Computational Intelligence in Machine Learning

Amit Kumar is DNA Forensics Professional, Entrepreneur, Engineer, Bioinformatician, and an IEEE Volunteer. In 2005, he founded the first Private DNA Testing Company Bio Axis DNA Research Centre (P.) Ltd in Hyderabad, India, with a US Collaborator. He has vast experience of training 1000+ crime investigating officers and helped 750+ criminal and non-criminal cases to reach justice by offering analytical services in his laboratory. His group also works extensively on genetic predisposition risk studies of cancers and has been helping many cancer patients from 2012 to fight and win the battle against cancer. He was a member of the IEEE Strategy Development and Environmental Assessment Committee (SDEA) of IEEE MGA. He is a senior member of IEEE and has been a very active IEEE volunteer at Section, Council, Region, Technical Societies of Computational Intelligence and Engineering in Medicine and Biology and IEEE MGA levels in several capacities. He has driven several IEEE conferences, conference leadership programs, entrepreneurship development workshops, innovation, and internship-related events. Currently, he is Managing Director of BioAxis DNA Research Centre (P) Ltd and IEEE MGA Nominations and Appointments committee member.  Jacek M. Zurada is Professor of Electrical and Computer Engineering and Director of the Computational Intelligence Laboratory at the University of Louisville, Kentucky, USA, where he served as Department Chair and Distinguished University Scholar. He received his M.S. and Ph.D. degrees (with distinction) in electrical engineering from the Technical University of Gdansk, Poland. He has published over 420 journal and conference papers in neural networks, deep learning, computational intelligence, data mining, image processing, and VLSI circuits. He has authored or co-authored three books, including the pioneering text Introduction to Artificial Neural Systems, co-edited the volumes Computational Intelligence: Imitating Life, Knowledge-Based Neurocomputing, and co-edited twenty volumes in Springer Lecture Notes on Computer Science. In addition to his pioneering neural networks textbook, his most recognized achievements include an extension of complex-valued neurons to associative memories and perception networks; sensitivity concepts applied to multilayer neural networks; application of networks to clustering, biomedical image classification, and drug dosing; blind sources separation; and rule extraction as a tool for prediction of protein secondary structure. Vinit Kumar Gunjan is an Associate Professor in the Department of Computer Science & Engineering at CMR Institute of Technology Hyderabad (affiliated to Jawaharlal Nehru Technological University, Hyderabad). An active researcher; published research papers in IEEE, Elsevier and Springer Conferences, authored several books and edited volumes of Springer series, most of which are indexed in SCOPUS database. Awarded with the prestigious Early Career Research Award in the year 2016 by Science Engineering Research Board, Department of Science & Technology Government of India. Senior Member of IEEE; an active Volunteer of IEEE Hyderabad section; volunteered in the capacity of Treasurer, Secretary and Chairman of IEEE Young Professionals Affinity Group and IEEE Computer Society. Was involved as organizer in many technical and non-technical workshops, seminars and conferences of IEEE and Springer. During his tenure, worked with top leaders of IEEE and was awarded with best IEEE Young Professional award in 2017 by IEEE Hyderabad Section.  Raman Balasubramanian is Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Roorkee. He received his Ph.D. in Mathematics (2001) from the Indian Institute of Technology Madras, India. He obtained his B.Sc. and M.Sc. in Mathematics from the University of Madras in 1994 and 1996. respectively. His research areas include computer vision, fractional transform theory, wavelet analysis, multimedia security, video skimming and summarization, medical imaging, machine learning, multilingual text recognition, EEG based pattern analysis, visualization, and volume graphics.

Machine Learning-based Project Resource Allocation Fitment Analysis System – (ML-PRAFS).- Electric Theft Detection using Un-supervised Machine Learning Based Matrix Profile and K Means Clustering Technique.- Placement Analysis – A New Approach to Ease the Recruitment Process.- Continuous Assessment Analyzer using Django.- Fuzzy Logic in Battery Energy Storage System (Bess).- Fault Classification of Cooling Fans using a CNN-based Approach.- Violence Recognition using Convolutional Neural Networks.- Automated Grading of Citrus Suhuiensis Fruit using Deep Learning Method.- The Future of Car Automation Field with Smart Driverless Technologies.- Diagnosis and Medicine Prediction for Covid-19 using Machine Learning Approach.- Automated Guided Vehicle Robot Localization with Sensor Fusion.- Implementation of Industrial Automation Water Distribution System Utilizing PLC: A Laboratory Set-up.- Control of Thin McKibben Muscles in an Antagonistic Pair Configuration.- Defect Severity Classification of Complex Composites using CWT and CNN.- Detection of Mobile Phone Usage While Driving using Computer Vision and Deep Learning.- Industry Revolution 4.0 Knowledge Assessment in Malaysia.