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Green Computational Intelligence

Sustainable Strategies and Emerging Technologies

Edited by Nitish Pathak, Neelam Sharma, Moolchand Sharma and Dac-Nhuong Le
Copyright: 2025   |   Expected Pub Date:2025/09/30
ISBN: 9781394383610  |  Hardcover  |  
530 pages

One Line Description
Transform your approach to technology and sustainability with this comprehensive guide to green computing and computational intelligence.

Audience
Researchers, students, educators, and industry professionals working towards sustainable practices in and using green technology

Description
The global pursuit of sustainability has placed an urgent emphasis on developing innovative and eco-friendly technological solutions. Green computing has the potential to revolutionize the way we evaluate sustainability with the use of energy-efficient algorithms for resource optimization, sustainable hardware design, and smart resource management. Recognizing the intersection of computational intelligence and environmental stewardship, this book seeks to address the pressing challenges of integrating green practices into the realm of computational intelligence and aligning them with global sustainable development goals. Through global expertise from researchers and industry professionals, this book comprehensively covers the many applications of these innovative new technologies, as well as the challenges surrounding their implementation.
Readers will find the book:
• Explores the convergence of environmental sustainability and advanced computational techniques, addressing the global call for energy-efficient and eco-friendly technological solutions;
• Integrates perspectives from computer science, engineering, environmental science, and artificial intelligence, providing a holistic view of green computing;
• Examines sustainable practices across diverse topics, including energy-efficient algorithms and resource optimization, sustainable hardware design, green software engineering, eco-friendly data centers, and smart resource management;
• Offers practical strategies for implementing sustainable computing practices while addressing theoretical and practical challenges;
• Highlights the role of computational intelligence in promoting sustainability, bridging the gap between technology development and environmental conservation.

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Author / Editor Details
Nitish Pathak, PhD is an associate professor in the Department of Computer Science and Engineering, the Bhagwan Parshuram Institute of Technology, Guru Gobind Singh Indraprastha University, New Delhi, India with over 19 years of teaching experience. He has authored and edited over ten books and published over 125 articles in international journals, conferences, patents, and book chapters. His research interests include intelligent computing techniques, empirical software engineering, trusted operating systems, cloud computing, the IoT, and artificial intelligence.

Neelam Sharma, PhD is a senior assistant professor in the Maharaja Agrasen Institute of Technology, Guru Gobind Singh Indraprastha University, New Delhi, India with over 19 years of teaching experience. She has published over 95 papers in international journals, conferences, patents, and book chapters. Her research focuses on wireless sensor networks, wireless body area networks, mobile communications, AI, IoT, information security, and computer graphics.

Moolchand Sharma, PhD is an assistant professor in the Department of Computer Science and Engineering at the Maharaja Agrasen Institute of Technology, Guru Gobind Singh Indraprastha University, New Delhi, India. He has published four books and several book chapters, as well as numerous scientific research publications in reputed international journals and conferences. His research areas include artificial intelligence, nature-inspired computing, security in cloud computing, machine learning, and search engine optimization.

Dac-Nhuong Le, PhD is an associate professor of Computer Science and Dean of the Faculty of Information Technology at Haiphong University, Vietnam with over 20 years of teaching experience. He has authored and edited over 35 books and numerous articles in international journals and conferences. His areas of research include soft computing, network communication, security and vulnerability, network performance analysis and simulation, cloud computing, IoT, and image processing for biomedicine.

