Feras Al-Obeidat | Zayed University (original) (raw)
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Papers by Feras Al-Obeidat
Information Retrieval Journal, Oct 8, 2023
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IEEE Transactions on Computational Social Systems, 2022
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Research Square (Research Square), Dec 1, 2022
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Neural Computing and Applications, Jul 9, 2023
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ACM Transactions on Asian and Low-Resource Language Information Processing, Apr 19, 2023
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IEEE Transactions on Computational Social Systems, 2022
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Journal of Business Research, 2019
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Intelligent Data Analysis, Jun 29, 2023
Link prediction is one of the most essential and crucial tasks in complex network research since ... more Link prediction is one of the most essential and crucial tasks in complex network research since it seeks to forecast missing links in a network based on current ones. This problem has applications in a variety of scientific disciplines, including social network research, recommendation systems, and biological networks. In previous work, link prediction has been solved through different methods such as path, social theory, topology, and similarity-based. The main issue is that path-based methods ignore topological features, while structure-based methods also fail to combine the path and structured-based features. As a result, a new technique based on the shortest path and topological features’ has been developed. The method uses both local and global similarity indices to measure the similarity. Extensive experiments on real-world datasets from a variety of domains are utilized to empirically test and compare the proposed framework to many state-of-the-art prediction techniques. Over 100 iterations, the collected data showed that the proposed method improved on the other methods in terms of accuracy. SI and AA, among the existing state-of-the-art algorithms, fared best with an AUC value of 82%, while the proposed method has an AUC value of 84%.
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Computers & Electrical Engineering, May 1, 2023
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Computer Science and Information Systems, 2023
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2022 International Conference on Computer and Applications (ICCA)
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Future Internet, Jul 17, 2023
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Personal and Ubiquitous Computing, Nov 16, 2017
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Communications in Computer and Information Science, 2020
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Informatics in Medicine Unlocked
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Shock
This study investigated the temporal dynamics of childhood sepsis by analyzing gene expression ch... more This study investigated the temporal dynamics of childhood sepsis by analyzing gene expression changes associated with proinflammatory processes. Five datasets, including four meningococcal sepsis shock (MSS) datasets (two temporal and two longitudinal) and one polymicrobial sepsis dataset, were selected to track temporal changes in gene expression. Hierarchical clustering revealed three temporal phases: early, intermediate, and late, providing a framework for understanding sepsis progression. Principal component analysis supported the identification of gene expression trajectories. Differential gene analysis highlighted consistent upregulation of vascular endothelial growth factor A (VEGF-A) and nuclear factor κB1 (NFKB1), genes involved in inflammation, across the sepsis datasets. NFKB1 gene expression also showed temporal changes in the MSS datasets. In the postmortem dataset comparing MSS cases to controls, VEGF-A was upregulated and VEGF-B downregulated. Renal tissue exhibited ...
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2020 IEEE/ACS 17th International Conference on Computer Systems and Applications (AICCSA), 2020
Context-based fake news detection provides means to define and describe a social context for news... more Context-based fake news detection provides means to define and describe a social context for news objects on social media, thereby facilitating detection of fake news through data analysis and patterns recognition. However, while content-based fake news detection has gained popularity with machine learning and NLP techniques, the context-based approach has seen very little exploitation. Therefore, it has become pertinent to significantly explore and integrate other technologies for context-based detection of fake news on social media. With semantic technologies capabilities to provide context-awareness for data, this paper analyses social media context and develops a taxonomy for entities classification. Furthermore, a semantic model is developed to describe classes extracted from the taxonomy towards fully semantically describing concepts, relations, instances, and axioms. The model would enhance fake news detection through semantic annotation for contextual features of news objects and datasets, providing a basis for patterns recognition, analysis, and identification of news articles on social media as either fake or not.
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Information Retrieval Journal, Oct 8, 2023
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Bookmarks Related papers MentionsView impact
IEEE Transactions on Computational Social Systems, 2022
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Research Square (Research Square), Dec 1, 2022
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Neural Computing and Applications, Jul 9, 2023
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ACM Transactions on Asian and Low-Resource Language Information Processing, Apr 19, 2023
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IEEE Transactions on Computational Social Systems, 2022
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Journal of Business Research, 2019
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Bookmarks Related papers MentionsView impact
Intelligent Data Analysis, Jun 29, 2023
Link prediction is one of the most essential and crucial tasks in complex network research since ... more Link prediction is one of the most essential and crucial tasks in complex network research since it seeks to forecast missing links in a network based on current ones. This problem has applications in a variety of scientific disciplines, including social network research, recommendation systems, and biological networks. In previous work, link prediction has been solved through different methods such as path, social theory, topology, and similarity-based. The main issue is that path-based methods ignore topological features, while structure-based methods also fail to combine the path and structured-based features. As a result, a new technique based on the shortest path and topological features’ has been developed. The method uses both local and global similarity indices to measure the similarity. Extensive experiments on real-world datasets from a variety of domains are utilized to empirically test and compare the proposed framework to many state-of-the-art prediction techniques. Over 100 iterations, the collected data showed that the proposed method improved on the other methods in terms of accuracy. SI and AA, among the existing state-of-the-art algorithms, fared best with an AUC value of 82%, while the proposed method has an AUC value of 84%.
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Computers & Electrical Engineering, May 1, 2023
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Computer Science and Information Systems, 2023
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2022 International Conference on Computer and Applications (ICCA)
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Future Internet, Jul 17, 2023
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Personal and Ubiquitous Computing, Nov 16, 2017
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Communications in Computer and Information Science, 2020
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Informatics in Medicine Unlocked
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Shock
This study investigated the temporal dynamics of childhood sepsis by analyzing gene expression ch... more This study investigated the temporal dynamics of childhood sepsis by analyzing gene expression changes associated with proinflammatory processes. Five datasets, including four meningococcal sepsis shock (MSS) datasets (two temporal and two longitudinal) and one polymicrobial sepsis dataset, were selected to track temporal changes in gene expression. Hierarchical clustering revealed three temporal phases: early, intermediate, and late, providing a framework for understanding sepsis progression. Principal component analysis supported the identification of gene expression trajectories. Differential gene analysis highlighted consistent upregulation of vascular endothelial growth factor A (VEGF-A) and nuclear factor κB1 (NFKB1), genes involved in inflammation, across the sepsis datasets. NFKB1 gene expression also showed temporal changes in the MSS datasets. In the postmortem dataset comparing MSS cases to controls, VEGF-A was upregulated and VEGF-B downregulated. Renal tissue exhibited ...
Bookmarks Related papers MentionsView impact
2020 IEEE/ACS 17th International Conference on Computer Systems and Applications (AICCSA), 2020
Context-based fake news detection provides means to define and describe a social context for news... more Context-based fake news detection provides means to define and describe a social context for news objects on social media, thereby facilitating detection of fake news through data analysis and patterns recognition. However, while content-based fake news detection has gained popularity with machine learning and NLP techniques, the context-based approach has seen very little exploitation. Therefore, it has become pertinent to significantly explore and integrate other technologies for context-based detection of fake news on social media. With semantic technologies capabilities to provide context-awareness for data, this paper analyses social media context and develops a taxonomy for entities classification. Furthermore, a semantic model is developed to describe classes extracted from the taxonomy towards fully semantically describing concepts, relations, instances, and axioms. The model would enhance fake news detection through semantic annotation for contextual features of news objects and datasets, providing a basis for patterns recognition, analysis, and identification of news articles on social media as either fake or not.
Bookmarks Related papers MentionsView impact