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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics
Oqaidi, Khalid
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Computer Science & IT Electrical & Electronics Engineering

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Predicting graduation in Moroccan open-access bachelors: early indicators and re-enrollment data Oqaidi, Khalid; Aouhassi, Sarah; Mansouri, Khalifa
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.v14i1.8580

Abstract

The primary aim of higher education institutions is the successful graduation of their students. This study explores open-access higher education in Morocco, introducing a predictive model for assessing the probability of students achieving a science bachelor's degree. We analyzed data from 2012 to 2022, initially encompassing 45,573 student entries, and narrowed it down to 14,054 records after data cleaning. Focusing on early academic indicators from enrollment onwards-excluding current program performance—we used popular machine learning classifiers to examine the predictive capacity for student graduation and early dropout. Our comparison included analyses with and without re-enrollment data. Upon analyzing various machine learning algorithms, we attained accuracies between 79% and 86%, identifying random forest (RF) as the superior model for predicting outcomes both with and without incorporating re-enrollment data. This analysis was grounded on initial indicators observed during enrollment and throughout subsequent years, deliberately excluding current academic performance metrics from consideration.
From YOLO V1 to YOLO V11: comparative analysis of YOLO algorithm (review) Beqqali Hassani, Imane; Benhida, Soufia; Lamii, Nabil; Oqaidi, Khalid; Ouiddad, Ahmed; Ghiadi, Soukaina
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i1.pp450-462

Abstract

Object detection in images or videos faces several challenges because the detection must be accurate, efficient and fast. The you only look once (YOLO) algorithm was invented to meet these criteria. But with the creation of several versions of this algorithm (from V1 to V11), it becomes difficult for researchers to choose the best one. The main objective of this review is to present and compare the eleven versions of the yolo algorithm in order to know when using the appropriate one for the study. The methodology used for this work is aligned with preferred reporting items for systematic reviews and meta-analyses (PRISMA) principles and the results demonstrate that the choice of the best version mainly depends on the priorities of the study. If the study prioritizes accuracy and detection of small objects, it should use YOLO V4, YOLO V5, YOLO V6, YOLO V7, YOLO V8, YOLO V9, YOLO V10 or YOLO V11. While studies that prioritize detection speed should use YOLO V5, YOLO V6, YOLO V7, YOLO V8, YOLO V10 or YOLO V11. In complex environment, researchers should avoid using YOLO V1, YOLO V2, YOLO V3, YOLO V5, YOLO V7 and YOLO V9. And researchers who are looking for a good accuracy and speed and a reduced number of parameters should use YOLO V10 or YOLO V11.
Co-Authors Aouhassi, Sarah Benhida, Soufia Beqqali Hassani, Imane Ghiadi, Soukaina Lamii, Nabil Mansouri, Khalifa Ouiddad, Ahmed