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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Evaluation and Research in Education (IJERE)
Fakhri, Youssef
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Computer Science & IT Education Electrical & Electronics Engineering Engineering

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Fastest Moroccan license plate recognition using a lightweight modified YOLOv5 model Fadili, Abdelhak; El Aroussi, Mohammed; Fakhri, Youssef
IAES International Journal of Artificial Intelligence (IJ-AI) 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/ijai.v14.i1.pp527-537

Abstract

Morocco is witnessing an alarming surge in road accidents. Automatic license plate recognition (ALPR) technology is vital in enhancing road safety. It en- ables applications like traffic management, law enforcement, and toll collection by automatically identifying vehicles on the roads. This paper integrated the ShuffleNet V2 architecture into the end-to-end YOLOv5 object detection sys- tem. The goal was to develop a model capable of accurately detecting Moroc- can license plates with an 87% accuracy rate. The proposed model was able to achieve high processing speeds of 60 frames per second (FPS) while maintain- ing a compact size of 1.3 megabytes and a limited computational requirement of 0.44 million floating-point operations. Compared to other models used in similar contexts, this model demonstrates superior performance and high com- patibility with embedded systems, making it a promising solution for addressing road safety challenges in Morocco.
Butterfly optimization-based ensemble learning strategy for advanced intrusion detection in internet of things networks Choukhairi, Mouad; Tahiri, Sara; Choukhairi, Ouail; Fakhri, Youssef; Amnai, Mohamed
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp3494-3505

Abstract

The massive growth in internet of things (IoT) devices has led to enhanced functionalities through their interconnections with other devices, smart infrastructures, and networks. However, increased connectivity also increases the risk of cyberattacks. To protect IoT systems from these threats, intrusion detection systems (IDS) employing machine learning (ML) techniques have been developed to identify cybersecurity threats. This paper introduces a novel ensemble IDS framework called butterfly optimization-based ensemble learning (BOEL). This framework integrates the butterfly optimization algorithm (BOA) with ensemble learning techniques to improve IDS detection performance in IoT networks. BOEL is designed to accurately detect various types of attacks in IoT networks by dynamically optimizing the weights of base learners, which are the four sophisticated ML gradient-boosting algorithms (GBM, CatBoost, XGBoost, and LightGBM) for each attack category, and identifying the best weight combination for ensemble models. Experiments conducted on two public IoT security datasets, CICIDS2017 and Bot-IoT, demonstrate the robustness of the proposed BOEL in intrusion detection across diverse IoT environments, achieving 99.795% accuracy on CICIDS2017 and 99.966% accuracy on Bot-IoT. These results outline the successful application of diverse learning approaches and highlight the framework’s potential to enhance IDS in addressing IoT cyber threats.
Comparative analysis of machine learning models for fake news detection in social media Eddine Elbaghazaoui, Bahaa; Amnai, Mohamed; Fakhri, Youssef; Choukri, Ali; Gherabi, Noreddine
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp1951-1959

Abstract

The rapid rise of information sharing on social media has amplified the spread of fake news, making its detection increasingly critical. As fake news continues to proliferate, the need for efficient detection mechanisms has become more urgent to protect users from misinformation and disinformation. This paper presents a comparative analysis of multiple machine learning models for detecting text based fake news on social media platforms. Using models such as gradient boosting, XGBoost, and linear support vector classifier (SVC) on the Infor mation Security and Object Technology (ISOT) fake news dataset, the study demonstrates that gradient boosting achieves the highest accuracy of 99.61%, while XGBoost provides a strong balance with 99.59% accuracy and a signifi cantly lower execution time, making it more suitable for real-time applications. These results offer valuable insights into the trade-offs between accuracy and computational efficiency, contributing to the development of more practical de tection systems and future research in the field.
Factors affecting engineering students’ self-perceived employability in Morocco Sabri, Zineb; Remaida, Ahmed; Abdellaoui, Benyoussef; Ait Madi, Abdessalam; Qostal, Aniss; Chadli, Fatima Ezzahra; Fakhri, Youssef; Moumen, Aniss
International Journal of Evaluation and Research in Education (IJERE) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijere.v14i3.31797

Abstract

In a dynamic socio-economic world, perceiving a career opportunity and job prospects has become complex. The number of unemployed individuals is rising despite the increasing number of students pursuing higher education. This study is suggested to enhance students’ professional insertion, guide their career development initiatives, and help them acquire the skills demanded by prospective employers, thereby increasing their likelihood of employment. For this goal, this study investigates the determinants impacting self-perceived employability (SPE) among engineering students. Following a quantitative approach to explain how personal and contextual factors impact perceived employability, more than 350 Moroccan engineering students responded to a questionnaire for data collection, which had an internal consistency of 0.90. Data analysis employing advanced statistical techniques using structural equations modeling (SEM) to conduct descriptive, regression, and mediation analysis. The findings highlight that academic performance, university contribution, and personal circumstances significantly influence perceived employability, while generic skills have a minor effect. Furthermore, personal determinants are identified as stronger than contextual ones. The results provide several recommendations to stakeholders such as university administrations, teaching staff, employers, the Ministry of Education, and graduates. Additionally, they offer an insightful exploration of the intricate interactions among factors that enhance employability.
Co-Authors Abdellaoui, Benyoussef Ait Madi, Abdessalam Amnai, Mohamed Chadli, Fatima Ezzahra Choukhairi, Mouad Choukhairi, Ouail Choukri, Ali Eddine Elbaghazaoui, Bahaa El Aroussi, Mohammed Fadili, Abdelhak Gherabi, Noreddine Moumen, Aniss Qostal, Aniss Remaida, Ahmed Sabri, Zineb Tahiri, Sara