Intelligent Computing and Optimization for Sustainable Development
This book presents insights into how Intelligent Computing and Optimization techniques can be used to attain the goals of Sustainable Development. It provides a comprehensive overview of the latest breakthroughs and recent developments in sustainable, intelligent computing technologies, applications...
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| Jazyk: | English |
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Milton
CRC Press
2025
Taylor & Francis CRC Press LLC |
| Vydanie: | 1 |
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| ISBN: | 9781032624563, 1032625813, 9781032625812, 1032624566, 9781040159910, 1040159893, 9781040159897, 9781032625829, 1032625821, 1040159915 |
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| Abstract | This book presents insights into how Intelligent Computing and Optimization techniques can be used to attain the goals of Sustainable Development. It provides a comprehensive overview of the latest breakthroughs and recent developments in sustainable, intelligent computing technologies, applications, and optimization techniques across various industries, including business process management, manufacturing, financial sector, agriculture, financial sector, supply chain management, and healthcare. It focuses on computational intelligent techniques and optimization techniques to provide sustainable solutions to many problems. Features: • Provides insights into the theory, implementation, and application of computational intelligence techniques in many industries. • Includes industry practitioner perspectives and case studies for a better understanding of sustainable solutions. • Highlights the role of intelligent computing and optimization as key technologies in decision-making processes and in providing cutting-edge solutions to real-world problems. • Addresses the challenges and limitations of computational approaches in sustainability, such as data availability, model uncertainty, and computational complexity, while also discusses emerging opportunities and future directions in the field. This book will be useful for professionals and scholars looking for up-to-date research on cutting-edge perspectives in the field of computational intelligent and optimization techniques in the areas of agriculture, industry, financial sector, business automation, renewable energy, optimization, and smart cities. |
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| AbstractList | This book presents insights into how Intelligent Computing and Optimization techniques can be used to attain the goals of Sustainable Development. It provides a comprehensive overview of the latest breakthroughs and recent developments in sustainable, intelligent computing technologies, applications, and optimization techniques across various industries, including business process management, manufacturing, financial sector, agriculture, financial sector, supply chain management, and healthcare. It focuses on computational intelligent techniques and optimization techniques to provide sustainable solutions to many problems.
Features:
Provides insights into the theory, implementation, and application of computational intelligence techniques in many industries.
Includes industry practitioner perspectives and case studies for a better understanding of sustainable solutions.
Highlights the role of intelligent computing and optimization as key technologies in decision-making processes and in providing cutting-edge solutions to real-world problems.
Addresses the challenges and limitations of computational approaches in sustainability, such as data availability, model uncertainty, and computational complexity, while also discusses emerging opportunities and future directions in the field.
