Article Per Year (5 Year)
p-Index From 2020 - 2025
0.817
P-Index
This Author published in this journals
All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) International Journal of Informatics and Communication Technology (IJ-ICT) Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
ATOUF, ISSAM
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
VEHICULAR QUEUE LENGTH MEASUREMENT BASED ON EDGE DETECTION AND VEHICLE FEATURE EXTRACTION alokaishi, wahban yahya; ZAARANE, ABDELMOGHIT; ATOUF, ISSAM; BENRABH, MOHAMED
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 11, No 4: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v11.i4.pp%p

Abstract

The queue length is an important parameter used by several companies of traffic management for quantitative analysis of traffic scene. In this paper, we propose a system to measure the queue length. The proposed system contains two principal operations for measuring the queue length the first operation is the motion detection, and the second one is the vehicle detection. The traffic scene is divided into a number of blocks, which have variable sizes based on the camera parameters and the distance between the camera and the position of the block. To minimize the execution time to accord the real-time application, we continuously apply the detection motion operation on the first block of the scene and the queue tail. The vehicle detection algorithm is based on the edge detection and the vehicle features extraction to improve the detection of vehicle and minimize the error of detection of the other things? edges (damaged of road, the mark of the road, the shadow of trees or building). The algorithm is applied to videos obtained by stationary camera. The obtained results demonstrate the robustness and accuracy of the proposed system.
Enhanced driving assistance: automated day and night vehicle detection system utilizing convolutional neural networks Zaarane, Abdelmoghit; Slimani, Ibtissam; Elhabchi, Mourad; Atouf, Issam
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1532-1542

Abstract

This paper presents an enhanced real-time vehicle detection system using convolutional neural networks (CNNs) for both daytime and night-time conditions. Initially, the system determines the time of capture by analyzing the upper part of input images. For daytime detection, it uses normalized cross-correlation and two-dimensional discrete wavelet transform (2D-DWT) techniques. Night-time detection involves identifying vehicle lamps through color thresholding and connected component techniques, followed by symmetry analysis and CNN classification. The dataset for training includes images from the Caltech Cars, AOLP, KITTI Vision, and night-time vehicle detection datasets, ensuring robust performance across various lighting conditions. Experiments demonstrate the system's high accuracy, achieving 99.2% during the day and 98.27% at night, meeting real-time requirements and enhancing driving assistance systems' reliability.
Metamaterial inspired miniaturized ultra-wideband monopole hexagonal antenna with triple band-filter functions Elhabchi, Mourad; Bour, Mohamed; Atouf, Issam; Zaarane, Abdelmoghit
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.7568

Abstract

In this letter, a new technique to the design of an ultra-wideband (UWB) monopole hexagonal antenna with triple band-rejected functions and to restrict the interferences with the exist bands is proposed, the design has the form of a hexagonal patch and a ground plane having rectangular shaped etched in the back side of the substrate to achieve the UWB behavior. The triple-band filter feature is generated by inserting a metamaterial (MTM) as a split ring resonator slots (SRRs) and a complementary split ring resonators (CSRRs) strip, thus no extra size is needed. The triple band-elimination is for 3.3-3.9 GHz centered at 3.5 GHz for 5G band, 4.99-5.4 GHz centered at 5.2 GHz for wireless local area network (WLAN) band, and 6.2-6.8 GHz centered at 6.5 GHz for IEEE INSAT/Supra-extended C-band. The antenna dimension has a compact size of 20×25×1.6 mm3. Current distribution on the antenna is used to analyze the effect of MTMs on the antenna operations. The simple structure and small size of the antenna makes it suitable for most of the wireless communication systems.
Predictive insights into student online learning adaptability: elevating e-learning landscape El Ghali, Mohamed; Atouf, Issam; El Guemmat, Kamal; Talea, Mohamed
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i3.pp892-902

Abstract

In Morocco’s rapidly transforming educational landscape, this study delves into students’ adaptability to online learning environments by integrating sophisticated artificial intelligence (AI) algorithms and hyperparameter optimization techniques. This research uses the comprehensive “online learning adaptivity” dataset to identify pivotal factors influencing student flexibility and effectiveness in e-learning platforms. We applied various AI models, with a particular emphasis on the CatBoost classifier, which exhibited exceptional predictive performance, achieving an accuracy rate near 98%. This high precision in predicting student adaptiveness offers essential insights into tailoring digital education systems. The results underscore the significant potential of machine learning technologies to enhance educational methodologies by catering to the diverse needs of students. Such capabilities are instrumental for educators and policymakers dedicated to refining e-learning strategies that effectively accommodate individual learning styles, ultimately improving the broader educational outcomes in Moroccan tertiary education. These findings advocate for a more nuanced understanding of the interplay between student behavior and technological solutions, providing a roadmap for developing more responsive and effective educational platforms.
Co-Authors alokaishi, wahban yahya Benrabh, Mohamed Bour, Mohamed El Ghali, Mohamed El Guemmat, Kamal Elhabchi, Mourad Slimani, Ibtissam Talea, Mohamed ZAARANE, ABDELMOGHIT