Elsevier Decision-Making Models: A Perspective of Fuzzy Logic and Machine Learning (Uncertainty, Computational Techniques, and Decision Intelligence)

Decision Making Models: A Perspective of Fuzzy Logic and Machine Learning presents the latest developments in the field of uncertain mathematics and decision science. The book aims to deliver a systematic exposure to soft computing techniques in fuzzy mathematics as well as artificial intelligence in the context of real-life problems and is designed to address recent techniques to solving uncertain problems encountered specifically in decision sciences. Researchers, professors, software engineers, and graduate students working in the fields of applied mathematics, software engineering, and artificial intelligence will find this book useful to acquire a solid foundation in fuzzy logic and fuzzy systems. Other areas of note include optimization problems and artificial intelligence practices, as well as how to analyze IoT solutions with applications and develop decision-making mechanisms realized under uncertainty. Introduces mathematics of intelligent systems which provides the usage of mathematical rigor such as precise definitions, theorems, results, and proofs Provides extended and new comprehensive methods which can be used efficiently in a fuzzy environment as well as optimization problems and related fields Covers applications and elaborates on the usage of the developed methodology in various fields of industry such as software technologies, biomedicine, image processing, and communications