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All Journal International Journal of Electrical and Computer Engineering International Journal of Evaluation and Research in Education (IJERE) Bulletin of Electrical Engineering and Informatics
Housni, Khalid
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Computer Science & IT Education Electrical & Electronics Engineering

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Text classification supervised algorithms with term frequency inverse document frequency and global vectors for word representation: a comparative study Labd, Zakia; Bahassine, Said; Housni, Khalid; Hamou Aadi, Fatima Zahrae Ait; Benabbes, Khalid
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp589-599

Abstract

Over the course of the previous two decades, there has been a rise in the quantity of text documents stored digitally. The ability to organize and categorize those documents in an automated mechanism, is known as text categorization which is used to classify them into a set of predefined categories so they may be preserved and sorted more efficiently. Identifying appropriate structures, architectures, and methods for text classification presents a challenge for researchers. This is due to the significant impact this concept has on content management, contextual search, opinion mining, product review analysis, spam filtering, and text sentiment mining. This study analyzes the generic categorization strategy and examines supervised machine learning approaches and their ability to comprehend complex models and nonlinear data interactions. Among these methods are k-nearest neighbors (KNN), support vector machine (SVM), and ensemble learning algorithms employing various evaluation techniques. Thereafter, an evaluation is conducted on the constraints of every technique and how they can be applied to real-life situations.
Deep learning-based methods for anomaly detection in video surveillance: a review Berroukham, Abdelhafid; Housni, Khalid; Lahraichi, Mohammed; Boulfrifi, Idir
Bulletin of Electrical Engineering and Informatics Vol 12, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i1.3944

Abstract

Detecting anomalous events in videos is one of the most popular computer vision topics. It is considered a challenging task in video analysis due to its definition, which is subjective or context-dependent. Various approaches have been proposed to address the anomaly detection problems. These approaches vary from hand-crafted to deep learning. Many researchers have gone into determining the best approach for effectively detecting anomalies in video streams while maintaining a low false alarm rate. The results proved that approaches based on deep learning offer very interesting results in this field. In this paper, we review a family of video anomaly detection approaches based on deep learning techniques, which are compared in terms of their algorithms and models. Moreover, we have grouped state-of-the-art methods into different categories based on the approach adopted to differentiate between normal and abnormal events, and the underlying assumptions. Furthermore, we also present publicly available datasets and evaluation metrics used in existing works. Finally, we provide a comparison and discussion on the results of various approaches according to different datasets. This paper can be a good starting point for such researchers to understand this field and review existing work related to this topic.
TawjihiNavigator: a novel hybrid information retrieval system for educational guidance in Morocco Silkhi, Hassan; Bakkas, Brahim; Housni, Khalid
International Journal of Evaluation and Research in Education (IJERE) 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/ijere.v14i5.32803

Abstract

In this paper, we propose a novel hybrid method for improving Arabic educational information retrieval (IR) in Moroccan high schools. Traditional search methods often struggle with Arabic’s rich morphology and educational terminology, hindering students’ access to accurate guidance information. The proposed method TawjihiNavigator that combines vector-based semantic search with lexical matching, enhanced by advanced Arabic natural language processing (NLP) techniques. Using a comprehensive dataset collected from official Ministry of education sources. To validate the IR-Abhato system, we integrate CAMeL Tools and Farasa stemmer for Arabic preprocessing, testing multiple embedding models including Word2Vec, FastText, and AraT5. The obtained results demonstrate that our hybrid method’s superiority over standalone vector and full-text search approaches, achieving a mean reciprocal rank (MRR) of 0.7987 and mean average precision (MAP) of 0.5628. The AraT5 model achieved the highest precision@5 score of 0.4500, specially in educational query processing. These findings indicate that our model enhances Arabic educational IR accuracy, that can be improve student decision-making processes.
Co-Authors Bahassine, Said Bakkas, Brahim Benabbes, Khalid Berroukham, Abdelhafid Boulfrifi, Idir Hamou Aadi, Fatima Zahrae Ait Labd, Zakia Lahraichi, Mohammed Silkhi, Hassan