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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Bouchaib Cherradi
Hassan II University of Casablanca
Computer Science & IT Electrical & Electronics Engineering

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Efficient feature descriptor selection for improved Arabic handwritten words recognition Soufiane Hamida; Oussama El Gannour; Bouchaib Cherradi; Hassan Ouajji; Abdelhadi Raihani
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp5304-5312

Abstract

Arabic handwritten text recognition has long been a difficult subject, owing to the similarity of its characters and the wide range of writing styles. However, due to the intricacy of Arabic handwriting morphology, solving the challenge of cursive handwriting recognition remains difficult. In this paper, we propose a new efficient based image processing approach that combines three image descriptors for the feature extraction phase. To prepare the training and testing datasets, we applied a series of preprocessing techniques to 100 classes selected from the handwritten Arabic database of the Institut Für Nachrichtentechnik/Ecole Nationale d'Ingénieurs de Tunis (IFN/ENIT). Then, we trained the k-nearest neighbor’s algorithm (k-NN) algorithm to generate the best model for each feature extraction descriptor. The best k-NN model, according to common performance evaluation metrics, is used to classify Arabic handwritten images according to their classes. Based on the performance evaluation results of the three k-NN generated models, the majority-voting algorithm is used to combine the prediction results. A high recognition rate of up to 99.88% is achieved, far exceeding the state-of-the-art results using the IFN/ENIT dataset. The obtained results highlight the reliability of the proposed system for the recognition of handwritten Arabic words.
Evaluating multi-state systems reliability with a new improved method Yasser Lamalem; Soufiane Hamida; Yassine Tazouti; Oussama El Gannour; Khalid Housni; Bouchaib Cherradi
Bulletin of Electrical Engineering and Informatics Vol 11, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i3.3509

Abstract

The computation of network reliability for a system with many states is an NP-hard issue. Finding all the minimum path vectors (d-MPs) lower boundary points for each level d is one of the few approaches for computing such dependability. This research proposed enhancements to the technique described in Chen's "Searching for d-MPs with rapid enumeration" paper. We propose additional adjustments to the method that creates the flow vector F in this enhancement. This decreases the number of required steps and the temporal complexity of the method. Comparing the newly suggested approach to the old algorithm reveals that the adjustment has increased the enumeration's efficiency and degree of complexity.
Healthcare monitoring system for automatic database management using mobile application in IoT environment Shawki Saleh; Bouchaib Cherradi; Oussama El Gannour; Nissrine Gouiza; Omar Bouattane
Bulletin of Electrical Engineering and Informatics Vol 12, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i2.4282

Abstract

In the last decade, healthcare systems have played an effective role in improving medical services by monitoring and diagnosing patients' health remotely. These systems, either in hospitals or in other health centers, have experienced significant growth with emerging technologies. They are becoming of great interest to many countries worldwide nowadays. Portable healthcare monitoring systems (HMS) depend on internet of things (IoT) technology due to its effectiveness and reliability in several sectors, as well as in the sector of telemedicine. This paper proposes a portable healthcare system in an IoT environment controllable via a smartphone application that aims to facilitate utilization. This proposed system can track physiological indicators of a patient's body as well as the environmental conditions where the patient lives in real-time and auto-manage databases. Moreover, this paper touched on a comparison between three servers, concerning data transfer speeds from the proposed system into the servers.
Parkinson’s diagnosis hybrid system based on deep learning classification with imbalanced dataset Asmae Ouhmida; Abdelhadi Raihani; Bouchaib Cherradi; Sara Sandabad
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp3204-3216

Abstract

Brain degeneration involves several neurological troubles such as Parkinson’s disease (PD). Since this neurodegenerative disorder has no known cure, early detection has a paramount role in improving the patient’s life. Research has shown that voice disorder is one of the first symptoms detected. The application of deep learning techniques to data extracted from voice allows the production of a diagnostic support system for the Parkinson’s disease detection. In this work, we adopted the synthetic minority oversampling technique (SMOTE) technique to solve the imbalanced class problems. We performed feature selection, relying on the Chi-square feature technique to choose the most significant attributes. We opted for three deep learning classifiers, which are long-short term memory (LSTM), bidirectional LSTM (Bi-LSTM), and deep-LSTM (D-LSTM). After tuning the parameters by selecting different options, the experiment results show that the D-LSTM technique outperformed the LSTM and Bi-LSTM ones. It yielded the best score for both the imbalanced original dataset and for the balanced dataset with accuracy scores of 94.87% and 97.44%, respectively.
Classifying toxicity in the Arabic Moroccan dialect on Instagram: a machine and deep learning approach Rabia Rachidi; Mohamed Amine Ouassil; Mouaad Errami; Bouchaib Cherradi; Soufiane Hamida; Hassan Silkan
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 1: July 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i1.pp588-598

Abstract

People crave interaction and connection with other people. Therefore, social media became the center of society’s life. Among the brightest social media platforms nowadays with a massive number of daily users there is Instagram, which is due to its distinctive features. The excessive revealing of personal life has put users in the spots of getting bullied and harassed and getting toxic revues from other users. Numerous studies have targeted social media to fight its harmful side effects. Nevertheless, most of the datasets that were already available were in English, the Arabic Moroccan dialect ones were not. In this work, the Arabic Moroccan dialect dataset has been extracted from the Instagram platform. Furthermore, feature extraction techniques have been applied to the collected dataset to increase classification accuracy. Afterward, we developed models using machine learning and deep learning algorithms to detect and classify toxicity. For the models’ evaluation, we have used the most used metrics: accuracy, precision, F1-score, and recall. The experimental results gave modest scores of around 70% to 83%. These results imply that the models need improvement due to the lack of available datasets and the preprocessing libraries to handle the Moroccan dialect of Arabic.
Enhancing learner performance prediction on online platforms using machine learning algorithms Mohammed Jebbari; Bouchaib Cherradi; Soufiane Hamida; Mohamed Amine Ouassil; Taoufiq El Harrouti; Abdelhadi Raihani
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.
Co-Authors Abdelhadi Raihani Asmae Ouhmida Hassan Ouajji Hassan Silkan khalid Housni Mohamed Amine Ouassil Mohammed Jebbari Mouaad Errami Nissrine Gouiza Omar Bouattane Oussama El Gannour Rabia Rachidi Sara Sandabad Shawki Saleh Soufiane Hamida Taoufiq El Harrouti Yasser Lamalem Yassine Tazouti