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All Journal International Journal of Electrical and Computer Engineering JOIN (Jurnal Online Informatika)
Abuzaraida, Mustafa Ali
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Computer Science & IT Electrical & Electronics Engineering

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Cyberbullying Detection in the Libyan Dialect Using Convolutional Neural Networks M. Elgoud, Sara; Abuzaraida, Mustafa Ali; S. Attarbashi, Zainab; Saip, Mohamed Ali
JOIN (Jurnal Online Informatika) Vol 10 No 2 (2025)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v10i2.1631

Abstract

ecently, the widespread use of social media has increased, leading to increased concerns about cyberbullying. It has become imperative to intensify efforts and methods to detect and manage cyberbullying through social media. Arabic has recently received increasing attention to improve the classification of Arabic texts. Given the multitude of Arabic dialects used on social media platforms by Arabic speakers to express their opinions and communicate with each other, applying this approach to Arabic becomes extremely challenging due to its structural and morphological complexity. Analyzing Arabic dialects using Natural Language Processing (NLP) tools can be more challenging than Standard Arabic. In this paper, the impact of using stopword removal and derivation techniques on detecting cyberbullying in the Libyan dialect was presented. The efficiency of text classification was compared when using a Libyan dialect word list alongside pre-generated Modern Standard Arabic (MSA) lists. The texts were classified using Convolutional Neural Network (CNN) classifiers, and the experiments showed that when using Libyan dialect words, the accuracy results were 92% and 83%, and when using only Standard Arabic stop words, the accuracy results were dropped to 91% and 77%. Based on these results, the higher accuracy was obtained when using the presented stop words list which it is specific to the Libyan dialect, and they had a positive impact on the results, better than Standard Arabic stop words.
Online handwriting Arabic recognition system using k-nearest neighbors classifier and DCT features Abuzaraida, Mustafa Ali; Elmehrek, Mohammed; Elsomadi, Esam
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 4: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i4.pp3584-3592

Abstract

With advances in machine learning techniques, handwriting recognition systems have gained a great deal of importance. Lately, the increasing popularity of handheld computers, digital notebooks, and smartphones give the field of online handwriting recognition more interest. In this paper, we propose an enhanced method for the recognition of Arabic handwriting words using a directions-based segmentation technique and discrete cosine transform (DCT) coefficients as structural features. The main contribution of this research was combining a total of 18 structural features which were extracted by DCT coefficients and using the k-nearest neighbors (KNN) classifier to classify the segmented characters based on the extracted features. A dataset is used to validate the proposed method consisting of 2500 words in total. The obtained average 99.10% accuracy in recognition of handwritten characters shows that the proposed approach, through its multiple phases, is efficient in separating, distinguishing, and classifying Arabic handwritten characters using the KNN classifier. The availability of an online dataset of Arabic handwriting words is the main issue in this field. However, the dataset used will be available for research via the website.
Co-Authors Elmehrek, Mohammed Elsomadi, Esam M. Elgoud, Sara S. Attarbashi, Zainab Saip, Mohamed Ali