Kaur N. Computational Optimization. Machine Learning and Fuzzy Systems 2026

Textbook in PDF format The book investigates the correlation between computational optimization methods, Data Science, Machine Learning, and mathematical analysis. This book provides profound insights into the mathematical analysis of Machine Learning models. In this book, case studies are presented in a variety of disciplines, such as healthcare , finance , and e-commerce. The latter illustrates the practical implementations of optimisation in the selection of features, hyperparameters, and models. Practical observations regarding the evaluation and enhancement of Machine Learning models in real-world scenarios, including agriculture, e-commerce, healthcare, and financial portfolio management, are included in this publication. This publication is intended for researchers, engineers, mathematicians, and computer scientists. The goal is to improve the utilisation of computational optimisation techniques in real-time scenarios by integrating theoretical concepts with practical applications. Conventional optimization techniques are converging with contemporary Artificial Intelligence (AI) approaches, particularly Machine Learning and fuzzy systems, as there is a continual demand for more precise, efficient, and adaptive optimization methods. This is a book that explores this emerging field by integrating the capabilities of Machine Learning with the adaptability of fuzzy logic in dynamic contexts. The objective is to provide readers, including researchers, graduate students, and professionals, with both academic context and practical examples of how these tools might collaboratively address optimization challenges in real-world scenarios. We begin with fundamental optimization approaches, then progressing to the essentials of Machine Learning, Deep Learning, and fuzzy logic systems. Subsequently, we examine hybrid and intelligent optimization frameworks. We demonstrate the efficacy of fuzzy rules in conjunction with data-driven learning, the enhancement of metaheuristic algorithms through Machine Learning, and the superiority of this combined approach over traditional methods across various domains, including bioinformatics, supply chain management, image processing, and control systems. Significant emphasis is placed on real-world case studies and algorithmic applications, prioritizing clarity and reproducibility. Each chapter concludes with mathematical models, algorithm pseudocode, flowcharts, and experimental data to facilitate the connection between theory and practice. The goal of ML, a branch of Artificial Intelligence (AI), is to develop systems that can improve their performance without explicit programming by learning from information. It includes the creation of techniques that allow computers to identify trends, create views, and become better over time. Since systems based on ML may generate forecasts and judgments depending on the information they are trained on, they are extremely useful in everyday situations. Both the quantity and the value of data have a significant impact on a model’s effectiveness, and data may come in either structured or unstructured forms. Unlike traditional programming, ML systems are capable of handling changing data patterns and progressively improve their accuracy. Once trained, ML systems can complete tasks on their own, reducing the need for human intervention. ML draws strength from several fields, such as statistics, computer science, and cognitive science. Despite significant theoretical and practical advances, ML approaches are limited in complex and uncertain environments. Traditional ML systems can be confused by incomplete data, inaccurate observations, and unclear information or relationships. The three types of ML are supervised learning, unsupervised learning, and reinforcement learning. Preface List of Authors Bibliometric Review of Bibliometric Studies on Machine Learning Research Trends Machine Learning and Computation-Enabled Smart Systems Optimization for Machin

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