Decision-Making Techniques and Methods for Sustainable Technological Innovation

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.

• Foreword xiiiPreface xvPart I: Frameworks for Sustainable Technological Innovation 11 Green Technology Planning in Developing Countries: An Innovative Decision-Making Framework 3Vamsidhar Talasila, Chandrashekhar Goswami and Muniyandy Elangovan1.1 Introduction 41.2 Related Works 51.3 Proposed Methodology 61.3.1 SWOT, G-TOPSIS and Integrated GASM Methods 61.3.2 SWOT–GASM Method 71.3.3 Process of Grey Analytical Hierarchy 71.3.4 Grey Numbers 91.3.5 G-TOPSIS Approach 101.4 Results and Discussion 131.4.1 Ranking of SWOT Factors 141.4.2 Grey Analytical Hierarchical Process Results 141.4.2.1 Overall Ranking of SWOT Subfactors 141.4.2.2 Ranking of Threats Subfactors 161.4.2.3 Ranking of Opportunities Subfactors 161.4.2.4 Ranking of Weaknesses Subfactors 171.4.2.5 Ranking of Strengths Subfactors 171.4.3 Grey TOPSIS Results 181.4.3.1 WO Strategies 191.4.3.2 ST Strategies 201.4.3.3 SO Strategies 211.4.3.4 WT Strategies 211.5 Conclusion 22References 222 Evaluating Sustainability Indicators for Green Building Manufacture with Fuzzy-Based MODM Technique 25Chandrshekhar Goswami, Muniyandy Elangovan and Puppala Ramya2.1 Introduction 262.2 Related Works 272.3 Proposed Method 282.3.1 Enhanced Fuzzy DEMATEL 292.4 Results and Discussion 322.5 Conclusion 41References 413 Sustainable Energy Options: Qualitative TOPSIS Method for Challenging Scenarios 45Muniyandy Elangovan, Puppala Ramya and Chandrashekhar Goswami3.1 Introduction 463.2 Related Works 483.3 Methods and Materials 493.3.1 Preliminaries 503.3.1.1 Models of Absolute Qualitative Order of Magnitude 503.4 Analytical Hierarchy Process Method to Compute Weights 513.5 The Proposed Q-TOPSIS Technique 523.6 Results and Discussion 533.6.1 A Q-TOPSIS Investigation that Demonstrates How to Choose Sustainable Energy Sources 533.6.1.1 Alternatives, Criteria, and Indicators for Sustainability Assessment 543.6.2 Results 543.6.3 Method Comparison 563.6.4 Results Comparison and Sensitivity Analysis 593.6.5 Enabling Specialists to Employ Various Degrees of Precision 623.7 Conclusion 64References 654 Sustainable Education in the Age of 5G and 6G Networks: An Analytical Perspective 69Kambala Vijaya Kumar, Yalanati Ayyappa, T. Preethi Rangamani, Eswar Patnala, Vinay Kumar Dasari and Gudipalli Tejo Lakshmi4.1 Introduction 704.2 Related Work 714.3 Methodology 724.3.1 Elements for Hierarchical Structure 724.3.2 Students 724.3.3 Teachers 724.3.4 Relationship Between Learning and Teaching 734.3.5 Teacher: Intermediary Between Students and Technology 734.3.6 Analytical Hierarchy Process 734.4 Result and Discussion 744.4.1 Target Layer 744.4.2 Layer of Criteria 774.4.3 Discussion 774.5 Conclusions 80References 81Part II: Sustainable Technology and Data Security 855 Optimizing Sustainable Image Encryption Strategies in Industry 5.0 Using VIKOR MCDM Methodology 87I. Shiek Arafat, R. Premkumar, M. Vidhyalakshmi, C. Priya and Muniyandy ElangovanIntroduction 88Image Encryption 89Multiple-Criteria Decision-Making (VIKOR) Method 93Conclusion 98References 996 Sustainable Cryptographic Solutions for IoT: Leveraging MOORA in Evaluating Algorithms for Limited-Resource Environments 101Muniyandy Elangovan, R. Premkumar and B. Swarna6.1 Introduction 1026.2 Materials and Method 1066.3 Analysis and Discussion 1096.4 Conclusion 113References 1147 Optimizing Microwave Device Performance with SPSS Analysis 119Muniyandy Elangovan, G. Dhanabalan and H. B. Michael Rajan7.1 Introduction 1207.2 Materials and Methods 1237.3 Results and Discussion 1257.4 Conclusion 135References 1368 Enhanced Microgrid Security: Naive Bayes Versus Random Forest in Attack Detection Accuracy 139A. Prince Kalvin Raj and S. Pushpa LathaIntroduction 140Materials and Methods 142Naive Bayes 143Novel Naive Bayes Algorithm Execution 143Random Forest 145Results and Discussion 146Conclusion 149References 1509 Enhancing the Accuracy of