This book addresses the adoption of intelligent algorithms for resolving challenges in different aspects of the society such as sport, cyber-security, COVID-19 pandemic, advertising, driving, smart environment-sensors, blockchain, cloud computing, and health. In addition, the book also covers machine learning fundamentals such as feature selection. The book presents practical simulation results and different illustrations in different chapters for easy understanding of concepts and approaches. The types of contributions in the book are as follows: original research, survey, and theoretical insight that describe advancement in the adoption of technique for resolving the broad range of challenges. Researchers, undergraduates, postgraduates, and industry experts will find the book as a valuable resource that bridges theory and practice.

This book addresses theories and empirical procedures for the application of machine learning and data mining to solve problems in cyber dynamics. It explains the fundamentals of cyber dynamics, and presents how these resilient algorithms, strategies, techniques can be used for the development of the cyberspace environment such as: cloud computing services; cyber security; data analytics; and, disruptive technologies like blockchain. The book presents new machine learning and data mining approaches in solving problems in cyber dynamics. Basic concepts, related work reviews, illustrations, empirical results and tables are integrated in each chapter to enable the reader to fully understand the concepts, methodology, and the results presented. The book contains empirical solutions of problems in cyber dynamics ready for industrial applications. The book will be an excellent starting point for postgraduate students and researchers because each chapter is design to have future research directions.

Addressing the applications of computational intelligence algorithms in energy, this book presents a systematic procedure that illustrates the practical steps required for applying bio-inspired, meta-heuristic algorithms in energy, such as the prediction of oil consumption and other energy products. Contributions include research findings, projects, surveying work and industrial experiences that describe significant advances in the applications of computational intelligence algorithms in energy. For easy understanding, the text provides practical simulation results, convergence and learning curves as well as illustrations and tables. Providing a valuable resource for undergraduate and postgraduate students alike, it is also intended for researchers in the fields of computational intelligence and energy.