Suhaib Kh. Hamed

SUHAIB KH. HAMED is currently a Ph.D. candidate in the Center for Software Technology and Management (SOFTAM), Faculty of Information Science and Technology (FTSM), Universiti Kebangsaan Malaysia (UKM). His expertise is Deep Learning, Supervised Learning, Classification, Feature Extraction, Data Fusion, Natural Language Processing, Image Processing, Sentiment Analysis, and Ontology. He received a master's degree from the Center for Artificial Intelligence Technology (CAIT), Faculty of Information Science and Technology (FTSM), Universiti Kebangsaan Malaysia (UKM) in 2016. He is also a researcher with the Sentiment Analysis Lab, CAIT. In administration, he was the head of the IT Department, Baghdad Electricity Distribution, 2019-2020.

Scopus Publications

Enhanced Feature Representation for Multimodal Fake News Detection Using Localized Fine-Tuning of Improved BERT and VGG-19 Models
Suhaib Kh. Hamed, Mohd Juzaiddin Ab Aziz, Mohd Ridzwan Yaakub
Arabian Journal for Science and Engineering, 2025
A data augmentation approach based on various GAN models to address class imbalance in fine-grained multimodal fake news datasets
Suhaib Kh. Hamed, Mohd Juzaiddin Ab Aziz, Mohd Ridzwan Yaakub
Computing, 2025
Improving Data Fusion for Fake News Detection: A Hybrid Fusion Approach for Unimodal and Multimodal Data
Suhaib Kh. Hamed, Mohd Juzaiddin Ab Aziz, Mohd Ridzwan Yaakub
IEEE Access, 2024
The proliferation of fake news, exacerbated by social media and modern technology, presents significant challenges across sectors such as health, the economy, politics, and national stability. This study addresses the limitations of current multimodal fake news detection models, which often struggle to effectively integrate heterogeneous modalities like text and images. We propose a hybrid data fusion approach (HF-TIM) that combines the early fusion of multimodal data with the late fusion of unimodal data, leveraging the strengths of both techniques to enhance detection accuracy. Our HF-TIM approach employs a Softmax classifier for early fusion and integrates it with unimodal features extracted from BERT and VGG-19 classifiers through a neural network-based meta-learning classifier. This approach captures the complementary and unique properties of each modality, resulting in a more comprehensive and robust fake news detection model. Experimental results demonstrate that the HF-TIM method significantly improves classification accuracy across various fake news categories by effectively addressing the complex interrelationships between text and images. Our fine-grained detection model, based on the HF-TIM method, achieved a detection accuracy of 93.4%, outperforming state-of-the-art models in related studies. The proposed hybrid fusion HF-TIM approach offers an innovative and effective solution for multimodal fake news detection, with potential applications extending to other domains.
A review of fake news detection approaches: A critical analysis of relevant studies and highlighting key challenges associated with the dataset, feature representation, and data fusion
Suhaib Kh Hamed, Mohd Juzaiddin Ab Aziz, Mohd Ridzwan Yaakub
Heliyon, 2023
Currently, social networks have become the main source to acquire news about current global affairs. However, fake news appears and spreads on social media daily. This disinformation has a negative influence on several domains, such as politics, the economy, and health. In addition, it further generates detriments to societal stability. Several studies have provided effective models for detecting fake news in social networks through a variety of methods; however, there are limitations. Furthermore, since it is a critical field, the accuracy of the detection models was found to be notably insufficient. Although many review articles have addressed the repercussions of fake news, most have focused on specific and recurring aspects of fake news detection models. For example, the majority of reviews have primarily focused on dividing datasets, features, and classifiers used in this field by type. The limitations of the datasets, their features, how these features are fused, and the impact of all these factors on detection models were not investigated, especially since most detection models were based on a supervised learning approach. This review article analyzes relevant studies for the few last years and highlights the challenges faced by fake news detection models and their impact on their performance. The investigation of fake news detection studies relied on the following aspects and their impact on detection accuracy, namely datasets, overfitting/underfitting, image-based features, feature vector representation, machine learning models, and data fusion. Based on the analysis of relevant studies, the review showed that these issues significantly affect the performance and accuracy of detection models. This review aims to provide room for other researchers in the future to improve fake news detection models.
