinson's disease in Asian populations,,,” vol. 36, pp. pp. 2908 e11-2908 e15, 2015. 8. Aleksander,, Sadikov, et al,,, “Feasibility of spirography features for objective assessment of motor function in Parkinson's disease,,,” Artificial Intelligence in Medicine,, vol. 13, pp. 54-62,, 2017. 9. D.Karthiga, Dr.P.Sumithra,, “A Survey of Predicting Parkinson's & A typical parkinson's disease in the Primordial stage by using classification techniques in Data Mining,,,” vol. 5, pp. 794-797, 2018. 10. Ahmed Abdulhakim Al-Absi,, Hee-Cheol Kim, Kim younga, Kueh Lee Hui, Mangal Sain, Satyabrata Aich,, , “A Supervised Machine Learning Approach using Different Feature Selection Techniques on Voice Datasets for Prediction of Parkinson's Disease,,” vol. 7, pp. 1116-1121, 2018. 11. I.L.Guan, , H.Lee, I.Lee,,, “Video analysis of Human Gait and Posture to determine Neurological disorders,,” vol. 1, 2008. 12. C.Harris, M.S.Nixon,, P.Huang,, , “Human gait recognition in canonical space using temporal templates,,” vol. 146, pp. 93-100, 1999. 13. Mujammed Hammad Memon, Shah Nazir,, Tanvir Ahmad, Asad Malik, Jian Ping Li, Amin ul Haq, Jalaluddin Khan, Mohammad Shahid, Ijaz Ahad, Amjad Ali,,, “Feature Selection based on L1-Norm Support Vector Machine ans Effective Recognition System for Parkinson's Disease Using Voice Recordings,,,” vol. 7, pp. 37718-37734, 2019. 14. Minhan Yi, Jifeng Guo, Yuan Zhang, Beisha Tang, Qiying Sun, Xinxiang Yan, Qian Xu, Xun Zhou,,, “Genetic Analysis of LRRK2R1628P in Parkinson's Disease in Asian Populations,,” pp. 2-6, 2017. 15. Y. Campos-Roca, L. Naranjo, J. Martin, C. Jperez,,, “ Addressing voice recording replications for Parkinson's disease detection,,” vol. 46, pp. 286-292, 2016. 16. J. Martin, L. Naranjo, C. J. Perez, Y. Campos-Roca,,, “A latent variable-based Bayesian Regression to address recording replications in Parkinson's disease,,” pp. 1447-1451, 2014. 17. R. Cmejla, M. Novotny, E. Ruzicka, J. Rusz,, , “Automatic Evaluation of Articuatory disorders in Parkinson's disease,,,” vol. 22, pp. 1366-1378, 2014. 18. F. Karabiber,, I.Canturk,, “A Machine learning system for the diagnosis of Parkinson's disease from speech signals and its application to multiple speech signal types,.,” vol. 41, pp. 5049-5059, 2016. 19. Prashant, Shrivastava, et al,,, “A survey of nature-inspired algorithms for feature selection to identify Parkinson's disease,,,” vol. 139, pp. 171-179, 2017. 20. C.B. Schneider, A.Storch, L.Klingelhofer et al,,, “Quantitative assessment of non-motor fluctuations in Parkinson's disease using the Non-Motor Symptoms ,,,” vol. 122, pp. 1673-1684, 2015. 21. L.M. Shulman, R.von Coellnand ,, “Clinical subtypes and genetic heterogeneity: of lumping and splitting in Parkinson disease,,,” vol. 29, pp. 727-734, 2016. 22. Dr.Hariganesh S,Gracy Annamary S,, “A Survey of Parkinson's Disease Using Data Mining Algorithms,,” vol. 5, pp. 4943-4944, 2014. 23. R. et al, Prashanth,, “High-accuracy detection of early Parkinson's disease through multimodal features and machine learning,,,” pp. 13- 21, 2016. 24. Tomas Arias-Vergara, Juan Camilo Vasquez-Correa, Bjorn Eskofier, J.R. Orozco-Arroyave, Elmar Noth, Jochen Klucken, , “Multimodal Assessment of Parkinson's disease: A Deep Learning Approach,,,” vol. 23, pp. 1618-1630, 2019. 25. D. Dougherty, J.Westin, K.Taha,,, “Classification of speech intelligibility in Parkinson's disease,,” vol. 34, pp. 35-45, 2015. 26. M.Venkateswara Rao, Tarigoppula V.S Sriram, DSVGK Kaladhar, G.V.SatyaNarayana, , “A Comparative And Prediction Analysis for the Diagnosis of Parkinsons Disease using Data Mining Techniques on Voice Datasets,,,” vol. 11, 2016. 27. R.B.Postuma, D.Berg, C.H.Adler et al,, , “MDS research criteria for prodromal Parkinson's disease,,,” vol. 30, pp. 1600-1611, 2015. 28. P.Porwik, W.Froelich, K.Wrobel,,, “Diagnosis of Parkinson's disease using speech samples and threshold-based classification,,,” pp. 1358-1363, 2015. 29. J.Martin, Y.Campos-Roca, L.Naranjo, C.J.Perez,,, “A two stage variable selection and classification approach for Parkinson's disease detection by using voice recording replications,,,” vol. 142, pp. 147-156, 2017. 30. A.U.Haq et al,, “Comparative analysis of the classification performance of machine learning classifiers and deep neural network classifier for prediction of parkinson disease,,” pp. 101-106, 2018. 31. B.S. Tang, K.Fan, Y.Q. Wang et al,,, “The GBA, DYRK1A and MS4A6A polymorphisms influence the age at onset of Chinese Parkinson Patents,,” vol. 621, pp. 133-136, 2016. 32. B.S.Tang, Y.Yang, L.Weng et al, , “Genetic identification is critical for the diagnosis of parkinsonism: a Chinese pedigree with early onset of parkinsonism,;,” p. 10, 2015. 33. N.Bregman, A.Thaler, T.Gurevich et al, , “Parkinson's disease phenotype is influenced by the severity of the mutations in the GBA gene,,” vol. 45, pp. 45-49, 2018. 34. C.Y.McLean, M.A.Nalls, J.Rick et al, , “Diagnosis of Parkinson's disease on the basics of clinical and genetic classification: A populaion-based modelling study,,” vol. 14, pp. 1002-1009, 2015. Authors: Youngkeun Choi, Jae Won Choi Prediction of Telecom Churn using Comparative Analysis of Three Classifiers of Artificial Neural Paper Title: Network Abstract: The purpose of this study is to evaluate existing individual neural network-based classifiers to compare performance measurements to improve the accuracy of deviance predictions. The data sets used in this white paper are related to communication deviance and are available to IBM Watson Analytics in the IBM community. This study uses three classifiers from ANN and a split validation operator from one data set to predict the departure of communications services. Apply different classification techniques to different classifiers to achieve the following accuracy with 75.63% for deep running, 77.63% for perceptron, and 77.95% for autoMLP. With a limited set of features, including the information of customer, this study compares ANN's classifiers to derive the best performance model. In particular, the study shows that telecom service companies with practical implications to manage potential departures and improve revenue.
4. Keywords: Artificial neural network, Telecom service, Churn; Deep learng, Perceptron, AutoMLP.
References: 17-20 1. Z. Zhang, R. Wang, W. Zheng, S. Lan, D. Liang, and H. Jin, “Profit maximization analysis based on data mining and the exponential retention model assumption with respect to customer churn problems,” In Data Mining Workshop (ICDMW), 2015 IEEE International Conference on (pp. 1093-1097). IEEE. 2. E. Shaaban, Y. Helmy, A. Khedr, and M. Nasr, “A Proposed Churn Prediction Model,” International Journal of Engineering Research and Applications, vol. 2, issue. 4, 2012, pp. 693-697. 3. A. Idris, A.Iftikhar, and Z. Rehman, “Intelligent churn prediction for telecom using GP-AdaBoost learning and PSO undersampling,” Cluster Computing, vol. 22, 2017, pp.7241-7255. 4. A. Anjum, A. Zeb, L. Afridi, P. Shah, et al., “Optimizing Coverage of Churn Prediction in Telecommunication Industry,” International Journal of Advanced Computer Science and Applications, vol. 8, issue 5, 2017, pp. 179-188. 5. W. Verbeke, D. Martens, C., Mues, and B. Baesens, “Building comprehensible customer churn prediction models with advanced rule induction techniques,” Expert Systems with Applications, vol. 38, issue 3, 2011, pp. 2354-2364. 6. I. M. Mitkees, S. M. Badr, and A. I. B. Elseddawy, “Customer churn prediction model using data mining techniques,” In Computer Engineering Conference (ICENCO), 2017 13th International (pp. 262-268). IEEE. 7. I. M. Mitkees, S. M. Badr, and A. I. B. Elseddawy, “Customer churn prediction model using data mining techniques,” In Computer Engineering Conference (ICENCO), 2017 13th International (pp. 262-268). IEEE. 5. Authors: Krishnandan Verma, Debozani Borgohain, B.R. Sharma
Paper Title: Soret Effect Through a Rotating Porous Disk of MHD Fluid Flow Abstract: The present study attempts to investigate numerically the problem due to rotating porous disk of 21-28 MHD fluid flow with Soret effect using Darcy-Forchheimer model in a steady laminar Newtonian fluid. By using similarity transformation the governing equations of continuity, momentum, energy and concentration are converted into a system of nonlinear ODE’s. Matlab’s built in solver bvp4c has been employed to solve numerically the coupled ODE’s. Numerical results are obtained for velocity (radial, axial and tangential), temperature and concentration profiles for various parameters and are illustrated graphically. The effect of suction parameter on the radial and tangential skin friction coefficients and rate of heat transfer are obtained and compared with the one available in literature. The results are found to be in good agreement. Numerical values of Skin-friction coefficient, Nusselt number and Sherwood number are obtained for different values of parameter.
Keywords-- bvp4c, Darcy- Forchheimer, Porous rotating disk, Soret effect.
References: 1. Von Karman T. Uber, 1921, “ Laminare und turbulente Reibung. Zeitschrift für Angewandte Mathematik und Mechanik”, 1:1233- 1255. 2. Cochran WG., 1934, “The flow due to a rotating disk”. Proceedings of the Cambridge Philosophical Society, 30:365-375. 3. Benton ER., 1966, “On the flow due to a rotating disk”, Journal of Fluid Mechanics, 24:781-800. 4. Millsaps, K and Pohlhausen, K., 1952, “Heat transfer by laminar flow from a rotating disk”, J. Aeronaut. Sci., 19, pp.120-126. 5. Sparrow, E. M., and Gregg, J.L., 1960, “Mass transfer, flow and heat transfer about a rotating disk”, ASME J. Heat transfer, Nov., 294- 302. 6. Hassan, A.L.A. and H.A. Attia, 1997,“Flow due to a rotating disk with Hall effect,” Physics Letters A, 228, 246-290. 7. Kelson, N. and A. Desseaux, 2000, “Notes on porous rotating disk flow,” ANZIAM J., 42(E), C837-C855. 8. Maleque, Kh.A. and M.A. Sattar, 2003, “Transient Convective Flow Due to a Rotating Disc with Magnetic Field and Heat Absorption Effects,” Journal of Energy, Heat and Mass Transfer, 25, 279-291. 9. Attia, H.A., 2009, “Steady flow over a rotating disk in porous medium with heat transfer nonlinear analysis”, Modelling and Control, Vol. 14, No. 1, 21-26. 10. Maleque, Kh. A., 2010, “Dufour and Soret effects on unsteady MHD convective heat and mass transfer flow due to rotating disk”, Latin American Applied Research, 40:105-111. 11. Sharma, B.R. and Borgohain, D., 2014, “Influence of Chemical reaction, Soret and Dufour effects on heat and mass transfer of a binary fluid mixture in porous medium over a rotating disk”, IOSR Journal of Mathematics, e-ISSN: 2278-5728, p-ISSN: 2319-765X. Vol. 10(6) Ver III, 73-78. 12. Dhanpat, B. S. and Singh, S.K., 2015,”Unsteady two layer film flow on a non uniform rotating disk in presence uniform transverse magnetic field”, Applied Mathematics and Computation, Vol. 258, 545-555. 13. EL-Dabe, Nabil, T., Hazim A. Attia and Mohamed A. I. Essawy, Ibrahim H. Abd-elmaksoud, Ahmed A. Ramadan and Alaa H. Abdel-Hamid, 2019, “Non-linear heat and mass transfer in a thermal radiated MHD flow of a power-law nanofluid over a rotating disk”, SN Applied Sciences (online), 1:551. 14. Nield, D. A. and Bejan, A.,2006, “Convection in Porous Media”, Third edition, ISBN: 0-387-29096-6, 978-0387-29096-6, 1-14. 15. P. Forchheimer., 1901, Wasserbewegung durch Boden. Zeitschrift des Vereines Deutscher Ingenieuer, 45 edition. 16. Gad-el-Hak M., 1999, “The fluid mechanics of microdevices”. The freeman scholar lecture. Journal of Fluids Engineering-Transactions of the ASME ;121: 5-33. 17. Cobble MH., 1977, “Magnetohydrodynamic flow with a pressure gradient and fluid injection”, Journal of Engineering Mathematics, 11:249-256. 18. Maleque A. K., Sattar A. M., 2005,”Steady laminar convective flow with variable properties due to a porous rotating disk”, Journal of Heat Transfer; 127: 1406-1409. 19. Osalusi E, Sibanda P., 2006, “On variable laminar convective flow properties due to a porous rotating disk in a magnetic field”, Romanian Journal of Physics,51 (9-10): 933-944. 20. Frusteri F, Osalusi E., 2007, “On MHD and slip flow over a rotating porous disk with variable properties”, International Communications in Heat and Mass Transfer, 14:34, 492-501. 6. Authors: Sai Venu Prathap Katari, K. Kishore Kumar, K Gopi
Paper Title: A Real-Time Haze Removal & Mosaicing using Rapid Prototype Hardware Abstract: In this paper, a pivotal technique was proposed that reduces the haze and combines the haze free 29-34 image to increase the Field of View (FoV) in real-time with a rapid prototype hardware device. The Initial focus is to reduce the haze in an image with Dark Channel Prior Technique and the FSD method is utilized to mosaic the haze free images. Low contrast may occur due to the scattering light, air particles or fog in nature which results in a haze image that needs to be reduced and enhance the image for better vicinity. Haze reduction approach depends on entropy and information fidelity. Our Haze free algorithm executes multiple phases such as dark channel prior computation, estimation and refinement of transmission map and restoration of RGB values. The second technique is the mosaic process that improves the field of view of a scene and the phases that execute are corner detection, extraction, geometric computation and blending. Our experimental results have shown better when compared to the other algorithms. The whole process is executed in real-time with a standalone device called Intel compute stick.
Keywords: Haze, Dark Channel Prior, Mosaic, Steerable filters, FAST, Corner detection.
References: 1. Wencheng W., Member, Xiaohui Y., “Recent Advances in Image Dehazing” IEEE/CAA Journal of Automatic Sinica, Vol. 4, No. 3, July 2017. 2. R. T. Tan, “Visibility in bad weather from a single image,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2008, pp. 1-8. 3. R. Fattal, “Single image dehazing,” ACM Transactions on Graphics (TOG), vol. 27, no. 3, pp. 72, 2008. 4. Lu, Huimin & Li, Yujie & Nakashima, Shota & Serikawa, Seiichi. Single Image Dehazing through Improved Atmospheric Light Estimation. Multimedia Tools and Applications. Vol. 7, Issue 1, May 2015. V10.1007/s11042-015-2977-7. 5. Merlin L., Agnel L. “Haze Image Enhancement Using Visibility Restoration Technique” IJLTET, Vol. 7, Issue 1, May 2016. 6. R. Gonzalez and E. Richard, “Woods, digital image processing,” ed: Prentice Hall Press, ISBN 0-201-18075-8, 2002. 7. S. G. Narasimhan and S. K. Nayar. "Contrast restoration of weather degraded images," IEEE Trans. Pattern Analysis and Machine Intelligence (TPAMI), vol. 25, no.6, pp. 713-724, 2003. 8. E. J. McCartney, Optics of the Atmosphere: Scattering by Molecules and Particles. New York, USA: John Wiley and Sons, Inc., 1976, pp. 1¡42. 9. M. Pedone and J. Heikkila, “Robust airlight estimation for haze removal from a single image,” in Proc. 2011 IEEE Computer Society Conf. Computer Vision and Pattern Recognition Workshops, Colorado Springs, CO, USA, 2011, pp. 90¡96. 10. K.S.V. Prathap, S.A.K.Jilani and P. R. Reddy “A Real-time Image Mosaic Performance Measurements” International Journal of Pure and Applied Mathematics, Vol. 118 No. 18 2018, 3699-3706 11. E. J. McCartney, “Optics of the atmosphere: scattering by molecules and particles,” New York, John Wiley and Sons, Inc. 1976. 12. S. G. Narasimhan and S. K. Nayar. “Vision and the atmosphere,” International Journal of Computer Vision (IJCV), vol. 48, no. 3, pp. 233-254, 2002. 13. S. G. Narasimhan and S. K. Nayar, “Removing weather effects from monochrome images,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2001. 14. K He, J Sun, X Tang, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR, Miami, 2009), pp. 1956–1963 15. K He, J Sun, X Tang, Single image haze removal using dark channel prior. IEEE Trans. Pattern Anal. Mach. Intell. 33(12), 2341–2353 (2010) 16. K.S.V. Prathap, S.A.K.Jilani and P. R. Reddy “A Real-time Image Mosaicing using Single Board Computer” Journal of Engineering and Applied Sciences Vol. 14, Issue 10, 3150-3157, 2019. 17. Edward Rosten, Reid Porter, and Tom Drummond. Faster and Better: “A Machine Learning Approach to Corner Detection”. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.32, No.1, January- 2010. 18. Alahi, Alexandre, Raphael Ortiz, and Pierre Vandergheynst. “Freak: Fast retina keypoint.” Computer Vision and Pattern Recognition (CVPR), IEEE Conference on. IEEE, 2012. 19. V. Vora, A. Suthar, Y. Makwana, and S. Davda, “Analysis of compressed image quality assessments” IJAEA, Jan. 2010 Authors: Rahul Nigam, Santosh Pawar
Paper Title: A Novel NOR-Type TCAM Deploy Dual-VT cell with OR-Type Cascade Match-Line Structure Abstract: We look over improvements in the schemes of large size content addressable memory (CAM). A CAM is a very important device that executes the routing table function within a single clock cycle in network router to transmit information over the network. CAMs are particularly popular in network switches to classify and sending information packets, they are also helpful in other different applications that require fast information retrieval from routing table. The primary CAM configuration challenge is to decrease power dissipation related with the lot of parallel activity in memory circuitry during search operation. As innovation going on in technology scaling, it continues minimizing the dynamic power dissipation of CAMs, however it also rises the leakage current of transistors. Thus, the static power is turning into a noteworthy bit of the whole power dissipation in CAMs. Here, we introduced a procedure which advantageous for high capacity Ternary Content Addressable Memory (TCAM) that minimize the static power dissipation in SRAM storage cell part and speed up activity in searching part of TCAM cell. We also divide whole memory into equivalent segments which improve performance of our design. We examine the different schemes and introduced the trade-offs of applying the techniques. Simulation and design have done by using Tanned EDA V.16 tool. For recreations of Low power TCAM structures we utilized predictive technology model (PTM) 45nm for high performance (HP) and low power (LP), which incorporate metal gate, high-k and stress effect of CMOS technology. 7. Keywords: Dual-VT, High capacity, Low power, OR-type Match-line, TCAM. 35-39 References: 1. L. Chisvin and R. J. Duckworth, “Content-addressable and associative memory: alternatives to the ubiquitous RAM”, in IEEE Computer Society, vol. 22, no. 7, pp. 51-64 (1989). 2. K. E. Grosspietsch, “Associative processors and memories: a survey”, in IEEE Micro, vol. 12, no. 3, pp. 12-19 (1992). 3. I. Arsovski and A. Sheikholeslami, “A current-saving match-line sensing scheme for content-addressable memories”, in IEEE International Solid-State Circuits Conference, Digest of Technical Papers, ISSCC, San Francisco, CA, USA, vol.1, pp. 304-494 (2003). 4. I. Arsovski and A. Sheikholeslami, “A mismatch-dependent power allocation technique for match-line sensing in content-addressable memories”, in IEEE Journal of Solid-State Circuits, vol. 38, no. 11, pp. 1958-1966 (2003). 5. K. Pagiamtzis and A. Sheikholeslami, “Content-addressable memory (CAM) circuits and architectures: a tutorial and survey”, in IEEE Journal of Solid-State Circuits, vol. 41, no. 3, pp. 712-727 (2006). 6. J. Zhang, Y. Ye and B. Liu, “A New Mismatch-Dependent Low Power Technique with Shadow Match-Line Voltage-Detecting Scheme for CAMs”, Proceedings of the International Symposium on Low Power Electronics and Design, pp. 135-138, Tegernsee, Germany (2006). 7. N. Mohan and M. Sachdev, “Low-Leakage Storage Cells for Ternary Content Addressable Memories”, in IEEE Transactions on Very Large Scale Integration (VLSI) Systems, vol. 17, no. 5, pp. 604-612 (2009). 8. A. T. Do, C. Yin, K. Velayudhan, Z. C. Lee, K. S. Yeo and T. T. H. Kim, “0.77-fJ/bit/search Content Addressable Memory Using Small Match Line Swing and Automated Background Checking Scheme for Variation Tolerance”, in IEEE Journal of Solid-State Circuits, vol. 49, no. 7, pp. 1487-1498 (2014). 9. J. Zhang, S. Zheng, F. Teng, Q. Ding and X. Chen, “An OR-type cascaded match line scheme for high-performance and EDP-efficient ternary content addressable memory”, IEEE Nordic Circuits and Systems Conference (NORCAS), Copenhagen, Denmark, pp. 1-6 (2016). 8. Authors: Osama M. Al-Habahbeh
Paper Title: Configuration of Hybrid Fuel-Electric Airplane Model Based on Full Flight Path Performance Abstract: The feasibility of enhancing the efficiency of hybrid fuel-electric airplane is investigated. The airplane 40-45 model considered in this work is a hybrid version of Aerosonde propelled by an integrated system of internal combustion engine (ICE) and electric motor (EM). Modified versions of Breguet equation are used to calculate the contribution of the ICE propulsion to the range and endurance of the airplane. On the other hand, the range and endurance components due to EM propulsion are calculated using Payne range strategy based on battery capacity. In order to find the most feasible propulsion configuration; multiple configurations are compared; including conventional all-fuel, full-electric, parallel-hybrid and fuel-first strategy (FFS), which is based on parallel-hybrid design, where fuel is burned during the early phases of the flight then the flight is completed in the fully-electric mode. The preceding propulsion types are investigated for all flight phases including takeoff, climb, cruise, descent, and landing. Impact on airplane weight due to additional equipment is considered. It is found that by adopting the FFS, range can be extended by 7% and endurance by 6% above the parallel-hybrid case. In terms of fuel consumption, implementing FFS yields a fuel saving of 6% relative to parallel hybrid.
Keywords: Fuel-first strategy, hybrid airplane, hybrid propulsion, parallel-hybrid. References: 1. C. Friedrich, P. A. Robertson. (2014). Design of a hybrid-electric propulsion system for light aircraft. 14th AIAA Aviat. Technol., Integration, and Operations Conf. Atlanta, Georgia. Available: https:// arc.aiaa.org/doi/10.2514/6.2014-3008. 2. G. G., Venson. (2013). Processo de Desenvolvimento de Aeronaves. [Personal Collection for Aircraft Design]. Aeronautical Engineering Program, Faculty of Mechanical Engineering, Federal University of Uberlândia, Uberlândia, Minas Gerais. Available: https://repositorio. ufu.br/bitstream/123456789/21986/1/DesenvolvimentoValidacaoSoftware.pdf. 3. M. Voskuijl, J. V. Bogaert, A. G. Rao. (2018). Analysis and design of hybrid electric regional turboprop aircraft. CEAS Aeronaut J., Vol. 9, pp.15-25. Available: https://repository.tudelft.nl/islandora/object/uuid. 4. I. H. Mengistu. (2011). Small internal combustion engine testing for a hybrid-electric remotely-piloted aircraft. Air Force Institute of Technology, Wright-Patterson Air Force Base, Ohio. Available: https://scholar.afit.edu/cgi/viewcontent.cgi?article=2344&context=etd. 5. M. Hepperle. (2012). Electric flight-potential and limitations. German Aerospace Center, Institude of Aerodynamics and Flow Technology, Braunschweig, Germany. Available: https://elib.dlr.de/ 78726/1/MP-AVT-209-09.pdf. 6. M. J. Miller. (2004). Propulsion systems for hybrid vehicles. The Institution of Electrical Engineers, London. Available: https://www. researchgate.net/publication/288369175_Propulsion_Systems_for_Modern_Hybrid_and_Electric_Vehicles. 7. C. Friedrich, P. A. Robertson. (2015). Hybrid-electric propulsion for aircraft. Journal of Aircraft, Vol. 52 (1). pp.176-189. Available: https://arc.aiaa.org/doi/10.2514/1.C032660. 8. T. Katrašnik, F. Tranc, O. S. Rodman. (2007). Analysis of the energy conversion efficiency in parallel and series hybrid powertrains. IEEE Transactions on Vehicular Technology. Vol. 56(6). pp.3649-3659. Available: https://trid.trb.org/view/841804. 9. J. Sliwinski, A. Gardi, M. Marino, R. Sabatini. (2017). Hybrid-electric propulsion integration in unmanned aircraft. Energy, Vol. 140, pp.1407-1416. Available: https://www.researchgate.net/publication/ 317264076_Hybrid- Electric_Propulsion_Integration_in_Unmanned_Aircraft. 10. A. X. Ang, A. G. Rao, T. Kanakis, W. Lammen. (2019). Performance analysis of an electrically assisted propulsion system for a short range civil aircraft. Proc IMechE Part G: J Aerospace Engineering. Vol. 233(4), pp.1490-1502. Available: https://journals.sagepub.com/doi/full/ 10.1177/0954410017754146. 11. J. Hoelzen, Y. Liu, B. Bensmann, C. Winnefeld, A. Elham, J. Friedrichs, R. H. Rauschenbach. (2018). Conceptual design of operation strategies for hybrid electric aircraft. Energies. Vol.11(1), pp.217. Available: https://doi. org/10.3390/en11010217. 12. K. T. Chau, Y. S. Wong. (2002). Overview of power management in hybrid electric vehicles. Energy conversion and management. Vol. 43(15), pp.1953-1968. Available: https://www.sciencedirect.com/ science/article/pii/S0196890401001480. 13. P. H. Stephen. (1999). A miniature power plant for very small, very long range autonomous aircraft. Technical report, US Department of Energy, Contract No. DE-FG03-96ER82187 (Phase II SBIR), The Insitu Group, Washington, USA. Available: https://www.sbir.gov/ sbirsearch/detail/ 332308. 14. Barnard Microsystems Ltd. (2019). First Atlantic crossing by an unmanned aircraft, Taunton, UK. Available: https://barnardmicrosystems.com/ UAV/milestones/atlantic_crossing_1.html. 15. Plettenberg Motors, Trinity Aries Limited, London, UK. (2020). Available: https://www.trinityaries.com/shop/b2b-products/ plettenberg -motors/plettenberg-hp-320-30-inrunner-brushless-dc-electric-motor. Authors: Komivi Démocrite Koudoufio, Anand Mohan Sharma, Gopal Singh Feasibility of Storing Lightning Energy Being Discharged through a Lightning Arrester by a Paper Title: Capacitor Abstract: Lightning phenomenon over the years has been subject of debate of scientists as well as that of people. For some, it is the manifestation of their deities, and, for others it is nothing but a simple display of colors along with fearsome sounds with a destructive power. We know by the work of scientists like Benjamin Franklin (18th century) that it was not what we used to think, and, has some explanations. We now, have understood how the clouds get charged, and how they, in turn, induce charges on the surface of the earth below them. The different types of strokes were also better understood along the way. With the grasp of the real phenomenon, scientists worked out its multiple threats to transmission lines, and therefore, came up with some protective devices to avoid the total catastrophe that could occur should these strokes be left without any preventive measures. Hence, many protective devices came to life with special applications; one of which the lightning arrester. The latter one helps a lot, especially nearby substations by grounding lightning induced energy. We also know that scientists have been discussing the possibility of capturing lightning energy, and, use it to compensate the deficit in energy demand from the world needs in terms of energy. We are using here a 9. capacitor-lightning arrester combination to try and store the lightning-induced energy in transmission lines. We shall carry out this work by making use of the ability of the capacitor while subjected to a surge, and then, find out about the energy it can store by getting charged up. we shall also make use of SmartDraw software for our 46-49 designs and models. The aim here is to target how we could possibly break the grounds in the mastering of the ever-lost lightning energy to ground.
Keywords: Lightning phenomenon; strokes; multiple threats; protective devices; lightning arrester; capturing.
References: 1. Sanketa Shivalli, “lightning phenomenon, its effects and sets a methodology to be followed to provide a solution to both the direct and indirect effects of a lightning strike.”, IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE), Volume 11, Issue 3Ver. I (May. – Jun. 2016), PP 44-50 2. Vladimir A. Rakov, “Lightning Discharge and Fundamentals of Lightning Protection”, Journal of Lightning Research, 2012, 4, (Suppl 1: M2) 3-11 3. A. Kalair, N. Abas and N. Khan, “Lightning Interactions with Humans and Lifelines”, Journal of Lightning Research, 2013, 5, 11-28 4. M.R. Ahmad, M.R.M. Esa, M. Rahma1, V. Cooray and E. Dutkiewicz, “Lightning Interference in Multiple Antennas Wireless Communication Systems”, Journal of Lightning Research, 2012, 4, 155-165 5. J B Gupta, “Switchgear and Protection”, Third edition, January 1, 2013. 10. Authors: Megha Sharma
Paper Title: Machine Learning to Calculate Trip Budget Abstract: Trip planning requires effort. Majority of which is consumed in balancing preferences of travel and 50-54 stay; with budget. This effort can be minimized using budget estimator. Summing up the total costs to calculate budget is ideally correct. Practically, budget can differ from individual to individual based on their nature. Some prefer to spend more while some less. Machine Learning could help predict human nature using feedback mechanism. Taking feedback about total cost incurred and comparing it to actual estimate could give insight about user nature to the system. In this paper, we have built a budget estimator that considers user preferences and uses regression algorithm to compute costs. It later asks user to input the actual cost incurred, correcting its previous estimate and uses the updated entry to drive data to be more user-specific. The system gives percent classification of 84% and percent recognition of 72.27%.
Keywords : Budget Estimator, Feedback, Machine Learning, Percent Classification, Percent Recognition
References: 1. Title: How to create a realistic travel budget that actually works. Available :https://budgetbakers.com/blog/create-realistic-travel- budget#:~:text=If%20you're%20still%20in,spending%20choices%20on%20the%20go. 2. Title: Preparing your dataset for machine learning : 8 basic techniques that make your data better. Available : https://www.altexsoft.com/blog/datascience/preparing-your-dataset-for-machine-learning-8-basic-techniques-that-make-your-data- better/ Authors: Olawale Adepoju, Devaraj Verma C
Paper Title: Prediction and Classification into Benign and Malignant using the Clinical Testing Features Abstract: Breast Cancer is the most often identified cancer among women and a major reason for the increased mortality rate among women. As the diagnosis of this disease manually takes long hours and the lesser availability of systems, there is a need to develop the automatic diagnosis system for early detection of cancer. The advanced engineering of natural image classification techniques and Artificial Intelligence methods has largely been used for the breast-image classification task. Data mining techniques contribute a lot to the development of such a system, Classification, and data mining methods are an effective way to classify data. For the classification of benign and malignant tumors, we have used classification techniques of machine learning in which the machine learns from the past data and can predict the category of new input. This study is a relative study on the implementation of models using Support Vector Machine (SVM), and Naïve Bayes on Breast cancer Wisconsin (Original) Data Set. With respect to the results of accuracy, precision, sensitivity, specificity, error rate, and f1 score, the efficiency of each algorithm is measured and compared. Our experiments have shown that SVM is the best for predictive analysis with an accuracy of 99.28% and naïve Bayes with an accuracy of 98.56%. It is inferred from this study that SVM is the well-suited algorithm for prediction.
Keywords: Breast Cancer, Data Mining, Machine Learning, Naïve Bayes, Support Vector Machine. 11. References: 1. Acha, B., Rangayann R.M, Desautels J.E. L. (2006). "Detection of micro calcifications in mammograms" .SPIE, Bellingham, Recent 55-61 Advances in Breast Imaging, Mammography, and Computer Aided Diagnosis of Breast Cancer. 2. Choudhari G, Swain D, Thakur D, Somase, K. (2012).”An adaptive approach to classify and detect the breast cancer using image processing", International Journal of Computer Applications (0975-8887) 45(17). 3. Dheeba J. WiselinJiji G. (2010) “Detection of micro calcification cluster in mammograms using neural network “, International Journal of Advanced Science and Technology. 4. Erickson, Carissa (2005). “Automated detection of breast cancer using saxs data and wavelet features", (Unpublished doctoral dissertation) university of Saskatchewan, Saskatoon. 5. V. Chaurasia and S. Pal, “Data Mining Techniques: To Predict and Resolve Breast Cancer Survivability,” vol. 3, no. 1, pp. 10–22, 2014. 6. AlirezaOsarech, BitaShadgar,”A Computer Aided Diagnosis System for Breast Cancer”, International Journal of Computer Science Issues, Vol. 8, Issue 2, March 2011 7. Mandeep Rana, Pooja Chandorkar, Alishiba Dsouza, “Breast cancer diagnosis and recurrence prediction using machine learning techniques”, International Journal of Research in Engineering and Technology Volume 04, Issue 04, April 2015. 8. S. Aruna and L. V Nandakishore, “KNOWLEDGE B ASED ANALYSIS OF VARIOUS STATISTICAL TOOLS IN DETECTING BREAST” pp. 37–45, 2011. 9. D. Delen, G. Walker, and A. Kadam, “Predicting breast cancer survivability: A comparison of three data mining methods” Artif.Intell. Med., vol. 34, pp. 113–127, 2005. doi:10.1016/j.artmed.2004.07.002 10. N. Khuriwal and N. Mishra, "Breast cancer diagnosis using adaptive voting ensemble machine learning algorithm," 2018 IEEMA Engineer Infinite Conference (eTechNxT), New Delhi, 2018, pp. 1-5, doi: 10.1109/ETECHNXT.2018.8385355. 11. Moore K. L, Agur A.M and Dalley A.F (2004). Essential Clinical Anatomy. (4nd Ed.). (W. Kluwer, Ed.) 12. Authors: A. K. Issa, M. H. Idris, M. I. Tikau
Paper Title: Animal Wastes as Thermoplastic Composite Reinforcement Materials for Sustainable Development Abstract: Nowadays solid wastes are becoming serious environmental challenges in Africa and most especially 62-66 in Nigeria. Many strategies that are employed by local, state and federal government to curb the situation are always sabotaged due to poor altitude to waste management and corruption. Among these solid wastes are animal waste viz: chicken feather, animal horn, hoof, bone and hair (wool), human hair, silk and so on. These wastes contain keratin and collagen which make them viable for reinforcement materials in developing thermoplastic composite apart from their lighter weight, availability and bio-degradability. This paper presents review on utilization of animal waste as workable alternative fibre to synthetic fibres which are not recyclable and biodegradable. Furthermore, it provides some important data that can facilitate the usage of these animal fibres in order to achieve both economic and social sustainable development.
Keywords: Animal fibre, Fibre-reinforced, Mechanical properties, thermoplastic References: 1. Ho, M. P. et al., (2012). Critical Factors on Manufacturing Processes of Natural Fibre Composites. Composites: Part (43), 3549–3562 2. Naidu, A. L. and Rao, P. S. V. R. (2016). A review on Chemical Behaviour of Natural Fibre Composite. Int. J. Chem. Sci , 14(4), 2223- 2238. 3. Agunsoye, J. O., Talabi, S. I., Awe, O. and Kelechi, H. (2013). Mechanical Properties and Tribological Behaviour of Recycled Polyethylene/Cow Bone Particulate Composite. Journal of Materials Science Research, 2(2). doi:10.5539/jmsr.v2n2p41 4. Kumar, D. and Rajendra, B. S. (2014). Mechanical and Thermal Properties of Horn Fibre Reinforced Polypropylene Composites. Procedia Engineering, Elsevier ltd 648-659. 5. Saxena, M., Pappu, A., Sharma, A., Haque, R. and Wankkhede, S. (2011). Composite Materials from Natural Resources: Recent Trends and Future Potentials, Advances in Composite Materials -Analysis of Natural and Man-Made Materials, Dr. Pavla Tesinova (Ed.), ISBN: 978-953-307-449- 8, InTech, Availablefrom: http://www.intechopen.com/books/advances-in-compositematerials- analysis-of-natural-andman-made-materials/composite-materials-from-natural-resources-recent-trends-and-future-potentials 6. Nohara, L. B., Cândido, G. M., Nohara, E. L. and Rezende, M. C. (2012). Processing of Carbon Fibre/Peicomposites Based On Aqueous Polymeric Suspension of Polyimide. intech Open Science Open Mind www.Intechopen.Com. 7. Popescu, C. and Wortmann, F. J. (2017, October). Wool- Structure and Mechanical Properties and Technical Products Based on Animal Fibre 8. Kima, N. K., Bhattacharyya, D. and Lin, R. J. T. (2013). Multi-functional Properties of Wool Fibre Composites. Advanced Materials Research -08-30, 747, 8-11. 9. Darshan, S. M., Suresha, B. and Divya, G. S. (2016). Waste Silk Fibre Reinforced Polymer Matrix Composites: A Review. Indian Journal of Advances in Chemical Science, 183-189. 10. Rao, P.D., kiran, C.U. and Prasad, K. E. (2017). Tensile Studies on Random Oriented Human Hair Fibre Reinforced Polyester Composites. Journal of Mechanical Engineering, 37- 44. 11. Elanchezhian, C., Vijaya, R. B., Sughan, M. U., Suseetharan, K. V. Varun, K., Vezhavendan, R. and.Kaosik, R. (2016). Evaluation of Mechanical Properties of Human Hair-Bombyx Mori Silk Fibre-reinforced Epoxy Based Bio-composite. ARPN Journal of Engineering and Applied Sciences, 5498-5505. 12. Kumar, A. (2014). A Study on Mechanical Behaviour of Hair Fibre Reinforced Epoxy Composite. Msc. Dissertation, National Institute of Technology, Rourkela, Mechanical Engineering . 13. Gupta, A. (2014). Human Hair ‘‘Waste’’ and Its Utilization: Gaps and Possibilities. Journal of Waste Management, 1-17. 14. Jagadeeshgouda, K. B., Reddy, P. R. and Ishwaraprasad, K. (2014). Experimental Study on Behaviour of Poultry Feather Fibre: A Reinforcing Material for Composite. International Journal of Research in Engineering and Technology, 3(2), 362-371. 15. Aranberri, I., Montes, S., Azcune, I., Rekondo, A and Grande, H. (2017). Fully Biodegradable Bio-composites with High Chicken Feather Content. Polymers , 9(593), 1-15. 16. Abdullahi, U. and Salihi, A. (2011). Characterization and Investigation into Potential Application of Kano Cattle Horn. Journal of Engineering and Technology, VI (1-2), 32-40. 17. Kumar, D., Boopathy, R. S. and Sangeetha, D. (2016). Investigation on Tribological Properties of Horn Fibre Reinforced Epoxy Composites. International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS, 16(3), 79-87. 18. Cheung, H., Ho, M., Lau, K., Cardona, F. and Hui, D. (2009). Natural fibre-reinforced Composites for Bioengineering and Environmental Engineering Applications. Composites, 655– 663. 19. Mishra, A. (2017). Investigations of Mechanical Characteristics of Chicken Feather-Teak Wood Dust Filled Epoxy Composites. International Journal of Engineering Research and Development, 13( 4), 01-09. 20. Tombolato, L., Novitskaya, E. E., Chen, P., Sheppard, F. A.and McKittrick, J. (2010). Microstructure, Elastic Properties and Deformation Mechanisms of Horn Keratin. Acta Biomaterialia, 319–330. 21. Grigore, M. E. (2017). Methods of Recycling, Properties and Application of Thermoplastic Composite. Recycling, 2(24), 2- 11 22. Savkin, A. N., Andronik, A. V., Gorunov, A. I., Sedov, A. A., and Sukhanov, M. A. (2014). Advanced Materials of Automobile Bodies. European Transport, 10(56). 23. Mallick, P. (2010). Thermoplastics and Thermoplastic–matrix Composites for Lightweight Automotive. USA: Woodhead Publishing Limited. 24. Isiaka, O. O. and Temitope, A. A. (2013). Influence of Cow Bone Particle Size Distribution on the Mechanical Properties of Cow bone Reinforced Polyester Composite. Biotechnology Research International, 1-5. 25. Atuanya, C.U. et al., (2015). Empirical Models for Estimating the Mechanical and Morphological Properties of RecycledLow-density Polyethylene/Snail Shell Bio-composites. Journal of the Association of Arab Universities for Basic Applied Sciences.Retrievedfrom: http://dx.doi.org/10.1016/j.jaubas.2015.01.001. 26. Alam, A. K., Beg, M. D. H., Shubhra, Q. T. H. and Khan, M. A. (2010). Study of Natural Fibre Reinforced Thermoplastic Composite and their Comparative Study. (pp. 1-12). ResearchGate. Retrieved October 29, 2014. 27. Oladele, I. O., Olajide, J. L. and Ogunbadejo, A. S.. (2015). The Influence of Chemical Treatment on the Mechanical Behaviour of Animal Fibre-Reinforced High Density Polyethylene Composites. American Journal of Engineering Research (AJER), 42, 19-26. 13. Authors: Sunil Kumar V, Ramesh Babu D R
Paper Title: Sybil Attack Detection in Vehicular Ad-hoc Networks using Direct Trust Calculation Abstract: Vehicular Ad-hoc Networks (VANETs) are gaining rapid momentum with the increasing number of 67-73 vehicles on the road. VANETs are ad-hoc networks where vehicles exchange information about the traffic, road conditions to each other or to the road-side infrastructures. VANETs are characterized by high mobility and dynamic topology changes due to the high-speed vehicles in the network. These characteristics pose security challenges as vehicles can be conceded. It is critical to address security for the sake of protecting private data of vehicle and to avoid flooding of false data which defeats the purpose of VANETs. Sybil attack is one of the attacks where a vehicle fakes multiple vehicle identity to compromise the whole network. In this work, a direct trust manager is introduced which derives the trust value of each of its neighbor nodes at a regular interval of time. If the trust value is deviated, it confirms sybil attack. The proposed system is compared with the existing system to prove improved sybil attack detection ratio, thus providing better security. NS2 environment is used to prove the simulation results. The experimental results show that the attack detection ratio of SAD-V-DTC is 5 times better than that of the existing system. The packet delivery ratio shows an improvement of 27.27% while the false positive shows a good increase of 65.80% than the existing system.
Keywords: VANET, Sybil Attack, RSA Algorithm, Location Certificate, Direct Trust Calculation, AODV, NS2.
