Data Science in the Medical Field

Seifedine Kadry is a Professor in the Department of Mathematics and Computer Science, at Norrof University College, in Norway. He has a Bachelor’s degree in 1999 from Lebanese University, MS degree in 2002 from Reims University (France) and EPFL (Lausanne), PhD in 2007 from Blaise Pascal University (France), HDR degree in 2017 from Rouen University. At present, his research focuses on data Science, education using technology, system prognostics, stochastic systems, and applied mathematics. He is an ABET program evaluator for computing, and ABET program evaluator for Engineering Tech. He is a Fellow of IET, Fellow of IETE, and Fellow of IACSIT. He is a distinguished speaker of IEEE Computer Society.Dr. Shubham Mahajan is an academic and researcher, member of IEEE, ACM, and IAENG. He earned a B.Tech from Baba Ghulam Shah Badshah University, an M.Tech from Chandigarh University, and a PhD from Shri Mata Vaishno Devi University. He is currently Assistant Professor at Amity University, Haryana, India. His research spans artificial intelligence and image processing, including video compression, image segmentation, fuzzy entropy, nature-inspired optimization, data mining, machine learning, robotics, and optical communications. He holds patents internationally and has published widely in high-impact venues; he has edited several Scopus-indexed books. He has received multiple awards for research excellence and travel support from IEEE, among others. He has served as IEEE Campus Ambassador at premier institutes and promotes international collaborations. He participates in technical program committees and editorial boards for conferences and journals, shaping discourse in AI and image processing.

• 1. PPH 4.0: a privacy-preserving health 4.0 framework with machine learning and cellular automata2. An automatic detection and severity levels of COVID-19 using convolutional neural network models3. Biosensors and disease diagnostics in medical field4. Brain tumor recognition and classification techniques5. Identifying the features and attributes of various artificial intelligence-based healthcare models6. Classification algorithms and optimization techniques in healthcare systems representation of dataset in medical applications7. A knowledge discovery framework for COVID-19 disease from PubMed abstract using association rule hypergraph8. Predictive analysis in healthcare using data science: leveraging big data for improved patient care9. Data science in medical field: advantages, challenges, and opportunities10. Decentralizing healthcare through parallel blockchain architecture: transmitting internet of medical things data through smart contracts in telecare medical information systems11. Machine learning in heart disease prediction12. U-Net-based approaches for brain tumor segmentation13. Explainable image recognition models for aiding radiologists in clinical decision making14. Prediction of heart failure disease using classification algorithms along with performance parameters15. Cancer survival prediction using artificial intelligence: current status and future prospects16. Heart disease prediction in pregnant women with diabetes using machine learning17. Healthcare using image recognition technology18. Integration of deep learning and blockchain technology for a smart healthcare record management system19. Internet of things based smart health and attendance monitoring system in an institution for COVID-1920. Medical diagnosis using image processing techniques21. Harnessing the potential of predictive analytics and machine learning in healthcare: empowering clinical research and patient care22. Predictive analysis in healthcare using data science23. Recommender systems in healthcare—an emerging technology24. Robotics: challenges and opportunities in healthcare25. A new era of the healthcare industry using Internet of Medical Things26. Single cell genomics unleashed: exploring the landscape of endometriosis with machine learning, gene expression profiling, and therapeutic target discovery27. Analyzing the success of the thriving machine prediction model for Parkinson’s disease prognosis: a comprehensive review