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Decision-Making Techniques and Methods for Sustainable Technological Innovation

Edited by Kanak Kalita, J.V.N. Ramesh, M. Elangovan and S. Balamurugan
Copyright: 2025   |   Expected Pub Date:2025/08/30
ISBN: 9781394242573  |  Hardcover  |  
284 pages

One Line Description
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.

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.

Description
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.

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Author / Editor Details
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.

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Table of Contents
Foreword
Preface
Part I: Frameworks for Sustainable Technological Innovation
1. Green Technology Planning in Developing Countries: An Innovative Decision-Making Framework
Vamsidhar Talasila, Chandrashekhar Goswami and Muniyandy Elangovan
1.1 Introduction
1.2 Related Works
1.3 Proposed Methodology
1.3.1 SWOT, G-TOPSIS and Integrated GASM Methods
1.3.2 SWOT–GASM Method
1.3.3 Process of Grey Analytical Hierarchy
1.3.4 Grey Numbers
1.3.5 G-TOPSIS Approach
1.4 Results and Discussion
1.4.1 Ranking of SWOT Factors
1.4.2 Grey Analytical Hierarchical Process Results
1.4.2.1 Overall Ranking of SWOT Subfactors
1.4.2.2 Ranking of Threats Subfactors
1.4.2.3 Ranking of Opportunities Subfactors
1.4.2.4 Ranking of Weaknesses Subfactors
1.4.2.5 Ranking of Strengths Subfactors
1.4.3 Grey TOPSIS Results
1.4.3.1 WO Strategies
1.4.3.2 ST Strategies
1.4.3.3 SO Strategies
1.4.3.4 WT Strategies
1.5 Conclusion
References
2. Evaluating Sustainability Indicators for Green Building Manufacture with Fuzzy-Based MODM Technique
Chandrshekhar Goswami, Muniyandy Elangovan and Puppala Ramya
2.1 Introduction
2.2 Related Works
2.3 Proposed Method
2.3.1 Enhanced Fuzzy DEMATEL
2.4 Results and Discussion
2.5 Conclusion
References
3. Sustainable Energy Options: Qualitative TOPSIS Method for Challenging Scenarios
Muniyandy Elangovan, Puppala Ramya and Chandrashekhar Goswami
3.1 Introduction
3.2 Related Works
3.3 Methods and Materials
3.3.1 Preliminaries
3.3.1.1 Models of Absolute Qualitative Order of Magnitude
3.4 Analytical Hierarchy Process Method to Compute Weights
3.5 The Proposed Q-TOPSIS Technique
3.6 Results and Discussion
3.6.1 A Q-TOPSIS Investigation that Demonstrates How to Choose Sustainable Energy Sources
3.6.1.1 Alternatives, Criteria, and Indicators for Sustainability Assessment
3.6.2 Results
3.6.3 Method Comparison
3.6.4 Results Comparison and Sensitivity Analysis
3.6.5 Enabling Specialists to Employ Various Degrees of Precision
3.7 Conclusion
References
4. Sustainable Education in the Age of 5G and 6G Networks: An Analytical Perspective
Kambala Vijaya Kumar, Yalanati Ayyappa, T. Preethi Rangamani, Eswar Patnala, Vinay Kumar Dasari and Gudipalli Tejo Lakshmi
4.1 Introduction
4.2 Related Work
4.3 Methodology
4.3.1 Elements for Hierarchical Structure
4.3.2 Students
4.3.3 Teachers
4.3.4 Relationship Between Learning and Teaching
4.3.5 Teacher: Intermediary Between Students and Technology
4.3.6 Analytical Hierarchy Process
4.4 Result and Discussion
4.4.1 Target Layer
4.4.2 Layer of Criteria
4.4.3 Discussion
4.5 Conclusions
References
Part II: Sustainable Technology and Data Security
5. Optimizing Sustainable Image Encryption Strategies in Industry 5.0 Using VIKOR MCDM Methodology
I. Shiek Arafat, R. Premkumar, M. Vidhyalakshmi, C. Priya and Muniyandy Elangovan
Introduction
Image Encryption
Multiple-Criteria Decision-Making (VIKOR) Method
Conclusion
References
6. Sustainable Cryptographic Solutions for IoT: Leveraging MOORA in Evaluating Algorithms for Limited-Resource Environments
Muniyandy Elangovan, R. Premkumar and B. Swarna
6.1 Introduction
6.2 Materials and Method
6.3 Analysis and Discussion
6.4 Conclusion
References
7. Optimizing Microwave Device Performance with SPSS Analysis
Muniyandy Elangovan, G. Dhanabalan and H. B. Michael Rajan
7.1 Introduction
7.2 Materials and Methods
7.3 Results and Discussion
7.4 Conclusion