This book will be useful for professionals and scholars looking for up-to-date research on cutting-edge perspectives in the field of computational intelligent and optimization techniques in the areas of agriculture, industry, financial sector, business automation, renewable energy, optimization, and smart cities. This book presents insights into how Intelligent Computing and Optimization techniques can be used for attaining the goals of Sustainable Development. It provides a comprehensive overview of the latest breakthroughs and recent developments in sustainable, intelligent computing technologies, applications, and optimisation techniques. This book presents insights into how Intelligent Computing and Optimization techniques can be used to attain the goals of Sustainable Development. It provides a comprehensive overview of the latest breakthroughs and recent developments in sustainable, intelligent computing technologies, applications, and optimization techniques across various industries, including business process management, manufacturing, financial sector, agriculture, financial sector, supply chain management, and healthcare. It focuses on computational intelligent techniques and optimization techniques to provide sustainable solutions to many problems. Features: • Provides insights into the theory, implementation, and application of computational intelligence techniques in many industries. • Includes industry practitioner perspectives and case studies for a better understanding of sustainable solutions. • Highlights the role of intelligent computing and optimization as key technologies in decision-making processes and in providing cutting-edge solutions to real-world problems. • Addresses the challenges and limitations of computational approaches in sustainability, such as data availability, model uncertainty, and computational complexity, while also discusses emerging opportunities and future directions in the field. This book will be useful for professionals and scholars looking for up-to-date research on cutting-edge perspectives in the field of computational intelligent and optimization techniques in the areas of agriculture, industry, financial sector, business automation, renewable energy, optimization, and smart cities. |
| Author | Grover, Veena Rathinam, Gopal Balusamy, Balamurugan Dhanaraj, Rajesh Kumar |
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| Copyright | 2025 selection and editorial matter, Veena Grover, Rajesh Kumar Dhanaraj, Balamurugan Balusamy, Gopal Rathinam; individual chapters, the contributors |
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| Keywords | Green Sustainability decision-making processes SDG Optimization techniques Deep Neural network Sustainable Process Engineering Sustainable Manufacturing Supply Chain Management Net Zero Robotic Process Management |
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| Snippet | This book presents insights into how Intelligent Computing and Optimization techniques can be used to attain the goals of Sustainable Development. It provides... This book presents insights into how Intelligent Computing and Optimization techniques can be used for attaining the goals of Sustainable Development. It... |
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| SubjectTerms | Artificial intelligence Automatic control engineering Computer architecture and logic design Computer programming / software engineering Computer science Computing and Information Technology decision-making processes Deep Neural network Development studies Digital and information technologies: Health and safety aspects Digital and information technologies: Legal aspects Digital and information technologies: social and ethical aspects Electronics and communications engineering Electronics engineering Green Sustainability Human–computer interaction Information technology: general topics Interdisciplinary studies Net Zero Operating systems Optimization techniques Real time operating systems Reference, Information and Interdisciplinary subjects Robotic Process Management SDG Software Engineering Supply Chain Management Sustainable Manufacturing Sustainable Process Engineering Technology, Engineering, Agriculture, Industrial processes |
| TableOfContents | 8.3 Optimization Techniques for Portfolio Management: Traditional and Modern Approaches with Esg Considerations 6.2 Outline of the Smart Manufacturing's Transformation Toward Industry 4.0 -- 6.2.1 Smart Manufacturing -- 6.2.2 Industry 3.0 -- 6.3 Industry 4.0 Transformation -- 6.3.1 Interconnectivity and IIoT -- 6.3.2 Data Analytics and AI -- 6.3.3 Cyber-physical Systems (CPS) -- 6.3.4 Advanced Automation and Robotics -- 6.3.5 Customization and Flexibility -- 6.3.6 Decentralized Decision-making -- 6.3.7 Human-machine Association -- 6.3.8 Sustainability and Resource Efficiency -- 6.4 Deep Learning: Understanding Its Fundamental Concepts -- 6.4.1 Neural Network Architecture: a Detailed