Optimization, Machine Learning, and Fuzzy Logic : Theory, Algorithms, and Applications

Toufik Mzili

ISBN 13: 9798369373521

Published by IGI Global, 2025

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - Optimization, machine learning, and fuzzy logic are fundamental in the field of computational intelligence, each contributing to solving complex problems across various domains. Optimization techniques focus on finding the best solutions to problems by improving efficiency and minimizing resources. Machine learning enables systems to learn from data, making predictions or decisions without being programmed. Fuzzy logic deals with uncertainty and imprecision, allowing for flexible decision-making processes. Together, these theories, algorithms, and applications solve challenges in fields such as engineering, finance, and healthcare, where traditional methods often fall short. The continued application and exploration of these disciplines may unveil new possibilities for advanced problem-solving and intelligent systems. Optimization, Machine Learning, and Fuzzy Logic: Theory, Algorithms, and Applications explores optimization techniques, fuzzy logic, and their integration with machine learning. It covers fundamental concepts, mathematical foundations, algorithms, and applications, providing a holistic understanding of these domains. This book covers topics such as disease detection, deep learning, and text analysis, and is a useful resource for engineers, data scientists, medical professionals, academicians, and researchers.

Seller Inventory # 9798369373521

Bibliographic Details

• Title: Optimization, Machine Learning, and Fuzzy Logic : Theory, Algorithms, and Applications
• Publisher: IGI Global
• Publication Date: 2025
• Language: English
• ISBN 13: 9798369373521
• Binding: Buch
• Condition: Neu
• Dimensions: 260W x 183H x 38D millimeters 260 millimeters width by 183 millimeters height by 38 millimeters depth
• Weight: 1348 grams

About this title

Synopsis

"This publication offers a comprehensive exploration of optimization techniques, fuzzy logic, and their integration with machine learning. It covers fundamental concepts, mathematical foundations, algorithms, and applications, providing a holistic understanding of these domains"-- Provided by publisher.

About the Authors

Dr. Mzili Toufik holds the position of Assistant Professor in the Department of Computer Science at the Faculty of Science, Chouaib Doukkali University. Recognized as a distinguished researcher, he has made noteworthy contributions to the fields of metaheuristics, optimization, and scheduling problems. His scholarly impact is evident through numerous publications in esteemed Q1 journals. Additionally, Dr. Mzili Toufik has demonstrated his expertise as a peer reviewer for several prestigious journals, showcasing his commitment to advancing the academic discourse in his field.

A competent professional with nearly 20 years of experience in Research and Development / Teaching chemical engineering graduates and post graduates. - Exercises judgment within generally defined practices in selecting methods & techniques for obtaining teaching solutions; ensuring compliance to quality measures and maintenance requisite documentation as well as records - Skills in managing the administrative activities entailing student management, faculty appraisal/training and upholding of the institution's motto - Proficiency in teaching Oil and Gas Pipeline and core chemical engineering subjects to Chemical Engineering Graduate and post graduate students. - Experience in developing curriculum to accommodate different learning styles & maximize students' comprehension - Received research awards - Exposure in working with T.A.N.A.P. Gas Pipeline Project at Worley Parsons. - Exposure in planning and organizing day-to-day research activities and resolving the procedural problem as appropriate to the timely accomplishment of research objectives - An effective leader with proven skills in leading teams during the project phase, training & guiding team members and enabling knowledge sharing among the team