Название: Soft Computing and Machine Learning: A Fuzzy and Neutrosophic View of Reality
Автор: Mohd Anas Wajid, Aasim Zafar, Mohammad Saif Wajid, Akib Mohi Ud Din Khanday, Pronaya Bhattacharya
Издательство: CRC Press
Год: 2025
Страниц: 323
Язык: английский
Формат: pdf (true), epub
Размер: 25.2 MB

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.

ML, with its formidable algorithms and data-­driven methodologies, has thrived on the principles of learning patterns from vast datasets to make predictions, classifications, and decisions. Its power lies in recognizing patterns, yet it often operates within the constraints of well-­defined, crisp data, struggling when faced with ambiguity or incomplete information.

Contrarily, NML transcends these limitations by embracing and manipulating uncertainty. It acknowledges not only the presence of true or false values but also considers a third parameter indeterminacy—where the truth value is neither true nor false but lies in between, accommodating partial truths or ambiguities within the dataset. This innovative framework introduces a paradigm shift, allowing AI systems to navigate and process information that would traditionally confound conventional ML models.

This book:

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.






ОТСУТСТВУЕТ ССЫЛКА/ НЕ РАБОЧАЯ ССЫЛКА ЕСТЬ РЕШЕНИЕ, ПИШЕМ СЮДА!