17 Best Books on Computational Intelligence

“Computational Intelligence” Book Review: The book focuses on the implementation of computational intelligence tools and techniques, covering foundational concepts and paradigms of evolutionary computation, neural networks, and fuzzy systems. It provides code examples in C and C++ throughout the text, as well as summary and exercise sections at the end of each unit. Review questions and case studies are also included for easy comprehension. This resource is particularly useful for graduate students and researchers in computer science.

“Computational Intelligence: Principles, Techniques and Applications” Book Review: The focus of this book is on the principles, techniques, and applications of computational intelligence, with topics ranging from an introduction to fuzzy sets and relations, to fuzzy logic and approximate reasoning. It also covers areas such as fuzzy logic in process control, fuzzy pattern recognition, fuzzy databases, and possibilistic reasoning. Each chapter includes exercises for students to practice and a conclusion with references for further reading. This book is intended for advanced students in computer science engineering.

“Computational Intelligence in Business Analytics: Concepts, Methods, and Tools for Big Data Applications” Book Review: This book explores the applications, tools, and methodologies of computational intelligence in the context of business analytics. It covers topics such as direct marketing customer segmentation, relationship management through customer profiling, customer retention, credit score analysis, hedge fund strategies, and fraud behavior and transaction detection. The book uses numerous examples and genetic algorithms to explain these topics in a clear and concise manner. It also includes several case studies for practical understanding. This book is particularly useful for computer engineering students interested in studying business analytics.

“Computational Intelligence: Methods and Techniques” Book Review: This book focuses on popular computational intelligence methods and techniques. It covers topics such as artificial intelligence, rough sets and type one fuzzy sets for knowledge representation, neural networks and their learning algorithms, evolutionary algorithms, data clustering methods, and flexible neuro fuzzy systems. Each chapter includes notes for quick review, and the book features labelled figures and numerous examples to aid understanding. It is suitable for advanced computer engineering students.

“Computational Intelligence: An Introduction” Book Review: This book serves as an introductory guide to computational intelligence. It covers essential chapters such as an overview of computational intelligence, artificial neural networks, supervised and unsupervised learning neural networks, and radial basis functional networks. Other topics covered include reinforcement learning and performance issues. Each chapter includes assignments with several questions and exercises. The book provides detailed explanations of all methods and applications, along with relevant code snippets and equations. This book is particularly useful for undergraduate students studying computer engineering.

“Computational Intelligence, Control and Computer Vision in Robotics and Automation” Book Review: The book explores the applications of computational intelligence in robotics and automation, focusing on control and computer vision. It covers various technologies and computing approaches used in robotics research, including control methods for single and multiple rigid robots. Research from different countries, including the USA, Canada, Mexico, India, and Malaysia, is also discussed. The book is suitable for advanced students in computer science engineering.

“Image Feature Detectors and Descriptors: Foundations and Applications (Studies in Computational Intelligence)” Book Review: The book covers theoretical and practical aspects of image feature detectors and descriptors, including feature extraction, detection, hardware implementation, and image segmentation. It serves as a comprehensive guide for practitioners and researchers interested in this field, with additional discussions on topics such as evolutionary algorithms, ordinal measures, and speech recognition.

“Computational Intelligence in Digital and Network Designs and Applications” Book Review: This book provides a concise overview of the latest developments in computational intelligence, covering algorithms, design methodologies, synthesis techniques, and the design of integrated circuits and systems. It is intended for both practitioners and researchers in computer science and electronic engineering. The book also delves into new approaches to biasing and sizing, as well as optimization techniques. It explores the application of high-performance digital, VLSI, radio-frequency, and mixed signal circuits and systems, including the design of analog and mixed signal and radio-frequency circuits.

“Computational Intelligence Techniques in Health Care (SpringerBriefs in Applied Sciences and Technology)” Book review: This book caters to those who have a keen interest in the application of computational intelligence in healthcare. It covers the latest research on emerging techniques and tools of computational intelligence and their applications in healthcare systems. The book also delves into advanced decision-making in healthcare, remote monitoring, healthcare logistics, operational excellence, and modern information systems. Various computational intelligence methods implemented in different areas of healthcare are discussed in this book, with contributions from practitioners, technology developers, and solution providers.

