Feras Al-Obeidat - Profile on Academia.edu (original) (raw)
Papers by Feras Al-Obeidat
Learning heterogeneous subgraph representations for team discovery
Information Retrieval Journal, Oct 8, 2023
Predicting heart disease risk in patients using various kinds of analytical models
Social Alignment Contagion in Online Social Networks
IEEE Transactions on Computational Social Systems, 2022
Research Square (Research Square), Dec 1, 2022
The team discovery task is concerned with finding a group of experts from a collaboration network... more The team discovery task is concerned with finding a group of experts from a collaboration network who would collectively cover a desirable set of skills. Most prior work for team discovery either adopt graph-based or neural mapping approaches. Graph-based approaches are computationally intractable often leading to sub-optimal team selection. Neural mapping approaches have better performance, however, are still limited as they learn individual representations for skills and experts and are often prone to overfitting given the sparsity of collaboration networks. Thus, we define the team discovery task as one of learning subgraph representations from heterogeneous collaboration network where the subgraphs represent teams which are then used to identify relevant teams for a given set of skills. As such, our approach captures local (node interactions with each team) and global (subgraph interactions between teams) characteristics of the representation network and allows us to easily map between any homogeneous and heterogeneous subgraphs in the network to effectively discover teams. Our experiments over two real-world datasets from different domains, namely the DBLP bibliographic dataset with 10, 647 papers and IMDB with 4, 882 movies, illustrate that our approach outperforms the state-of-the-art baselines on a range of ranking and quality metrics. More specifically, in terms *These authors are ordered alphabetically.
Denoising histopathology images for the detection of breast cancer
Neural Computing and Applications, Jul 9, 2023
Comparative Analysis of Machine Learning Algorithms for Author Age and Gender Identification
ACM Transactions on Asian and Low-Resource Language Information Processing, Apr 19, 2023
Emotion identification from text has recently gained attention due to its versatile ability to an... more Emotion identification from text has recently gained attention due to its versatile ability to analyze human-machine interaction. This work focuses on detecting emotions from textual data. Languages, like English, Chinese, and German are widely used for text classification, however, limited research is done on resource-poor oriental languages. Roman Urdu (RU) is a resource-constrained language extensively used across Asia. This work focuses on predicting emotions from RU text. For this, a dataset is collected from different social media domains and based on Paul Ekman's theory it is annotated with six basic emotions, i.e., happy, surprise, angry, sad, fear, and disgusting. Dense word embedding representations of different languages is adopted that utilize existing pre-trained models. BERT is additionally pre-trained and fine-tuned for the classification task. The proposed approach is compared with baseline machine learning and deep learning algorithms. Additionally, a comparison of the current work is also performed with different approaches for the same task. Based on the empirical evaluation, the proposed approach performs better than the existing state-of-the-art with an average accuracy of 91%.
Discovering the Correlation Between Phishing Susceptibility Causing Data Biases and Big Five Personality Traits Using C-GAN
IEEE Transactions on Computational Social Systems, 2022
Journal of Business Research, 2019
With the terrific growth of digital data and associated technologies, there is an emerging trend,... more With the terrific growth of digital data and associated technologies, there is an emerging trend, where industries become rapidly digitized. These technologies are providing great opportunities to identify and resolve different problems. In particular, the telecommunication industry is facing a serious problem of customer churn relating to, the customers who are going to abandon their established relation with the business/network in the near future. This problem cannot only affect the rapid growth of the business but can also affect the revenues. Therefore, many customer churn prediction (CCP) models have been introduced but not yielding the desired performance in CCP. This is because there can be many factors, that contribute to customer churn which are still unexplored. In this paper, we focus on determining the effectiveness of the factors, i.e. lower and upper distance between the samples, are considered by the proposed model for the CCP. Further, we demonstrate a novel solution pertaining to the telecommunication sector showing the hidden factors considered for predicting the customer churn. Finally, we investigate the effects of both types of samples: those samples that are low distance and the upper distance (in terms of relevance) to the majority samples in given publicly available dataset. As a result of the study, we found that lower distance test set (LDT) samples have obtained best performance as compare to upper distance test set (UDT) samples in term of increased in the accuracy, f-measures, precision and recall when the uncertain sample size increases. Because the classification performance on upper distance samples remain almost the same when the size of samples increased in the test set.
