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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:
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.
Foreword xiii
Preface xv
Part I: Frameworks for Sustainable Technological Innovation 1
1 Green Technology Planning in Developing Countries: An Innovative Decision-Making Framework 3Vamsidhar 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 25Chandrshekhar 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 45Muniyandy 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 69Kambala 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 87I. 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 101Muniyandy 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 SPSS Analysis 119Muniyandy 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
References 136
8 Enhanced Microgrid Security: Naive Bayes Versus Random Forest in Attack Detection Accuracy 139A. Prince Kalvin Raj and S. Pushpa Latha
Introduction 140
Materials and Methods 142
Naive Bayes 143
Novel Naive Bayes Algorithm Execution 143
Random Forest 145
Results and Discussion 146
Conclusion 149
References 150
9 Enhancing the Accuracy of Detecting Air Pollution Using Random Forest Algorithm Comparison with Support Vector Machine 153M. Santhosh and K. Nattar Kannan
9.1 Introduction 154
9.2 Materials and Methods 157
9.2.1 Data Preparation 159
9.2.2 Random Forest Algorithm 159
9.2.3 Support Vector Machine Algorithm 160
9.2.4 Statistical Analysis 161
9.2.5 Results and Discussion 161
9.3 Conclusion 165
References 166
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 171P. Sai Sateesh and Vijaya Bhaskar K.
10.1 Introduction 172
10.2 Materials and Methods 173
10.2.1 Problem Description 173
10.2.2 Logistic Regression 174
10.2.3 Linear Regression 175
10.2.4 Statistical Analysis 175
10.3 Results and Discussion 176
10.3.1 Analysis of Iterative Results 176
10.3.2 Statistical Analysis and t Test Comparisons 177
10.3.3 Comparison of Overall Accuracy 179
10.3.4 Discussion on Results 179
10.3.5 Limitations and Future Directions 179
10.4 Conclusion 180
References 181
11 Optimizing Uber Data Analysis Using Decision Tree and Random Forest 183I. Vasanth Kumar and K. Nattar Kannan
11.1 Introduction 184
11.2 Materials and Methods 188
11.2.1 Study Design 188
11.2.2 Dataset Description 189
11.2.3 Data Preparation 189
11.2.4 Decision Tree 190
11.2.5 Random Forest 191
11.2.6 Statistical Analysis 193
11.2.7 Methodology Summary 193
11.3 Results and Discussion 194
11.4 Conclusion 199
References 200
12 Decision-Making in Malware Detection Through Advanced Imaging Techniques 203Rohan 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
12.4.1 Metrics 206
12.4.2 Training Models from Scratch 207
12.4.3 Using Pretrained Models as Feature Extractors 207
12.4.4 Retraining Parts of A Pretrained Model 207
12.4.5 Ensemble Approach 207
12.5 Results and Comparisons 207
12.6 Research Gap and Future Works 208
12.7 Conclusion 209
References 210
13 Enhancing Decision-Making in Indian Legal Systems: Automating Document Analysis with Named Entity Recognition 211Gaurav 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
13.4.1 Data Collection 215
13.4.2 Data Annotation 216
13.4.3 Legal Domain Adaptation 216
13.4.4 Evaluation Metrics 217
13.5 Results and Discussion 218
13.5.1 Token-Wise Comparison with Gold Standard 218
13.5.2 Accuracy is an Unsuitable Metric 219
13.5.3 Performance of the Model 221
13.5.4 Evaluation Metric Computed Value 221
13.6 Conclusion 221
References 222
14 Classification of Indian Legal Judgment Documents Through Innovative Technology to Aid in Decision-Making 223Ujjwal Pandey, Sukanya G. and Priyadarshini J.
