This book is an essential guide for anyone looking to drive sustainable technological innovation, providing a comprehensive toolkit of decision-making methods and real-world applications to effectively manage technology in the era of Industry 5.0.
Sustainable technological innovation is critical for building a more sustainable future. As the world faces increasing environmental challenges, there is a pressing need for new and innovative technologies that can reduce resource consumption, mitigate environmental impacts, and promote sustainable development. This book focuses on the vital role of decision-making processes in achieving sustainability through technological innovation in the context of Industry 5.0. By delving into various decision-making methods and approaches employed to facilitate sustainable technological innovation across essential industries such as manufacturing, agriculture, and energy, the book will present both theoretical and applied research on managing technology, including decision-making connected to Industry 4.0 and 5.0, artificial intelligence, and other revolutionary techniques.
The book covers a wide range of topics, including multiple attribute decision theory, multiple objective decision-making, patent mining, big data analytics, and other decision-making methods and techniques, and features case studies and reviews that highlight real-world applications of sustainable technological innovation in different industries. The exploration of various decision-making methods and approaches for sustainable technological innovation makes this book an essential guide for those looking toward a sustainable Industry 5.0.
Readers will find the book:
- Emphasizes the role of decision-making processes in enabling sustainable technological innovation, providing a unique perspective on the subject;
- Covers a wide range of topics related to decision-making for sustainable technological innovation, including decision theory, multiple attribute and objective decision-making, patent mining, big data analytics, and case studies;
- Provides real-world examples and case studies that demonstrate the effectiveness of decision-making processes in promoting sustainable technological innovation across various industries;
- Features the latest research and developments in the field, ensuring that readers are up-to-date on the most current thinking on decision-making for sustainable technological innovation.
Audience
Researchers, practitioners, and students in the fields of computer science, data science, engineering, and mathematics, specifically interested in decision analytics and machine learning algorithms.
Kanak Kalita, PhD is an associate professor in the Department of Mechanical Engineering, Rajalakshmi Institute of Technology, Chennai, India. He has authored over 75 research articles, edited eight books, and given over 20 expert lectures. His research interests include machine learning, fuzzy decision making, metamodeling, process optimization, the finite element method, and composites.
J.V.N. Ramesh, PhD is an assistant professor in the Department of Computer Science and Engineering at Koneru Lakshmaiah University with over 18 years of teaching experience. He published several papers in national and international conferences and journals, as well as six textbooks. His research interests include wireless sensor networks, computer networks, deep learning, machine learning, and artificial intelligence.
M. Elangovan, PhD is currently working as a visiting professor at the Applied Science Research Centre, Applied Science Private University, Amman, Jordan. He has published over 90 articles in international journals and conferences and completed a number of consultancy projects. His research focuses on hydrodynamics, design, underwater marine vehicles, and industrial robots.
S. Balamurugan, PhD is the Director of Research and Development at Intelligent Research Consultancy Services. He has published 45 books, over 200 articles in international journals and conferences, and 35 patents. His research interests include artificial intelligence, soft computing, augmented reality, Internet of Things, big data analytics, cloud computing, and wearable computing.
