Bitan Misra

This book explores one of the most critical and emerging fields in artificial intelligence, machine unlearning. As data privacy concerns grow and regulations like GDPR demand compliance, this book provides a comprehensive guide to selectively removing learned information from machine learning models without sacrificing performance or requiring complete retraining. Covering foundational principles, advanced algorithms, benchmarking tools, and real-world case studies in healthcare, finance, and social media, the book bridges the gap between theory and practice. It also addresses ethical, legal, and societal implications, offering insights into creating trustworthy AI systems. This book is an essential resource for understanding and implementing machine unlearning in the era of responsible AI. It is for researchers, AI practitioners, and professionals seeking to understand and implement machine unlearning for data privacy, ethical AI design, and regulatory compliance.

Innovation and Knowledge Management in Manufacturing Industry: Case Studies and Applications is a valuable resource to organizations in the modern manufacturing setting where change is fast, competition is intense, and where technological changes take place continuously. The book provides a thorough analysis of the crucial connection between the practices of innovation and knowledge management systems and introduces a detailed framework that is meant to help manufacturers cope with the challenges related to digital transformation and the incorporation of Industry 4.0 technologies. It contains practical case studies, which are the successful experiences of knowledge management and innovation strategies in practice and they contain the valuable insights and best practices that have been learned by the leaders in the industry. Some of the major issues covered include the assessment of Industry 4.0 maturity, the overall organization of the innovation cycle, and the development of the efficient knowledge transfer methods that enable employees to exchange information and contribute to the consistent improvement. Addressing the needs of manufacturing executives, knowledge management professionals, and industrial engineers, the book can equip the readers with the knowledge, and strategies that will help them improve the levels of innovation in their organization and efficiently manage the knowledge resources. Through innovation, this book will expand readers' organizational potential and inspire them about the future of manufacturing.

This book explores the concept of employee engagement, which is crucial for a company's success, as it is closely linked to job satisfaction and employee morale. Engaged workers tend to be more productive, efficient, and committed to the values and objectives of the company. Artificial intelligence plays a significant role in HR analytics, particularly in automating tedious tasks like data collection and organization from multiple sources. AI-driven employee engagement software can analyze employee feedback, surveys, and social media posts to capture the overall sentiment of the workforce. This allows HR teams to gain insights into employee experiences and perceptions, uncover areas for improvement, and address any issues that require attention. By leveraging past data and employing machine learning algorithms, AI enables HR professionals to predict turnover rates, forecast employee engagement levels, and identify potential flight risks.

IoMT Applications in Healthcare 5.0 introduces the concept of Healthcare 5.0, emphasizing the transformative potential of intelligent diagnosis, remote treatment, and smart health management. Sections define IoMT and its multi-tiered architecture, discussing its pivotal role in improving electronic device productivity in healthcare, explore IoMT applications in Healthcare 5.0, discuss remote physical therapy using IoMT in Healthcare 5.0, delve into the security and privacy considerations of IoMT in healthcare, and address how to overcome challenges and future directions in IoMT. The book concludes by summarizing key findings, providing a comprehensive overview of the discussed topics. Written by experts in the field, this is a key text for Biomedical Engineers, Data Scientists, and IoT Developers and specialists who work in the Internet of Medical Things. Graduate students of IoT engineering, biology, biostatistics, biomedical sciences, bioinformatics, health-informatics, and Interdisciplinary sciences will also find this an invaluable resource.

This book explains basic ideas behind several methods used in artificial intelligence-based knowledge management techniques. It also shows how these techniques are applied in practical contexts in different education sectors. The book discusses AI-based knowledge management applications, AI-empowered knowledge management in primary and higher education, and technical and ethical challenges and opportunities.