Recent Advances in Computational Intelligence Applications for Biometrics and Biomedical Devices

Aditya Khamparia, Deepak Gupta

• Publisher: Academic Press
• Year: 2025
• Topic: Biological Sciences
• eBook ISBN:
  9780443330131
• Subtopic: Biotechnology
• Index: Biological Sciences

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. - Covers recent computational advancements in biomedical-driven biometrics for connected healthcare - Explores intelligent computational multimodal technologies for healthcare, incorporating deep learning, parallel computing, and distributed computing - Presents security aspects of next-generation healthcare technologies, including machine learning, deep learning, and blockchain integration with IoT for information fusion, data security, and trust management - Provides both fundamental and high-level concepts, making it suitable for both industry professionals and beginners - Includes applications of cloud and pervasive computing technologies in biometrics for real-world scenarios

Table of contents

• Title of Book
• Chapter 1. AI-assisted wireless capsule endoscopy devices for gastrointestinal tract screening: A review
• Chapter 2. A survey on suicidal rate trends in society using machine learning models
• Chapter 3. Securing healthcare systems using biometrics: A state-of-the-art systematic literature review and future research directions
• Chapter 4. Security vulnerabilities against fingerprint biometric systems
• Chapter 5. Evolutionary optimization for biometric feature identification and recognition: A survey
• Chapter 6. Multimodal biomedical system for mental disorder detection using emotion recognition
• Chapter 7. Transformative IoT applications: Advancing healthcare and industry
• Chapter 8. Anomaly detection in IoT using fox swarm intelligence
• Chapter 9. Computational medical imaging for biometrics using MRI and X-ray
• Chapter 10. AI cybersecurity in healthcare for cyber-physical systems
• Chapter 11. Social media analytics using deep neural networks for mental healthcare applications
• Chapter 12. Blockchain and fog computing for secure and scalable storage of blood pressure sensor data in biometric systems
• Chapter 13. Deep learning–based noninvasive brain feature fusion for Alzheimer's disease classification
• Chapter 14. Decentralized consent management using blockchain for IoT-based EHR management systems
• Chapter 15. Machine learning–based gait freezing detection and classification in Parkinson's patients using biometric insights
• Chapter 16. Experiments on peach leaf disease detection using light weight transfer learning models
• Chapter 17. Predictive detection of breast cancer with artificial neural network and support vector machine
• Chapter 18. Energy efficiency in IoT-based WSNs
• Chapter 19. An explainable hybrid TabTransformer–Random Forest model for biometric security in IoMT Healthcare systems
• Chapter 20. Comparative analysis of Explainable Artificial Intelligence (XAI) techniques on cancer dataset—addressing the “Black Box Problem”
• Chapter 21. Risk monitoring strategy for confidentiality of healthcare information
• Chapter 22. Adaptalytics of students’ to online learning using machine learning classifiers and sequential neural network
• Index