Managerial Conflict Among the Software Development Team
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%.
Cyber security and beyond: Detecting malware and concept drift in AI-based sensor data streams using statistical techniques
Computers & Electrical Engineering, May 1, 2023
Computer Science and Information Systems, 2023
Online discussion forums are repositories of valuable information where users interact and articu... more Online discussion forums are repositories of valuable information where users interact and articulate their ideas and opinions, and share experiences about numerous topics. These online discussion forums are internet-based online communities where users can ask for help and find the solution to a problem. A new user of online discussion forums becomes exhausted from reading the significant number of irrelevant replies in a discussion. An automated discussion thread summarizing system (DTS) is necessary to create a candid view of the entire discussion of a query. Most of the previous approaches for automated DTS use the continuous bag of words (CBOW) model as a sentence embedding tool, which is poor at capturing the overall meaning of the sentence and is unable to grasp word dependency. To overcome these limitations, we introduce the LSTM Auto-encoder as a sentence embedding technique to improve the performance of DTS. The empirical result in the context of the proposed approach's average precision, recall, and F-measure with respect to ROGUE-1 and ROUGE-2 of two standard experimental datasets demonstrates the effectiveness and efficiency of the proposed approach and outperforms the state-of-the-art CBOW model in sentence embedding tasks and boost the performance of the automated DTS model.
Twitter sentiment analysis to understand students' perceptions about online learning during the Covid'19
2022 International Conference on Computer and Applications (ICCA)
Future Internet, Jul 17, 2023
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
Personal and Ubiquitous Computing, Nov 16, 2017
Traffic classification in computer networks has very significant roles in network operation, mana... more Traffic classification in computer networks has very significant roles in network operation, management, and security. Examples include controlling the flow of information, allocating resources effectively, provisioning quality of service, detecting intrusions, and blocking malicious and unauthorized access. This problem has attracted a growing attention over years and a number of techniques have been proposed ranging from traditional port-based and payload inspection of TCP/IP packets to supervised, unsupervised, and semi-supervised machine learning paradigms. With the increasing complexity of network environments and support for emerging mobility services and applications, more robust and accurate techniques need to be investigated. In this paper, we propose a new supervised hybrid machine-learning approach for ubiquitous traffic classification based on multicriteria fuzzy decision trees with attribute selection. Moreover, our approach can handle well the imbalanced datasets and zero-day applications (i.e., those without previously known traffic patterns). Evaluating the proposed methodology on several benchmark real-world traffic datasets of different nature demonstrated its capability to effectively discriminate a variety of traffic patterns, anomalies, and protocols for unencrypted and encrypted E.-S. M. El-Alfy
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
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 ...
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.
Sentiment Analysis of Using ChatGPT in Education
WSEAS TRANSACTIONS ON ADVANCES in ENGINEERING EDUCATION
This paper presents a study on the use of the Chat Generative Pretrained Transformer (ChatGPT) in... more This paper presents a study on the use of the Chat Generative Pretrained Transformer (ChatGPT) in education. In this work, we propose a sentiment analysis model of tweets related to the use of the ChatGPT in education. The purpose of this research is to identify common sentiments, topics, and perspectives that are expressed towards ChatGPT in the education field based on the data collected from Twitter. Twitter was used to collect 11830 tweets about the use of ChatGPT in education. Topics and emotions expressed in the tweets were extracted using NLP algorithms and organized into distinct groups. Also, the most frequent words in the positive and negative opinion words are determined. The findings of the paper indicate that most tweets about ChatGPT are either positive or neutral, with a small percentage expressing negative sentiments. In addition, the study analyzes the sentiments expressed in tweets about the employment of ChatGPT in education using four different classifiers: Naive...