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Table of Contents
Preface
Part I: AI and IoT for Sustainable Technologies and Smart Systems
1. Artificial Intelligence and IoT for Smart Farming: Environmental Sustainability
Kshatrapal Singh, Yogesh Kumar Sharma, Vijay Shukla, Dhiraj Gupta and Arun Kumar Rai
1.1 Introduction
1.2 Robots in Agriculture
1.2.1 Irrigation
1.2.2 Weeding
1.3 Drones in Agriculture
1.4 Proposed Idea
1.5 Conclusion
1.6 Future Scope
References
2. Importance of Sustainability in Technology
Sarika Kumari, Nitish Pathak and Neelam Sharma
2.1 Introduction to Sustainability in Technology
2.1.1 Definition of Sustainability in the Technological Context
2.1.2 Importance of Integrating Sustainability into Technological Advancements
2.1.3 Balancing Innovation with Ecological Responsibility
2.2 Fundamental Areas of Sustainable Technology
2.2.1 Efficient Utilization of Energy
2.2.2 Minimization of Material Resources
2.2.3 Waste Reduction and Recycling Strategies
2.3 Green Computational Intelligence and Environmental Sustainability
2.3.1 Role of Green Computational Intelligence in Sustainable Technology
2.3.2 Development of Power-Efficient Computational Algorithms
2.3.3 Strategies for Reducing Energy Consumption in Computational Processes
2.3.4 Integration of Renewable Energy Sources with Computing Systems
2.4 Technological Innovations Supporting Sustainability
2.4.1 Green Data Centers and Their Environmental Impact
2.4.2 Environmentally Friendly Materials in Product Manufacturing
2.4.3 Trends in Recycling and Sustainable Disposal of Technological Products
2.5 Case Studies and Real-World Applications
2.5.1 Examples of Successful Sustainable Technologies in Practice
2.5.2 Case Studies of Energy-Efficient Algorithms and Systems
2.5.3 Demonstrations of Low-Carbon Technologies in the Tech Industry
2.6 Barriers and Challenges in Achieving Sustainability in Technology
2.6.1 Technical, Financial, and Logistical Challenges in Adopting Sustainable Practices
2.6.2 Policy and Regulatory Barriers to Sustainability in Technology
2.6.3 Resistance within Industries to Implement Green Technologies
2.7 The Future of Sustainability in Technology
2.7.1 Long-Term Vision for Sustainable Technological Development
2.7.2 The Evolving Role of Researchers, Technologists, and Policymakers
2.7.3 Predictions and Trends in the Green Technology Sector
2.8 Conclusion
References
3. Energy-Efficient Spectrum Allocation Strategy for Cognitive Radio Network with Compressive Sensing and Relevance Vector Machine
Manish Sharma and Somya Dubey
3.1 Introduction
3.2 Challenges in Traditional Spectrum Allocation Methods
3.2.1 Energy Inefficiency
3.2.2 Computational Overhead
3.2.3 Inflexibility and Inefficiency in Dynamic Environments
3.3 Literature Survey
3.4 Proposed Modified Compressive Sensing–Based Spectrum Sensing Framework
3.5 Simulation Analysis
3.6 Conclusion
References
4. Exploring Predictive Models for Software Maintenance: Current Approaches and Emerging Trends
Yogita Khatri and Urvashi Rahul Saxena
4.1 Introduction
4.2 Discussion
4.2.1 Data Source-Based Studies
4.2.2 Model-Based Studies
4.2.3 Metric-Based Studies
4.2.4 Pre-Processing–Based Studies
4.2.5 Project-Based Studies
4.2.6 Task-Based Studies
4.3 Suggestions to Address Challenges in SFP
4.3.1 Mindful Selection of Performance Measures for the True Evaluation of the SFP Model
4.3.2 Mindful Selection of Statistical Testing Technique for Effective Validation
4.3.3 Validation on Big Corpus of Data
4.3.4 Mindful Selection of Learner
4.4 Conclusion and Future Directions
References
5. Green Computing Strategies: Enhancing Energy Efficiency in Computing Systems
Deepika Bhatia, Oshi Sharma and Swyam Sharma
