Mohammed M. Hassoun Al-Jawad
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Mohammed M. Hassoun Al-Jawad has a total of 8 co-authors. Here are the top co-authors by number of shared publications:
- Ahmed F. Al-Mukhtar1 shared publication
- Ala’a R. Al-Shamasneh
University of Malaya
1 shared publication - Ali Majeed Hasan
College of Medicine, Al-Nahrain University, Baghdad 10001, Iraq
1 shared publication - Anwar Alnawas
Department of Computer Engineering, Faculty of Technology, Gazi University, Turkey/Nasiriyah Technical Institute, Southern Technical University, Iraq
1 shared publication - Hadil Shaiba
College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
1 shared publication - Hamid Abdullah Jalab
Information and Communication Technology Research Group, Scientific Research Center, Al-Ayen University , 64001 , Thi Qar , Iraq
1 shared publication
Latest Publications
- Open Access
- 1 April 2022
- Published by ITMO University in Scientific and Technical Journal of Information Technologies, Mechanics and Optics
- Vol. 22 (2) , 308
Constructing twitter corpus of Iraqi Arabic Dialect (CIAD) for sentiment analysis
The number of Twitter users in Iraq has increased significantly in recent years. Major events, the political situation in the country, had a significant impact on the content of Twitter and affected the tweets of Iraqi users. Creating an Iraqi Arabic Dialect corpus is crucial for sentiment analysis to study such behaviors. Since no such corpus existed, this paper introduces the Corpus of Iraqi Arabic Dialect (CIAD). The corpus has been collected, annotated and made publicly accessible to other researchers for further investigation. Furthermore, the created corpus has been validated using eight different combinations of four feature-selections approaches and two versions of Support Vector Machine (SVM) algorithm. Various performance measures were calculated. The obtained accuracy, 78 %, indicates a promising potential application.
- 1 April 2022
- Published by ITMO University in Scientific and Technical Journal of Information Technologies, Mechanics and Optics
- Vol. 22 (2) , 308
Open AccessConstructing twitter corpus of Iraqi Arabic Dialect (CIAD) for sentiment analysis
The number of Twitter users in Iraq has increased significantly in recent years. Major events, the political situation in the country, had a significant impact on the content of Twitter and affected the tweets of Iraqi users. Creating an Iraqi Arabic Dialect corpus is crucial for sentiment analysis to study such behaviors. Since no such corpus existed, this paper introduces the Corpus of Iraqi Arabic Dialect (CIAD). The corpus has been collected, annotated and made publicly accessible to other researchers for further investigation. Furthermore, the created corpus has been validated using eight different combinations of four feature-selections approaches and two versions of Support Vector Machine (SVM) algorithm. Various performance measures were calculated. The obtained accuracy, 78 %, indicates a promising potential application.
- Open Access
Classification of Covid-19 Coronavirus, Pneumonia and Healthy Lungs in CT Scans Using Q-Deformed Entropy and Deep Learning Features
Many health systems over the world have collapsed due to limited capacity and a dramatic increase of suspected COVID-19 cases. What has emerged is the need for finding an efficient, quick and accurate method to mitigate the overloading of radiologists’ efforts to diagnose the suspected cases. This study presents the combination of deep learning of extracted features with the Q-deformed entropy handcrafted features for discriminating between COVID-19 coronavirus, pneumonia and healthy computed tomography (CT) lung scans. In this study, pre-processing is used to reduce the effect of intensity variations between CT slices. Then histogram thresholding is used to isolate the background of the CT lung scan. Each CT lung scan undergoes a feature extraction which involves deep learning and a Q-deformed entropy algorithm. The obtained features are classified using a long short-term memory (LSTM) neural network classifier. Subsequently, combining all extracted features significantly improves the performance of the LSTM network to precisely discriminate between COVID-19, pneumonia and healthy cases. The maximum achieved accuracy for classifying the collected dataset comprising 321 patients is 99.68%.
Open AccessClassification of Covid-19 Coronavirus, Pneumonia and Healthy Lungs in CT Scans Using Q-Deformed Entropy and Deep Learning Features
Many health systems over the world have collapsed due to limited capacity and a dramatic increase of suspected COVID-19 cases. What has emerged is the need for finding an efficient, quick and accurate method to mitigate the overloading of radiologists’ efforts to diagnose the suspected cases. This study presents the combination of deep learning of extracted features with the Q-deformed entropy handcrafted features for discriminating between COVID-19 coronavirus, pneumonia and healthy computed tomography (CT) lung scans. In this study, pre-processing is used to reduce the effect of intensity variations between CT slices. Then histogram thresholding is used to isolate the background of the CT lung scan. Each CT lung scan undergoes a feature extraction which involves deep learning and a Q-deformed entropy algorithm. The obtained features are classified using a long short-term memory (LSTM) neural network classifier. Subsequently, combining all extracted features significantly improves the performance of the LSTM network to precisely discriminate between COVID-19, pneumonia and healthy cases. The maximum achieved accuracy for classifying the collected dataset comprising 321 patients is 99.68%.