Solving with Bees

This book is a comprehensive volume, which delves into the versatile world of Artificial Bee Colony (ABC) algorithms, their variants, and myriad applications in a wide range of fields. This book is designed to be an essential resource for researchers, practitioners, students, and anyone intrigued by the fascinating realm of swarm intelligence and optimization. This book serves as a bridge between the theoretical foundations of ABC algorithms and their practical implementations across diverse domains. The book offers a deep understanding of these algorithms and how they can be harnessed to tackle complex real-world challenges.

Covers basic concepts of artificial bee colony algorithm and its variants Presents its various applications Illustrates different case studies

Autorentext

Dr. Khalid Raza is an Associate Professor at the Department of Computer Science, Jamia Millia Islamia (Central University), New Delhi, India. He also served as a ICCR Chair Visiting Professor at the Faculty of Computer & Information Sciences, Ain Shams University, Cairo, Egypt academic session 2017-2018. Dr. Raza has over 13 years of teaching and research experience at undergraduate, postgraduate and doctoral levels. He has contributed over 140 peer-reviewed articles in refereed international journals, conference proceedings, and as book chapters. Dr. Raza has authored/edited 10 Books published by reputed publishers like Springer-Nature, Elsevier, and CRC Press. Dr. Raza is an Academic Editor of PeerJ Computer Science International Journal, Guest Editor of the Natural Product Communications (SAGE), and Mini-Reviews in Medicinal Chemistry (Bentham Science). He has an active collaboration with the scientists from leading institutions of India and abroad. He is an Honorary Research Fellow of INIT International University, Malaysia. Dr. Raza has been featured in the list of World's Top 2% Scientists released by Stanford University (USA) in collaboration with Elsevier for the two consecutive years 2021 and 2022.

Inhalt

Fundamentals of Artificial Bee Colony Algorithms and Its Variants.- The Rise of Artificial Bee Colony Algorithms in Data Science and Machine Learning is Notable.- Swarm Intelligence for Optimization: A Bee's-Eye View on Multi-objective and Dynamic Challenges.- Artificial Bee Colony Algorithms in Control Systems, Robotics and Automation.- Integrating Artificial Bee Colony Algorithms for Deep Learning Model Optimization: A Comprehensive Review.- An IoMT Enabled Iterative Artificial Bee Colony Approach Using Federated Learning for Detection of Heart Disease.- Optimal Design of a Biomedical Amplifier for Minimum Offset Using a Modified ABC Algorithm.- Using Honeybees for Gene Expression Profiling: The Artificial Bee Colony Algorithm to Identify Robust Gene Biomarkers for Clinical Diagnosis.- Smart Diagnostics for Diabetic Retinopathy: Integrating Artificial Bee Colony Algorithms into Medical Image Analysis.- Artificial Bee Colony Algorithms in Gene Expression Studies: A Case Study.- Artificial Bee Colony Algorithm in Multi-Omics Analysis: A Case Study.