5.1 Introduction
5.2 Energy-Aware Computing Architectures
5.2.1 Circuit-Level Power Management
5.2.2 System-Level Energy Adaptation
5.2.3 Hardware and Software Co-Design for Energy Efficiency
5.3 Optimization Techniques for Power Efficiency
5.3.1 Dynamic Voltage and Frequency Scaling (DVFS)
5.3.2 Clock and Power Gating
5.3.3 Advanced Cooling Mechanisms and Thermal Management
5.4 Green Cloud Computing and Virtualization Techniques
5.4.1 Virtualization for Resource Optimization
5.4.2 Sustainable Data Center Architectures
5.4.3 Workload Distribution for Energy Conservation
5.5 Machine Learning for Energy Optimization in Computing Systems
5.5.1 Predictive Analytics for Power Consumption
5.5.2 Autonomous System Optimization
5.5.3 Adaptive Machine Learning Algorithms for Energy Efficiency
5.6 Conclusion
References
6. The Resurgence of Predictive Models of ESG Technology Strives for a Sustainable Paradigm
Sonali Srivastava, Manisha Singh and Anubha Vashisht
6.1 Introduction
6.2 ESG Technology
6.3 Importance of Predictive Models in ESG Implementation
6.4 Current Landscape of ESG Technology in India
6.5 Regulatory Frameworks and Compliance Issues
6.6 Discussions
6.7 Applications of Predictive Models in ESG Technology
6.8 Challenges and Opportunities
6.9 Conclusion and Future Scope
References
7. Eco-Intelligence: The Pivotal Role of Artificial Intelligence (AI) and Machine Learning (ML) in Shaping Sustainable Practices Across Industries
R. Venkatesh, Nitish Pathak, P.G. Akila and Neelam Sharma
7.1 Introduction
7.1.1 The Need for Sustainable Practices
7.1.2 The Role of Technology in Sustainability
7.2 Understanding Eco-Intelligence
7.2.1 Definition and Conceptual Framework
7.2.2 AI and ML: A Brief Overview
7.3 Applications Across Industries
7.3.1 Agriculture
7.3.1.1 Precision Farming
7.3.1.2 Crop Monitoring and Management
7.3.2 Energy
7.3.2.1 Smart Grids
7.3.2.2 Renewable Energy Optimization
7.3.3 Manufacturing
7.3.3.1 Resource Efficiency
7.3.3.2 Waste Reduction
7.3.4 Transportation
7.3.4.1 Autonomous Vehicles
7.3.4.2 Logistics Optimization
7.4 Case Studies of Eco-Intelligence in Action
7.4.1 Successful Industry Implementations
7.4.2 Lessons Learned and Best Practices
7.5 Challenges to Adoption
7.5.1 Technological Barriers
7.5.2 Economic Considerations
7.5.3 Social and Cultural Challenges
7.6 Ethics and Responsibility in Eco-Intelligence
7.6.1 Ethical Considerations in AI Deployment
7.6.2 The Importance of Inclusivity and Equity
7.7 Future Directions and Trends
7.7.1 Emerging Technologies
7.7.2 Policy Implications and Recommendations
7.8 Conclusion
References
8. Enhancing the Operational Efficiency of FPOs by Using Predictive Model Building in Machine Learning Through Feature Selection Method
P. Sreelakshmi, Santosh Basavaraj and Helen Josephine V. L.
8.1 Introduction
8.2 Literature Review
8.2.1 Research Gap
8.2.2 Motivation for the Study
8.2.3 FPOs Ecosystem in the Study Area
8.3 Methodology
8.3.1 Objectives of the Study
8.3.2 Results
8.3.3 Feature Selection
8.4 Discussion
8.4.1 Unique Implications of the Study
8.4.2 Theoretical Implications
8.4.3 Managerial Implications
8.5 Conclusion
8.5.1 Limitations of the Study
8.5.2 Scope for Further Research
References
9. Business Intelligence through Forecasting and Trend Analysis on Global Renewable Energy Consumption
A. Kannammal, E. Chandra Blessie, Dinesh Kumar T. and Vignesh N.
9.1 Introduction
9.2 Literature Review
9.2.1 Survey on Energy Usage and Economic Development
9.2.2 Literature Survey for the Energy Demand Research Project
9.2.3 Literature Review on the Association among Energy Features, Situation, and Financial Development
9.3 Motivation and Scope