Learning heterogeneous subgraph representations for team discovery
Information Retrieval Journal, Oct 8, 2023
Predicting heart disease risk in patients using various kinds of analytical models
Social Alignment Contagion in Online Social Networks
IEEE Transactions on Computational Social Systems, 2022
Research Square (Research Square), Dec 1, 2022
The team discovery task is concerned with finding a group of experts from a collaboration network... more The team discovery task is concerned with finding a group of experts from a collaboration network who would collectively cover a desirable set of skills. Most prior work for team discovery either adopt graph-based or neural mapping approaches. Graph-based approaches are computationally intractable often leading to sub-optimal team selection. Neural mapping approaches have better performance, however, are still limited as they learn individual representations for skills and experts and are often prone to overfitting given the sparsity of collaboration networks. Thus, we define the team discovery task as one of learning subgraph representations from heterogeneous collaboration network where the subgraphs represent teams which are then used to identify relevant teams for a given set of skills. As such, our approach captures local (node interactions with each team) and global (subgraph interactions between teams) characteristics of the representation network and allows us to easily map between any homogeneous and heterogeneous subgraphs in the network to effectively discover teams. Our experiments over two real-world datasets from different domains, namely the DBLP bibliographic dataset with 10, 647 papers and IMDB with 4, 882 movies, illustrate that our approach outperforms the state-of-the-art baselines on a range of ranking and quality metrics. More specifically, in terms *These authors are ordered alphabetically.
Denoising histopathology images for the detection of breast cancer
Neural Computing and Applications, Jul 9, 2023
Comparative Analysis of Machine Learning Algorithms for Author Age and Gender Identification
ACM Transactions on Asian and Low-Resource Language Information Processing, Apr 19, 2023
Emotion identification from text has recently gained attention due to its versatile ability to an... more Emotion identification from text has recently gained attention due to its versatile ability to analyze human-machine interaction. This work focuses on detecting emotions from textual data. Languages, like English, Chinese, and German are widely used for text classification, however, limited research is done on resource-poor oriental languages. Roman Urdu (RU) is a resource-constrained language extensively used across Asia. This work focuses on predicting emotions from RU text. For this, a dataset is collected from different social media domains and based on Paul Ekman's theory it is annotated with six basic emotions, i.e., happy, surprise, angry, sad, fear, and disgusting. Dense word embedding representations of different languages is adopted that utilize existing pre-trained models. BERT is additionally pre-trained and fine-tuned for the classification task. The proposed approach is compared with baseline machine learning and deep learning algorithms. Additionally, a comparison of the current work is also performed with different approaches for the same task. Based on the empirical evaluation, the proposed approach performs better than the existing state-of-the-art with an average accuracy of 91%.
Discovering the Correlation Between Phishing Susceptibility Causing Data Biases and Big Five Personality Traits Using C-GAN
IEEE Transactions on Computational Social Systems, 2022
Journal of Business Research, 2019
With the terrific growth of digital data and associated technologies, there is an emerging trend,... more With the terrific growth of digital data and associated technologies, there is an emerging trend, where industries become rapidly digitized. These technologies are providing great opportunities to identify and resolve different problems. In particular, the telecommunication industry is facing a serious problem of customer churn relating to, the customers who are going to abandon their established relation with the business/network in the near future. This problem cannot only affect the rapid growth of the business but can also affect the revenues. Therefore, many customer churn prediction (CCP) models have been introduced but not yielding the desired performance in CCP. This is because there can be many factors, that contribute to customer churn which are still unexplored. In this paper, we focus on determining the effectiveness of the factors, i.e. lower and upper distance between the samples, are considered by the proposed model for the CCP. Further, we demonstrate a novel solution pertaining to the telecommunication sector showing the hidden factors considered for predicting the customer churn. Finally, we investigate the effects of both types of samples: those samples that are low distance and the upper distance (in terms of relevance) to the majority samples in given publicly available dataset. As a result of the study, we found that lower distance test set (LDT) samples have obtained best performance as compare to upper distance test set (UDT) samples in term of increased in the accuracy, f-measures, precision and recall when the uncertain sample size increases. Because the classification performance on upper distance samples remain almost the same when the size of samples increased in the test set.
