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Somaya Al-maadeed
Also published under:Somaya Al-Maadeed, Somaya Ali Al-Maadeed, Somaya Al Maadeed, S. Al-Maadeed, Sumaya Ali Al-Maadeed, Somayya Al-Maadeed, Sumaya Al-Maadeed, Sumaya Al Maadeed
Affiliation
Computer Science and Engineering Department, Qatar University, Doha, Qatar
Topic
Deep Learning,Convolutional Neural Network,Machine Learning,Support Vector Machine,Convolutional Layers,Neural Network,Computer Vision,Deep Neural Network,K-nearest Neighbor,Classification Accuracy,Machine Learning Models,Object Detection,Training Set,Attention Module,Bounding Box,Confusion Matrix,Decision Tree,Detection Task,False Positive Ratio,Histogram Of Oriented Gradients,Learning Algorithms,Machine Learning Techniques,Medical Imaging,Performance Of Method,Recurrent Neural Network,Support Vector Machine Classifier,Video Content,Average Precision,Binary Classification,CNN Model,Camera Model,Channel-wise Attention,Classification Performance,Convolutional Neural Network Model,Crowd Density,Decision Boundary,Deep Learning Architectures,Deep Learning Techniques,Density Map,Digital Video,False Negative,Fingerprint,Graphical User Interface,Identification Of Sources,Image Classification,Image Processing,Image Regions,Large-scale Datasets,Learning Disabilities,Long Short-term Memory,
Biography
Somaya Al-Maadeed received the Ph.D. degree in computer science from Nottingham University. She supervised students through research projects related to computer vision, AI, and biomedical image applications. She is currently a Professor with the Computer Science Department, Qatar University. She is the Coordinator of the Computer Vision Research Group, Qatar University. She enjoys excellent collaboration with national and international institutions and industry. She is the principal investigator of several funded research projects related to medical imaging. She published extensively in computer vision and pattern recognition and delivered workshops on teaching programming. In 2015, she was elected as the IEEE Chair of the Qatar Section. She filled IPs related to cancer detection from images and light.