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Aly A. Fahmy
Researcher at Cairo University
Publications - 80
Citations - 2736
Aly A. Fahmy is an academic researcher from Cairo University. The author has contributed to research in topics: Support vector machine & Optimization problem. The author has an hindex of 19, co-authored 80 publications receiving 1740 citations. Previous affiliations of Aly A. Fahmy include Zagazig University.
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Proceedings ArticleDOI
Genetic Algorithms for community detection in social networks
TL;DR: The most popular objectives proposed over the past years are used and it is shown how those objective correlate with each other, and their performances when they are used in the single-objective Genetic Algorithm and the Multi-Objective genetic Al algorithm and the community structure properties they tend to produce.
Book ChapterDOI
A Hybrid EEG Signals Classification Approach Based on Grey Wolf Optimizer Enhanced SVMs for Epileptic Detection
TL;DR: The experimental results proved that the proposed GWO-SVM approach, able to detect epileptic and could thus further enhance the diagnosis of epilepsy with accuracy 100%.
Proceedings Article
An approach for analyzing and correcting spelling errors for non-native Arabic learners
TL;DR: The proposed error detection mechanism is applied on top of Buckwalter's Arabic morphological analyzer in order to demonstrate the capability of the approach in detecting possible spelling errors and the correction mechanism adopts a rule-based edit distance algorithm.
Journal ArticleDOI
Sheep Identification Using a Hybrid Deep Learning and Bayesian Optimization Approach
TL;DR: Sheep identities were recognized by a deep convolutional neural network using facial bio-metrics to obtain the best possible accuracy, and the approach outperforms previous approaches for sheep identification.
Book ChapterDOI
Community Detection Algorithm Based on Artificial Fish Swarm Optimization
TL;DR: Artificial Fish Swarm optimization (AFSO) has been used as an effective optimization technique to solve the community detection problem with the advantage that the number of communities is automatically determined in the process.