Decision-Making Models: A Perspective of Fuzzy Logic and Machine Learning

Decision Making Models: A Perspective of Fuzzy Logic and Machine Learning provides a comprehensive introduction to soft computing techniques in fuzzy mathematics and artificial intelligence, addressing recent techniques to solving uncertain problems in decision sciences. It is useful for researchers, professors, software engineers, and graduate students in applied mathematics, software engineering, and artificial intelligence.

Format: Paperback / softback
Length: 678 pages
Publication date: 07 August 2024
Publisher: Elsevier Science Publishing Co Inc

Decision Making Models: A Perspective of Fuzzy Logic and Machine Learning is a comprehensive book that explores the latest developments in uncertain mathematics and decision science. It aims to provide a systematic introduction to soft computing techniques in fuzzy mathematics and artificial intelligence, with a focus on real-life problems. The book is designed to address recent techniques for solving uncertain problems encountered in decision sciences, making it useful for researchers, professors, software engineers, and graduate students working in applied mathematics, software engineering, and artificial intelligence.

One of the key topics covered in the book is fuzzy logic, which is a mathematical framework for dealing with uncertainty. Fuzzy logic uses fuzzy sets, which are sets that have a degree of membership, to represent uncertain information. This allows for the creation of complex decision-making models that can handle a wide range of scenarios. The book provides a detailed explanation of fuzzy logic concepts, including fuzzy sets, fuzzy operators, and fuzzy reasoning. It also discusses the applications of fuzzy logic in various fields, such as decision-making, control systems, and robotics.

Another important topic covered in the book is machine learning. Machine learning is a subfield of artificial intelligence that involves the use of algorithms to learn from data. The book provides a comprehensive introduction to machine learning concepts, including supervised learning, unsupervised learning, and reinforcement learning. It also discusses the applications of machine learning in various fields, such as image recognition, natural language processing, and recommendation systems.

The book also covers optimization problems and artificial intelligence practices. Optimization problems involve finding the best solution to a problem from a set of possible solutions. Artificial intelligence practices involve the use of algorithms to solve problems that are too complex for humans to solve. The book provides a detailed explanation of optimization problems and artificial intelligence practices, including linear programming, nonlinear programming, and genetic algorithms.

In addition to the technical topics, the book also discusses the ethical implications of decision-making. Decision-making can have significant consequences for individuals, organizations, and society as a whole. The book provides a discussion of ethical principles and how they can be applied to decision-making. It also discusses the role of decision-making in social and political contexts.

The book is well-written and easy to read. It includes numerous examples and illustrations to help readers understand the concepts. The book also includes a glossary of terms and a bibliography of relevant resources.

One of the strengths of the book is its focus on real-life problems. The book provides numerous examples of real-life problems that have been solved using fuzzy logic and machine learning. These examples help readers to understand the practical applications of these techniques.

However, the book also has some limitations. One of the limitations is that it can be quite technical. This can make it difficult for readers who are not familiar with the technical aspects of fuzzy logic and machine learning. The book also does not provide a comprehensive overview of all the techniques that are available in these fields.

Despite these limitations, Decision Making Models: A Perspective of Fuzzy Logic and Machine Learning is a valuable book for researchers, professors, software engineers, and graduate students working in applied mathematics, software engineering, and artificial intelligence. The book provides a systematic introduction to soft computing techniques in fuzzy mathematics and artificial intelligence, with a focus on real-life problems. It also covers optimization problems and artificial intelligence practices, as well as the ethical implications of decision-making.

In conclusion, Decision Making Models: A Perspective of Fuzzy Logic and Machine Learning is a comprehensive book that explores the latest developments in uncertain mathematics and decision science. It provides a systematic introduction to soft computing techniques in fuzzy mathematics and artificial intelligence, with a focus on real-life problems. The book is well-written and easy to read, and it includes numerous examples and illustrations to help readers understand the concepts. However, the book also has some limitations, such as its technical nature and its lack of a comprehensive overview of all the techniques that are available in these fields. Despite these limitations, the book is a valuable resource for researchers, professors, software engineers, and graduate students working in applied mathematics, software engineering, and artificial intelligence.

Weight: 1360g
Dimension: 234 x 193 x 35 (mm)
ISBN-13: 9780443161476