Closed Loop Vision Based Ball Balancer.- A Novel Network Learning For Image Compressive Sensing.- COVID – 19 Severıty Predıctıons: An Analysis Usıng Correlatıon Measures.- A Novel Methodology for Comparative Analysis of Power Quality Improvement for a 3 Phase DC/AC Embedded DC/DC Converter.- Performance of Photovoltaic based ZETA Converter Water Pumping Application.- Performance Analysis of Radial Distribution System by Optimal Deployment of DG and DSTATCOM Considering Network Reconfiguration using a SAR Algorithm.
Dr. Subhransu Sekhar Dash is presently Professor and Head in the Department of Electrical Engineering, Government College of Engineering, Keonjhar, Odisha, India. He received his Ph.D. degree from the College of Engineering, Guindy, Anna University, Chennai, India. He has more than 22 years of research and teaching experience. His research areas are AI techniques application to power system, modeling of FACTS controller, power quality, and smart grid. He is Visiting Professor at Francois Rabelais University, POLYTECH, France. He has published more than 220 research articles in peer-reviewed international journals and conferences. Professor Dash has played a role as Convenor and Program or General Chair for many international conferences. He is also one of the editors of few of the books titled Introduction to FACTS and Power Quality Management of IRD Publication and Vijay Nicole Imprints Private Limited, respectively.
Bijaya Ketan Panigrahi is Professor in the Department of Electrical Engineering, Indian Institute of Technology Delhi. His research interests include the security of cyber-physical systems, digital signal processing, and soft computing applications to power systems. Panigrahi received a Ph.D. in Electrical Engineering. He is an associate editor for IEEE Systems Journal and a senior member of the IEEE.
Swagatam Das received the B.E. Tel.E., M.E. Tel.E. (Control Engineering specialization), and Ph.D. degrees, all from Jadavpur University, India, in 2003, 2005, and 2009, respectively. Swagatam Das is currently serving as Associate Professor at the Electronics and Communication Sciences Unit of the Indian Statistical Institute, Kolkata, India. His research interests include evolutionary computing, pattern recognition, multi-agent systems, and wireless communication. Dr. Das has published more than 300 research articles in peer-reviewed journals and international conferences. He is the founding co-editor-in-chief of Swarm and Evolutionary Computation, an international journal from Elsevier. He has also served as or is serving as the associate editors of the Pattern Recognition (Elsevier), Neurocomputing (Elsevier), Information Sciences (Elsevier), IEEE Trans. on Systems, Man, and Cybernetics: Systems, IEEE Computational Intelligence Magazine, IEEE Access, and so on. He is an editorial board member of Progress in Artificial Intelligence (Springer), Applied Soft Computing (Elsevier), Engineering Applications of Artificial Intelligence (Elsevier), and Artificial Intelligence Review (Springer). Dr. Das has 20,500+ Google Scholar citations and an H-index of 67 till date. He has been associated with the international program committees and organizing committees of several regular international conferences including IEEE CEC, IEEE SSCI, SEAL, GECCO, and SEMCCO. He has acted as the guest editors for special issues in journals like IEEE Transactions on Evolutionary Computation and IEEE Transactions on SMC, Part C. He is the recipient of the 2012 Young Engineer Award from the Indian National Academy of Engineering (INAE). He is also the recipient of the 2015 Thomson Reuters Research Excellence India Citation Award as the highest cited researcher from India in Engineering and Computer Science category between 2010 and 2014.
This book presents the peer-reviewed proceedings of the Sixth International Conference on Intelligent Computing and Applications (ICICA 2020), held at Government College of Engineering, Keonjhar, Odisha, India, during December 22–24, 2020. The book includes the latest research on advanced computational methodologies such as neural networks, fuzzy systems, evolutionary algorithms, hybrid intelligent systems, uncertain reasoning techniques, and other machine learning methods and their applications to decision-making and problem-solving in mobile and wireless communication networks.