This book presents key computational intelligence approaches applied across diverse areas of computer science, demonstrating their effectiveness in solving real-world problems. It highlights how nature-inspired methods are shaping the future of intelligent systems and offering powerful, efficient, and elegant solutions to complex challenges.
Computational intelligence (CI) extends far beyond the optimization of complex computations. It provides robust, generic, and adaptable mechanisms for addressing challenging problems across science and technology where traditional mathematical reasoning encounters uncertainty, nonlinearity, and complexity.
In computer science, CI strongly influences algorithm design, system architectures, and optimization schemes. Unlike classical artificial intelligence, which is largely rule- and logic-based, computational intelligence relies on nature-inspired methodologies that model learning, adaptation, and evolution.
Traditionally, CI has been built upon three foundational paradigms: Neural networks, fuzzy systems, and evolutionary computation. Neural networks emulate the structure and learning behavior of the human brain; fuzzy systems incorporate linguistic reasoning to manage uncertainty and imprecise data; and evolutionary computation draws inspiration from biological evolution, incorporating mechanisms such as selection, mutation, and reproduction.
Today, CI has expanded to include machine learning methods, swarm intelligence, support vector machines, and chaotic systems. These techniques enable faster, more accurate, and less complex decision-making across a wide range of computational problems.
Table of Contents:
1. Artificial Intelligence in Healthcare: Revolutionizing Medical Innovations 2. Beyond Human Insight: How AI is Crafting the Next Era of Healthcare Innovation 3. Stacked Machine Learning for Accurate Prediction of Dual-band Circular Patch Antenna Resonant Frequencies 4. Cyber Security Threat Detection using Computational Intelligence 5. Optimizing Agile Software Development Process: A Focus on User Story Splitting Techniques 6. Enhancing Data Security Through Secure Data Transactions: Techniques, and Future Directions 7. Optimizing Mental Health Interventions Through AI-based Therapies 8. Intelligent Blockchain Architectures: Design, Challenges, and Innovations 9. Shifting of Origin and Error Minimization for Realistic Representation of Basic Geometrical Entities on a Computer Screen 10. Big Data Analytics using Computational Intelligence 11. ML-driven approaches to improve cyber security in financial transactions
About the Author :
Prof. (Dr.) Sushil Chandra Dimri, M.Tech., Ph.D. (Computer Science), is currently working with Graphic Era deemed to be University as professor in the Department of Computer Science and Engineering. His teaching industry experience is more than 26 years. Dr. Sushil Chandra Dimri has authored 9 books on mathematics and computer science, i.e. Engineering Mathematics, Computer Based Numerical Techniques, Probability and Statistics, Graphical User Interface, Web Programming, Fundamental of Computers, Algorithms-design and Analysis, C-programming and Mathematical Foundation of Computers. He has published 60+ research papers in national and international journals. His areas of interest are machine learning techniques, designing algorithms and resource optimization. He is an editor and reviewer for many reputed journals.
Prof. (Dr.) Lata Nautiyal is an accomplished computer science researcher and academician with extensive expertise in software engineering and cyber security. With over a decade of experience in academic teaching, research, supervision, and leadership, she has demonstrated exceptional ability to work both independently and as a collaborative team member. Her research interests focus on cyber security and security requirements engineering, striving to innovate and enhance technology to better align with user needs and expectations. She has published 22+ research papers in various national and international journals and is an editor and reviewer for various international journals.
Dr. Akshay Kumar received his B.Sc. and M.Sc. degrees in science from Chaudhary Charan Singh University, Meerut, India, in 2010 and 2012, and his Ph.D. degree major in mathematics and minor in computer science from G. B. Pant University of Agriculture and Technology, Pantnagar, India, in 2017. He is currently working as an Assistant Professor in the Department of Mathematics, Graphic Era Hill University, Dehradun in India. He has more than 8 years teaching experience. He is a regular reviewer for more than 60 international journals including Elsevier, Springer, Emerald, MDPI, Cambridge University Press, World Scientific, River Publishers, GI Global Publisher and many other publishers. He has published 100 plus research papers in many national and international journals of repute and presented his works at national and international conferences. Also, he has published 7 books (edited) with international publishers. His fields of research are reliability theory, fuzzy reliability, signature reliability and applied mathematics.
Dr. Richa Indu gained her Ph.D. in computer science and engineering from Graphic Era (Deemed to be University), Dehradun, India and her M.Tech (Hons) in computer science and engineering from Uttarakhand Technical University, Dehradun. Furthermore, she has completed an M.Sc. (Gold medalist) in information technology at Hemavati Nandan Bahuguna University, Srinagar (Garhwal), India. She has made some noteworthy contributions with 7 published research articles in reputed journals, 5 conference papers, and 2 book chapters. Her research interests encompass data sciences, programming languages, and algorithm design, reflecting her effort to advance the frontiers of technology.
