Autorentext

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.

Klappentext

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.

  Inhalt

  Foreword xiii

  Preface xv

  Part I: Frameworks for Sustainable Technological Innovation 1

  1 Green Technology Planning in Developing Countries: An Innovative Decision-Making Framework 3
  Vamsidhar Talasila, Chandrashekhar Goswami and Muniyandy Elangovan

  1.1 Introduction 4

  1.2 Related Works 5

  1.3 Proposed Methodology 6

  1.3.1 SWOT, G-TOPSIS and Integrated GASM Methods 6

  1.3.2 SWOT-GASM Method 7

  1.3.3 Process of Grey Analytical Hierarchy 7

  1.3.4 Grey Numbers 9

  1.3.5 G-TOPSIS Approach 10

  1.4 Results and Discussion 13

  1.4.1 Ranking of SWOT Factors 14

  1.4.2 Grey Analytical Hierarchical Process Results 14

  1.4.2.1 Overall Ranking of SWOT Subfactors 14

  1.4.2.2 Ranking of Threats Subfactors 16

  1.4.2.3 Ranking of Opportunities Subfactors 16

  1.4.2.4 Ranking of Weaknesses Subfactors 17

  1.4.2.5 Ranking of Strengths Subfactors 17

  1.4.3 Grey TOPSIS Results 18

  1.4.3.1 WO Strategies 19

  1.4.3.2 ST Strategies 20

  1.4.3.3 SO Strategies 21

  1.4.3.4 WT Strategies 21

  1.5 Conclusion 22

  References 22

  2 Evaluating Sustainability Indicators for Green Building Manufacture with Fuzzy-Based MODM Technique 25
  Chandrshekhar Goswami, Muniyandy Elangovan and Puppala Ramya

  2.1 Introduction 26

  2.2 Related Works 27

  2.3 Proposed Method 28

  2.3.1 Enhanced Fuzzy DEMATEL 29

  2.4 Results and Discussion 32

  2.5 Conclusion 41

  References 41

  3 Sustainable Energy Options: Qualitative TOPSIS Method for Challenging Scenarios 45
  Muniyandy Elangovan, Puppala Ramya and Chandrashekhar Goswami

  3.1 Introduction 46

  3.2 Related Works 48

  3.3 Methods and Materials 49

  3.3.1 Preliminaries 50

  3.3.1.1 Models of Absolute Qualitative Order of Magnitude 50

  3.4 Analytical Hierarchy Process Method to Compute Weights 51

  3.5 The Proposed Q-TOPSIS Technique 52

  3.6 Results and Discussion 53

  3.6.1 A Q-TOPSIS Investigation that Demonstrates How to Choose Sustainable Energy Sources 53

  3.6.1.1 Alternatives, Criteria, and Indicators for Sustainability Assessment 54

  3.6.2 Results 54

  3.6.3 Method Comparison 56

  3.6.4 Results Comparison and Sensitivity Analysis 59

  3.6.5 Enabling Specialists to Employ Various Degrees of Precision 62

  3.7 Conclusion 64

  References 65

  4 Sustainable Education in the Age of 5G and 6G Networks: An Analytical Perspective 69
  Kambala Vijaya Kumar, Yalanati Ayyappa, T. Preethi Rangamani, Eswar Patnala, Vinay Kumar Dasari and Gudipalli Tejo Lakshmi

  4.1 Introduction 70

  4.2 Related Work 71

  4.3 Methodology 72

  4.3.1 Elements for Hierarchical Structure 72

  4.3.2 Students 72

  4.3.3 Teachers 72

  4.3.4 Relationship Between Learning and Teaching 73

  4.3.5 Teacher: Intermediary Between Students and Technology 73

  4.3.6 Analytical Hierarchy Process 73

  4.4 Result and Discussion 74

  4.4.1 Target Layer 74

  4.4.2 Layer of Criteria 77

  4.4.3 Discussion 77

  4.5 Conclusions 80

  References 81

  Part II: Sustainable Technology and Data Security 85

  5 Optimizing Sustainable Image Encryption Strategies in Industry 5.0 Using VIKOR MCDM Methodology 87
  I. Shiek Arafat, R. Premkumar, M. Vidhyalakshmi, C. Priya and Muniyandy Elangovan

  Introduction 88

  Image Encryption 89

  Multiple-Criteria Decision-Making (VIKOR) Method 93

  Conclusion 98

  References 99

  6 Sustainable Cryptographic Solutions for IoT: Leveraging MOORA in Evaluating Algorithms for Limited-Resource Environments 101
  Muniyandy Elangovan, R. Premkumar and B. Swarna

  6.1 Introduction 102

  6.2 Materials and Method 106

  6.3 Analysis and Discussion 109

  6.4 Conclusion 113

  References 114

  **7 Optimizing Microwave Device Performance with …