9.3.1 Forecasting and Trend Analysis
9.4 The Proposed Method
9.4.1 Objective
9.4.2 Dataset
9.5 Exploratory Data Analysis
9.6 Pipeline and Workflow
9.6.1 Dashboard Creation
9.6.2 Tableau - Interactive Exploratory
9.7 Analysis and Visualization
9.7.1 Renewable Energy Forecast
9.7.2 Country-Wise Forecast
9.7.3 Continental-Wise Forecast
9.8 Conclusion
Bibliography
10. Generative AI Framework for Asian Healthcare Organizations
M. Suchitra, Binoy Varghese Cheriyan, Afsar Shaik and Maneesha Panguluru
10.1 Introduction
10.1.1 The History of the Healthcare Industry
10.1.2 Diverse Population and Healthcare Challenges
10.1.3 Overview of Generative AI
10.1.4 Importance of Generative AI in Healthcare
10.1.5 Trends Shaping the Future of Healthcare
10.1.6 Scope of the Chapter
10.2 Literature Survey of Generative AI in Healthcare
10.3 Challenges and Opportunities in Implementing Generative AI
10.4 Case Studies from Asian Healthcare
10.4.1 Medical Image Generation for Diagnostic Purposes
10.4.2 Drug Discovery Using Generative AI Algorithms
10.4.3 Personalized Treatment
10.4.4 Synthetic Data Generation for Training AI Models
10.5 Success Stories
10.6 Future Directions
10.7 Conclusion
Bibliography
11. Harnessing AI and Machine Learning for a Sustainable Environment
Pranati Rakshit, Priyanshu Pal and Sabyasachi Pramanik
11.1 Introduction
11.2 AI and ML Techniques in Environmental Applications
11.2.1 Predictive Modeling and Forecasting
11.2.2 Optimization Algorithms for Resource Efficiency
11.2.3 Data-Driven Environmental Monitoring and Analysis
11.2.4 Emerging AI Technologies for Environmental Science
11.3 Key Applications of AI and ML in Environmental Sustainability
11.3.1 Climate Change Mitigation and Adaptation
11.3.2 Precision Agriculture and Sustainable Food Production
11.3.3 Wildlife Conservation and Biodiversity Protection
11.3.4 Pollution Monitoring and Waste Management
11.3.5 Renewable Energy Management and Optimization
11.4 Case Studies: Real-World Implementations of AI for Environmental Goals
11.4.1 AI in Climate Action Projects
11.4.2 Precision Agriculture Success Stories
11.4.3 Wildlife Conservation Using Machine Learning
11.4.4 Renewable Energy Efficiency Programs
11.4.5 Pollution Control and Smart Waste Management Solutions
11.5 Technical, Ethical, and Social Challenges in AI for Environmental Sustainability
11.5.1 Data Privacy and Security Concerns
11.5.2 Carbon Footprint and Environmental Costs of AI Systems
11.5.3 Ethical Implications of AI in Environmental Applications
11.5.4 Interdisciplinary Collaboration for Effective AI Deployment
11.6 Future Directions and Emerging Opportunities in AI for Sustainability
11.6.1 Next-Generation AI Technologies for Environmental Science
11.6.2 Policy Support and AI for Environmental Governance
11.6.3 Green AI and Energy-Efficient Technologies
11.6.4 Global Expansion and Scalability of AI Solutions
11.7 Conclusion
References
12. Internet of Things (IoT) for Sustainable Resource Management
Manjushree Nayak, Bharata Roshan Sahu, Sumesha Das and Satyam Singh
12.1 Introduction
12.2 Literature Review
12.3 Why IoT for Sustainable Resource Management?
12.4 Overview
12.5 Sustainability in IoT
12.6 Case Study: Smart IoT-Based Water Management for Agricultural Uses in California
12.7 Case Study: IoT-Enabled Smart Waste Management in the City of Barcelona
12.8 Conclusion
References
Part II: Green AI, Computational Intelligence, and Smart Applications
13. Driving Business Success through Green Innovation and Sustainable Practices
Nitin N. Sakhare, Rushikesh S. Tanksale, Rupali A. Mahajan, Manoj Jagdale and Disha S. Wankhede
13.1 Introduction
13.1.1 The Imperative for Eco-Friendly Strategies