14.1 Introduction 223
14.2 Literature Survey 225
14.3 Dataset 227
14.3.1 Collection Methodology 227
14.3.2 Preprocessing 228
14.3.3 Exploratory Analysis 229
14.4 Proposed Methodology and Experimentation 230
14.4.1 System Architecture 230
14.4.2 Experimentation 233
14.5 Evaluation 234
14.5.1 Precision 235
14.5.2 Recall 237
14.5.3 F1 Score 238
14.6 Conclusion and Future Work 239
References 239
Appendix A. System Specifications and Hyperparameters 240
15 Revolutionizing Recruitment in Industry 5.0: An Efficient AI and Machine Learning-Based Applicant Tracking System 243Shola Usharani, Gayathri Rajakumaran, Priyadarshini Jayaraju and Anuttam Anand
15.1 Introduction and Technical Background 244
15.1.1 The Impact of Technology on the Hiring Process 245
15.1.2 AI and Machine Learning in Hiring 245
15.1.3 Social Media and Hiring 246
15.1.4 Virtual Reality and Gamification in Hiring 247
15.2 Benefits of Technology in the Hiring Industry 248
15.3 Methodology 249
15.3.1 Research Design 249
15.3.2 Sampling 250
15.3.3 Data Collection 252
15.3.4 Data Analysis 253
15.3.5 Research Gaps 254
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
Vamsidhar Talasila1, Chandrashekhar Goswami2 and Muniyandy Elangovan3,4*
1Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, India
2Faculty of Computing and Informatics, Sir Padampat Singhania University, Udaipur, Rajasthan, India
3Department of Biosciences, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, India
4Applied Science Research Center, Applied Science Private University, Amman, Jordan
Green technologies are crucial for formulating and creating national environmental and sustainability plans. Development and implementation of approaches for accelerating green technologies in emerging economies are possible as a result of an increasing focus on GT approaches in strategic decision-making. As a result, this research creates comprehensive strategy structure research on SWOT (strengths, weaknesses, opportunities, and threats) analysis for successful planning of the green technology (GT) industry. The SWOT analysis examines both internal and outside factors as well as related aspects that are crucial for Pakistan development of GT. To calculate the weights of the 21 subfactors, the Grey analytical structured model approach is then used. Finally, 18 approaches created for sustainable GT planning are prioritized using the Grey techniques for order performance by similarity to ideal solution. The possibility for high agricultural output, decreased foreign investment because of security and terrorist concerns, declining costs of GTs, and the chance to establish a good reputation are crucial various components for comprehensive GT planning, according to the findings. On the other hand, it has been found that the best approaches involve enhancing national security to draw international direct investment, lowering taxes, duties on the import of GT, relaxing regulations for regional businesses going through a transition, and developing plans and initiatives that encourage green innovations in the agricultural sector. The systematic, complete structure for sustainable GT development in Pakistan is being proposed for the first time in this research. Our work closes a gap in the process of preparing and carrying out strategic plans and offers policymakers solutions to deal with roadblocks to adopting GT strategy.
Keywords: Decision-making framework, GT, SWOT, Grey analytical structured model (GASM)
Innovative green technology (GT) is becoming more and more necessary due to the dual restrictions on resources and the environment. Green industrial design may enhance environmental quality, encourage sustainable economic development, and translate technical innovation into GT innovation [1]. GT is a sort of invention that may benefit both the consumers and businesses while also significantly reducing negative environmental impacts. It encompasses technological innovation in areas such as environmental management, waste reduction, waste recycling, and the creation of eco-friendly goods. Due to the rising concern about the status of the environment, innovative GT has consistently attracted attention as a crucial component of green innovation [2]. Environmental regulation has the potential to support the green transformation of the economy via two channels: the creation of environmentally friendly technologies and the upgrading of industrial structures. On the other hand, the functions that different degrees of economic growth have in determining the impacts of environmental legislation are often neglected [3]. Sustainable development goals may be attained on a regional and global level due in large part to GT. Additionally, it promotes social advancement and lessens the negative effects of economic growth on the environment. GT has a bright future in fostering economic success in underdeveloped nations. However, the adoption of GT within a particular nation may be impacted by interconnecting environmental as well as social variables [4]. However, it is also accurate that governments, particularly those in developing countries, must continue to work toward promoting economic growth and raising living standards. This is furthermore in addition to the readily apparent fact that diminishing greenhouse gas emissions is important for the longterm sustainability of the global economy [5]. GT planning is a complex process that involves assessing a variety of factors such as technological feasibility, economic viability, environmental impact, and social acceptability. To facilitate this process, an innovative decision-making framework can be used to evaluate the potential benefits and risks associated with different GT options [6]. This framework involves several stages, starting with the identification of key stakeholders and their objectives, followed by the development of a set of criteria to assess the suitability of different GT options. These criteria should be based on a variety of factors, including environmental impact, social and economic benefits, and technological feasibility.
