Prediction of the Future Pandemic: Artificial Intelligence and Nanotechnology Approaches provides a comprehensive account of the applications, challenges, and breakthroughs in using AI and nanotechnology for pandemic prediction. The book emphasizes interdisciplinary collaboration among experts and a global perspective on the prediction of future pandemics. Sections provide a comprehensive exploration of cutting-edge methodologies that leverage AI and nanotechnology to predict, analyze, and combat future pandemics. This book aims to bridge the gap between theoretical concepts and practical applications, offering insights into the integration of these advanced technologies for more accurate, timely, and ethical pandemic predictions.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. List of contributors - p. xi 7. Chapter 1. The role of emerging technology in pandemic predictions - p. 1 8. Chapter 2. Interdisciplinary perspectives on future pandemic prediction - p. 11 9. Chapter 3. Role of artificial intelligence in pandemic prediction - p. 25 10. Chapter 4. Nanotechnology applications in pandemic prediction - p. 37 11. Chapter 5. AI and nanotechnology integration and future pandemic prediction - p. 53 12. Chapter 6. Comparative analysis of AI and nanotech approaches for pandemic prediction - p. 69 13. Chapter 7. Quantum leaps in pandemic prediction: AI synergy in the prediction of future pandemics - p. 105 14. Chapter 8. AI healer helps redefining pandemic response - p. 119 15. Chapter 9. Role of nanomaterials in prevention strategies - p. 129 16. Chapter 10. Viral detection: nanosensors in quaraetntine enforcement - p. 167 17. Chapter 11. Future implications, opportunities, and limitations in pandemic prediction - p. 203 18. Chapter 12. Pandemics of tomorrow: how AI and nanotech will transform global health - p. 231 19. Chapter 13. Machine learning in pandemic: transformative applications, models, and future directions - p. 275 20. Chapter 14. Bipolar intuitionistic fuzzy graph centrality-based decision-making model to prioritize locations for healthcare camp during COVID-19 - p. 297 21. Chapter 15. Enhancing healthcare accessibility for tuberculosis prevention: a bipolar fuzzy ARAS decision-making approach - p. 311 22. Chapter 16. A systematic review of the policy with regard to mode of delivery among COVID-19-positive mothers - p. 329 23. Chapter 17. Virtual reality as a pandemic solution: adapting education, working, tourism, and industrial in COVID-19 situation - p. 381 24. Index - p. 395
Prediction of the Future Pandemic: Artificial Intelligence and Nanotechnology Approaches provides a comprehensive account of the applications, challenges, and breakthroughs in using AI and nanotechnology for pandemic prediction. The book emphasizes interdisciplinary collaboration among experts and a global perspective on the prediction of future pandemics. Sections provide a comprehensive exploration of cutting-edge methodologies that leverage AI and nanotechnology to predict, analyze, and combat future pandemics. This book aims to bridge the gap between theoretical concepts and practical applications, offering insights into the integration of these advanced technologies for more accurate, timely, and ethical pandemic predictions.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. List of contributors - p. xi 7. Chapter 1. The role of emerging technology in pandemic predictions - p. 1 8. Chapter 2. Interdisciplinary perspectives on future pandemic prediction - p. 11 9. Chapter 3. Role of artificial intelligence in pandemic prediction - p. 25 10. Chapter 4. Nanotechnology applications in pandemic prediction - p. 37 11. Chapter 5. AI and nanotechnology integration and future pandemic prediction - p. 53 12. Chapter 6. Comparative analysis of AI and nanotech approaches for pandemic prediction - p. 69 13. Chapter 7. Quantum leaps in pandemic prediction: AI synergy in the prediction of future pandemics - p. 105 14. Chapter 8. AI healer helps redefining pandemic response - p. 119 15. Chapter 9. Role of nanomaterials in prevention strategies - p. 129 16. Chapter 10. Viral detection: nanosensors in quaraetntine enforcement - p. 167 17. Chapter 11. Future implications, opportunities, and limitations in pandemic prediction - p. 203 18. Chapter 12. Pandemics of tomorrow: how AI and nanotech will transform global health - p. 231 19. Chapter 13. Machine learning in pandemic: transformative applications, models, and future directions - p. 275 20. Chapter 14. Bipolar intuitionistic fuzzy graph centrality-based decision-making model to prioritize locations for healthcare camp during COVID-19 - p. 297 21. Chapter 15. Enhancing healthcare accessibility for tuberculosis prevention: a bipolar fuzzy ARAS decision-making approach - p. 311 22. Chapter 16. A systematic review of the policy with regard to mode of delivery among COVID-19-positive mothers - p. 329 23. Chapter 17. Virtual reality as a pandemic solution: adapting education, working, tourism, and industrial in COVID-19 situation - p. 381 24. Index - p. 395