References: 1. A. Luckshetty, S. Dontal, S. Tangade and S. S. Manvi, "A survey: Comparative study of applications, attacks, security and privacy in VANETs," 2016 International Conference on Communication and Signal Processing (ICCSP), IEEE, Melmaruvathur, 2016, pp. 1594- 1598. 2. H. Hamed, A. Keshavarz-Haddad and S. G. Haghighi, "Sybil Attack Detection in Urban VANETs Based on RSU Support,” Iranian Conference on Electrical Engineering (ICEE), Mashhad,2018,pp.602-606. 3. T. Zhou, R. R. Choudhury, P. Ning and K. Chakrabarty, "P2DAP — Sybil Attacks Detection in Vehicular Ad Hoc Networks," in IEEE Journal on Selected Areas in Communications, vol. 29, no. 3, pp. 582-594, March 2011. 4. S. Chang, Y. Qi, H. Zhu, J. Zhao and X. Shen, "Footprint: Detecting Sybil Attacks in Urban Vehicular Networks," in IEEE Transactions on Parallel and Distributed Systems, vol. 23, no. 6, pp. 1103-1114, June 2012. 5. Salam Hamdan, Amjad Hudaib & Arafat Awajan (2019): Detecting Sybil attacks in vehicular ad hoc networks, International Journal of Parallel, Emergent and Distributed Systems. 6. H. Hamed, A. Keshavarz-Haddad and S. G. Haghighi, "Sybil Attack Detection in Urban VANETs Based on RSU Support," Iranian Conference on Electrical Engineering (ICEE), Mashhad, IEEE, 2018, pp. 602-606. 7. M. S. Mohamed, P. Dandekhya and A. Krings, "Beyond passive detection of sybil attacks in VANET," 2017 6th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), IEEE, Noida, 2017, pp. 384-390. 8. Y. Yao et al., "Voiceprint: A Novel Sybil Attack Detection Method Based on RSSI for VANETs," 2017 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), IEEE, Denver, CO, 2017, pp. 591-602. 9. J. Jenefa, E. A. Mary Anita,” Secure Vehicular Communication Using ID Based Signature Scheme,” Springer Science+Business Media, 2017. 10. T. M. de Sales, H. O. Almeida, A. Perkusich, L. de Sales and M. de Sales, "A privacy-preserving authentication and Sybil detection protocol for vehicular ad hoc networks," 2014 IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, 2014, pp. 426-427. 11. N. Varshney, T. Roy and N. Chaudhary, "Security protocol for VANET by using digital certification to provide security with low bandwidth," 2014 International Conference on Communication and Signal Processing, IEEE, Melmaruvathur, 2014, pp. 768-772. 12. H. Hamed, A. Keshavarz-Haddad and S. G. Haghighi, "Sybil Attack Detection in Urban VANETs Based on RSU Support," Iranian Conference on Electrical Engineering (ICEE), Mashhad,2018,pp.602-606. 13. K. Lim, K. M. Tuladhar and H. Kim, "Detecting Location Spoofing using ADAS sensors in VANETs," 2019 16th IEEE Annual Consumer Communications & Networking Conference (CCNC), Las Vegas, NV, USA, 2019, pp. 1-4. 14. S. A. Syed and B. V. V. S. Prasad, "Merged technique to prevent SYBIL Attacks inVANETs," 2019 International Conference on Computer and Information Sciences (ICCIS), IEEE, Sakaka, Saudi Arabia, 2019, pp. 1-6. 15. M. Baza et al., "Detecting Sybil Attacks using Proofs of Work and Location in VANETs," in IEEE Transactions on Dependable and Secure Computing, doi: 10.1109/TDSC.2020.299 Authors: Suspended Paper Title: Abstract: 14. Keywords: 74-78 References:
Authors: Tanjima Akhter, Md. Ariful Islam, Farhana Akhtar, Azizul Hakim Suzan, Kamrunnahar
Paper Title: COVID-19: Herd Immunity Projection in Bangladesh Abstract: Herd Immunity is the opposition to the spread of infectious disease like COVID-19 within a population that appears if an adequately high proportion of individuals are immune to the infectious disease. According to infectious disease law, if at least 70 percent of a population becomes resistant to a particular disease or infectious disease, they can no longer spread the disease to the remaining 30 percent of susceptible people. If 70 to 90 percent of total populations in a country are infected by COVID-19, then herd immunity will be obtained and by this way the novel corona virus can be annihilated from that country. According to the World Health Organization, a person infected with corona virus can infect 2.5 people. At least 90 percent of people need to be infected with corona virus to have herd immunity. If there is to be herd immunity in the case of Bangladesh, there are 161.4 million people here, so about 145.5 million people will have to be infected with corona virus. If the infection continues in Bangladesh, there is no way out until herd immunity comes. If Bangladesh makes a decision to go to herd immunity, it is necessary to know the death and infected status in 15. herd immunity stage. In this paper, a model based on Malthusian theory has proposed to make the projection of herd immunity with the status of infected cases, cured cases and death in Bangladesh. The model of exponential 79-85 growth has implemented to evaluate the time of herd immunity and also to estimate the total death and infected cases. The proposed model has validated using MATLAB and Microsoft excel. Keywords: COVID-19, Projection, Exponential growth, Malthusian theory, Herd immunity.
References: 1. Fotios Petropoulos, Spyros Makridakis, Forcasting the novel corona virus COVID-19, Plos ONE. 2. Jeffrey R. Chasnov. Mathematical Biology Lecture notes for MATH 4333 3. Mathematical models in population biology and epidemiology, 2nd edition, Springer-Verlag New York, Fred Brauer, Carlos Castillo- Chavez 4. https://www.worldometers.info/coronavirus/country/bangladesh/ 5. https://www.who.int/bangladesh/emergencies/coronavirus-disease-(covid-19)-update/coronavirus-disease-(covid-2019)-bangladesh- situation-reports 6. https://corona.gov.bd/ 7. https://iedcr.gov.bd/ Authors: Shruthi G., Ayesha A. 16. Paper Title: Image Processing Concepts for Brain Tumor MRI Image Classification Abstract: The current generation is witnessing a radical change in technology with the rise of artificial 86-92 intelligence. The application of artificial intelligence on different domain indicates the widespread involvement of this technology in the years to come. One such application is on medical image classification such as brain tumor classification. The process of medical image classification involves techniques from the image processing domain to process set of MRI image data in order to extract prominent feature that eases the classification process. The classifier model learns the MRI image data to predict the occurrence of the tumor cells. The objective of this paper is to provide knowledge pertaining to various approaches implemented in the field of machine learning applied to medical image classification as preparation of the MRI dataset to a standard form is the key for developing classifier model. the paper focus to analyses different types of preprocessing methods, image segmentation, and feature extraction methodologies and inscribes to points out the astute observation for each of techniques present in image processing methodologies. As predicting tumor cells is a challenging task because of its unpredictable shape. Hence emulating an appropriate methodology to improve the accuracy and efficiency is important as it aids in constructing a classifier model that can accelerate the process of prediction and classification for the brain tumor MRI imagery.
Keywords: brain tumor, image processing, image segmentation, preprocessing methods, feature extraction methods, GLCM, PCA
References: 1. D. Addiati, U. U. Sheikh and S. A. R. A. Bakar, "Restoration of out of Focus Barcode Images Using Wiener Filter," 2009 Fifth International Conference on Signal Image Technology and Internet Based Systems, Marrakesh, 2009, pp. 133-137 2. Ramesh Babu Vallabhaneni, V. Rajesh, Brain tumour detection using mean shift clustering and GLCM features with edge adaptive total variation denoising technique, Alexandria Engineering Journal, Volume 57, Issue 4, 2018,Pages 2387-2392,ISSN 1110-0168, 3. Mamun, Md. Al & Hossain, Dr Md Ali & Uddin, Md. Palash. (2018). Brain Tumor Detection Using Anisotropic Filtering, SVM Classifier and Morphological Operation From MR Images. 10.1109/IC4ME2.2018.8465613. 4. Baselice, F., Ferraioli, G., Pascazio, V., & Schirinzi, G. (2017). Enhanced Wiener Filter for Ultrasound image denoising. EMBEC NBC 2017,2018, Springer Singapore, pp 65-68, 5. Telrandhe, Swapnil & Kendhe, Ankita & Pimpalkar, Amit. (2016). Detection of Brain Tumor from MRI images by using Segmentation &SVM. 6. Kamil Dimililer, Ahmet İlhan,Effect of Image Enhancement on MRI Brain Images with Neural Networks,Procedia Computer Science,Volume 102,2016,Pages 39-44,ISSN 1877-0509 7. Manisha, & bansal, n. (2018). Improved Median Filtering in Image Denoise. International Journal of Advance Research and Innovative Ideas in Education, 4(5), 169-175. 8. A. Sehgal, S. Goel, P. Mangipudi, A. Mehra and D. Tyagi, "Automatic brain tumor segmentation and extraction in MR images," 2016 Conference on Advances in Signal Processing (CASP), Pune, 2016, pp. 104-107, doi: 10.1109/CASP.2016.7746146. 9. Galiano, Gonzalo & Velasco, Julián. (2014). On a new formulation of nonlocal image filters involving the relative rearrangement. 10. G. Mirajkar and B. Barbadekar, "Automatic segmentation of brain tumors from MR images using undecimated wavelet transform and gabor wavelets," 2010 17th IEEE International Conference on Electronics, Circuits and Systems, Athens, 2010, pp. 702-705, doi: 10.1109/ICECS.2010.5724609. 11. Xue W, Mou X, Zhang L, Bovik AC, Feng X. Blind image quality assessment using joint statistics of gradient magnitude and Laplacian features. IEEE Trans Image Process. 2014;23(11):4850-4862. doi:10.1109/TIP.2014.2355716 12. G. Singh and M. A. Ansari, "Efficient detection of brain tumor from MRIs using K-means segmentation and normalized histogram," 2016 1st India International Conference on Information Processing (IICIP), Delhi, 2016, pp. 1-6, doi: 10.1109/IICIP.2016.7975365. 13. Kumari, Apurva & Sahdev, Sidharth & Sahoo, Subhendu Kumar. (2015). Improved Single Image and Video Dehazing Using Morphological Operation. 2015 International Conference on VLSI Systems, Architecture, Technology and Applications, VLSI-SATA 2015. 10.1109/VLSI-SATA.2015.7050486. 14. Sepideh Yazdani, Rubiyah Yusof, Alireza Karimian, Mohsen Pashna & Amirshahram Hematian (2015) Image Segmentation Methods and Applications in MRI Brain Images, IETE Technical Review, 32:6, 413-427, DOI: 10.1080/02564602.2015.1027307 15. Hiralal, Reshma nd Menon, Hema P., Corchado Rodriguez, Juan Manuel. Mitra, Sushmita and Thampi, Sabu M. and El-Alfy, El- Sayed, A Survey of Brain MRI Image Segmentation Methods and the Issues Involved, Intelligent Systems Technologies and Applications 2016,Springer International Publishing, pp 245-259 16. Asra Aslam, Ekram Khan, M.M. Sufyan Beg,Improved Edge Detection Algorithm for Brain Tumor Segmentation,Procedia Computer Science,Volume 58,2015,Pages 430-437,ISSN 1877-0509,https://doi.org/10.1016/j.procs.2015.08.057.(http://www.sciencedirect.com/ science/article/pii/S1877050915021687) 17. J. Canny, A computational approach to edge detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, (6) (1986) 679–698. 18. Hasan, S M Kamrul et al. “Two-step verification of brain tumor segmentation using watershed-matching algorithm.” Brain informatics vol. 5,2 8. 14 Aug. 2018, doi:10.1186/s40708-018-0086-x 19. S. Beucher, F. Meyer, The morphological approach to segmentation: the watershed transformation, Optical Engineering, New York- Marcel Dekker Incorporated 34 (1992) 433–433 20. Sujan, M., Alam, N., Abdullah, S., Jahirul, M., 2016. A Segmentation based Automated System for Brain Tumor Detection. International Journal of Computer Applications, 153(10), 41-49 21. Umit Ilhan, Ahmet Ilhan,Brain tumor segmentation based on a new threshold approach,Procedia Computer Science,Volume 120,2017,Pages 580-587,ISSN 1877-0509,https://doi.org/10.1016/j.procs.2017.11.282.(http://www.sciencedirect.com/science/article/ pii/S1877050917324948) 22. N. K. Verma, P. Gupta, P. Agrawal and Y. Cui, "MRI brain image segmentation for spotting tumors using improved mountain clustering approach," 2009 IEEE Applied Imagery Pattern Recognition Workshop (AIPR 2009), Washington, DC, 2009, pp. 1-8. 23. J. Liu, J. K. Udupa, D. Odhner, et al., A system for brain tumor volume estimation via MR imaging and fuzzy connectedness. Computerized Medical Imaging and Graphics, Vol.29, No.1, 21-34, 2005. 24. Eman Abdel-Maksoud, Mohammed Elmogy, Rashid Al-Awadi,Brain tumor segmentation based on a hybrid clustering technique,Egyptian Informatics Journal,Volume 16, Issue 1,2015,Pages 71-81,ISSN 1110-8665,https://doi.org/10.1016/j.eij.2015.01.003.(http://www.sciencedirect.com/science/article/pii/S1110866515000043) 25. M. R. Kaus, S. K. Warfield, A. Nabavi, et al., Automated Segmentation of MR Images of Brain Tumors1,Radiology, Vol.218, No.2, 586-591, 2001. 26. Arumugam, Meena & Raja, R.. (2013). Spatial Fuzzy C Means PET Image Segmentation of Neurodegenerative Disorder. Indian J. Comput. Sci. Eng.. 4. 27. H. E. M. Abdalla and M. Y. Esmail, "Brain Tumor Detection by using Artificial Neural Network," 2018 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE), Khartoum, 2018, pp. 1-6, doi: 10.1109/ICCCEEE.2018.8515763. 28. Sharma, Minakshi and Mukharjee, Sourabh,Brain Tumor Segmentation Using Genetic Algorithm and Artificial Neural Network Fuzzy Inference System (ANFIS),Advances in Computing and Information Technology,2013,Springer Berlin Heidelberg,Berlin, Heidelberg,pages 329--339 29. Zulpe, Nitish & Pawar, V. (2012). GLCM textural features for Brain Tumor Classification. IJ CSI. 9. 354-359. 30. Xue, Wufeng & Mou, Xuanqin & Zhang, Lei & Bovik, Alan & Feng, Xiangchu. (2014). Blind Image Quality Assessment Using Joint Statistics of Gradient Magnitude and Laplacian Features. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. 23. 10.1109/TIP.2014.2355716. 31. Arunadevi, Baladhandapani; Deepa, Subramaniam N. "Brain tumor tissue categorization in 3D magnetic resonance images using improved PSO for extreme learning machine." The Free Library 01 April 2013 32. Geeta Palki, Ashwini Patil, Sandeep Kumar, Shrivatsa Perur, Shubham Kumar, 2017, A Novel MRI Brain Images Classifier Using PCA and SVM, INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT) Volume 06, Issue 06 (June 2017), http://dx.doi.org/10.17577/IJERTV6IS060136 33. Irem Ersöz Kaya, Ayça Çakmak Pehlivanlı, Emine Gezmez Sekizkardeş, Turgay Ibrikci,PCA based clustering for brain tumor segmentation of T1w MRI images,Computer Methods and Programs in Biomedicine,Volume 140,2017,Pages 19-28,ISSN 0169- 2607,https://doi.org/10.1016/j.cmpb.2016.11.011.(http://www.sciencedirect.com/science/article/pii/S0169260716304199) 34. Jolliffe, I. T., & Cadima, J. (2016). Principal component analysis: a review and recent developments. Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 374(2065), 20150202. https://doi.org/10.1098/rsta.2015.0202 35. Saravanan, S.Karthigaivel, R.Magudeeswaran, V.2020/03/29, brain tumor image segmentation technique in image processing using ICA-LDA algorithm with ARHE model,Journal of Ambient Intelligence and Humanized Computing 36. M. Gupta, B. V. V. S. N. P. Rao and V. Rajagopalan, "Brain Tumor Detection in Conventional MR Images Based on Statistical Texture and Morphological Features," 2016 International Conference on Information Technology (ICIT), Bhubaneswar, 2016, pp. 129-133, doi: 10.1109/ICIT.2016.037. 37. Frucci, Maria & Sanniti di Baja, Gabriella. (2007). Object Detection in Watershed Partitioned Gray-Level Images. 4826. 94-103. 10.1007/978-3-540-76300-0_10. 38. Kaur, Navneet & Mahajan, Nitish. (2016). A Novel Technique for Analysis of Ice Properties using GABOR Filtering. Indian Journal of Science and Technology. 9. 10.17485/ijst/2016/v9i44/98672. 39. Kumar, D. (2015). A new Method on Brain MRI Image Preprocessing for Tumor Detection. International journal of scientific research in science, engineering and technology, 1(1), 40-44. 40. Li, Yuhong & Dou, Qi & Jia, Fucang & Qin, Jing & Heng, Pheng-Ann. (2015). Automatic Brain Tumor Segmentation from MR Images via a Multimodal Sparse Coding Based Probabilistic Model. 41-44. 10.1109/PRNI.2015.18. 41. Muneer, K. & Joseph, K.. (2018). Performance Analysis of Combined k-mean and Fuzzy-c-mean Segmentation of MR Brain Images. 10.1007/978-3-319-71767-8_71. 42. Rathi, V. & Palani, S.. (2011). Detection and characterization of brain tumor using segmentation based on HSOM, wavelet packet feature spaces and ANN. ICECT 2011 - 2011 3rd International Conference on Electronics Computer Technology. 6. 10.1109/ICECTECH.2011.5942097. 43. Jolliffe IT, Cadima J. Principal component analysis: a review and recent developments. Philos Trans A Math Phys Eng Sci. 2016;374(2065):20150202. doi:10.1098/rsta.2015.0202 44. Geeta Palki, Ashwini Patil, Sandeep Kumar, Shrivatsa Perur, Shubham Kumar, 2017, A Novel MRI Brain Images Classifier Using PCA and SVM, INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT) Volume 06, Issue 06 (June 2017), http://dx.doi.org/10.17577/IJERTV6IS060136 45. Samanta, A. , Khan, A. (2017). 'Computer Aided Diagnostic System for Detection and Classification of a Brain Tumor through MRI Using Level Set Based Segmentation Technique and ANN Classifier'. World Academy of Science, Engineering and Technology, Open Science Index 126, International Journal of Biomedical and Biological Engineering, 11(6), 340 - 347. 46. Zhang, Hanyu & Hung, Che-Lun & Min, Geyong & Guo, Jhih-Peng & Liu, Meiyuan & Hu, Xiaoye. (2019). GPU-Accelerated GLRLM Algorithm for Feature Extraction of MRI. Scientific Reports. 9. 10.1038/s41598-019-46622-w. 47. Perur, Shrivatsa. (2017). A Novel MRI Brain Images Classifier Using PCA and SVM. International Journal of Engineering and Technical Research. 6. 189-192. 10.17577/IJERTV6IS060136. 48. Győrfi, Á., Karetka-Mezei, Z., Iclănzan, D., Kovács, L., & Szilágyi, L. (2019). A Study on Histogram Normalization for Brain Tumour Segmentation from Multispectral MR Image Data. In I. Nyström, Y. Hernández Heredia, & V. Milián Núñez (Eds.), Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - 24th Iberoamerican Congress, CIARP 2019, Proceedings (pp. 375- 384). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11896 LNCS). Springer. https://doi.org/10.1007/978-3-030-33904-3_35 17. Authors: Tejeshree J. Bhangale, S M Shinde Analysis of Proportional Integral Controller and Fuzzy Logic Controller for Single Phase Induction Paper Title: Motor Abstract: Nowadays problems are occurring due to power quality issues which causes major impression in 93-100 power system. Most of the times problem is of harmonics in the supply system. It occurs due to Non-Linear Load and it could be rectified by using filters, controllers and artificial intelligence. In this paper, the Fuzzy logic controller is designed using Takagi- Sugeno fuzzy inference system to eliminate harmonics and uncertainty in the supply system thereby improving the response of whole system. The Selective Harmonic Elimination Pulse Width Modulation (SHEPWM) control scheme is used to generate the necessary pulses to eliminate the lower order harmonics. The results for both fuzzy logic controller (FLC) and PI Controller are compared, and the simulation is designed in MATLAB Simulink.
Keywords: Fuzzy logic controller, PI Controller, Voltage Source Inverter, SHEPWM, THD
References: 1. Zainal Salam. “DC to AC conversion (inverters)” Power Electronics and Drives (Version 2), 2002. encon.fke.utm.my/courses/see_5433/inverter.pdf 2. Mohammed H. Rashid. “Power Electronics” Prentice-Hall of India Private Limited, Second Edition, 1994, pp 225-233 3. Bimal K. Bose. “Modern Power Electronics and AC Drives” Pearson education,2003, pp 191-194 4. Jyh-Shing Roger Jang. “Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence”, Prentice-Hall, 1996, pp 47-91 5. Robert Fuller. “Neural Fuzzy Systems” ISBN 951-650-624-0, ISSN 0358-5654, Åbo,1995, pp 87-91 6. Mohammed K. Al-shuqfa. “Investigation of some Uninterruptible Power Supply (UPS) Concepts” M.Sc. Thesis, University of Mosul,2001, pp 4-20 7. Siriroj Sirisukprasert. “Optimized Harmonic Stepped-Waveform for multilevel inverter” M. Sc. Thesis, Blacksburg Virginia University,1999, pp 1-141 8. M. Mohaddes, P.G. McLaren, A.M. Gole. “Control of Optimal PWM Voltage Source Inverter using Sigmoid and Picewise Linear Artificial Neural Network” Graduate Student conference GRADCON’98 Winnipeg, MB, Canada, 1998, pp 35-38 9. Zahra Bayt, “Low order harmonics elimination in multilevel inverters using fuzzy logic controller considering the variations of dc voltage sources”, Proc. IEEE Int. Conf. on Electrical Machines and systems, INSPEC Accession Number: 12389545, 2011, pp 1-6 10. E. R. (Randy) Collins, Jr., Senior Member, IEEE, J. R. (Trey) Shirley “An Experimental Investigation of Third Harmonic Current Distortion in Single-Phase Induction Motors” 978-1-4244-1770-4,2008, pp 1-7 Authors: Lokasani Bhanuprakash, Muhammed Anaz Khan, Sunnam Nagaraju, A Ravindra Effect of Fibre Orientation on the Tensile and Flexural Properties of Glass Fibre Reinforced Epoxy Paper Title: Angle-Ply Laminated Composites Abstract: The present work is aimed at studying glass fibre reinforced epoxy angle-ply laminated composites under in-plane and out-of-plane loads. Three symmetric laminates were fabricated at different combination of fibre ply orientations through a simple hand layup technique. The prepared laminates were characterized for tensile and flexural strength measurements according to the ASTM standards D3039 and D7264, respectively. Symmetric laminates consisting of fibre plies orienting in the direction of applied load have demonstrated greater resistance against tensile loads, whereas laminate system consisting of adjacent plies oriented in different angles promoted binding strength of the matrix which in turn resulted in enhanced flexural strength values.
Keywords: Angle-ply composites, Fibre orientation, Tensile properties, Flexural properties, GFRP composites.
References: 1. P. K. Mallick, Fibre reinforced composites: Materials, manufacturing and design (3rd edition), Boca Raton, Florida: CRC Press, 2007. 2. Lokasani Bhanuprakash, Sampath Parashuram, Soney Varghese, Experimental investigation on graphene oxides coated carbon fibres/epoxy hybrid composites: Mechanical and electrical properties, Composites Science and Technology, Vol. 179, 2019, pp 134- 18. 144. 3. Lokasani Bhanuprakash, Abins Ali, Rashad Mokkoth, Soney Varghese, Mode I and Mode II interlaminar interlaminar fracture behavior of E-glass fiber reinforced epoxy composites modified with reduced exfoliated graphite oxide, Polymer Composites, 2018, pp 1-13. 101-105 4. Kakur Naresh, Shankar Krishnapillai, Velmurugan Ramachandran, Comparative study of a neat epoxy and unidirectional carbon/epoxy composites under tensile and impact loading, Solid State Phenomena, Vol. 267, 2017, pp 87-92. 5. Abhay Shivanagere, S.K.Sharma, P.Goyal, Modelling of glass fibre reinforced polymer (gfrp) for aerospace applications, Journal of Engineering Science and Technology, Vol. 13 (11), 2018, pp 3710-3728. 6. O.S.David-West, N.V.Alexander, D.H.Nash, W.M.Banks, Energy absorption and bending stiffness in CFRP laminates: The effect of 45° plies, Thin-Walled Structures, Vol.46, 2008, pp 860-869. 7. Ali Umran Al-saadi, Thiru Aravintan, Weena Lokugea, Effects of fibre orientation and layup on the mechanical properties of the pultruded glass fibre reinforced polymer tubes, Engineering Structure, Vol. 198, 2019, pp 109448. 8. G.Czel, M.Jalalvand, M.R.Wisnom, Design and characterization of advanced psedo-ductile unidirectional thin-ply carbon/epoxy-glass/ epoxy hybrid composites, Composite Structures, Vol. 142, 2016, pp 362-370. 9. V.Ben W Kim, Arnold H Mayer, Influence of fibre direction and mixed-mode ratio on delamination fracture toughness of carbon/epoxy laminates, Composites Science and Technology, Vol. 63, 2003, pp 695-713. 10. K.Grigoriou, A.P.Mouritz, Influence of ply stacking pattern on the structural properties of quasi-isotropic carbon-epoxy laminates in fire, Composites Part A, Vol. 99, 2017, pp 113-120. 11. ASTM D339/D3039M-17, Standard test method for tensile properties of polymer matrix composite materials, ASTM International, West Conshohocken, PA, 2017, www.astm.org 12. ASTM D7264/D7264M-15, Standard test method for flexural properties of polymer matrix composite materials, ASTM International, West Conshohocken, PA, 2015, www.astm.org Authors: SUSPEND PAPER
Paper Title: SUSPEND PAPER
19. Abstract: SUSPEND PAPER Keywords: 106-111 References:
20. Authors: C.Raveena, Sri Kalaivani.R , Yagna.B , Rakshitha.T.R
Paper Title: Deep Submerged Image Enhancement and Restoration Process using CNN Abstract: In oceanographic studies, underwater imagery plays a vital role. Underwater imaging has some of the 112-117 advanced applications such as hand-held stereo-cam, fish-pond monitoring, etc. The major sources of quality degradation in most of the underwater imaging processes are scattering and absorption which occurs due to light assimilation. In this paper, we propose a two step-strategy in which the former is the enhancement process and latter is the restoration process. Our unavoidable selective and quantitative appraise uncover that our upgraded pictures and recordings have better accessibility in the dark locales, progressed global and local contrast and better edge sharpness. In order to get rid of image quality impairments, we follow a method which involves only a single image. The major advantage of this method is that it does not require a specialized image-capturing equipment. Moreover, our substantiation gives a better accuracy by deploying Convolutional Neural Network (CNN) algorithm.
Keywords: CNN, haze removal, colour correction, underwater image enhancement. References: 1. C. O. Ancuti, C. Ancuti, C. De Vleeschouwer and P. Bekaert, "Color Balance and Fusion for Underwater Image Enhancement," in IEEE Transactions on Image Processing, vol. 27, no. 1, pp. 379-393, Jan. 2018. 2. Yan-Tsung Peng and Pamela C, “Underwater Image Restoration Based on Image Blurriness and Light Absorption,” IEEE transactions on image processing, vol. 26, no. 4, April 2017. 3. H. Yang, P. Chen, C. Huang, Y. Zhuang and Y. Shiau, "Low Complexity Underwater Image Enhancement Based on Dark Channel Prior," 2011 Second International Conference on Innovations in Bio-inspired Computing and Applications, Shenzhan, 2011, pp. 17-20. 4. A. Khan, S. S. A. Ali, A. S. Malik, A. Anwer and F. Meriaudeau, "Underwater Image Enhancement by WaveletBased Fusion," 2016 IEEE International Conference on Underwater System Technology: Theory and Applications (USYS), Penang, 2016, pp. 83-88. 5. J. Y. Chiang and Y. Chen, "Underwater Image Enhancement by Wavelength Compensation and Dehazing," in IEEE Transactions on Image Processing, vol. 21, no. 4, pp. 1756-1769, April 2012. 6. Cosmin Ancuti, Codruta Orniana Ancuti, Tom Haber, and Philippe Bekaert, “Enhancing underwater images and videos by fusion,” in 7. Chong-Yi Li, Ji-Chang Guo, Run-Min Cong, Yan-Wei Pang, and Bo Wang, “Underwater image enhancement by dehazing with minimum information loss and histogram distribution prior,” IEEE Transactions on Image Processing, vol. 25, no. 12, pp. 5664–5677, 2016. 8. Yoav Y Schechner and Yuval Averbuch, “Regularized image recovery in scattering media,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 9, 2007. 9. John Y Chiang and Ying-Ching Chen, “Underwater image enhancement by wavelength compensation and dehazing,” IEEE Transactions on Image Processing, vol. 21, no. 4, pp. 1756– 1769, 2012. 10. Mahesh M Subedar and Lina J Karam, “Increased depth perception with sharpness enhancement for stereo video,” in IS&T/SPIE Electronic Imaging. International Society for Optics and Photonics, 2010, pp. 75241B– 75241B. 11. Yin Zhao, Zhenzhong Chen, Ce Zhu, Yap- Peng Tan, and Lu Yu, “Binocular just-noticeable- difference model for stereoscopic images,” IEEE Signal Processing Letters, vol. 18, no. 1, pp. 19– 22, 2011. 12. Shijie Zhang, Jing Zhang, Shuai Fang, and Yang Cao, “Underwater stereo image enhancement using a new physical model,” in Image Processing (ICIP), 2014 IEEE International Conference on. IEEE, 2014, pp. 5422–5426. 13. Dongliang Cheng, Dilip K Prasad, and Michael S Brown, “Illuminant estimation for color constancy: why spatial-domain methods work and the role of the color distribution,” JOSA A, vol. 31, no. 5, pp. 1049–1058, 2014. 14. Simone Bianco, Claudio Cusano, and Raimondo Schettini, “Color constancy using cnns,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2015, pp. 81–89. 15. Simone Bianco, Claudio Cusano, and Raimondo Schettini, “Single and multiple illuminant estimation using convolutional neural networks,” arXiv preprint arXiv: 1508.00998, 2015. 21. Authors: Anshul Shrivastava, Savita Maru
Paper Title: Seismic Response Analysis of RCC Frame Building using FREI Abstract: An analysis is given for the medium-rise building with fixed base and other with base isolation 118-125 device. The device hereby used for base isolation is Fiber-reinforced Elastomeric Isolator (FREI). The Fiber- reinforced Elastomeric Isolator is an isolator which uses fiber fabric material instead of steel thin plates. The main purpose of using FREI is to analyze the seismic response of medium-rise RCC frame building. As in past many research work have been carried out for low-rise building. Therefore, in this paper (G+4), (G+6), (G+8) Storey building are taken for study and analysis has been demonstrated using commercial software ETABS v17. The design of Fiber Reinforced Elastomeric Isolators is broadly based on the guidelines of ASCE 7-10. The response spectrum analysis of different storey are based on Indian Design Standard. In addition to the design of FREI, a comparison between fixed based and base isolated RCC frame building has been carried out in the form of time period, displacement, drift at various load combination. The above parameter is taken to check whether it can withstand the loads and seismic forces without any failure.
Keywords: Response Spectrum Analysis, FREI, Storey Drift, Lateral Displacement.
References: 1. Chopra, A. K. (2001). Dynamics of structures: Theory and applications to earthquake engineering, 2nd Ed., Prentice Hall, Upper Saddle River, N.J. 2. Das A, Dutta A, Deb SK. (2012). ‘Modeling of fiber-reinforced elastomeric base isolators’. 3. World Conference on Earthquake Engineering, Lisbon 2012. 4. Das A, Dutta A, Deb SK. (2014). ‘Performance of fiber-reinforced elastomeric base isolators 5. Dezfuli FH, Alam MS. (2014). ‘Performance of carbon fiber-reinforced elastomeric isolators 6. FEMA 356 (2000), ―Prestandard and Commentary for the Seismic Rehabilitation of Buildings,ǁ Federal Emergency Management Agency, Washington,DC 7. IS : 1893.(Part-1) (2002). Criteria for Earthquake Resisting Design for Structures. Part 1 General Provision and Buildings (Sixth Revision), BIS, New Dehli, India 8. IS : 456. (2000). Plain and Reinforced Concrete – Code of Practice (Fourth Revision, BIS, New Dehli, India 9. Kelly J.M. (1986). ‘Aseismic Base Isolation: review and bibliography’ Soil Dynamics and Earthquake Engineering Volume 5, Issue 4, October 1986, Pages 202-216. 10. Kelly JM, Takhirov SM. (2001), ‘Analytical and experimental study of fiber-reinforced elastomeric isolator’. PEER Report, 2001/11, Pacific Earthquake Engineering Research Center, University of California, Berkeley, USA, 2001. 11. Kelly JM, Takhirov SM. (2002). ‘Analytical and experimental study of fiber-reinforced strip isolators’. PEER Report, 2002/11, Pacific Earthquake Engineering Research Center, University of California, Berkeley, USA, 2002. 12. Kelly JM. (1999). ‘Analysis of fiber-reinforced elastomeric isolators’. Journal of Seismology and Earthquake Engineering 1999; 2 (1): 19–34. 13. Konstantinidis D, Kelly JM. (2014). ‘Advances in low cost seismic isolation with rubber’. Tenth U.S. National Conference on Earthquake Engineering, July 211-25, 2014, Anchorage, Alaska. 14. Moon BY, Kang GJ, Kang BS, Kelly JM. (2002). ‘Design and manufacturing of fiber reinforced elastomeric isolator for seismic isolation’. Journal of Materials Processing Technology 2002; 130–131: 145–150. 15. Moon BY, Kang GJ, Kang BS, Kelly JM. (2003). ‘Mechanical properties of seismic isolation system with fiber-reinforced bearing of strip type’. International Applied Mechanics 2003; 39 (10): 1231–1239. 16. Nezhad HT, Tait MJ, Drysdale RG. (2009). ‘Simplified analysis of a low-rise building seismically isolated with stable un-bonded fiber reinforced elastomeric isolators’. Canadian Journal of Civil Engineering 2009, 36(7):1182–1194. 17. Nezhad HT. (2014). ‘Horizontal stiffness solutions for un-bonded fiber reinforced elastomeric bearings’. Structural Engineering and Mechanics 2014, 49(3):395–410. 18. Osgooei PM, Tait MJ, Konstantinidis D. (2014b). ‘Finite element analysis of un-bonded square fiber-reinforced elastomeric isolators (FREIs) under lateral loading in different directions’. Composite Structures 2014, 113:164–173. 19. Pinarbasi S, Mengi Y. (2008). ‘Elastic layers bonded to flexible reinforcements’. International Journal of Solids and Structures 2008; 45 (3): 794–820. 20. Russo G, Pauletta M. (2013). ‘Sliding instability of fiber-reinforced elastomeric isolators in un-bonded applications’. Engineering Structures 2013, 48:70–80. 21. Spizzuoco M, Calabrese A, Serino G. (2014). ‘Innovative low-cost recycled rubber-fiber reinforced isolator: experimental test and finite element analyses’. Engineering Structures 2014, 76:99–111. 22. Strauss A, Apostolidi E, Zimmermann T, Gerhaher U, Dritsos S. (2014). ‘Experimental investigations of fiber and steel reinforced elastomeric bearings: shear modulus and damping coefficient’. Engineering Structures 2014, 75:402–413. 23. Toopchi-Nezhad H, Tait MJ, Drysdale RG. (2011). ‘Bonded versus un-bonded strip fiber reinforced elastomeric isolators: finite element analysis’. Composite Structures 2011; 93 (2): 850–859. 24. Tsai HC, Kelly JM. (2001), ‘Stiffness analysis of fiber-reinforced elastomeric isolator’. PEER Report, 2001/05, Pacific Earthquake Engineering Research Center, University of California, Berkeley, USA, 2001. 25. Tsai HC. (2004). ‘Compression stiffness of infinite-strip bearings of laminated elastic material interleaving with flexible reinforcements’. International Journal of Solids and Structures 2004; 41 (24): 6647–6660. 26. Tsai HC. (2006). ‘Compression stiffness of circular bearings of laminated elastic material interleaving with flexible reinforcements’. International Journal of Solids and Structures 2006; 46 (11): 3484–3497. 27. Van Engelen NC, Tait MJ, Konstantinidis D. (2012). ‘Horizontal behaviour of stable unbonded fiber reinforced elastomeric isolators (SU-FREIs) with holes’. Proc. 15th World Conference on Earthquake Engineering, Lisbon, Portugal, 2012. 22. Authors: Suman Jana, Pabitra Kumar Biswas
Paper Title: Feasibility Analysis of Supercapacitor for Lightning Energy Conversion System Abstract: Lightning is a very high voltage electrostatic emission consisting of highly charged electrostatic 126-133 particles. However, limited literature is available in the field of the Lightning energy harvesting area due to the hazards involved and the intermittent nature of lightning. A lightning bolt does have convenient energy. Still, empirically, energy harnessing and storage is a difficult task due to confusion existed in the selection of impulse storage device. This paper presents a feasibility analysis of supercapacitors to store energy extracted from a high voltage surge. The analysis is performed by connecting the impulse generator as a source of the supercapacitor. Different loads are connected sequentially with the supercapacitor to assess the applicability of the proposed numerical model. The impulse generator is designed in the simulation model as a replica of the Tesla coil hardware constructed for the lightning energy conversion system. Lightning energy conversion system is an innovative system which can convert high voltage lightning in storable form. Also, a real-time pulse has been generated for the Seven level inverter by interfacing Atmel ATMEGA328P-PU microcontroller in the MATLAB environment. The computational model used in this paper produced 0.0027% to 0.7% SOC percentage of supercapacitor for single waveform and different loads. This paper may drive the lightning energy research towards a positive influence by its conceptual resolution.
Keywords: Supercapacitor, High voltage test, Multi-Level Inverter, Energy Storage, Lightning.
References: 1. A. Danielou, The Myths and Gods of India: The Classic Work on Hindu Polytheism, The Princeton Bollingen Series, Inner Traditions, 1991. 2. T. A. G. Rao, Elements of Hindu iconography, Motilal Banarsidass, 1993. 3. S. H. S. Jones et. al., A Greek-English Lexicon, Oxford. Clarendon Press, Oxford, 1940. 4. H. E. Davidson, Gods and Myths of Northern Europe, Penguin, Harmondsworth, Middlesex, 1990. 5. V. A. Rakov, M. A. Uman, Lightning: Physics and Effects, Cambridge University Press, 2003. 6. J. R. Holton et. al., Encyclopedia of atmospheric sciences, Academic Press 3 (2003) 459-483, 1216-1227. 7. T. Takahashi, Riming electrification as a charge generation mechanism in thunderstorms, Journal of the atmospheric sciences 35 (1978) 1536-1548. 8. M. Stolzenburg, T. C. Marshall, Charge Structure and Dynamics in Thunderstorms, Space Science Reviews 137 (2008) 355-372. 9. C. T. R. Wilson, The electric field of a thundercloud and some of its effects, Proceedings of the Physical Society of London 37(1) (1924) 32D-37D. 10. D. J. DePaolo et al., Origin and evolution of earth: Research questions for a changing planet, The National Academies Press, 2008. 11. M. A. Uman, The lightning discharge, Vol. 39, Academic Press Inc, 1987. 12. T. Koopman et al., Channeling of an Ionizing Electrical Streamer by a Laser Beam, Journal of Applied Physics 42 (1971) 1883-1886. 13. S. N. Washington, D.C., Artificial Lightning, American Association for the Advancement of Science 69 (1782). 14. N. Khan et al., Artificial Lightning, Laser-triggered lightning discharge 4 (61). 15. X. Qie et. al., Triggering Lightning Experiments: an Effective Approach to the Research of Lightning Physics, Journal of aerospace lab. 5 (2012) 1-12. 16. R. Gunn, Diffusion charging of atmospheric droplets by ions and the resulting combination coefficients, Journal of meteorology 11 (5) (1954) 339-347. 17. M. A. Uman, D. K. MClain, Magnetic Field of Lightning Return Stroke, Journal of Geophysical Research 74 (28) (1959) 6899-6910. 18. J. S. Nisbet, A Dynamic Model of Thundercloud Electric Fields, Journal of The Atmospheric Sciences 40 (28) (1983) 2855-2873. 19. S. Soula, S. Chauzy, Multilevel measurement of the electric field underneath a thundercloud and dynamical evolution of a ground space charge layer, Journal of Geophysical Research 96 (D12) (1991) 22327-22336. 20. P. M. Bitzer, Global distribution and properties of continuing current in lightning, Journal of Geophysical Research: Atmospheres (121) (2016) 1-9. 21. M. Jayaraju et. al., Impulse voltage generator modelling using Matlab, World Journal of Modelling and Simulation 4 (1) (2008) 57-63. 22. S. E. Meiners, An impulse generator simulation circuit, Miami University (2002) 1-50. 23. R. Hasbrouck, Mitigating lightning hazards, Science and Technology Review (1969) 4-12. 24. V. A. Rakov, A Review of Positive and Bipolar Lightning Discharges, American Meteorological Society (2003) 767-776. 25. R. K. K. N. Singh, A. Manivannan, Matlab based simulation of thermoelectric-photovoltaic hybrid system, International Journal of Engineering Research and Applications 3 (2) (2013) 975-979. 26. J. Ho et al., Historical Introduction to Capacitor Technology, IEEE Electrical Insulation Magazine 26 (1) (2010) 20-25. 27. R. Signorelli et al., Electrochemical Double-Layer Capacitors Using Carbon Nanotube Electrode Structures, Proceedings of the IEEE 97 (11) (2009) 1837-1847. 28. S. Jana et al., Methodological model of Lightning Energy Plant integrated with different energy extraction processes to harness Lightning Energy, IEEE Proceedings of International Conference on Electrical Energy Systems 4 (2018) 229-233. 29. V. A. Rakov, F. Rachidi, Overview of Recent Progress in Lightning Research and Lightning Protection, IEEE Transaction on electromagnetic compatibility 51 (3) (2009) 428-442. 30. A. Kumar et. al., Study of Transient Behaviour of the Capacitor Voltage Transformer, International Journal of Advance Research in Electrical, Electronics and Instrumentation Engineering 4 (5) (2015) 3993-4000. 31. Y. Fuyuan, Characterization, Analysis and Modeling of an Ultracapacitor, World Electric Vehicle Journal 4 (2010) 358-369. 32. B. E. Conway, Electrochemical Supercapacitors: Scientific Fundamentals and Technological Applications, Springer (1999) 1-8. 33. B. E. Conway, Transition from 'Supercapacitor' to 'Battery' Behavior in Electrochemical Energy Storage, Journal of electrochemical society 6 (138) (1991) 1539-1548. 34. F. Khoucha et al., A Comparison of Symmetrical and Asymmetrical Three-Phase H-Bridge Multilevel Inverter for DTC Induction Motor Drives, IEEE Transactions on Energy Conversion 26 (1) (2011) 64-72. 35. E. Levi et al., Analytical Determination of DC-Bus Utilization Limits in Multiphase VSI Supplied AC Drives, IEEE Transactions on Energy Conversion 23 (2) (2008) 433-443. Authors: Mohamed Y Mohsen, Hany I Ahmed, Hany S Riad, Amr A Abdel Rahman
Paper Title: Effect of Longitudinal Forces Due to Loads on Prestressed Mono-Block Sleeper Spacing Abstract: The present paper proposes scientific and practical methodology to update the concept of the constant sleeper spacing along the railway track to be reset according to the affecting normal forces caused by passenger and freight trains. The proposed methodology has developed a suitable sleeper spacing plan according to three cases which are train acceleration, uniform speed and braking on -5 ‰, 0 ‰ and 5‰ grades. The study aims to determine the actual acceleration length, braking length, longitudinal forces, displacement index and finally the suitable sleeper spacing for each part on the track, then calculating the saving in sleepers for the following cases: passenger train runs on single or double track, freight train runs on single or double track and mixed traffic (passenger and freight) runs on single or double track.
Keywords: feasibility study on sleeper spacing longitudinal forces on railway track, pre-stressed mono-block 23. concrete sleeper, railway normal forces, sleeper spacing, track creep
References: 134-149 1. American Railway Engineering Association, “Concrete Ties,” 1982. 2. Gallego I., “Vertical Track Stiffness as a New Parameter Involved in Designing High-Speed Railway Infrastructure,” ASCE, Journ. of Transp. Eng., Vol. 137, No 12, 2011. 3. Esveld, C., “Modern railway track second edition,” Zaltbommel, MRT-Productions, 2001. 4. Lichtberger, B., “Track compendium: Formation, permanent way, maintenance, economics,” Eurailpress, 2005. 5. Profillidis, V. “Railway Management and Engineering,” Section of Transportation, Democritus Thrace University, Greece, Vol. 2, 2014. 6. Buekette J., “Concrete Sleepers,” Track Course, RIA, London, 1983. 7. European Standard, “Prestressed Monoblock Concrete Sleepers,” European Committee for Standardization, Brussels, 1994. 8. Profillidis, V., “Applications of Finite Element Analysis in the Rational Design of Track Bed Structures,” Computers and Structures, Vol. 22, No 3, 1986. 9. Prud’homme A., “La Voie,” RGCF, Paris, 1970. 10. UIC, “Factors affecting Track Maintenance Costs and their Relative Importance,” Paris, 1992. 11. Panagiotopoulos P., “Hemivariational Inequalities: Applications in Mechanics and Engineering,” Springer, Berlin, 1993. 12. http://www.clag.org.uk/wheelbase.html 24. Authors: Dipak Ranjan Jana, Sumalatha Emmela, Ch.Monika, D.Archana, K.Thulasi Priya, K.Yamini
Paper Title: Detection and Prevention of Wheel Unbalancing and Tire Burst in Moving Vehicles Abstract: Fatal accidents are increasing day-by-day due to the failure of wheel bearing, unbalancing of wheel 150-153 and tyre bursting due to increase in the temperature. Bearing is the most important mechanical device on which the wheel performance of a vehicle depends. Lack of proper periodic maintenance of the bearing leads to the failure of bearing, which results in wheel misalignment. Hence, tyres with wheels come out from the axial in moving condition, which results in accidents. Bearing failure can also be due to bearing buckling, scratches, nicks, discoloration, corrosion and crack. This can be due to lack of lubrication or overheating etc. Also due to improper tyre pressure, harsh braking and increase in the temperature of the tyre, tyre gets heated up causing tyre bursting which leads to fatal accidents. The main objective is to detect tyre temperature and wheel alignment deviation, thereby providing indication through audio-visual system which prevents accidents of the vehicle and the driver from an injury or death. Hence, we have used ARDUINO UNO, ULTRASONIC SENSOR, LEDS, DHT-11 SENSOR and BUZZER.
