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All Journal International Journal of Electrical and Computer Engineering
Abdullah M. Alghamdi
Imam Abdulrahman Bin Faisal University
Computer Science & IT Electrical & Electronics Engineering

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Gender recognition from unconstrained selfie images: a convolutional neural network approach Saddam Bekhet; Abdullah M. Alghamdi; Islam F. Taj-Eddin
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i2.pp2066-2078

Abstract

Human gender recognition is an essential demographic tool. This is reflected in forensic science, surveillance systems and targeted marketing applications. This research was always driven using standard face images and hand-crafted features. Such way has achieved good results, however, the reliability of the facial images had a great effect on the robustness of extracted features, where any small change in the query facial image could change the results. Nevertheless, the performance of current techniques in unconstrained environments is still inefficient, especially when contrasted against recent breakthroughs in different computer vision research. This paper introduces a novel technique for human gender recognition from non-standard selfie images using deep learning approaches. Selfie photos are uncontrolled partial or full-frontal body images that are usually taken by people themselves in real-life environment. As far as we know this is the first paper of its kind to identify gender from selfie photos, using deep learning approach. The experimental results on the selfie dataset emphasizes the proposed technique effectiveness in recognizing gender from such images with 89% accuracy. The performance is further consolidated by testing on numerous benchmark datasets that are widely used in the field, namely: Adience, LFW, FERET, NIVE, Caltech WebFaces andCAS-PEAL-R1.
A genetic algorithm for shortest path with real constraints in computer networks Fahad. A. Alghamdi; Ahmed Younes Hamed; Abdullah M. Alghamdi; Abderrazak Ben Salah; Tamer Hashem Farag; Walaa Hassan
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i1.pp435-442

Abstract

The shortest path problem has many different versions. In this manuscript, we proposed a muti-constrained optimization method to find the shortest path in a computer network. In general, a genetic algorithm is one of the common heuristic algorithms. In this paper, we employed the genetic algorithm to find the solution of the shortest path multi-constrained problem. The proposed algorithm finds the best route for network packets with minimum total cost, delay, and hop count constrained with limited bandwidth. The new algorithm was implemented on four different capacity networks with random network parameters, the results showed that the shortest path under constraints can be found in a reasonable time. The experimental results showed that the algorithm always found the shortest path with minimal constraints.
Co-Authors Abderrazak Ben Salah Ahmed Younes Hamed Fahad. A. Alghamdi Islam F. Taj-Eddin Saddam Bekhet Tamer Hashem Farag Walaa Hassan