A
Ahmad Taher Azar
Researcher at Prince Sultan University
Publications - 458
Citations - 12351
Ahmad Taher Azar is an academic researcher from Prince Sultan University. The author has contributed to research in topics: Computer science & Control theory. The author has an hindex of 47, co-authored 389 publications receiving 8847 citations. Previous affiliations of Ahmad Taher Azar include Misr University for Science and Technology & Yahoo!.
Papers
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Journal ArticleDOI
Association between dialysis dose improvement and nutritional status among hemodialysis patients.
TL;DR: All the available evidence in hemodialysis patients confirms the close association between dialysis dose and biochemical outcome and highlights the existence of relationship between malnutrition and outcome among these patients.
Proceedings Article
Automatic computer aided segmentation for liver and hepatic lesions using hybrid segmentations techniques
TL;DR: The sophisticated hybrid system was proposed in this paper which is capable to segment liver from abdominal CT and detect hepatic lesions automatically and provided good quality results, which could segment liver and extract lesions from abdominalCT in less than 0.15 s/slice.
Book ChapterDOI
Robust Adaptive Supervisory Fractional Order Controller for Optimal Energy Management in Wind Turbine with Battery Storage
TL;DR: Results of extensive simulation studies prove that the proposed supervisory control system guarantees to track reference signals with a high harmonic performance despite external disturbance uncertainties.
Journal ArticleDOI
Hybrid Tolerance Rough Set: PSO Based Supervised Feature Selection for Digital Mammogram Images
TL;DR: This paper proposes an approach based on the tolerance rough set model, which has the flair to deal with real-valued data whilst simultaneously retaining dataset semantics, and results obtained show an increase in the diagnostic accuracy.
Proceedings ArticleDOI
Retinal vessel segmentation based on possibilistic fuzzy c-means clustering optimised with cuckoo search
TL;DR: This paper presents an approach to automatic vessel segmentation in retinal images that utilises possibilistic fuzzy c-means (PFCM) clustering to overcome the problems of the conventional fuzzy c -means objective function.