13.1.2 Green Innovation: The Cornerstone of Sustainability
13.1.3 Water Conservation and Management: A Critical Resource
13.1.4 Waste Management and Circular Economy: Reducing and Reusing
13.1.5 Sustainable Finance and Investment: Aligning Capital with Sustainability Goals
13.1.6 Measuring Environmental Impact: Techniques and Tools
13.2 Green Innovation: Exploring New Frontiers
13.2.1 Definition of Green Innovation
13.2.2 Key Green Innovation Issues
13.2.3 Importance and Drivers of Green Innovation
13.2.4 Emerging Trends in Green Innovation
13.2.5 Case Studies in Green Innovation
13.2.6 Business Benefits of Green Innovation
13.3 Water Conservation and Management: Strategies for Efficiency
13.3.1 Importance of Water Conservation
13.3.2 Implementing Water Management Practices
13.3.3 Case Studies in Water Conservation and Management
13.3.4 Economic and Environmental Benefits
13.4 Waste Management and Reduction: Building a Circular Economy
13.4.1 Principles of Waste Management
13.4.2 Circular Economy and Business Applications
13.4.3 Case Studies and Best Practices
13.5 Sustainable Finance and Investment: Getting Capital in Line with the Environment
13.5.1 Introduction to Sustainable Finance
13.5.2 Green Bonds and Investment Opportunities
13.5.3 Case Studies in Sustainable Investment
13.6 Measuring Environmental Impact: Tools and Techniques
13.6.1 Assessing Environmental Impact
13.6.2 Reporting and Transparency
13.6.3 Continuous Improvement and Future Outlook
13.7 Conclusion
References
14. Applications of Green Computational Intelligence
Deepika Bhatia, Moksh Adlakha and Tanisha Sharma
14.1 Introduction: Green Computational Intelligence
14.2 Case Studies in Green Technology Implementations
14.2.1 Transportation: Electrification of Public Transport in Shenzhen, China
14.2.1.1 Implementation Phases and Process
14.2.1.2 Environmental and Economic Impact
14.2.2 Tourism: Sustainable Eco-Lodges in Costa Rica
14.2.2.1 Sustainable Design and Operation
14.2.2.2 Community and Environmental Impact
14.2.2.3 Impact on the Tourism Industry
14.3 Applications of Green Computing: Healthcare, Agriculture, Manufacturing
14.4 Urban Sustainability and Smart Cities
14.5 Conclusion
References
15. MA-Dense-Res-CapsNet: Multiple Attention Dense Residual Capsule Network for Classification of Lung Carcinoma
S. Poonkodi and M. Kanchana
15.1 Introduction
15.2 Related Work
15.2.1 Handcraft-Based Techniques
15.2.2 Machine Learning Techniques
15.2.3 Deep Learning Techniques
15.2.3.1 CNN
15.2.3.2 CNN with Attention Mechanism
15.2.3.3 Capsule Network
15.2.3.4 Residual Capsule Network
15.3 Materials and Methods
15.3.1 Dataset
15.3.2 Preprocessing
15.3.3 CLAHE
15.3.4 GABF
15.3.5 MA-Dense-Res-CapsNet Model
15.3.6 Framework of DRFEM
15.3.7 Framework of MSC Attention Block
15.3.8 Framework of DRFEB
15.3.9 The Framework of Residual Blocks
15.3.10 The Capsule Network with Attention Mechanism
15.4 Experimental Result
15.4.1 Experimental Setup
15.4.2 Performance Metrics
15.4.3 Analysis of APTx Activation Function
15.4.4 Experimental Comparisons
15.5 Conclusion
Bibliography
16. Refined U-Net++ Architecture: Deep Learning for the Segmentation and Classification of Tomato Plant Diseases Utilizing Aquila Optimization
Jayanthi V. and Kanchana M.
16.1 Introduction
16.2 Related Work
16.3 Problem Statement
16.4 Tomato Disease Detection Model
16.4.1 Segmentation
16.4.2 Feature Selection
16.4.3 Classification
16.5 Conclusion
References
17. ANN Modeling for the Smart Design of Monopile Foundation in the Offshore Wind Turbine Structures
R. Mohana and S.M. Leela Bharathi
17.1 Introduction
17.2 Methodology for Assessing the Dynamic Design Factors
17.2.1 ANN Modeling for the Prediction of Design Parameters of Monopile Foundation
