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Ahmed Chiheb Ammari
Also published under:Ahmed Ammari, Ahmed C. Ammari, A. Ammari
Affiliation
Sultan Qaboos University, Muscat, Oman
Topic
Adversarial Attacks,Adversarial Training,Assault,Chest X-ray,Convolutional Neural Network,Convolutional Neural Network Architecture,Convolutional Neural Network Model,Deep Learning,Fast Gradient Sign Method,Generative Adversarial Networks,Neural Network,Projected Gradient Descent,Access Latency,Adversarial Examples,Adversarial Robustness,Artificial Neural Network,Authentication,Average Accuracy,Big Model,C++ Programming,Cancer Screening,Cell Size,Center Of Mass,Central Feature,Chest X-ray Images,Cloud Computing,Cloud Providers,Clustering Algorithm,Collaborative Relationships,Computer Vision System,Computing Services,Control Input,Convolutional Network,Convolutional Neural Network Design,Coupling Terms,Critical Conditions,Crowdsourced Data,Data Block,Data Center,Data Placement,Data Placement Strategy,Decision Boundary,Deep Learning Models,Deep Learning Techniques,Deep Neural Network,Detection In Images,Disgust,Distortion Values,Dynamic Model,Embedded System,
Biography
Ahmed Chiheb Ammari (Senior Member, IEEE) received the B.S. degree from the Ecole Nationale des Ingénieurs de Monastir, Tunisia, in 1993, and the M.Sc. and Ph.D. degrees from the Institut National Polytechnique de Grenoble, France, in 1993 and 1996, respectively, all in electrical engineering. Since 1997, he has been a Faculty Member with the Institut National des Sciences Appliques et de Technologies, Carthage University, Tunisia. He is currently an Associate Professor with the Department of Electrical and Computer Engineering, College of Engineering, Sultan Qaboos University, Oman. His current research interests include multicore and multiprocessor system-on-chip, energy-efficient computing of battery-operated portable devices, inductive data and power transfer for implantable medical devices, hybrid electric energy storage for electric vehicles and renewable energy systems, and system-level modeling and optimization for smart grid and data centers.