Keywords: Wheel Bearing, Unbalancing, Maintenance, Alignment.
References: 1. Hawes, James; Fisher, John; Mercer, Todd (2008), Tire Pressure Monitoring Systems Guide, Mitchel1. 2. Ryosuke Matsuzaki and Akira Todoroki, “Wireless Monitoring of Automobile Tires for Intelligent Tyres”, Sensors 2008, 8, pp.8123- 8138. 3. S Patwardhan, M.Tomizuka, Wei-Bin Zhang and Peter Devlin, “Theory and Experiments of Tire Blow-Out Effects and Hazard Reduction Control for Automated Vehicle Lateral Control System”, Proceedings of the American Control Conference Baltimore, Maryland June 1994, pp. 1207-1209. 4. Mulla Minaz, Soni Drupad, Tale Hetal, Gandhi Maulik and Dr.Sheshang Degadwala, “Wheel Alignment Detection using raspberry pi”, 2nd International Conference on Current Research Trends in Engineering and Technology (IJSRSET), volume 4, issue 5, June 2017, pp 261-270. 5. Bala Krishnan.T, Hariraman.R, Jayachandran.R and Jayasri Meenachi.V, “Vehicle Integrated Wheel Alignment Alert System”, International Journal of Scientific and Engineering Research, volume-7, Issue-5, May-2016 pp. 79-81. 6. Chetan P. Chaudhari, Bhushan B. Thakare, Saurabh R. Patil, Shrikant U. Gunjal, “A Study of Bearing and its types”, IJARSE, Volume No: 4, special Issue 1, March 2015. 7. Leo Louis, “Working Principle of Arduino and Using it as a tool for Study and Research”, International Journal of Control, Automation, Commuincation and Systems (IJCAS), Vol.1, No.2, April, 2016. 8. “Arduino UNO for Beginners-Projects, Programming and Parts”, makerspaces.com 9. www.google.com 10. Shweta G. Barhe and Balaji G.Gawalwad, “ Measurement of Wheel Alignment using IR Sensor”, International Journal of Innovative Research in Computer and Communication Engineering, volume 4, issue 5, May, 2016. 11. Dr. Porag Kalita, “Automotive Tyres: Study on Vehicle Computerized Wheel Alignment”, International Journal of Computer Engineering in Research Trends (IJCERT), volume 3, issue 2, February-2016, pp.70-75. 12. S.Jenkins Godfrey and S.Senthil Murugan,” Stability control of Vehicles during Tyre Burst with Auto Expanding Rims”, Journal of Automation and Automobile Engineering, volume 3, Issue 2, 2018. 13. Min Li, JiYin Zhao, XingWen Chen and YaNing Yang,” Research and Design of Direct Type Automobile Tire Burst Early-Warning System”, International Conference on Computer Science, Environment, Ecoinformatics and Education, pp.291-297, 2011. Authors: Dipak Ranjan Jana, Turaka Sowmya, M.Leela Priyanka, N.V.Bhargavi, P.Sannihitha, P.Sandhya
Paper Title: A System and Method for Detection of Obstacles on Moving Vehicles on Either Side 3600 Abstract: This work provides information to determine the sudden hazardous living or non–living materials in front of vehicles on either side, i.e.180 degree across will indicate the drivers for stopping the vehicles automatically with ANDON and BUZZER. Then the vehicle will automatically turn on either side safely. For Sudden detection of obstacles, specifically waterfall at certain height, rock rolling down, landslides, earthquake, animals, abnormal things and tree fallen on the road 90 degree on either side. ANDON and BUZZER system is for visual indication along with voice monitoring for indication to front and back vehicles. Successful display of distance and identified object will be displayed in the LCD. The mainly used components for this project are the use of preventing and corrective action through ARDUINO MEGA, ULTRASONIC SENSORS, VIBRATION SENSOR SW-420 and LDR MODULE.
25. Keywords: ANDON and BUZZER, Embedded, Landslides.
References: 154-157 1. Bulas, J.C. Tyres, road surfaces and reducing accidents – anoverview. A report on research carried out for the AAFoundation for Road Safety Research and the County,Surveyors’ Society, 2004. Availablewww.roadsafetyfoundation.org/media/11323/aa_foundation_fdn34.pdf . Accessed on 11th June 2014 2. Road traffic accidents in hilly regions of northern India: What has to be done?Anil Kumar Joshi,ChitraJoshi,Mridu Singh,and Vikram Singh 3. Automatic Road Accident Detection using Ultrasonic Sensor by Usman Khalil DHA Suffa University ,Adnan Nasir University of Texas Arlington Arlington, TX, 76019 S.M. Khan NED UET, T. Javid, S.A. Raza, A. Siddiqui Hamdard University. 4. A.M. Flynn, “Combining sonar and infrared sensors for mobile robot navigation,” The International Journal of Robotics Research, vol. 7 5. KarvinenTero, KarvinenKimmo and Valtokari Ville. Les capteurs pour Arduino et Raspberry Pi. Edition Dunod, 2014. 6. Shradhakukade and Prof. U.W. Hore "Intelligent Traffic Management System Based on Smart Internet ofVehicles." International Journal of Advanced Research in Electrical, Electronics and InstrumentationEngineering Vol. 7, Issue 3, March 2018. 7. https://create.arduino.cc/projecthub/albertoz/obstacle -avoiding-robot-fb30e4 8. https://en.wikipedia.org/wiki/Robotics 9. 9. www.google.com 26. Authors: Shivaji G. Patil, Ravindra K. Lad Identification of Strengths and Weaknesses of Jalyukt Shivar Abhiyan by Assessment of Works in Paper Title: Tal-Purandar, Dist-Pune Abstract: Maharashtra is the third largest state in India and nearly 58 % of population lives in the rural area 158-164 which depends largely on agriculture for their livelihood. Due to various negative externalities of lack of water availability Government of Maharashtra declared a comprehensive water conservation programme named as Jalyukta Shivar Abhiyan (JSA). In this study, the methodology adopted to identify the strengths and weaknesses of JSA by conducting theoretical assessment of various water conservation activities carried out under JSA in three villages in Purandar taluka in Pune district and also few works as per Shirpur pattern in Dhule district, Maharashtra, India. Firstly, it was studied to know whether various activities conducted were based on scientific and engineering principles and the effectiveness of water conservation activities carried out on the village. Secondly, the effect of local community participation in these activities was studied to suggest ways for increase in participation for enhancement in groundwater recharge in the study area. The strengths and weaknesses were identified from assessment of JSA, which include technical gaps observed in planning and actual implementation of these works. It was also seen that public awareness regarding JSA in drought affected villages was poor due to which community participation was also poor. It is concluded that these strengths and weaknesses could be used to make some changes in policy and structure of JSA to improve effectiveness of scheme and also increase local community participation for enhancement groundwater recharge and also to increase effectiveness of water conservation activities under JSA.
Keywords: Assessment, community participation, groundwater recharge, water conservation
References: 1. A. K. Bhattacharya, “Artificial groundwater recharge with a special reference to India,” Int. Journal of Research Reviews in Applied Sciences, 4(2), 2010, pp. 214-221. 2. A. Kolekar, A. B. Tapase, Y. M. Ghugal and B. A. Konnur, “Impact analysis of soil and water conservation structures - Jalyukt Shivar Abhiyan – A case study,” Proceedings of Int. Congress & Exhibition Sustainable Civil Infrastructures, GeoMEast, 2019, pp. 47-53. 3. E. R. Dawale, P.S. Wanjari, S. A. Patil and M. A. Lokhande, “Generation of potential treatment maps for the development of water resources and water conservation using remote sensing & GIS: A strategy for Jalyukt Shivar Abhiyan,” 19th ESRI India Users Conference, 2018, pp. 1-9. 4. J. Bharati and P. S. Datta, “An initiative for community participation and rehabilitation of a watershed ecosystem in a mountainous area in India,” Proceedings of 3rd WEPA Int. Forum on Water Environmental Governance in Asia and IGES, Japan, 2008, pp. 7-13. 5. K. B. Ramappa, B. S. Reddy and S. K. Patil, “Water conservation in India: An institutional perspective,” Ecology, Environment & Conservation, 20(1), 2014, pp. 303-311. 6. K. Jadhav and D. Kulkarni, “Impact assessment of Jalyukt Shivar Abhiyan for Padali Helgaon village Tal-Karad, Dist-Satara,” Int. J. of Recent Technology & Engineering, 8(2), 2019, pp. 1044-1049. 7. K. Sonawane, S. Nikalje, A. Hiremath and A. Kale, “Water conservation structure: Farm pond - A case study,” Vishwakarma Journal of Engineering Research, 2(4), 2018, pp. 195-201. 8. M. Kumari and J. Singh, “Water conservation: Strategies and solutions,” Int. J. of Advanced Research & Review, 1(4), 2016, pp. 75- 79. 9. M. Moglia, S. Cook and S. Tapsuwan, “Promoting water conservation: Where to from here,” Water, MDPI, 10, 2018, 1510, doi: 10.3390/w10111510. 10. P. A. Vedpathak and P. A. Hangargekar, “Impact assessment of Jalyukt Shivar structures on five villages in Ambajogai,” Int. J. of Applied Science, Engineering &Technology, 7(V), 2019, pp. 2620-2626. 11. P. N. Thakare, V. S. Tekale and P. S. Telange, “Knowledge of beneficiary farmers about Jalyukt Shivar Campaign,” Int. J. of Current Microbiology & Applied Science, 7(8), 2018, pp. 2936-2940. 12. R. T. Pachkor and D. K. Parbat, “Assessment of works under Jalyukt Shivar Campaign - A case study of Pusad region,” Int. J. for Research in Applied Science & Engineering Technology, 5(IV), 2017, pp. 1614-1619. 13. U. P. Potekar and S. K. Pawar, “Overview on Jalyukt Shivar Abhiyan and micro irrigation in Maharashtra state,” Research Front, 1, 2017, pp. 54-57. 14. V. M. Sanade, S. S. Dongare, V. D. Hande, S. D. Patil, D. D. Siddheshwar and P. S. Lokhande, “A research paper on Jalyukt Shivar Abhiyan assessment (Sonavade) and design of water-efficient village (Save),” Int. Research J. of Engineering & Technology, 6(6), 2019, pp. 2200-2204. 15. W. J. Cosgrove and D. P. Loucks, “Water management: current and future challenges and research directions,” Water Resources Research, 51, 2015, pp. 4823-4839. 16. Z. A. Ahmed and R.T. Pachkor, “Jalyukt Shivar-A Combat to water stresses in Maharashtra,” Int. J. of Applied Science, Engineering &Technology, 3(X), 2015, pp. 102-108. Authors: Paresh G Chaudhary, G.R.Selokar
Paper Title: Stress Optimization of Engine Support Bracket Abstract: This paper briefs about the several facts about the Engine Bracket which plays a vital role in any Automobile. Engine bracket is sustaining heavier shocks on roads so it’s being very crucial to analyses this component on stress point of view and minimize the stress to maximum possible extent. An attempt is made in this paper to reduce the stress occurring in the component by varying the materials and by changing the geometrical conditions and to find out best combination of material as well as geometry which will ultimately satisfy all engineering conditions. The design part of the component has covered with the help of CATIA software and the whole analysis part is done with the help of ANSYS software. The current paper will also cover some historical references of the relevant data and conclusions from them so that future researches can easily grab those references. Ultimately this paper will result in suggesting the best combination of material and geometry which will sustain at mentioned engineering frequency.
27. Keywords: Finite Element Analysis, Stress, Stress Reduction, Non Linear FEA, Engine Mounting.
References: 165-168 1. Kim K., Choi I., Design optimization analysis of body attachment for NVH performance improvements, SAE technical paper series 2003-01-1604. 2. M.V. Aditya Nag, Material Selection and Computational Analysis on DOHC V16 Engine’s Mounting Bracket Using COMSOL Multiphysics Software. Proceedings of the 2012 COMSOL, conference in Bangalore. 3. M.V. Aditya Nag, Topology optimization of engine mounting bracket, in HTC Technical Presentations 2012, hyper work technology conference 4. Umesh S. Ghorpade, D. S. Chavan, Vinaay Patil, Mahendra Gaikwad, “finite element analysis and natural frequency optimization of engine bracket” IJMIE, Vol-2, Iss-3, Page no. 1-6, 2012. 5. P.D.Jadhav, Ramakrishna, “Finite Element Analysis of Engine Mount Bracket”, International Journal of Advancement in Engineering Technology, Management & Applied Science, ISSN 2349–3224, Volume-1 Issue -4,Sept.2014, pp-1-10.] 6. Haval Kamal Asker, comparison of the mechanical properties of different models of automotive engine mounting, ARPN Journal of Engineering and Applied Sciences, VOL. 8, NO. 6, JUNE 2013. 7. Abdolvahab Agharkakli, Dig Vijay Pradip Wagh, Linear Characterization of Engine Mount and Body Mount for Crash Analysis, International Journal of Engineering and Advanced Technology (IJEAT), ISSN: 2249 – 8958, Volume-3, Issue-2, December 2013. 8. Analysis of Engine Support Bracket for Frequency and Stress Optimization, International Journal of Innovative Technology and Exploring Engineering (IJITEE), ISSN: 2278-3075, Volume-9 Issue-6, April 2020. DOI: 10.35940/ijitee.F4281.049620,PP-996-1000. 28. Authors: Namana A, Nishkala Gowda B Y, Pratheeksha C, Prajwal K S, Charunayana V
Paper Title: Cattle Diseases Prediction using IOT and ML - A Review Abstract: Due to the everchanging environment, the cattle’s are in risk of getting affected by diseases and this 169-173 in turn affects the economy. There will low productivity, less yield . It is still hard to forestall farm animals sicknesses using current monitoring systems that tune cattle activity and consequently the environmental situations of cattle .In this paper, we design a cattle health monitoring system using IOT and ML to a prevent livestock diseases, like anthrax disease, using dedicated sensors. We collect information using various. With the assistance of machine learning algorithm, we will predict the disease and send notification to the respective cattle owner and also the doctor in charge.
Keywords: accelerometer, Dallas temperature, ESP8266 Wi-Fi module, IOT, Machine learning, microphone, NodeMCU. References: 1. Amruta Helwatkar, Daniel Riordan, Joseph Walsh, “Sensor Technology for Animal Health Monitoring”, Proceedings of the 8th International Conference on Sensing Technology, Sep. 2-4, 2014, Liverpool, UK. 2. Bhisham Sharma, Deepika Koundal―Cattle health monitoring system using wireless sensor network‖, IET Wireless Sensor Systems , Volume 8, Issue 4, August 2018, p. 143 – 151. 3. Anshul Awasthi, Daniel Riordan ,.―Non-invasive sensor technology for the development of a dairy cattle health monitoring system‖, 12 October 2016. 4. D.Aswini, S.Santhya, T.Shri Nandheni N.Sukirthini , ―Cattle health and environment monitoring system‖,IRJET,volume -04 issue - 03,March 2017. 5. V Gokul, Sitaram Tadepalli, ―Implementation of smart infrastructure and non-invasive wearable for real time tracking and early identification of diseases in cattle farming using iot , 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) ,Feb 2017. 6. Niels Rutten, Henk Hogeveen, Annet Velthuis, ―Invited review:Sensors to support health management on dairy farms‖, journal of Dairy Science Volume 96, Number 4,Feb 22,2013. 7. Torben Godsk, Mikkel Baun Kjaergaard, ‖High Classification Rates for Continuous Cow Activity Recognition using Low-cost GPS Positioning Sensors and Standard Machine Learning Techniques‖,Volume 6870,2011. 8. Kae Hsiang Kwong, Tsung Ta Wu ,Hock Guan Goh, Bruce Stephen, Michael Gilroy, Craig Michie, and Ivan Andonovic, ―Wireless Sensor Networks in Agriculture: Cattle Monitoring for Farming Industries‖ , Piers Online, Volume 5,Number 1, January 2009,Glasgow,UK. 9. Carmen T.Agouridis, Timothy S.Stombaugh, Stephen R.Workman, Benjamin K.Koostra,Dwayne R.Edwards, and Eric S.Vanzant, ―Suitability of a GPS Collar for Grazing Studies‖ ,Volume 47,Number 4,2004 Transcation of American Scoiety of Agriculture Engineers[ASAE]. 10. S.Jegadeesan, Dr.G.K.D. Prasanna Venkatesan, ―Smart Cow Health Monitoring, Farm Environmental Monitoring And Control System Using Wireless Sensor Networks‖, Volume 7, Issue 1, Jan-March 2016, Tamil Nadu, India 11. Anselemi B.Lukonge, Dr.Shubi Kaijage, Ramadhani 12. S. Sinde,‖Review of Cattle Monitoring System Using Wireless Network‖, Volume 3,Issue 5,May 2014,Arusha,Tanzania. 29. Authors: Emmanuel A. Ubom, Victor E. Idigo, Ubong Ukommi Primary User Protection Contour and No-Talk Zone Characterization for TV Whitespace Spectrum Paper Title: Reuse in Nigeria. Abstract: To encourage secondary spectrum access within the TV broadcast bands in Nigeria, the 174-180 propagation properties of TV signals on the VHF and UHF frequency ranges were empirically studied through measurements carried from two TV stations. The Pathloss exponent for the VHF band was found to be 1.9 with
a characterised Pathloss equation for VHF band computed asPL ( dB )=84.04+19.03 log10 ( d ), where (d)is the distance from the transmitter to the receiver. The UHF band Pathloss exponent was computed to be 1.8 with
a Pathloss equation characterised asPL ( dB )=57.35+17.96 log10 ( d ). The findings re-echoed the need for specific prediction model to accurately estimate the service coverage of TV stations and facilitate effective utilization of spatial TV white space as it was found that there were divergence in coverage prediction between the measured model and some of the conventional models. Using the protection view point, the protection
contour in kilometers for TV signals propagating in the UHF band in Nigeria was characterized to be dr p=¿ P + 32.65 t . d r ¿ 10[ 17.96 ] . Where ( p is the protection contour radius modeled as a function of the transmit power of the TV station in decibels with reference to one milliwatt (dBm) for co-channel and adjacent channel coverage. Similarly, the no-talk-zone in kilometers was characterized as a function of the transmit power of the secondary
user device in dBm for co-channel usage to be d¿ ¿ modeled as a function of the secondary user transmit power
Ps. The separation distance in kilometers from the TV station to the possible secondary user transmitter beyond P +P −57.11 which no interference exist was computed to have a relationship equal to antilog t s . This ⟦ 17.96 ⟧ model will facilitate TVWS co-channel coexistence using the specified equation to determine the separation distances between television transmitters and secondary user transmitters.
Keywords: Primary User Protection Contour, Separation Distance, Isolation Distance
References: 1. E. A. Ubom, V. E. Idigo, A.C.O. Azubogu, C.O. Ohaneme, and T.L. Alumona, “Path loss Characterization of Wireless Propagation for South – South Region of Nigeria”. International Journal of Computer Theory and Engineering, Vol. 3, No. 3, June 2011 pp. 360-364. 2. V.H. MacDonald, “The cellular concept,” The Bell Systems Technical Journal, vol. 58, no. 1, 1979, pp. 15-43. 3. S. Faruque, “Propagation prediction based on environment classification and fuzzy logic approximation”. In Proceedings of the International Conference on Communications, ICCC/SUPERCOMM’96, Dallas, TX, USA, 23–27 June 2002. 4. N. Faruk A.A. Ayeni and Y.A.Adediran, “On the Study of Empirical Path Loss Models for Accurate Prediction of TV Signal for Secondary Users”. Progress in Electromagnetics Research B, Vol. 49, 2013, pp 155–176. 5. Z. Nadir, N. Elfadhil, and F. Touati, “Pathloss Determination using Okumura-Hata Model and Spline Interpolation for Missing Data for Oman”, Proceedings of the World Congress on Engineering 2008 Vol I WCE 2008, July 2 - 4, 2008, London, U.K. Page 422-425. 6. K. Mano, “Coverage estimation for Mobile Cellular Network. From Signal strength Measurements” submitted PhD Dissertation to Department of Electrical Engineering, The University of Texas at Dallas, 1999. Accessed on the 26th of June 2018 @ citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.58.4998&rep=rep1&type=pdf. 7. http://www.awe-communications.com/Propagation/Rural/HO/index.htm. Accessed 22nd of June 2019. 8. A. R. Mishra, “Advanced Cellular Network Planning and Optimisation” – Wiley 2007. 9. R. Nave, "Blue sky and Rayleigh Scattering". Hyperphysics. Georgia State University.http://hyperphysics.phy- astr.gsu.edu/HBASE/atmos/blusky.html] 10. M. Awad, K. T. Wong & Z. Li “An Integrative Overview of the Open Literature's Empirical Data on the Indoor Radiowave Channel's Temporal Properties” IEEE Transactions on Antennas & Propagation, vol. 56, no. 5, 2008, pp. 1451-1468. 11. G. Naik, S. Singhal, A. Kumar, and A. Karandikar, “Quantitative assessments of TV Whitespaces in India” , 2015 available online @ http://www.dynamicspectrumalliance.org/assets/Quantitative%20Assessment%20of%20TV%20White%20Space%20in %20India_02032015.pdf Authors: Geetha Peethambaran, Chandrakant Naikodi, Suresh Lakshmi Narasimha Setty
Paper Title: Evaluating Optimal Differentially Private Learning – Shallow and Deep Techniques Abstract: Data analytics is an evolving arena in today’s technological evolution. Big data, IoT and machine learning are multidisciplinary fields which pave way for large scale data analytics. Data is the basic ingredient in all type of analytical tasks, which is collected from various sources through online activity. Data divulged in these day-to-day activities contain personal information of individuals. These sensitive details may be disclosed when data is shared with data analysts or researchers for futuristic analysis. In order to respect the privacy of individuals involved, it is required to protect data to avoid any intentional harm. Differential privacy is an algorithm that allows controlled machine learning practices for quality analytics. With differential privacy, the outcome of any analytical task is unaffected by the presence or absence of a single individual or small group of individuals. But, it goes without saying that privacy protection diminishes the usefulness of data for analysis. Hence privacy preserving analytics requires algorithmic techniques that can handle privacy, data quality and efficiency simultaneously. Since one cannot be obtained without degrading the other, an optimal solution that balances the attributes is considered acceptable. The work in this paper, proposes different optimization techniques for shallow and deep learners. While evolutionary approach is proposed for shallow learning, private deep learning is optimized using Bayesian method. The results prove that the Bayesian optimized private deep learning model gives a quantifiable trade-off between the privacy, utility and performance
Keywords: Bayesian, Deep Learning, Privacy, Private Learning, Shallow Learning
30. References: 1. Latanya Sweeney “Achieving k-anonymity Privacy Protection Using Generalization and Suppression”,May 2002, International Journal on Uncertainty, Fuzziness and Knowledge-based Systems, 10 (5), 2002; 571-588. 181-187 2. Cynthia Dwork and Aaron Roth. The algorithmic foundations of differential privacy. Foundations and Trends in Theoretical Computer Science, 9(3 4):211–407, 2014. 3. https://www.macobserver.com/analysis/google-apple-differential-priv acy/ 4. Kobbi Nissim, et al. Differential Privacy: A Primer for a Non-technical Audience. February 14, 2018.https://diffprivlib.readthedocs.io 5. www.tensorflow.org 6. https://archive.ics.uci.edu/ml 7. Marler, R. T. and Arora, J. S. (2004). Survey of multi-objective optimization methods for engineering. Structural and multidisciplinary optimization, 26(6):369–395 8. parvizi, Mahdi & Shadkam, Elham & jahani, Niloofar. (2015). A Hybrid COA/ε-Constraint Method for Solving Multi-Objective Problems. International Journal in Foundations of Computer Science & Technology. 5. 27-40. 10.5121/ijfcst.2015.5503. www.medium.com 9. www.towardsdatascience.com 10. Deb, K., Pratap, A., Agarwal, S., and Meyarivan, T. A Fast and Elitist Multiobjective Genetic Algorithm: NSGA–II. IEEE Transactions on Evolutionary Computation 6, 2 (2002), 182–197. 11. Rinku Dewri, Darrell Whitley, Indrajit Ray and Indrakshi Ray,” A Multi-Objective Approach to Data Sharing with Privacy Constraints and Preference Based Objectives”, Proceedings of GECCO 2009 12. www.cs.torontu.edu 13. Bargav Jayaraman and David Evans,” Evaluating Differentially Private Machine Learning in Practice”, Proceedings of the 28th USENIX Security Symposium, 978-1-939133-06-9, August 14–16, 2019 14. Thanh Dai Nguyen(B), Sunil Gupta, Santu Rana, and Svetha Venkatesh,” A Privacy Preserving Bayesian Optimization with High Efficiency”, LNAI 10939, pp. 543–555, 2018. 15. Borja Balle, B. Avent, J. Gonzalez, T. Diethe and A. Paleyes,” Learning the Privacy-Utility Trade-off with Bayesian Optimization” 16. Guillermo Campos Ciro, Frederic Dugardin, Farak Yalaoui, Russell Key, “A NSGA-II and NSGA-III comparison for solving an open shop scheduling problemwith resource constarints,2405,8963,2016. 31. Authors: Omkar Patil, Umesh Chavan
Paper Title: Rule Based Expert System for Error Log Analysis Abstract: Humans have been using their domain expertise intelligently and skillfully for making decisions 188-192 in solving a problem. These decisions are made based on the knowledge that they have acquired through experience and practice over a course of time, which will be lost after the expert’s life ends. Hence, this expert knowledge is required to be stored to a database and a machine could be intelligently programmed which could use this knowledge to make decisions, known as an Expert System (ES). This system tries to emulate the decision-making skills of a domain expert by gathering knowledge of the domain experts, storing it to a knowledge base in rule format, and then using those rules to analyze the given data and provides solutions to the problems. These Expert Systems can be utilized to analyze the system log files, find issues logged into those log statements and provide solutions to the errors that are found in those logs.
Keywords: Artificial Intelligence, Expert System, Inference Engine, Knowledge base, Log Analysis, Log statements, Rules.
References: 1. Pratama et al., “Expert system for diagnosing vertebrate animals with visual prolog 8.0,” Proc. - 2018 3rd Int. Conf. Inf. Technol. Inf. Syst. Electr. Eng. ICITISEE 2018, pp. 100–104, 2019. 2. Z. Dong, J. Zhao, J. Duan, M. Wang, and H. Wang, “Research on Agricultural Machinery Fault Diagnosis System Based on Expert System,” Proc. 2018 2nd IEEE Adv. Inf. Manag. Commun. Electron. Autom. Control Conf. IMCEC 2018, no. Imcec, pp. 2057–2060, 2018. 3. A. Sajid and K. Hussain, “Rule Based (Forward Chaining/Data Driven) Expert System for Node Level Congestion Handling in Opportunistic Network,” Mob. Networks Appl., vol. 23, no. 3, pp. 446–455, 2018. 4. D. A. Sanders, A. Gegov, M. Haddad, F. Ikwan, D. Wiltshire, and Y. C. Tan, “A rule-based expert system to decide on direction and speed of a powered wheelchair,” Adv. Intell. Syst. Comput., vol. 868, pp. 822–838, 2018. 5. B. Cánovas-Segura, A. Morales, J. M. Juarez, M. Campos, and F. Palacios, “A lightweight acquisition of expert rules for interoperable clinical decision support systems,” Knowledge-Based Syst., vol. 167, pp. 98–113, 2019. 6. W. He, P. L. Qiao, Z. J. Zhou, G. Y. Hu, Z. C. Feng, and H. Wei, “A New Belief-Rule-Based Method for Fault Diagnosis of Wireless Sensor Network,” IEEE Access, vol. 6, pp. 9404–9419, 2018. 7. F. Aguado, P. Cabalar, J. Fandinno, B. Muñiz, G. Pérez, and F. Suárez, “A Rule-Based System for Explainable Donor-Patient Matching in Liver Transplantation,” Electron. Proc. Theor. Comput. Sci., vol. 306, pp. 266–272, 2019. 8. E. K. Gebre-Amanuel, F. G. Taddesse, and A. T. Assalif, “Web based expert system for diagnosis of cattle disease,” MEDES 2018 - 10th Int. Conf. Manag. Digit. Ecosyst., pp. 66–73, 2018. 9. F. Başçiftçi and E. Avuçlu, “An expert system design to diagnose cancer by using a new method reduced rule base,” Comput. Methods Programs Biomed., vol. 157, pp. 113–120, 2018. 10. X. Xu, X. Yan, C. Sheng, C. Yuan, D. Xu, and J. Yang, “A Belief Rule-Based Expert System for Fault Diagnosis of Marine Diesel Engines,” IEEE Trans. Syst. Man, Cybern. Syst., pp. 1–17, 2017. 11. M. Peña, F. Biscarri, J. I. Guerrero, I. Monedero, and C. León, “Rule-based system to detect energy efficiency anomalies in smart buildings, a data mining approach,” Expert Syst. Appl., vol. 56, pp. 242–255, 2016. 12. J. S. Jadhav, D. K. Nalawade, and D. M. M. Bapat, “Rule-Based Expert System and Its Application with Special Reference to Crimes Against Women,” 3rd Int. Conf. Work. Recent Adv. Innov. Eng. ICRAIE 2018, vol. 2018, no. November, pp. 1–4, 2019 32. Authors: Sumanta Kuila, Namrata Dhanda, Subhankar Joardar
Paper Title: Classification of Heart Arrhythmia in ECG Signals using PCA and SVM Abstract: Electro cardiogram (ECG) signals records the vital information about the condition of heart of an 193-198 individual. In this paper, we are aiming at preparing a model for classification of different types of heart arrhythmia. The MIT-BIH public database for heart arrhythmia has been used in the case of study. There are basically thirteen types of heart arrhythmia. The Principal Component Analysis (PCA) algorithm has been used to collect various important features of heart beats from an ECG signal. Then these features are trained and tested under Support Vector Machine (SVM) algorithm to classify the thirteen classes of heart arrhythmia. In the paper the proposed algorithm has been discussed and the outcome results have been validated. The result shows that the accuracy of our classifier in our research work is more than 91% in most of the cases.
Keywords: Arrhythmia, Electrocardiogram, MIT-BIH database, Principal Component Analysis, Support Vector Machine.
References: 1. Syed Muhammad Anwar, Maheen Gul, Muhammad Majid , Majdi Alnowami, "Arrhythmia Classification of ECG Signals Using Hybrid Features" Hindawi, Computational and Mathematical Methods in Medicine Volume 2018, Article ID 1380348. 2. Narendra Kohli, Nishchal K. Verma, and Abhishek Roy, "SVM based Methods for Arrhythmia Classification in ECG" International conference on Computer & Communication Technology, pp.486-490,IEEE 2010. 3. Arjon Turnip, M. Ilham Rizqywan, Dwi E. Arjon Turnip, M. Ilham Rizqywan, Dwi E. Kusumandari, Mardi Turnip,Poltak Sihombing, "Classification of ECG signal with Support Vector Machine Method for Arrhythmia Detection", IOP Conf. Series: Journal of Physics: Conf. Series 970 (2018), ICIESC-2017. 4. Raghu Nanjundegowda, Vaibhav Meshram, "Arrhythmia recognition and classification using kernel ICA and higher order spectra", International Journal of Engineering & Technology, Vol 7, pp.256-262 , 2018. 5. Jalal A. Nasiri, Mahmoud Naghibzadeh, H. Sadoghi Yazdi, Bahram Naghibzadeh, "ECG Arrhythmia Classification with Support Vector Machines and Genetic Algorithm", Third UKSim European Symposium on Computer Modeling and Simulation, pp. 187-192, 2009. 6. Mohammad Sarfraz, Ateeq Ahmed Khan, Francis F. Li, "Using Independent Component Analysis to Obtain Feature Space for Reliable ECG Arrhythmia Classification", IEEE International Conference on Bioinformatics and Biomedicine, pp. 62-67 ,2014. 7. Yang Wu, Liqing Zhang, "ECG Classification Using ICA Features and Support Vector Machines", Springer-Verlag Berlin Heidelberg,ICONIP 2011, Part I, LNCS 7062, pp. 146–154, 2011. 8. Maedeh Kiani Sarkaleh, Asadollah Shahbahrami, "Classification of ECG Arrhythmia using Discrete Wavelet Transform and Neural Network", International Journal of Computer Science, Engineering and Applications , Vol.2, No.1, pp 1-13 ,February 2012. 9. Siva A, Hari Sundar M, Siddharth S, Nithin, Rajesh CB, "Classification of Arrhythmia using Wavelet Transform and Neural Network Model", Journal of Bioengineering & Biomedical Science, Volume 8, Issue 1 ,April 2018. 10. Parham Ghorbanian, Ali Ghaffari, Ali Jalali1, C Nataraj, "Heart Arrhythmia Detection Using Continuous Wavelet Transform and Principal Component Analysis with Neural Network Classifier", Computing in Cardiology,Vol. 37, pp.669−672, 2010. 11. Yinsheng Ji, Sen Zhang, Wendong Xiao, "Electrocardiogram Classification Based on Faster Regions with Convolutional Neural Network", Sensors, MDPI, Sensors 2019. 12. Vijaya Arjunan R, "ECG Signal Classification based on Statistical Features with SVM Classification" , International Journal for Advance Signal & Image , Vol. 2, No.1,pp. 5-10 ,2016. 13. Harpreet Kaur, Rupinder Kaur, "ECG Arrhythmia Detection using PCA and Elman Neural Network", International Journal of Science and Research,Volume 3 Issue 10, pp. 894-896, October 2014. 14. Qibin Zhao,Liqing Zhang, "ECG Feature Extraction and Classification Using Wavelet Transform and Support Vector Machines", IEEE, pp.1089-1092, 2005. 15. Usha Desai, Roshan Joy Martis, C. Gurudas Nayak, K. Sarika,Sagar G. Nayak, "Discrete Cosine Transform Features in Automated Classification of Cardiac Arrhythmia Beats", Emerging Research in Computing, Information, Communication and Applications,Springer, pp.153-162 ,2015. 16. Yasushi Kikawa, Koji Oguri, "A Study for Excluding Incorrect Detections of Holter ECG Data Using SVM", Springer-Verlag Berlin Heidelberg 2004, ICONIP 2004, LNCS 3316, pp. 1223–1228, 2004. 17. Ary L. Goldberger, Luis A. N. Amaral, Leon Glass, Jeffrey M. Hausdorff, Plamen Ch. Ivanov," Complex Physiologic Signals Physio Bank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for, Circulation is published by the American Heart Association, 7272 Greenville Avenue, Dallas, TX 75231, May 2014. 18. Hongqiang Li, Danyang Yuan, Xiangdong Ma, Dianyin Cui & Lu Cao, "Genetic algorithm for the optimization of features and neural networks in ECG signals classification", Scientific Reports, January 2017. 19. Inan Gülera, Elif Derya Übeyl,"ECGbeat classifier designed by combined neural network model", Pattern Recognition , Elsevier Journal, pp 199-2008 ,June 2004. 20. Stanislaw Osowski, Linh Tran Hoai, Tomasz Markiewicz, "Support Vector Machine-Based Expert System for Reliable Heartbeat Recognition", IEEE Transactions on Biomedical Engineering, Vol 51, No 4, April 2004. 21. Jimena Rodríguez, Alfredo Goñi, Arantza Illarramendi,"Real-Time Classification of ECGs on a PDA", IEEE Transactions on Biomedical Engineering, Vol 9, No 1, March 2005. 22. Can Ye, Miguel Tavares Coimbra, B.V.K. Vijaya Kumar, "Arrhythmia Detection and Classification using Morphological and Dynamic Features of ECG Signals", 32nd Annual International Conference of the IEEE EMBS Buenos Aires, Argentina,pp. 1918-1921, August 31 - September 4, 2010. Authors: Latika Mahajan, Shubhada Thakare
Paper Title: Memory Optimization of Map Image Abstract: In this paper study the compression method for digital map images. The digital maps are stored and distributed electronically using raster image compression format. In this paper study the different compression technique for the digital map images, which support storage size, decompression of image and smooth transition. For the compression number of methods are used, in this describe the compression technique with their factor. The system is therefore capable of improving the overall performance of the system under test.
Keywords: universal codebook, row-column reduction, Discrete wavelet transform, discrete cosine transform
References: 1. SaifalZabir “Color maps and graphs compression” IEEE 17th International conference on image processing, september 2010,p.p.-26- 29. 2. A.H.M.Jaffar Iqubal Barbhuiya tahera akhtar laskar,k.Henachandean “An approach for color image compression of JPEG and PNG imagr using DCT and DWT” sixth interntional conference on computational intellienece and communication network,2014.,pp.123- 133. 3. Khosla, a. Xiao,J. Torralba,A. etal, “Memorabitily of image regions” in pereira, F.Burges,C.J.C. Bottou,L. etal „Advances in neural information processing systems 25‟,2012,pp.296-304. 4. Guo, C. Zhang, “aA Noval multiresolutiion spatiotemporal sailency detection model and its application in image and video 33. compression”, IEEE Trans. Image processing,2010,pp. 185-198. 5. Matei Mancas, Olivier Le Meur, “Memorabiity of natural Scence : the role of Attention”,2013 IEEE International conference on image processing,pp. 196-200. 199-201 6. Haichuan Ma, Dounf Liu, Ruiquin Xiong Wu, “A CNN-Based Image Compression Scheme Compatable With JPEG-2000”, IEEE Conference of Image Processing,2019. 7. Pasi Frantic, Eugene Ageenko, Pavelkopylov and Berger, “Compression of Map Image For Real Time Application” image and vision computing,2004, https://www.sciencedirect.com/science/article/pii/S02628856040011 43 8. P. Isola,J. Xiao, A. Torralaba and A. Oliva, “ What makes an image memorable?”, IEEE Conference On computer Vision And Pattern Recognition (CVPR), pp. 145-152,2011. 9. Meera Thapar Khaanna, Chetan Ralekar, Anurika Goell, Santanu Chaudhury,Brejesh Lall “Memorability based image compression” 2019,pp. 1490-1501 . 10. Huynh-Thu, Q., Ghanbari, M. “Scope of validity of PSNR in image/video quality assessment”, Electron. Lett., 2008, pp. 800–801. 11. Wang, Z., Bovik, A.C., Sheikh, H.R., etal“Image quality assessment: from error visibility to structural similarity”, IEEE Trans. Image Process., 2004, 13,(4), pp. 600–612. http://pubs.sciepub.com/ijdeaor/1/2/4/ijdeaor-1-2-4.pdf. 12. L. Itti and C. Koch. A saliency-based search mechanism for overt and covert shifts of visual attention.Vision Research, 40:1489–1506, 2000. 13. 13. M. Li, W. Zuo, S. Gu, D. Zhao, and D. Zhang, “Learning convolutional networks for content-weighted image compression,” in CVPR, 2018, pp. 3214–3223. 14. Y. Dai, D. Liu, and F. Wu, “A convolutional neural network approach for post-processing in HEVC intra coding,” in MMM. Springer, 2017, pp. 28–39. 15. M.M.Chowdhury, and A.Khatun, “Image Compression using Discrete Wavelet Transform”, International Journal of Computer Science Issues, Vol. 9,Issue 4,No.1, July, 2012, pp. 327-330. 16. Y.Sukanya,and J.Preeti,”Analysis of image compression Algorithm using Wavelet Transform with GUI in”, IJERT, eISSN:2319- 1163,pISSN:2321-7308,Vol. 2, Issue:10, oct, 2013. 34. Authors: Mohammad Israil, M S Jafiri, Pushpendra Kumar Sharma
Paper Title: Experimental Evaluation of Combined Effect of Flexure, Shear and Tension on SFRC Beams Abstract: Short discrete fiber reinforcement is the right choice in the concrete matrix. De-bounding and 202-205 pulling out of fibers requires more force, thereby increasing the durability and confrontation to repeat and dynamics loads. Fibers substantially decrease the fragility of concrete and advance its engineering characteristics, like load bearing capacity, resistance against impact load, flexural, tensile and fatigue etc. Behavior of SFRC in tension, compression, flexure and shear has already been studied separately but no or very little work has been done on combined state flexure, tension, shear, torsion etc. Present study involves the investigation of the behavior of SFRC composite M20 beams with varying percentage fiber content (0.0, 0.50, 0.75 & 1.0%) by volume under the combined state of tension and shear and flexure. The testing beam size was taken as100 mm × 100 mm × 500 mm. Straight fibers 28 mm long and 0.28 mm diameter were castoff. The specimen beams were tested applying direct tension of 0, 5, 7 and 10kN. For different fiber percent by weight beams were tested, all the direct tension values were applied to each of the three beams i.e. total of 48 beams were casted and tested accordingly. For the beam under combined effect of tension, flexure and shear, when tested it was observed that ultimate central deflection and ultimate bending stress were found to decrease for a particular percentage increment of fiber added along with increase of tension. It was also observed that for a specific tension value, deflection increases with increase of fiber percentage at ultimate load in beams. Bending stress increases at tension 10 KN for all percentages of fiber content.
Keywords: SFRC, Shotcrete, flexure, shear, deflection
References: 1. S. Niyogi, and G. Dwarkanathan “Fibre Reinforced Beams Under Moment and Shear”, Journal of Structural Engg., 111(3), 516-527 (1985). 2. G. Krishna “Performance Evaluation of Steel Fibre reinforced Shotcrete”, National Seminar on Advances in Concrete Technology and Concrete Structures in Future, Annamalai University (2003). 3. C. Tan, R. Hamid, and M. Kasmuri, “Dynamic Stress-Strain Behaviour of Steel Fiber Reinforced High-Performance Concrete with Fly Ash”, Advances in Civil Engineering, Vol. 2012, Article ID 907431, Pp. 1-6. 4. L.F.A., Bernando and S.M.R., Lopes “Neutral axis depth versus flexural ductility in high strength concrete beams”. Journal of structural engineering, Vol. 130, No.3, 2004. 5. A. M. Shende, A. M. Pande, and M. Gulfam Pathan “Experimental Study on Steel Fibre Reinforced Concrete for M-40 Grade”, International Referred Journal of Engineering and Science (IRJES), Vol. 1, Issue, 2012, PP. 043-048. Authors: Abhishek R Patil, Suraj D Shinde
Paper Title: Quantum of Embodied Energy for Residential Buildings Abstract: Embodied energy(E.E) is the total amount of energy that is required in the production of a material which include all the processes, from the mining and processing of natural resources to manufacturing, transport and product delivery and this helps in choice of materials and construction methods, to maximize the energy efficiency of a building during its operation. The E.E. in products and energy conservancy are the ecological features included in eco-labelling schemes. Embodied energy in materials is a significant aspect in GB rating systems. This paper discusses the influence of different construction units on the E.E. of the structure.
Keywords: Embodied energy (E.E), Buildings, Cement, Steel and Brick.