References
8. Enhanced Microgrid Security: Naive Bayes Versus Random Forest in Attack Detection Accuracy
A. Prince Kalvin Raj and S. Pushpa Latha
Introduction
Materials and Methods
Naive Bayes
Novel Naive Bayes Algorithm Execution
Random Forest
Results and Discussion
Conclusion
References
9. Enhancing the Accuracy of Detecting Air Pollution Using Random Forest Algorithm Comparison with Support Vector Machine
M. Santhosh and K. Nattar Kannan
9.1 Introduction
9.2 Materials and Methods
9.2.1 Data Preparation
9.2.2 Random Forest Algorithm
9.2.3 Support Vector Machine Algorithm
9.2.4 Statistical Analysis
9.2.5 Results and Discussion
9.3 Conclusion
References
Part III: AI and Decision-Making in Industry 5.0
10. Efficient Human Threat Recognition Using Novel Logistic Regression Compared Over Linear Regression with Improved Accuracy
P. Sai Sateesh and Vijaya Bhaskar K.
10.1 Introduction
10.2 Materials and Methods
10.2.1 Problem Description
10.2.2 Logistic Regression
10.2.3 Linear Regression
10.2.4 Statistical Analysis
10.3 Results and Discussion
10.3.1 Analysis of Iterative Results
10.3.2 Statistical Analysis and t Test Comparisons
10.3.3 Comparison of Overall Accuracy
10.3.4 Discussion on Results
10.3.5 Limitations and Future Directions
10.4 Conclusion
References
11. Optimizing Uber Data Analysis Using Decision Tree and Random Forest
I. Vasanth Kumar and K. Nattar Kannan
11.1 Introduction
11.2 Materials and Methods
11.2.1 Study Design
11.2.2 Dataset Description
11.2.3 Data Preparation
11.2.4 Decision Tree
11.2.5 Random Forest
11.2.6 Statistical Analysis
11.2.7 Methodology Summary
11.3 Results and Discussion
11.4 Conclusion
References
12. Decision-Making in Malware Detection Through Advanced Imaging Techniques
Rohan Alroy B., Shivaprakash S. J., Akshat Chauhan and Jayasudha M.
12.1 Introduction
12.2 Literature Review
12.3 Proposed Architecture
12.4 Methodology
12.4.1 Metrics
12.4.2 Training Models from Scratch
12.4.3 Using Pretrained Models as Feature Extractors
12.4.4 Retraining Parts of A Pretrained Model
12.4.5 Ensemble Approach
12.5 Results and Comparisons
12.6 Research Gap and Future Works
12.7 Conclusion
References
13. Enhancing Decision-Making in Indian Legal Systems: Automating Document Analysis with Named Entity Recognition
Gaurav Pendharkar, Sukanya G. and Priyadarshini J.
13.1 Introduction
13.2 Related Work
13.3 Proposed Architecture
13.4 Proposed Methodology
13.4.1 Data Collection
13.4.2 Data Annotation
13.4.3 Legal Domain Adaptation
13.4.4 Evaluation Metrics
13.5 Results and Discussion
13.5.1 Token-Wise Comparison with Gold Standard
13.5.2 Accuracy is an Unsuitable Metric
13.5.3 Performance of the Model
13.5.4 Evaluation Metric Computed Value
13.6 Conclusion
References
14. Classification of Indian Legal Judgment Documents Through Innovative Technology to Aid in Decision-Making
Ujjwal Pandey, Sukanya G. and Priyadarshini J.
14.1 Introduction
14.2 Literature Survey
14.3 Dataset
14.3.1 Collection Methodology
14.3.2 Preprocessing
14.3.3 Exploratory Analysis
14.4 Proposed Methodology and Experimentation
14.4.1 System Architecture
14.4.2 Experimentation
14.5 Evaluation
14.5.1 Precision
14.5.2 Recall
14.5.3 F1 Score
14.6 Conclusion and Future Work
References
Appendix A. System Specifications and Hyperparameters
15. Revolutionizing Recruitment in Industry 5.0: An Efficient AI and Machine Learning–Based Applicant Tracking System
Shola Usharani, Gayathri Rajakumaran, Priyadarshini Jayaraju and Anuttam Anand
15.1 Introduction and Technical Background
15.1.1 The Impact of Technology on the Hiring Process
15.1.2 AI and Machine Learning in Hiring
15.1.3 Social Media and Hiring
15.1.4 Virtual Reality and Gamification in Hiring
15.2 Benefits of Technology in the Hiring Industry
15.3 Methodology
15.3.1 Research Design
15.3.2 Sampling
15.3.3 Data Collection
15.3.4 Data Analysis
15.3.5 Research Gaps
15.4 Research Methodology and Evaluation Metrics
15.5 Applicant Tracking System Predicted Outcomes and Calculations
15.6 Results
15.7 Conclusion
References
Index

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