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All Journal International Journal of Electrical and Computer Engineering
Mahmoud Hammad
Jordan University of Science and Technology
Computer Science & IT Electrical & Electronics Engineering

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A transfer learning with deep neural network approach for diabetic retinopathy classification Mohammed Al-Smadi; Mahmoud Hammad; Qanita Bani Baker; Sa’ad A. Al-Zboon
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.pp3492-3501

Abstract

Diabetic retinopathy is an eye disease caused by high blood sugar and pressure which damages the blood vessels in the eye. Diabetic retinopathy is the root cause of more than 1% of the blindness worldwide. Early detection of this disease is crucial as it prevents it from progressing to a more severe level. However, the current machine learning-based approaches for detecting the severity level of diabetic retinopathy are either, i) rely on manually extracting features which makes an approach unpractical, or ii) trained on small dataset thus cannot be generalized. In this study, we propose a transfer learning-based approach for detecting the severity level of the diabetic retinopathy with high accuracy. Our model is a deep learning model based on global average pooling (GAP) technique with various pre-trained convolutional neural net- work (CNN) models. The experimental results of our approach, in which our best model achieved 82.4% quadratic weighted kappa (QWK), corroborate the ability of our model to detect the severity level of diabetic retinopathy efficiently.
Transfer deep learning approach for detecting coronavirus disease in X-ray images Mohammed Al-Smadi; Mahmoud Hammad; Qanita Bani Baker; Saja Khaled Tawalbeh; Sa’ad A. Al-Zboon
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 6: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i6.pp4999-5008

Abstract

Currently, the whole world is fighting a very dangerous and infectious disease caused by the novel coronavirus, called COVID-19. The COVID-19 is rapidly spreading around the world due to its high infection rate. Therefore, early discovery of COVID-19 is crucial to better treat the infected person as well as to slow down the spread of this virus. However, the current solution for detecting COVID-19 cases including the PCR test, CT images, epidemiologically history, and clinical symptoms suffer from high false positive. To overcome this problem, we have developed a novel transfer deep learning approach for detecting COVID-19 based on x-ray images. Our approach helps medical staff in determining if a patient is normal, has COVID-19, or other pneumonia. Our approach relies on pre-trained models including Inception-V3, Xception, and MobileNet to perform two tasks: i) binary classification to determine if a person infected with COVID-19 or not and ii) a multi-task classification problem to distinguish normal, COVID-19, and pneumonia cases. Our experimental results on a large dataset show that the F1-score is 100% in the first task and 97.66 in the second task.
Using deep learning models for learning semantic text similarity of Arabic questions Mahmoud Hammad; Mohammed Al-Smadi; Qanita Bani Baker; Sa’ad A. Al-Zboon
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.pp3519-3528

Abstract

Question-answering platforms serve millions of users seeking knowledge and solutions for their daily life problems. However, many knowledge seekers are facing the challenge to find the right answer among similar answered questions and writer’s responding to asked questions feel like they need to repeat answers many times for similar questions. This research aims at tackling the problem of learning the semantic text similarity among different asked questions by using deep learning. Three models are implemented to address the aforementioned problem: i) a supervised-machine learning model using XGBoost trained with pre-defined features, ii) an adapted Siamese-based deep learning recurrent architecture trained with pre-defined features, and iii) a Pre-trained deep bidirectional transformer based on BERT model. Proposed models were evaluated using a reference Arabic dataset from the mawdoo3.com company. Evaluation results show that the BERT-based model outperforms the other two models with an F1=92.99%, whereas the Siamese-based model comes in the second place with F1=89.048%, and finally, the XGBoost as a baseline model achieved the lowest result of F1=86.086%.
Towards enhancing the user experience of ChIP-Seq data analysis web tools Mahmoud Hammad; Qanita Bani Baker; Mohammed Al-Smadi; Wesam Alrashdan
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp5236-5247

Abstract

Deoxyribonucleic acid (DNA) sequencing is the process of locating the sequence of the main chemical bases in the DNA. Next-generation sequencing (NGS) is the state-of-the-art DNA sequencing technique. The NGS technique advanced the biological science in analyzing human DNA due to its scalability, high throughput, and speed. Analyzing human DNA is crucial to determine the ability of a person to develop certain diseases and his ability to respond to certain medications. ChIP-sequencing is a method that combines chromatin immunoprecipitation (ChIP) with NGS sequencing to analyze protein interactions with DNA to identify binding sites. Many online web tools have been developed to conduct ChIP-Seq data analysis to either discover or find motifs, i.e., patterns of binding sites. Since these ChIP-Seq web tools need to be used by clinical practitioners, they must comply to the web-related usability tasks including effectiveness, efficiency and satisfaction to enhance the user experience (UX). To that end, we have conducted an empirical study to understand their UX design. Specifically, we have evaluated the usability of 8 widely used ChIP-Seq web tools against 6 known usability quality metrics. Our study shows that the design of the studied ChIP-Seq web tools does not follow the UX design principles.
Co-Authors Mohammed Al-Smadi Qanita Bani Baker Saja Khaled Tawalbeh Sa’ad A. Al-Zboon Wesam Alrashdan