Seifedine KardySeifedine Kadry has a bachelor’s degree in 1999 from Lebanese University, an MS degree in 2002 from Reims University (France) and EPFL (Lausanne), Ph.D. in 2007 from Blaise Pascal University (France), an HDR degree in 2017 from Rouen University (Frane). His research currently focuses on Data Science, medical image recognition using AI, education using technology, and applied mathematics. He is an IET Fellow and IETE Fellow, member of European Academy of Sciences and Arts. Professor Kadry's most significant contribution to medical image analysis and processing is his thorough and rigorous approach to developing and documenting different Deep Learning models to analyze medical images for various diseases. He was one of the first researchers to develop a classification methodology to classify Focal and Non-Focal EEG by combining optimized entropy features towards classification. Therefore, he showed that entropy features are very good concerning EEG classification for better classification accuracy. In this approach, the maximum computation time of the selected features is 0.054 seconds, opening the window for real-time processing. Furthermore, Prof. Kadry was the first to introduce a heart rate measuring strategy using LAB color facial video. RGB videos are used by most of the nonintrusive-based systems as it is appropriate for experiments. Still, they must be developed extensively before being implemented in real-time applications. Furthermore, heart rate monitoring using RGB videos is inefficient outdoors because light significantly contributes to RGB videos. The proposed algorithm using LAB, The presented algorithm seems to be very powerful, quite practical, and easy to use in the regular observation of home care patients. His work on developing machine learning and deep learning models to analyze medical images has encouraged the development of AI models for the Covid-19 pandemic. His team proposes a deep learning framework for classifying COVID-19 pneumonia infection from normal chest CT scans. In this regard, a 15-layered convolutional neural network architecture is developed, which extracts deep features from the selected image samples – collected from the Radiopeadia. Deep features are collected from two different layers, the average global pool and fully connected layers, which are later combined using the max-layer detail (MLD) approach. Subsequently, a Correntropy technique is embedded in the main design to select the most discriminant features from the features pool. Finally, a one-class kernel extreme learning machine classifier is utilized for the final classification to achieve an average accuracy of 95.1% and sensitivity, specificity & precision rate of 95.1%, 95%, & 94%, respectively. Read More Read Less