Explainable Artificial Intelligence for Biomedical and Healthcare Applications

Aditya Khamparia has expertise in Teaching, Entrepreneurship, and Research & Development of more than 10 years. He is currently working as Assistant Professor and Coordinator of Department of Computer Science, Babasaheb Bhimrao Ambedkar University, Satellite Centre, Amethi, India. He received Ph.D. degree from Lovely Professional University, Punjab in May 2018. He has completed his M. Tech. from VIT University and B. Tech. from RGPV, Bhopal. He completed his PDF from UNIFOR, Brazil. He has more than 100 research papers along with book chapters including more than 20 papers in SCI indexed Journals with cumulative impact factor of above 50 to his credit. Additionally, He has authored, edited and editing 10 books. He has been featured in the list of top 2% scientist/researcher databases worldwide. In India, Rank 1 as a researcher in the field of healthcare applications (as per Google Scholar citation. 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 interests include machine learning, deep learning, educational technologies, computer vision. Deepak Gupta received a B.Tech. Degree in 2006 from the Guru Gobind Singh Indraprastha University, Delhi, India. He received an M.E. degree in 2010 from Delhi Technological University, India, and Ph. D. degree in 2017 from Dr. APJ Abdul Kalam Technical University (AKTU), Lucknow, India. He completed his Post-Doc from the National Institute of Telecommunications (Inatel), Brazil, in 2018. He has co-authored more than 207 journal articles, including 168 SCI papers and 45 conference articles. He has authored/edited 60 books, published by IEEE-Wiley, Elsevier, Springer, Wiley, CRC Press, DeGruyter, and Katsons. He has filled four Indian patents. He is the convener of the ICICC, ICDAM, ICCCN, ICIIP & DoSCI Springer conferences series. He is Associate Editor of Computer & Electrical Engineering, Expert Systems, Alexandria Engineering Journal, Intelligent Decision Technologies. He is the recipient of the 2021 IEEE System Council Best Paper Award. He has been featured in the list of top 2% scientist/researcher databases worldwide. In India, Rank 1 as a researcher in the field of healthcare applications (as per Google Scholar citation) and Ranked #78 in India among Top Scientists 2022 by Research.com. He is also working towards promoting Startups and also serving as a Startup Consultant. He is also a series editor of "Elsevier Biomedical Engineering" at Academic Press, Elsevier, "Intelligent Biomedical Data Analysis" at De Gruyter, Germany, and "Explainable AI (XAI) for Engineering Applications" at CRC Press. He is appointed as Consulting Editor at Elsevier. Accomplished productive collaborative research with grants of approximately $144000 from various international funding agencies, and he is Co-PI in an International Indo-Russian Joint project of Rs 1.31CR from the Department of Science and Technology.

1. Exploring Explainable AI: Techniques and Comparative Analysis. 2. Introduction to Explainable Artificial Intelligence in Biomedical and Healthcare Applications. 3. Smart Healthcare System: Automated Methods for diagnosis of diseases using Digital Twin Technology. 4. Explainable AI unlocks the Potential of AI in Biomedical Research and Practice. 5. An Intuitive Ensemble modelling with X-AI architecture for Autism classification. 6. Mental Disorder Management Using Explainable Artificial Intelligence. 7. Unlocking Insights: Data Analysis and Processing Empowered by Explainable AI. 8. Revolutionizing Healthcare: The Role of Artificial Intelligence in Transforming eHealth care. 9. Mental Disorders Management Using Explainable Artificial Intelligence (XAI). 10. Machine Learning Approach to Predict Adverse Effects of mRNA Vaccination: A Comparative Study of Classification Models and Ensemble Learning Techniques. 11. Explainable Artificial Intelligence (EAI): For Health Care Applications and Improvements. 12. Challenges and Imperatives for Equitable and Ethical Development of Explainable AI in Healthcare. 13. A Comprehensive Analysis of the Convergence Between Deep Learning Technologies and Bioinformatics, Catalyzing Groundbreaking Innovations in Biological Data Interpretation. 14. An Exhaustive Exploration of Explainable AI-Driven Applications in Healthcare, Enhancing Diagnostic Accuracy, Treatment Efficacy, and Patient Trust. 15. An In-Depth Exploration of Data Analysis and Processing Through the Prism of Explainable Artificial Intelligence Paradigms. 16. Implications of Artificial Intelligence in Disease Diagnosis