Internet of Things enabled Machine Learning for Biomedical Applications

The text begins by highlighting the benefits of the internet of things enabled machine learning in the healthcare sector, examines the diagnosis of diseases using machine learning algorithms, and examines security and privacy issues in the healthcare systems using the internet of things.

Dr. Neha Goel is working as Professor in the Department of Electronics & Communication Engineering, RKGIT, Ghaziabad, India. She has Ph.D. degree from SRM University, Chennai, in 2019. She has 18 years of rich experience in teaching and research and development activities. Her area of interest is VLSI design, CMOS design, Internet of Things, and machine learning. She has guided several B.Tech and M.Tech Projects and has published 45 papers in various national/ international journals and conferences. She has received many grants and has published Four patents. She has also attended various workshops and seminars in various fields. Dr. Ravindra Kumar Yadav is Professor and Head of the Department of Electronics & Communication Engineering, RKGIT, Ghaziabad, India. He has B.E., M.E., and Ph.D. degrees in the field of Electronics & Communication Engineering. He has 26 years of rich experience in teaching, research and development activities, administration and managing, and establishing higher educational institutions. He has guided several B. Tech. and M.Tech. projects and is also guiding Ph.D. students from IIT Dhanbad as a co-guide. He has 90 papers to his credit, published in international/national journals, conferences, and symposiums. Prof. Yadav is reviewer for several national/international journals of high repute. He has chaired/participated in technical sessions at multiple international and national conferences/seminars held throughout the country.

1. ML and IoT coupled Bio-Medical applications in Healthcare: Smart Growth and Upcoming Challenges. 2. Recent Advances in Ubiquitous Sustainable Healthcare Systems. 3. IoT enabled Healthcare System using Machine Learning. 4. An Efficient Architecture for Classification of Super Resolution Enhanced Human Chromosome Images. 5. Applications of Machine Learning to the Impact of IoT in Biomedical Applications. 6. Ovarian Cancer Detection Using IoT-Based Intelligent Assistant and Blockchain Technology. 7. Blood oxygen level and pulse rate measurement using hemodialysis using IoT and Computational Intelligence. 8. Dental Shade Matching using machine Learning Models. 9. Brain Tumor Detection for Recognising Critical Brain Damage in Patients Using Computer Vision. 10. Smart Therapist: The Mental Health detector. 11. Medical Image Analysis based on Deep Learning Approach and Internet of Medical Things (IoMT) for early Diagnosis of Retinal disease. 12. Intelligent E-Learning Platform Consolidating Web of Things and Chat-GPT. 13. Issues and Challenges in security and privacy with E-health care: a thorough literature analysis. 14. Harnessing the Power of Distributed Cloud and Edge Computing for Advanced Healthcare Systems. 15. Securing Cloud-Based IoT: Exploring the Significance of Lightweight Cryptography for Enhanced Security. 16. Security and Privacy in the Internet of Medical Things (IoMT)-Based Healthcare: Ensuring Trust and Safety. 17. A Comprehensive Study of the Problem and Challenges Associated with Machine Learning Enabled IOT in Biomedical Applications. 18. A Machine Learning-enabled Internet of Things Model for Cloud-based Biomedical Applications. 19. Machine Learning Enabled IoT for Biomedical applications: Problem and challenges. 20. IOT driven Machine learning mechanisms for Healthcare Applications.