• Foreword

• Preface

Part I: Foundations and Overview of Big data Analytics

• Introduction

• Fundamental Concepts of Big Data Analytics

• Exploring Big Data Analytics

• Data Collection and Pre-Processing in Big Data

Part II: Computational Intelligence Approaches for Big Data Analytics

• Artificial Neural Networks for Big data Analytics

• Fuzzy Logic for Big Data Analytics

• Evolutionary Computation for Big Data Analytics

• Swarm Intelligence for Big Data Analytics

• Conclusion of the book

• References

• Index

• Comprehensive Coverage: Offers a detailed exploration of Big Data Analytics, covering both foundational concepts and advanced computational techniques such as Artificial Neural Networks, Fuzzy Logic, Evolutionary Computation, and Swarm Intelligence.

• Real-World Case Studies: Includes numerous practical case studies that demonstrate the application of Big Data technologies across diverse industries, providing readers with hands-on examples of problem-solving.

• Step-by-Step Exercises: Provides structured exercises and research problems at the end of each chapter to enhance understanding and encourage active learning and practical application of concepts.

• Multi-Disciplinary Approach: Bridges the gap between theory and practice by integrating computational intelligence methods with data science, making the book relevant for students, researchers, and professionals from various disciplines.

• Future-Oriented Insights: Discusses future trends in Big Data Analytics, including hybrid algorithms and automated feature selection, offering readers a forward-looking perspective on emerging technologies.

• Extensive Reference Material: Contains a comprehensive reference section, making it a valuable resource for academic research and further reading on cutting-edge developments in Big Data and Computational Intelligence.