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. Anomaly Detection in IoT using Fox Swarm Intelligence
2. Computational Medical Imaging for Biometrics using MRI and X-ray
3. AI Cybersecurity in Healthcare for Cyber-Physical Systems
4. Securing Healthcare Systems using Biometrics: A State-of-the-Art
Systematic Literature Review and Future Research Directions
5. Social Media Analytics Using Deep Neural Networks for Mental Healthcare
Applications
6. Energy Efficiency in IoT-based WSNs
7. Predictive Detection of Breast Cancer with Artificial Neural Network and
Support Vector Machine
8. Evolutionary Optimization for Biometric Feature Identification and
Recognition: A Survey
9. Comparative Analysis of Explainable Artificial Intelligence (XAI)
Techniques on Cancer Dataset- addressing the Black Box Problem
10. Risk Monitoring Strategy for Confidentiality of Healthcare Information
11. Security Vulnerabilities against Fingerprint Biometric Systems
12. Adaptalytics of Students To Online Learning Using Machine Learning
Classifiers And Sequential Neural Network
13. A Survey on Suicidal Rate Trends in Society using Machine Learning
Models
14. AI assisted Wireless Capsule Endoscopy Devices for Gastrointestinal Tract
Screening: A Review
Dr. Aditya Khamparia has expertise in teaching, entrepreneurship, and research and development of 11 years. He is presently working as Assistant Professor in Babasaheb Bhimrao Ambedkar University, Satellite Centre, Amethi, India. He received his Ph.D. degree from Lovely Professional University, Punjab, India in May 2018. He has completed his M. Tech. from VIT University, Vellore, Tamil Nadu, India and B. Tech. from RGPV, Bhopal, Madhya Pradesh, India. He has completed his PDF from UNIFOR, Brazil. He has published around 105 research papers along with book chapters including more than 25 papers in SCI indexed Journals with cumulative impact factor of above 100 to his credit. Additionally, he has authored and edited eleven books. Furthermore, he has served the research field as a Keynote Speaker/Session Chair/Reviewer/TPC member/Guest Editor and many more positions in various conferences and journals. His research interest include machine learning, deep learning for biomedical health informatics, educational technologies, and computer vision. Dr. Aditya Khamparia has expertise in teaching, entrepreneurship, and research and development of 11 years. He is presently working as Assistant Professor in Babasaheb Bhimrao Ambedkar University, Satellite Centre, Amethi, India. He received his Ph.D. degree from Lovely Professional University, Punjab, India in May 2018. He has completed his M. Tech. from VIT University, Vellore, Tamil Nadu, India and B. Tech. from RGPV, Bhopal, Madhya Pradesh, India. He has completed his PDF from UNIFOR, Brazil. He has published around 105 research papers along with book chapters including more than 25 papers in SCI indexed Journals with cumulative impact factor of above 100 to his credit. Additionally, he has authored and edited eleven books. Furthermore, he has served the research field as a Keynote Speaker/Session Chair/Reviewer/TPC member/Guest Editor and many more positions in various conferences and journals. His research interest include machine learning, deep learning for biomedical health informatics, educational technologies, and computer vision.
