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{\bf{Principles of Hybrid Intelligent Systems}} is an edited research volume that that presents a coherent and application-oriented view of hybrid intelligence—an emerging paradigm that integrates machine learning, deep learning, classical artificial intelligence, signal processing, optimization techniques, and domain knowledge to address complex real-world challenges. As isolated AI models often struggle with issues of robustness, interpretability, and generalization, hybrid intelligent systems provide a principled framework by combining complementary methodologies. This volume establishes the foundational principles of hybrid intelligence while emphasizing its capability to deliver reliable, scalable, and context-aware decision-making in data-intensive and dynamic environments. 

The edited volume brings together contributions spanning a wide range of contemporary domains, including medical image understanding and clinical decision support, biomedical signal interpretation and health monitoring, smart agriculture and precision farming, industrial IoT monitoring and predictive maintenance, financial risk assessment and fraud detection, autonomous mobility and traffic prediction, remote sensing and environmental change detection, natural language understanding for low-resource languages, emotion recognition using multimodal human signals, cybersecurity and intrusion detection, smart education and personalized learning systems, energy management and smart grid optimization, vision–language models for robotics and human–machine interaction, sustainable development and decision support, and AI-enabled tools for software automation and intelligent code analysis. Intended for researchers, academicians, postgraduate students, and industry professionals, this book serves as a comprehensive reference on the principles, architectures, and practical deployments of hybrid intelligent systems, while also outlining future research directions in this rapidly evolving field.

     

