Series Editor(s): Dr. S. Balamurugan Scope: The world is on the cusp of several breakthroughs in artificial intelligence (AI), rapidly transforming industries and the research field. The "Leading-Edge Breakthroughs in Artificial Intelligence" book series is dedicated to exploring the most pioneering developments in artificial intelligence. The series aims to highlight both theoretical and practical advancements, offering insights on several cutting-edge advancements in AI and how the same could be deployed to solve complex problems in fields such as healthcare, finance, manufacturing, robotics, marketing, energy management, agriculture, autonomous systems, and many more. It also addresses critical issues like AI governance, ethics, trust, security, transparency, and societal impacts, making it an essential resource for researchers, engineers, and industry leaders looking to leverage AI for future breakthroughs. About the Series Editor(s): Submission to the series: Published and Forthcoming Titles Neuro-Symbolic AI: Concepts and Applications Decision Intelligence: Concepts, Applications, and Case Studies Human-Centered AI: Frameworks and Applications Responsible AI: Principles and Practices Platform Engineering: Concepts, Challenges, and Applications Composite AI: Fundamentals, Challenges, and Applications AI Trust, Risk and Security Management: Framework, Principles, and Practices Adaptive AI: Fundamentals, Challenges, and Applications Digital Immune System: Principles and Practices Visual AI: Fundamentals, Challenges, and Applications Causal AI: Fundamentals and Applications Embodied AI: Concepts, Challenges, and Applications Agentic AI: Concepts, Applications, and Case Studies Data-Centric AI : Concepts, Applications and Case Studies Sovereign AI: Frameworks, Principles, and Practices Accelerated AI : Energy Efficient Computing Systems for Large Language Models (LLMs) Actionable AI: Powered by Large Action Models (LAMs) Affective AI: Concepts, Applications, and Case Studies AI-Augmented Software Engineering: Principles and Practices Ambient Invisible Intelligence: Fundamentals, Challenges, and Applications Artificial Cognition: Technology, Applications, and Challenges Artificial General Intelligence: Principles and Practices Autonomous AI Agents: Foundations and Applications Principles of Confidential Computing Disinformation Security for Generative AI Applications: Concepts, Methodologies, and Use Cases Embedded AI: Concepts, Challenges, and Applications Graph Neural Networks (GNNs): Concepts and Applications Industry Cloud Platforms: Fundamentals, Applications, and Case Studies Multi-Modal Generative AI: Frameworks, Principles, and Practices Objective-Driven AI: Concepts, Applications, and Case Studies Physical AI for Robotics and Automation: Concepts, Techniques, and Applications Predictive AI: Concepts, Applications, and Case Studies Prescriptive AI: Concepts, Applications, and Case Studies Shadow AI: Concepts, Applications, and Security Small Language Models (SLMs) for Compact AI: Architectures, Principles, and Applications |