Article Per Year (5 Year)
p-Index From 2019 - 2024
0.983
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering International Journal of Power Electronics and Drive Systems (IJPEDS) Indonesian Journal of Electrical Engineering and Computer Science
Abdelhadi Raihani
Hassan II university of Casablanca
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

Published : 5 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 5 Documents
Search

 1 
A new modular nanogrid energy management system based on multi-agent architecture Meryem Hamidi; Abdelhadi Raihani; Mohamed Youssfi; Omar Bouattane
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 13, No 1: March 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v13.i1.pp178-190

Abstract

The emergence of renewable energy sources with controllable loads gave the opportunity to the consumers to build their own Microgrids. However, the intermittence of renewable energy sources such as wind and photovoltaic leads to some challenges in terms of balancing generation and consumption. This paper aims to present a novel multi-agent model based intelligent control scheme to balance the home/building alternative current (AC)-direct current (DC) load demands and renewable energy sources. The new proposed scheme consists of a three-level hierarchical multi agent system based on cooperation, communication and interaction between intelligent agents to fulfill the load's requirements. Then, the proposed multi agent framework is simulated using four different nanogrids to prove its effectiveness using different temporal profiles for loads and generators. The proposed model is designed to be modular, so that it can be considered as a sample from a set of similar modules, assigned to different buildings to allow efficient energy sharing and balancing. The used approach in this concept is inspired from auto-similar systems, which is well suited and easy to implement on multi agent systems. A co-simulation in MATLAB and JAVA/JADE platforms has been performed regarding the production-consumption of the 24 hours baseline period.
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.
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.
Optimal interdigitated electrode sensor design for biosensors using multi-objective particle-swarm optimization Issa Sabiri; Hamid Bouyghf; Abdelhadi Raihani
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.pp2608-2617

Abstract

Interdigitated electrodes (IDEs) are commonly employed in biological cellular characterization techniques such as electrical cell-substrate impedance sensing (ECIS). Because of its simple production technique and low cost, interdigitated electrode sensor design is critical for practical impedance spectroscopy in the medical and pharmaceutical domains. The equivalent circuit of an IDE was modeled in this paper, it consisted of three primary components: double layer capacitance, Cdl, solution capacitance, CSol, and solution resistance, RSol. One of the challenging optimization challenges is the geometric optimization of the interdigital electrode structure of a sensor. We employ metaheuristic techniques to identify the best answer to problems of this kind. multi-objective optimization of the IDE using multi-objective particle swarm optimization (MOPSO) was achieved to maximize the sensitivity of the electrode and minimize the Cut-off frequency. The optimal geometrical parameters determined during optimization are used to build the electrical equivalent circuit. The amplitude and phase of the impedance versus frequency analysis were calculated using EC-LAB® software, and the corresponding conductivity was determined.
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 Asmae Ouhmida Bouchaib Cherradi Hamid Bouyghf Hassan Ouajji Issa Sabiri Meryem Hamidi Mohamed Amine Ouassil Mohamed Youssfi Mohammed Jebbari Omar Bouattane Oussama El Gannour Sara Sandabad Soufiane Hamida Taoufiq El Harrouti