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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics
Alazaidah, Raed
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Computer Science & IT Electrical & Electronics Engineering

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Arabic fake news detection using hybrid contextual features Turki, Hussain Mohammed; Daoud, Essam Al; Samara, Ghassan; Alazaidah, Raed; Qasem, Mais Haj; Aljaidi, Mohammad; Abuowaida, Suhaila; Alshdaifat, Nawaf
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp836-845

Abstract

Technology has advanced and social media users have grown dramatically in the last decade. Because social media makes information easily accessible, some people or organizations distribute false news for political or commercial gain. This news may influence elections and attitudes. Even though English fake news is widely detected and limited, Arabic fake news is hard to recognize owing to a lack of study and data collection. Wara Arabic bidirectional encoder representations from transformers (WaraBERT), a hybrid feature extraction approach, combines word level tokenization with two Arabic bidirectional encoder representations from transformers (AraBERT) variants to provide varied features. The study also discusses eliminating stopwords, punctuations, and tanween markings from Arabic data. This study employed two datasets. The first, Arabic fake news dataset (AFND), has 606,912 records. Second dataset Arabic news (AraNews) has 123,219 entries. WaraBERT-V1 obtained 93.83% AFND accuracy using the bidirectional long short-term memory (BiLSTM) model. However, the WaraBERT-V2 technique obtained 81.25%, improving detection accuracy above previous researchers for the AraNews dataset. These findings show that WaraBERT outperforms word list techniques (WLT), term frequency-inverse document frequency (TF-IDF), and AraBERT on both datasets.
Progression of polymeric nanostructured fibres for pharmaceutical applications Abu Owida, Hamza; I. Al-Nabulsi, Jamal; M. Turab, Nidal; Al-Ayyad, Muhammad; Alazaidah, Raed; Alshdaifat, Nawaf
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.7315

Abstract

Electrospinning has emerged as a simple and cost-effective technique for producing polymer nanofibers, offering a versatile approach for creating nanostructured fibers from a wide range of polymer materials. The pharmaceutical field has particularly welcomed the advent of electrospun nanofibers, as they hold immense potential for revolutionizing drug delivery systems. The recent surge of interest in electrospun nanofibers can be attributed to their unique characteristics, including elasticity and biocompatibility, which make them highly suitable for various biomedical applications. By incorporating functional ingredients into blends of nanostructured fibers, the capabilities and reliability of drug delivery devices have been significantly enhanced. This review aims to provide a comprehensive summary of recent research endeavors focusing on the concept of nanofibrous mesh and its multifaceted applications. With an emphasis on the simplicity of fabrication and the virtually limitless combinations of materials achievable through this approach, nanofibrous meshes hold the promise of transforming specific treatment modalities. By streamlining the delivery of therapeutic agents and offering enhanced control over drug release kinetics, nanofibrous meshes may herald a new era in targeted and personalized medicine.
Heart disease detection using machine learning Al-Habahbeh, Mohammad; Alomari, Moath; Khattab, Hebatullah; Alazaidah, Raed; Alshdaifat, Nawaf; Abuowaida, Suhaila; Alqatan, Saleh; Arabiat, Mohammad
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.8324

Abstract

Heart disease continues to be a major worldwide health issue, requiring accurate prediction models to improve early identification and treatment. This research aims to address two main objectives in light of the increasing prevalence of heart-related disorders. Firstly, it aims to determine the most efficient classifier for identifying heart disease among twenty-nine different classifiers that represent six distinct learning strategies. Furthermore, the research seeks to identify the most effective method for selecting features in heart disease datasets. The results show how well different classifiers and feature selection methods work by using two datasets with different features and judging performance using four important criteria. The evaluation results demonstrate that the RandomCommittee classifier outperforms in diagnosing heart illness, displaying strong skills across various learning strategies. This classifier exhibits favorable results in terms of accuracy, precision, recall, and F1-score metrics, hence confirming its appropriateness for predictive modeling in heart-related datasets. Moreover, the paper examines feature selection methods, specifically aiming to determine the most effective method for enhancing the predicted accuracy of heart disease models. The prediction models' overall performance is enhanced by their capacity to accurately identify and prioritize pertinent variables, thereby facilitating the early detection and management of heart-related problems.
Co-Authors Abu Owida, Hamza Abuowaida, Suhaila Al-Ayyad, Muhammad Al-Habahbeh, Mohammad Aljaidi, Mohammad Alomari, Moath Alqatan, Saleh Alshdaifat, Nawaf Arabiat, Mohammad Daoud, Essam Al I. Al-Nabulsi, Jamal Khattab, Hebatullah M. Turab, Nidal Qasem, Mais Haj Samara, Ghassan Turki, Hussain Mohammed