Overview -- 6.4.1.1 Neuron and Layer -- 6.4.1.2 Artificial Neural Networks (ANNs) -- 6.4.1.3 Activation Functions -- 6.4.1.4 Feed Forward Propagation -- 6.4.1.5 Feed Forward Neural Networks (FNNs) -- 6.4.1.6 Back Propagation -- 6.4.1.7 Deep Neural Networks -- 6.4.1.8 Convolutional Neural Networks (CNNs) -- 6.4.1.9 Recurrent Neural Networks (RNNs) -- 6.4.1.10 Gated Recurrent Units (GRUs) and Long Short-term Memory (LSTM) -- 6.4.1.11 Autoencoders -- 6.4.1.12 Gans (Generative Adversarial Networks) -- 6.4.1.13 Transformers -- 6.4.1.14 Capsule Networks -- 6.4.1.15 Transfer Learning -- 6.4.1.16 Deep Learning Libraries and Frameworks -- 6.5 Deep Learning Integration in Smart Manufacturing -- 6.5.1 Quality Control -- 6.5.2 Predictive Maintenance -- 6.5.3 Process Optimization -- 6.5.4 Supply Chain Management -- 6.5.5 Energy Management -- 6.5.6 Human-Machine Collaboration -- 6.5.7 Customization and Flexibility -- 6.5.8 Quality and Process Control -- 6.5.9 Continuous Improvement -- 6.6 Deep Learning Implementation at Various Stages of Smart Manufacturing -- 6.6.1 Data Collection and Pre-processing -- 6.6.2 Design and Prototyping -- 6.6.3 Production Planning -- 6.6.4 Quality Control and Inspection -- 6.6.5 Process Monitoring and Optimization -- 6.6.6 Predictive Maintenance 3.9 Using DNN Technologies to Create Inclusive and Resilient Smart Cities -- 3.9.1 Making Decisions Based on Data -- 3.9.2 Improving Infrastructure Resilience and Planning for Future Maintenance -- 3.9.3 Raising the Bar for Crisis Intervention -- 3.9.4 Transportation Networks That Are Easy to Access -- 3.9.5 Sustainable Energy and Minimizing Energy Waste -- 3.10 Conclusion and Future Directions -- References -- 4 Digital Task Optimisation with Resource Allocation in Business Process Management Using Machine Learning Model -- 4.1 Introduction -- 4.2 Related Works -- 4.3 Proposed Business Process Management -- 4.4 Resource Allocation Using Markov Decision Entropy Q-cluster Bayesian Network -- 4.5 Network Optimisation Using Heuristic Swarm Colony Vector Optimisation Model -- 4.6 Results and Discussion -- 4.7 Conclusion and Future Scope -- References -- 5 Design of an Efficient Multimodal Deep Learning Framework for Assessing Mental Workload Using Eye Tracking and Physiological Parameters -- 5.1 Introduction -- 5.2 Literature Review -- 5.3 Proposed Design of an Efficient Multimodal Cross Learning Framework for Assessing Mental Workload Using Eye Tracking and Physiological Parameters -- 5.4 Result Evaluation and Comparative Analysis -- 5.5 Conclusion and Future Scope -- 5.5.1 Future Scope -- References -- 6 Deep Learning in Smart Manufacturing: Advancements, Applications, and Challenges -- 6.1 An Introduction to Industry 4.0: the Fourth Industrial Revolution -- 6.1.1 Key Pillars of Industry 4.0 -- 6.1.1.1 Interconnectivity -- 6.1.1.2 Data Analytics and Artificial Intelligence (AI) -- 6.1.1.3 Advanced Automation -- 6.1.1.4 Decentralized Decision-making -- 6.1.1.5 Customization and Flexibility -- 6.1.1.6 Sustainability and Resource Efficiency -- 6.1.1.7 Human-machine Collaboration -- 6.1.2 Impact and Implications 6.6.7 Human-machine Interaction -- 6.6.8 Energy Management -- 6.6.9 Supply Chain Management -- 6.6.10 Customization and Mass Production -- 6.6.11 Continuous Improvement -- 6.7 Real-time Case Studies for the Application of Deep Learning in Smart Manufacturing -- 6.7.1 Bmw's Smart Manufacturing with Deep Learning -- 6.7.2 Foxconn's Industrial AI Development Centre -- 6.7.3 General Electric's Wind Turbine Monitoring -- 6.7.4 Siemens' AI in Manufacturing -- 6.7.5 Hyundai's Robotics and AI Integration -- 6.8 Applications of Deep Learning in Industry 4.0 -- 6.9 Challenges in Integrating Deep Learning with Smart Manufacturing -- 6.10 Conclusion -- 6.11 Future Scope -- References -- 7 Fuzzy Optimization Techniques in Agricultural Field Using Supply Chain Management -- 7.1 Introduction -- 7.2 Results and Discussion -- 7.3 Conclusion -- References -- 8 Computational Intelligence Techniques for Banking and Financial Sectors -- 8.1 Introduction -- 8.1.1 Overview of Sustainable Finance -- 8.1.2 Importance of Incorporating Esg Elements -- 8.1.3 Challenges and Opportunities in Sustainable Portfolio Management -- 8.1.4 Opportunities -- 8.1.4.1 Innovation and Technology -- 8.1.4.2 Growing Demand -- 8.1.4.3 Risk Mitigation -- 8.1.4.4 Impactful Investing -- 8.2 Intelligent Computing