Bijaya Ketan Panigrahi – författare

Recent Trends in Swarm Intelligence Enabled Research for Engineering Applications focuses on recent, up-to-date technologies, combining other intelligent tools with swarm intelligence techniques to yield robust and failsafe solutions to real world problems. This book aims to provide audiences with a platform to learn and gain insights into the latest developments in hybrid swarm intelligence. It will be useful to researchers, engineers, developers, practitioners, and graduate students working in the major and interdisciplinary areas of computational intelligence, communication systems, computer networks, and soft computing.With the advent of data-intensive applications, the elimination of redundancy in disseminated information has become a serious challenge for researchers who are on the lookout for evolving metaheuristic algorithms which can explore and exploit the information feature space to derive the optimal settings for specific applications. Swarm intelligence algorithms have developed as one of the most widely used metaheuristic techniques for addressing this challenge in an effective way. Inspired by the behavior of a swarm of bees, these swarm intelligence techniques emulate the corresponding natural instincts to derive optimal solutions for data-intensive applications.

This book presents high-quality, original contributions (both theoretical and experimental) on Information Security, Machine Learning, Data Mining and Internet of Things (IoT). It gathers papers presented at ICETIT 2019, the 1st International Conference on Emerging Trends in Information Technology, which was held in Delhi, India, in June 2019.

This volume constitutes the thoroughly refereed post-conference proceedings of the 5th International Conference on Swarm, Evolutionary, and Memetic Computing, SEMCCO 2014, held in Bhubaneswar, India, in December 2014.

The total of 96 papers presented in this volume was carefully reviewed and selected from 250 submissions for inclusion in the proceedings. The papers cover a wide range of topics in swarm, evolutionary, memetic and other intelligent computing algorithms and their real world applications in problems selected from diverse domains of science and engineering.

This proceedings bring together contributions from researchersfrom academia and industry to report the latest cutting edge research made inthe areas of Fuzzy Computing, Neuro Computing and hybrid Neuro-Fuzzy Computingin the paradigm of Soft Computing. The FANCCO 2015 conference explored newapplication areas, design novel hybrid algorithms for solving different realworld application problems. After a rigorous review of the 68 submissions fromall over the world, the referees panel  selected 27 papers to be presented at theConference. The acceptedpapers have a good, balanced mix of theory and applications. The techniquesranged from fuzzy neural networks, decision trees, spiking neural networks,self organizing feature map, support vector regression, adaptive neuro fuzzyinference system, extreme learning machine, fuzzy multi criteria decisionmaking, machine learning, web usage mining, Takagi-Sugeno Inference system,extended Kalman filter, Goedel type logic, fuzzy formal concept analysis, biclusteringetc. The applications ranged from social network analysis, twitter sentimentanalysis, cross domain sentiment analysis, information security, educationsector, e-learning, information management, climate studies, rainfallprediction, brain studies, bioinformatics, structural engineering, sewage waterquality, movement of aerial vehicles,  etc.

From nature, we observe swarming behavior in the form of ant colonies, bird flocking, animal herding, honey bees, swarming of bacteria, and many more.  It is only in recent years that researchers have taken notice of such natural swarming systems as culmination of some form of innate collective intelligence, albeit swarm intelligence (SI) - a metaphor that inspires a myriad of computational problem-solving techniques.  In computational intelligence, swarm-like algorithms have been successfully applied to solve many real-world problems in engineering and sciences. This handbook volume serves as a useful foundational as well as consolidatory state-of-art collection of articles in the field from various researchers around the globe.  It has a rich collection of contributions pertaining to the theoretical and empirical study of single and multi-objective variants of swarm intelligence based algorithms like particle swarm optimization (PSO), ant colony optimization (ACO), bacterial foraging optimization algorithm (BFOA), honey bee social foraging algorithms, and harmony search (HS).  With chapters describing various applications of SI techniques in real-world engineering problems, this handbook can be a valuable resource for researchers and practitioners, giving an in-depth flavor of what SI is capable of achieving.