Soft Computing and Machine Learning

This reference text covers the theory and applications of soft computing and machine learning and presents readers with the intelligent fuzzy and neutrosophic rules that require situations where classical modeling approaches cannot be utilized, such as when there is incomplete, unclear, or imprecise information at hand or inadequate data. It further illustrates topics such as image processing, and power system analysis.

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Discusses soft computing techniques including fuzzy Logic, rough sets, neutrosophic sets, neural networks, generative adversarial networks, and evolutionary computation. Examines novel and contemporary advances in the fields of soft computing, fuzzy computing, neutrosophic computing, and machine learning systems, as well as their applications in real life. Serves as a comprehensive reference for applying machine learning and neutrosophic sets in real-world applications such as smart cities, healthcare, and the Internet of Things. Covers topics such as image processing, bioinformatics, natural language processing, supply chain management, and cybernetics. Illustrates classification of neutrosophic machine learning, neutrosophic reinforcement learning, and applications of neutrosophic machine learning in emerging industries. The text is written for senior undergraduate students, graduate students, and academic researchers in the fields of electrical engineering, electronics and communications engineering, computer science and engineering, and information technology.

Size: A4(20*28cm)

Printing: 80 gm – color

Cover: Softcover