Applied Soft Computing: Techniques and Applications explores a variety of modern techniques that deal with estimated models and give resolutions to complex real-life issues. Involving the concepts and practices of soft computing in conjunction with other frontier research domains, this book explores a variety of modern applications in soft computing, including bioinspired computing, reconfigurable computing, fuzzy logic, fusion-based learning, intelligent healthcare systems, bioinformatics, data mining, functional approximation, genetic and evolutionary algorithms, hybrid models, machine learning, meta heuristics, neuro fuzzy system, and optimization principles. The book acts as a reference book for AI developers, researchers, and academicians as it addresses the recent technological developments in the field of soft computing.
Soft computing has played a crucial role not only with the theoretical paradigms but is also popular for its pivotal role for designing a large variety of expert systems and artificial intelligent-based applications. Beginning with the basics of soft computing, this book deeply covers applications of soft computing in areas such as approximate reasoning, artificial neural networks, Bayesian networks, big data analytics, bioinformatics, cloud computing, control systems, data mining, functional approximation, fuzzy logic, genetic and evolutionary algorithms, hybrid models, machine learning, meta heuristics, neuro fuzzy system, optimization, randomized searches, and swarm intelligence.
This book is destined for a wide range of readers who wish to learn applications of soft computing approaches. It will be useful for academicians, researchers, students, and machine learning experts who use soft computing techniques and algorithms to develop cutting-edge artificial intelligence-based applications.