Transforming Industries, Empowering Societies

Contents<br>Contributors xiii<br>Foreword xv<br>Preface xvii<br>Part I<br>Transformation towards Industry 5.0<br>1. Transitioning from traditional artificial intelligence to emerging trends: Exploring paradigm shifts, challenges, and opportunities<br>Anamika Anu, Jagrati Nagdiya and Sheril Thomas<br>1.1. Introduction<br>1.2. Paradigm shifts<br>1.3. Technology<br>1.4. Computational power<br>1.5. Cognitive understanding<br>1.6. Traditional artificial intelligence approaches<br>1.7. Limitations of early artificial intelligence systems<br>1.8. Emerging trends in artificial intelligence<br>1.9. Artificial intelligence-powered solutions<br>1.10. Challenges and ethical considerations<br>1.11. Data privacy, security, and interpretability<br>1.12. Challenges and opportunities in the transition<br>1.13. Conclusion<br>References<br>2. Human-machine collaboration in Industry 5.0 using Big Data analytics<br>Samiksha Khule, Muskan Sihare, Rakhi Arora, Nitin Dixit, Gaurav Dubey and Yogesh Kumar Sharma<br>2.1. Introduction<br>2.2. Technologies of Industry 5.0<br>2.3. Creative applications of Industry 5.0<br>2.4. The role of vision transformers in industry 4.0 and Industry 5.0<br>2.5. Principles of Industry 5.0<br>2.6. Literature review<br>2.7. Challenges in Industry 5.0<br>2.8. Limitations in Industry 5.0<br>2.9. Conclusion<br>References<br>3. Implications of Industry 5.0 for Society 5.0: A systematic literature review<br>Ganesh Narkhede, Gajanan Ghuge and Madahavi Mohite<br>3.1. Introduction<br>3.2. Literature review<br>3.3. Results and discussion<br>3.4. Conclusion<br>References<br>4. Cloud security through robust cryptographic measures: Overview, advances, and application<br>Radha Nishant Deoghare, Prachi Nishant Shah-Bahekar, Shradha Nishant Tawade and Sapana Nishant Kolambe<br>4.1. Introduction<br>4.2. Related Work<br>4.3. Proposed Approach<br>4.4. Result Analysis<br>4.5. Conclusion<br>References<br>5. Mesocaps: Enhancing deepfake detection 1<br>Umesh Pranjal Shirsat, Shivani Joshi, Siddhi Shinde, Vaibhav Garje, Amit Joshi and Suraj Sawant<br>5.1. Introduction<br>5.2. Literature review<br>5.3. Deepfake generation<br>5.4. Deepfake detection<br>5.5. Gap analysis<br>5.6. Methodology<br>5.7. Model architecture<br>5.8. MesoNet<br>5.9. Capsule network<br>5.10. Results and discussion<br>5.11. Experimental setup<br>5.12. Performance metrics and comparison<br>5.13. Conclusion and future scope<br>References<br>Part II<br>Transformation in Healthcare 5.0<br>6. Digital health evaluation: A roadmap ahead<br>Pranali Chavhan, Namrata Kharate, Prashant Anerao and Gajanan Chavhan<br>6.1. Introduction<br>6.2. Current Approaches to Digital Health Evaluation<br>6.3. A Roadmap for Future Evaluation<br>6.4. Case Studies<br>6.5. Digital Health: Barrier and Solution<br>6.6. Conclusion<br>References<br>7. Adapting online medical services for the well-being diverse patients<br>Jyoti Deshmukh, Vijay Rathod, Nilesh Sable and Gitanjali Shinde<br>7.1. Introduction<br>7.2. Telemedicine Strategy Implementation in 2019 During the COVID-19 Era<br>7.3. Technological Solutions for Telemedicine<br>7.4. Related Information<br>7.5. COVID-19 Pandemic: The Remote Medication Network for Neurorehabilitation<br>7.6. Conclusion<br>References<br>8. Revolutionizing healthcare using digital twins: Monitoring, analysis, and advancement<br>Rakhi Arora, Nitin Dixit, Jigyasa Mishra, Muskan Sihare, Samiksha Khule and Yogesh Kumar Sharma<br>8.1. Introduction<br>8.2. Literature Survey<br>8.3. Enabling Technologies and Data Sources<br>8.4. Digital Twin In Healthcare—Applications<br>8.5. Integration of Artificial Intelligence in Human Digital Twins<br>8.6. Limitations Associated