References: 1. Richard Haynes., (2010), ―Embodied Energy Calculations within Life Cycle Analysis of Residential Buildings‖. 35. 2. Ramesha T., Prakasha Ravi, Shukla K.K., (2010), ―Life cycle energy analysis of buildings: An overview‖, Elsevier journal of Sustainable cities and Environment, vol. 42, pp. 1592-1600. 3. Dixit Manish K., Fernández Jose L., Lavy Sarel, Culp Charles H., (2012), ―Need for an embodied energy measurement protocol for 206-210 buildings: A review paper‖, Elsevier journal of Renewable and Sustainable Energy Reviews, vol. 16, pp. 3731-3735. 4. Gillian F. Menzies, (2011), ―Embodied energy considerations for existing buildings‖, Historic Scotland Technical Paper 13. 5. Reddy B.V. V. and Jagadish K.S., (2003), ―Embodied energy of common and alternative building materials and technologies‖, Elsevier journal of Building and Environment, vol. 35, pp. 129-135. 6. M. Kaniuma, (2000), ―Estimation of embodied CO2 by general equilbrium model‖,Global environment division,NIES. 7. Reddy B.V.V., (2004), ―Sustainable Building Technologies‖, Current Science, vol 87, No7, pp 899 –907. 8. Lee Bruno, Marija Trckab, Jan L.M. Hensenb, (2011), ―Embodied energy of building materials and green building rating systems—A case study for industrial halls‖, Elsevier journal of Sustainable cities and Environment, vol. 1, pp. 67-71. 9. M. Ali, (July 2003), ―Energy Efficient Architecture and Building Systems to Address Global Warming‖, Leadership and Management in Engineering, vol. 8, pp, 113-123. 10. Yeoa Dong Hun and Rene D. Gabbaib, (2011), ―Sustainable design of reinforced concrete structures through embodied energy optimization‖, Elsevier journal of Sustainable cities and Environment, vol. 43, pp. 2028-2033. 11. Estokova Adriana and Porhincak Milan, (2014), ―Environmental analysis of two building material alternatives in structures with the aim of sustainable construction‖, Clean Techn Environ Policy, pp. 14-75. 12. Janssen R.M.J., (2014), ―Assessing onsite energy usage: an explorative study”, Department of Construction Management & Engineering, University of Twente, pp. 1-7. 36. Authors: Shifali Sharma, Parveen Kumar, Anita Suman
Paper Title: Handover Schemes in Wireless Networks: Its Advancements and Trends Abstract: Heterogeneous Network (HetNet) are widely employed networks. These networks bear the 211-214 responsibility of providing services to customers. But, there are some complexities that occur because of changing the base stations of different regions. To sustain the stability of the services, handover is required to be placed in the network which transfers the request from initial access point or parent station to another. An important study has been conducted to attain better and effective communication results in HetNets. This review displayed a literature survey that recognizes the approaches utilized to execute a handover. Along with this, the whole procedure of HO is also given. This paper may help n analyzing different mechanisms in future research.
Keywords: Heterogeneous Network, handover, VHO process, handover management.
References: 1. Anita Singhrova and Dr. Nupur Prakash, “Adaptive VHO decision algorithm for wireless Het Net”, IEEE, Computer Scociety, ,2009, pp:476-481 2. Anita Singhrova1 Dr. Nupur Prakash, “A Review of Vertical Handoff Decision Algorithm in Het Nets”, 3. I. F. Akyildiz, J. McNair, J. S. M. Ho, H. Uzunalioglu and Wenye Wang, "Mobility management in next-generation wireless systems," Proceedings of the IEEE, vol. 87, pp. 1347-1384, 1999. 4. Thomas Hofer, Wieland Schwinger, Mario Pichler, Gerhard Leonhartsberger, Josef Altmann and Werner Retschitzegger, ContextAwareness on Mobile Devices - the Hydrogen Approach, Proceedings of the 36th Annual Hawaii International Conference on System Sciences (HICSS'03), Track 9 Volume 9, 2003. 5. Anind K. Dey and Gregory D. Abowd, Towards a Better Understanding of Context and Context-Awareness, HUC '99 Proceedings of the 1st international symposium on Handheld and Ubiquitous Computing, 1999. 6. Sassi Maaloul, Mériem Afif, Sami Tabbane, “Handover Decision in Het Nets”, 2016 IEEE 30th International Conference on Advanced Information Networking and Applications, pp: 588-595 7. Hoshang Karanjekar, Prof. Avinash Agrawal, “Review on VHO Techniques among Het Nets Int. J. Advanced Networking and Applications Volume: 5 Issue: 5 Pages: 2066-2069” 8. P. Chan, R. Sheriff, Y. Hu, P. Conforto, C. Tocci, Mobility management incorporating fuzzy logic for a heterogeneous IP environment, IEEE Communications Magazine 39 (12) (2001) 42–51 9. E. Stevens-Navarro, V. Wong, Comparison between vertical handoff decision algorithms for heterogeneous wireless networks, in: Proceedings of IEE Vehicular Technology Conference (VTC-Spring), vol. 2, 2006, pp. 947–951.] 10. Meriem Kassar, Brigitte Kervella, Guy Pujolle, “An overview of VHO decision strategies in heterogeneous wireless networks, 2008. 2607-2620 11. K. Pahlavan, P. Krishnamurthy, A. Hatami, M. Ylianttila, J. Makela, R. Pichna, J. Vallstron, Handoff in hybrid mobile data networks, IEEE Personal Communications 7 (2) (2000) 34–47.[ 12. J. Makela, M. Ylianttila, K. Pahlavan, Handoff decision in multiservice networks, in: The 11th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, 2000 (PIMRC 2000), vol. 1, 2000, pp. 655–659. 13. P. Chan, Y. Hu, R. Sheriff, Implementation of fuzzy multiple objective decision making algorithm in a heterogeneous mobile environment, in: IEEE Wireless Communications and Networking Conference, 2002 (WCNC 2002), vol. 1, 2002, pp. 332–336.] 14. I. Kustiawan, K. H. Chi.: Handoff Decision Using a Kalman Filter and Fuzzy Logic in Heterogeneous Wireless Networks. IEEE Communication Letters 19, 1–4 (2015) 15. A. Calhan, C. Ceken.: Artificial neural network based vertical handoff algorithm for reducing handoff latency. Wireless Personal Communications 71, 2399–2415 (2013) 16. S. H. Alsamhi, N. S. Rajput.: An intelligent hand-off algorithm to enhance quality of service in high altitude platforms using neural network. Wireless Personal Communications 82, 2059– 2073 (2015) 17. N. M. Alotaibi, S. S. Alwakeel.: A neural network based handover management strategy for Het Nets. 14th IEEE International Conference on Machine Learning and Applications, 1210–1214 (2015) 18. K. Ahuja, B. Singh, R. Khanna.: Particle swarm optimization based network selection in heterogeneous wireless environment. OptikInternational Journal for Light and Electron Optics 125, 214–219 (2014) 19. Yan, X., Sekercioglu, Y. A., Narayanan, S.: A survey of VHO decision algorithms in fourth generation heterogeneous wireless networks. Computer Networks 54, 1848–1863 (2010) 20. Nassr, N., Guizani, S., Al-Masri, E.: Middleware vertical handoff manager: a neural networkbased solution. IEEE International Conference on Communications, 5671–5676 (2007) 21. Capka, J., Boutaba, R.: Mobility prediction in wireless networks using neural networks. Management of Multimedia Networks and Services 3271, 320–333 (2011) 22. Chai, R., Cheng, J., Pu, X., Chen. Q.: Neural network based vertical handoff performance enhancement in heterogeneous wireless networks. Wireless Communications, Networking and Mobile Computing (WiCOM), 1–4, (2011). 23. Nan, W., Wenxiao, S., Shaoshuai, F., Shuxiang, L.: PSO-FNN based vertical handoff decision algorithm in heterogeneous wireless networks. 2nd International Conference on Challenges in Environmental Science and Computer Engineering 11, 55–62 (2011) Ben- Mubarak, M., Ali, B. M., Noordin, N. K., Ismail, A., Ng, C. K.: Fuzzy logic based selfadaptive handover algorithm for mobile WiMAX. Wireless Personal Communications 71, 1421–1442 (2013) 24. H. Y. Huang, C. Y. Wang, and R. H. Hwang, “Context-Awareness Handoff Planning in Heterogeneous Wireless Networks”, Lecture Notes in Computer Science, Volume 6406 pp. 430-444, SpringerVerlag Berlin Heidelberg 2010. 25. P. TalebiFard and V. C.M Leung, “A Dynamic Context-Aware Access Network Selection for Handover in Het Net Environments”, IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) pp. 385-390, June 2011. 26. J. M. Barja, C. T. Calafate, J. C. Cano and P. Manzoni, “An overview of VHO techniques: Algorithms, protocols and tools”, Computer Communications, Vol 34, No. 8 pp. 985-997, June 2011 27. X. Yan, Y. A. Sekercioglu and S. Narayanan, “A survey of VHO decision algorithms in Fourth Generation heterogeneous wireless networks”, Journal Computer Networks: The International Journal of Computer and Telecommunications Networking Volume 54 Issue 11 pp. 1848-1863, August 2010. 28. K. Savitha and Dr. C. Chandrasekar, “VHO decision schemes using SAW and WPM for Network selection in Heterogeneous Wireless Networks”, Global Journal of Computer Science and Technology Volume 11 Issue 9 Version 1.0 pp. 19-24, May 2011. 29. Hongwei Liao, Ling Tie and Zhao Du, A VHO Decision Algorithm Based on Fuzzy Control Theory, Proceedings of the First International Multi-Symposiums on Computer and Computational Sciences (IMSCCS'06) pp. 309-313, June 2006. 30. Rashid A. Saeed, HafizalMohamad, BorhanuddinMohd. Ali and Mazlan Abbas, WiFi/WiMAX Heterogeneous Seamless Handover, Third International Conference on Broadband Communications, Information Technology & Biomedical Applications, pp. 169-174, 12/ 2008. 31. Z. Dai, R. Fracchia, J. Gosteau, P. Pellati and G. Vivier, VHO criteria and algorithm in IEEE 802.11 and 802.16 hybrid networks, IEEE International Conference on Communications 2008 (ICC '08), pp. 2480–2484, Mai 2008. 32. XuHaibo, TianHui and Zhang Ping, A novel terminal-controlled handover scheme in heterogeneous wireless networks, Journal Computers and Electrical Engineering, Volume 36 Issue 2 pp. 269– 279, March 2010. 33. K. Piamrat, A. Ksentini, J. M. Bonnin and C. Viho, “Radio resource management in emerging heterogeneous wireless networks”, Computer Communications, February 2010. 37. Authors: Kirankumar Y. Bendigeri, Santosh B. Kumbalavati, Jayashree D. Mallapur
Paper Title: Circular Based Node Placement in Wireless Sensor Networks Abstract: Wireless Sensor Network (WSN) is one of the promising technologies in today’s world. 215-221 Applications can be from home, science, industry, medical and so on. In every field it is proving to be one of the best methods adopted like for example patient monitoring from home to hospitals using sensors and internet at a low cost is possible. In this paper study on node placement is considered as most of the sensor network has random based deployment of nodes during simulation. Random node placements have densely placed nodes in a certain area and sometimes only few nodes are deployed in a same area. This is purely because of random distribution of nodes and thus has impact on overall performance of WSN. Proposed method considers circular based deployment for routing in WSN with its own algorithm. Circular based node deployment is a combination of random and grid based approach and proves to be effective way of routing packets to destination. Simulation results show that performance of network in terms of computation is better than the existing methods.
Keywords: Wireless Sensor Network, simulation, Random.
References: 1. V.A. Petrushin, G. Wei, O. Shakil, D. Roqueiro, V. Gershman, Multiple-sensor indoor surveillance system, in: Proceedings of the 3rd Canadian Conference on Computer and Robot Vision (CRV’06), Que´bec city, June 2006. 2. L. Krishnamurthy et al., Design and deployment of industrial sensor networks: experiences from a semiconductor plant and the North Sea, in: Proceedings of the 3rd ACM Conference on Embedded Networked Sensor Systems (Sen-Sys’05), San Diego, CA, November 2005 3. A. Brooks, A. Makarenko, T. Kaupp, S. Williams, H. Durrant-Whyte, Implementation of an indoor active sensor network, in: Proceedings of the 9th International Symposium on Experimental Robotics Singapore, June 2004. 4. Y. Zhang, W. Huangfu and Z. Zhang, "Robust Deployment for Data Collecting under Random Node Failures in Wireless Sensor Networks," 2015 IEEE 12th Intl Conf on Ubiquitous Intelligence and Computing and 2015 IEEE 12th Intl Conf on Autonomic and Trusted Computing and 2015 IEEE 15th Intl Conf on Scalable Computing and Communications and Its Associated Workshops (UIC- ATC-ScalCom), Beijing, 2015, pp. 328-331. 5. Z. u. Rahman et al., "On Utilizing Static Courier Nodes to Achieve Energy Efficiency with Depth Based Routing for Underwater Wireless Sensor Networks," 2016 IEEE 30th International Conference on Advanced Information Networking and Applications (AINA), Crans-Montana, 2016, pp. 1184-1191. 6. R. M. Gomathi, J. M. L. Manickam and T. Madhukumar, "Energy preserved mobicast routing protocol with static node for underwater acoustic sensor network," International Conference on Innovation Information in Computing Technologies, pp. 1-8, Chennai, 2015. 7. H. Y. Chang, Y. H. Huang and T. L. Lin, "A Novel Relay Placement Algorithm Based on Puzzle Games for Indoor Wireless Sensor Networks," 2014 Tenth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, Kitakyushu, pp. 682-685, 2014. 8. N. P. Mohapatra and S. K. Behera, “Relay Node and Cluster Head Placement in Wireless Sensor Networks”, International Conference on Information and Network Technology, Vol. 37, 2012 9. Selmic, Rastko R., Jinko Kanno, Jack Buchart, and Nicholas Richardson. "Quadratic Optimal Control of Wireless Sensor Network Deployment." In Proc. Cyberspace Research Workshop, Shreveport, Louisiana. 2007. 10. Abidin, H. Zainol, Norashidah Md Din, and Nurul Asyikin Mohd Radzi. "Deterministic static sensor node placement in Wireless Sensor Network based on territorial predator scent marking behavior." International Journal of Communication Networks and Information Security 5, no. 3 (2013): 186. 11. Cha, HyunSoo, Ki-Hyung Kim, and SeungWha Yoo. "A node placement algorithm for avoiding congestion regions in wireless sensor networks." In Ubiquitous and Future Networks (ICUFN), 2011 Third International Conference on, pp. 202-207. IEEE, 2011. 12. Chen, Qinyin, Y. Hu, Zhe Chen, Vic Grout, D. Zhang, H. Wang, and H. Xing. "Improved relay node placement algorithm for Wireless Sensor Networks application in Wind Farm." In Smart Energy Grid Engineering (SEGE), 2013 IEEE International Conference on, pp. 1-6. IEEE, 2013. 13. W. Li, “Wireless Sensor Network placement Algorithm”, Retrieved, 28th March, 2017 14. Z. Lu, Y. Wen, R. Fan, S. L. Tan and J. Biswas, "Toward Efficient Distributed Algorithms for In-Network Binary Operator Tree Placement in Wireless Sensor Networks," in IEEE Journal on Selected Areas in Communications, vol. 31, no. 4, pp. 743-755, April 2013. 15. C. Ma, W. Liang and M. Zheng, "Set-covering-based algorithm for delay constrained relay node placement in Wireless Sensor Networks," 2016 IEEE International Conference on Communications (ICC), pp. 1-6, Kuala Lumpur, 2016. 16. S. Misra, N. E. Majd and H. Huang, "Approximation Algorithms for Constrained Relay Node Placement in Energy Harvesting Wireless Sensor Networks," in IEEE Transactions on Computers, vol. 63, no. 12, pp. 2933-2947, Dec. 2014. 17. L. Wang, X. Fu, J. Fang, H. Wang and M. Fei, "Optimal node placement in industrial Wireless Sensor Networks using adaptive mutation probability binary Particle Swarm Optimization algorithm," Seventh International Conference on Natural Computation, pp. 2199-2203, Shanghai, 2011,. 18. Z. Wang and D. Wei, "Constrained RN placement algorithm in two-tiered wireless sensor networks," 2012 International Conference on Systems and Informatics (ICSAI2012), pp. 99-102, Yantai, 2012. 19. A. Willig, "Placement of relayers in wireless industrial sensor networks: An approximation algorithm," 2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), pp. 1-6, Singapore, 2014. 20. Heinzelman, W., Chandrakasan, A., and Balakrishnan, H., "Energy-Efficient Communication Protocols for Wireless Microsensor Networks", Proceedings of the 33rd Hawaaian International Conference on Systems Science (HICSS), January 2000. 21. Peng-Jun Wan and Chih-Wei Yi, "Coverage by randomly deployed wireless sensor networks," in IEEE Transactions on Information Theory, vol. 52, no. 6, pp. 2658-2669, June 2006. 22. W. Zhu, S. Xianhe, L. Cuicui and C. Jianhui, "Relay Node Placement Algorithm Based on Grid in Wireless Sensor Network," 2013 Third International Conference on Instrumentation, Measurement, Computer, Communication and Control, Shenyang, 2013, pp. 278- 283. 38. Authors: Ramandeep Kaur, V. Devendran
Paper Title: Content Based Image Retrieval: A Review Abstract: Image recovery was one of the most thrilling and vibrant fields of computer vision science. Content- 222-228 based image retrieval systems (CBIR) are used to catalog, scan, download and access image databases automatically. Color & texture features are significant properties for content-based image recovery systems. The content-based image retrieval (CBIR) is therefore an attractive source of accurate and quick retrieval. Number of techniques has been established in recent years to improve the performance of CBIR. This paper discusses why CBIR is important nowadays along with the limitations and benefits. Apart from applications, various feature extraction techniques used in CBIR are also discussed.
Keywords: Image recovery, Image Processing, CBIR, Feature Extraction
References: 1. Alhassan, A. K., & Alfaki, A. A. (2017). Color and texture fusionbased method for content-based Image Retrieval. 2017 International Conference on Communication, Control, Computing and Electronics Engineering (ICCCCEE). 2. Ali, A., & Sharma, S. (2017). Content based image retrieval using feature extraction with machine learning. 2017 International Conference on Intelligent Computing and Control Systems (ICICCS). 3. Azuela, H. S., J., Santiago, R., López, A., Peña Ayala, A., Cuevas Jimenez, E. V., & Rubio Espino, E. (2014). Alternative formulations to compute the binary shape Euler number. IET Computer Vision, 8(3), 171–181. 4. Balan, S., & Sunny, L. Z. (2018). Survey on Feature Extraction Techniques in Image Processing. International Journal for Research in Applied Science & Engineering Technology (IJRASET), 6(3), 217- 222. 5. Bansal A., Agarwal R., & Sharma R. K. (2016). Statistical feature extraction based iris recognition system. Indian Academy of Sciences, 41(5), 507-518. 6. Chatterjee, S., Dey, D., Munshi, S., & Gorai, S. (2019). Extraction of features from cross correlation in space and frequency domains for classification of skin lesions. Biomedical Signal Processing and Control, 53, 101581. 7. Chaugule, A., & Mali, S. N. (2014). Evaluation of Texture and Shape Features for Classification of Four Paddy Varieties. Journal of Engineering, 2014, 1–8. 8. Choudhary, R., Raina, N., Chaudhary, N., Chauhan, R., & Goudar, R. H. (2014). An integrated approach to Content Based Image Retrieval. 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI). 9. He, T., Liu, Y., Yu, Y., Zhao, Q., & Hu, Z. (2019). Application of Deep Convolutional Neural Network on Feature Extraction and Detection of Wood Defects. Measurement, 107357. 10. Hoang, N.-D. (2019). Automatic detection of asphalt pavement raveling using image texture based feature extraction and stochastic gradient descent logistic regression. Automation in Construction, 105, 102843. 11. Kaur, M., & Sohi, N. (2016). A novel technique for content based image retrieval using color, texture and edge features, 2016 International Conference on Communication and Electronics Systems (ICCES). 12. Kumar, K., Li, J.-P., & Zain-Ul-Abidin. (2015). Complementary feature extraction approach in CBIR. 2015 12th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP). 13. Latif, A., Rasheed, A., Sajid, U., Ahmed, J., Ali, N., Ratyal, N. I., Khalil, T. (2019). Content-Based Image Retrieval and Feature Extraction: A Comprehensive Review. Mathematical Problems in Engineering, 2019, 1–21. 14. Liu, Z., Lai, Z., Ou, W., Zhang, K., & Zheng, R. (2020). Structured optimal graph based sparse feature extraction for semi-supervised learning. Signal Processing, 107456. 15. Matsuoka, Y. R., R. Sandoval, G. A., Q. Say, L. P., Y. Teng, J. S., & Acula, D. D. (2018). Enhanced Intelligent Character Recognition (ICR) Approach Using Diagonal Feature Extraction and Euler Number as Classifier with Modified One-Pixel Width Character Segmentation Algorithm. 2018 International Conference on Platform Technology and Service (PlatCon). 16. Meena, M., Singh, A. R., & Bharadi, V. A. (2016). Architecture for Software as a Service (SaaS) Model of CBIR on Hybrid Cloud of Microsoft Azure. Procedia Computer Science, 79, 569–578. 17. Mistry, Y., Ingole, D. T., & Ingole, M. D. (2017). Content based image retrieval using hybrid features and various distance metric. Journal of Electrical Systems and Information Technology, 5(3), 874- 888. 18. Mohamed A., Nasein D., Hassan H., & Haron H. (2015). A Review on Feature Extraction and Feature Selection for Handwritten Character Recognition. (IJACSA) International Journal of Advanced Computer Science and Applications, 6(2). 19. Neha J. Pithadia1, & Nimavat, V. D. (2015). A Review on Feature Extraction Techniques for Optical Character Recognition. International Journal of Innovative Research in Computer and Communication Engineering, 3(2), 1263-1268. 20. Pal, M., Bhattacharyya, S., & Sarkar, T. (2018). Euler number based feature extraction technique for gender discrimination from offline Hindi signature using SVM & BPNN classifier. 2018 Emerging Trends in Electronic Devices and Computational Techniques (EDCT). 21. Patel M. N., & Tandel P. (2016). A Survey on Feature Extraction Techniques for Shape based Object Recognition,” International Journal of Computer Applications, 137(6), 16-22. 22. Putri, R. D., Prabawa, H. W., & Wihardi, Y. (2017). Color and texture features extraction on content-based image retrieval. 2017 3rd International Conference on Science in Information Technology (ICSITech). 23. Raveena, P. V., James, A., & Saravanan, C. (2017). Extended zone based handwritten Malayalam character recognition using structural features. 2017 Second International Conference on Electrical, Computer and Communication Technologies (ICECCT). 24. Schroder, J., Goetze, S., & Anemuller, J. (2015). Spectro-Temporal Gabor Filterbank Features for Acoustic Event Detection. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 23 (2), 2198–2208. 25. Seth N., & Jindal S. (2017). A Review on content based image retrieval, International Journal of Computers and Technology 15(14). 26. Soora, N. R. & Deshpande, P. S. (2017). Review of Feature Extraction Techniques for Character Recognition. IETE Journal of Research, 68(3), 280-295. 27. Stubendek, A., & Karacs, K. (2018). Shape Recognition Based on Projected Edges and Global Statistical Features. Mathematical Problems in Engineering, 2018, 1–18. 28. Tahir M., & Fahiem M. A. (2014). A Statistical-Textural-Features Based Approach for Classification of Solid Drugs Using Surface Microscopic Images. Computational and Mathematical Methods in Medicine, 2014, 1-12. 29. Unar, S., Wang, X., Wang, C., & Wang, Y. (2019). A decisive content based image retrieval approach for feature fusion in visual and textual images. Knowledge-Based Systems. 30. Vithlani P., & Kumbharana C. K. (2015). Structural and Statistical Feature Extraction Methods for Character and Digit Recognition. International Journal of Computer Applications, 120(4), 43-47. 31. Wang, H., Li, Z., Li, Y., Gupta, B. B., & Choi, C. (2018). Visual saliency guided complex image retrieval. Pattern Recognition Letters. 32. Zhu, L. (2014). Accelerating Content-Based Image Retrieval via GPU-Adaptive Index Structure. The Scientific World Journal, 2014, 1–11. Authors: Meiappane. A, Giridharan. S, Jayaram. V, Manikandan. K, Vishnu. M
Paper Title: Automatic Attendance Management System under Unconstrained Video using Face Recognition Abstract: Attendance Management System under unconstrained video using face recognition technology has made a great variation from the traditional method of attendance marking system. This attendance management system has been developed under the domain of Deep Learning by using Face recognition. Automatic Attendance Management under unconstrained video using face recognition systems which automatically mark attendance by detecting end to end face from the frames obtained from live stream video of surveillance camera which placed in center of the classroom. From the recognized faces, it will be compared with stored images in database, then the attendance report will be generated and it also provides attendance reports to parents of the absentee’s student.
Keywords: Automatic Attendance system, Attendance marking, Face recognition, Deep learning.
References: 1. Nandhini R, Duraimurugan N, S.P.Chokkalingam, “Face Recognition Based Attendance System”, International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958, Volume-8, Issue-3S, February 2019. 39. 2. Radhika R, Hari Prasanth P, Karthik A, Mohanraj K, Navin Kumar M, “ Automatic Attendance Marking using Face Recognition and SMS Alert using IoT”, International Journal for Research in Applied Science & Engineering Technology (IJRASET) ISSN: 2321-9633; IC Value: 45.98; SJ Impact Factor: 6.887, Volume 7 Issue III, Mar 2019. 229-232 3. Abhilasha Varshney, Sakshi Singh, Suneet Srivastava, Suyash Chaudhary, Tanuja, “Automated Attendance System using Face Recognition”, International Research 4. Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 05 | May 2019. 5. Senthamil Selvi K, Chitrakala P, Antony Jenitha A, “Face Recognition Based Attendance Marking System”, International Journal of Computer Science and Mobile Computing, Vol.3 Issue.2, February- 2014. 6. Narayan T. Deshpande, Dr. S.Ravishankar, “Face Detection and Recognition using Viola-Jones algorithm and Fusion of PCA and ANN”, Advances in Computational Sciences and Technology ISSN 0973-6107 Volume 10, Number 5 (2017). 7. Pooja Malusare , Shivangi Shewale,”Face and Person 8. Recognition from Unconstrained Video”, IJESC Vol. 7 Issue No. 2, February 2017. 9. K.Senthamil Selvi, P.Chitrakala, A.Antony Jenitha,‖Face Recognition Based Attendance Marking System‖, IJCSMC, Vol. 3, Issue. 2, February 2014. 10. Y. Khem Puthea, Rudy Hartanto and Risanuri Hidayat, ― A Review paper on Attendance Marking System based on Face Recognition. 11. V. Shehu and A. Dika, ―Using real time computer vision algorithms in automatic attendance management systems, Inf. Technol. Interfaces (ITI), 2010. 12. P. Wagh, R. Thakare, J. Chaudhari, and S. Patil, “Attendance system based on face recognition using 13. eigenfaces and PCA algorithms,” in 2015 International Conference on Green Computing and Internet of Things (ICGCIoT), 2015. 14. K G Shreyas Dixit, Mahima Girish Chadaga, Sinchana S Savalgimath, G Ragavendra Rakshith, Naveen Kumar M R,” Evaluation and Evolution of Object Detection Techniques YOLO and R-CNN”, International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-8, Issue-2S3, July 2019. Authors: N.A.S. Musa Asri, S.H.Y.S. Abdullah, F.A. Halim Yap, R. Yusof, S.M. Sharun Paper Title: Low Cost Semi-Automatic Acrylic Cutter and Bending Machine Abstract: In the current market, the acrylic cutter and bending machine is mostly available in big size and equipped with advanced system processes for large industrial application. This is undesirable since the cost of the machine can be quite expensive for a small industry. This paper proposed the development of a low-cost semi-automatic acrylic cutter and bending machine to cater to the small industry requirement. The design methodology has been accomplished using engineering design method to propose a suitable design semi- automatic acrylic cutter and bending machine based on commercially available design. A 3D representation of the machine was generated with the help of Autodesk Inventor 2019 to visualize all the details concerning the semi-automatic acrylic cutter and bending machine. The semi-automatic acrylic cutter and bending machine are designed for cutting, bending, and finishing acrylic sheet in simple manual operation. The machine was fabricated using industrial material specifications such as plywood and hollow steel to ensure the effectiveness of semi-automatic acrylic cutter and bending machine. This semi-automatic acrylic cutter and the bending machine was also equipped with a table that can be adjusted to different heights. The performance of the machine was tested by cutting and bending different thicknesses of the acrylic sheet and polishing of the acrylic sheet. The results found that the semi-automatic acrylic cutter and bending machine is capable of handling the thickness of the acrylic sheet up until 5 mm in a single operation. The developed machine was able to cut the acrylic sheet up to 5 mm thickness and bend from 0 to 180 degrees. Thus, this machine could provide a highly ergonomic solution that responds to the needs of small industrial applications.
40. Keywords: Acrylic, Bending, Cutter, Machine, Semi-automatic. 233-237 References: 1. A.R. Torabi, B. Saboori, S.K. Mohammadian, M.R. Ayatollahi. (2018). Brittle failure of PMMA in the presence of blunt V-notches under combined tension-tear loading: Experiments and stress-based theories. Polymer Testing. doi: https://doi.org/10.1016/j.polymertesting.2018.10.002 2. E. Pawar. (2016). A Review Article on Acrylic PMMA. IOSR Journal of Mechanical and Civil Engineering, 13(2), 1-4. https://doi.org/ 10.9790/1684-1302010104 3. J.S. Reinaldo, L.M. Pereira, E.S. Silva, T.C.P. Macedo, I.Z. Damasceno, E.N. Ito. (2019). Thermal, mechanical and morphological properties of multicomponent blends based on acrylic and styrenic polymers, Polymer Testing. 82, 106265 doi: https://doi.org/10.1016/ j.polymertesting.2019.106265 4. A. Ruhl, S. Kolling, J. Schneider. (2017). Characterization and Modeling of Poly(methyl Methacrylate) and Thermoplastic Polyurethane for the Application in Laminated Setups, Mechanics of Materials. 113, 102-111. doi: 10.1016/j.mechmat.2017.07.018 5. A.E. Saputro, M. Darwis. (2020). Rancang bangun mesin laser engraver and cutter untuk membuat kemasan modul praktikum berbahan akrilik, Jurnal Pengelolaan Laboratorium Pendidikan, 2(1), 40-50. 6. N. Sohaimi (2019). “Design and Develop a Semi-Automatic Acrylic Cutter Machine,” theses at Universiti Sultan Zainal Abidin, Kuala Terengganu, Malaysia, unpublished. 7. S.B., Mohamed, R. Ab Rashid, M., Muhamad, J., Ismail. (2019). Cutting Parameters and the Machinability Performance. In Down Milling Trimming Process Optimization for Carbon Fiber-Reinforced Plastic (pp. 15-27). Springer, Singapore. 8. H.P.S., Abdul Khalil, C.K. Saurabh, M. Asniza, Y.Y. Tye, M.R. Nurul Fazita, M.I. Syakir, H.M. Fizree. (2017). Nanofibrillated cellulose reinforcement in thermoset polymer composites, In Cellulose-Reinforced Nanofibre Composites, pp. 1-24. Woodhead Publishing. 9. A.I Yusra, H.A. Khalil, M.S. Hossain, Y. Davoudpour, A.A. Astimar, A. Zaidon, A.M. Omar. (2015). Characterization of plant nanofiber-reinforced epoxy composites. BioResources, 10(4), 8268-8280. 41. Authors: V Amit M Naidu , S.D. Shelare, S.M. Awatade
Paper Title: Design of Components of 4 Stroke Engine using Hybrid Metal Matrix Abstract: In the present study of Aluminum Metal Matrix Composites (AMMCs), specifically speaking, in 238-242 our case Al-SiC-graphite have been discussed on the basis of literature review, comprising of similar characteristics of base aluminum alloy but not completely identical. Aluminum metal matrix composites have different properties when compared with Cast Iron and Aluminum. This difference in properties gives us a chance to design components which may give better results than the conventional ones. In a new approach, we are trying to develop new components for 4 Stroke Petrol Engine namely piston & connecting rod, which would have better Tensile Strength, Young’s Modulus & Low Thermal Conductivity as compared to the pure Aluminum. Paper Setup must be in A4 size with Margin: Top 0.7”, Bottom 0.7”, Left 0.65”, 0.65”, Gutter 0”, and Gutter Position Top. Paper must be in two Columns after Authors Name with Width 8.27”, height 11.69” Spacing 0.2”. Whole paper must be with: Font Name Times New Roman, Font Size 10, Line Spacing 1.05 EXCEPT Abstract, Keywords (Index Term), Paper Tile, References, Author Profile (in the last page of the paper, maximum 400 words), All Headings, and Manuscript Details (First Page, Bottom, left side).Paper Title must be in Font Size 24, Bold, with Single Line Spacing. Authors Name must be in Font Size 11, Bold, Before Spacing 0, After Spacing 16, with Single Line Spacing. Please do not write Author e-mail or author address in the place of Authors name. Authors e-mail, and their Address details must be in the Manuscript details. Abstract and Keywords (Index Term) must be in Font Size 9, Bold, Italic with Single Line Spacing. All MAIN HEADING must be in Upper Case, Centre, and Roman Numbering (I, II, III…etc), Before Spacing 12, After Spacing 6, with single line spacing. All Sub Heading must be in Title Case, Left 0.25 cm, Italic, and Alphabet Numbering (A, B, C…etc), Before Spacing 6, After Spacing 4, with Single Line Spacing. Manuscript Details must be in Font Size 8, in the Bottom, First Page, and Left Side with Single Line Spacing. References must be in Font Size 8, Hanging 0.25 with single line spacing. Author Profile must be in Font Size 8, with single line spacing. Fore more details, please download TEMPLATE HELP FILE from the website. Keywords: Al-SiC-Graphite Specimen, Melting Metallurgy Metal matrix.
References: 1. David Weiss Eck Industries, Inc., Design of Aluminum Metal Matrix Components for Casting, 2005-01-1689., SAE TECHNICAL PAPER SERIES. 2. Ashok Kr. Mishra, Rakesh Sheokand, Intl Journal of Sci & Research Publications, Vol 2, 10, Oct 2012. 3. Y.Wu, H.Liu and E.J.Lavernia, Scripta Metallurgica et Materiallia,1993, 41- 61. 4. J.JenixRino,D.Chandramohan, (IJSR), India Online ISSN: 2319-7064. 5. D.J.Lloyd, Comp.Sci, and Tech., 35, 1989, pp.159-179. 6. M.K.Surappa and P.K.Rohatgi, Journal of Material Science. 16, 1981, pp 982-993. 7. P.S.Robi et.al, Mater.Chact., 27, 1991, pp. 11- 18. 8. Karl Ulrich Kainer, Metal Matrix Composites. Custom-made Materials for Automotive and Aerospace Engineering, ISBN: 3-527- 31360-5. 9. S. K. Jo, W. J. Lee, Y. H. Park, I. M. Park, Tribol Lett (2012) 45:101–107. 10. S.Das and S.V.Prasad, Wear, 133, 1989, pp 173-187. 11. B.P.Krishnan, M.K.Surappa and P.K.Rohtagi, JMS., 16, 1981, 1209-1216. 12. S.Basavarajappa, G.Chandramohan, ISRS on Materials and Engineering December 20- 22, 2004. 13. Shyam Bahadur, Fundamentals of Friction and Wear. 14. S.Das et al., Mater.Trans., JIM, 32 (2), 1991, pp.189-194. 15. T.P.Murali, M.K.Surappa and P.K.Rohatgi, Metall. Trans., 13B, 1992, pp 485-494. 16. K. U. Kainer (Ed.), Metallische Verbundwerkstoffe, DGM Informationsgesellschaft, Oberursel (1994). 17. W.Henning, E. Köhler, Maschinenmarkt 1995, 101, 50–55. 18. S. Mielke, N. Seitz, Grosche, Int. Conf. on Metal Matrix Composites, The Institute of Metals, London (1987), pp. 4/1–4/3. 19. H. P. Degischer, Schmelzmetallurgische Herstellung von Metallmatrix-Verbundwerkstoffen, in Metallische Verbundwerkstoffe, K.U.Kainer (Ed.), DGM Informationsgesellschaft, Oberursel (1994), pp.139–168. 20. Lanxide Electronic Components, Lanxide Electronic Components, Inc., Newark USA (1995). 21. C. Fritze, Infiltration keramischer Faserformkörper mit Hilfe des Verfahrens des selbstgenerierenden Vakuums, Dissertation TU Clausthal (1997). 22. DURALCAN Composites for Gravity Castings, Duralcan USA, San Diego (1992). 23. DURALCAN Composites for High-Pressure Die Castings, Duralcan USA, San Diego (1992). 24. C. W. Brown, W.Harrigan, J. F. Dolowy, Proc. Verbundwerk 90, Demat, Frankfurt (1990), pp. 20.1–20.15. 25. Manufacturers of Discontinuously Reinforced Aluminum (DRA), DWA Composite Specialities, Inc., Chatsworth USA (1995). 26. G. Leatham, A. Ogilvy, L. Elias, Proc. Int. Conf. P/M in Aerospace, Defence and Demanding Applications, MPIF, Princeton, USA (1993), pp. 165–175. 27. Cospray Ltd. Banbury, U.K., 1992. 28. Keramal Aluminum-Verbundwerkstoffe, Aluminum Ranshofen Ges.m.b.H., Ranshofen, Österreich (1992). 29. F. Koopmann, Kontrolle Heft 1/2 (1996), pp. 40–44. 30. Nunna Durga Prasanth* , Dr.B Venkataraman, Experimental investigation and analysis of piston by using hybrid metal matrix, IJESRT, ISSN: 2277-9655, 2015. 31. Er. Rachit Marwaha, Mr. Rahul Dev Gupta, Er. Krishan Kant Sharma “Determination & Experimental Investigation Of Weight Loss On Al/Sic/Gr - Metal Matrix Hybrid Composite By Taguchi Method” Volume 2, Issue 11, November 2013. 32. R. Ravi Raja Malarvannan and P. Vignesh “Experimental Investigation and Analysis of Piston by using Composite Materials” Vol 04, Article-K100; November 2013. 33. Joel Hemanth, Trans. AFS, 5, 1999, 769. 34. G. B. Veeresh Kumar, C. S. P. Rao, N. Selvaraj, JMMCE, Vol. 10, pp.59-91, 2011. 35. Harish K.Garg Intl Journal of Latest Research in Science and Technology Vol.1, Issue 1 :36-44,May- June(2012). 36. Haizhi Ye, ASM Int JMEPEG (2003) 12:288-297. 37. V.C.Uvaraja, International Journal of Engineering Technology Volume 2, 2012. 42. Authors: Nikitha M, Pratheksha Jerline L, Aishwarya T, Karthikaikannan D Solution of Economic Load Dispatch problem using Conventional methods and Particle Swarm Paper Title: Optimization Abstract: Economic load dispatch is the method to find the optimum power output of the generators in a 243-249 network cost-effectively with adherence to all the constraints. In this paper, the Economic Load Dispatch (ELD) problem has been tested on IEEE 14 Bus System by implementing conventional methods like Classical Coordination method, Gradient method, Modified Coordination method, and Particle Swarm Optimization (PSO). Conventional methodologies provide the solution in the simplest way but it does not handle the constraints effectively. Modified coordination method provides a better solution without the use of B- coefficients and the calculation of penalty factors is much easier because they can be obtained from the already available solution of FDLF involving some computations. PSO also provides a better solution but the initial design parameters are slightly difficult to determine. The performance of all the methods is compared and results reveal that the Modified coordination method proves to be the fastest among other solutions particularly if larger systems are involved.
Keywords: constraints, loss, optimum, penalty factor.
References: 1. Bhowmik P. S, Bose S. P, Rajan D. V, Deb S, “Power Flow Analysis of Power System Using Power Perturbation Method,” IEEE Power Engineering and Automation Conference, Wuhan, China, September, 2011. 2. M. Gholamghasemi, E. Akbari, M. B. Asadpoor, “A new solution to the non-convex economic load dispatch problems using phasor particle swarm optimization,” Applied Soft Computing Journal, 2019, pp. 111-124. 3. Hadi Saadat, Power system analysis. WCB/McGraw-Hill (USA), 1999, pp. 268-270. 4. D. P. Kothari, I. J. Nagrath, Modern Power System Analysis, Tata McGraw-Hill (India), 2003. 5. J. Nanda, L. Hari, M. L. Kothari, and J. Henry, “Extremely fast economic load dispatch algorithm through modified co-ordination equations,” IEE Proceedings C (Generation, Transmission and Distribution), 1992, pp. 39-46. 6. M. Sudhakaran, P. Ajay-D-Vimal Raj, and T.G. Palanivelu, “Application of Particle Swarm Optimization for Economic Load Dispatch Problems,” International Conference on Intelligent Systems Applications to Power Systems, Toki Messe, Niigata, Japan, 2007. 7. Vipandeep Kour, Lakhwinder Singh, “Comparative Analysis of Lambda Iteration Method and Particle Swarm Optimization for Economic Emission Dispatch Problem,” International Journal of Engineering Research & Technology (IJERT), 2017. 8. Manoj Mahajan, Shelly Vadhera, “Economic load dispatch of different bus systems using particle swarm optimization,” IEEE Fifth Power India Conference, 2012. 9. M. A. Abido, "Environmental/Economic power dispatch using multiobjective evolutionary algorithms," IEEE Transactions on Power Systems, vol. 18, no. 4, pp. 1529-1537, 2003. 10. Uma Sharma, Beaulah Moses, “Analysis and optimization of economic load dispatch using soft computing techniques,” 2016 International Conference on Electrical, Electronics and Optimization Techniques (ICEEOT). Authors: Amandeep Singh, Amit Chhabra
Paper Title: Review of Techniques used to find the Possibility of Getting Heart Related Disease. Abstract: As with the changing lifestyle and people consuming high calorie diet increases the heart disease rate among the humans. Over the last decade heart related diseases are one of the leading cause of death cases every year. It is very hard to notice the symptoms of any heart related disease at early stage and in many cases it leads to sudden death before ever knowing the first symptom of any heart related problem. With the advancement of technology there are many devices which are used to perform several tests in the medical field and with the emerging trend of Machine learning doctors can be aided to find symptoms of heart disease . There is huge amount of patients health data collected by healthcare institutes which can be used for data mining and infer relationship between data and helps in predicting heart diseases. The machine learning models trained on patients record data which shows symptoms is used to predict the probability for having a heart disease.
Keywords: Machine learning , supervised learning , unsupervised learning , heart disease.
References: 10. Carlos Ordonez. “Association Rule Discovery With the Train and Test 43. 11. Approach for Heart Disease Prediction” IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, VOL. 10, NO. 2, 2006 250-253 12. Shadab A. Pattekari, Asma A. Parveen, Enginering Khaja, Banda Nawaz. “PREDICTION SYSTEM FOR HEART DISEASE USING NAIVE BAYES” Semantic scholar 2012 13. Jabbar, M. A., Deekshatulu, B. L., & Chandra, P. “Heart disease prediction using lazy associative classification.” International Mutli- Conference on Automation, Computing, Communication, Control and Compressed Sensing (iMac4s) 2013 14. Krishnan. J, S., & S, G. . “Prediction of Heart Disease Using Machine Learning Algorithms.”1st International Conference on Innovations in Information and Communication Technology (ICIICT). 2019 15. G. Subbalakshmi, Montana Tech, Edgar Dist, Ramesh M. Tech. “Decision Support in Heart Disease Prediction System using Naive Bayes.” Semantic scholar 2011 16. Latha Parthiban and R.Subramanian. “Intelligent Heart Disease Prediction System using CANFIS and Genetic Algorithm.” International Journal of Biological and Medical Sciences 3:3 2008 17. Chaitrali S. Dangare , Sulabha S. Apte . “Improved Study of Heart Disease Prediction System using Data Mining Classification Techniques.” International Journal of Computer Applications (0975 – 888) 2012 18. Palaniappan, S., & Awang, R. “Intelligent heart disease prediction system using data mining techniques.” 2008 IEEE/ACS International Conference on Computer Systems and Applications. doi:10.1109/aiccsa.2008.4493524 2008 19. Alloghani, Mohamed & Al-Jumeily, Dhiya 20. & Mustafina, Jamila & Hussain, Abir & Aljaaf, Ahmed. “A Systematic Review on Supervised and Unsupervised Machine Learning Algorithms for Data Science.” 10.1007/978-3-030-22475-2_1. 2020 21. Jyoti Soni , Ujma Ansari , Dipesh Sharma. “Predictive Data Mining for Medical Diagnosis: An Overview of Heart Disease Prediction.” International Journal of Computer Applications (0975 – 8887) Volume 17– No.8. 2011 44. Authors: Reshmitha Bethi, Sujatha Allipuram
Paper Title: On the Secrecy Outage of Wiretap Channel Abstract: In wireless data transmission, providing security over communication channels has become a 254-259 growing concern. Traditionally cryptography is used to provide secrecy. However, physical layer studies show that it allows a huge potential in providing secrecy. In this paper, secrecy outage probability is derived for Rician fading channels. A new secrecy metric Generalized Secrecy Outage Probability(GSOP) derivation is considered to overcome the limitation of traditional Outage probability for both passive and active cases of eavesdropping.
