Kamalakanta Muduli – författare

With a need to find alternatives to the traditional face-to-face teaching and learning during the pandemic, this topical volume discusses the best use of technologies in higher education, addressing the growing role of AI in the digital transformation of higher education systems. With chapters from eminent professors, researchers, and others involved in higher education from a selection of different countries, the volume looks at blockchain and IoT technologies to create smart universities now and for the future.

With a need to find alternatives to the traditional face-to-face teaching and learning during the pandemic, this topical volume discusses the best use of technologies in higher education, addressing the growing role of AI in the digital transformation of higher education systems. With chapters from eminent professors, researchers, and others involved in higher education from a selection of different countries, the volume looks at blockchain and IoT technologies to create smart universities now and for the future.

This book addresses issues related to the integration of digital evolutionary technologies and provides solutions to various challenges encountered during the implementation process. With real-time case studies, the book explains the smart technologies available and their operational applications and benefits in the manufacturing sector.

Smart Manufacturing Technologies for Industry 4.0: Integration, Benefits, and Operational Activities assists in the understanding of the shifting paradigm in the manufacturing sector towards smart manufacturing and spotlights these technologies and the effects they are having on existing industries. It showcases Industry 4.0 as a promising research area in its infancy and offers insights into the role smart technologies are playing now and into the future. The book focuses on smart technologies’ rudiments, implementation, and integration for organizational development and offers insights on how to achieve resiliency through and because of these technologies.

This book presents real-time implementation discussions along with case studies that emphasize benefits and operational activities for engineers and managers. It’s also a very useful book for technology developers, academicians, data scientists, industrial engineers, researchers, and students interested in uncovering the latest innovations in a field that seeks current research on products and services.

This book addresses issues related to the integration of digital evolutionary technologies and provides solutions to various challenges encountered during the implementation process. With real-time case studies, the book explains the smart technologies available and their operational applications and benefits in the manufacturing sector.

Smart Manufacturing Technologies for Industry 4.0: Integration, Benefits, and Operational Activities assists in the understanding of the shifting paradigm in the manufacturing sector towards smart manufacturing and spotlights these technologies and the effects they are having on existing industries. It showcases Industry 4.0 as a promising research area in its infancy and offers insights into the role smart technologies are playing now and into the future. The book focuses on smart technologies’ rudiments, implementation, and integration for organizational development and offers insights on how to achieve resiliency through and because of these technologies.

This book presents real-time implementation discussions along with case studies that emphasize benefits and operational activities for engineers and managers. It’s also a very useful book for technology developers, academicians, data scientists, industrial engineers, researchers, and students interested in uncovering the latest innovations in a field that seeks current research on products and services.

The text comprehensively discusses computational models including artificial neural networks, agent-based models, and decision field theory for reliability engineering. It will serve as an ideal reference text for graduate students and academic researchers in the fields of industrial engineering, manufacturing engineering, computer engineering, and materials science.

The text covers swarm intelligence techniques including ant colony optimization, particle swarm optimization, cuckoo search, and genetic algorithms for solving complex industrial problems of the manufacturing industry as well as predicting reliability, maintainability, and availability of several industrial components.

The text comprehensively discusses computational models including artificial neural networks, agent-based models, and decision field theory for reliability engineering. It will serve as an ideal reference text for graduate students and academic researchers in the fields of industrial engineering, manufacturing engineering, computer engineering, and materials science.

The text covers swarm intelligence techniques including ant colony optimization, particle swarm optimization, cuckoo search, and genetic algorithms for solving complex industrial problems of the manufacturing industry as well as predicting reliability, maintainability, and availability of several industrial components.

This book discusses smart technologies and their influence in the field of manufacturing and industrial systems engineering, in the context of performability enhancement, and explores the development of the workforce for the execution of such smart and advanced technologies.

Smart Technologies for Improved Performance of Manufacturing Systems and Services discusses the integration of smart technology into the production process and supply chain to enhance the overall performance of manufacturing industries. As well as emphasizing the fundamentals of smart technologies, such as artificial intelligence, big data, and cyber-physical systems, it highlights the role that machine learning plays along with other smart technologies. Real-time case studies highlight the applications of smart digital technologies, and research insights into the area of performability and overall sustainable development round out the great range of discussions this reference book has to offer.

Managers and stakeholders seeking coverage on techniques and methods for integration into their organizations, as well as students and researchers in the field will find this book very useful.

