Soft Computing and Machine Learning: A Fuzzy by Mohd Anas Wajid(.PDF)+

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File Size: 25.2 MB

Soft Computing and Machine Learning: A Fuzzy and Neutrosophic View of Reality by Mohd Anas Wajid, Aasim Zafar, Mohammad Saif Wajid, Akib Mohi Ud Din Khanday, Pronaya Bhattacharya
Requirements: .ePUB, .PDF reader, 25.2 MB
Overview: 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. The theory of Machine Learning (ML) is an interdisciplinary domain that converges statistical, probabilistic, computer science, and algorithmic elements. It involves iterative learning from data, unveiling concealed insights to construct intelligent applications. In the dynamic domain of Artificial Intelligence (AI), ML has been the cornerstone of numerous technological advancements, revolutionizing industries and reshaping how we perceive data analysis and predictive modeling. However, amidst this robust framework, a newer paradigm known as Neutrosophic Machine Learning (NML) has emerged, offering a novel perspective on handling uncertainty, imprecision, and indeterminacy within datasets.
Genre: Non-Fiction > Tech & Devices

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