Nature-Inspired Approaches to Engineering and Healthcare Solutions

Nilanjan Dey (Senior Member, IEEE) received the B.Tech., M.Tech. in information technology from West Bengal Board of Technical University and Ph.D. degrees in electronics and telecommunication engineering from Jadavpur University, Kolkata, India, in 2005, 2011, and 2015, respectively. Currently, he is Professor with the Techno International New Town, Kolkata and a visiting fellow of the University of Reading, UK. He is the Editor-in-Chief of International Journal of Ambient Computing and Intelligence, Associate Editor of IEEE Transactions on Technology & Society, series Co-Editor of Springer Tracts in Nature-Inspired Computing and Data-Intensive Research from Springer Nature and Advances in Ubiquitous Sensing Applications for Healthcare from Elsevier etc. Furthermore, he is an Editorial Board Member of IEEE Data Descriptions. He is a Fellow of IETE and member of IE, ISOC etc.

• Nature-Inspired Algorithms for Optimization of Microwave Circuits: Applications in Filters and Amplifiers.- Impact Of Optimization Algorithms With Energy Storage Systems In Frequency Management Of Standalone/Interconnected Power Systems.- Nature-Inspired Feature Selection in Unsupervised Learning.- Metaheuristic-Driven Design Optimization of Parabolic Trough Collectors: A Study of Nature-Inspired Algorithms.- Hybrid Optimization Algorithm based on ANFIS and Jaya: Application for Compliant Microgripper in Cell Phone Vibration Motor Assembly.- Ant Colony Optimization based approach to efficiently identify Suspicious processes in Memory.- Application of the ACO Algorithm-based controller in interlinked Nuclear Power Plant performance for frequency regulation.- Implementation of NSGA-III in Solving Constrained Multi-Objective Arduous Engineering Design Problems.- Nature-Inspired Computing for Engineering and Health Care Problem Solving.- Nature inspired computing for diagnosis in central nervous system disorder.- Enhancing Medical Image Analysis with Advanced Optimization Techniques: A Comparative Study of Machine Learning Model Optimizers.