with Healthcare Digital Twins<br>8.7. Conclusion<br>References<br>9. Wellbeing of working mothers based on decision making: A data science approach 1<br>Jyoti Deshmukh, Vijay Rathod, Nilesh Sable and Gitanjali Shinde<br>9.1. Introduction<br>9.2. Exploration of Wearable Devices<br>9.3. Experimental Methods<br>9.4. Monitoring of Fetal Movement<br>9.5. The Wearable Device Design<br>9.6. The Patient Data Possession<br>9.7. Energy Assessment<br>9.8. Fetal Movement Extraction of Feature<br>9.9. Design of Phantom—The Simulation System for Fetal Movement<br>9.10. Conclusion<br>References<br>Part III<br>Transformation in agriculture<br>10. Navigating the agricultural landscape: Artificial intelligence and Industry 5.0 insights<br>Pradnya Samit Mehta and Sanved Narwadkar<br>10.1. Overview of Artificial Intelligence in Agriculture<br>10.2. Role of Artificial Intelligence in Decision-making<br>10.3. Precision Agriculture Techniques<br>10.4. Data-driven Crop Yield Predictions<br>10.5. Climate and Weather Impact Assessment Strategies With Artificial Intelligence<br>10.6. Holistic Approach With Artificial Intelligence for Industry 5.0 Society 5.0<br>10.7. Smart Irrigation Systems for Artificial Intelligence Advancements in Farming: A Revolution an Agriculture<br>10.8. Case Studies Demonstrating Increased Water Efficiency and Crop Yield<br>10.9. Conclusion<br>References<br>11. Industry 5.0 unveiled, precision agriculture empowered: Integrating recommendation and prediction systems for transparent farming transactions<br>Kaustubh Vitthal Rathod, Devesh Rathi and Sankalp Naranje<br>11.1. Introduction<br>11.2. Methodology<br>11.3. Results and Discussion<br>11.4. Conclusion<br>11.5. Future Scope<br>References<br>12. Enhancing agricultural resilience through synergistic human–AI collaboration in Industry 5.0<br>Yogesh Kumar Sharma, Samiksha Khule, Gaurav Dubey, Rakhi Arora, Nitin Dixit and Muskan Sihare<br>12.1. Introduction<br>12.2. Literature Review<br>12.3. Industry 5.0 Technologies<br>12.4. Industry 4.0 vs Industry 5.0<br>12.5. Challenges of Industry 5.0<br>12.6. Industry 5.0: Applications<br>12.7. Industry 5.0: Limitations<br>12.8. Future Directions<br>12.9. Conclusion<br>References<br>13. Cultivating the future of agriculture where digital twin meets artificial intelligence<br>Muskan Sihare, Samiksha Khule, Rakhi Arora, Nitin Dixit, Gaurav Dubey and Yogesh Kumar Sharma<br>13.1. Introduction<br>13.2. Literature Review<br>13.3. Digital Twin Definition<br>13.4. Digital Twin in Agriculture<br>13.5. Artificial Intelligence for the Digital Twin<br>13.6. Artificial Intelligence and Digital Twin Convergence<br>13.7. Agriculture Has Undergone Distinct Phases of Evolution<br>13.8. The Industrial Revolution&rsquo;s Phases Can Be Compared With The Development of Agricultural Technology<br>13.9. Digital Agriculture Tools<br>13.10. Application of Digital Twins in Agriculture<br>13.11. Benefits and Challenges<br>13.12. The Future Pathways for Digital Twins<br>13.13. Conclusion<br>References<br>14. Explainable artificial intelligence for plant disease diagnosis<br>Diana Susan Joseph and Pranav M Pawar<br>14.1. Introduction<br>14.2. Related Works<br>14.3. Methods of Explainable Artificial Intelligence<br>14.4. Explainable Artificial Intelligence For Sustainable Agriculture<br>14.5. Research Directions of Artificial Intelligence in Agriculture With Explainable Artificial Intelligence<br>14.6. Conclusion<br>References<br>Part IV<br>Transformation in Design & Manufacturing<br>15. Challenges, opportunities, and frameworks for human-centric design and manufacturing in Industry 5.0<br>Prashant Anerao, Namrata Kharate, Yashwant Shrirang Munde and Pranali Chavhan<br>15.1. Introduction to Industry 