Techniques for Financial Analysis: Application of AI and Ml in Financial Analysis and Predictive Modeling -- 8.2.1 AI and Ml in Financial Analysis -- 8.2.1.1 Enhanced Data Processing -- 8.2.1.2 Pattern Recognition -- 8.2.1.3 Risk Assessment -- 8.2.1.4 Fraud Detection -- 8.2.2 Predictive Modelling in Finance -- 8.2.2.1 Time Series Analysis -- 8.2.2.2 Regression Analysis -- 8.2.2.3 Neural Networks -- 8.2.2.4 Sentiment Analysis -- 8.2.2.5 Data Collection -- 8.2.2.6 Esg Integration -- 8.2.2.7 Sustainability Sentiment Analysis -- 8.2.2.8 Risk Mitigation -- 8.2.2.9 Performance Monitoring Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Editor Biography -- List of Contributors -- 1 A Journey to Computational Intelligence in Sustainable Development -- 1.1 Introduction -- 1.1.1 Problem Statement -- 1.1.2 Research Methodology -- 1.2 Literature Review -- 1.3 Finding and Discussion -- 1.4 Results -- 1.4.1 Comparative Analysis Between Various Computational Intelligence Techniques -- 1.4.2 Most Effective Ci Technique -- 1.4.3 Examples of Applications of Computational Intelligence -- 1.4.4 Challenges Faced in Adoption of Ci Techniques -- 1.5 Impact of Study -- 1.6 Future Scope of Research -- 1.7 Conclusion -- References -- 2 Businesses Combining Artificial Intelligence Concentrating on Sustainable Development Goals: A Win-win Situation -- 2.1 Introduction -- 2.1.1 Importance of Sustainability in the 21st Century -- 2.1.2 Artificial Intelligence in Error Prediction for the Industry -- 2.1.2.1 Latest Data on Sustainability Challenges (Climate Change, Biodiversity Loss, Resource Scarcity, Inequality) -- 2.1.3 Artificial Intelligence Role as Transformative Dynamism -- 2.2 The Supremacy of AI in Sustainability -- 2.3 Artificial Intelligence Complements Human Expertise in Sustainability Efforts -- 2.4 The Extended Expertise of AI in Managing Diverse Issues Related to Sustainability -- 2.5 Solicitation of Artificial Intelligence in Environment Fortification -- 2.5.1 Applications of AI in Climate Modeling and Prediction -- 2.5.2 Ai-based Solutions on Carbon Capture and Emission Reduction -- 2.5.3 Advancements in Weather Forecasting and Disaster Preparedness Through AI -- 2.6 Contemporaneous Scenario of the Indian AI Market -- 2.6.1 Market Size by Type of Sector -- 2.6.2 Sector-wise AI Adoption in India -- 2.6.3 Artificial Intelligence Proficiencies Among Indian Enterprises 2.6.4 A Portion of Open AI Jobs Crossways Years of Experience -- 2.7 Challenges of Implementing AI with Business for Sustainability -- 2.8 Conclusion -- 2.9 Future Scope -- References -- 3 Deep Neural Networks (DNNs) for Sustainable Development in Smart City -- 3.1 DNNs: Machine Learning Revolutionaries -- 3.1.1 The Anatomy of Deep Neural Networks -- 3.1.2 Acquiring Knowledge Through Data -- 3.1.3 Applications Across Various Industries and Concerns -- 3.2 DNN's Significance in Smart Cities -- 3.2.1 Requirements and Related Works -- 3.2.1.1 Developing Energy Administration -- 3.2.2 Interoperable DNN Framework -- 3.2.3 Importance of DNN in Smart Cities -- 3.2.3.1 Handling Decisions Efficiently -- 3.3 Intelligent Traffic Management Using DNN -- 3.3.1 Applications of DNNs in Intelligent Traffic Management -- 3.3.1.1 Optimizing Traffic Lights -- 3.3.2 Traffic Detection Using DNN -- 3.3.3 Benefits of DNN-based Intelligent Traffic Management -- 3.3.4 Data Security in DNN-based Itm -- 3.3.4.1 Practical Tips -- 3.3.5 Applications -- 3.3.6 Limitations and Future Scope -- 3.4 Dnn for Waste Management and Environmental Sustainability -- 3.5 Data Security and Privacy Concerns in DNN-enabled Smart Cities -- 3.5.1 Algorithms from Literature to Improve Security and Privacy -- 3.5.2 Mitigation Strategies -- 3.6 Integration of DNN and IoT for Smart City Problems -- 3.6.1 IoT-enabled Smart City Architecture -- 3.6.1.1 IoT Sensors -- 3.6.1.2 Communication Infrastructure -- 3.6.1.3 Cloud Platforms -- 3.6.1.4 DNN Algorithms -- 3.6.1.5 Centralized Control Centres and Various Smart City Applications -- 3.7 Obstacles and Prospects for Using DNN for Sustainable Smart Cities -- 3.7.1 Challenges -- 3.7.1.1 Responsibility and the Ability to Advance -- 3.7.2 Future Directions -- 3.8 Frameworks of Policy and Governance for the Evolution of Smart Cities Assisted by DNN |
| Title | Intelligent Computing and Optimization for Sustainable Development |
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