17.2.1.1 Fixed Base Frequency
17.2.1.2 Nondimensional Stiffness Factor
17.2.1.3 Flexibility Factors and Natural Frequency
17.2.2 Numerical Study
17.3 Discussion on Results
17.3.1 Soil-Pile Structure Interaction
17.3.2 Comparison of Numerical Results
17.4 Conclusion
17.5 Future Research Directions
References
18. Green Artificial Intelligence: Eco-Friendly Technology for Futures and Intelligent Cities
Nitesh Kumar, Sudha Bharti, Nitish Pathak and Neelam Sharma
18.1 Introduction
18.2 Urban Artificial Intelligence: Smart Cities as Role Models for Fostering Sustainability and Innovation Since Extraordinary Measures
18.3 Green Artificial Intelligence
18.4 Sustainability of Artificial Intelligence
18.5 AI for Smart City Transformation
18.6 Policy Directions for Making AI Greener and Cities Smarter
18.7 Eco-Friendly Implications of AI and Green Technology
18.8 Research Methodology
18.9 Conclusion
References
19. Machine Learning for Energy Optimization in Computing Systems
Monika Bansal, Nishi Jain, Nitish Pathak and Neelam Sharma
19.1 Introduction
19.2 The Need for Energy-Efficient Computing Systems
19.2.1 Environmental Impact
19.2.2 Cost Reduction
19.2.3 Growing Computational Demands
19.2.4 Regulatory Compliance and Social Responsibility
19.2.5 Technological Advancements
19.3 Machine Learning Techniques for Achieving Energy-Efficient Computing Systems
19.3.1 Hardware-Level Techniques
19.3.2 Software-Level Techniques
19.3.3 Networking-Level Techniques
19.4 Energy Optimization – Key Applications and Literature Survey
19.4.1 Data Center Optimization
19.4.2 Cloud Computing
19.4.3 Edge Computing and IoT Devices
19.4.4 High-Performance Computing (HPC)
19.5 Green Artificial Intelligence (Green AI): Future for Energy Optimization
19.6 Conclusion
References
20. The Synergy Between Green Computational Intelligence and Cybersecurity
Aparna Agarwal and Khoula Al Harthy
20.1 Introduction
20.1.1 Overview of Green Computational Intelligence (GCI) and Cybersecurity
20.1.2 Scope of the Chapter
20.2 Core Components of GCI in Cybersecurity
20.2.1 Energy-Efficient Machine Learning Models for Security
20.2.2 Edge Computing and Renewable-Powered Data Centers
20.2.3 Lightweight Encryption and Quantum-Resistant Cryptography
20.2.4 Energy-Saving Blockchain Protocols
20.3 Cybersecurity Risks in Energy-Efficient Systems
20.3.1 Expanded Attack Surfaces in Decentralized Networks
20.3.2 Case Study: Healthcare and Energy Sector Vulnerabilities
20.3.3 Threat Mitigation Strategies
20.4 Emerging Research in Energy-Efficient Cybersecurity Solutions
20.4.1 Challenges of Expanding Attack Surfaces in Decentralized Networks
20.4.2 Case Studies: Security Vulnerabilities in Healthcare and Renewable Energy Sectors
20.4.2.1 Healthcare Sector: IoT-Enabled Medical Systems
20.4.2.2 Renewable Energy Sector: Vulnerabilities in Smart Grids
20.4.3 Quantum-Safe Encryption and Green AI for Intrusion Detection
20.4.4 Threat Mitigation Strategies for GCI
20.5 Generative AI in GCI-Driven Cybersecurity
20.5.1 Green Cryptographic Algorithms
20.5.2 Generative AI in GCI-Driven Cybersecurity
20.5.3 Decentralized Security Through Blockchain
20.5.4 Federated Learning for Secure and Efficient IoT Networks
20.6 Future Directions and Collaborative Research
20.6.1 Sustainable Blockchain Technologies
20.6.2 Scalable AI-Driven Security Systems
20.6.3 Teamwork for Sustainable Cybersecurity
20.6.4 A Roadmap for Eco-Friendly Digital Systems
20.7 Conclusion
20.7.1 Work Together on Research
20.7.2 Set Global Standards
20.7.3 Support Through Policies
20.7.4 Lead by Example in Industries
Bibliography
Index

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