The rest of the essay is structured as follows: related works are discussed in Section 1.2, the suggested approach is explained in Section 1.3, the results and discussion are presented in Section 1.4, and the paper is concluded in Section 1.5.
The four areas in the research [7] of IoT (Internet of Things) applications in agriculture include cattle breeding, environment-controlled planting, open-field planting, aquaculture, and aquaponics. It is advised that the emphasis on deploying agricultural IoT systems be broadened beyond the growing cycle to the life cycle of agricultural products. With the deployment of GTs in mind, operational recognition indications are examined from the standpoint of extended life cycle theory. Fuzzy analytic hierarchy process with triangular fuzzy evaluation is applied to the identifying system following the principles and logic of the suggested recognition system, and a workable computing process is developed [8]. The paper [9] was designed using the DMAIC (define, measure, analyze, improve, and control) technique, and the implementation of the GLS (Green Lean Six Sigma) was suggested based on theoretical components. It was discovered that the integration of GLS is complemented by enablers, a toolkit, and implementation techniques. The suggested architecture offers a route for GLS implementation via wise project selection.
The study by Andenæs and colleagues [10] identified and discussed the main building technical issues related to blue-green roofing and to provide a framework for risk management. Research data on blue-green roof faults and their causes have been gathered via literature and document analyses, qualitative interviews, and expert meetings. The current study [11] covers a few key sources of bioactive phenol compounds as well as cutting-edge extraction methods. The approaches make use of supercritical, microwave, and ultrasound technologies. The review will also emphasize how to best extract phenolic bioactives from plant-based materials using response surface methodology, a statistical approach. The research [12, 13] provided an integrated GT framework to address a vacuum in the existing body of literature by highlighting the most important characteristics of GTs applicable to Pakistan. The article [13, 14] built a multilayer index system for green building influencing elements and introduced the green financial supporting factor. The WINGS (weighted influence nonlinear gauge system) model is improved with the use of a radial basis function neural network (RBF-WINGS model), which also determines the direct strength- influence matrix. For the assessment of green rating (GR) schemes of current planning bodies (PBs), the research suggests a TOPSIS (technique for order performance by similarity to ideal solution) decision approach based on a cloud model that can get beyond the ambiguity and complexity of scheme selection. The methodology and preparation procedures are explained, and the definitions of the quantitative concepts and parameters are provided [14, 15]. In the study [15-17], the Malmquist index and data envelopment analysis are used to determine how effective GT innovation is in strategically growing sectors.
In this section, we discuss in detail an innovative decision-making framework for GT planning to facilitate growth in developing countries. GT planning involves the identification of opportunities and approaches to facilitate the development and adoption of GT in developing countries. This includes assessing the current state of technology, identifying areas where GT can be most effective, and developing policies and programs to support its implementation.
By using an innovative decision-making framework, GT planning can be a more effective and efficient process, helping to facilitate the growth of sustainable and environmentally friendly technologies in developing countries.
In this research, GT approaches are evaluated and prioritized for greater sustainable development in developing countries using SWOT (strengths, weaknesses, opportunities, and threats) analysis, GASM (Grey analytical structured model), and G-TOPSIS (Grey group TOPSIS) technique, which...
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