Feras Al-Obeidat | Zayed University (original) (raw)

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Papers by Feras Al-Obeidat

Research paper thumbnail of Learning heterogeneous subgraph representations for team discovery

Information Retrieval Journal, Oct 8, 2023

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Research paper thumbnail of Predicting heart disease risk in patients using various kinds of analytical models

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Research paper thumbnail of Social Alignment Contagion in Online Social Networks

IEEE Transactions on Computational Social Systems, 2022

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Research paper thumbnail of Learning Heterogeneous Subgraph Representations for Team Discovery

Research Square (Research Square), Dec 1, 2022

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Research paper thumbnail of Denoising histopathology images for the detection of breast cancer

Neural Computing and Applications, Jul 9, 2023

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Research paper thumbnail of Comparative Analysis of Machine Learning Algorithms for Author Age and Gender Identification

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Research paper thumbnail of Towards Enhanced Identification of Emotion from Resource-Constrained Language through a novel Multilingual BERT Approach

ACM Transactions on Asian and Low-Resource Language Information Processing, Apr 19, 2023

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Research paper thumbnail of Discovering the Correlation Between Phishing Susceptibility Causing Data Biases and Big Five Personality Traits Using C-GAN

IEEE Transactions on Computational Social Systems, 2022

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Research paper thumbnail of Customer churn prediction in telecommunication industry using data certainty

Journal of Business Research, 2019

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Research paper thumbnail of Managerial Conflict Among the Software Development Team

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Research paper thumbnail of Enhancing link prediction efficiency with shortest path and structural attributes

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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Research paper thumbnail of Cyber security and beyond: Detecting malware and concept drift in AI-based sensor data streams using statistical techniques

Computers & Electrical Engineering, May 1, 2023

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Research paper thumbnail of Sentence embedding approach using LSTM auto-encoder for discussion threads summarization

Computer Science and Information Systems, 2023

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Research paper thumbnail of Twitter sentiment analysis to understand students' perceptions about online learning during the Covid'19

2022 International Conference on Computer and Applications (ICCA)

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Research paper thumbnail of Self-Healing in Cyber–Physical Systems Using Machine Learning: A Critical Analysis of Theories and Tools

Future Internet, Jul 17, 2023

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Research paper thumbnail of Hybrid multicriteria fuzzy classification of network traffic patterns, anomalies, and protocols

Personal and Ubiquitous Computing, Nov 16, 2017

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Research paper thumbnail of The Socio-economic Impacts of Social Media Privacy and Security Challenges

Communications in Computer and Information Science, 2020

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Research paper thumbnail of A dual covari-ant biomarker approach to Kawasaki disease, using vascular endothelial growth factor A and B gene expression; implications for coronary pathogenesis

Informatics in Medicine Unlocked

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Research paper thumbnail of A Transcriptomic Appreciation of Childhood Meningococcal and Polymicrobial Sepsis from a Pro-Inflammatory and Trajectorial Perspective, a Role for Vascular Endothelial Growth Factor a and B Modulation?

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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Research paper thumbnail of A Semantic Model for Context-Based Fake News Detection on Social Media

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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Research paper thumbnail of Learning heterogeneous subgraph representations for team discovery

Information Retrieval Journal, Oct 8, 2023

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Research paper thumbnail of Predicting heart disease risk in patients using various kinds of analytical models

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Social Alignment Contagion in Online Social Networks

IEEE Transactions on Computational Social Systems, 2022

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Research paper thumbnail of Learning Heterogeneous Subgraph Representations for Team Discovery

Research Square (Research Square), Dec 1, 2022

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Research paper thumbnail of Denoising histopathology images for the detection of breast cancer

Neural Computing and Applications, Jul 9, 2023

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Research paper thumbnail of Comparative Analysis of Machine Learning Algorithms for Author Age and Gender Identification

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Towards Enhanced Identification of Emotion from Resource-Constrained Language through a novel Multilingual BERT Approach

ACM Transactions on Asian and Low-Resource Language Information Processing, Apr 19, 2023

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Research paper thumbnail of Discovering the Correlation Between Phishing Susceptibility Causing Data Biases and Big Five Personality Traits Using C-GAN

IEEE Transactions on Computational Social Systems, 2022

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Research paper thumbnail of Customer churn prediction in telecommunication industry using data certainty

Journal of Business Research, 2019

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Research paper thumbnail of Managerial Conflict Among the Software Development Team

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Research paper thumbnail of Enhancing link prediction efficiency with shortest path and structural attributes

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%.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Cyber security and beyond: Detecting malware and concept drift in AI-based sensor data streams using statistical techniques

Computers & Electrical Engineering, May 1, 2023

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Research paper thumbnail of Sentence embedding approach using LSTM auto-encoder for discussion threads summarization

Computer Science and Information Systems, 2023

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Research paper thumbnail of Twitter sentiment analysis to understand students' perceptions about online learning during the Covid'19

2022 International Conference on Computer and Applications (ICCA)

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Research paper thumbnail of Self-Healing in Cyber–Physical Systems Using Machine Learning: A Critical Analysis of Theories and Tools

Future Internet, Jul 17, 2023

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Research paper thumbnail of Hybrid multicriteria fuzzy classification of network traffic patterns, anomalies, and protocols

Personal and Ubiquitous Computing, Nov 16, 2017

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Research paper thumbnail of The Socio-economic Impacts of Social Media Privacy and Security Challenges

Communications in Computer and Information Science, 2020

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Research paper thumbnail of A dual covari-ant biomarker approach to Kawasaki disease, using vascular endothelial growth factor A and B gene expression; implications for coronary pathogenesis

Informatics in Medicine Unlocked

Bookmarks Related papers MentionsView impact

Research paper thumbnail of A Transcriptomic Appreciation of Childhood Meningococcal and Polymicrobial Sepsis from a Pro-Inflammatory and Trajectorial Perspective, a Role for Vascular Endothelial Growth Factor a and B Modulation?

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

Research paper thumbnail of A Semantic Model for Context-Based Fake News Detection on Social Media

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