Keywords: Active eavesdropper, fading, secrecy outage probability, wireless communication.
References: 1. C.E. Shannon, “Communication theory of secrecy systems,” Bell Syst.Tech. Journ., vol. 29, pp. 656–715, 1949. 2. A. D. Wyner, “The wire-tap channel,” Bell Syst. Tech. Journ., vol. 54, pp.1355–1387, 1975. 3. I. Csiszár and J. Körner, “Broadcast channels with confidential messages,” IEEE Trans. Inf. Theory, vol. IT-24, no. 3, pp. 339–348, May 1978. 4. S. K. Leung-Yan-Cheong and M. E. Hellman, “The gaussian wiretap channel,” IEEE Trans. on Inform. Theory, vol. 24, no. 4, pp. 451456 July 1978. 5. U. Maurer, “Secret key agreement by public discussion from common information,”IEEETrans.Inf.Theory,vol.39,no.3,pp.733– 742,May 1993. 6. P. Parada and R. Blahut, “Secrecy capacity of simo and slow fading channels,” in Proc. IEEE Int. Symp. Information Theory (ISIT 2005), Adelaide, Australia, Sep. 2005, pp. 2152–2155. 7. P. K. Gopala, L. Lai, and H. El-Gamal, “On the secrecy capacity of fading channels,” in Proc. IEEE Int. Symp. Information Theory, Nice, France, Jun. 2007, pp. 1306–1310. 8. Y. Liang, H. V. Poor, and S. Shamai (Shitz), “Secrecy capacity region of fading broadcast channels,” in Proc. IEEE Int. Symp. Information Theory, Nice, France, Jun. 2007, pp. 1291–1295. 9. Z. Li, R. Yates, and W. Trappe, “Secrecy capacity of independent parallel channels,” in Proc. 44th Annu. Allerton Conf. Communications, Control and Computing, Monticello, IL, Sep. 2006, pp. 841–848. 10. Matthieu Bloch, J.Barros, Miguel R. D. Rodrigues and Steven W.McLaughlin, ”Wireless Information-Theoretic Security,” IEEE Trans. on Inform. Theory, vol. 54, no. 6 June 2008. 11. Miguel R.D.Rodrigues, João Barros “Secrecy Capacity of Wireless Channels” , Proc. IEEE Int. Symp. Inf. Theory (ISIT), pp. 356-360, Jul. 2006. 12. Matthieu Bloch, Andrew Thangaraj, Steven W. McLaughlin, and Jean-Marc Merolla, “LDPC-based Gaussian key reconciliation,” in Proc. of the IEEE International Workshop on Information Theory, Punta del Este, Uruguay, March 2006. 13. Xian Liu “Probability of Strictly Positive Secrecy Capacity of Ricain-Rician Fading Channel,” IEEE WIRELESS COMMUNICATIONS LETTERS, vol. 2, no. 1 February 2013. 14. Jiangbo Si, Zan Li, Julian Cheng, Caijan Zhong “Sececy Performance of Multi-antenna Wiretap Channels With Diversity Combining Over Correlated Rayleigh Fading Channels, ” IEEE Trans. on Wireless Communication. 15. R. Price, “Some non-central F-distributions expressed in closed form, ”Biometrika, vol. 51, pp. 107–122, 1964 16. Biao He, Xiangyun Zhou, A. Lee Swindlehurst “On Secrecy Metrics for Physical Layer Security over Quasi-Static Fading Channels” IEEE Trans. on Wireless Communication. 17. Theodore Rappaport, Wireless Communications: Principles and Practice, 2nd Edition, Prentice Hall, 2001. 18. Andrea Goldsmith, Wireless Communications, 2004. Authors: Aakash Jannumahanthi, Sivanesan Murugesan
Paper Title: Prediction of Diesel Engine Performance using Support Vector Regression Technique Abstract: Extensive research has been carried out on the prediction of diesel engine performance. Machine learning techniques such as support vector regression technique makes the performance predictions simpler. Support vector regression is a regression algorithm used to minimize the error with a threshold value and tries to fit the best line with a threshold value. In this paper, a detailed study of diesel engine performance using support vector regression and performance metrics such as brake thermal efficiency and accuracy are explored. Findings specify that support vector regression is an efficient technique for diesel engine performance that validates and compares the actual performance with high accuracy. For engine performance, the support vector machine supports to reduce the time and cost of testing.
Keywords: Support Vector Regression, Engine Performance, Brake thermal efficiency.
References: 1. Corinna Cortes and Vapnik, "Support-Vector Networks" Machine Learning, 20, 273-297 (1995) 2. Kongara Venkatesh, Sivanesan Murugesan, "Prediction of Engine Emissions using Linear Regression Algorithm in Machine Learning" International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-9 Issue-7, May 2020 3. Ayon Dey, "Machine Learning Algorithms: A Review" International Journal of Computer Science and Information Technologies, Vol. 7 (3, 2016, 1174-1179). 4. Xianglong Luo, Danyang Li, and Shengrui Zhang. "Traffic Flow Prediction during the Holidays Based on DFT and SVR," Journal of 45. Sensors, 2019. 5. Yufeng Li, and Jinghui Xu, "Multi-sensor Aviation Fuel Quantity Measurement Algorithm Based on SVR," AIP Conference Proceedings, 2122, 020060, 2019. 6. Qingsong Zuo, Xinning Zhu, Zhiqiang Liu, Jianping Zhang, Gang Wu, and Yuelin Lib, "Prediction of the performance and emissions 260-264 of a spark-ignition engine fueled with butanol-gasoline based on support vector regression," Environmental American Institute of Chemical Engineers Journals, 2018. 7. M Ghanbari1, G Najafi1, B Ghobadian1, R Mamat, M M Noor and A Moosavian1, "Support vector machine to predict diesel engine performance and emission parameters fueled with nano-particles additive to diesel fuel," IOP Conf. Series: Materials Science and Engineering, 100 012069, 2015. 8. Harsh S. Dhiman, Dipankar Deb, Josep M. Guerrero, "Hybrid machine intelligent SVR variants for wind forecasting and ramp events," Renewable and Sustainable Energy Reviews 108, 369–379, 2019. 9. Theodore B. Trafalis and Huseyin Ince, "Support Vector Machine for Regression and Applications to Financial Forecasting," International Joint Conference on Neural Networks, 2000. 10. Sivanesan Murugesan. Lakshmikanthan Chinnasamy, Abhijeet Patil.," Developing & Simulating the Duty Cycle on Engine Dynamometer based on Engine RWUP, "SAE Technical Paper," 2015, 2015-26-0024. 11. Subramanian, R., et al. "Studies on Performance and Emission Characteristics of Multi-Cylinder Diesel Engine Using Hybrid Fuel Blends as Fuel," Journal of Scientific and Industrial Research, 2011. 12. Shaomin Wu, Artur Akbarov " Support Vector Regression for warranty claim forecasting," European Journal of Operational Research, 2011. 13. Srihari S., Dr. Thirumalini S., and Prashanth, K., "An experimental study on the performance and emission characteristics of PCCI-DI engine fuelled with diethyl ether-biodiesel-diesel blends," Renewable Energy, 2017, vol. 107, pp. 440 – 447. 14. Kadir Kavaklioglu, "Modeling and prediction of Turkey's electricity consumption using Support Vector Regression," Applied Energy, 2011. 15. Wei-Chiang Hong," Chaotic particle swarm optimization algorithm in a support vector regression electric load forecasting model," Energy Conversion and Management, 2009. 16. M N V R S S Sumanth and Sivanesan Murugesan, "Experimental Investigation of Wall Wetting Effect on Hydrocarbon Emission in Internal Combustion Engine," IOP Conf. Series: Materials Science and Engineering 577 (2019) 012029 46. Authors: Youngkeun Choi, Jae Won Choi
Paper Title: The Prediction of Application for Loan using Machine Learning Technique Abstract: Machine learning techniques are used to verify the many kinds of loan prediction problems. This 265-268 study pursueS two major goals. Firstly, this paper is to understand the role of variables in loan prediction modeling better. Secondly, the study evaluates the predictive performance of the decision trees. The corresponding variable information is drawn from a third-party website, international challenge on the popular internet platform Kaggle (www.kaggle.com), which provides data in the title of ‘Loan Prediction’ that was uploaded by Amit Parajapet. We used decision tree which is a powerful and popular machine learning algorithm to this date for predicting and classifying big data. Based on these results, first, women seem to be more likely to get to loan than men. credit history, self-employed, property area, and applicant income also show significance with loan prediction. This study contributes to the literature regarding loan prediction by providing a global model summarizing the loan prediction determinants of customers’ factors.
Keywords: Machine learning, Decision tree, Artificial intelligence, Financial service, Loan prediction. References: 1. K. Amira, H. Ibrahim and A. Ajith, “Modeling Consumer Loan Default Prediction Using Ensemble Neural Networks,” International Conference on Computing, Electrical and Electronics Engineering, 2013, pp. 719 – 724. 2. E. W. T. Ngai, L Xiu and D. C. K. Chau, “Application of data mining techniques in customer relationship management: A literature review and classification,” Expert Systems with Applications, 2009, Vol. 36, pp. 2592–2602. 3. Chitra and S. Uma, “An Ensemble Model of Multiple Classifiers for Time Series Prediction,” International Journal of Computer Theory and Engineering, 2010, Vol. 2, No. 3, pp. 454–458. 4. S. Akkoç, “An empirical comparison of conventional techniques, neural networks and the three stage hybrid Adaptive Neuro Fuzzy Inference System ( ANFIS ) model for credit scoring analysis : The case of Turkish credit card data,” Elsevier Europezan Journal of Operational Research, 2012, Vol. 222, No. 1, pp. 168–178. 5. S. Sarwesh and K. M. Sadhna, “A Review of Ensemble Technique for Improving Majority Voting for Classifier,” International Journal of Advanced Research in Computer Science and Software Engineering, 2014, Vol. 3, No. 1, pp. 177- 180. Authors: Shikha Ujjainia, Pratima Gautam, S. Veenadhari,
Paper Title: Crop Yield Prediction using Regression Model Abstract: This research is done to find out the production dependability of crop with various physical circumstances. The prediction can also be done of a crop yield by using the model of regression and it is mainly discussed in this paper. Machine learning is an emerging research area in Agriculture, particularly in crop yield analysis and prediction. There are some complex data which are tough to decode or find by everyone, the strategies of machine learning can be used in this scenario and automatically the valuable underlining pattern can be accessed. Various complex decision-making activities can be performed when the feature of machine learning will enable the knowledge and patterns which are unseen about any problem. The future events can also be predicted. In the growing season as possible, a farmer is focused on conceptualizing how much yield they except. Like many other regions, the amount of agricultural data is increasing at the daily source. This paper aims to predict crop yield on the collected agricultural dataset. The regression analysis model is used to test the accuracy and effective predictions of the rice crop yield in India. Linear regression is used to establish a relationship between various environmental variables like temperature, rainfall, etc and the crop yield. It is important to measure the possible production of rate of crop and the farmers will be benefitted by the result of this prediction. As financial impact is attached of the farmers with the yield production, the research will support them to avoid any loss. The accuracy of the prediction through regression model is also observed in this research paper.
47. Keywords: Machine learning, Regression model, Linear regression, Yield prediction.
References: 269-273 1. A.L. Samuel, Some Studies in Machine Learning Using the Game of Checkers I, D. N. L. Levy (ed.). New York: Computer Games I, 1959. 2. K. Liakos, P. Busato, M. Dimitrios, S Pearson, and D Bochtis, “Machine Learning in Agriculture: A Review,” Sensors, vol. 18, no. 8, pp. 1-29, August 2018. 3. A. Singh, B. Ganapathysubramanian, A. K. Singh, and S. Sarkar, “Machine Learning for High-Throughput Stress Phenotyping in Plants,” Trends in Plant Science, vol. 21, no.2, pp. 110-124, February 2016. 4. R. Kumar, M. P. Singh, P. Kumar and J. P. Singh, “Crop Selection Method to Maximize Crop Yield Rate using Machine Learning Technique,” International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy, and Materials (ICSTM), pp. 138-145, May 2015. 5. Olive, David J. "Multiple linear regression." In Linear Regression, pp. 17-83. Springer, Cham, 2017. 6. Van Gerven, M. and Bohte, S., Artificial neural networks as models of neural information processing. Frontiers in Computational Neuroscience, 11, p.114, 2017. 7. Schönbrodt, F., Testing fit patterns with polynomial regression models., 2016. 8. N. Chumerin and M. Van Hulle, “Comparison of Two Feature Extraction Methods Based on Maximization of Mutual Information,” Proc. IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing, 2006, pp. 343–348. 9. S. Khalid, T. Khalil and S. Nasreen, "A survey of feature selection and feature extraction techniques in machine learning," Science and Information Conference, London, pp. 372- 378, August 2014. 10. H. Motoda and H. Liu, “Feature selection, extraction and construction,” Sixth PacificAsia Conference on Knowledge Discovery and Data Mining (PAKDD), pp. 67–72, 2002. 11. L. Ladha and T. Deepa, “Feature Selection Methods And Algorithms,” International Journal on Computer Science and Engineering (IJCSE), vol. 3, no. 5, pp. 1787-1797, May 2011. 12. P. Surya, and I. L. Aroquiaraj, “ Crop Yield Prediction In Agriculture Using Data Mining Predictive Analytic Techniques, ” International Journal of Research and Analytical Reviews (IJRAR), vol. 5, no. 4, pp. 783-787, December 2018. 48. Authors: Niharika Mishra, Sameer Bajpai, Esh Narayan
Paper Title: Speed Control of DC Motor using PID Controller FED H-Bridge Abstract: In the present scenario, DC motor is widely used in industries. So, if DC motor is used for 274-282 industrial purpose, the controlling is necessary. But there are various methods to control any system or plant such as via Proportional controller (P), Integral controller (I), Derivative controller (D), PI controller, PD controller, PID controller. Each controller is used on the basis of requirement. Proportional controller reduces the rise time, improves the steady state accuracy, and reduces the steady state error. But Integral controllers eliminate the steady state error but the process is too slow so it produces the worse transient response. Derivative controller improves the transient response, reduces the overshoots and improves the stability. So, for obtaining the accurate output of any plant, PID controller is best for many others. And for operating the DC motor in forward and backward both, H-bridge MOSFET is also used in this dissertation. Any other power electronics device is not suitable.
Keywords: Proportional controller (P), Integral controller (I), MOSFET, Derivative controller (D), Matlab etc.
References: 1. https://www.electrical4u.net/electrical-basic/motor-motor-works-graphical presentation/ 2. https://www.kinmoremotor.com/news-detail/22.htmlhttps://instrumentationtools.com/top-50-electrical-engineering-questions-answers/ 3. Fleming’s left hand rule https://www.electrical4u.com/fleming-left-hand-rule-and-fleming-right-hand-rule/ 4. https://tricalmachines.wordpress.com/2013/07/05/dc-motorstypescharacteristic/ 5. Javed Saman, Jha Sumit, Sajid Hasan, Vishu Kumar UGStudent, Electrical Engineering Department Moradabad institute of Technology, Moradabad, IJSRMS ISSN: 2349-3771 Volume 3 Issue 1, pg: 80-87 6. “Speed Control of Separately Excited DC Motor” Moleykutty George Faculty of Engineering and Technology, Multimedia University Melaka Campus, 75450 Melaka, Malaysia , American Journal of Applied Sciences 5 (3): 227-233, 2008 ISSN 1546-9239 7. Kaustubh S. Deshmukh, Rutuja S Hiware Master in Technology, Electrical Engineering, G H Raisoni College of Engineering and Technology, Nagpur, Maharashtra, India (IJSR) ISSN (Online): 2319-7064 8. Vikramarajan Jambulingam Electrical and Electronics Engineering, VIT University, India. IJEDR | Volume 4, Issue 2 | ISSN: 2321- 9939 9. International Journal of Electrical and Electronics Research ISSN 2348-6988 (online) Vol. 3, Issue 1, pp: (289-295), Month: January - March 2015 10. Yadav D.K ,M.Tech student, Department of Electrical Engineering, Nehru Kamla Institute of Technology, Sultanpur- 228118 (U.P.), India (E-mail: [email protected]). 11. Analysis of Speed Control of DC Motor –A review study Tripathi Nikhil , Singh Rameshwar , yadav Renu International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 www.irjet.net 12. Singh Vijay and Garg Vijay Kumar International Journal of Electronic and Electrical Engineering ISSN 0974-2174, Volume 7, Number 4 (2014), 13. Md. Kamruzzaman Russel, Bhuyan Muhibul Haque, 01- 02 December 2012 (ICECTE2012), RUET, Rajshahi-6204, Bangladesh 14. Krunal Shah,Vidhi Shah,Deepak Mistry , International Journal of Electrical and Electronics Research ISSN 2348-6988 (online) Vol. 3, Issue 1, pp: (254-259), Month: January - March 2015, Available at: www.researchpublish.com 15. R.Ranjani, R.Preethii, S.Jerine Sumitha International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering (An ISO 3297: 2007 Certified Organization) 16. Islam Ariful, A.K.M. Shamim1, Hadaate Ullah and Mohammad Arif Sobhan Bhuiyan https://www.researchgate.net/publication/322655778_Design_and_Implementation_of_a_Lowcost_MOSFET_Based_Chopper_Drive_ DC_Motor_Speed_Control Authors: Naveen Kumar S, M Dakshayini, Raghavendra R Biradar, Rajashekargouda G S
Paper Title: Blockchain Based Security Solution for Medical Data Abstract: Existing health organizations maintain their patients and medical data using centralised management approach. This system is more vulnerable to data breaches which leads to security threats and patients don’t have control over their data. According to German cybersecurity company Greenbone Networks, the patient records, 121 millions of medical images and scans from India has been leaked which includes details such as the name of the patient, their date of birth, the national ID, name of the medical institution, their medical history, physician names and other details. According to poneman cost of data breach study, the cost of the data breach for healthcare organizations approximated to be $380 per record. According to 2016 Breach Barometer Report, 27,314,647 patient records were affected. The patient don’t have control over their data and data can be misused. Hyperledger fabric framework based Blockchain technology is most desirable solution to prevent data manipulation and data theft .It also facilitates patient to have control over their data. Hyperledger fabric is a permissioned distributed ledger framework and provide high degrees of confidentiality, flexibility, and scalability.
Keywords: medical data, centralized management approach, blockchain, Hyperledger fabric framework 49. References: 1. Harshini V M, Shreevani Danai, Usha H R, Manjunath R Kounte School of Electronics and Communication Engineering REVA University, Bangalore, India. “Health Record Management through Blockchain Technology”. In Proceedings of the Third International 283-287 Conference on Trends in Electronics and Informatics (ICOEI 2019). 2. Rexford Nii Ayitey Sosu, Kester Quist-Aphetsi, Laurent Nana, Ghana Technology University College. “A Decentralized Cryptographic Blockchain Approach for Health Information System”. In 2019 International Conference on Computing, Computational Modelling and Applications. 3. Asaph Azaria, Ariel Ekblaw, Thiago Vieira and Andrew Lippman Media Lab.“MedRec:UsingBlockchainforMedicalDataAccessandPermissionManagement”. In 2016 2nd International Conference on Open and Big Data. 4. Yiheng Liang Department of Computer Science Bridgewater State University. “Identity Verification and Management of Electronic Health Records with Blockchain Technology”. 5. Nabil Rifi, Elie Rachkidi, Nazim Agoulmine, Nada Chendeb Taher COSMO, IBISC Laboratory, University of Evry, France. “Towards Using Blockchain Technology for eHealth Data Access Management”. In 2017 Fourth International Conference on Advances in Biomedical Engineering (ICABME). 6. Jamal N. Al-Karaki, Amjad Gawanmeh, Meryeme Ayachek, Ashraf Mashaleh ,Department of Information Security Engineering Technology, Abu Dhabi Polytechnic, MBZ city, Abu Dhabi, UAE .“DASS-CARE: A Decentralized, Accessible, Scalable, and Secure Healthcare Framework using Blockchain”. 7. Leila Ismail(Member, IEEE), Huned Marewala, and Sherali Zeadally ,College of Information Technology, UAE University, Al Ain 15551, UAE. “Lightweight Blockchain for Healthcare”. This work was supported by the Emirates Center for Energy and Environment Research of the United Arab Emirates University under Grant 31R101. 8. Xueping Liang, Juan Zhao, Sachin Shetty, Jihong Liu, Danyi Li, 1 Institute of Information Engineering, Chinese Academy of Sciences, Beijing, 100093, China. “Integrating Blockchain for Data Sharing and Collaboration in Mobile Healthcare Applications”. 50. Authors: Pawan S Nadig, Pooja G, Kavya D, R Chaithra, Radhika A D
Paper Title: Kannada Text to Speech Conversion System Abstract: The following paper describes the design of a system which does text to speech generation for one of 288-293 the regional language’s Kannada. The printed document of Kannada text is given as input to the system, the system then converts the document to an image format. Pre-processing is done to stabilize the intensity of the images and clear the artifacts. This process boosts the precision and interpretability of an image. Optical Character Recognition (OCR) is used to unsheathe the segmented characters from a particular image and are matched with the characters that have been stored in the dataset. Once the matched characters are extracted it is stored in a suitable format and then the TTS engine is deployed to convert the saved Kannada characters to a speech format. The obtained speech output corresponds to the characters which are collected after processing the input text.
Keywords: Image, Kannada, OCR, Pre-processing, Text-to-Speech
References: 1. B.M. Sagar, Dr. Shobha G, And Dr. Ramakanth Kumar P, OCR for printed Kannada text to Machine editable format using Database approach, 9th WSEAS International Conference on AUTOMATION and INFORMATION (ICAI'08) Bucharest, Romania, June 24-26, 2008. 2. Malti Bansal, Shivam Sonkar, Text Image to Speech Conversion using Matlab and Microsoft SAPI, IJEECS ISSN 2348-117X, vol. 6, Issue 11 November 2017. 3. Chaw Su Thu Thu and Theingi Zin. "Implementation of Text to Speech Conversion," International Journal of Engineering Research & Technology (IJERT), ISSN: 2278-0181, vol. 3, Issue 3, March, 2014.B. Smith, “An approach to graphs of linear forms (Unpublished work style),” unpublished. 4. Sagar G.K and Shreekanth T, Real Time Implementation of Optical Character Recognition Based TTS System using Raspberry pi, International Journals of Advanced Research in Computer Science and Software Engineering, July 2017. 5. Asha G. Hagargund, Sharsha Vanria Thota, Mitadru Bera, Eram Fatima and Shaik, Image to Speech Conversion for Visually Impaired, International Journal of Latest Research in Engineering and Technology (IJLRET), ISSN: 2454-5031, vol 03, June 2017, pp. 09-15C. J. Kaufman, Rocky Mountain Research Lab., Boulder, CO, private communication, May 1995. 6. Jisha Gopinath, S Aravind , Pooja Chandran, and S S Saranya, Text to Speech Conversion System using OCR, International Journal of Emerging Technology and Advanced Engineering, January 2015. 7. Jarlin James, Michael Aldo, Adellina Andrew, Image Text to Speech Conversion using OCR Technique in Raspberry PI, International Journal of Scientific & Engineering Research vol. 9, Issue 3, March-2018. 8. Nilesh Jondhale and Dr. Sudha Gupta, Reading text extracted from an image using OCR and android Text to Speech, International Journal of Latest Engineering and Management Research (IJLEMR), ISSN: 2455-4847, vol. 03, April 2018, pp. 64-67. 9. K Nirmala Kumari and Meghana Reddy J, Image Text to Speech Conversion Using OCR Technique in Raspberry Pi, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, vol. 5, May 2016. 10. Suhas R. Mache, Manasi R. Baheti, and C. Namrata Mahender, Review on Text-To-Speech Synthesizer, International Journal of Advanced Research in Computer and Communication Engineering, vol. 4, August 2015. 11. John Colaco and Sangam Borkar, Design and Implementation of Konkani text to Speech Generation System using OCR Technique, International Journal of Scientific and Research Publications, vol. 6, September 2016, ISSN 2250-3153. 12. M. Nagamani, S.Manoj Kumar and Uday Bhaskar, Image to Speech Conversion System for Telugu Language, International Journal of Engineering Science and Innovative Technology (IJESIT), volume 2, November 2013, ISSN: 2319-5967. 13. K.N. Natei, J. Viradiya, and S. Sasikumar, Extracting Text from Image Document and Displaying Its Related Information, K.N. Natei Journal of Engineering Research and Application, ISSN : 2248-9622,vol. 8, Issue5 (Part -V) May 2018, pp 27-33. 14. A.G. Ramakrishnan, H.R. Shivakumar, Lakshmi Chithambaran, Text to Speech Synthesis in Indian Languages, Apr 7, 2018. 15. Yamini D. Patil and Nandkishor C. Patil, Optical Character Recognition Based Text To Speech Synthesis, Journal of Emerging Technologies and Innovative Research (JETIR), ISSN: 2349-5162, vol. 2, July 2015. 16. Aravind Sasikumar, Text to Speech Conversion System using OCR, vol. 5, January 2015 17. Akshay.A, Amrrith.N P, Dwishanth.P and Rekha.V, A Survey on Text to Speech Conversion, International Journal of Trend in Research and Development, vol. 5(2), ISSN: 2394-9333, Mar - Apr 2018. 18. Anusha Joshi, Deepa Chabbi, Suman M, Suprita Kulkarni, Text to speech system for Kannada language, IEEE Xplore: 12 November 2015. Authors: Laxmi Balappa Bavage Simulation and Implementation of Single Phase Quasi Z-Source Series Resonance DC DC Converter Paper Title: for Photovoltaic Application Abstract: The paper presents quasi-Z source series resonant dc–dc converter with high performance for PV applications. Their multimode operation property features wide input voltage and load regulation range. Multimode operation achieved by pulse width modulation and phase shift modulation which gives boost and buck operating modes. Paper includes experimental setup which ensures 13volts ripple free output voltage.
Keywords: DC–DC converter, module-level power electronics (MLPE), module-integrated converter, quasi- Z-source (qZS) converter, renewable energy, resonant converter, solar photovoltaic (PV)
51. References: 1. S. B. Kjaer, J. K. Pedersen, and F. Blaabjerg, “A review of single phase grid-connected inverters for photovoltaic modules,” IEEE Trans. Ind. Appl., vol. 41, no. 5, pp. 1292–1306, Sep./Oct. 2005. 294-298 2. Andrii Chub, Dmitri Vinnikov, “Single-Switch Galvanically Isolated Quasi-Z-Source DC-DC Converter” in IEEE 5th International Conference on Power Engineering, Energy and Electrical Drives, 2015. 3. L. Liivik, A. Chub, D. Vinnikov, and J. Zakis, “Experimental study of high step-up quasi-Z-source DC-DC converter with synchronous rectification,” in Proc. 9th Int. Conf. Compat. Power Electron., Jun. 24–26, 2015, pp. 409–414. 4. L. Liivik, A. Chub, J. Zakis, and I. Rankis, “Analysis of buck mode realization possibilities in quasi-Z-source DC-DC converters with voltage doubler rectifier,” in Proc. IEEE 5th Int. Conf. Power Eng. Energy Elect. Drives, May 11–13, 2015, pp. 1–6. 5. Vinnikov and I. Roasto, “Quasi-Z-source-based isolated DC/DC converters for distributed power generation,” IEEE Trans. Ind. Electron., vol. 58, no. 1, pp. 192–201, Jan. 2011. 6. L. Liivik,D.Vinnikov, and T. Jalakas, “Synchronous rectification in quasi- Z-source converters: Possibilities and challenges,” in Proc. IEEE Int. Conf. Intell. Energy Power Syst., Jun. 2–6, 2014, pp. 32–35.. 7. D. Vinnikov, A. Chub, I. Roasto, and L. Liivik, “Multi-mode quasi- Z-source series resonant DC/DC converter for wide input voltage range applications,” in Proc. IEEE Appl. Power Electron. Conf. Expo., Mar. 20–24, 2016, pp. 1–7. 8. Book “Power Electronics” by P.S. Bhimra 52. Authors: Edwin Juvenal Cedeño Herrera, Gloris Denisse Cedeño Batista, Gloris Batista Mendoza, Hector Bedon Overcoming the Communication Challenges in Wireless Sensor and Actuator Networks Isolated Paper Title: using DTN Technologies Abstract: Wireless Sensor and Actuator Networks (WSAN) have represented a significant advance in wireless communication environments and their convergence with the phenomenon called the Internet of Things (IoT). One of the challenges studied in the WSAN field is to propose communication mechanisms to interconnect wireless sensor networks in isolated areas. Various studies have revealed the difficulty of achieving connectivity in this type of network. In this sense, we address the challenge of interconnecting isolated WSANs, which do not have end-to-end connection, with the Internet network infrastructure. For this, we consider the following characteristics of these environments, disruptive communications, long delays; devices with limited resources, very short transfer times and in contexts of mobility. We propose an integrated architecture that allows us to offer a service management framework based on the capabilities provided by WSAN, through an infrastructure based on cloud technologies. The functionalities that characterize service architectures in telecommunication networks and the integration of WSAN with cloud-based architectures are analyzed. Architectural capabilities such as Machine-to-Machine (M2M) and Machine Type Communication (MTC) are considered. The proposed architecture allows deliver applications and services can be reachable and shared with any host connected to Internet. WSAN data, hosted at remote sites or with limited communications, can be processed, stored and analyzed in the cloud, or locally by components of the architecture. The communication with the sensor network and actuators, is iterant, because of architecture provides support for long-delayed and disruptive tolerant services.
Keywords: Remote Wsan, Mobile Service, Dtn, Internet of Things, Service Architecture, Service Delegation.
References: 1. M. Asim, H. Mokhtar, and M. Merabti, “A Fault Management Architecture for Wireless Sensor Network,” in 2008 International Wireless Communications and Mobile Computing Conference, Aug. 2008, pp. 779–785, doi: 10.1109/IWCMC.2008.135. 2. [F. Wang and J. Liu, “Networked Wireless Sensor Data Collection: Issues, Challenges, and Approaches,” IEEE Communications Surveys Tutorials, vol. 13, no. 4, pp. 673–687, Fourth 2011, doi: 10.1109/SURV.2011.060710.00066. 3. M. A. M. Marinho, E. P. de Freitas, J. P. C. Lustosa da Costa, A. L. F. de Almeida, and R. T. de Sousa, “Using cooperative MIMO techniques and UAV relay networks to support connectivity in sparse Wireless Sensor Networks,” in 2013 International Conference on Computing, Management and Telecommunications (ComManTel), Jan. 2013, pp. 49–54, doi: 10.1109/ComManTel.2013.6482364. 4. K. Mikhaylov and J. Tervonen, “Data Collection from Isolated Clusters in Wireless Sensor Networks Using Mobile Ferries,” in 2013 27th International Conference on Advanced Information Networking and Applications Workshops, Mar. 2013, pp. 903–909, doi: 10.1109/WAINA.2013.87. 5. E. P. de Freitas et al., “UAV relay network to support WSN connectivity,” in International Congress on Ultra Modern Telecommunications and Control Systems, Oct. 2010, pp. 309–314, doi: 10.1109/ICUMT.2010.5676621. 299-306 6. T. Huang, T. Sang, and Y. Yu, “An architecture for integrating wireless sensor networks into IP network,” in 2010 International Conference on Intelligent Computing and Integrated Systems, Oct. 2010, pp. 910–912, doi: 10.1109/ICISS.2010.5657038. 7. R. Piyare et al., “Integrating Wireless Sensor Network into Cloud services for real-time data collection,” in 2013 International Conference on ICT Convergence (ICTC), Oct. 2013, pp. 752–756, doi: 10.1109/ICTC.2013.6675470. 8. Perumal and P. Rajasekaran, “WSN Integrated Cloud for Automated Telemedicine (ATM) Based e-Healthcare.” /paper/WSN- INTEGRATED-CLOUD-FOR-AUTOMATED-TELEMEDICINE-(-)-Perumal-Rajasekaran/ dc78f78be01338934f03fb20cd6986e2757ec7f3 (accessed Jul. 23, 2020). 9. D. Guinard and V. Trifa, “Towards the Web of Things: Web Mashups for Embedded Devices,” in Book Towards the Web of Things: Web Mashups for Embedded Devices, 2009, pp. 1506–1518. 10. N. Blum, J. Müller, F. Schreiner, and T. Magedanz, “Telecom Applications, APIs and Service Platforms,” in Evolution of Telecommunication Services: The Convergence of Telecom and Internet: Technologies and Ecosystems, E. Bertin, N. Crespi, and T. Magedanz, Eds. Berlin, Heidelberg: Springer, 2013, pp. 25–46. 11. “ts_102690v020101p.pdf.” Accessed: Jul. 23, 2020. [Online]. Available: https://www.etsi.org/deliver/etsi_ts/102600_102699/102690/02.01.01_60/ts_102690v020101p.pdf. 12. [“A 3GPP IP Multimedia Subsystem-Based System Architecture for a M2M Horizontal Service Platform.” http://connection.ebscohost.com/c/articles/98924660/3gpp-ip-multimedia-subsystem-based-system-architecture-m2m-horizontal- service-platform (accessed Jul. 23, 2020). 13. [“Specification # 23.888.” https://portal.3gpp.org/desktopmodules/Specifications/SpecificationDetails.aspx?specificationId=968 (accessed Jul. 23, 2020). 14. M. R. Kosanovi, “Connecting Wireless Sensor Networks To Internet,” p. 14. 15. A. E. Kouche, “Towards a wireless sensor network platform for the Internet of Things: Sprouts WSN platform,” in 2012 IEEE International Conference on Communications (ICC), Jun. 2012, pp. 632–636, doi: 10.1109/ICC.2012.6364196. 16. B. Li and J. Yu, “Research and Application on the Smart Home Based on Component Technologies and Internet of Things,” Procedia Engineering, vol. 15, pp. 2087–2092, Jan. 2011, doi: 10.1016/j.proeng.2011.08.390. 17. N. Mitton, S. Papavassiliou, A. Puliafito, and K. S. Trivedi, “Combining Cloud and sensors in a smart city environment,” J Wireless Com Network, vol. 2012, no. 1, p. 247, Aug. 2012, doi: 10.1186/1687-1499-2012-247. 18. “SenseWeb: An Infrastructure for Shared Sensing,” Oct. 01, 2007. https://www.computer.org/csdl/magazine/mu/2007/04/mmu2007040008/13rRUyYSWpl (accessed Jul. 23, 2020). 19. R. Moeller and A. Sleman, “Wireless networking services for implementation of ambient intelligence at home,” in 2008 7th International Caribbean Conference on Devices, Circuits and Systems, Apr. 2008, pp. 1–5, doi: 10.1109/ICCDCS.2008.4542655. 20. B. Priyantha, A. Kansal, M. Goraczko, and F. Zhao, “Tiny Web Services for Sensor Device Interoperability,” in 2008 International Conference on Information Processing in Sensor Networks (ipsn 2008), Apr. 2008, pp. 567–568, doi: 10.1109/IPSN.2008.33. 21. A. Al-Yasiri and A. Sunley, “Data aggregation in wireless sensor networks using the SOAP protocol,” J. Phys.: Conf. Ser., vol. 76, p. 012039, Jul. 2007, doi: 10.1088/1742-6596/76/1/012039. 22. 22. A. Wolff, S. Michaelis, J. Schmutzler, and C. Wietfeld, “Network-centric Middleware for Service Oriented Architectures across Heterogeneous Embedded Systems,” in 2007 Eleventh International IEEE EDOC Conference Workshop, Oct. 2007, pp. 105–108, doi: 10.1109/EDOCW.2007.20. 53. Authors: Oumar Sacko, Stephen Musyoki, Vitalice K. Oduol Paper Title: A Novel Transceiver Model for Polar Transform Optical (PTO-) OFDM for Visible Light Communication (VLC) Based on Peak to Average Power Ratio Reduction using Precoding Abstract: Indoor visible light communication (VLC) has the potential of providing high data rates for short- range wireless communication with a relative spatial elevated security in contrast to a radiofrequency wireless one. To support that high data stream, Orthogonal Frequency Division Multiplexing (OFDM) is used; however, due to the limited operational bandwidth of the commercial white light-emitting diode (LED), signal processing techniques are used to increase the efficiency of the OFDM and to adapt OFDM to VLC systems. As a major concern, the intensity modulation direct detection necessary for VLC requires positive real signal, this is dealt with by imposing Hermitian pre-possessing or Cartesian to polar conversion post-processing to the OFDM. The use of the Cartesian to polar converter allows the transmission of complex OFDM symbols through the intensity modulation channel. A polar transform optical (PTO-) OFDM presented here as an improvement and simplification of previous polar optical OFDM schemes gives an efficient transceiver architecture. Nevertheless, both OFDM transmission techniques for Visible optical links, similar to radiofrequency (RF), suffer greatly from irregular excessive Peak-to-Average power ratio (PAPR). Higher PAPR reduces the power efficiency of the On- Off Keying (OOK) based on pulse amplitude modulation (PAM). Furthermore, it also is recommendable to reduce the PAPR for conformity with eye safety. A precoding technique is proposed to reduce the PAPR of intensity-modulated for direct detectability of the OFDM signal destined for the wireless optical link using Cartesian-to-Polar conversion. Based on the enhanced processing at the front ends and using MATLAB simulation, it is proven that the presented model can improve the link parameters including the bit error rate (BER) and signal to noise ratio (SNR) and bandwidth efficient compared to Hermitian modified ones.
Keywords: Intensity modulation Direct detection, PAPR precoding, Polar Transform Optical OFDM, Pulse Coded Modulation, Visible light communication.
References: 1. X. Guo, Y. Guo, and S. Li, “Experimental Investigation of Zadoff-Chu Matrix Precoding for Visible Light Communication System with OFDM Modulation,” Advances in Condensed Matter Physics, 2018. https://www.hindawi.com/journals/acmp/2018/5173285/ (accessed Feb. 22, 2020). 2. F. Zafar, M. Bakaul, and R. Parthiban, “Laser-diode-based visible light communication: Toward gigabit class communication,” IEEE Communications Magazine, vol. 55, no. 2, pp. 144–151, 2017. 3. N. Chi, Y. Zhou, S. Liang, F. Wang, J. Li, and Y. Wang, “Enabling technologies for high-speed visible light communication employing CAP modulation,” Journal of Lightwave Technology, vol. 36, no. 2, pp. 510–518, 2018. 4. K. Bandara, P. Niroopan, and Y.-H. Chung, “PAPR reduced OFDM visible light communication using exponential nonlinear companding,” in 2013 IEEE International Conference on Microwaves, Communications, Antennas and Electronic Systems (COMCAS 2013), Oct. 2013, pp. 1–5, doi: 10.1109/COMCAS.2013.6685269. 5. H. Elgala, S. K. Wilson, and T. D. Little, “Optical polar OFDM: On the effect of time-domain power allocation under power 307-315 and dynamic-range constraints,” in IEEE Wireless Communications and Networking Conference (WCNC), New Orleans, LA, USA, Mar. 2015, pp. 31–35. 6. J. Lian and M. Brandt-Pearce, “Magnitude-Phase Optical OFDM for IM/DD Communication Systems,” in 2018 52nd Asilomar Conference on Signals, Systems, and Computers, 2018, pp. 702–706. 7. Z. Ghassemlooy, W. Popoola, and S. Rajbhandari, Optical Wireless Communications: System and Channel Modelling with MATLAB®, 1st ed. CRC Press, 2012. 8. S. B. Slimane, “Peak-to-average power ratio reduction of OFDM signals using broadband pulse shaping,” in Proceedings IEEE 56th Vehicular Technology Conference, Sep. 2002, vol. 2, pp. 889–893 vol.2, doi: 10.1109/VETECF.2002.1040728. 9. Y. S. Cho, J. Kim, W. Y. Yang, and C. G. Kang, MIMO-OFDM Wireless Communications with MATLAB. John Wiley & Sons, 2010. 10. A. K. Gurung, F. S. Al-Qahtani, A. Z. Sadik, and Z. M. Hussain, “Power savings analysis of clipping and filtering method in OFDM systems,” in 2008 Australasian Telecommunication Networks and Applications Conference, 2008, pp. 204–208. 11. Xianbin Wang, T. T. Tjhung, and C. S. Ng, “Reduction of peak-to-average power ratio of OFDM system using a companding technique,” IEEE Transactions on Broadcasting, vol. 45, no. 3, pp. 303–307, Sep. 1999, doi: 10.1109/11.796272. 12. S. Thompson, J. G. Proakis, and J. R. Zeidler, “The effectiveness of signal clipping for PAPR and total degradation reduction in OFDM systems,” presented at the IEEE GLOBCOM, Jan. 2005, pp. 5 pp. – 2811, doi: 10.1109/GLOCOM.2005.1578271. 13. G. Sikri, “Peak to Average Power Ratio Reduction in OFDM System over PAM, QAM and QPSK Modulation,” in Advances in Computing and Information Technology, Springer, 2012, pp. 685–690. 14. V. P. T. Ijyas and M. I. Al-Rayif, “Low Complexity Joint PAPR Reduction and Demodulation Technique for OFDM Systems,” IETE Journal of Research, vol. 0, no. 0, pp. 1–11, Oct. 2019, doi: 10.1080/03772063.2019.1674194. 15. A. Skrzypczak, P. Siohan, and J.-P. Javaudin, “Analysis of the Peak-To-Average Power Ratio for OFDM/OQAM,” in 2006 IEEE 7th Workshop on Signal Processing Advances in Wireless Communications, Jul. 2006, pp. 1–5, doi: 10.1109/SPAWC.2006.346413. 16. S. Y. Le Goff, S. S. Al-Samahi, B. K. Khoo, C. C. Tsimenidis, and B. S. Sharif, “Selected mapping without side information for PAPR reduction in OFDM,” IEEE Transactions on Wireless Communications, vol. 8, no. 7, pp. 3320–3325, Jul. 2009, doi: 10.1109/TWC.2009.070463. 17. H. Boche and B. Farrell, “On the peak-to-average power ratio reduction problem for orthogonal transmission schemes,” Internet Mathematics, vol. 9, no. 2–3, pp. 265–296, 2013. 18. P. Varahram, W. A. Azzo, and B. M. Ali, “IDRG–PTS scheme with low complexity for peak-to-average power ratio reduction in OFDM systems,” Journal of the Chinese Institute of Engineers, vol. 36, no. 6, pp. 677–683, Sep. 2013, doi: 10.1080/02533839.2012.740593. 19. S. B. Slimane, “Reducing the peak-to-average power ratio of OFDM signals through precoding,” IEEE Transactions on vehicular technology, vol. 56, no. 2, pp. 686–695, 2007. 20. Y. Hong, X. Guan, L.-K. Chen, and J. Zhao, “Experimental demonstration of an OCT-based precoding scheme for visible light communications,” in 2016 Optical Fiber Communications Conference and Exhibition (OFC), 2016, pp. 1–3. 21. L. Song and J. Shen, Evolved cellular network planning and optimization for UMTS and LTE. CRC press, 2010. 22. S. Bhattacharjee, M. Rakshit, S. Sil, and A. Chakrabarti, “Reduction of Peak-to-Average Power Ratio of OFDM System Using Triangular Distribution Based Modified Companding Scheme,” IETE Journal of Research, vol. 64, no. 5, pp. 660–672, 2018. 23. J. Armstrong and B. J. Schmidt, “Comparison of asymmetrically clipped optical OFDM and DC-biased optical OFDM in AWGN,” IEEE Communications Letters, vol. 12, no. 5, pp. 343–345, 2008. 54. Authors: Anusuya Ramasamy, Abel Adane Changare Hybrid Fuzzy Knowledge Based Prediction Model for the Software Development and Maintenance Paper Title: Quality in Software Engineering Approach Abstract: The main arena of Software Engineering development with ood design, development, coding, 316-321 testing, implementation, deployment of the software, finally maintaining the software with good functionality. For the development of software many organizations are investing more and more budget in their revenue. Software Engineering development has several categories of data presented in software engineering such as Graphical User Interface, Usage graphs, writing text, realities and images. Significant information be able to be obtained from this composite data by well recognized data mining techniques such as association, classification, clustering etc. By discovery hidden patterns by data mining software engineering data is made illegal. Software Engineering development has many objectives in software engineering such as Code and Design optimization, Project documentation, Development cost estimation etc. Variety of significant data mining method in each phase of software development life cycle supports in realizing these objectives proficiently and the failure rate of software is decreased. . This paper focused a new hybrid model like combination of Fuzzy Logic and knowledge management offers a significant method for developing models for software quality prediction. This research paper explains about exercise of estimate and valuation at a particular organization by developments and represents the outcomes attained with a fuzzy based classification and knowledge model for the fuzzy knowledge management predication for the quality of software engineering Approach. This result illustrate that the significance of Average Error Evaluation Efficiency observed and used in fuzzy logic is lesser than Average Error Evaluation Efficiency used in another regression multiple regression; while the value of prediction is higher value that other prediction models is used before. Thus Results demonstrate that Hybrid fuzzy knowledge management predication for the quality of software engineering can be used as alternative for predicting the Software Development and Maintenance Quality (SDMQ).
