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.
Edited by:
Kanak Kalita (Rajalakshmi Institute of Technology Chennai India),
J. V. N. Ramesh (Koneru Lakshmaiah Education Foundation,
Vaddeswaram,
India),
M. Elangovan (Applied Science Private University,
Amman,
Jordan),
S. Balamurugan (Intelligent Research Consultancy Services (iRCS),
Coimbatore,
India)
Imprint: Wiley-Scrivener
Country of Publication: United States
ISBN: 9781394242573
ISBN 10: 1394242573
Series: Industry 5.0 Transformation Applications
Pages: 288
Publication Date: 10 October 2025
Audience:
Professional and scholarly
,
Undergraduate
Format: Hardback
Publisher's Status: Active
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 1.4 Results and Discussion 13 1.5 Conclusion 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 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 3.4 Analytical Hierarchy Process Method to Compute Weights 51 3.5 The Proposed Q-TOPSIS Technique 52 3.6 Results and Discussion 53 3.7 Conclusion 64 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 4.4 Result and Discussion 74 4.5 Conclusions 80 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 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 7 Optimizing Microwave Device Performance with SPSS Analysis 119 Muniyandy Elangovan, G. Dhanabalan and H. B. Michael Rajan 7.1 Introduction 120 7.2 Materials and Methods 123 7.3 Results and Discussion 125 7.4 Conclusion 135 8 Enhanced Microgrid Security: Naive Bayes Versus Random Forest in Attack Detection Accuracy 139 A. Prince Kalvin Raj and S. Pushpa Latha 9 Enhancing the Accuracy of Detecting Air Pollution Using Random Forest Algorithm Comparison with Support Vector Machine 153 M. Santhosh and K. Nattar Kannan 9.1 Introduction 154 9.2 Materials and Methods 157 9.3 Conclusion 165 Part III: AI and Decision-Making in Industry 5.0 169 10 Efficient Human Threat Recognition Using Novel Logistic Regression Compared Over Linear Regression with Improved Accuracy 171 P. Sai Sateesh and Vijaya Bhaskar K. 10.1 Introduction 172 10.2 Materials and Methods 173 10.3 Results and Discussion 176 10.4 Conclusion 180 11 Optimizing Uber Data Analysis Using Decision Tree and Random Forest 183 I. Vasanth Kumar and K. Nattar Kannan 11.1 Introduction 184 11.2 Materials and Methods 188 11.3 Results and Discussion 194 11.4 Conclusion 199 12 Decision-Making in Malware Detection Through Advanced Imaging Techniques 203 Rohan Alroy B., Shivaprakash S. J., Akshat Chauhan and Jayasudha M. 12.1 Introduction 204 12.2 Literature Review 204 12.3 Proposed Architecture 205 12.4 Methodology 206 12.5 Results and Comparisons 207 12.6 Research Gap and Future Works 208 12.7 Conclusion 209 13 Enhancing Decision-Making in Indian Legal Systems: Automating Document Analysis with Named Entity Recognition 211 Gaurav Pendharkar, Sukanya G. and Priyadarshini J. 13.1 Introduction 212 13.2 Related Work 213 13.3 Proposed Architecture 214 13.4 Proposed Methodology 215 13.5 Results and Discussion 218 13.6 Conclusion 221 14 Classification of Indian Legal Judgment Documents Through Innovative Technology to Aid in Decision-Making 223 Ujjwal Pandey, Sukanya G. and Priyadarshini J. 14.1 Introduction 223 14.2 Literature Survey 225 14.3 Dataset 227 14.4 Proposed Methodology and Experimentation 230 14.5 Evaluation 234 14.6 Conclusion and Future Work 239 15 Revolutionizing Recruitment in Industry 5.0: An Efficient AI and Machine Learning–Based Applicant Tracking System 243 Shola Usharani, Gayathri Rajakumaran, Priyadarshini Jayaraju and Anuttam Anand 15.1 Introduction and Technical Background 244 15.2 Benefits of Technology in the Hiring Industry 248 15.3 Methodology 249 15.4 Research Methodology and Evaluation Metrics 255 15.5 Applicant Tracking System Predicted Outcomes and Calculations 256 15.6 Results 262 15.7 Conclusion 262 References 263 Index 265
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.
