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This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Computer Science International Journal of Educational Innovation and Research
Belaid BOUIKHALENE
Soultan Moulay Slimane University Beni Mellal
Computer Science & IT Education Engineering Languange, Linguistic, Communication & Media Mathematics Other

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Metaverse Applications in Education 4.0: A Decade of Systematic Literature Review Ghoulam, Khalid; Bouikhalene, Belaid
International Journal of Educational Innovation and Research Vol. 3 No. 2 (2024)
Publisher : Universitas Majalengka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31949/ijeir.v3i2.9733

Abstract

The advent of Education 4.0, which aligns with the fourth industrial revolution, has been significantly influenced by advancements in digital technologies. Central to this evolution is the metaverse, a virtual shared space that merges augmented reality, virtual reality, and physical reality. This paper delves into the metaverse's applications within Education 4.0, highlighting its potential to revolutionize learning experiences, enhance collaboration, and improve access to quality education. By reviewing current literature and case studies, we identify the primary benefits, such as increased engagement, personalized learning, and broader accessibility. Additionally, we address the challenges associated with metaverse integration, including technical limitations, privacy concerns, and the need for new pedagogical approaches. Through a mixed-methods research approach, combining qualitative and quantitative data along with expert interviews, this paper provides a comprehensive overview of the metaverse's role in future education. The findings suggest that while significant hurdles remain, the metaverse offers a promising avenue for transforming educational practices to meet the demands of the digital age, ultimately fostering more dynamic, inclusive, and effective learning environments. Future research should focus on evaluating long-term impacts and developing standards for metaverse applications in education.
Forecasting livestock feed sales using machine learning techniques: an analysis of the Moroccan market Nebri, Mohamed Amine; Moussaid, Abdellatif; Bouikhalene, Belaid
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp1139-1150

Abstract

Agriculture, especially cereals, is important in sustaining economies and food security globally. This study delves into the Moroccan agricultural landscape, specifically focusing on predicting livestock feed sales to assist cereal company producers in optimizing production, streamlining supply chain operations, and enhancing customer satisfaction. Data collected from various markets across Morocco, including sales dates and locations, was combined with climate data and analyzed using advanced machine learning techniques, particularly the gradient boosting regression (GBR) algorithm, which achieved high accuracy with a mean absolute error (MAE) of 0.0203 and a root mean square error (RMSE) of 0.0281. The evaluation of multiple regression models revealed promising results, demonstrating the effectiveness of predictive models in accurately forecasting sales. These findings contribute valuable insights to sales forecasting in the cereal industry by considering weather conditions, production methods, and livestock-related variables, highlighting the importance of leveraging advanced machine learning techniques for optimizing production processes and meeting market demands efficiently in the agribusiness sector.
Intelligent assessment of harmonic distortion compliance in reverse osmosis systems Lahlou, Cherki; Bouikhalene, Belaid; Bengourram, Jamaa; Latrache, Hassan
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp4371-4381

Abstract

This study explores the critical challenge of harmonic distortion compliance in reverse osmosis (RO) desalination systems, with a focus on aligning with international standards, specifically IEC 61000, IEEE 519, and EN 50160. High-power equipment, particularly high-pressure pumps (HPP), introduces significant harmonic distortions, threatening power quality and operational reliability. To address this issue, we integrated advanced machine learning (ML) algorithms, namely decision tree (DT), random forest (RF), support vector machine (SVM), and multi-layer perceptron (MLP) to assess harmonic compliance and predict total harmonic distortion (THD) under four operational scenarios. All data used for training and testing were obtained from real-time measurements taken at a large-scale desalination plant using a power quality analyzer (QUALISTAR CA 8336), which guarantees the practical relevance of the analysis. The models were trained on harmonic order and amplitude data and evaluated using accuracy, precision, recall, and F1-score metrics. Among the models, MLP demonstrated superior performance, achieving an accuracy of 99.11% and an F1-score of 98.9%, making it a robust tool for harmonic compliance assessment. SVM and RF also showed commendable results, while DT proved effective for basic analysis. This research underscores the potential of AI-driven approaches in mitigating harmonic-related challenges, optimizing power quality, and enhancing operational efficiency in RO plants. These findings offer a pathway toward more reliable and energy-efficient industrial operations.
Co-Authors Ghoulam, Khalid Jamaa Bengourram, Jamaa Lahlou, Cherki Latrache, Hassan Moussaid, Abdellatif Nebri, Mohamed Amine