This book comprehensively covers an extensive array of topics within the realm of computational intelligence. The scope encompasses fuzzy logic, elucidating the principles and applications of this mathematical framework designed to handle uncertainty and imprecision in data. It delves into the fundamentals of neural networks, exploring their applications in diverse fields such as pattern recognition, image processing, and natural language processing.

• Covers a wide range of topics related to computational intelligence, including fuzzy logic, neural networks, and explainable artificial intelligence interpretation
• Discusses the challenges and limitations of computational intelligence techniques, such as the interpretability of artificial intelligence models and the need for ethical considerations in artificial intelligence applications
• Explains neural networks in natural language processing, neural networks in computer vision, and applications of neural networks in real-world scenarios
• Presents applications of computational intelligence in diverse areas such as robotics, transportation, web mining, healthcare, and finance
• Illustrates reinforcement learning-based control systems, adaptive control systems, neuro-fuzzy control systems, and applications of intelligent control systems

The text is primarily written for senior undergraduates, graduate students, and academic researchers in diverse fields, including electrical engineering, electronics and communication engineering, computer science and engineering, and information technology.

Chapter 0. Front Matter. Section I. Foundations of Machine Learning and Intelligent Systems. Chapter 1. Engineering Students Employability for Campus Placement: A State-of-the-Art Survey Using Machine Learning-Based Approaches. Chapter 2. Enhancing E-commerce Sales Prediction: Overcoming Data and Complexity Challenges with LSTM Models. Chapter 3. A Review: How to Employ Generative AI to Build Informative Recommendation Systems. Section II. Computational Intelligence, Optimization, and Cybersecurity. Chapter 4. Applications of Computational Intelligence. Chapter 5. Optimizing and Improving the Quality of Mined Association Rules Using a Hybrid Genetic Algorithm and Grey Wolf Algorithm. Chapter 6. Critical Role of Artificial Intelligence in Cybersecurity in the Digital Era. Section III. AI Applications in Business, IoT, and Human Behavior. Chapter 7. Gender Dynamics in AI-Driven Online Fashion Purchases: An Impulse Buying Analysis. Chapter 8. Machine Learning Algorithms Enable Data-Driven Decision-Making by Internet of Things Devices. Section IV. AI in Healthcare and Bioinformatics. Chapter 9. Applications of Machine Learning for Cardiovascular Diseases Prediction. Chapter 10. Comparative Performance Analysis of Machine Learning and Hardware Techniques for DNA Sequence Alignment. Chapter 11. Biological Sequence Alignment Using Burrows-Wheeler Transformation: An Implementation Roadmap. Chapter 12. Early Detection of Parkinson's Disease Using Machine Learning and Deep Learning Techniques: A Multimodal Analysis of Voice and Spiral Image Data. Section V. Networking, Cloud, and Steganography. Chapter 13. Machine Learning for Networking and Cybersecurity. Chapter 14. AI-Driven Predictive Analytics for Multi-Cloud Management. Chapter 15. Local Feature-Based Steganographic Scheme Using Chaos Theory and Arnold Scrambling. Chapter 16. The Convergence of Artificial Intelligence and 5G/6G Networks: Challenges, Recent Advancements, and Future Trends. Section VI. Defense, Emotion Recognition, and Explainable AI. Chapter 17. Vidya Cipher: Revolutionizing Financial Security with Cryptographic Intelligence. Chapter 18. Army Vehicle Detection Using YOLO-v7, YOLO-v8, and YOLO-v9. Chapter 19. Emotion Detection and Anomaly Recognition in Crowds: Spotting Suspicious Behaviour. Chapter 20. Explainable AI: Bridging the Gap Between Machine Learning and Interpretability Potential in Humans. Section VII. Real-Time Systems and Agriculture. Chapter 21. Emergency Medical System Based on Users’ Real-Time Location. Chapter 22. A Perspective Analysis of Agricultural Yield Prediction with Advanced AI and Intelligent Systems.