Recent Advances in Computational Intelligence Applications for Biometrics and Biomedical Devices focuses on the intersection of biometric-driven computational approaches and techniques within a connected multi-modal environment, particularly emphasizing their applications in healthcare. The book explores cutting-edge methodological approaches that leverage technologies like blockchain and integrate them with information fusion, data security for medical devices, and trust management. Readers with find this to be a comprehensive overview of the topics covered, including machine learning and deep learning for biomedical-based biometrics, computational medical imaging techniques, security strategies for healthcare systems, AI technology for multimodal biometrics, and feature reduction techniques.
Other sections cover blockchain and fog computing models for medical sensor data storage and evolutionary optimization for biometric feature identification and recognition, amongst others.
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. Contributors - p. xvii 7. Chapter 1. AI-assisted wireless capsule endoscopy devices for gastrointestinal tract screening: A review - p. 1 8. Chapter 2. A survey on suicidal rate trends in society using machine learning models - p. 13 9. Chapter 3. Securing healthcare systems using biometrics: A state-of-the-art systematic literature review and future research directions - p. 25 10. Chapter 4. Security vulnerabilities against fingerprint biometric systems - p. 35 11. Chapter 5. Evolutionary optimization for biometric feature identification and recognition: A survey - p. 45 12. Chapter 6. Multimodal biomedical system for mental disorder detection using emotion recognition - p. 71 13. Chapter 7. Transformative IoT applications: Advancing healthcare and industry - p. 89 14. Chapter 8. Anomaly detection in IoT using fox swarm intelligence - p. 99 15. Chapter 9. Computational medical imaging for biometrics using MRI and X-ray - p. 113 16. Chapter 10. AI cybersecurity in healthcare for cyber-physical systems - p. 123 17. Chapter 11. Social media analytics using deep neural networks for mental healthcare applications - p. 135 18. Chapter 12. Blockchain and fog computing for secure and scalable storage of blood pressure sensor data in biometric systems - p. 151 19. Chapter 13. Deep learning–based noninvasive brain feature fusion for Alzheimer's disease classification - p. 171 20. Chapter 14. Decentralized consent management using blockchain for IoT-based EHR management systems - p. 197 21. Chapter 15. Machine learning–based gait freezing detection and classification in Parkinson's patients using biometric insights - p. 217 22. Chapter 16. Experiments on peach leaf disease detection using light weight transfer learning models - p. 239 23. Chapter 17. Predictive detection of breast cancer with artificial neural network and support vector machine - p. 257 24. Chapter 18. Energy efficiency in IoT-based WSNs - p. 275 25. Chapter 19. An explainable hybrid TabTransformer–Random Forest model for biometric security in IoMT Healthcare systems - p. 285 26. Chapter 20. Comparative analysis of Explainable Artificial Intelligence (XAI) techniques on cancer dataset—addressing the “Black Box Problem” - p. 301 27. Chapter 21. Risk monitoring strategy for confidentiality of healthcare information - p. 315 28. Chapter 22. Adaptalytics of students’ to online learning using machine learning classifiers and sequential neural network - p. 321 29. Index - p. 343
Recent Advances in Computational Intelligence Applications for Biometrics and Biomedical Devices focuses on the intersection of biometric-driven computational approaches and techniques within a connected multi-modal environment, particularly emphasizing their applications in healthcare. The book explores cutting-edge methodological approaches that leverage technologies like blockchain and integrate them with information fusion, data security for medical devices, and trust management. Readers with find this to be a comprehensive overview of the topics covered, including machine learning and deep learning for biomedical-based biometrics, computational medical imaging techniques, security strategies for healthcare systems, AI technology for multimodal biometrics, and feature reduction techniques.
Other sections cover blockchain and fog computing models for medical sensor data storage and evolutionary optimization for biometric feature identification and recognition, amongst others.
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. Contributors - p. xvii 7. Chapter 1. AI-assisted wireless capsule endoscopy devices for gastrointestinal tract screening: A review - p. 1 8. Chapter 2. A survey on suicidal rate trends in society using machine learning models - p. 13 9. Chapter 3. Securing healthcare systems using biometrics: A state-of-the-art systematic literature review and future research directions - p. 25 10. Chapter 4. Security vulnerabilities against fingerprint biometric systems - p. 35 11. Chapter 5. Evolutionary optimization for biometric feature identification and recognition: A survey - p. 45 12. Chapter 6. Multimodal biomedical system for mental disorder detection using emotion recognition - p. 71 13. Chapter 7. Transformative IoT applications: Advancing healthcare and industry - p. 89 14. Chapter 8. Anomaly detection in IoT using fox swarm intelligence - p. 99 15. Chapter 9. Computational medical imaging for biometrics using MRI and X-ray - p. 113 16. Chapter 10. AI cybersecurity in healthcare for cyber-physical systems - p. 123 17. Chapter 11. Social media analytics using deep neural networks for mental healthcare applications - p. 135 18. Chapter 12. Blockchain and fog computing for secure and scalable storage of blood pressure sensor data in biometric systems - p. 151 19. Chapter 13. Deep learning–based noninvasive brain feature fusion for Alzheimer's disease classification - p. 171 20. Chapter 14. Decentralized consent management using blockchain for IoT-based EHR management systems - p. 197 21. Chapter 15. Machine learning–based gait freezing detection and classification in Parkinson's patients using biometric insights - p. 217 22. Chapter 16. Experiments on peach leaf disease detection using light weight transfer learning models - p. 239 23. Chapter 17. Predictive detection of breast cancer with artificial neural network and support vector machine - p. 257 24. Chapter 18. Energy efficiency in IoT-based WSNs - p. 275 25. Chapter 19. An explainable hybrid TabTransformer–Random Forest model for biometric security in IoMT Healthcare systems - p. 285 26. Chapter 20. Comparative analysis of Explainable Artificial Intelligence (XAI) techniques on cancer dataset—addressing the “Black Box Problem” - p. 301 27. Chapter 21. Risk monitoring strategy for confidentiality of healthcare information - p. 315 28. Chapter 22. Adaptalytics of students’ to online learning using machine learning classifiers and sequential neural network - p. 321 29. Index - p. 343
