Intelligent Computing and Optimization for Sustainable Development

• Main Authors: Grover, Veena, Dhanaraj, Rajesh Kumar, Balusamy, Balamurugan, Rathinam, Gopal
• Format: eBook
• Language: English
• Published: Milton CRC Press 2025; Taylor & Francis; CRC Press LLC
• Edition: 1
• Subjects: Artificial Intelligence; Automatic control engineering; Business & Economics; Computer architecture and logic design; Computer programming / software engineering; Computer science; Computers; Computing and Information Technology; decision-making processes; Deep Neural network; Development; 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 Development & Engineering; Software Engineering; Supply Chain Management; Sustainable Development; Sustainable Manufacturing; Sustainable Process Engineering; Technology, Engineering, Agriculture, Industrial processes; Green Sustainability; decision-making processes; SDG; Optimization techniques; Deep Neural network; Sustainable Process Engineering; Sustainable Manufacturing; Supply Chain Management; Net Zero; Robotic Process Management

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...

Table of Contents:

• 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