This book explores the role of computational intelligence techniques in addressing power quality challenges in modern power systems. It examines the integration of multiple energy sources, renewable energy systems, and energy storage units while analyzing nonlinearities in power plants. The book introduces optimization methods, including machine learning, genetic algorithms, and neural networks, to enhance power system stability, reliability, and efficiency. It bridges theoretical insights with real-world applications in modern power networks. • Explores the use of AI techniques, including genetic algorithms and fuzzy logic, for power system analysis and optimization. • Examines power quality issues such as frequency deviations, tie-line power flow variations, and area control errors• Discusses the tuning of secondary controllers, including PID controllers, using advanced optimization algorithms• Addresses PQ challenges posed by renewable energy integration and energy storage units• Covers real-time system monitoring and control for maintaining power quality under nonlinear operating conditions• Provides a structured approach to analysing system behaviour using mathematical modeling and time-domain simulationsThis book serves as a valuable resource for researchers, professionals, and students in power system engineering, computational intelligence, and smart grid technologies.

This book is a practical guide to the integration of generative AI into human resources, covering use cases across recruitment, onboarding, training, and retention. It offers strategic guidance, ethical frameworks, and technical insights to help organisations deploy generative AI effectively while safeguarding fairness, privacy, and compliance. It covers AI-driven applications in talent acquisition, onboarding, employee engagement, career development, and decision-making, while addressing key concerns such as algorithmic bias, data privacy, and regulatory compliance. With practical case studies and deployment templates, the book provides a structured approach to evaluating and integrating generative AI tools within HR systems. From resume screening to personalised learning paths and predictive analytics, this book bridges the gap between AI technologies and HR operations to help organisations build agile, ethical and employee-centric workplaces. Examines generative AI’s applications across recruitment, onboarding, L&D, engagement, and retentionProvides strategic frameworks for integrating AI into HR ecosystemsIncludes case studies and practical deployment examples from real organizationsAddresses key issues of AI bias, explainability, data privacy and complianceOffers guidance on selecting and evaluating HR-specific AI toolsExplains core AI concepts for both technical and non-technical HR professionalsIncludes future-focused insights into AI-driven workforce planning and employee experience designThis book is for HR professionals, business leaders, AI developers, and organizational strategists seeking to understand, implement, and manage generative AI solutions in the HR domain.

This book is a practical guide to the integration of generative AI into human resources, covering use cases across recruitment, onboarding, training, and retention. It offers strategic guidance, ethical frameworks, and technical insights to help organisations deploy generative AI effectively while safeguarding fairness, privacy, and compliance. It covers AI-driven applications in talent acquisition, onboarding, employee engagement, career development, and decision-making, while addressing key concerns such as algorithmic bias, data privacy, and regulatory compliance. With practical case studies and deployment templates, the book provides a structured approach to evaluating and integrating generative AI tools within HR systems. From resume screening to personalised learning paths and predictive analytics, this book bridges the gap between AI technologies and HR operations to help organisations build agile, ethical and employee-centric workplaces. Examines generative AI’s applications across recruitment, onboarding, L&D, engagement, and retentionProvides strategic frameworks for integrating AI into HR ecosystemsIncludes case studies and practical deployment examples from real organizationsAddresses key issues of AI bias, explainability, data privacy and complianceOffers guidance on selecting and evaluating HR-specific AI toolsExplains core AI concepts for both technical and non-technical HR professionalsIncludes future-focused insights into AI-driven workforce planning and employee experience designThis book is for HR professionals, business leaders, AI developers, and organizational strategists seeking to understand, implement, and manage generative AI solutions in the HR domain.

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 role of computational intelligence techniques in addressing power quality challenges in modern power systems. It examines the integration of multiple energy sources, renewable energy systems, and energy storage units while analyzing nonlinearities in power plants. The book introduces optimization methods, including machine learning, genetic algorithms, and neural networks, to enhance power system stability, reliability, and efficiency. It bridges theoretical insights with real-world applications in modern power networks. • Explores the use of AI techniques, including genetic algorithms and fuzzy logic, for power system analysis and optimization. • Examines power quality issues such as frequency deviations, tie-line power flow variations, and area control errors• Discusses the tuning of secondary controllers, including PID controllers, using advanced optimization algorithms• Addresses PQ challenges posed by renewable energy integration and energy storage units• Covers real-time system monitoring and control for maintaining power quality under nonlinear operating conditions• Provides a structured approach to analysing system behaviour using mathematical modeling and time-domain simulationsThis book serves as a valuable resource for researchers, professionals, and students in power system engineering, computational intelligence, and smart grid technologies.

This book is a practical guide to the integration of generative AI into human resources, covering use cases across recruitment, onboarding, training, and retention. It offers strategic guidance, ethical frameworks, and technical insights to help organisations deploy generative AI effectively while safeguarding fairness, privacy, and compliance. It covers AI-driven applications in talent acquisition, onboarding, employee engagement, career development, and decision-making, while addressing key concerns such as algorithmic bias, data privacy, and regulatory compliance. With practical case studies and deployment templates, the book provides a structured approach to evaluating and integrating generative AI tools within HR systems. From resume screening to personalised learning paths and predictive analytics, this book bridges the gap between AI technologies and HR operations to help organisations build agile, ethical and employee-centric workplaces. Examines generative AI’s applications across recruitment, onboarding, L&D, engagement, and retentionProvides strategic frameworks for integrating AI into HR ecosystemsIncludes case studies and practical deployment examples from real organizationsAddresses key issues of AI bias, explainability, data privacy and complianceOffers guidance on selecting and evaluating HR-specific AI toolsExplains core AI concepts for both technical and non-technical HR professionalsIncludes future-focused insights into AI-driven workforce planning and employee experience designThis book is for HR professionals, business leaders, AI developers, and organizational strategists seeking to understand, implement, and manage generative AI solutions in the HR domain.

This book is a practical guide to the integration of generative AI into human resources, covering use cases across recruitment, onboarding, training, and retention. It offers strategic guidance, ethical frameworks, and technical insights to help organisations deploy generative AI effectively while safeguarding fairness, privacy, and compliance. It covers AI-driven applications in talent acquisition, onboarding, employee engagement, career development, and decision-making, while addressing key concerns such as algorithmic bias, data privacy, and regulatory compliance. With practical case studies and deployment templates, the book provides a structured approach to evaluating and integrating generative AI tools within HR systems. From resume screening to personalised learning paths and predictive analytics, this book bridges the gap between AI technologies and HR operations to help organisations build agile, ethical and employee-centric workplaces. Examines generative AI’s applications across recruitment, onboarding, L&D, engagement, and retentionProvides strategic frameworks for integrating AI into HR ecosystemsIncludes case studies and practical deployment examples from real organizationsAddresses key issues of AI bias, explainability, data privacy and complianceOffers guidance on selecting and evaluating HR-specific AI toolsExplains core AI concepts for both technical and non-technical HR professionalsIncludes future-focused insights into AI-driven workforce planning and employee experience designThis book is for HR professionals, business leaders, AI developers, and organizational strategists seeking to understand, implement, and manage generative AI solutions in the HR domain.

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.