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Razali Yaakob
Also published under:R. Yaakob
Affiliation
Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Serdang, Malaysia
Topic
Convolutional Neural Network,Deep Neural Network,Machine Learning,Student Model,Teacher Model,Advanced Techniques,Adversarial Domain Adaptation,Adversarial Domain Adaptation Method,Adversarial Training,Anatomical Landmarks,Approaches In The Literature,Attention Map,Average Accuracy,Benchmark Datasets,Boolean Variable,Bounding Box,Candidate Solutions,Chest X-ray,Class Labels,Communication Overhead,Communication Rounds,Computer-aided Diagnosis,Convolutional Neural Network Model,Covariance Matrix,Data Distribution,Data Pre-processing,Deep Convolutional Neural Network,Detection Model,Detection Of Tuberculosis,Detection Results,Domain Adaptation,Domain Adaptation Methods,Domain Dataset,Domain Method,Domain Shift,Domain-invariant Features,Domain-invariant Representations,Down Syndrome Patients,Eigenvectors,Electronic Health Records,Eye Region,Face Detection,Face Images,Face Recognition,Facial Features,False Detection Rate,Feasible Solution,Feature Extraction Techniques,Federated Learning,General Lack,
Biography
Razali Yaakob (Member, IEEE) received the bachelor’s degree in computer science and the master’s degree in computer science from Universiti Putra Malaysia, in 1996 and 1999, respectively, and the Ph.D. degree from the University of Nottingham, U.K., in 2008. Currently, he is the Dean with the Faculty of Computer Science and Information Technology, Universiti Putra Malaysia. His research interests include artificial neural networks, pattern recognition, and evolutionary computation in game playing. He is a member of the Intelligent Computing Group at the faculty.