Managerial Conflict Among the Software Development Team
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%.
Cyber security and beyond: Detecting malware and concept drift in AI-based sensor data streams using statistical techniques
Computers & Electrical Engineering, May 1, 2023
Computer Science and Information Systems, 2023
Online discussion forums are repositories of valuable information where users interact and articu... more Online discussion forums are repositories of valuable information where users interact and articulate their ideas and opinions, and share experiences about numerous topics. These online discussion forums are internet-based online communities where users can ask for help and find the solution to a problem. A new user of online discussion forums becomes exhausted from reading the significant number of irrelevant replies in a discussion. An automated discussion thread summarizing system (DTS) is necessary to create a candid view of the entire discussion of a query. Most of the previous approaches for automated DTS use the continuous bag of words (CBOW) model as a sentence embedding tool, which is poor at capturing the overall meaning of the sentence and is unable to grasp word dependency. To overcome these limitations, we introduce the LSTM Auto-encoder as a sentence embedding technique to improve the performance of DTS. The empirical result in the context of the proposed approach's average precision, recall, and F-measure with respect to ROGUE-1 and ROUGE-2 of two standard experimental datasets demonstrates the effectiveness and efficiency of the proposed approach and outperforms the state-of-the-art CBOW model in sentence embedding tasks and boost the performance of the automated DTS model.
Twitter sentiment analysis to understand students' perceptions about online learning during the Covid'19
2022 International Conference on Computer and Applications (ICCA)
Future Internet, Jul 17, 2023
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
Personal and Ubiquitous Computing, Nov 16, 2017
Traffic classification in computer networks has very significant roles in network operation, mana... more Traffic classification in computer networks has very significant roles in network operation, management, and security. Examples include controlling the flow of information, allocating resources effectively, provisioning quality of service, detecting intrusions, and blocking malicious and unauthorized access. This problem has attracted a growing attention over years and a number of techniques have been proposed ranging from traditional port-based and payload inspection of TCP/IP packets to supervised, unsupervised, and semi-supervised machine learning paradigms. With the increasing complexity of network environments and support for emerging mobility services and applications, more robust and accurate techniques need to be investigated. In this paper, we propose a new supervised hybrid machine-learning approach for ubiquitous traffic classification based on multicriteria fuzzy decision trees with attribute selection. Moreover, our approach can handle well the imbalanced datasets and zero-day applications (i.e., those without previously known traffic patterns). Evaluating the proposed methodology on several benchmark real-world traffic datasets of different nature demonstrated its capability to effectively discriminate a variety of traffic patterns, anomalies, and protocols for unencrypted and encrypted E.-S. M. El-Alfy
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
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 ...
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.
Sentiment Analysis of Using ChatGPT in Education
WSEAS TRANSACTIONS ON ADVANCES in ENGINEERING EDUCATION
This paper presents a study on the use of the Chat Generative Pretrained Transformer (ChatGPT) in... more This paper presents a study on the use of the Chat Generative Pretrained Transformer (ChatGPT) in education. In this work, we propose a sentiment analysis model of tweets related to the use of the ChatGPT in education. The purpose of this research is to identify common sentiments, topics, and perspectives that are expressed towards ChatGPT in the education field based on the data collected from Twitter. Twitter was used to collect 11830 tweets about the use of ChatGPT in education. Topics and emotions expressed in the tweets were extracted using NLP algorithms and organized into distinct groups. Also, the most frequent words in the positive and negative opinion words are determined. The findings of the paper indicate that most tweets about ChatGPT are either positive or neutral, with a small percentage expressing negative sentiments. In addition, the study analyzes the sentiments expressed in tweets about the employment of ChatGPT in education using four different classifiers: Naive...