Master AI-powered automation techniques for next-generation industrial and societal applications Intelligence-driven Automation Techniques delivers a comprehensive roadmap for transforming traditional automation into intelligent, AI-powered systems. Written by leading experts from premier institutions, this edited volume addresses integrating machine learning and deep learning into automation systems across industrial, healthcare, robotics, and infrastructure domains. The book delivers detailed coverage of deep learning architectures, intelligent digital twins, AI-based decision-making algorithms, and industrial communication protocols. Readers gain practical insights into autonomous robots, intelligent navigation, medical imaging with AI, brain-computer interfaces, autonomous transportation, smart agriculture, and enterprise automation. Each chapter combines theoretical foundations with real-world implementation strategies addressing specific domain requirements. Readers will find: Comprehensive coverage of intelligent automation across seven major application domains including industrial systems, robotics, healthcare, infrastructure, and smart environments Practical algorithm designs and implementation strategies for integrating AI and machine learning into existing automation frameworks Real-world applications spanning autonomous transportation, medical nanorobots, intelligent monitoring systems, and AI-optimized aviation operations In-depth analysis comparing intelligent automation systems with traditional approaches, identifying positive and negative impacts across different sectors Intelligence-driven Automation Techniques is essential for researchers advancing automation theory, graduate students specializing in AI and robotics, and professionals implementing intelligent systems in industrial, healthcare, or infrastructure settings. This comprehensive reference uniquely bridges theoretical concepts with practical implementations across the full spectrum of emerging automation applications.
