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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics
Kissi, Mohamed
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Computer Science & IT Electrical & Electronics Engineering

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An improved Arabic text classification method using word embedding Sabri, Tarik; Bahassine, Said; El Beggar, Omar; Kissi, Mohamed
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.pp721-731

Abstract

Feature selection (FS) is a widely used method for removing redundant or irrelevant features to improve classification accuracy and decrease the model’s computational cost. In this paper, we present an improved method (referred to hereafter as RARF) for Arabic text classification (ATC) that employs the term frequency-inverse document frequency (TF-IDF) and Word2Vec embedding technique to identify words that have a particular semantic relationship. In addition, we have compared our method with four benchmark FS methods namely principal component analysis (PCA), linear discriminant analysis (LDA), chi-square, and mutual information (MI). Support vector machine (SVM), k-nearest neighbors (K-NN), and naive Bayes (NB) are three machine learning based algorithms used in this work. Two different Arabic datasets are utilized to perform a comparative analysis of these algorithms. This paper also evaluates the efficiency of our method for ATC on the basis of performance metrics viz accuracy, precision, recall, and F-measure. Results revealed that the highest accuracy achieved for the SVM classifier applied to the Khaleej-2004 Arabic dataset with 94.75%, while the same classifier recorded an accuracy of 94.01% for the Watan-2004 Arabic dataset.
Design and development of a fuzzy explainable expert system for a diagnostic robot of COVID-19 Beggar, Omar El; Ramdani, Mohammed; Kissi, Mohamed
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp6940-6951

Abstract

Expert systems have been widely used in medicine to diagnose different diseases. However, these rule-based systems only explain why and how their outcomes are reached. The rules leading to those outcomes are also expressed in a machine language and confronted with the familiar problems of coverage and specificity. This fact prevents procuring expert systems with fully human-understandable explanations. Furthermore, early diagnosis involves a high degree of uncertainty and vagueness which constitutes another challenge to overcome in this study. This paper aims to design and develop a fuzzy explainable expert system for coronavirus disease-2019 (COVID-19) diagnosis that could be incorporated into medical robots. The proposed medical robotic application deduces the likelihood level of contracting COVID-19 from the entered symptoms, the personal information, and the patient's activities. The proposal integrates fuzzy logic to deal with uncertainty and vagueness in diagnosis. Besides, it adopts a hybrid explainable artificial intelligence (XAI) technique to provide different explanation forms. In particular, the textual explanations are generated as rules expressed in a natural language while avoiding coverage and specificity problems. Therefore, the proposal could help overwhelmed hospitals during the epidemic propagation and avoid contamination using a solution with a high level of explicability.
Detection and mitigation of DDoS attacks in SDN based intrusion detection system Chouikik, Meryem; Ouaissa, Mariyam; Ouaissa, Mariya; Boulouard, Zakaria; Kissi, Mohamed
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.7570

Abstract

Software defined networks (SDN) have completely revolutionized the management and operation of networks. This novel technology entails a distinctive approach to management. Amidst the advancements, a notable security concern arises in the form of distributed denial of service (DDoS) attacks. To counteract this attack, the deployment of intrusion detection systems (IDS) assumes paramount importance. IDS plays a critical role in monitoring network traffic, promptly detecting irregularities that may signify a potential denial of service (DoS) assault. This study delves into a comprehensive exploration of a DDoS attack on an SDN network using the OpenDaylight controller and the Mininet emulator. Furthermore, the assessment extends to evaluating the DDoS attack's repercussions and the effectiveness of IDS in mitigating such risks. Various performance metrics, including throughput according to delay time, are monitored to gauge network performance under duress. The difference in throughput curves when comparing scenarios with and without IDS highlights the significant impact of intrusion detection. When the IDS was absent, there was a noticeable increase in oscillations, indicating greater network susceptibility. On the other hand, the presence of an IDS created a more regulated environment, reducing variances and promoting a more stable network.
Co-Authors Bahassine, Said Beggar, Omar El Boulouard, Zakaria Chouikik, Meryem El Beggar, Omar Ouaissa, Mariya Ouaissa, Mariyam Ramdani, Mohammed Sabri, Tarik