5.0<br>15.2. Challenges and Opportunities<br>15.3. Framework of Industry 5.0 in Design and Manufacturing<br>15.4. Key Considerations for Implementation<br>15.5. Roadmap Ahead<br>15.6. Conclusion<br>References<br>16. Transformation in manufacturing industry: Review and future trends<br>Mansi Subhedar and Suyog Dasnurkar<br>16.1. Introduction<br>16.2. Collaborative Robots<br>16.3. Digital Twins and Simulations<br>16.4. Virtual Reality and Augmented Reality for Industrial Testing<br>16.5. AI and ML in Manufacturing<br>16.6. Challenges for Transformations in the Manufacturing Industry<br>16.7. Future Directions<br>16.8. Conclusion<br>References<br>17. The pivotal role of artificial intelligence in digital twins: A case study<br>Nalini Jagtap, Trisha Singh and Eshwari Sonawane<br>17.1. Introduction<br>17.2. Literature Survey<br>17.3. Core Functionalities of Artificial Intelligence in Digital Twins<br>17.4. Case Studies and Applications<br>17.5. Conclusion<br>References<br>18. Developing artificial intelligence applications in manufacturing using digital twin-driven machine learning technology1<br>Dixit Nitin, Rakhi Arora, Vijay Sharma, Muskan Sihare, Samiksha Khule and Bhawna Ojha<br>18.1. Introduction<br>18.2. Background and Recent Advances<br>18.3. Framework for Digital Twin-driven Industrial Artificial Intelligence<br>18.4. Digital Twin in Machine Learning<br>18.5. Conclusion<br>References<br>Part V<br>Energy and sustainable development<br>19. The role of optimization techniques in achieving sustainable artificial intelligence 1<br>Hanan Hussain and S. Tamizharasan<br>19.1. Introduction<br>19.2. Related Works<br>19.3. Optimization Techniques for Sustainable Artificial Intelligence<br>19.4. Challenges and Open Issues in Achieving Sustainable Artificial Intelligence<br>19.5. Conclusion<br>References<br>20. Smart disaster management: Leveraging machine learning and remote sensing for informed decision-making<br>Ruta Prabhu, Anupama Jawale, Hiral Patel, Disha Gandhi, Shivwani Nadar and Riddhi Lonandkar<br>20.1. Introduction<br>20.2. Literature Review<br>20.3. Methods for Disaster Monitoring<br>20.4. Overview of Various Algorithms for Disaster and Hazard Detection<br>20.5. Tsunami Detection<br>20.6. Conclusion<br>References<br>21. Navigating ethical complexities in energy transitions<br>Bhawna Ojha, Yogesh Kumar Sharma, Khemchand Shakywar and Aniket Arya<br>21.1. Introduction<br>21.2. Understanding Industry 5.0<br>21.3. Benefits and Challenges of Industry 5.0 Implementation<br>21.4. Ethical Complexities in Energy Transitions<br>21.5. Addressing Ethical Complexities Through Industry 5.0<br>21.6. Stakeholder Engagement and Collaboration7<br>21.7. Future Outlook and Recommendations<br>21.8. Conclusion<br>References<br>22. Revolutionizing energy storage for a smart society<br>Asmita Kalamkar, Gitanjali Shinde, Riddhi Mirajkar, ParikshitMahalle,Namrata Kharate and Prashant Anerao<br>22.1. Background and Context<br>22.2. Green Computing: Principles and Practices<br>22.3. Renewable Energy Integration<br>22.4. Case Studies and Applications<br>22.5. Challenges and Barriers<br>22.6. Conclusion<br>References<br>23. Green energy storage: Bridging sustainability and smart industries<br>Riddhi Mirajkar, Gitanjali Shinde, Snehal Rathi, Vidula Meshram, Pankaj Chandre and Pranali Chavhan<br>23.1. Introduction<br>23.2. Fundamentals of Green Energy Storage<br>23.3. Advanced Energy Storage Technologies<br>23.4. Artificial Intelligence and Internet of Things in Smart Energy Storage<br>23.5. Integrating Green Energy Storage in Industry 5.0<br>23.6. Policy and Regulatory Frameworks<br>23.7. Challenges and Future Prospects of Energy Storage<br>23.8. Conclusion<br>References<br>Index