Adaptive AI in Sensor Informatics: Methods, Applications, and Implications explores the growing need for efficient, interpretable, and reliable adaptive AI systems tailored to wireless sensor networks. The book highlights how adaptive AI strengthens collaboration between humans and artificial intelligence by enabling transparent decision-making processes. Aimed at academics, professionals, and students, it provides an accessible yet thorough guide to understanding the intersection of adaptive AI and sensor informatics, focusing on practical implementation and the development of models that are both trustworthy and user-friendly. Readers will gain insight into the essential role adaptive AI plays in advancing wireless sensor networks across various sectors.
The book also examines the unique challenges and opportunities that arise when deploying adaptive AI in real-world sensor environments. It offers actionable advice for designing AI models that comply with regulations and support user confidence, especially in areas such as healthcare, environmental monitoring, smart cities, and industrial automation.
• Draws on the latest research and methods to provide valuable insight into the efficiency of AI-based systems, particularly within the realm of wireless sensors and related domains
• Explores and explains the critical role played by adaptive AI and sensor informatics in healthcare, finance, and autonomous vehicles, where the synergy of AI and sensor data plays a pivotal role
• Presents relevant case studies, practical demonstrations, and empirical evidence to substantiate the efficacy of AI-enabled sensor systems
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. xiii 7. About the editors - p. xvii 8. Preface - p. xix 9. Acknowledgments - p. xxi 10. 1. Artificial intelligence in sensor technology - p. 1 11. 2. Artificial intelligence for sensor data analysis - p. 25 12. 3. Role of AI in wireless sensor network - p. 51 13. 4. Leveraging adaptive artificial intelligence and sensor fusion for enhanced process optimization and monitoring in chemical engineering - p. 77 14. 5. Adaptive artificial intelligence for intelligent fault detection and correction in sensor systems - p. 133 15. 6. Sensor-integrated smart wearable system for cardiac arrest monitoring using explainable artificial intelligence and machine learning - p. 153 16. 7. Sensor-driven artificial intelligence for early diabetes prediction using machine learning models - p. 181 17. 8. Smart AI: adapting to sensor data in practical applications - p. 203 18. 9. Enhancing quality of life with automatic artificial intelligence sensors - p. 229 19. 10. Enhanced data processing and decision-making using edge computing, Internet of Things, and adaptive artificial intelligence - p. 259 20. 11. Transformation of urban infrastructure using federated learning in artificial intelligence-driven sensor cloud architectures - p. 277 21. 12. Future directions and emerging trends of adaptive artificial intelligence in wireless sensor networks, Internet of Things, and Industrial Internet of Things - p. 291 22. Index - p. 309
Adaptive AI in Sensor Informatics: Methods, Applications, and Implications explores the growing need for efficient, interpretable, and reliable adaptive AI systems tailored to wireless sensor networks. The book highlights how adaptive AI strengthens collaboration between humans and artificial intelligence by enabling transparent decision-making processes. Aimed at academics, professionals, and students, it provides an accessible yet thorough guide to understanding the intersection of adaptive AI and sensor informatics, focusing on practical implementation and the development of models that are both trustworthy and user-friendly. Readers will gain insight into the essential role adaptive AI plays in advancing wireless sensor networks across various sectors.
The book also examines the unique challenges and opportunities that arise when deploying adaptive AI in real-world sensor environments. It offers actionable advice for designing AI models that comply with regulations and support user confidence, especially in areas such as healthcare, environmental monitoring, smart cities, and industrial automation.
• Draws on the latest research and methods to provide valuable insight into the efficiency of AI-based systems, particularly within the realm of wireless sensors and related domains
• Explores and explains the critical role played by adaptive AI and sensor informatics in healthcare, finance, and autonomous vehicles, where the synergy of AI and sensor data plays a pivotal role
• Presents relevant case studies, practical demonstrations, and empirical evidence to substantiate the efficacy of AI-enabled sensor systems
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. xiii 7. About the editors - p. xvii 8. Preface - p. xix 9. Acknowledgments - p. xxi 10. 1. Artificial intelligence in sensor technology - p. 1 11. 2. Artificial intelligence for sensor data analysis - p. 25 12. 3. Role of AI in wireless sensor network - p. 51 13. 4. Leveraging adaptive artificial intelligence and sensor fusion for enhanced process optimization and monitoring in chemical engineering - p. 77 14. 5. Adaptive artificial intelligence for intelligent fault detection and correction in sensor systems - p. 133 15. 6. Sensor-integrated smart wearable system for cardiac arrest monitoring using explainable artificial intelligence and machine learning - p. 153 16. 7. Sensor-driven artificial intelligence for early diabetes prediction using machine learning models - p. 181 17. 8. Smart AI: adapting to sensor data in practical applications - p. 203 18. 9. Enhancing quality of life with automatic artificial intelligence sensors - p. 229 19. 10. Enhanced data processing and decision-making using edge computing, Internet of Things, and adaptive artificial intelligence - p. 259 20. 11. Transformation of urban infrastructure using federated learning in artificial intelligence-driven sensor cloud architectures - p. 277 21. 12. Future directions and emerging trends of adaptive artificial intelligence in wireless sensor networks, Internet of Things, and Industrial Internet of Things - p. 291 22. Index - p. 309