This book discusses smart technologies and their influence in the field of manufacturing and industrial systems engineering, in the context of performability enhancement, and explores the development of the workforce for the execution of such smart and advanced technologies.

Smart Technologies for Improved Performance of Manufacturing Systems and Services discusses the integration of smart technology into the production process and supply chain to enhance the overall performance of manufacturing industries. As well as emphasizing the fundamentals of smart technologies, such as artificial intelligence, big data, and cyber-physical systems, it highlights the role that machine learning plays along with other smart technologies. Real-time case studies highlight the applications of smart digital technologies, and research insights into the area of performability and overall sustainable development round out the great range of discussions this reference book has to offer.

Managers and stakeholders seeking coverage on techniques and methods for integration into their organizations, as well as students and researchers in the field will find this book very useful.

This book presents cutting-edge findings that draw on the use of AI, the Industrial Internet of Things, Blockchain, and Co-Analytics for the development of Circular Economy (CE) models to make organizational activities more sustainable. A further goal is the development of Digital Technology (DT)–enabled support tools that can be used to further analyze the impact of DT and CE–enabled operational practices used to achieve resource and environmental sustainability.

Digital Technology Enabled Circular Economy: Models for Environmental and Resource Sustainability discusses the integration of digital technology-enabledcircular economy models into the manufacturing industries and its advantages for sustainability. It emphasizes the fundamentals and applications and their enactment, as well as integration for the overall organizational development. The book explains the role of digital technologies in food supply chains and multi-life cycle materials for sustainable development and highlights the development of the workforce to facilitate the implementation of smart and advanced technologies. This book presents the development of DT–enabled support tools used to analyze the impact of DT and CE–enabled operational practices on an organization and efforts to achieve resource and environmental sustainability. Case studies that demonstrate how smart digital technology can help firms improve their long-term performance by embracing circular operation methods are also included.

Students, academicians, and researchers, as well as managers and stakeholders who are interested in smart, sustainable production, and consumption, together with managers and stakeholders who are interested in ways of implementing them in their organizations, will find this book of interest. It will demonstrate, via the use of real-world case studies, how smart digital technology can help firms improve their long-term performance by embracing circular operating methods.

This book presents cutting-edge findings that draw on the use of AI, the Industrial Internet of Things, Blockchain, and Co-Analytics for the development of Circular Economy (CE) models to make organizational activities more sustainable. A further goal is the development of Digital Technology (DT)–enabled support tools that can be used to further analyze the impact of DT and CE–enabled operational practices used to achieve resource and environmental sustainability.

Digital Technology Enabled Circular Economy: Models for Environmental and Resource Sustainability discusses the integration of digital technology-enabledcircular economy models into the manufacturing industries and its advantages for sustainability. It emphasizes the fundamentals and applications and their enactment, as well as integration for the overall organizational development. The book explains the role of digital technologies in food supply chains and multi-life cycle materials for sustainable development and highlights the development of the workforce to facilitate the implementation of smart and advanced technologies. This book presents the development of DT–enabled support tools used to analyze the impact of DT and CE–enabled operational practices on an organization and efforts to achieve resource and environmental sustainability. Case studies that demonstrate how smart digital technology can help firms improve their long-term performance by embracing circular operation methods are also included.

Students, academicians, and researchers, as well as managers and stakeholders who are interested in smart, sustainable production, and consumption, together with managers and stakeholders who are interested in ways of implementing them in their organizations, will find this book of interest. It will demonstrate, via the use of real-world case studies, how smart digital technology can help firms improve their long-term performance by embracing circular operating methods.

Industry 4.0 and 5.0 applications will revolutionize production, enabling smart manufacturing machines to interact with their environments. These machines will become self-aware, self-learning, and capable of real-time data interpretation for self-diagnosis and prevention of production issues. They will also self-calibrate and prioritize tasks to enhance production quality and efficiency.

Computational Intelligence in Industry 4.0 and 5.0 Applications examines applications that merge three key disciplines: computational intelligence (CI), Industry 4.0, and Industry 5.0. It presents solutions using Industrial Internet of Things (IIoT) technologies, augmented by CI-based techniques, modeling, controls, estimations, applications, systems, and future scopes. These applications use data from smart sensors, processed through enhanced CI methods, to make smart automation more effective.

