Advances in Intelligent Systems and Machine Learning

This timely volume offers a comprehensive exploration of cutting-edge developments in intelligent systems and machine learning, making it an essential resource for researchers, practitioners, and graduate students. Bridging theoretical foundations with practical implementations, the book addresses real-world applications in healthcare, finance, smart systems, and cybersecurity. Each chapter is authored by leading experts and includes case studies, algorithms, and comparative evaluations to demonstrate practical relevance. The book also highlights emerging trends such as explainable AI, federated learning, and hybrid intelligence, offering readers a forward-looking perspective. Designed for both academic and industry audiences, it provides actionable insights into deploying ML models in complex environments. With contributions from a global network of scholars, it reflects diverse perspectives and interdisciplinary approaches. Its accessible structure and rich content make it a valuable reference for curriculum enhancement, research development, and solution-driven innovation across multiple domains of AI and machine learning.