“Computational Collective Intelligence” Book review: This book targets students interested in learning about Computational Collective Intelligence and is particularly suitable for undergraduates studying Information Technology and computers. It serves as a respected platform for scientific research in computer-based methods of collective intelligence and their applications in areas such as group decision making, consensus computing, knowledge integration, semantic web, social networks, and multi-agent systems. Additionally, the book offers insights into cognitive learning through artificial intelligence.

“Computational Intelligence for Big Data Analysis: Frontier Advances and Applications (Adaptation, Learning, and Optimization)” Book Review: The intended audience for this book is postgraduate computer engineering students, as well as working professionals seeking a reference guide. The book delves into the topics of big data analysis and cloud computing, providing in-depth coverage and discussing potential applications in scientific computing. It also addresses the persistent issues in big data analysis and computational intelligence techniques, along with real-life scenarios and challenges encountered in cloud computing. With numerous figures, graphs, and practical case studies, the book offers a simplified understanding of the concepts.

“Applications of Computational Intelligence in Biology: Current Trends and Open Problems (Studies in Computational Intelligence)” Book Review: This book explores the utilization of computational intelligence in Biology, covering topics such as bioinformatics, computational genomics, protein structure prediction, modeling, and analysis. It serves as a valuable reference for both students and researchers in this field, discussing various computational techniques that can aid in data analysis and understanding. The primary objective of this book is to facilitate an exchange of expertise and concerns between computational intelligence and biology researchers, with contributions from both disciplines.

“Artificial Organic Networks (Studies in Computational Intelligence)” Book Review: Academic researchers and graduate students in computational intelligence, intelligent control, systems approximation, and complex networks can benefit from this book. It discusses the use of artificial hydrocarbon networks (AHNs), which are based on biological inspiration, as machine learning approximation models. The book covers different algorithms for approximation problems and explains how to create and characterize artificial organic networks and AHNs using traditional organic chemistry concepts. It also includes simulated and experimental examples of using fuzzy logic and genetic algorithms with organic neural networks, and explores modeling problems suitable for AHNs such as inference, classification, approximation, and audio-signal filtering. Moreover, the book proposes future directions for the development and implementation of AHNs. Additionally, the book provides a downloadable LabVIEW™ toolkit from the book’s page on springer.com, which allows students to design and implement their organic neural networks.

“Geometrical Multiresolution Adaptive Transforms: Theory and Applications (Studies in Computational Intelligence)” Book Review: The book delves into the adaptive methods of image approximation, with a particular focus on multi-smoothlets. In addition, it presents several novel concepts, such as the blurred multi-horizon class for approximating images with multiple edges, as well as the introduction of the shift-invariant multi-smoothly transform and sliding multi-smoothlets. The book is a valuable resource for mathematicians and computer scientists and can serve as a professional reference for students, researchers, and engineers. It also poses several open problems related to the use of geometrical multiresolution adaptive methods in image processing, analysis, and compression.

“Optimization of PID Controllers Using Ant Colony and Genetic Algorithms (Studies in Computational Intelligence)” Book Review: The book is a valuable resource for those interested in genetic algorithms and optimization algorithms. It covers a range of topics including the history and general applications of algorithms, as well as the basic concepts behind them. Additionally, the book delves into more specific areas such as real-time control algorithms, applications of genetic algorithms, ant colony-based algorithms, and control parameters of PID. Each topic is presented with detailed information and practical examples, making it an ideal resource for those looking to implement these techniques in system control.

“Computational Intelligence in Reliability Engineering: Evolutionary Techniques in Reliability Analysis and Optimization (Studies in Computational Intelligence)” Book Review: This is a comprehensive book that explores the use of evolutionary techniques in reliability analysis and optimization. The authors provide a detailed overview of the fundamental concepts and methodologies involved in computational intelligence, including genetic algorithms, neural networks, and fuzzy logic. The book covers a wide range of topics, from system reliability and maintenance to risk analysis and optimization. This book is a valuable resource for researchers and practitioners in the field of reliability engineering who are interested in applying computational intelligence techniques to their work.