Detecting Air Pollution Using Random Forest Algorithm Comparison with Support Vector Machine 153M. Santhosh and K. Nattar Kannan9.1 Introduction 1549.2 Materials and Methods 1579.2.1 Data Preparation 1599.2.2 Random Forest Algorithm 1599.2.3 Support Vector Machine Algorithm 1609.2.4 Statistical Analysis 1619.2.5 Results and Discussion 1619.3 Conclusion 165References 166Part III: AI and Decision-Making in Industry 5.0 16910 Efficient Human Threat Recognition Using Novel Logistic Regression Compared Over Linear Regression with Improved Accuracy 171P. Sai Sateesh and Vijaya Bhaskar K.10.1 Introduction 17210.2 Materials and Methods 17310.2.1 Problem Description 17310.2.2 Logistic Regression 17410.2.3 Linear Regression 17510.2.4 Statistical Analysis 17510.3 Results and Discussion 17610.3.1 Analysis of Iterative Results 17610.3.2 Statistical Analysis and t Test Comparisons 17710.3.3 Comparison of Overall Accuracy 17910.3.4 Discussion on Results 17910.3.5 Limitations and Future Directions 17910.4 Conclusion 180References 18111 Optimizing Uber Data Analysis Using Decision Tree and Random Forest 183I. Vasanth Kumar and K. Nattar Kannan11.1 Introduction 18411.2 Materials and Methods 18811.2.1 Study Design 18811.2.2 Dataset Description 18911.2.3 Data Preparation 18911.2.4 Decision Tree 19011.2.5 Random Forest 19111.2.6 Statistical Analysis 19311.2.7 Methodology Summary 19311.3 Results and Discussion 19411.4 Conclusion 199References 20012 Decision-Making in Malware Detection Through Advanced Imaging Techniques 203Rohan Alroy B., Shivaprakash S. J., Akshat Chauhan and Jayasudha M.12.1 Introduction 20412.2 Literature Review 20412.3 Proposed Architecture 20512.4 Methodology 20612.4.1 Metrics 20612.4.2 Training Models from Scratch 20712.4.3 Using Pretrained Models as Feature Extractors 20712.4.4 Retraining Parts of A Pretrained Model 20712.4.5 Ensemble Approach 20712.5 Results and Comparisons 20712.6 Research Gap and Future Works 20812.7 Conclusion 209References 21013 Enhancing Decision-Making in Indian Legal Systems: Automating Document Analysis with Named Entity Recognition 211Gaurav Pendharkar, Sukanya G. and Priyadarshini J.13.1 Introduction 21213.2 Related Work 21313.3 Proposed Architecture 21413.4 Proposed Methodology 21513.4.1 Data Collection 21513.4.2 Data Annotation 21613.4.3 Legal Domain Adaptation 21613.4.4 Evaluation Metrics 21713.5 Results and Discussion 21813.5.1 Token-Wise Comparison with Gold Standard 21813.5.2 Accuracy is an Unsuitable Metric 21913.5.3 Performance of the Model 22113.5.4 Evaluation Metric Computed Value 22113.6 Conclusion 221References 22214 Classification of Indian Legal Judgment Documents Through Innovative Technology to Aid in Decision-Making 223Ujjwal Pandey, Sukanya G. and Priyadarshini J.14.1 Introduction 22314.2 Literature Survey 22514.3 Dataset 22714.3.1 Collection Methodology 22714.3.2 Preprocessing 22814.3.3 Exploratory Analysis 22914.4 Proposed Methodology and Experimentation 23014.4.1 System Architecture 23014.4.2 Experimentation 23314.5 Evaluation 23414.5.1 Precision 23514.5.2 Recall 23714.5.3 F1 Score 23814.6 Conclusion and Future Work 239References 239Appendix A. System Specifications and Hyperparameters 24015 Revolutionizing Recruitment in Industry 5.0: An Efficient AI and Machine Learning–Based Applicant Tracking System 243Shola Usharani, Gayathri Rajakumaran, Priyadarshini Jayaraju and Anuttam Anand15.1 Introduction and Technical Background 24415.1.1 The Impact of Technology on the Hiring Process 24515.1.2 AI and Machine Learning in Hiring 24515.1.3 Social Media and Hiring 24615.1.4 Virtual Reality and Gamification in Hiring 24715.2 Benefits of Technology in the Hiring Industry 24815.3 Methodology 24915.3.1 Research Design 24915.3.2 Sampling 25015.3.3 Data Collection 25215.3.4 Data Analysis 25315.3.5 Research Gaps 25415.4 Research Methodology and Evaluation Metrics 25515.5 Applicant Tracking System Predicted Outcomes and Calculations 25615.6 Results 26215.7 Conclusion 262References 263Index 265