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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Abuowaida, Suhaila
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Journal : International Journal of Electrical and Computer Engineering

 1 
The performance of artificial intelligence in prostate magnetic resonance imaging screening Abu Owida, Hamza; R. Hassan, Mohammad; Ali, Ali Mohd; Alnaimat, Feras; Al Sharah, Ashraf; Abuowaida, Suhaila; Alshdaifat, Nawaf
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp2234-2241

Abstract

Prostate cancer is the predominant form of cancer observed in men worldwide. The application of magnetic resonance imaging (MRI) as a guidance tool for conducting biopsies has been established as a reliable and well-established approach in the diagnosis of prostate cancer. The diagnostic performance of MRI-guided prostate cancer diagnosis exhibits significant heterogeneity due to the intricate and multi-step nature of the diagnostic pathway. The development of artificial intelligence (AI) models, specifically through the utilization of machine learning techniques such as deep learning, is assuming an increasingly significant role in the field of radiology. In the realm of prostate MRI, a considerable body of literature has been dedicated to the development of various AI algorithms. These algorithms have been specifically designed for tasks such as prostate segmentation, lesion identification, and classification. The overarching objective of these endeavors is to enhance diagnostic performance and foster greater agreement among different observers within MRI scans for the prostate. This review article aims to provide a concise overview of the application of AI in the field of radiology, with a specific focus on its utilization in prostate MRI.
Automated classification of brain tumor-based magnetic resonance imaging using deep learning approach Owida, Hamza Abu; AlMahadin, Ghayth; Al-Nabulsi, Jamal I.; Turab, Nidal; Abuowaida, Suhaila; Alshdaifat, Nawaf
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp3150-3158

Abstract

The treatment of brain tumors poses significant challenges and contributes to a significant number of deaths on a global scale. The process of identifying brain tumors in medical practice involves the visual analysis of photographs by healthcare experts, who manually delineate the tumor locations. However, this approach is characterized by its time-consuming nature and susceptibility to errors. In recent years, scholars have put forth automated approaches to early detection of brain tumors. However, these techniques face challenges attributed to their limited precision and significant false-positive rates. There is a need for an effective methodology to identify and classify tumors, which involves extracting reliable features and achieving precise disease classification. This work presents a novel model architecture that is derived from the EfficientNetB3. The suggested framework has been trained and assessed on a dataset consisting of 7,023 magnetic resonance images. The findings of this study indicate that the fused feature vector exhibits superior performance compared to the individual vectors. Furthermore, the technique that was provided showed superior performance compared to the currently available systems and attained a 100% accuracy rate. As a result, it is viable to employ this technique within a clinical environment for the purpose of categorizing brain tumors based on magnetic resonance images scans.
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.
A novel convolutional neural network architecture for Alzheimer’s disease classification using magnetic resonance imaging data Abuowaida, Suhaila; Mustafa, Zaid; Aburomman, Ahmad; Alshdaifat, Nawaf; Iqtait, Musab
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp3519-3526

Abstract

Accurate categorization of Alzheimer’s disease is crucial for medical diagnosis and the development of therapeutic strategies. Deep learning models have shown significant potential in this endeavor; however, they often encounter difficulties due to the intricate and varied characteristics of Alzheimer’s disease. To address this difficulty, we suggest a new and innovative architecture for Alzheimer’s disease classification using magnetic resonance data. This design is named Res-BRNet and combines deep residual and boundary-based convolutional neural networks (CNNs). Res-BRNet utilizes a methodical fusion of boundary-focused procedures within adapted spatial and residual blocks. The spatial blocks retrieve information relating to uniformity, diversity, and boundaries of Alzheimer’s disease, although the residual blocks successfully capture texture differences at both local and global levels. We conducted a performance assessment of Res-BRNet. The Res-BRNet surpassed conventional CNN models, with outstanding levels of accuracy (99.22%). The findings indicate that Res-BRNet has promise as a tool for classifying Alzheimer’s disease, with the ability to enhance the precision and effectiveness of clinical diagnosis and treatment planning
Co-Authors Abu Owida, Hamza Aburomman, Ahmad Al Sharah, Ashraf Al-Habahbeh, Mohammad Al-Momani, Adai Al-Nabulsi, Jamal I. Alazaidah, Raed Alhaj, Mohammad Alhenawi, Esraa Ali, Ali Mohd Alidmat, Omar Aljaidi, Mohammad Almaghthawi, Ahmed AlMahadin, Ghayth Almatarneh, Sattam Alnaimat, Feras Alomari, Moath Alqaryouti, Marwan Harb Alqatan, Saleh Alsharafat, Wafa Alshdaifat, Nawaf Arabiat, Mohammad Chan, Huah Yong Daoud, Essam Al Elsoud, Esraa Abu Hassan, Mohammad Ibrahim, Abdullah Iqtait, Musab Issa, Abedalhakeem Khattab, Hebatullah Mustafa, Zaid Owida, Hamza Abu Qasem, Mais Haj R. Hassan, Mohammad Sadeq, Ala Eddin Samara, Ghassan Sharadqeh, Ahmad Turab, Nidal Turki, Hussain Mohammed