6th International Conference on Machine Learning for Networking (MLN'2023)
Keynote: Osman Salem, University Paris Cité, France
Title: The Art of the Possible with Machine Learning in IoMTs
Abstract
The Internet of Medical Things (IoMTs) is rapidly transforming healthcare delivery by collecting massive amounts of data from smart medical devices. One of the keys to leveraging these massive and heterogeneous data lies in the application of Machine Learning. This presentation will explore the ways in which Machine Learning algorithms, including deep learning, can be used to analyze, interpret, and extract information from IoMTs, thus providing innovative solutions for diagnosing, monitoring, and treating patients more effectively and efficiently. We will discuss how Machine Learning can be used in medicine to improve healthcare and open new opportunities for medical research. We will also present the specific challenges encountered in the healthcare domain, such as data privacy, security, federated learning, and model validation in real life scenario.
Biography
Osman Salem received his Ph.D. in Computer Science in 2006 from the Paul Sabatier University Toulouse III, and his HDR in 2017 from the University of Paris Cité. From 2006, he was a postdoctoral researcher at the Computer Science Department of ENST Bretagne, Brest, France. From 2008, he is an associate professor in cybersecurity at the University Paris Cité. He has published seven chapters in different books and about 150 technical papers in specialized journals and conference proceedings. His research interests include cybersecurity, anomaly detection, epilepsy seizure prediction, heart diseases, and machine learning. He is a member of the editorial board of several journals.