Divneet Singh Kapoor – författare
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Intelligent Systems for Neurocognition and Human-Robot-Computer Interaction explores the transformative potential of intelligent systems in enhancing human well-being through advancements in neurocognitive technologies, robotics, and AI-driven healthcare. This book addresses the fragmented knowledge at the intersection of these fields, enabling readers to understand the latest advancements and discover practical strategies for implementation. It highlights the importance of ethical considerations and governance in deploying AI and robotics in healthcare settings. Each chapter covers various aspects, including personalized medicine, human-robot interaction, cognitive robotics, wearable technologies, and the role of AI in mental health. The book also discusses the implications of cloud computing, big data, and human-computer interaction in enhancing cognitive and emotional well-being.Explores how intelligent systems enhance human well-being through neurocognitive technologies, robotics, and AI-driven healthcareProvides real-world case studies and applications demonstrating how intelligent systems are deployed in healthcare, rehabilitation, and personal well-beingAddresses ethical concerns and governance frameworks for the responsible development and deployment of AI and robotics in healthcare
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LLMs in Practice: Real World Applications, Challenges and Success Stories offers a deeply applied, interdisciplinary perspective on how Large Language Models (LLMs) are being integrated into the real world-spanning industries, healthcare, education, governance, mental health, creative domains, and intelligent systems. The book presents a blend of technical insights, sector-specific applications, governance frameworks, and ethical considerations. Designed for both academic and professional audiences, it equips readers to responsibly deploy LLMs while fostering innovation, equity, and scalability. LLMs in Practice: Real World Applications, Challenges & Success Stories addresses a significant gap in current literature by offering a focused and practice-oriented examination of how Large Language Models (LLMs) are being applied across diverse real-world domains. While there is widespread academic and public interest in generative AI, there exists no single resource that cohesively captures its deployment frameworks, sector-specific applications, ethical considerations, and pedagogical integration-especially from a multidisciplinary and global perspective. This book provides deployment guidance, prompt optimization, and reliability strategies; governance frameworks, risk mitigation tools, and audit strategies; and offers case studies, instructional models, project templates, career-aligned examples, and skill-building paths.Provides a comprehensive understanding of how LLMs are transforming sectors such as healthcare, education, law, and businessServes as a reference for researchers, practitioners, and innovators seeking to design, evaluate, and scale generative AI systemsSupports educators and students by offering structured resources for teaching, learning, and project-based engagement with LLMsPromotes responsible innovation by highlighting frameworks for ethical governance, transparency, and inclusive AI adoption across varied socio-economic contexts