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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Soufiane Hamida
Hassan II University of Casablanca
Computer Science & IT Electrical & Electronics Engineering 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.
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.
Hybrid convolutional neural network-bidirectional long short-term memory model for Arabic sentence readability assessment Mohamed Amine Ouassil; Mohammed Jebbari; Rabia Rachidi; Mouaad Errami; Soufiane Hamida; Bouchaib Cherradi; Abdelhadi Raihani
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2849-2862

Abstract

In the current educational landscape, a large number of educators prefer using generative artificial intelligence techniques to produce textual content to be presented for learning. However, these generated texts may not meet the specific needs of learners or align with their abilities. Many traditional methods and techniques can be employed to assess the complexity of a text, such as traditional readability formulas, but these techniques are time consuming and labor-intensive. In this paper, we introduce a deep learning approach for automatically evaluating the readability of Arabic texts by analyzing and classifying sentences into different difficulty levels within educational content. The initial stage of the proposed approach is preprocessing textual content and leveraging natural language processing (NLP) methodologies for feature extraction, such as Word2Vec. The approach then concentrates on refining and evaluating a deep learning model to classify text into different readability levels. This paper introduces a hybrid classification model that combines convolutional neural networks (CNNs) and bidirectional long short-term memory (BiLSTM) layers, attaining an accuracy of 96.68%. This model demonstrates the significance of applying hybrid deep learning models in analyzing educational materials and establishes a foundation for subsequent progress in the field of automated Arabic readability assessment.
Simulation and optimization of inverse kinematics algorithms for real-time target tracking in inertial stabilization platforms Abderahman Kriouile; Soufiane Hamida; Abdoul Latif Abdou Moussa
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.10921

Abstract

Two-axis gimbal systems are engineered to maintain visual lock on target objects by actively counterbalancing movements, regardless of whether these originate from the target or the mounting platform. This research investigates the creation and refinement of inverse kinematics (IK) algorithms for achieving accurate, instantaneous target tracking in inertial stabilization platforms (ISPs), commonly referred to as gimbal systems. Such platforms are essential in applications requiring exceptional stability and precision, including surveillance operations, navigational systems, and scientific investigations. The study commences with a comprehensive analysis of the mathematical foundations underlying IK, with particular attention to the challenges posed by real-time processing requirements. To address these obstacles, sophisticated optimization methods are employed, with an emphasis on reducing computational latency and improving tracking accuracy. The developed algorithms are tested within a Simscape Multibody simulation environment, enabling thorough evaluation across various operational scenarios. Validation incorporates both simulated conditions and practical field tests to confirm the algorithms' durability and functional effectiveness. Results demonstrate significant improvements in both tracking precision and system reactivity, providing a foundation for more efficient and reliable gimbal systems in challenging dynamic environments.
Improving skin diseases prediction through data balancing via classes weighting and transfer learning Oussama El Gannour; Soufiane Hamida; Yasser Lamalem; Mohamed Amine Mahjoubi; Bouchaib Cherradi; Abdelhadi Raihani
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.5999

Abstract

Skin disease prediction using artificial intelligence has shown great potential in improving early diagnosis and treatment outcomes. However, the presence of class imbalance within skin disease datasets poses a significant challenge for accurate prediction, particularly for rare diseases. This study proposes a novel approach to address class imbalance through data balancing using classes weighting, coupled with transfer learning techniques, to enhance the performance of skin disease prediction models. Two experiments were conducted using a tuned EfficientNetV2L based classifier. In the first experiment, a default dataset structure was utilized for training and testing. The second experiment involved employing classes weighting approach to balance the dataset. The effectiveness of the proposed approach is evaluated using the ISIC 2018 dataset, which comprises a diverse collection of skin lesion images. By assigning appropriate weights to different classes based on their prevalence, the proposed method aims to balance the representation of rare disease classes. To evaluate the performance of the proposed methodology, several performance evaluation metrics, including accuracy, precision, and recall, were employed. These findings revealed that the balanced dataset achieved enhanced generalization, mitigating the biases associated with class imbalance. As a result, the efficacy of artificial intelligence models is enhanced.
Toward enhanced skin disease classification using a hybrid RF-DNN system leveraging data balancing and augmentation techniques Soufiane Hamida; Driss Lamrani; Oussama El Gannour; Shawki Saleh; Bouchaib Cherradi
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.6313

Abstract

Significant health concerns are associated with skin diseases, and accurate and timely diagnosis is essential for effective treatment and patient management. To improve the classification of cutaneous diseases, we propose a novel hybrid system that incorporates the strengths of random forest (RF) and deep neural network (DNN) algorithms. The system employs data augmentation and balancing techniques to enhance model performance and generalizability. The HAM10000 dataset of diverse dermatoscopic images is used for training and evaluation in this study. In the hybrid system proposed, the RF model provides an initial diagnosis based on patient-reported symptoms, while the DNN analyzes images of skin lesions, resulting in more precise and efficient diagnoses. Using hyper-parameter optimization, we fine-tune the system for optimal performance. The evaluation demonstrates the accuracy of the hybrid model, which achieves a classification accuracy of 96.8% overall. According to our findings, the hybrid system demonstrates exceptional efficacy in six of seven skin disease classes. Variations in sensitivity and reliance on data quality and quantity are however cited as limitations. Nevertheless, this hybrid system has the potential to revolutionize skin disease diagnosis and treatment.
Co-Authors Abdelhadi Raihani Abderahman Kriouile Abdoul Latif Abdou Moussa Bouchaib Cherradi Driss Lamrani Hassan Ouajji Hassan Silkan khalid Housni Mohamed Amine Mahjoubi Mohamed Amine Ouassil Mohammed Jebbari Mouaad Errami Oussama El Gannour Rabia Rachidi Shawki Saleh Yasser Lamalem Yassine Tazouti