Keywords: Software Quality Prediction, Fuzzy Logic, knowledge management, Software Development
References: 1. Nilsson, M.; Griggs, D.; Visbeck, M. Policy: Map the interactions between Sustainable Development Goals. Nat. News 2016, 534, 320 2. Yu, M.; Zhu, F.; Yang, X.; Wang, L.; Sun, X. Integrating Sustainability into Construction Engineering Projects: Perspective of Sustainable Project Planning. Sustainability 2018, 10, 784. 3. Hardy-Vallee, B. The Cost of Bad Project Management. Business Journals, 7 February 2012. 4. Lacko, B. Evaluation of Software Projects with Mta; Vsb-Tech University Ostrava: Ostrava, Czech Republic, 2012; ISBN 978-80- 248-2669-1. 5. Doležal, J. Prediction in Project Using Markov Chains. Available online: https://www.vutbr.cz/studenti/zav-prace?zp_id=34323 (accessed on 15 March 2018). 6. Šviráková, E. Methods for Project Tracking in Creative Environment. Acta Inform. Pragensia 2017, 6, 32–59. 7. Rasheed, S.; Wang, C.; Lucena, B. Risk Leveling in Program Environments—A Structured Approach for Program Risk Management. Sustainability 2015, 7, 5896–5919. 8. Nguyen, L.H.; Watanabe, T. The Impact of Project Organizational Culture on the Performance of Construction Projects. Sustainability 2017, 9, 781. 9. Relich, M. A computational intelligence approach to predicting new product success. In Proceedings of the 11th International Conference on Strategic Management and Its Support by Information Systems, Uherske Hradiste, Czech Republic, 21–22 May 2015; pp. 142–150. 10. Naeni, L.M.; Shadrokh, S.; Salehipour, A. A fuzzy approach for the earned value management. Int. J. Proj. Manag. 2011, 29, 764–772. 11. Schwalbe, K. Řízení projektů v IT: Kompletní průvodce; Vyd. 1; Computer Press: Brno, Czech Republic, 2011; ISBN 978-80-251- 2882-4. 12. McManus, J. Risk Management in Software Development Projects; Routledge: Abingdon, UK, 2012; ISBN 978-1-136-36791-5. 13. Boehm, B.W. Software Risk Management: Principles and Practices. Nasirzadeh 1991, 8, 32–41. 14. Rudnik, K.; Deptula, A.M. System with probabilistic fuzzy knowledge base and parametric inference operators in risk assessment of innovative projects. Expert Syst. Appl. 2015, 42, 6365–6379. 15. Nasirzadeh, F.; Khanzadi, M.; Rezaie, M. Dynamic modeling of the quantitative risk allocation in construction projects. Int. J. Proj. Manag. 2014, 32, 442–451. 16. Liu, Z.-C.; Ye, Y. Models for comprehensive evaluating modeling of investment project risk with trapezoid fuzzy linguistic information. J. Intell. Fuzzy Syst. 2015, 28, 151–156. 17. Rodriguez, A.; Ortega, F.; Concepcion, R. A method for the evaluation of risk in IT projects. Expert Syst. Appl. 2016, 45, 273–285. 18. Zwikael, O.; Pathak, R.D.; Singh, G.; Ahmed, S. The moderating effect of risk on the relationship between planning and success. Int. J. Proj. Manag. 2014, 32, 435–441. 19. Doskocil, R.; Skapa, S.; Olsova, P. Success Evaluation Model for Project Management. E M Ekon. Manag. 2016, 19, 167–185. 20. RIPRAN—Metoda pro analýzu projektových rizik. Available online: http://ripran.eu/ (accessed on 30 January 2018). 21. Ginevičius, T.; Kaklauskas, A.; Kazokaitis, P. Knowledge model for integrated construction project management. Bus. Theory Pract. 2011, 12, 162–174. 22. Matthies, B.; Coners, A. Double-loop learning in project environments: An implementation approach. Expert Syst. Appl. 2018, 96, 330–346. 23. Zhang, J.; Li, H.; Wang, S.H.-M. Analysis and Potential Application of the Maturity of Growth Management in the Developing Construction Industry of a Province of China: A Case Study. Sustainability 2017, 9, 143. 24. Janicek, P. Systems Conception of Problem-Solving. In Engineering Mechanics 2017; Acad Sci Czech Republic, Inst Thermomechanics: Prague, Czech Republic, 2017; pp. 402–405. ISBN 978-80-214-5497. 25. Dr. Anusuya Ramasamy, Mr. Gergito Kusse Duba and Mr.Addisu Mulugeta. 2020. “Integrated datamining with knowledge stroke disease”, International Journal of Current Research, 12, (04), 10948-10952. 26. R.Anusuya Dr. R.Uma Maheswari. “An Efficient Cancer Classification Using mid value K-means and Naïve Bayes”, Journal of Science, Computing and Engineering Research, 1(1), 01-06, Mar-Apr 2020. 27. Zadeh, L.A. Fuzzy sets. Inf. Control 1965, 8, 338–353. 28. Chen, S.-H.; Kaboudan, M.; Du, Y.-R. The Oxford Handbook of Computational Economics and Finance; Oxford University Press: Oxford, UK, 2018; ISBN 978-0-19-984437-1. 29. Meyer, A.; Zimmermann, H.-J. Applications of Fuzzy Technology in Business Intelligence. Int. J. Comput. Commun. Control 2011, 6, 428–441. [ 30. Tripathy, B.K.; Sooraj, T.R.; Mohanty, R.K. A New Approach to Fuzzy Soft Set Theory and Its Application in Decision Making. In Computational Intelligence in Data Mining, Cidm, Vol 2; Behera, H.S., Mohapatra, D.P., Eds.; Springer-Verlag Berlin: Berlin, Germany, 2016; Volume 411, pp. 305–313. ISBN 978-81-322-2731-1. 31. Zadeh, L.A. Fuzzy logic as the logic of natural languages. In Analysis and Design of Intelligent Systems Using Soft Computing Techniques; Melin, P., Castillo, O., Ramirez, E.G., Kacprzyk, J., Pedrycz, W., Eds.; Springer-Verlag Berlin: Berlin, Germany, 2007; Volume 41, pp. 1–2. ISBN 978-3-540-72431-5. 32. Dostál, P. Advanced Decision Making in Business and Public Services, 1st ed.; Akademické nakladatelství CERM: Brno, Czech Republic, 2011; ISBN 978-80-7204-747-5. 33. Doskočil, R. An evaluation of total project risk based on fuzzy logic. Bus. Theory Pract. 2016, 15, 23–31. 34. Fassinger, R.E.; Shullman, S.L.; Buki, L.P. Future Shock: Counseling Psychology in a VUCA World. Couns. Psychol. 2017, 45, 1048– 1058. 35. Zandhuis, A. ISO21500: Guidance on Project Management—A Pocket Guide; Van Haren Publishing: Zaltbommel, The Netherlands, 2013; p. 51. 36. Cooper, D.; Bosnich, P.; Grey, S.; Purdy, G.; Raymond, G.; Walker, P.; Wood, M. Project Risk Management Guidelines: Managing Risk with ISO 31000 and IEC 62198; Wiley: Hoboken, NJ, USA, 2014. 37. Kerzner, H.R. Project Management: A Systems Approach to Planning, Scheduling, and Controlling; John Wiley & Sons: Hoboken, NJ, USA, 2017; ISBN 978-1-119-16535-4. 38. Ariely, D. The Upside of Irrationality: The Unexpected Benefits of Defying Logic at Work and at Home; HarperCollins: London, UK, 2010; ISBN 978-0-00-735479-5. 39. McCrindle, M.; Wolfinger, E. The ABC of XYZ: Understanding the Global Generations; University of New South Wales Press: Sydney, Australia; London, UK, 2010; ISBN 978-1-74223-035-1. 40. Simonin, B.L. The Importance of Collaborative Know-How: An Empirical Test of the Learning Organization. Acad. Manag. J. 1997, 40, 1150–1174. Authors: M D Asif, Hemalatha J N, Anil Baby
Paper Title: Constant on Time Controller for Voltage Regulator with and without Adaptive Voltage Positioning Abstract: Advancement in electronics prompted for incremental usage of power supplies in digital circuits. In devices such as Central Processing Unit (CPU), Graphic Processing Unit (GPU) a Voltage regulator (VR) is utilized for microprocessors powering application. During load transients, microprocessor input voltage losses stability. High speed devices need quick response from the converters under transient conditions. Therefore, futuristic electronic devices demand a VR with new control schemes to operate at low operating voltage with improved efficiency thereby improving light load efficiency. In this paper a comparative study about the performance of Constant on Time (COT) controller with and without Adaptive Voltage Positioning (AVP) technique is presented. . The aim of the paper is to present the idea of AVP with COT during transients. Since load transient demands large number of capacitors to maintain stable voltage but increases the cost and volume. Thus, to overcome the transient time frame problem, voltage spikes and to reduce the number of capacitors. The converters need to operate under a new control scheme i.e. COT controller with AVP. Simulation of the voltage regulator is carried out for both Non AVP and AVP compliant schematics in LTSpice software. The simulation results show Non-AVP topology with output voltage spikes for 445uF but the AVP compliant topology with 222uF shows smooth output voltage transition. 55. Keywords: Adaptive voltage positioning; constant ON-Time control; dc-dc converters. 322-328
References: 1. Van Wyk J. D and Lee F.C, “On a Future for Power Electronics”, IEEE Journal Of Emerging and Selected Topics in Power Electronics, vol. 1, no. 2, pp. 59-72, June2013. 2. Intel document "Voltage Regulator-Down (VRD)"http://www.intel.com/assets/pdf/designguide/313214.pdf dated November 2006. 3. P. K. Sim and Z. Salam, "Some issues on the design of a voltage regulator module (VRM) for microprocessor power supply," Proceedings. National Power Engineering Conference, 2003. PECon 2003., Bangi, Malaysia, 2003, pp. 110-116. 4. K. Yao et al., "Adaptive voltage position design for voltage regulators", Nineteenth Annual IEEE Applied Power Electronics Conference and Exposition, 2004. APEC '04., Anaheim, CA, USA, 2004. 5. Himanshu and R. Khanna, "Various control methods for DC-DC buck converter", 2012 IEEE Fifth Power India Conference, Murthal, 2012, pp. 1-4 6. R. Panguloori, D. Kastha, A. Patra and G. Capodivacca, "High performance voltage regulator for high step-down DC-DC conversion", 2008 34th Annual Conference of IEEE Industrial Electronics, Orlando, FL, 2008. 7. J. Sun, M. Xu, Y. Ren and F. C. Lee, "Light-Load Efficiency Improvement for Buck Voltage Regulators", in IEEE Transactions on Power Electronics, vol. 24, no. 3, pp. 742-751, March 2009. 8. K. Hu, B. Chen and C. Tsai, "A digitally controlled buck converter with current sensor-less adaptive voltage positioning (AVP) mechanism", 2017 International Symposium on VLSI Design, Automation and Test (VLSI-DAT), Hsinchu, 2017. 9. C. Chen, S. Lu, S. Hsiao, Y. Chen and J. Huang, "A Current-Mode Buck Converter With Reconfigurable On-Chip Compensation and Adaptive Voltage Positioning", in IEEE Transactions on Power Electronics, vol. 34, no. 1, pp. 485-494, Jan. 2019. 56. Authors: Shivangi Garg, Prachee Tiwari, Shubham Gupta, Brijesh Prasad, Hritik Mohan
Paper Title: Intelligent Door Lock System Abstract: The intelligent door lock system supercharged by Amazon net Services. The objective of our project 329-331 is to enhance security in official and residential places by automation and ease-of-access. A guest once he reaches the door step and press the bell button, greets the visitant by his/her specific identity, apprize the landlord concerning the visitor and associate in nursing keep in mind an unknown visitor. That place landlord will identify the identity of visitor by call on - "Alexa, who is at the door?" and Alexa will obey the instruction given by the owner. We propose to design a custom Alexa skill which helps in identifying the visitors and navigate him/her inside the place or home without moving anywhere. Security is important concern in today's world. In these days’ homes are primarily equipped with a minimum of one Virtual help Devices like Alexa, Google Assistant etc. that everybody uses it all the time.
Keywords: Intelligent Door Lock System, Alexa, Security
References: 1. L.Kamelia, S. R. Alfin Noorhassan, M. Sanjaya, and W.S. Edi Mulyana, “Door-automation system using bluetooth-based android for mobile phone,” ARPN J. Eng. Appl. Sci., 2014. 2. “(PDF) AUTOMATIC PASSWORD BASED DOOR LOCK SYSTEM Hamza Saeed Khan Academia.edu.”https://www.academia.edu/20828187/AUTOMATIC_PASSWORD_BASED_DOOR_LOCK_SYSTEM. 3. A. Mishra, S. Sharma, S. Dubey, and S. K. Dubey, “PASSWORD BASED SECURITY LOCK SYSTEM,” 2014. Available:www.ijates.com. 4. D. Pavithra and R. Balakrishnan, “IoT based monitoring and control system for home automation,” in Global Conference on Communication Technologies, GCCT 2015, Nov. 2015, pp. 169–173, doi: 10.1109/GCCT.2015.7342646. 5. M. Ibrahim, A. Elgamri, S. Babiker, and A. Mohamed, “Internet of things based smart environmental monitoring using the Raspberry- Pi computer,” in 2015 5th International Conference on Digital Information Processing and Communications, ICDIPC 2015, Nov. 2015, pp. 159–164, doi: 10.1109/ICDIPC.2015.7323023. 6. Rabail Shafique Satti, Sidra Ejaz, Madiha Arshad, “A Smart 7. Visitors Notification System With Automatic Secure Door Lock Using Mobile Communication Technology”, International Journal of Computer and Communication System Engineering, Vol. 02 No.01 February 2015. 8. A.O.Oke, O.M.Olaniyi, O.T. Arulogun, O.M. Olaniyan, “Development Of A Microcontroller-Controlled Security Door System.”, The Pacific Journal of Science and Technology, Volume 10. Number 2. November 2009 (Fall). 9. S. Nazeem Basha, Dr. S.A.K. Jilani, Mr. S. Arun, “An Intelligent Door System Using Raspberry Pi And Amazon Web Services Iot”, International Journal of Engineering Trends and Technology (IJETT), Volume 33 Number 2- March 2016. Authors: Y Sudha, P Pooja, K Rama, P Shweta
Paper Title: Speed Control of AC Motor Abstract: An enormous number of motors have been using in our daily life ranging from home appliances to industrial machinery. During the last decade, there has been tremendous change industry automation and home automation. Electric motor is always the essential part in home appliances as well as industrial applications in the automation. There is always a need to control the speed of the motor to run the appliances smoothly. Other important part of the appliances is the microcontroller. Microcontroller is always the essential part in all the embedded system applications because of its economical price and easy to customize to any specific need. Electronic devices like thyristors, diodes have been widely used in industrial applications to control the speed of the motor The Objective of this paper is to control the induction motor speed by using triac and zero crossing detector. Usually in many industrial applications speed varying is important to run the process in different stages, this paper demonstrates the mechanism of varying the speed of the AC Motor. The circuit is equipped with microcontroller and the induction motor. Induction motor is controlled by using triac and zero crossing 57. detector by controlling the pulses of input AC signal. Microcontroller input is connected with two input modules that is one increment switch and one decrement switch. Increment switch is used to increase the speed of motor by varying in steps that is already dumped the code in microcontroller and also same for decrement switch. An 332-334 LCS screen has been used to display the speed of the motor. Touch screen is used to operate the motor.
Keywords: Microcontroller, LCD screen, Rectifier, PWM, Embedded systems ,AC Motor, Triac , Zero Crossing detector.
References: 1. Shilpa V. Kailaswar, Prof . R.A.Keswani/International journal of Engineering Research and application (IJERA), pp. 1732-1736. Speed Control Of Three Induction Motor by V/F method for batching motion system 2. S.M.Wankhede,R.M. Holmukhe, Miss A.M. Kadam ,MissP.R.Shinde,* P.S.Chaudhari. Microcontroller Based Control of Three Phase Induction Motor Using PWM technique. 3. Application Note-017, PWM Motor Drives –Theory and Measurement Considerations. 4. The 8052 Microcontroller and Embedded System Pearson Education-M.A.Mazidi. 5. Power Electronics-M.Rashid. 6. Electrical Technology-Theraja 58. Authors: Simran Koul, Simriti Koul, Prajval Mohan, Lakshya Sharma, Pranav Narayan Detection of Cyber-Attack in Broad-Scale Smart Grids using Deep and Scalable Unsupervised Paper Title: Machine Learning System Abstract: The increase in the reliability, efficiency and security of the electrical grids was credited to the 335-344 innovation of the smart grid. It is also a fact that the smart grids a very dependable on the digital communication technology that in turn gives rise to undiscovered weaknesses which have to be reconsidered for dependable and coherent power distribution. In this paper, we propose an unsupervised anomaly detection which is mainly focused the statistical correlation among the data. The main aim is to create a scalable anomaly detection system suitable for huge-scale smart grids, which are capable to denote a difference between a real fault from a disruption and an intelligent cyber-attack. We have presented a methodology that applies the concept of attribute extraction by the use of Symbolic Dynamic Filtering (SDF) to decrease compilation drift whilst uncovering usual interactions among subsystems. Results of simulation obtained on IEEE 39, 118 and 2848 bus systems confirm the execution of the method, proposed in this paper, under various working conditions. The results depict a precision of almost 99 percent, along with 98 percent of true positive rate and less than 2 percent of false positive rate.
Keywords: Anomaly, cyber-attack, smart grid, statistical property, machine learning, unsupervised learning
References: 1. J. Dagle, “Postmortem analysis of power grid blackouts-the role of measurement systems”, IEEE Power and Energy Magazine, vol. 4, no. 5, pp. 30-35, Sept 2006. 2. M. Masera, I. Nai Fovino, “Effects of intentional threats to power substation control systems”, Int. Jour. Cri. Infr., vol. 4, no. 1, pp.129143, 2008. 3. T. Morris, S. Pan, J. Lewis, J. Moorhead, B. Reaves, N. Younan, R. King, M. Freund, V. Madani, “Cybersecurity testing of substation phasor measurement units and phasor data concentrators”, in Proc. of CSIIRW, pp. 12-14, Oct. 2011. 4. Prajval Mohan, Pranav Narayan, Lakshya Sharma, Tejas Jambhale, Simran Koul, "Iterative SARSA: The Modified SARSA Algorithm for Finding the Optimal Path". International Journal of Recent Technology and Engineering (IJRTE). ISSN: 2277-3878, Volume-8 Issue-6, March 2020. 5. H. Haddad Pajouh, R.Javidan, R.Khaymi, A. Dehghantanha and K.R.Choo, “A Two-layer Dimension Reduction and Two-tier Classification Model for Anomaly-Based Intrusion Detection in IoT Backbone Networks”, IEEE Trans. on Eme. Topics in Computing, 2016. 6. R. Khan, K. Mclaughlin, D. Laverty, S. Sezer, “Design and implementation of security gateway for synchrophasor based realtime control and monitoring in smart grid”, IEEE Access, vol. 5, no. , pp. 11626-11644, June 2017. 7. H. Karimipour, V. Dinavahi, “Robust Massively Parallel Dynamic State Estimation of Power Systems Against Cyber-Attack”, IEEE Access, vol. 6, pp. 2984-2995, Dec. 2017. 8. M. Ozay, I. Esnaola, F. T. Yarman Vural, S. R. Kulkarni, and H. V. Poor,“Machine learning methods for attack detection in the smart grid,” IEEE Trans. on Neural Net. & Learning Syst., vol. 27, no. 8, pp. 1773–1786, Aug 2016. 9. Prajval Mohan, Adiksha Sood, Lakshya Sharma, Simran Koul, Simriti Koul, “PC-SWT: A Hybrid Image Fusion Algorithm of Stationary Wavelet Transform and Principal Component Analysis”. International Journal of Engineering and Advanced Technology (IJEAT)’, ISSN: 2249-8958, Volume-9 Issue-5, June 2020. 10. S. Pan, T. Morris, and U. Adhikari, “Developing a hybrid intrusion detection system using data mining for power systems”, IEEE Trans. Smart Grid, vol. 6, no. 6, pp. 3104_3113, Nov. 2015. 11. J. Landford et al., “Fast sequence component analysis for attack detection in synchrophasor networks”, 5th Int. Conf. Smart Cities Green ICT Syst. (SmartGreens), Rome, Italy, 2016. 12. S. Ahmed, Y. Lee, S. Hyun and I. Koo, “Feature Selection-Based Detection of Covert Cyber Deception Assaults in Smart Grid Communications Networks Using Machine Learning”, IEEE Access, vol. 6, pp. 27518-27529, 2018. 13. Simran Koul, Yash Raj, Simriti Koul, “Analyzing Cyber Trends in Online Financial Frauds using digital Forensics Techniques”. ‘International Journal of Innovative Technology and Exploring Engineering (IJITEE)’, ISSN: 2278-3075 (Online), Volume-9 Issue-9, July 2020, Page No. 446-451. 14. Y. He, G. J. Mendis and J. Wei, “Real-Time Detection of False Data Injection Attacks in Smart Grid: A Deep Learning-Based Intelligent Mechanism”, IEEE Trans. on Smart Grid, vol. 8, no. 5, pp. 2505-2516, Sept. 2017. 15. S. Mohammadi, H. Mirvaziri, M. G. Ahsaee, H. Karimipour, “Cyber Intrusion Detection by Combined Feature Selection Algorithm”, Journal of Inf. Sec. and App., pp. 80-88, vol. 44, Feb. 2019. 16. C. A. Murthy, “Bridging feature selection and extraction: compound feature generation”, IEEE Transactions on Knowledge and Data Engineering, vol. 29, no. 4, pp. 757-770, 1 April 2017. 17. A Bergen and V. Vittal, “Power Systems Analysis”, Prentice-Hall, Second ed., 2000. 18. Y. Liu, M. K. Reiter, and P. Ning, “False data injection attacks against state estimation in electric power grids”, ACM Trans. Inf. Syst. Secur., vol. 14, pp. 1-33, 2011. 19. M. Ozay, I. Esnaola, F. T. Yarman Vural, S. R. Kulkarni, and H. V. Poor, “Sparse attack construction and state estimation in the smart grid: Centralized and distributed models,” IEEE J. Sel. Areas Commun., vol. 31, no. 7, pp. 1306–1318, Jul. 2013. 20. S. Sarkar, S. Sarkar, K. Mukherjee, A. Ray, A. Srivastav, “Multisensor data interpretation and semantic fusion for fault detection in aircraft gas turbine engines”, Journal of Aerospace Engineering, vol. 227, no. 12, pp. 1988–2001, Dec. 2013. 21. R. D. Zimmerman, C. E. Murillo-Sánchez, and R. J. Thomas, “MATPOWER: Steady-state operations, planning, and analysis tools for power systems research and education,” IEEE Trans. Power System, vol. 26, no. 1, pp. 12–19, Feb. 2011. 22. Simriti Koul, “Contribution of Cognitive Science and Artificial Intelligence in the Simulation of the Complex Human Mind”. ‘International Journal of Recent Technology and Engineering (IJRTE)’. 23. Simran Koul, “Contribution of Artificial Intelligence and Virtual Worlds Towards Development of Super Intelligent AI Agents”. ‘International Journal of Engineering and Advanced Technology (IJEAT)’, ISSN: 2249-8958 (Online), Volume-9 Issue-5, June 2020, Page No. 800-809 59. Authors: Jogamohan Medak, Partha Pratim Gogoi
Paper Title: Critical Scrutiny of Page Replacement Algorithms: FIFO, Optimal and LRU Abstract: Virtual memory plays an important role in memory management of an operating system. A process or 345-348 a set of processes may have a requirement of memory space that may exceed the capacity of main memory. This situation is addressed by virtual memory where a certain memory space in secondary memory is treated as primary memory, i.e., main memory is virtually extended to secondary memory. When a process requires a page, it first scans in primary memory. If it is found then, process continues to execute, otherwise a situation arises, called page fault, which is addressed by page replacement algorithms. This algorithms swaps out a page from main memory to secondary memory and replaced it with another page from secondary memory in addition to the fact that it should have minimum page faults so that considerable amount of I/O operations, required for swapping in/out of pages, can be reduced. Several algorithms for page replacement have been formulated to increase the efficiency of page replacement technique. In this paper, mainly three page replacement algorithms: FIFO, Optimal and LRU are discussed, their behavioural pattern is analysed with systematic approach and a comparative analysis of these algorithms is recorded with proper diagram.
Keywords: Belady’s anomaly, FIFO, hit ratio, LRU, Optimal, page fault, Virtual memory.
References: 1. A. Silberschatz, P.B. Galvin, G. Gange, Operating System Concepts. Wiley India Edition, 8Th Edition,2010, ch. 9. 2. W. Stallings, Operating System Internals Design and Principles, Prentice Hall, 7th Edition, 2012 ch. 8. 3. J. Kumari, S. Kuamr, D. Prasad, “A Comparison of Page Replacement Algorithms: A Survey” IJSER, valume 7, Issue 12, December- 2016. 4. G. Rexha, E. Elmazi, I. Tafa, “ A Comparision of Three Page Replacement Algorithms: FIFO, LRU and Optimal” AJIS MCSER Publishing, Rome-Italy.Vol 4. No 2 S2. August 2015. 5. M. Saktheeswari, K. Sridharan, “ A Stdy on Page Replacement Algorithms”, IJTES., Vol 3., No. 2, 2012. 6. “Page Replacement Algorithm” , 2020. Available at https://en.wikipedia.org/wiki/Page_replacement_algorithm. 7. “Belady’s Anomaly”,2020., Available at https://en.wikipedia.org/wiki/B%C3%A9l%C3%A1dy%27s_anomaly. 8. “Page Replacement Algorithms in Operating Systems”, 2020., Available at https://www.geeksforgeeks.org/page-replacement- algorithms-in-operating-systems/. 9. “Page Replacement Algorithms”, 2020., available at https://www.javatpoint.com/os-page-replacement-algorithms. 10. “Optima Page Replacement Algorithm”, 2020., Available at https://www.geeksforgeeks.org/optimal-page-replacement-algorithm/? ref=rp. 11. “Program for page replacement Algorithms|Set 2(FIFO)”, 2020. Available at https://www.geeksforgeeks.org/program-page- replacement-algorithms-set-2-fifo/?ref=rp. 12. “ Program for Least Recently Used(LRU)”,2020., Available at https://www.geeksforgeeks.org/program-for-least-recently-used-lru- page-replacement-algorithm/?ref=rp. Authors: M.Sundar Rajan, Samuel Kefale, Abraham Mesfin
Paper Title: Variable Frequency Drive Source Based Efficiency Measurement of an Induction Motor Abstract: Induction motor loss separation and efficiency measurement needs loading dynamometers and other tools as like variable voltage sinusoidal power supply. These are costly and not always usable except though a loading tool is usable. Variable frequency drives are also commonly utilized for running induction machinery and are readily accessible and low cost. Nevertheless, their usage in lieu of a constant frequency sinusoidal power supply to calculate system performance precisely is interesting, but potentially difficult because of the PWM output voltage. This paper provides few studies into the usage of variable frequency drives. The usage of the machine, the measurement criterion and the protocols shall be reported and addressed. The output presented describes the possibility of the suggested idea of calculating machine effectiveness with a PWM power source.
Keywords: Induction machines, PWM supply, Variable Frequency Drive (VFD). 60. References: 1. P. S. Dainez, E. Bim, D. Glose and R. M. Kennel,"Modeling and parameter identification of a double-starinduction machines," IEEE 349-352 International Electric Machines &Drives Conference (IEMDC), Coeur d'Alene, ID, , pp. 749-755, 2015. 2. K.Vijaya Bhaskhar Reddy, G.V. Siva Krishna Rau, Professor, Dept. of Electrical Engineering, Andhra University, Waltair, A.P, India-" Modeling and Simulation of Modified Sine PWM VSI fed asynchronous machine drive" - International Journal of Electrical Engineering and Technology (!JEET), Volume 3, Issue 2, pp . 343-351,luly - September (2012). 3. Ajay Kumar Mauria, Yogesh K. Chauhan, R. K. Mishra-"Fuel Cell Integrated with Five Level VSI for Industrial Pump Applications" - International Journal of Renewable Energy Research, Vol.3, No.2, 2013. 4. Jianhui Wangu , Fenglong Shen, and Yongkui Man-"Research on Control System of Three- Level Inverter Based on Indirect Field Orientation" - IEEE 978-1-4577 -2074-1112,2012. 5. P.Thirumuraugan, R. Preethy-"Closed Loop Control of Multilevel Inverter Using SVPWM for Grid Connected Photovoltaic System" - International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering-Vol. 2, Issue 4, April 2013. 6. Dr. Keith Corzene -"Operation and Design of Multilevel Inverters" - University of Missouri, June 2005. 7. Madhusudhan Singh, Arpit Agarwal, Namrata Kaira" Performance Evaluation of Multilevel Inverter with Advance PWM Control Techniques" IEEE Trans 978-1-4673-0934-9112, 2012 8. K.Suria, Suresh and M. Vishnu Prasad-"PV Cell Based Five Level Inverter Using Multicarrier PWM" -International Journal of Modern Engineering Research (IJMER) YoU, Issue.2, pp- 545-551 ISSN: 2249-6645. 61. Authors: Rama Naga Kiran Kumar. K, Ramesh Babu. I The File System Recommendations to Reduce the Space and Time Parameters in Hadoop File Paper Title: Storage and Map Reduce Processing of Big Data Applications Abstract: The study of Hadoop Distributed File System (HDFS) and Map Reduce (MR) are the key aspects of 353-356 the Hadoop framework. The big data scenarios like Face Book (FB) data processing or the twitter analytics such as storing the tweets and processing the tweets is other scenario of big data which can depends on Hadoop framework to perform the storage and processing through which further analytics can be done. The point here is the usage of space and time in the processing of the above-mentioned huge amounts of the data definitely leads to higher amounts of space and time consumption of the Hadoop framework. The problem here is usage of huge amounts of the space and at the same time the processing time is also high which need to be reduced so as to get the fastest response from the framework. The attempt is important as all the other eco system tools also depends on HDFS and MR so as to perform the data storage and processing of the data and alternative architecture so as to improve the usage of the space and effective utilization of the resources so as to reduce the time requirements of the framework. The outcome of the work is faster data processing and less space utilization of the framework in the processing of MR along with other eco system tools like Hive, Flume, Sqoop and Pig Latin. The work is proposing an alternative framework of the HDFS and MR and the name we are assigning is Unified Space Allocation and Data Processing with Metadata based Distributed File System (USAMDFS).
Keywords: Analytics, Hadoop Framework, Meta Data based File system, Eco System, Unified Space Allocation.
References: 1. Ivanliton Polato, “A Comprehensive view of Hadoop Research- A Systematic Literature Review, Volume 46,November 2019,PP:1-25. 2. Konstantin Shvachko,” The Hadoop Distributed File System” IEEE 2010. 3. Mohd Rehan Ghazi, “Hadoop, MapReduce and HDFS: A Developers Perspective” Procedia Computer Science ,2015. 4. Wu Jun ,” Study of New Materials Design based on Hadoop”, MATEC Web of Conference 61,07016(2016). 5. Himi Egemen Ciritogulu, “A Heterogeneity –aware replica deletion for HDFS” Journal of Big data, October 2019. 6. Sujit Roy,”Hadoop Periodic Jobs Using Data Blocks to Achieve Efficiency”, Indian Journal of Research in Compueter Science and Information Technology,Vol:3, Issue:3,2018. 7. Ronald C.Taylor, “ An Overview of the Hadoop/Map Reduce/HBase/ framework and its current applications in bioinformatics”,BMC Bio Informatics,2010. 8. Jason.C.Cphen, “Towards a Trusted HDFS storage platform”,ACM Digital Library,Vol:19,No:3,July 2014. 9. Aibo Song, “A memory-Based Hadoop Framework for Large Data Storage”,Resource management in Virtaulized Clouds,Hindawi publishers,Volume 2016 10. Tafiq Hassanin, “Severly Imbalanced Big data challenges : Investigating Data Sampling approaches”,Springer, 30 November 2019. 11. Konstantin, The Hadoop Distributed File System,”Symantic Scholar.org,October 2013. 12. D.Borthakur ,”The Hadoop Distributed File System Architecture and Design”, 2017,Apache.org. [13].H.Liao, ”Multi-Dimensional index on Hadoop Distributed File System” 2010, IEEE explore ieee.org. [14].J.Zhang “A Distributed Cache for Hadoop Distributed File system in real-cloud services,ACM 2012. [15].S.Jin,”Design of trusted file System based on Hadoop,2012, Springer. 13. UmaPavan Kumar,”Integration of Hadoop and IOT for better analytics” TEST Engineering and Management,February 2020. 14. UmaPavan Kumar,” Various Issues in Hadoop Distributed File System, Map Reduce and Future Research Directions, International Journal of Pure and Applied Mathematics. 15. Rama Naga Kiran Kumar,”Hadoop Based File System Revision with Derby and Virtual Local File System Models., International Journal of Advanced Science and Technology, Vol.29, June 2020. Authors: Nitesh Reddy M, Sakthivel R, Sharath Reddy M, Varun Hemanth L Design and Implementation of Perceptron Neuron in Machine Learning for Handwritten Character Paper Title: Recognition Abstract: Due to the exponential increase of electronic devices that are connected to the Internet, the amount of data that they produce have grown to the same extent. In order to face the processing of these data, the use of some automatic learning algorithms, also known as Machine Learning, has become widespread. The most popular is the one known as neural networks. These algorithms need a great deal of resources to compute all their operations, and because of that, they have been traditionally implemented in application specific integrated circuits. However, recently there have been a boom in implementations in field programmable gate arrays, also known as FPGAs. These allow greater parallelism in the implementation of the algorithms. Field Programmable Gate Arrays (FPGA) implementation based feature extraction method is proposed in this paper. This particular application is handwritten offline digit recognition. The classification depends on simple 2 layer Multi-Layer Perceptron (MLP). The particular feature extraction approach is suitable for execution of FPGA because it is utilized with subtraction and addition operations. From Standard database handwritten digit images of normalized 40×40 pixel the features are extracted by the proposed method. It has been discovered by experiential outcomes that 85% accuracy is achieved by proposed system. Overall, as compared to other systems, it is less complex, more accurate and simple. Further this project explains IEE-754 format single precision floating point MAC unit’s FPGA implementation which is utilized for feeding the neurons weighted inputs in artificial neural networks. Data representation range is improved by floating point numbers utilization to a higher number from smaller number that is highly suggested for Artificial Neuron Network. The code is developed in HDL, simulated and synthesis results are extracted using Xilinx synthesis tools .In order to validate its computational accuracy of the FFT, an MATLAB validation script is used to verify the output of HDL with standard reference model.
Keywords: FP MAC Unit, Handwritten character recognition, Machine Learning, Multilayer Percepteron
References: 1. K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE Conference on 62. Computer Vision and Pattern Recognition, 2016, pp. 770–778. 2. A. Graves, A.-r. Mohamed, and G. Hinton, “Speech recognition with deep recurrent neural networks,” in Acoustics, speech and signal processing (icassp), 2013 357-363 3. ieee international conference on. IEEE, 2013, pp. 6645– 6649. 4. B. Huval, T. Wang, S. Tandon, J. Kiske, W. Song, J. Pazhayampallil, M. Andriluka, P. Rajpurkar, T. Migimatsu, R. Cheng-Yue et al., “An empirical evaluation of deep learning on highway driving,” arXiv preprint arXiv:1504.01716, 2015. 5. R. Collobert and J. Weston, “A unified architecture for natural language processing: Deep neural networks with multitask learning,” in Proceedings of the 25th international conference on Machine learning. ACM, 2008, pp. 160–167. 6. R. Burbidge, M. Trotter, B. Buxton, and S. Holden, “Drug design by machine learning: support vector machines for pharmaceutical data analysis,” Computers & chemistry, vol. 26, no. 1, pp. 5–14, 2001. 7. S. Han, H. Mao, and W. J. Dally, “Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding,” arXiv preprint arXiv:1510.00149, 2015. 8. A. Ren, Z. Li, C. Ding, Q. Qiu, Y. Wang, J. Li, X. Qian, and B. Yuan, “Sc-dcnn: Highly-scalable deep convolutional neural network using stochastic computing,” in Proceedings of the Twenty-Second International Conference on Architectural Support for Programming Languages and Operating Systems. ACM, 2017, pp. 405–418. 9. Y. LeCun, J. S. Denker, S. A. Solla, R. E. Howard, and L. D. Jackel, “Optimal brain damage.” in NIPs, vol. 2, 1989, pp. 598– 605. 10. L. Y. Pratt, Comparing biases for minimal network construction with back-propagation. Morgan Kaufmann Pub, 1989, vol. 1. 11. Mohamed Al-Ashrafy, Ashraf Salem and Wagdi Anis, “An Efficient Implementation of Floating Point Multiplier, “proceeding of 2011 IEEE. 12. Guillermo Marcus, Patricia Hinojosa, Alfonso Avila and Juan Nolazco-Flores, “A Fully Synthesizable Single-Precision, FloatingPoint Adder/Subtractor and Multiplier in VHDL for General and Educational Use”, Proceedings of the Fifth IEEE International Caracas Conference on Devices, Circuits and Systems, Dominican Republic. 13. Xilinx Inc, ISE, at http://www.xilinx.com. 14. Behrooz Parhami, Computer Arithmetic: Algorithms and Hardware Designs, 1st ed. Oxford: Oxford University Press, 2000 15. John G. Proakis and Dimitris G. Manolakis (1996), “Digital Signal Processing: Principles. Algorithms and Applications”, Third Edition. 16. Patterson, D. & Hennessy, J. (2005), Computer Organization and Design: The Hardware/software Interface, Morgan Kaufmann. 17. Mentor Graphics Inc, FPGA Advantage, at http://www.mentor.com/fpgaadvantage. 18. IEEE Standards Board, IEEE-754, IEEE Standard for Binary Floating-Point Arithmetic, New York: IEEE, 1985. 19. Lamiaa S.A.Hamid, Khaled A.Sheata, Hassan El-Ghitani, Mohamed Elsaid (2010),“ Design of Generic Floating Point Multiplier and Adder/Subtractor Units”, in proceedings of the 12th IEEE international Conference on computer modeling and Simulation. 20. Design of FPGA based Handwriting Image Recognition System ASME JOURNALS-AMSE IIETA publication-2017-series Lei Wang, Ziheng Yang, Guangqiang Xu, Meili Fu, Yu Wang 21. FPGA based Farsi Handwritten Digit Recognition System Marzieh Moradi , Mohammad Ali Pournima , Farbod Razzazi. 63. Authors: Rakesh Kumar Singh, Ajay Kumar Bharti
Paper Title: An Analytical Study on Variants of LEACH Protocol Abstract: There are many remote areas where traditional computer networks cannot render services due to 364-369 unavailability of infrastructure. Among these infrastructure less networks, most popular choice for researchers are wireless sensor network in the modern era. Wireless Sensor networks perform the communication in remote areas where it is difficult to deploy the layout of network. Clustering hierarchy (LEACH) protocol is still a landmark as energy saving protocol for the researchers of wireless sensor network (WSN) even after 20 years of its existence. Since its inception, many modifications of LEACH protocol have been proposed. All the routing protocols have been divided into two categories namely single hop and multi hop scenarios. In this paper, we studied and surveyed various LEACH based routing protocols presented by researchers so far and discussed the advantages and functioning of them in comparison to LEACH protocol. The paper also discusses the merits and demerits of different successors of LEACH. In the end, paper concludes with future research directions in the Wireless sensor network area.
Keywords: LEACH, single hop and multi hop, Wireless Sensor Network, Clustering, Cluster Head, Routing protocol.