Industry 4.0 integrates data and intelligent automation into manufacturing, using technologies like CI, the IoT, the IIoT, and cloud computing. It transforms data into actionable insights for decision-making and process optimization, essential for modern competitive businesses managing high-speed data integration in production processes. Currently, Industries 4.0 and 5.0 are undergoing significant transformations due to advances in applying artificial intelligence (AI), big data analytics, telecommunication technologies, and control theory. These applications are increasingly multidisciplinary, integrating mechanical, control, and information technologies. However, they face such technical challenges as parametric uncertainties, external disturbances, sensor noise, and mechanical failures. To address these, this book examines such CI technologies as fuzzy logic, neural networks, and reinforcement learning and their application to modeling, control, and estimation. It also covers recent advancements in IIoT sensors, microcontrollers, and big data analytics that further enhance CI-based solutions in Industry 4.0 and 5.0 systems.

Industry 4.0 and 5.0 applications will revolutionize production, enabling smart manufacturing machines to interact with their environments. These machines will become self-aware, self-learning, and capable of real-time data interpretation for self-diagnosis and prevention of production issues. They will also self-calibrate and prioritize tasks to enhance production quality and efficiency.

Computational Intelligence in Industry 4.0 and 5.0 Applications examines applications that merge three key disciplines: computational intelligence (CI), Industry 4.0, and Industry 5.0. It presents solutions using Industrial Internet of Things (IIoT) technologies, augmented by CI-based techniques, modeling, controls, estimations, applications, systems, and future scopes. These applications use data from smart sensors, processed through enhanced CI methods, to make smart automation more effective.

Industry 4.0 integrates data and intelligent automation into manufacturing, using technologies like CI, the IoT, the IIoT, and cloud computing. It transforms data into actionable insights for decision-making and process optimization, essential for modern competitive businesses managing high-speed data integration in production processes. Currently, Industries 4.0 and 5.0 are undergoing significant transformations due to advances in applying artificial intelligence (AI), big data analytics, telecommunication technologies, and control theory. These applications are increasingly multidisciplinary, integrating mechanical, control, and information technologies. However, they face such technical challenges as parametric uncertainties, external disturbances, sensor noise, and mechanical failures. To address these, this book examines such CI technologies as fuzzy logic, neural networks, and reinforcement learning and their application to modeling, control, and estimation. It also covers recent advancements in IIoT sensors, microcontrollers, and big data analytics that further enhance CI-based solutions in Industry 4.0 and 5.0 systems.

Industry 4.0 and Industry 5.0 applications will revolutionize production, enabling smart manufacturing machines to interact with their environments. These machines will become self-aware, self-learning, and capable of real-time data interpretation for self-diagnosis and prevention of production issues. They will also self-calibrate and prioritize tasks to enhance production quality and efficiency.

Computational Intelligence for Analysis of Trends in Industry 4.0 and 5.0 examines the trends in applications that merge three key disciplines: Computational Intelligence (CI), Industry 4.0, and Industry 5.0. It presents solutions using industrial Internet of Things (IIoT) technologies, augmented by CI-based techniques, modeling, controls, estimations, applications, systems, and future scopes. These applications use data from smart sensors, processed through enhanced CI methods, to make smart automation more effective.

Industry 4.0 integrates data and intelligent automation into manufacturing, using technologies like CI, Internet of Things (IoT), IIoT, and cloud computing. It transforms data into actionable insights for decision-making and process optimization, essential for modern competitive businesses managing high-speed data integration in production processes. Currently, Industry 4.0 and Industry 5.0 are undergoing significant transformations due to advances in applying artificial intelligence (AI), big data analytics, telecommunication technologies, and control theory. These trends are increasingly multidisciplinary, integrating mechanical, control, and information technologies. However, they face technical challenges such as parametric uncertainties, external disturbances, sensor noise, and mechanical failures. To address these issues, this book examines trends such as CI technologies as fuzzy logic, neural networks, and reinforcement learning and their application to modeling, control, and estimation. It also covers recent advancements in IIoT sensors, microcontrollers, and big data analytics that further enhance CI-based solutions in Industry 4.0 and Industry 5.0 systems.

Industry 4.0 and Industry 5.0 applications will revolutionize production, enabling smart manufacturing machines to interact with their environments. These machines will become self-aware, self-learning, and capable of real-time data interpretation for self-diagnosis and prevention of production issues. They will also self-calibrate and prioritize tasks to enhance production quality and efficiency.

