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All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electrical Engineering and Computer Science
Hamida, Soufiane
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Computer Science & IT Electrical & Electronics Engineering

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Enhancing learner performance prediction on online platforms using machine learning algorithms Jebbari, Mohammed; Cherradi, Bouchaib; Hamida, Soufiane; Ouassil, Mohamed Amine; El Harrouti, Taoufiq; Raihani, Abdelhadi
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 1: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i1.pp343-353

Abstract

E-learning has emerged as a prominent educational method, providing accessible and flexible learning opportunities to students worldwide. This study aims to comprehensively understand and categorize learner performance on e-learning platforms, facilitating timely support and interventions for improved academic outcomes. The proposed model utilizes various classifiers (random forest (RF), neural network (NN), decision tree (DT), support vector machine (SVM), and K-nearest neighbors (KNN)) to predict learner performance and classify students into three groups: fail, pass, and withdrawn. Commencing with an analysis of two distinct learning periods based on days elapsed (≤120 days and another exceeding 220 days), the study evaluates the classifiers’ efficacy in predicting learner performance. NN (82% to 96%) and DT (81%-99.5%) consistently demonstrate robust performance across all metrics. The classifiers exhibit significant performance improvement with increased data size, suggesting the benefits of sustained engagement in the learning platform. The results highlight the importance of selecting suitable algorithms, such as DT, to accurately assess learner performance. This enables educational platforms to proactively identify at-risk students and offer personalized support. Additionally, the study highlights the significance of prolonged platform usage in enhancing learner outcomes. These insights contribute to advancing our understanding of e-learning effectiveness and inform strategies for personalized educational interventions.
Modeling and enhancing inverse kinematics algorithms for real-time target tracking in inertial stabilization systems Kriouile, Abderahman; Hamida, Soufiane; Moussa, Abdoul Latif Abdou
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp1544-1556

Abstract

This study develops a two-axis gimbal system designed to maintain a target within its field of view by compensating for motion of either the target or the platform. The focus is on inertial stabilization platforms (ISPs), where accurate, real-time tracking is essential for applications such as surveillance, navigation, and scientific observation. The research prioritizes the design and optimization of inverse kinematics algorithms to enhance system performance. A detailed analysis of mathematical models underpins the development, addressing challenges in real-time processing with advanced optimization techniques to minimize latency and maximize accuracy. The proposed algorithms achieve a mean tracking error of 0.002 m and a mean convergence time of 2.12 seconds, surpassing traditional methods in precision and efficiency. Performance is evaluated within a simulation framework using Simscape Multibody, testing the algorithms under various conditions. Validation extends to real-world scenarios to ensure robustness and practical applicability. The results demonstrate significant improvements in tracking accuracy and responsiveness, offering a reliable solution for dynamic environments. This work paves the way for more efficient gimbal systems, contributing to advancements in technologies requiring stable and precise tracking in dynamic and challenging settings.
Co-Authors Cherradi, Bouchaib El Harrouti, Taoufiq Jebbari, Mohammed Kriouile, Abderahman Moussa, Abdoul Latif Abdou Ouassil, Mohamed Amine Raihani, Abdelhadi