References: 1. W. B. Heinzelman, A. P. Chandrakasan, and H. Balakrishnan, “An application-specific protocol architecture for wireless microsensor networks,” Wireless Communications, IEEE Transactions on, vol. 1, no. 4, pp. 660-670, 2002. 2. Amandeep Kaur, Er. Swaranjee Singh and Navjot Kaur, “ Review of LEACH Protocol and Its Types,” International Journal of Emerging Engineering Research and Technology Volume 3, Issue 5, May 2015, PP 20-25. 3. Amit parmar and Ankit Thakkar,” An Improved Modified Leach-C Algorithm For Energy Efficient Routing Protocol In Wsn,” Nirma Univeristy Journal Of Engineering And Technology Vol. 4, N0. 2, Jul-Dec 2015. 4. B. Manzoor, N. Javaid, O. Rehman, M. Akbar, Q. Nadeem, A. Iqbal and M. Ishfaq, ”Q-LEACH: A New Routing Protocol for WSNs,” International Workshop on Body Area Sensor Networks (BASNet-2013). 5. Deepa S., C. N. Marimuthu and Dhanvanthri V, “Enhanced Q-Leach Routing Protocol For Wireless Sensor Networks,” ARPN Journal of Engineering and Applied Sciences, Vol. 10, No. 9, May 2015. 6. Jiman Hong, Joongjin Kook, Sangjun Lee, Dongseop Kwon and Sangho Yi, “T-LEACH: The method of threshold-based cluster head replacement for wireless sensor networks,” Inf. Syst. Frontiers, vol. 11, no. 5, pp. 513_521, 2009. 7. H. Junping, J. Yuhui, and D. Liang, “A time-based cluster-head selection algorithm for LEACH,'' IEEE Symp. Comput. Commun. (ISCC), Jul. 2008, pp. 1172_1176. 8. M. Tong and M. Tang, “LEACH-B: An improved LEACH protocol for wireless sensor network,” 6th Int. Conf. Wireless Commun.Netw. Mobile Comput. (WiCOM), Sep. 2010, pp. 1_4. 9. A. S. D. Sasikala and N. Sangameswaran, “Improving the energy efficiency of LEACH protocol using VCH in wireless sensor network,” Int. J. Eng. Develop. Res., vol. 3, no. 2, pp. 918_924, 2015. 10. Rajashree.V.Biradar , Dr. S. R. Sawant, Dr. R. R. Mudholkar and Dr. V.C. Patil, “ Multihop Routing In Self-Organizing Wireless Sensor Networks,” IJCSI International Journal of Computer Science Issues, Vol. 8, Issue 1, January 2011. 11. V. Loscrì, G. Morabito, and S. Marano, “A Two-Levels Hierarchy for Low-energy Adaptive Clustering Hierarchy (TL-LEACH),” Vehicular Technology Conference, 1988, IEEE 38th · October 2005. 12. Wassim Jerbi, Abderrahmen Guermazi and Hafedh Trabelsi, “O-LEACH: The problem of orphan nodes in the LEACH of routing protocol for wireless sensor networks,”. 13. Muhamnmad Omer Farooq, Abdul Basit Dogar and Ghalib Asadullah Shah, “MR-LEACH: Multi-hop Routing with Low Energy Adaptive Clustering Hierarchy,” 2010 Fourth International Conference on Sensor Technologies and Applications. 14. Kiran Jadav and Dhara Vadher, “A Comparative Study on Cluster Routing Based on leach in wireless sensor network” International Research Journal of Engineering and Technology (IRJET), Volume: 03 Issue: 03,2016. 15. B. Kishore Kumar Reddy, “A Design on Clustering Routing Protocol for Improving an Distributed Network Using Leach Protocol”, International Journal of Advance Research in Computer Science and Management Studies, Volume 2, Issue 3, March 2014. 16. Kiranjot and Er. Manoj Kumar, “Review Paper on LEACH and Its Descendant Protocols in Wireless Sensor Networks”, IJLTEMAS, Volume VI, 2017. 17. B. Gong, L. Li, S. Wang, and X. Zhou, “Multi-hop routing protocol with unequal clustering for wireless sensor networks”, In ISECS International Colloquium on Computing, Communication, Control, and Management, CCCM '08., Vol. 2, 2008, pp. 552 -556. 18. W.R. Heinzelman, A. Chandrakasan, “Energy efficient Communication Protocol for Wireless Microsensor Networks”, In: IEEE Computer Society Proceedings of the 33rd Hawaii International Conference on System Sciences (HICSS '00),Vol. 8, 2000, pp. 8020. 19. K. Maraiya, K. Kant, and N. Gupta, “Efficient Cluster Head Selection Scheme for Data Aggregation in Wireless Sensor Network,” International Journal of Computer Applications, vol. 23, 2011. 20. J. Wang, X. Yang, Y. Zheng et al., “An Energy-Efficient Multi-hop Hierarchical Routing Protocol for Wireless Sensor Networks,” International Journal of Future Generation Communication & Networking, vol. 5, no. 4, 2012. 21. I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, ‘‘Wireless sensor networks: A survey,’’ Comput. Netw., vol. 38, no. 4, pp. 393– 422, 2002. A. Abed, A. Alkhatib, and G. S. Baicher, ‘‘Wireless sensor network architecture,’’ in Proc. Int. Conf. Comput. Netw. Commun. Syst. (CNCS), vol. 35. Singapore, 2012, pp. 11–15. 22. S. Pino-Povedano, R. Arroyo-Valles, and J. Cid-Sueiro, ‘‘Selective forwarding for energy-efficient target tracking in sensor networks,’’ Signal Process., vol. 94, pp. 557–569, Jan. 2014. 23. N. A. Pantazis, S. A. Nikolidakis, and D. D. Vergados, ‘‘Energy- efficient routing protocols in wireless sensor networks: A survey,’’ IEEE Commun. Surveys Tuts., vol. 15, no. 2, pp. 551–591, 2nd Quart. 2013. 24. Z. J. Haas, J. Y. Halpern, and L. Li, ‘‘Gossip-based ad hoc routing,’’ IEEE/ACM Trans. Netw., vol. 14, no. 3, pp. 479–491, Jun. 2006. 25. C. Intanagonwiwat, R. Govindan, D. Estrin, J. Heidemann, and F. Silva, ‘‘Directed diffusion for wireless sensor networking,’’ IEEE/ACM Trans. Netw., vol. 11, no. 1, pp. 2–16, Feb. 2003. 26. Braginsky and D. Estrin, ‘‘Rumor routing algorthim for sensor networks,’’ in Proc. 1st ACM Int. Workshop Wireless Sensor Netw. Appl., New York, NY, USA, 2002, pp. 22–31. 27. J. Kulik,and H. Balakrishnan, ‘‘Negotiation-based protocols for disseminating information in wireless sensor networks,’’Wireless Netw., vol.8, no.2, pp.169–185, 2002. 28. W. R. Heinzelman, A. Chandrakasan, and H. Balakrishnan, ‘‘Energy- efficient communication protocol for wireless microsensor networks,’’ in Proc. 33rd Annu. Hawaii Int. Conf. Syst. Sci., vol. 2, Jan. 2000, p. 10. 29. O. Younis and S. Fahmy, ‘‘HEED: A hybrid, energy-efficient, distributed clustering approach for adhoc networks,’’ IEEE Trans. Mobile Comput., vol. 3, no. 4, pp. 366–379, Oct. 2004. 30. S. Lindsey and C. S. Raghavendra, ‘‘PEGASIS: Power-efficient gathering in sensor information systems,’’ in Proc. IEEE Aerosp. Conf., vol. 3. Mar. 2002, pp. 3-1125–3-1130. 31. M. Ye, C. Li, G. Chen, and J. Wu, ‘‘EECS: An energy efficient clustering scheme in wireless sensor networks,’’ in Proc. 24th IEEE Int. Perform., Comput., Commun. Conf., Apr. 2005, pp. 535–540. 32. 32.Y. Jin, L. Wang, Y. Kim, and X. Yang, ‘‘EEMC: An energy-efficient multi- level clustering algorithm for large-scale wireless sensor networks,’’ Comput. Netw., vol. 52, no. 3, pp. 542–562, 2008. 33. 33, A. Manjeshwar and D. P. Agrawal, ‘‘TEEN: A routing protocol for enhanced efficiency in wireless sensor networks,’’ in Proc. 15th Int. Parallel Distrib. Process. Symp., Apr. 2000, pp. 2009–2015. 34. 34. L.Buttyán and P. Schaffer, ‘‘Panel: Position-based aggregator node election in wireless sensor networks,’’in Proc. IEEE Int. Conf. Mobile Adhoc Sensor Syst., Oct. 2007, pp. 1–9. Authors: Kavitha S, Uma Maheswari N, Venkatesh R
Paper Title: An Intrusion Detection System- Techniques and Algorithms of Machine and Deep Learning Abstract: Computer networks are vital component for today’s development of science and technology, due to the emergence of limitless communication pattern and exponential count of network devices cyber security become crucial for this world to secure the most valuable data or information which is more vulnerable for attack by the intruders. New pattern of intrusion and attacks are created in everyday manner by potential intruders and they should be identified by efficient Intrusion Detection Systems (IDSs), also proper counter should be applied for. The paper surveys about the discussion of various machine /deep learning technology and algorithm related to Intrusion Detection System (IDSs) for the real time performance of the system. Finally the literature review investigated gives some open issues which will need to be considered for further research in the field of network security.
Keywords: Network Security, Intrusion Detection System, Signature-Based IDS, Anomaly-Based IDS, Machine Learning, Deep Learning, Artificial Neural Network, Deep Belief Network
References: 1. B. Mukherjee, L. T. Heberlein, and K. N. Levitt, ``Network intrusion detection,'' IEEE Netw., vol. 8, no. 3, pp. 26_41, May 1994. 2. K. Scarfone, P. Mell, P. Mell, Guide to intrusion detection and prevention systems (IDPS), NIST special publication, (2007). 3. A. Milenkoski, M. Vieira, S. Kounev, A. Avritzer, and B. D. Payne,“Evaluating Computer Intrusion Detection Systems:A Survey of Common Practices,” Acm Comput. Surv., vol. 48, no. 1, pp. 1–41, 2015 4. F. A. Khan, A. Gumaei, A Comparative Study of Machine Learning Classifiers for Network Intrusion Detection, In International Conference on Artificial Intelligence and Security, pp. 75-86. Springer, Cham, 2019. 5. S. Pouyanfar et al., “A Survey on Deep Learning: Algorithms, Techniques, and Applications,” ACM Comput. Surv., vol. 51, no. 5, p. 92, 2018. 6. Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning. nature 521 (7553): 436,” Google Sch., 2015. 7. Lazarevic, Aleksander, Yipin Kumar and Jaideep Srivastava, "Intrusion Detection: A Survey", managing cyber Threats, Springer US, 2005, pp 19-78, 2005. 8. G. Karatas and O. K. Sahingoz, “Neural network based intrusion detection systems with different training functions,” in Digital Forensic and Security (ISDFS), 2018 6th International Symposium on. IEEE,2018, pp. 1–6. 9. G. Karatas¸, “Genetic algorithm for intrusion detection system,” in Signal Processing and Communication Application Conference (SIU), 2016 24th. 64. IEEE, 2016, pp. 1341–1344. 10. O. Can and O. K. Sahingoz, “An intrusion detection system based on neural network,” in 2015 23nd Signal Processing and Communications Applications Conference (SIU), May 2015, pp. 2302–2305. 370-376 11. R. G. Smith and J. Eckroth, “Building AI Applications: Yesterday, Today, and Tomorrow,” Ai Mag., vol. 38, no. 1, pp. 6–22, 2017. 12. P. Louridas and C. Ebert, “Machine Learning,” IEEE Softw., vol. 33, no. 5, pp. 110–115, 2016. 13. M. I. Jordan and T. M. Mitchell, “Machine learning: Trends, perspectives, and prospects,” Science, vol. 349, no. 6245, pp. 255–260, 2015. 14. L. Deng and D. Yu, “Deep learning: methods and applications,” Found. Trends® Signal Process., vol. 7, no. 3, pp. 197–387, 2014. 15. Y. Lecun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015. 16. I. M. Coelho, V. N. Coelho, E. J. D. S. Luz, L. S. Ochi, F. G. Guimarães, and E. Rios, “A GPU deep learning metaheuristic based model for time series forecasting,” Appl. Energy, vol. 201, no. 1, pp. 412–418, 2017. 17. Sydney Mambwe Kasongo, Yanxia Sun, A Deep Learning Method With Wrapper Based Feature Extraction For Wireless Intrusion Detection System, Computers & Security (2020), doi: https://doi.org/10.1016/j.cose.2020.101752 18. R. Vijayanand and D. Devaraj, "A Novel Feature Selection Method Using Whale Optimization Algorithm and Genetic Operators for Intrusion Detection System in Wireless Mesh Network," in IEEE Access, vol. 8, pp. 56847-56854, 2020 19. Mohammad Mehedi Hassan , Abdu Gumaei , Ahmed Alsanad , Majed Alrubaian , Giancarlo Fortino , A Hybrid Deep Learning Model for Efficient Intrusion Detection in Big Data Environment, Information Sciences(2019) 20. R. Vinayakumar, M. Alazab, K. P. Soman, P. Poornachandran, A. Al-Nemrat and S. Venkatraman, "Deep Learning Approach for Intelligent Intrusion Detection System," in IEEE Access, vol. 7, pp. 41525-41550, 2019. 21. Mengmeng Ge, Xiping Fu, Naeem Syed, Zubair Baig, Gideon Teo, Antonio Robles-Kelly, “Deep Learning-based Intrusion Detection for IoT Networks”, 2019 IEEE 24th Pacific Rim International Symposium on Dependable Computing (PRDC), Kyoto, Japan, 2019, pp. 256-25609. 22. Nagaraj Balakrishnan, Arunkumar Rajendran, Danilo Pelusi, Vijayakumar Ponnusamy, “Deep Belief Network enhanced Intrusion Detection System to Prevent Security Breach in the Internet of Things, Internet of Things (2019), doi: https://doi.org/10.1016/j.iot.2019.100112 23. F. Farahnakian and J. Heikkonen, "A deep auto-encoder based approach for intrusion detection system," 2018 20th International Conference on Advanced Communication Technology (ICACT), Chuncheon-si Gangwon-do, Korea (South), 2018, pp. 178-183. 24. M. Al-Qatf, Y. Lasheng, M. Al-Habib and K. Al-Sabahi, "Deep Learning Approach Combining Sparse Autoencoder With SVM for Network Intrusion Detection," in IEEE Access, vol. 6, pp. 52843-52856, 2018. 25. W. Chen, F. Kong, F. Mei, G. Yuan and B. Li, "A Novel Unsupervised Anomaly Detection Approach for Intrusion Detection System," 2017 IEEE 3rd international conference on big data security on cloud (bigdatasecurity), IEEE international conference on high performance and smart computing (hpsc), and ieee international conference on intelligent data and security (ids), Beijing, 2017, pp. 69-73 26. Weiwei Chen, Fangang Kong, Feng Mei, GuiginYuan, Bo Li, “a novel unsupervised Anamoly detection Approach for Intrusion Detection System”, 2017 IEEE 3rd International Conference on big data security on cloud, May 16-18,2017, Zhejiang, China. 27. Manoj s. Koli, Manik K. Chavan, “An Advanced method for detection of botnet traffic using Internal Intrusion Detection”, 2017 International Conference on (ICICCT), March 10-11, 2017, Sangli, India. 28. Panagiotis I. Radogloa-Grammatikis; Panagiotis G. Sarigannidis, "Flow anamoly based Intrusion Detection System for Android Mobile Devices", 2017 6th International Conference on MOCAST, May 4-6, 2017, Kazani, Greece. 65. Authors: Anoosh G P, Chetan G, Mohan Kumar M, Priyanka BN, Nagashree Nagaraj
Paper Title: Generating Video from Images using GANs Abstract: Generative adversarial networks are a category of neural networks used extensively for the generation 377-380 of a wide range of content. The generative models are trained through an adversarial process that offers a lot of potential in the world of deep learning. GANs are a popular approach to generate new data from random noise vector that are similar or have the same distribution as that in the training data set. The Generative Adversarial Networks (GANs) approach has been proposed to generate more realistic images. An extension of GANs is the conditional GANs which allows the model to condition external information. Conditional GANs have seen increasing uses and more implications than ever. We also propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models, a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. Our work aims at highlighting the uses of conditional GANs specifically with Generating images. We present some of the use cases of conditional GANs with images specifically in video generation.
Keywords: Generative adversarial networks (GANs), Generative Model, Discriminative Model, Video Generation.
References: 1. Abadi, M. and Andersen, D. G. (2016). Learning to protect communications with adversarial neural cryptography. arXiv preprint arXiv:1610.06918. 2. Ackley, D. H., Hinton, G. E., and Sejnowski, T. J. (1985). A learning algorithm for Boltzmann machines. Cognitive Science, 9, 147{169. 3. Bengio, Y., Thibodeau-Laufer, E., Alain, G., and Yosinski, J. (2014). Deep generative stochastic networks are trainable by backprop. In ICML'2014. 4. Brock, A., Lim, T., Ritchie, J. M., and Weston, N. (2016). Neural photo editing with introspective adversarial networks. 5. Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I., and Abbeel, P. (2016a). Infogan: Interpretable representation learning by information maximizing generative adversarial nets. 6. Isola, P., Zhu, J.-Y., Zhou, T., and Efros, A. A. (2016). Image-to-image translation with conditional adversarial networks. arXiv preprint arXiv:1611.07004. 7. Dziugaite, G. K., Roy, D. M., and Ghahramani, Z. (2015). Training generative neural networks via maximum mean discrepancy optimization. arXiv preprint arXiv:1505.03906. 8. Finn, C., Goodfellow, I., and Levine, S. (2016b). Unsupervised learning for physical interaction through video prediction. NIPS. 9. Geoff Penington (geoffp), Mae Hwee Teo (maehwee), and Chao Wang (cwang15), June 9th, 2019. Generating Video from Images. 10. Carl Vondrick, Hamed Pirsiavash, Antonio Torralba. Generating Videos with Scene Dynamics. 11. Radford, A., Metz, L., and Chintala, S. (2015). Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434. 12. Goodfellow, JeanPouget-Abadie, MehdiMirza, BingXu, DavidWarde-Farley, SherjilOzair, AaronCourville, YoshuaBengio. Generative Adversarial Nets 66. Authors: Ravishankar B S, Vijayendra V K, K.T.Veeramanju Evaluation of Distributed Generation Impact on Reliability of a Distribution System using Paper Title: DIgSILENT PowerFactory Abstract: As an effective supplement to the centralized fossil fuel based traditional generation, Distributed 381-389 Generation (DG) has become an effective alternative choice and has been rapidly increasing since past few years due to growing demand for electricity and the new policies of governing bodies for usage of green energy. In overall power system, distribution systems are more vulnerable to faults and reliability aspects of such systems becomes an important issue. With higher penetration of DG into the distribution network, it will be necessary to study the impact of such generation on the various aspects of distribution system. Thus, increase in rate of penetration DGs into the distribution system on one side and increased faults in distribution network on another side, will make the study of impact of DG integration on distribution system reliability an interesting topic of research. The present work focuses on evaluation of impacts of integration of such DGs on reliability of local distribution network, typically in an urban scenario By using the simulation method using DIgSILENT PowerFactory software, the impacts of integration of DG in terms of enhancement in distribution system reliability indices and reduction in system losses for different scenarios are studied and presented in this paper. Based on the simulation results obtained and after analysis of the distribution system, overall results are summarized by focusing on the installation of suitable capacity of DG and the location of DG which are important factors affecting the system losses and system reliability indices.
Keywords: Distributed Generation (DG), RBTS, Simulation, DIgSILENT PowerFactory. Radial distribution system.
References: 1. A.T.Mohamed, A.A.Helal, S.M.El safty, “Distribution System Reliability Evaluation in Presence of DG”, IEEE, 2019. 2. Ashutosh Barik, Srinivasalu G and Balakrishna P, “An effective Method of optimal DG location, Type and Size to Deal with Power System Constraints”, ICSETS, IEEE, 2019. 3. Sreevidya.L, S.U.Prabha and S.Sathiya, “Evaluation of the Reliability of Distribution System with Distributed Generation using ETAP”, International Journal of Soft Computing and Engineering (IJSCE), ISSN: 2231-2307, Volue-8 Issue-5, January 2019. 4. Mingze Zhang et al, “Reliability Evaluation of Distribution Power Unit Based on DG Power Contribution”, IEEE PES Innovative Smart Gird Technologies Asia, 2019. 5. K.Kirubarani, APeer Fathima, “Distribution System Reliability Assessment for Improved Feeder Configurations”, IEEE PES Innovative Smart Gird Technologies Asia, 2019. 6. Rudresh B.Magadum and D.B Kulkarni, “Optimal Location and Sizing of Multiple DG for Efficient Operation of Power System”, IEEE, 2018. 7. Mohamed Mostafa, Mostafa Elshahed, Mohamed S.Esobki, “The Impact of Distributed Energy Resources on the Reliability of Smart Distribution System”, Majlesi Journal of Electrical Engineering, Vol.12, No.4, December 2018. 8. Muhammad Zahid Kamaruzaman, Noor Izzri Abdul Wahab and Mohammad Nasrun Mohd Nasir, “Reliability Assessment of Power system with Renewable Source using ETAP”, Proceedings of the SMART-2018, IEEE conference ID: 44078, International conference on system modeling & advancement in research trends, November-2018. 9. J.Senthil kumar, P.Venkatesh, S.Charles Raja, J.Jeslin Drusila Nesamlar, C.Palanichamy, “Reliability Enhancement of Small and Medium Distribution System with Renewable Genertions and Reclosers”, IEEE, 2018. 10. Vikas Singh Bhadoria, Nidhi Singal Pal, Vivek Shrivastava and Shiva Pujan Jaiswal, “Reliability Improvement of Distribution system by Optimal Sitting and Sizing of Disperse Generation”, International Journal of Reliability, Quality and Safety Engineering, Vol.2, No.6 (2017) 1740006. 11. Juan Li, Honglian Zhou, Erbiao Zhou, Jingjie Xue, Zifa Liu and Xuyang Wang, “Comprehensive Evaluation of Impacts of High Penetration of Distributed Generation Integration in Distribution Network, IEEE, 2017. 12. Sanaullaha Ahmad, Azzam Ul Asar, Sana Sardar and Babar Noor, “Impact of Distributed Generation on the Reliability of Local Distribution System”, International Journal of Advanced Computer Science and Applications, Vol.8, No.6, 2017. 13. Hadi Suyono, Wiono, Rini Nur Hasanah and Syamsu Dhuba, “Power Distribution System Reliability Improvement due to Injection of Distributed Generation”, IEEE, 2017. 14. A.Ngaopitakkul, C.Jettanasen, “The Effects of Multi-Distributed Generator on Distribution System Reliability”, IEEE, 2017. 15. M.R.Siddappaji, Dr.K.Thippeswamy, “Reliability Indices Evaluation and Optimal Placement of Distributed Generation for Loss Reduction in Distribution System by using Fast Decoupled Method”, International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS-2017). 16. K. Prakash, F. R. Islam, K. A. Mamun, A. Lallu and M. Cirrincione, “Reliability of Power Distribution Networks With Renewable Energy Sources”, IEEE, 2017. 17. Fabian C.Oreke, D.C Idoniboyeobu, “Reliability Assessment of Electrical Energy Distribution System – A cas study of Port Harcourt Distribution Network”, IJRASET, Vol-5, Issue VI, June 2017. 18. Sanaullaha Ahmad, Sana Sardar, Babar Noor and Azzam Ul Asar, “Analyzing Distributed Generation Impact on the Reliability of Electric Distribution Network”, International Journal of Advanced Computer Science and Applications, Vol.7, No.10, 2016. 19. Ulas Eminoglu, Ridvan Uyan, “Reliability Analyses of Electrical Distribution System: A case study”, International Refereed Journal of Engineering and Science (IRJES), Vol.5, Issue 12, December-2016, PP.94-102. 20. Prabhjot Kaur, Sandeep Kaur and Rintu Khanna, “Optimal Placement and Sizing of DG Comparison of Different Techniques of DG Placement”, 1st IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES-2016). 21. V.S.S.Sailaja, Dr.P.V.N.Prasad, “Determination of Optimal Distributed Generation Size for Losses, Protection Co-Ordination and Reliability Evaluation Using ETAP”, Biennial International Conference on Power and Energy Systems:Towards Sustainable Energy (PESTSE), 2016. 22. Maad Al Owaifeer, Mohammad AlMuhaini, “Reliability Analysis of Distribution Systems with Hybrid Renewable Energy and Demand Side Management”, 13th International Multi-Conference on Systems, Signals & Devices, 2016. 23. P.Chandhra Sekhar, R.A.Deshpande, V.Sankar, “Evaluation and Improvement of Reliability Indices of Electrical Power Distribution System”, IEEE, 2016. 24. Noppatee Sabpayakon, Somporn Sirisumrannukul, “Power losses reduction and reliability improvement in distribution system with Very Small Power Producers”, Elsevier, 2016. 25. Vasilike Vita, Tareafa Alimardan and Lambros Ekonomou, “The Impact of Distributed Generation in the Distribution Networks’ Voltage Profile and Energy Losses”, IEEE European Modeling Symposium, 2015. 26. Rohit K.Mathew, Ashok.S and Kumaravel S, “Analyzing the Effect of DG on Reliability of Distribution Systems”, IEEE, 2015. 27. Oleboge K.P.Mokaka, Kehinde O.Awodele, “Reliability Evaluation of Distribution Networks Using NEPLAN & DIgSILENT Power Factory”, IEEE 2013. 28. Atthapol Ngaopitakkul et al, “A Reliability Impact and Assessment of Distributed Generation Integration to Distribution System”, Energy and Power Engineering, 2013, 5, 1043-1047, Scientific Research. 29. I.Waseem, “Reliability Benefits of Distributed Generation as a Backup source”, IEEE, 2009. 30. Kola Sampangi Sambaiah, “A Review on Optimal Allocation and Sizing Techniques for DG in Distribution Systems”, International Journal of Renewable Energy Research, Vol.8, No.3, 2018. 31. Prem Prakash, Dheeraj K. Khatod, “Optimal sizing and siting techniques for distributed generation in distribution systems: A review”, Renewable and Sustainable Energy Reviews 57 (2016) 111-130, Elseiver, 2015. 32. Pavlos S.Georgilakis and Nikos D. Hatziargyriou, “Optimal Distributed Generation Placement in Power Distribution Networks: Models, Methods and Future Research”, IEEE transactions on Power Systems, Vol.28, No.3, August-2013. 33. Andrew Keane, Luis F.Ochoa, Carmen L.T Borges and others, “State-of-the-Art Techniques and Challenges Ahead for Distributed Generation Planning and Optimization”, IsEEE Transaction on Power Systems, Vol.28, No.2, 2013. 34. Roy Billinton and Satish Jonnavithula, “A Test System For Teaching Overall Power System Reliability Assessment”, IEEE Transactions on Power Systems, Vol.11, No.4, November 1996. 35. R.N.Allan, R.Billinton and others, “A Reliability Test System for Educational Purposes – Basic Distribution System Data and Results”, IEEE Transactions on Power Systems, Vol.6, No.2, May-1991. Authors: Shitanshu Jain, S.C.Jain, Santosh Vishwakarma
Paper Title: Analysis of Text Classification with various Term Weighting Schemes in Vector Space Model Abstract: Term Weighting Scheme (TWS) is a key component of the matching mechanism when using the vector space model In the context of information retrieval (IR) from text documents, the this paper described a new approach of term weighting methods to improve the classification performance. In this study, we propose an effective term weighting scheme, which gives highest accuracy with compare to the text classification methods. We compared performance parameter of KNN and Naïve Bayes Classification with different Weighting Method, Weight information gain, SVM and proposed method.We have implemented many term-weighting methods (TWM) on Amazon data collections in combination with Information-Gain and SVM and KNN algorithm and Naïve Bayes Algorithm.
67. Keywords: Text Mining, Text Classification, Term Weighting, KNN, Naïve Bayes, SVM
References: 390-393 1. J. Han and M. Kamber, Data Mining- Concepts and Techniques, 3rd edition San Francisco,USA, Morgan Kaufmann, Boston, MA, USA, Elsevier, 2006. 2. Ramzan Talib, Muhammad Kashif Hanif, ShaeelaAyesha , and Fakeeha Fatima ,Text Mining Techniques, Applications and Issues, International Journal of Advanced Computer Science and Applications, Volume 7 No. 11, 2016. 3. Chauhan Shrihari and Amish Desai, A Review on Knowledge Discovery using Text Classification Techniques in Text Mining, International Journal of Computer Applications (0975 – 8887), Volume 111 , No 6, February 2015. 4. M. Balamurugan, E.Iyswarya, "A Trend Analysis of Information Retrieval Models" International Journal of Advanced Research in Computer Science, Volume 8, No. 5, May-June 2017. 5. Salton, G. , and Buckley, C. 1988, Term-weighting approaches in automatic text retrieval, Inf. Process. Manage, 24(5), 513–523. 6. Salton G. and Mc-Gill M, Introduction to Modern Information Retrieval, McGraw-Hill Book Company, New-York, NY, 1983. 7. S.Brindha , Dr. K.Prabha , Dr. S.Sukumaran, The Comparison Of Term Based Methods Using Text Mining, International Journal of Computer Science and Mobile Computing, IJCSMC, Volume-5, Issue.-9, September 2016, page 112 – 116. 8. Dey, Lopamudra, et al., Sentiment Analysis of Review Datasets Using Naive Bayes and K-NN Classifier, arXiv preprint arXiv- 1610.09982, (2016). Authors: Shazia Begum, B K Kolhapure Comparative Study on Progressive Collapse of An Irregular (Lshaped) Flat Slab Building by Linear Paper Title: Static Analysis using ETABS Abstract: Concrete and steel structures influences the construction of multi-storey structures. The aid of progressive collapse increases when there is a failure of one or more load bearing structural elements. Thereafter, this case study is carried out to determine the prospective of the progressive collapse of an irregular (L shaped) building due to the failure or removal of two adjacent columns at a time present in the ground floor. Failure may occur because of the natural or manmade accidental loads like explosion or seismic loads, collision of vehicles, etc. Columns at different locations were removed and the slab loads had been increased as per the General Services Administration (GSA) guidelines and the results in terms of Demand Capacity Ratios (DCR) are compared for all the cases. The Demand to Capacity Ratios were calculated for the interested columns. It is observed that when the interior columns were removed then the possibility of progressive collapse is more. This study has been made for the case or earthquake forces for corresponding zone II and zone V. 68. Keywords: Progressive collapse, demand capacity ratio, Column removal, ETABS. 394-401
References: 1. SaumiaMeenathethilAlex ,SreedeviLekshmi “Study of Progressive Collapse Analysis of Flat Slab Building” International Journal of Science and Research (IJSR) ISSN (Online): 2319-7064 2015 2. Mr. Muralidhara G. B , Mrs. Swathi Rani K. S , Mr. MeleseWorku “Seismic Parametric Study on Different Irregular Flat Slab Multi- Story Building” International Journal of Engineering Research & Technology (IJERT) Vol. 5 Issue 04, April-2016 3. Kevin A. Giriunas and HalilSezen “Progressive collapse analysis of an existing building” ohio state university May-2009 4. SewerynKokot, ArmelleAnthoine, Paolo Negro and Goergesolomos “Static and dynamic analysis of a reinforced concrete flat slab frame building for progressive collapse” AISC 40:205-217 July-2012 5. Mohamed zanjir, “Vulnerability of buildings with flat plates and flat slabs to progressive collapse” university of Ottawa April-2012. 6. Russell, Justin “Progressive collapse of reinforced concrete flat slab structures”. PhD thesis, University of Nottingham. July-2015 7. Yogesh T. Birajdar ,Dr. Nagesh L. Shelke “Progressive Collapse Analysis of Multi-Storied RCC Building”. IJSRST | Volume 3 | Issue 6 | Print ISSN: 2395-601 2017 Authors: Rahul S. Jain, Nikhil P.Wyawahare, Arpit Doshi
Paper Title: Detection of Component Assembly Error using Computer Vision: A Review Abstract: Object recognition (OR) is a main capability needed by most AI vision systems. The most recent R&D on this domain has been gaining incredible ground in numerous ways. OR has a variety of uses. In this paper we talk about applications of OR system in manufacturing industry. In recent era scenario increased level of process automation in production industry also demands process automation of quality examination with lesser human intervention.
Keywords: Detection System, Object Extraction, Object Recognition, Object counting, Deep Learning, Computer vision, AI, Machine Learning. 69. References: 1. R. Kirpan , Pooja I. Baviskar, Shivani D. Khawase, Anjali S. Mankar, Karishma A. Ramteke, “Object detection in rasberry pi” 402-405 International Journal of Engineering Science and Computing, Volume 7,issue 3,March 2017. 2. Abdul Vahab, Maruti S Naik, Prasanna G Raikar, Prasad S R “Applications of Object Detection System” International Research Journal of Engineering and Technology (IRJET) Volume: 06 Issue: 04 | Apr 2019. 3. “Object Recognition within Smart Manufacturing” 29th International Conference on Flexible Automation and Intelligent Manufacturing (FAIM2019), June 24-28, 2019, University of Limerick, Ireland. 4. Gianluca Giuffrida, Gabriele Meoni and Luca Fanucci “A YOLOv2 Convolutional Neural Network-Based Human–Machine Interface for the Control of Assistive Robotic Manipulators” Published: 31 May 2019. 5. Yang-Lang Chang , Amare Anagaw , Lena Chang , Yi Chun Wang , Chih-Yu Hsiao and Wei-Hong Lee. “Ship Detection Based on YOLOv2 for SAR Imagery” Published: 2 April 2019. 6. Nematullo Rahmatov, Anand Paul, Faisal Saeed, Won-Hwa Hong, HyunCheol Seo and Jeonghong Kim “Machine learning–based automated image processing for quality management in industrial Internet of Things” International Journal of Distributed Sensor Networks 2019, Vol. 15(10). 70. Authors: Sri Murni Dewi, Lilya Susanti, Ming Narto Wijaya,
Paper Title: Reactivity Index and Strength Development of High Strength Concrete with GGBFS Cement Abstract: The slag cement industry in Indonesia is growing in tandem with the smelter industry as a supplier 406-412 of slag material. The use of slag cement instead of ordinary cement can reduce CO2 emissions. This research aimed to design the mixture composition of slag cement and ordinary cement for high-strength concrete. Standard concrete cylinders and concrete beams were tested to gain the compressive, tensile and flexural strength. The testing results indicate that generally, the concrete mixture compositions of low GGBFS (25%) gained their optimum strength at the age of 28 days while concrete with high composition of GGBFS (55%) achievedsimilar strength at the age of 90 days.A mixture using higher percentage replacement of GGBFS might attain its optimum strength at the longer ages. The use of Silica Fume (SF) in high-strength concrete mixtures with GGBFS found ineffective to increasethe concrete strength as the results indicate that concretes with SF have lower strength compared with non-SF concrete mixtures.
Keywords: Binder reactivity, concrete ages, GGBFS, High-Strength Concrete (HSC), Silica Fume (SF), strength development.
References: 1. Wang SY, McCaslin E and White CE. "Effects of magnesium content and carbonation on the multiscale pore structure of alkali- activated slags." Cement and Concrete Research. 2020;130:105979. 2. Gong K and White CE. "Impact of chemical variability of ground granulated blast-furnace slags on the phase formation in alkali- activated slag pastes." Cement and Concrete Research. 2016; 89:310-319. 3. ASTM International. "ASTM C 989-04 Standard Specification for Ground Granulated Blast-Furnace Slag for Use in Concrete". 2004. 4. Velli VM. "High performance concrete with GGBFS and robo sand. International Journal of Engineering Science and Technology." 2010; 2(10):5107-5113. 5. Ravi Ch and Amudhavalli NK. "Study on high performance concrete using GGBFS and robosand (M50 grade)." International Journal of Civil Engineering and Technology.2018; 9(8): 551-561. 6. Venkatesan G and Tamizhazhagan T. "Ultra high strength concrete." International Journal of Innovative Research in Science Engineering and Technology.2016; 5(3): 4412-4418. 7. Veena P, Ramakrishna Nand Rao S. "A study on compatibility of super plasticizers with GGBS blended cement concrete using OPC 53- S." International Journal of Engineering Trends and Technology.2017; 50(3):173-179. 8. Valeti C, Sumukh C and Vasugi V. "Compatibility assessment of commercial cements and superplasticizers." International Journal of Scientific & Engineering Research 2017; 8(6):17-22. 9. Lee SY et. al. "Predicting compressive strength development of concrete with GGBFS using chemical reaction rate." https://www.researchgate.net/. 10.Yang HM. "Evaluation of strength development in concrete with ground granulated blast furnace slag using apparent activation energy." Materials.2020; 13(442). 11.Ramesh RL, Nagaraja PS and Deepa K. "Strength of high performance concrete with GGBS." International Journal of Engineering and Techniques 2018; 4(3):117-122. 12.Sai PP. "A review of ternary blended hybrid fibre reinforced concrete." International Journal of Civil Engineering and Technology 2018; 9(1):314-319. 13.Dewi SM, Susanti L and Suseno H. "Effects of GGBFS to the compressive strength, workability and time span between mixing and compacting of concrete paste ." International Journal of Civil Engineering and Technology.2019; 10(3):1404-1412. 14.Lu L and Ouyang D. "Properties of cement mortar and ultra-high strength concrete incorporating graphene oxide nanosheets." Nanomaterials. 2017;7 (187):7070187. 71. Authors: M. Udaya, D. Sony, D. Krishna Reddy
Paper Title: Design of IRNSS Tracking System using 1.5 bit ADPLL and Correlator Abstract: IRNSS is an indigenous satellite navigation system consisting of 7 satellites, that provide accurate 413-420 positioning in the Indian sub-continent region. Each IRNSS satellite transmits a signal which contains information regarding satellite orbital and clock parameters (known as navigation message). The purpose of the receiver is to demodulate the satellite signal and extract navigation message, the receiver must know certain parameters of the signal like its doppler shift and code offset. However, in real-time, due to relative velocity of the satellite and ionospheric interference, these parameters vary with time. Therefore, the receiver must continuously perform the tracking operation to update the varying parameters. Existing tracking systems are based on SDR and SoC’s, which require high-performance processors and iterative algorithms to perform both carrier and phase tracking. Though they are highly accurate, these designs are complex and expensive. In this paper, 1.5-bit ADPLL is used to track the carrier. This design does not require numerous computational loops to perform tracking of the carrier, thus reducing the complexity of the design. This work includes simulation results for 1.5-bit ADPLL. In this work, 2-bit, 1.5-bit, and modified 1.5-bit correlators are simulated and synthesized. It was found that modified 1.5-bit correlator architecture is less complex compared to 2-bit correlator and offers better SNR compared to 1.5-bit correlator. Therefore, modified 1.5-bit correlator is used for code tracking. The IRNSS signal tracking is performed in ModelSim. The system utilizes 77 standard LUTs and exhibit maximum settling time of 714µs and 31.28ms for carrier tracking and code tracking, respectively.
Keywords: Receiver, ADPLL, correlator, satellite navigation.
References: 1. M. Braasch and A. Dempster, "Tutorial: GPS receiver architectures, front-end and baseband signal processing," in IEEE Aerospace and Electronic Systems Magazine, vol. 34, no. 2, pp. 20-37, Feb. 2019.W.-K. Chen, Linear Networks and Systems (Book style). Belmont, CA: Wadsworth, 1993, pp. 123–135. 2. Dr. S. Muthukumar, M. K. Subramaniya Raman, D Jagadeeshwaran, V Saveetha, 2020, Signal Acquisition of IRNSS Signals, International Journal Of Engineering Research & Technology (IJERT) Volume 09, Issue 06 (June 2020). 3. D.J.R.Van Nee and A.J.R.M. Coenen, "New Fast GPS code-acquisition technique using FFT," in Electronics Letters, vol. 27, no. 2, pp. 158-160, 17 Jan. 1991. 4. A.R.Yashaswinia P.Siva Nagendra Reddy G.N.Kodanda Ramaiahb “Generation and implementation of IRNSS Standard Positioning Signal” Engineering Science and Technology, an International Journal Volume 19, Issue 3, September 2016, Pages 1381-1389. 5. E. Schmidt, D. Akopian and D. J. Pack, "Development of a Real-Time Software-Defined GPS Receiver in a LabVIEW-Based Instrumentation Environment," in IEEE Transactions on Instrumentation and Measurement, vol. 67, no. 9, pp. 2082-2096, Sept. 2018, doi: 10.1109/TIM.2018.2811446. 6. E. Wang, S. Zhang, Q. Hu, J. Yi and X. Sun, "Implementation of an Embedded GPS Receiver Based on FPGA and MicroBlaze," 2008 4th International Conference on Wireless Communications, Networking and Mobile Computing, Dalian, 2008, pp. 1-4, doi: 10.1109/WiCom.2008.335. 7. S. B. Totad, K. S. Rakesh, N. N. Nagendra and M. Sowmya, "Design and simulation of tracking loops for IRNSS receiver in SIMULINK®," 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS), Chennai, 2017, pp. 1548-1553, doi: 10.1109/ICECDS.2017.8389705 8. Borre, K., Akos, D.M., Bertelsen, N., Rinder, P., Jensen, S.H. “A Software-Defined GPS and Galileo Receiver- A Single-Frequency Approach” 2007, 978-0-8176-4540-3. A. K. Chaudhary and M. Kumar, "Design and implementation of ADPLL for Digital communication applications," 2017 2nd International Conference for Convergence in Technology (I2CT), Mumbai, 2017, pp. 397-401, doi: 10.1109/I2CT.2017.8226159. 9. Hongwei Zhou1, Tian Jin2, and Fangyao Lü2 &ldquo “Design of 1.5 bit quantization correlator in satellite navigation software receiver” rdquo; Journal of Systems Engineering and Electronics Vol. 27, No. 2, April 2016, pp. 449 − 456. 10. J. Tao, W. Yu. “A Real-time GPS software receiver correlator design for embedded platform”. Proc. of Institute of Navigation Global Navigation Satellite Systems Conference, 2011: 808−812. 11. Y. Yang, X. Ba and J. Chen, "A Novel VLSI Architecture for Multi-Constellation and Multi-Frequency GNSS Acquisition Engine," in IEEE Access, vol. 7, pp. 655-665, 2019. 12. Xilinx “MicroBlaze Processor Reference Guide” UG984 (v2017.3) October 4, 2017. Authors: Sanjitha M, Sri Lakshmi J, S Sameeksha, Ravi V
Paper Title: A Predictive Classification Method for Email Phishing Attacks using Random Forest and A-R trees Abstract: Cyber-attacks are the attempts made by an individual or an organization deliberately, to breach the information system mainly computers of another individual or organization. These attacks have risen in recent years due to various reasons posing the need for systems that can use adaptive learning techniques to detect and mitigate these attacks at an early stage. Phishing is one of the significant cyber-attacks. According to global security report 2019, phishing was the major cause of attacks in corporate networks. Phishing attack uses disguised email to achieve its goal. In this attack, attacker masquerade himself as a trusted individual or a company and trick the email recipient into clicking malicious links or attachments. The proposed method provides a testbed for detecting and mitigating various types of phishing attacks. Machine learning techniques are used to build an intelligent system which can detect phishing attacks. This application uses random forest algorithm with AR-Trees (acceptance-rejection tree algorithm) to determine the attacks by considering various datasets available online and new datasets dynamically constructed for making the system ready to mitigate future phishing attacks.
Keywords: AR-Trees, Random Forest.
References: 72. 1. Gupta BB, Tewari A, Jain AK, Agrawal DP. Fighting against phishing attacks: state of the art and future challenges. Neural ComputAppl. 2017;28(12):3629–54. 2. The Phishing Guide Understanding & Preventing Phishing Attacks By: Gunter Ollmann, Director of Security Strategy, IBM Internet Security Systems, 2007 421-424 3. Phishing: Cutting the Identity Theft Line Published by Wiley Publishing, Inc. 10475 Crosspoint Boulevard Indianapolis, IN 46256 www.wiley.com, 2005, Rachael Lininger and Russell Dean Vines. 4. Anti-Phishing Working Group (APWG), “Phishing activity trends report—first quarter 2013. http://antiphishing.org/reports/apwgtrendsreportq12013.pdf, accessed September 2014 5. Pierluigi Paganini (2014) Phishing: a very dangerous cyber threat. http://resources.infosecinstitute.com/phishing-dangerous-cyber- threat/2012. Accessed on Sept 2014 6. M. Khonji, Y. Iraqi, and A. Jones, “Phishing detection: a literature survey,” IEEE Communications & Surveys Tutorials, vol. 15, no. 4, pp. 2091–2121, 2013. View at: Publisher Site | Google Scholar 7. Adwan Yasin and Abdelmunem Abuhasan.An intelligent classification model for phishing email detection,2016. 8. Srishti Rawal, Bhuvan Rawal, Aakhila Shaheen and Shubam Malik .Phishing detection in emails using machine learning.International Journal of Applied Information Systems,12:21-24,10 2017. 9. https://web.cs.hacettepe.edu.tr/~selman/phish-iris-dataset/ 10. https://www.phishtank.com/developer_info.php 11. Andronicus A. Akinyelu and Aderemi O. Adewumi. Classification of phishing email using random forest machine learning technique. Journal of Applied Mathematics, 2014:425731, Apr 2014. 12. Calhoun, P., Hallett, M.J., Su, X. et al. Random forest with acceptance–rejection trees. Comput Stat (2019). https://doi.org/10.1007/s00180-019-00929-4 13. https://github.com/pcalhoun1/AR-Code 14. https://sites.google.com/site/aslugsguidetopython/data-analysis/pandas/calling-r-from-python 15. https://research.aalto.fi/en/datasets/phishstorm-phishing-legitimate-url-dataset 16. Muhammet Baykara and Zahit Ziya Gürel. Detection of phishing attacks. 2018 6th International Symposium on Digital Forensic and Security (ISDFS). 73. Authors: Sonali. C. Rangari, Gunwanti. S. Shende, Mohan. M. Renge
Paper Title: CMOS-NAND Based Gate Driver Card of IGBT for Fault Diagnosis in VSI Abstract: In industrial application, over loading condition, short circuit is the dominant fault in the motor 425-428 drives. There are many reasons for the fault but due to this insulated gate bipolar transistor (IGBT) gets damaged which affect the whole system. Protecting IGBT and hence protecting motor drive system at fault condition is the crucial and insistent part of the protection. The short circuit and open circuit fault in an IGBT can be detected by several techniques. The paper presents an idea to deals with the short circuit fault such as fault under over load condition in the typical applications like winder machine. In this paper both the fault diagnosis and clearance of the fault are done. Fault diagnosis is based on the voltage between the collector and the emitter (VCE) of the IGBT which can be done by actual short circuit between these points in the simulation. Voltages of healthy and faulty condition are analyzed. In the fault clearance, CMOS-NAND based circuitry is used called EH10 card for inverter. It shows that this card has ability to detect the fault across the switch and give the signal to microcontroller for its clearance by making GATE signal of the IGBTs zero. This protection of Inverter using realization of CMOS-NAND based GATE driver card is verified using MATLAB simulation results.