Computational Intelligence for Analysis of Trends in Industry 4.0 and 5.0 examines the trends in applications that merge three key disciplines: Computational Intelligence (CI), Industry 4.0, and Industry 5.0. It presents solutions using industrial Internet of Things (IIoT) technologies, augmented by CI-based techniques, modeling, controls, estimations, applications, systems, and future scopes. These applications use data from smart sensors, processed through enhanced CI methods, to make smart automation more effective.

Industry 4.0 integrates data and intelligent automation into manufacturing, using technologies like CI, Internet of Things (IoT), IIoT, and cloud computing. It transforms data into actionable insights for decision-making and process optimization, essential for modern competitive businesses managing high-speed data integration in production processes. Currently, Industry 4.0 and Industry 5.0 are undergoing significant transformations due to advances in applying artificial intelligence (AI), big data analytics, telecommunication technologies, and control theory. These trends are increasingly multidisciplinary, integrating mechanical, control, and information technologies. However, they face technical challenges such as parametric uncertainties, external disturbances, sensor noise, and mechanical failures. To address these issues, this book examines trends such as CI technologies as fuzzy logic, neural networks, and reinforcement learning and their application to modeling, control, and estimation. It also covers recent advancements in IIoT sensors, microcontrollers, and big data analytics that further enhance CI-based solutions in Industry 4.0 and Industry 5.0 systems.

The Asia-Pacific region, home to some of the world’s fastest-growing economies, faces a range of complex challenges, including environmental degradation, the increasing frequency of natural hazards, and rapid urbanization. Addressing these issues, which many countries across the globe are facing, requires innovative, interdisciplinary approaches to promote sustainable development and enhance resilience. Geographic information systems (GIS), when combined with multi-criteria decision-making (MCDM) techniques and advanced technologies such as artificial intelligence (AI), offer powerful tools to tackle these multifaceted problems. AIGIS integrates AI with GIS to derive insights from geospatial data. The fusion of AI techniques with GIS enhances data analysis, visualization, and decision-making. Artificial Intelligence, Geographic Information Systems, and Multi-Criteria Decision-Making for Improving Sustainable Development explore how these integrated tools can support decision-making processes aimed at advancing sustainable development.

Drawing on research and insights from diverse disciplines, the book looks at how GIS, MCDM, and AI can provide solutions for disaster risk reduction, environmental monitoring, urban planning, and natural resource management. Through diverse case studies and theoretical explorations, this book highlights the value of integrated geospatial tools in facilitating informed decision-making and fostering resilient societies in the face of evolving challenges. It covers a wide range of topics, including the following:

Bringing together the work of academicians, practitioners, and decision-makers, the book reflects the growing recognition towards effective and sustainable solutions to complex problems, which require a multidimensional approach, integrating scientific, economic, and social considerations. By providing the latest research and practical applications of MCDM, AI, and GIS, it contributes to ongoing efforts to build a more sustainable and resilient future for the Asia-Pacific region, as well as for the world.

The Asia-Pacific region, home to some of the world’s fastest-growing economies, faces a range of complex challenges, including environmental degradation, the increasing frequency of natural hazards, and rapid urbanization. Addressing these issues, which many countries across the globe are facing, requires innovative, interdisciplinary approaches to promote sustainable development and enhance resilience. Geographic information systems (GIS), when combined with multi-criteria decision-making (MCDM) techniques and advanced technologies such as artificial intelligence (AI), offer powerful tools to tackle these multifaceted problems. AIGIS integrates AI with GIS to derive insights from geospatial data. The fusion of AI techniques with GIS enhances data analysis, visualization, and decision-making. Artificial Intelligence, Geographic Information Systems, and Multi-Criteria Decision-Making for Improving Sustainable Development explore how these integrated tools can support decision-making processes aimed at advancing sustainable development.

Drawing on research and insights from diverse disciplines, the book looks at how GIS, MCDM, and AI can provide solutions for disaster risk reduction, environmental monitoring, urban planning, and natural resource management. Through diverse case studies and theoretical explorations, this book highlights the value of integrated geospatial tools in facilitating informed decision-making and fostering resilient societies in the face of evolving challenges. It covers a wide range of topics, including the following:

Bringing together the work of academicians, practitioners, and decision-makers, the book reflects the growing recognition towards effective and sustainable solutions to complex problems, which require a multidimensional approach, integrating scientific, economic, and social considerations. By providing the latest research and practical applications of MCDM, AI, and GIS, it contributes to ongoing efforts to build a more sustainable and resilient future for the Asia-Pacific region, as well as for the world.