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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Alikhashashneh, Enas Ahmad
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A robust method for detecting fake news using both machine and deep learning algorithms Alikhashashneh, Enas Ahmad; Nahar, Khalid M.O.; Abual-Rub, Mohammed; Alkhaldy, Hedaya M.
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1816-1826

Abstract

Spreading fake news and false information on social media is very common and can be done effortlessly due to the huge number of users of each of the various social media platforms. Another reason for having such a speedy spread of fake news (which makes about 40% of the information published on social media platforms) is the inability of these platform to verify the authenticity of the news before allowing it to be published. This research will use information technology to detect fake news/ false information and change this kind of technology from being the cause of the problem to a tool to solve it. This research provides a method that uses both machine learning (ML) and deep learning (DL) algorithms to detect fake information versus real information and compare the performance of the algorithms. The results of this research indicate that the algorithms that use term frequency inverse document frequency (TF-IDF) have achieved better results than the algorithms that use Word2Vec. Long short-term memory (LSTM) algorithm, however, has achieved the best performance; of 99% accuracy -when using TF-IDF, and 94% -when using Word2Vec.
Recognizing AlMuezzin and his Maqam using deep learning approach Shatnawi, Nahlah Mohammad; Nahar, Khalid M. O.; Al-Issa, Suhad; Alikhashashneh, Enas Ahmad
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i2.pp1360-1372

Abstract

Speech recognition is an important topic in deep learning, especially to Arabic language in an attempt to recognize Arabic speech, due to the difficulty of applying it because of the nature of the Arabic language, its frequent overlap, and the lack of available sources, and some other limitations related to the programming matters. This paper attempts to reduce the gap that exists between speech recognition and the Arabic language and attempts to address it through deep learning. In this paper, the focus is on Call for Prayer (Aladhan: ناذآلا ) as one of the most famous Arabic words, where its form is stable, but it differs in the notes and shape of its sound, which is known as the phonetic Maqam (Maqam: ماقملا  يتوصلا ). In this paper, a solution to identify the voice of AlMuezzin ( نذؤملا ), recognize AlMuezzin, and determine the form of the Maqam through VGG-16 model presented. The VGG-16 model examined with 4 extracted features: Chroma feature, LogFbank feature, MFCC feature, and spectral centroids. The best result obtained was with chroma features, where the accuracy of Aladhan recognition reached 96%. On the other hand, the classification of Maqam with the highest accuracy reached of 95% using spectral centroids feature.
Co-Authors Abual-Rub, Mohammed Al-Issa, Suhad Alkhaldy, Hedaya M. Nahar, Khalid M. O. Nahar, Khalid M.O. Shatnawi, Nahlah Mohammad