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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI)
Nouh, Said
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Journal : International Journal of Electrical and Computer Engineering

 1 
Enhancing feature selection with a novel hybrid approach incorporating genetic algorithms and swarm intelligence techniques Benghazouani, Salsabila; Nouh, Said; Zakrani, Abdelali; Haloum, Ihsane; Jebbar, Mostafa
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.pp944-959

Abstract

Computing advances in data storage are leading to rapid growth in large-scale datasets. Using all features increases temporal/spatial complexity and negatively influences performance. Feature selection is a fundamental stage in data preprocessing, removing redundant and irrelevant features to minimize the number of features and enhance the performance of classification accuracy. Numerous optimization algorithms were employed to handle feature selection (FS) problems, and they outperform conventional FS techniques. However, there is no metaheuristic FS method that outperforms other optimization algorithms in many datasets. This motivated our study to incorporate the advantages of various optimization techniques to obtain a powerful technique that outperforms other methods in many datasets from different domains. In this article, a novel combined method GASI is developed using swarm intelligence (SI) based feature selection techniques and genetic algorithms (GA) that uses a multi-objective fitness function to seek the optimal subset of features. To assess the performance of the proposed approach, seven datasets have been collected from the UCI repository and exploited to test the newly established feature selection technique. The experimental results demonstrate that the suggested method GASI outperforms many powerful SI-based feature selection techniques studied. GASI obtains a better average fitness value and improves classification performance.
Predicting academic performance: toward a model based on machine learning and learner’s intelligences Rafiq, Jamal Eddine; Abdelali, Zakrani; Amraouy, Mohammed; Nouh, Said; Bennane, Abdellah
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp645-653

Abstract

With the rapid evolution of online learning environments, the ability to predict students' academic performance has become crucial for personalizing and enhancing the educational experience. In this article, we present a predictive model based on machine learning techniques, designed to be integrated into online learning platforms using the competency-based approach. This model leverages features from four key dimensions: demographic, social, emotional, and cognitive, to accurately predict learners' academic performance. We detail the methodology for collecting and processing learning traces, distinguishing between explicit traces, such as demographic data, and implicit traces, which capture learners' interactions and behaviors during their learning process. The analysis of these data not only improves the accuracy of performance predictions but also provides valuable insights into skill acquisition and learners' personal development. The results of this study demonstrate the potential of this model to transform online education by making it more adaptive and focused on individual learners' needs.
Optimal design, decoding, and minimum distance analysis of Goppa codes using heuristic method Aylaj, Bouchaib; Nouh, Said; Belkasmi, Mostafa
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5411-5421

Abstract

Error-correcting codes are crucial to ensure data reliability in communication systems often affected by transmission noise. Building on previous successful applications of our heuristic method degenerate quantum simulated annealing (DQSA) to Bose–Chaudhuri–Hocquenghem (BCH) and quadratic residue (QR) codes. This paper proposes two algorithms designed to address two coding problems for Goppa codes. DQSA-dmin computes the minimum distance (dmin) while DQSA-Dec, serves as a hard decoder optimized for additive white gaussian noise (AWGN) channels. We validate DQSA-dmin comparing its computed minimum distances with theoretical estimates for algebraically constructed Goppa codes, showing accuracy and efficiency. DQSA-dmin further used to find the optimal Goppa codes that reach the lower bound of dmin for linear codes known in the literature and stored in Marcus Grassl's online database. Indeed, we discovered 12 Goppa codes reaching this lower bound. For DQSA-Dec, experimental results show that it obtains a bit error rate (BER) of 10-5 when SNR=7.5 for codes with lengths less than 65, which is very interesting for a hard decoder. Additionally, a comparison with the Paterson algebraic decoder specific to this code family shows that DQSA-Dec outperforms it with a 0.6 dB coding gain at BER=10-4. These findings highlight the effectiveness of DQSA-based algorithms in designing and decoding Goppa codes.
Co-Authors Abdelali, Zakrani Amraouy, Mohammed Aylaj, Bouchaib Azzouazi, Mohamed Belkasmi, Mostafa Bellfkih, El Mehdi Benghazouani, Salsabila Bennane, Abdellah Chemseddine Idrissi, Imrane El Assad, Mohammed El Kasmi Alaoui, Seddiq Haloum, Ihsane Jebbar, Mostafa Louartiti, Khalid Mouline, Jamal Rafiq, Jamal Eddine Zakrani, Abdelali