Keywords: Voltage Source Inverter, Short Circuit Fault, IGBT fault Diagnosis, EH10 card
References: 1. E. Flores, A. Claudio, J. Aguayo, L. Hernández “Fault Detection Circuit Based on IGBT Gate Signal”IEEE LATIN AMERICA TRANSACTIONS, VOL. 14, NO. 2, FEB. 2016 2. Vijay Bolloju,Jun Yang, "Influence of Short Circuit conditions on IGBT Short circuit current in motor drives," 978-1-4244-8085- 2/11/$26.00 ©2011 IEEE 3. Marjan Alavi1. Ming Luo, Danwei Wang, Haonan Bai,"IGBT Fault Detection for Three Phase Motor Drives using Neural Networks ", IEEE conference 2016 4. Fuhrmann, J., Eckel, H.-G., & sebstian Klauke, “Short-circuit behavior of series-connected high-voltage IGBTs”, 2016 18th European Conference on Power Electronics and Applications (EPE’16 ECCE Europe). doi:10.1109/epe.2016.7695445 5. Chuannuo, X., Xuezhen, C., & Tongqing, Z ”An Analysis Model and Test Monitoring Device for IGBT Intermittent Fault”, 2020 5th International Conference on Control and Robotics Engineering (ICCRE). doi:10.1109/iccre49379.2020.9096472 6. Min-Sub Kim, Byoung-Gun Park, Rae-Young Kim, and Dong-Seok Hyun “A Novel Fault Detection Circuit forShort-circuit Faults of IGBT”, 978-1-4244-8085-2/11/$26.00 ©2011 IEEE pp- 359-363, 2011. 7. Salvatore Musumeci, Rosario Pagano, Angelo Raciti, Leonard0 Fragapane, Maurizio Melito " Experimental Investigation On The Short Circuit Behaviors of Robust IGBT Devices " Fourth IEEE International Caracas Conference on Devices, Circuits and Systems, Aruba, April 17-19, 2002 8. Bhalla, S. Shekhawat, J. Gladish, J. Yedinak, G. Dolny “IGBT Behavior During Desat Detection and Short Circuit Fault Protection” Proceedings of 1998 International Symposium on Power SemiconductorDevices & ICs, Kyoto. 9. S. Mohsenzade, M. Zarghany, and S. Kaboli “A Series Stacked IGBT Switch with Robustness against Short Circuit Fault for Pulsed Power Applications”DOI 10.1109/TPEL.2017.2712705, IEEE Transactions on Power Electronics. 10. Rahul Chokhawala and Guissepe Castino,”IGBT fault current limiting circuit”, IEEE industry magazine September/October 1995 Authors: Santosh E, Anguraja R
Paper Title: Adaptable SVC Design Methodology for Power Quality Improvements in Steel Melting Shops Abstract: In 2019, India was the second-largest steel producer with total crude steel production of 112.3 metric ton [12]. There were lots of development actions taken in the starting of 90’s to promote more investments on producing steel and making it a bigger industry supporting country’s economy. Even though large amount of produced steel is utilized within the country for infrastructure, automobile and other consumable industries, still India is the seventh-largest exporter of steel. Also, Steel industries are not new to India. The oldest was TISCO and it started its production in 1907. Being said that, we have come long way in technology and science that all the steel plants need to be modernized and adapted to become more efficient, economical and productive. This paper presents one of such technology that being developed in the modern engineering word to make it adaptable in the steel industries where – Efficiency, energy consumption, quality and production can be improved significantly.
74. Keywords: steel melting shop, SVC, Harmonics, Passive filters.
References: 429-432 1. IEEE-519-1992: Recommended Practices and Requirements for Harmonic Control in Electrical Power Systems. 2. IEEE Std 18.-2002, IEEE Standard for Shunt Power Capacitors. 3. IEEE Std 1531-2003 - IEEE Guide for Application and Specification of Harmonic Filters. 4. IEEE Std 1031-2011 - IEEE Guide for the Functional Specification of Transmission Static Var Compensators. 5. IEEE Std 1303-1994 - IEEE Guide for Static Var Compensator Field Tests. 6. Circutor TM - AR6 – User manual in English. 7. H. A. Kazem, “Harmonic mitigation techniques applied to power distribution networks,” Advances in Power Electronics, 2013, 1-10, 2013. 8. Power Systems Harmonics Fundamentals, Analysis and Filter Design, Springer, 2001. 9. H. Akagi. “New trends in active filters for power conditioning”, IEEE Trans. on Industry Applications, vol. 32, pp. 1312-1322, (1996). 10. L. S. Czarnecki. “An overview of methods of harmonic suppression in distribution systems”, in Proc. of the IEEE Power Engineering Society Summer Meeting, vol. 2, pp. 800-805, (2000). 11. Harmonics and Power System, Francisco C. De LA ROSA, Distribution Control System, Inc, Hazelwood, Massouri, USA 75. Authors: Saranya Saetang
Paper Title: User-Centered Design for an Online Learning Abstract: Online learning has been studied for a long time. It has many benefits and challenges. In the period of 433-436 COVID-19 spreading, many universities and schools have more concerns for their students of the virus infection. Thus, the online classroom has been set to be a teaching method for students. However, it is quite a new normal practice, especially in Thailand, to have an online instead of a face-to-face classroom. Therefore, this study is a path for preparing to conduct online learning. This study aims to provide guidelines to design effective online learning based on students' opinions. By analyzing the open-end questionnaires with 37 participants, the results reveal that blended learning is the most preferred learning pedagogical approach. Moreover, the metaphor factors, including subjects' characteristic, class period, class size, activities and assessment that were suitable for online learning according to the students' opinion, were suggested. Finally, some obstacles that students have faced in online learning were presented, and some solutions were proposed
Keywords: privacy invasion, reaction, technology, Thai students
References: 1. C. R. Graham, "Blended learning systems: Defnition, current trends and future directions," In Handbook of blended learning: Global Perspectives, local designs. San Francisco, CA: Pfeiffer,2006. 2. H. Kanuka, C. Brooks, and N. Saranchuck, "Flexible learning and cost effective mass offerings," The Improving University Teaching (IUT) Vancouver CA, 2009. 3. A.D. Dumford and A.L. Miller, "Online learning in higher education: Exploring advantages and disadvantages for engagement", Journal of Computing in Higher Education, 30 (3), 2018, pp. 452-465. 4. K. L Milheim, "Facilitation across cultures in the online classroom," International Journal of Learning, Teaching and Educational Research, 5(1), 2014. 5. H. Amro, G. Maxwell, and L. Kupczynski, "Faculty Perceptions of Student Performance in the Online Classroom", E-Learning and Digital Media 10(3),2013, pp. 294-304. 6. H. Wei and C. Chou, "Online learning performance and satisfaction: Do perceptions and readiness matter?," Distance Education,41(1),2020, pp. 48–69. 7. Sun and X. Chen, "Online education and its effective practice: A research review", Journal of Information Technology Education: Research, 15, 2016, pp.157–190. 8. Poudel, "Blended Modality: A Choice of the Students in Higher Education," The European Journal of Open, Distance and E-Learning, 23, 2020. 9. Abras, D. Maloney-Krichmar, J. Preece, "User-Centered Design," In Bainbridge, W. Encyclopedia of Human-Computer Interaction. Thousand Oaks: Sage Publications, 2004. 10. A. Norman, "Cognitive engineering," In D. A. Norman and S. W. Draper (eds) user-centred Systems Design, Hillsdale, NJ: Lawrence Erlbaum Associates Inc., 1986. 11. K. Eason, Information technology and organizational change. London: Taylor and Francis, 1987. 12. J. Pearson, G. Buchanan, and H. Thimbleby, "HCI design principles for ereaders", Proceedings of Books Online: '10 Proceedings of the Third Workshop on Research Advances in Large Digital Book Repositories and Complementary Media, vol. 10, ACM, NY, 2010, pp. 15‐24. Authors: Sriram S, Anguraja R
Paper Title: Selective Coordination and its impact on Low-Voltage System Design & Arc Flash Abstract: This paper focus on total selective coordination of low voltage systems for critical facilities and based on reliability requirements. Critical facilities which include Data Centres, Health Care Facilities & Emergency Systems. It also discussed the importance of achieving total selective coordination and the impact on network design and how it is related to arc flash incident energy. It also states the National Electric Code requirements for the implementation of selective coordination based on system reliability requirements. The difficulties in achieving selectivity from Grid side and an inhouse generation side and the reliability benefits on critical facilities are discussed.
Keywords: Selectivity, Dynamic Impedance, ATS, Critical Facility, NEC, Coordination, TCC, bolted fault current, Electrode configuration, Arc flash.
References: 1. A. D. Thomas, M. G. Aidan And J. N. Charles, “Selective Coordination Versus Arc Flash - The Great Debate And Update,” Ieee, 2008. 2. “Nfpa 70,” In National Electric Code, Nfpa, 2017, P. 44. 3. Ed Larsen And J. Degnan, “Selective Coordination In Lv Power Distribution System. Is This Level Important?,” In Ieee/Ias Industrial And Commercial Power Systems Technical Conference, Clearwater Beach, Fl, Usa, 2008. 4. Ed Larsen, “A New Approach To Low-Voltage Circuit Breaker Short-Circuit Selective Coordination - Ed Larsen,” In 2008 Ieee/Ias Industrial And Commercial Power Systems Technical Conference, Clearwater Beach, Fl, Usa, 2008. 76. 5. S. Yong, T. Michalak, “Optimizing System Coordination And Overcurrent Protection With Zone Selective Interlocking,” In Annual Ieee Conference On Textile, Fiber And Film Industry, Atlanta, Ga, Usa, Usa, 1990. 6. Gary H. Fox, “Methods For Limiting Arc Flash Hazards While Maintaining System Selectivity,” In 2010 Ieee-Ias/Pca 52nd Cement 437-440 Industry Technical Conference, Colorado Springs, Co, Usa, 2010. 7. R. Kumar, D. Reed, R. Morris And S. Terry, “Higher Withstand Mcc For Better Selective Coordination,” In Conference Record Of 2010 Annual Pulp & Paper Industry Technical Conference, San Antonio, Tx, Usa, 2010. 8. “Ieee Recommended Practice For Protection And Coordination Of Industrial And Commercial Power Systems,” Industrial & Commercial Power Systems Standards Development Committee, 001. 9. “Ieee Std 241-1990, Recommended Practice For Electric Power Systems In Commercial Buildings,” Ieee Industry Applications Society, Pp. 330, 348-378. 10. “Ieee Std 446-1995, Recommended Practice For Emergency And Standby Power Systems For Industrial And Commercial Applications,” Ieee Industry Applications Society, Pp. 68-72. 11. “Ul 489 - Molded-Case Circuit Breakers, Molded-Case Switches, And Circuit-Breaker Enclosures,” No. 13, 2016. 12. “Ansi C37.50-2012,” National Electrical Manufacturers Association, 2012. 13. “Digestplus Online Catalog,” 2020. [Online]. Available: Https://Www.Schneider-Electric.Us/En/Work/Support/Resources-And-Tools/ Digestplus/. [Accessed 1 March 2020]. 14. “Selectivity Guidelines For Square D™ Panelboards,” 10 01 2016. [Online]. Available: Https://Www.Se.Com/Us/En/Download/Document/0100db0604/. [Accessed 1 March 2020]. 15. “Short Circuit Selective Coordination For Low Voltage Circuit Breakers,” 29 November 2016. [Online]. Available: Https://Www.Se.Com/Us/En/Download/Document/0100db0501/. [Accessed 1 March 2020]. 16. “Ieee Std 1100-2005, Recommended Practice For Powering And Grounding Electronic Equipment,” Ieee Industry Applications Society, 2005. 17. “Ieee Std 81-1962, Recommended Guide For Measuring Ground Resistance And Potential Gradients In The Earth,” 1962. 18. “Ieee Std 1584-2002, Guide For Performing Arc Flash Hazard Calculations,” 2002. 19. “Ieee 1584 - Guide For Performing Arc-Flash Hazard Calculations,” 2018. 20. “Recommended Practice For Electrical Equipment Maintenance,” Nfpa 70b, 2019. 21. “Standard For Electrical Safety In The Workplace,” Nfpa 70e, 2018. 77. Authors: Kavya K, Savita K Shetty
Paper Title: Classification of Breast Cancer Histopathology Images using Machine Learning Algorithms Abstract: Machine Learning (ML), provides system the capacity to learn instinctively and allows systems to 441-444 improve themselves with past experience and without being programmed specifically. In the field of Medical Science, ML plays important role. ML is being used to develop new practices in medical science which deals with huge patient data. Breast Cancer is a chronic disease commonly diagnosed in women. According to the survey by WHO, rank of breast cancer is at number one as compared to other cancers in female. BC has two kinds of tumour: Benign Tumour (BT), and Malignant Tumour (MT). BTs are treated as non-cancerous cells. MTs are treated as cancerous cells. The unidentified MTs in time stretch to other organs. Treatment procedure for BT and MT is different. So, it is salient to determine precisely whether a tumour is BT or MT. In this proposed model, Histopathology Images are used as dataset. These Histopathology images are pre- processed using Gaussian Blur and K-means Segmentation. The pre-processed data fed into feature extraction model. ML algorithms such as Support Vector Machine (SVM), Random Forest (RF) and Convolution Neural Network (CNN) are applied to extracted features. Performance of these algorithms is analysed using accuracy, precision, recall and F1-score. CNN gives the highest accuracy with 87%.
Keywords: Machine Learning (ML), Breast Cancer (BC), histopathology.
References: 1. International Agency of Research on Cancer, Available: https://www.iarc.fr/cards_page/iarc-research/ 2. Sara Laghmati, Machine Learning based System for Prediction of Breast Cancer Severity, 2018. 3. Dana Bazazch, Comparative Study of Machine Learning Algorithms for Breast Cancer Detection and Diagnosis, 2019. 4. Ayush Sharma, Sudhanshu Kulshrestha, Sibi Daniel, Machine Learning Approaches for Breast Cancer Diagnosis and Prognosis, 2017. 5. PanuwatMekha, NutnichaTeeyasuksaet, Deep Learning Algorithms for Predicting Breast Cancer Based on Tumor Cells, 2019. 6. Zhan Xiang, Breast Cancer Diagnosis from Histopathological Image based on Deep Learning, 2019. 7. Yuqian Li, Classification of Breast Cancer Histology Images Using Multi-Size and Discriminative Patches Based on Deep Learning, 2019. 8. Asoke Nath, Image Denoising algorithms: A comparative study of different filtration approaches used in image restoration‖, International conference on communication systems and network Technologies, 2013. 9. Sukhjinder Kaur, Noise Types and Various Removal Techniques, International Journal of Advanced Research in Electronics and Communication Engineering (IJARECE) Volume 4, Issue 2, February 2015. 10. H.P. Ng, S.H. Ong, K.W.C. Foong1, P.S. Goh, W.L. Nowinski, Medical Image Segmentation Using K-means Clustering and improved Watershed Algorithm, 2006. 11. K. P. Murphy, Machine Learning: A Probabilistic Perspective. Adaptive Computation and Machine Learning. Cambridge, Mass.: MIT Press, 2012. 12. International Agency for Research on Cancer (IARC) and World Health Organization (WHO). GLOBOCAN 2018: Age standardized (World) incidence and mortality rates, breast. [Online]. Available: https://gco.iarc.fr/today/data/factsheets/cancers/20-Breast- factsheet.pdf 13. Youness Khourdifi, Mohamed Bahaj: Applying Best Machine Learning Algorithms for Breast Cancer Prediction and Classification, 2018 78. Authors: L. Manjunatha, B. V. Sachin, H. Sharada Bai Ductility Behaviour of Rectangular Compression Members Retrofitted by Modified Technique of Paper Title: FRP Wrapping Abstract: This paper presents an experimental investigation on ductility behaviour of reinforced concrete 445-452 compression members, rectangular in cross section, modified to elliptical shape in cross section by bonding precast segment covers followed by Carbon Fiber Reinforced Polymer wrapping (CFRP) under concentric and eccentric loading conditions. Eighteen reinforced concrete rectangular compression members of size 100mm×150mm in cross section and 300mm in height were prepared using normal-strength concrete. Reinforcement ratio was kept at minimum, to simulate compression members that need retrofitting. Out of eighteen specimens, nine specimens were converted to elliptical shape in cross section. From nine remaining rectangular specimens, three specimens retained as it is without wrapping FRP and designated as Group1, remaining six specimens were wrapped with one and two layers of CFRP and designated as Group2. Out of nine elliptical specimens, three specimens were retained as it is without wrapping FRP and designated as Group3, remaining six elliptical specimens were wrapped with one and two layers of CFRP and designated as Group4. Specimens were tested upto failure under monotonic axial compression with concentric and eccentric load conditions. From the experimental results, it is observed that rectangular compression members shape modified to ellipse in cross section and then wrapped with CFRP show outstanding increase in the ultimate load carrying capacity which may be due to increased cross sectional area and effective confinement of FRP wrapping. As the number of layers of CFRP increases the ultimate load carrying capacity increases. With increase in eccentricity, the ultimate loads of the compression members were found to be decreased. Elliptical specimens wrapped with one and two layers of CFRP reported exponential increase in deformation ductility under concentric load condition and considerable increase under eccentric load condition compared to rectangular specimens wrapped with CFRP.
Keywords: Compression Members, CFRP Wrapping, Precast Segments, Elliptical Columns
References: 1. Chastre, C., & Silva, M. A. G. (2010). Monotonic axial behavior and modelling of RC circular columns confined with CFRP. Engineering Structures, 32(8), 2268–2277. https://doi.org/10.1016/j.engstruct.2010.04.001 2. Eid, R., & Paultre, P. (2017). Compressive behavior of FRP-confined reinforced concrete columns. Engineering Structures, 132, 518– 530. https://doi.org/10.1016/j.engstruct.2016.11.052 3. Hadi, M. N.S. (2006). Behaviour of FRP wrapped normal strength concrete columns unde eccentric loading. Composite Structures, 72(4), 503–511. https://doi.org/10.1016/j.compstruct.2005.01.018 4. Hadi, M. N.S. (2007). Behaviour of FRP strengthened concrete columns under eccentric compression loading. Composite Structures, 77(1), 92–96. https://doi.org/10.1016/j.compstruct.2005.06.007 5. Hadi, Muhammad N.S. (2006). Comparative study of eccentrically loaded FRP wrapped columns. Composite Structures, 74(2), 127– 135. https://doi.org/10.1016/j.compstruct.2005.03.013 6. Hadi, Muhammad N.S. (2009). Behaviour of eccentric loading of FRP confined fibre steel reinforced concrete columns. Construction and Building Materials, 23(2), 1102–1108. https://doi.org/10.1016/j.conbuildmat.2008.05.024 7. Hadi, Muhammad N.S., Jameel, M. T., & Sheikh, M. N. (2017). Behavior of Circularized Hollow RC Columns under Different Loading Conditions. Journal of Composites for Construction, 21(5). https://doi.org/10.1061/(ASCE)CC.1943-5614.0000808 8. Hadi, Muhammad N.S., Pham, T. M., & Lei, X. (2013). New method of strengthening reinforced concrete square columns by circularizing and wrapping with fiber-reinforced polymer or steel straps. Journal of Composites for Construction, 17(2), 229–238. https://doi.org/10.1061/(ASCE)CC.1943-5614.0000335 9. Harries, K. A., & Carey, S. A. (2003). Shape and “gap” effects on the behavior of variably confined concrete. Cement and Concrete Research, 33(6), 881–890. https://doi.org/10.1016/S0008-8846(02)01085-2 10. Ilki, A., Peker, O., Karamuk, E., Demir, C., & Kumbasar, N. (2008). FRP retrofit of low and medium strength circular and rectangular reinforced concrete columns. Journal of Materials in Civil Engineering, 20(2), 169–188. https://doi.org/10.1061/(ASCE)0899- 1561(2008)20:2(169) 11. Lin, G., & Teng, J. G. (2017). Three-Dimensional finite-element analysis of FRP-Confined circular concrete columns under eccentric loading. Journal of Composites for Construction, 21(4), 1–15. https://doi.org/10.1061/(ASCE)CC.1943-5614.0000772 12. Luca, A. De, Asce, M., Nanni, A., & Asce, F. (2011). Single-Parameter Methodology for the Prediction of the Stress-Strain Behavior of FRP-Confined RC Square Columns. 15(3), 384–392. https://doi.org/10.1061/(ASCE)CC.1943-5614.0000179. 13. Parvin, A., & Schroeder, J. M. (2008). Investigation of eccentrically loaded CFRP-confined elliptical concrete columns. Journal of Composites for Construction, 12(1), 93–101. https://doi.org/10.1061/(ASCE)1090-0268(2008)12:1(93) 14. Pessiki, B. S., Harries, K. A., Kestner, J. T., Sause, R., & Ricles, J. M. (2001). Axial Behavior of Reinforced Concrete Columns Confined With FRP Jackets. Journal of Composites for Construction, 5(November), 237–245. 15. Pham, T. M., Doan, L. V., & Hadi, M. N. S. (2013). Strengthening square reinforced concrete columns by circularisation and FRP confinement. Construction and Building Materials, 49, 490–499. https://doi.org/10.1016/j.conbuildmat.2013.08.082 16. Saleem, S., Hussain, Q., & Pimanmas, A. (2017). Compressive Behavior of PET FRP-Confined Circular, Square, and Rectangular Concrete Columns. Journal of Composites for Construction, 21(3). https://doi.org/10.1061/(ASCE)CC.1943-5614.0000754 17. Teng, J. G., & Lam, L. (2002). Compressive behavior of carbon fiber reinforced polymer-confined concrete in elliptical columns. Journal of Structural Engineering, 128(12), 1535–1543. https://doi.org/10.1061/(ASCE)0733-9445(2002)128:12(1551) 18. Teng, J. G., Wu, J. Y., Casalboni, S., Xiao, Q. G., & Zhao, Y. (2016). Behavior and modeling of fiberreinforced polymer-confined concrete in elliptical columns. Advances in Structural Engineering, 19(9), 1359–1378. https://doi.org/10.1177/1369433216642122 19. Wang, D. Y., Wang, Z. Y., Smith, S. T., & Yu, T. (2016). Size effect on axial stress-strain behavior of CFRP-confined square concrete columns. Construction and Building Materials, 118, 116–126. https://doi.org/10.1016/j.conbuildmat.2016.04.158 20. Yan, Z., Pantelides, C. P., & Duffin, J. B. (2011). Concrete column shape modification with FRP and expansive cement concrete. Advances in FRP Composites in Civil Engineering - Proceedings of the 5th International Conference on FRP Composites in Civil Engineering, CICE 2010, 824–828. https://doi.org/10.1007/978-3-642-17487-2_181 21. Zeng, J. J., Guo, Y. C., Gao, W. Y., Li, J. Z., & Xie, J. H. (2017). Behavior of partially and fully FRP-confined circularized square columns under axial compression. Construction and Building Materials, 152, 319–332. https://doi.org/10.1016/j.conbuildmat.2017.06.152
Authors: Qistina Donna Lee Abdullah, Aimuni Athirah Binti Latif
Paper Title: Variables Analysis of Tourism Apps Development in Influencing Tourist Travel Experience Abstract: Dumping of apps that emphasized on how to develop tourism industry and help travel organization to promoting their business. However, every app has own advantages and disadvantages. Producing an apps need a properly research about needs and demands of tourist or traveller itself. The tourism apps have its own influence towards tourists when it’s come to their satisfaction of travelling. Producing of tourism apps can be expansion if the producer increases the requirements, by doing a research of tourist’s needs and demand. Every tourist has their own needs and demands that are influenced by their preference, identity and family background. These factors are developed from many criteria, such as their culture which influence their identity, and their power of buying which influences their taste and preferences. Tourism industry is a wide field, before certain products are produced and certain services are offered, the producers need to analyse their target market. Thus, the success of a tourism App depends on how well and deep the producer has managed to explore and study about the target users. Research that linked between product and user needs on tourism apps are a big deal to explore. Successful of apps depends on how producer of tourism apps study about the users. The finding of paper is variables analysis of tourism apps that can be used to developing new prototype of tourism apps based on tourist needs. 79. This paper will introduce the best variables that have been analysing to be interesting features that can be including in tourism apps. 453-457 Keywords: Tourism Sector, Tourism Organization, Apps, Travellers, Development Tourism Apps
References: 1. Abdullah, Q.D.L., & Hamid, S.A. (2018). Public-Private Partnership (PPP) in Managing Arts, Cultural and Tourism Sector. Journal of Tourism, Hospitality & Culinary Arts, 10(1), 1-14. 2. Black, G. (2015). The Five Stages of Travel and the Digital Travellers. Digital Tourism Scotland. Retrieved From https://visitcairngorms.com/assets/files/Gordon_Black_DTS 3. Cristescu, C, G. (2016). The significance of tourist apps on a tourist experience. Master Thesis. Aalborg University. Aalborg. Denmark. 4. Krejcie, R.V., & Morgan, D.W. (1970). Determining Sample Size for Research Activities. Educational and Psychological Measurement, 30, 607-610 5. Mccabe, S. (2009). Who is a Tourist? Conceptual and Theoratical Developments. Philosophical Issues in Tourism. International Journal Tribe, (25–42). 6. Meyers, D., 2013. When Conversation still trumps the web, can websites really inspire people to travel? Retrieved from http://www.tnooz.com/article/when-‐conversation-‐still-‐trumps-‐the-‐web-‐can-‐websites-‐ really-‐inspire-‐people-‐to-‐travel/ 80. Authors: Ramesh G, Sandeep Kumar N, Champa H. N
Paper Title: OHKWR: Offline Handwritten Kannada Words Recognition using SVM Classifier with CNN Abstract: In field of handwriting recognition, Robust algorithms for recognition and character segmentation are 458-466 presented for multilingual Indian archive images of Devanagari and Latin scripts. These report basically suffer from their format organizations, low print and local skews quality and contain intermixed messages (machine- printed and manually written). In order to overcome these drawbacks, a character segmentation algorithm is proposed for kannada handwriting recognition. In this work, in initial steps we are obtained the segmentation paths by using the characters of structural property and also the graph distance theory whereas overlapped and connected character are separated. Finally, we are calculated results by using the SVM classifier. In proposed recognition of character, they are three new geometrical shapes based on new features such as center pixel of character is obtained by first and second feature and third feature is calculation purpose we are used in neighborhood information of text pixels. Benchmarking results represent that proposed algorithms have best work identified with other contemporary methodologies, where best recognition rates and segmentation are obtained.
Keywords: Convolutional Neural Network, Computer Vision, character recognition, Word recognition, SVM classifier.
References: 1. Parul Sahare and Sanjay B. Dhok “Multilingual Character Segmentation and Recognition Schemes for Indian Document Images”. IEEE, 2018. 2. R Sanjeev Kunte and R D Sudhaker Samuel "A simple and efficient optical character recognition system for basic symbols in printed Kannada text" 32(5):521-533 · October 2008. 3. Celine Mancas - Thillou, Bernard Gosselin Facult´e Polytechnique de Mons, Avenue Copernic 1, 7000 Mons, Belgium. “Character Segmentation-by-Recognition Using Log-Gabor Filters” August 2006. 4. Yungang Zhang Changshui Zhang “A New Algorithm for Character Segmentation of License Plate” July 2003. 5. T V Ashwin and P S Sastry “A font and size-independent OCR system for printed Kannada documents using support vector machines” Vol. 27, Part 1, February 2002. 6. Rohana K. Rajapakse, A. Ruvan Weerasinghe and E. Kevin Seneviratne "A Neural network based character recognition system for sinhala script” 7. M. Blumenstein1 and B.Verma1 "Neural-based Solutions for the Segmentation and Recognition of Difficult Handwritten Words from a Benchmark Database" January 1995. 8. M. Mahadeva Prasad, M. Sukumar, and A. G. Ramakrishnan, "Divide and conquer technique in online handwritten Kannada character recognition”, Proceedings of the international workshop on multilingual OCR. ACM, 2009. 9. Ramakrishnan, A. G., and J. Shashidhar. "Development of OHWR system for Kannada." VishwaBharat@ tdil 39 (2013): 40. 10. Joshi, Niranjan, et al. "Comparison of elastic matching algorithms for online Tamil handwritten character recognition." Frontiers in Handwriting Recognition, 2004. IWFHR-9 2004. Ninth International Workshop on. IEEE, 2004. 11. Oivind Due Trier, Anil.K.Jain and Torfinn Taxt, Feature Extraction Methods for Character Recognition – A Surve, July 1995. 12. Y. LeCun, F.J. Huang, L. Bottou, “Learning methods for generic object recognition with invariance to pose and lighting,” Proc. Computer Vision and Pattern Recognition Conference (CVPR), IEEE Press, 2004. 13. M. Ranzato, F. Huang, Y. Boureau, Y. LeCun, “Unsupervised learning of invariant feature hierarchies with applications to object recognition,” Proc. Computer Vision and Pattern Recognition Conference (CVPR), IEEE Press, 2007. 14. Y. LeCun, L. Bottou, Y. Bengio, P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE, vol. 86(11), pp. 2278-2324, 1998. 15. V. Vapnik, “Statistical Learn Theory,” John Wiley, New York, 1998. 16. C. Cortes, V. Vapnik, “Support vector networks,” Machine Learning, vol. 20, pp. 273-297, 1995. 17. C. Burges, “A tutorial on support vector machines for pattern recognition,” Data Mining Knowledge Discovery, vol. 2(2), pp. 121-167, 1998. 18. R Prajna, Ramya V R, Mamatha H R “A Study of different Text Line Extraction Techniques for Multi-font and Multi-size Printed Kannada Documents 1-2 Aug. 2014. 19. Wangsheng Zhu, Qin Chen, Chuanyi Wei, and Ziyang Li “A segmentation algorithm based on image projection for complex text layout” 05 October 2017. 20. Yifan Jiang ; Hyunhak Shin ; Hanseok Ko “Precise Regression for Bounding Box Correction for Improved Tracking Based on Deep Reinforcement Learning” 2018. 21. S. Karthik and K. S. Murthy, “Deep belief network based approach to recognize handwritten kannada characters using distributed average of gradients,” Cluster Computing, pp. 1–9, 2018. 22. Ramesh. G, J. Manoj Balaji, Ganesh. N. Sharma, Champa H.N “Recognition of Off-line Kannada Handwritten Characters by Deep Learning using Capsule Network” International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958, Volume-8 Issue-6, August, 2019 23. Ramesh. G, J. Manoj Balaji, Ganesh. N. Sharma, Champa H.N “Offline Kannada Handwritten Character Recognition Using Convolutional Neural Networks” IEEE-WIECON, 2019 81. Authors: Arunabhaskar Karri, Kona Vinay Praveen Kumar
Paper Title: Joint Hypergraph Learning using feature fusion for Image Retrieval Abstract: As the picture sharing sites like Flicker become increasingly well known, broad researchers focus on 467-470 tagbased picture recovery (TBIR). It is one of the essential approaches to discover pictures contributed by social clients. In this exploration field, label data and various visual highlights have been explored. Be that as it may, most existing strategies utilize these visual includes independently or successively. In this paper, we propose a worldwide and neighborhood visual highlights combination way to deal with get familiar with the significance of pictures by hypergraph approach. A hypergraph is built first by using worldwide, neighborhood visual highlights and tag data. At that point, we propose a pseudo-significance input system to get the pseudopositive pictures. At last, with the hypergraph and pseudo importance input, we receive the hypergraph learning calculation to figure the pertinence score of each picture to the inquiry. Trial results illustrate the adequacy of the proposed methodology.
Keywords: hypergraph, pseudo-positive, successively, Flicker.
References: 1. X. Li, C. Snoek, and M. Worring. Learning tag relevance by neighbor voting for social image retrieval. Proceedings of the ACM International Conference on Multimedia information retrieval, 2008: 180-187. 2. X. Yang, X. Qian, Y. Xue. Scalable Mobile Image Retrieval by Exploring Contextual Saliency. IEEE Transactions on Image Processing 24(6): 1709-1721 (2015). 3. G. Qi, C. Aggarwal, Q. Tian, et al. Exploring Context and Content Links in Social Media: A Latent Space Method, in IEEE Transactions on Pattern Analysis and Machine Intelligence, May 2012. 4. S. Lee, W. D. Neve. Visually weighted neighbor voting for image tag relevance learning. Multimedia Tools and Applications, 1-24, 2013. 5. K. Yang, M. Wang, X. Hua, and H. Zhang. Social Image Search with Diverse Relevance Ranking. Proceedings of the IEEE International Conference on Magnetism and Magnetic Materials, 2010:174-184. 6. L. Chen, S. Zhu, Z. Li. Image retrieval via improved relevance ranking. In Control Conference, pp. 4620-4625, IEEE, 2014. 7. D. Liu, X. Hua, M. Wang, and H. Zhang. Boost Search Relevance For Tag-Based Social Image Retrieval. Proceedings of the IEEE International Conference on Multimedia and Expo, 2009:1636-1639. 8. Y. Gao, M. Wang, H. Luan, J. Shen, S. Yan, and D. Tao. Tag-based social image search with visual-text joint hypergraph learning. Proceedings of the ACM International Conference on Multimedia information retrieval, 2011:1517-1520. 9. D. Wu, J. Wu, M. Lu. A Two-Step Similarity Ranking Scheme for Image Retrieval. In Parallel Architectures, Algorithms and Programming, pp. 191-196, IEEE, 2014. 10. Y. Hu, M. Li. Multiple-instance ranking: Learning to rank images for image retrieval. In Computer Vision and Pattern Recognition, CVPR 2008. IEEE Conference on (pp. 1-8). 11. G. Agrawal, R. Chaudhary. Relevancy tag ranking. In Computer and Communication Technology, pp. 169-173, IEEE, 2011. 12. L. Wu, R. Jin. Tag completion for image retrieval. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 35(3), 716-727, 2013. 13. B. Wang, Z. Li, M. Li. Large-scale duplicate detection for web image search. In Multimedia and Expo, 2006 IEEE International Conference on (pp. 353-356). [14] Xueming Qian, Dan Lu, and Xiaoxiao Liu. Tag Based Image Search by Social Re-ranking. IEEE TRANSACTIONS ON MULTIMEDIA, 2016. Authors: Khaja Moinuddin, B K Kolhapure
Paper Title: Effective Location of Shear Wall and Bracings for Multistoried Asymmetrical Building. Abstract: Earthquake happens all around the globe and it is a natural calamity and can occur across the world. It affects the structure by producing tough ground signals. To overwhelmed the Earthquake there is establishment of Shear wall and Bracing to increase the crosswise stiffness, ductility of the structure. To plan a building storey drift and crosswise displacement are crucial. The building is analyzed by Linear static and Linear dynamic method by E-tab software. In present paper G+25 multistoried building is analyzed by insertion of Shear wall and bracing at Corners, End and central core of the structure. The responses like Displacement, Storey drift, Time period and Base shear is calculated and equated.
Keywords: E-TAB, Shear Wall, Bracings, Linear static method, Linear dynamic method. 82. References: 1. Mohd Atif, Prof. Lakshmikant Vairagde, Vikrant Nair “Comparative Study on Seismic Analysis of Multistorey Building Stiffened 471-477 With Bracing and Shear Wall” International Research Journal of Engineering and Technology (IRJET) Vol.02 Issue no.5 August 2. Varsha R. Harne “Comparative Study of Strength of RC Shear Wall at Different Location on Multi-storied Residential Buildin. “International Journal of Civil Engineering Research”.ISSN 2278-3652 Vol 5,No.4(2014),pp.391-400 3. Prof.Bhosale Ashwini Tanaji, Prof. Shaik A.N. “Analysis of Reinforced Concrete Building with Different Arrangement of Concrete and Steel Bracing System” IOSR Journal of Mechanical and Civil Engineering (IOSR-JMCE). 4. Md. Samdani Azad, Syed Hazni Abd Gani “Comparative study of seismic analysis of multistory buildings with shear walls and bracing systems”. “International Journal of Advanced Structure and Geotechnical Engineering” ISSN 2319-5347,Vol.05,no.03,July 2016 5. Umesh. R .Biradar, Shivraj Mangalgi “Seismic Response of Reinforced Concrete by using different Bracing Systems” International Journal of Research in “Engineering and Technology” (IJRET) Vol. 3, Issue 09 Sept. 2014 ISSN: 2319-1163 ISSN: 2321-7308 6. 6.Anuj Chandiwala, “Earthquake Analysis of Building Configuration with Different Position of Shear Wall” international journal of emerging technology and advanced engineering, ISSN 2250-2459, Volume 2, Issue 12, December 2012. 7. 7.P. V. Sumanth Chowdary Senthil Pandian. M, “A comparative study on rcc structure with and without shear wall” ijsrd - international journal for scientific research & development| vol. 2, issue 02, 2014 | issn (online): 2321-0613 83. Authors: Avinash Rai, Shivani Sonker
Paper Title: Optical Character Reader & Text To Speech Conversion using Correlations & Speech Synthesis Abstract: In the modern era of image processing, recognizing content or information from an image is process 478-483 of electronic conversion into machine encoded text. Advanced systems that are capable of producing high accuracy for multi-font recognition are now becoming commonplace, and with the support of digital consent formatting. Some programs are able to retrieve formats that are very close to the original page including images, columns, and other non-text items. Proposed system is able to recognize text from an image and convert it into editable text along with speech conversion. System uses Correlation model for OCR (Optical Character Recognition) and Speech Synthesis for TTS (Text To Speech) conversion. Correlation is a measurement of the similarities between two similar objects such as the predefined alphabets and recognizing a combination of those alphabets from an image. Speech synthesis is an artificial expression of human speech. The computer program that has been used this feature is called a speech computer as well as speech synthesizer that can be implemented on the basis of software or hardware primitives. The text-to-speech system (TTS) converts a standard language text into a speech; some programs provide figurative language presentations such as typed text in speech. System is capable enough to acquire high level of accuracy with less false recognition. It is required to built an effective text scanner that can recognize text from an image with less error rate. System has been implemented in MATLAB and various pre-processing filters have been applied for better enhancement and extraction. Hand written text can also be recognized with an effective manner.
Keywords: OCR, TTS, Speech Synthesis, Correlation Model, Machine Encoding, Image Processing. References: 1. Google Patents, OCR (Optical Character Recognition) and TTS (Text To Speech) based low-vision reading visual aid system , https://patents.google.com/patent/CN104966084A/en, Accessed- 07 July 2015. 2. Mathur, Geetika & Rikhari, Suneetha. (2017). ISSN: 2454-132X Impact factor: 4.295 A Review on Recognition of Indian Handwritten Numerals. 3. S. C. Madre and S. B. Gundre, "OCR Based Image Text to Speech Conversion Using MATLAB," 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India, 2018, pp. 858-861, doi: 10.1109/ICCONS.2018.8663023. 4. S. Gupta and S. Gupta, "Character Recognition and Speech Synthesis using Adaptive Neuro Fuzzy Inference System," 2018 International Conference on Advances in Computing, Communication Control and Networking (ICACCCN), Greater Noida (UP), India, 2018, pp. 1091-1096, doi: 10.1109/ICACCCN.2018.8748742. 5. J. J. Mullani, M. Sankar, P. S. Khade, S. H. Sonalkar and N. L. Patil, "OCR BASED SPEECH SYNTHESIS SYSTEM USING LABVIEW : Text to Speech Conversion System using OCR," 2018 Second International Conference on Computing Methodologies and Communication (ICCMC), Erode, 2018, pp. 7-14, doi: 10.1109/ICCMC.2018.8487731. 6. N. K. Sawant and S. Borkar, "Devanagari Printed Text to Speech Conversion using OCR," 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. 504-507, doi: 10.1109/I-SMAC.2018.8653685. 7. Mathur, A. Pathare, P. Sharma and S. Oak, "AI based Reading System for Blind using OCR," 2019 3rd International conference on Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, India, 2019, pp. 39-42, doi: 10.1109/ICECA.2019.8822226. 8. Qixiang Ye and David Doermann,,” Detection and Recognition in Imagery: A Survey”, IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGNCE VOL. 37, NO. 7, JULY 2015. 9. Amit Choudhary, Rahul Rishi, Savita Ahlawat,” Off-Line Handwritten Character Recognition using Features Extracted from Binarization Technique”, 2013 AASRI Conference on Intelligent Systems and Control. 10. Pratik Madhukar Manwatkar and Shashank H. Yadav, ”Text Recognition from Images”, EEE Sponsored 2nd International Conference on Innovations in Information,Embedded and Communication systems (ICIIECS)2015. 11. Kumuda T and L Basavaraj,” Edge Based Segmentation Approach to Extract Text from Scene Images”, 2017 IEEE 7th International Advance Computing Conference. 12. Xiaoming Huang, Tao Shen, Run Wang, Chenqiang Gao,” Text Detection and Recognition in Natural Scene Images”, 2015 International Conference on Estimation, Detection and Information Fusion (ICEDlF 2015). 13. Jagruti Chandarana, Mayank Kapadia,” Optical Character Recognition”, International Journal of Emerging Technology and Advanced Engineering 2014. 14. Gupta Mehula, Patel Ankita, Dave Namrata, Goradia Rahul, and Saurin Sheth,” Text-Based Image Segmentation Methodology”, 2nd International Conference on Innovations in Automation and Mechatronics Engineering, ICIAME 2014. 15. Rodolfo P. dos Santos, Gabriela S. Clemente, Tsang Ing Ren and George D.C. Calvalcanti,” Text Line Segmentation Based on Morphology and Histogram Projection”, 2009 10th International Conference on Document Analysis and Recognition. 16. Anitha Mary M.O. Chacko and P.M. Dhanya,” A Comparative Study of Different Feature Extraction Techniques for Offline Malayalam Character Recognition”, Springer India 2015 Computational Intelligence in Data Mining - Volume 2, Smart Innovation, Systems and Technologies 32, DOI 10.1007/978-81322-2208-8_2 17. Mustain Billah, Sajjad Waheed, Abu Hanifa, “An Optical Character Recognition System from Printed Text and Text Image using Adaptive Neuro Fuzzy Inference System”, International Journal of Computer Applications Volume 130 - No.16, November 2015. Authors: Jaskarn Singh, Amit Chhabra Indian Stock Markets Data Analysis and Prediction using Macroeconomics Indictors in Machine Paper Title: Learning Abstract: Machine Learning plays a unique role in the world of stock market when it comes to the trend prediction. Machine learning library MLIB helps in determining the future values of stocks. With the help of this research one can find the ups and downs of stock market by providing a signal for the same and done by analyzing the previous stock data. This study is based on analysis of stock data from 2000 to 2009 which includes top fifty companies of various sectors from all over India. Six stock data indicators known as, Bollinger Band, Relative Strength Index(RSI), Stochastic Oscillator, Williams % R, Moving Average Convergence Divergence (MACD), Rate of Change applied on the nineteen years of stock data then results of these indicators are compiled and finally with the use of machine learning libraries like Numpy, Pandas, Matplotlib, Sklearn a random forest algorithm is applied on the compiled result to predict the stock movement , these libraries which splits the results into two sets training set and testing set which also boost up the result and gives you the better prediction.
Keywords: Random Forest, Stock, Machine Learning. 84. References: 1. Aithal, P., Dinesh, A., & Geetha, M. (2019). Identifying Significant Macroeconomic Indicators for Indian Stock Markets. IEEE 484-486 Access, 7, 143829-143840. 2. M, H., E.A., G., Menon, V., & K.P., S. (2018). NSE Stock Market Prediction Using Deep-Learning Models. Procedia Computer Science, 132, 1351-1362 3. Alsubaie, Y., Hindi, K., & Alsalman, H. (2019). Cost-Sensitive Prediction of Stock Price Direction: Selection of Technical Indicators. IEEE Access, 7, 146876-146892. 4. Kanade, P. (2020). Machine Learning Model for Stock Market Prediction. International Journal For Research In Applied Science And Engineering Technology, 8(6), 209-216. 5. Vijh, M., Chandola, D., Tikkiwal, V., & Kumar, A. (2020). Stock Closing Price Prediction using Machine Learning Techniques. Procedia Computer Science, 167, 599-606. 6. Oriani, F., & Coelho, G. (2016). Evaluating the impact of technical indicators on stock forecasting. 2016 IEEE Symposium Series On Computational Intelligence (SSCI). 7. Giri, A., & Joshi, P. (2017). The Impact of Macroeconomic Indicators on Indian Stock Prices: An Empirical Analysis. Studies In Business And Economics, 12(1), 61-78. 8. Chen, C., & Shih, P. (2019). A Stock Trend Prediction Approach based on Chinese News and Technical Indicator Using Genetic Algorithms. 2019 IEEE Congress On Evolutionary Computation (CEC). 9. Sharma, N., & Juneja, A. (2017). Combining of random forest estimates using LSboost for stock market index prediction. 2017 2Nd International Conference For Convergence In Technology (I2CT). 10. Gao, T., & Chai, Y. (2018). Improving Stock Closing Price Prediction Using Recurrent Neural Network and Technical Indicators. Neural Computation, 30(10), 2833-2854.