Fake News Detection Model on Social Media by Leveraging Sentiment Analysis of News Content and Emotion Analysis of Users’ Comments
Suhaib Kh. Hamed, Mohd Juzaiddin Ab Aziz, Mohd Ridzwan Yaakub
Sensors, 2023
Nowadays, social media has become the main source of news around the world. The spread of fake news on social networks has become a serious global issue, damaging many aspects, such as political, economic, and social aspects, and negatively affecting the lives of citizens. Fake news often carries negative sentiments, and the public’s response to it carries the emotions of surprise, fear, and disgust. In this article, we extracted features based on sentiment analysis of news articles and emotion analysis of users’ comments regarding this news. These features were fed, along with the content feature of the news, to the proposed bidirectional long short-term memory model to detect fake news. We used the standard Fakeddit dataset that contains news titles and comments posted regarding them to train and test the proposed model. The suggested model, using extracted features, provided a high detection accuracy of 96.77% of the Area under the ROC Curve measure, which is higher than what other state-of-the-art studies offer. The results prove that the features extracted based on sentiment analysis of news, which represents the publisher’s stance, and emotion analysis of comments, which represent the crowd’s stance, contribute to raising the efficiency of the detection model.
DISINFORMATION DETECTION ABOUT ISLAMIC ISSUES ON SOCIAL MEDIA USING DEEP LEARNING TECHNIQUES
Suhaib Kh. Hamed, Mohd Juzaiddin Ab Aziz, Mohd Ridzwan Yaakub
Malaysian Journal of Computer Science, 2023
Nowadays, many people receive news and information about what is happening around them from social media networks. These social media platforms are available free of charge and allow anyone to post news or information or express their opinion without any restrictions or verification, thus contributing to the dissemination of disinformation. Recently, disinformation about Islam has spread through pages and groups on social media dedicated to attacking the Islamic religion. Many studies have provided models for detecting fake news or misleading information in many domains, such as political, social, economic, and medical, except in the Islamic domain. Due to this negative impact of spreading disinformation targeting the Islamic religion, there is an increase in Islamophobia, which threatens societal peace. In this paper, we present a Bidirectional Long Short-Term Memory-based model trained on an Islamic dataset (RIDI) that was collected and labeled by two separate specialized groups. In addition, using a pre-trained word-embedding model will generate Out-Of-Vocabulary, because it deals with a specific domain. To address this issue, we have retrained the pre-trained Glove model on Islamic documents using the Mittens method. The results of the experiments proved that our proposed model based on Bidirectional Long Short-Term Memory with the retrained Glove model on the Islamic articles is efficient in dealing with text sequences better than unidirectional models and provides a detection accuracy of 95.42% of Area under the ROC Curve measure compared to the other models.
A Review of Fake News Detection Models: Highlighting the Factors Affecting Model Performance and the Prominent Techniques Used
Suhaib Kh. Hamed, Mohd Juzaiddin Ab Aziz, Mohd Ridzwan Yaakub
International Journal of Advanced Computer Science and Applications, 2023
—In recent times, social media has become the primary way people get news about what is happening in the world. Fake news surfaces on social media every day. Fake news on social media has harmed several domains, including politics, the economy, and health. Additionally, it has negatively affected society's stability. There are still certain limitations and challenges even though numerous studies have offered useful models for identifying fake news in social networks using many techniques. Moreover, the accuracy of detection models is still notably poor given we deal with a critical topic. Despite many review articles, most previously concentrated on certain and repeated sections of fake news detection models. For instance, the majority of reviews in this discipline only mentioned datasets or categorized them according to labels, content, and domain. Since the majority of detection models are built using a supervised learning method, it has not been investigated how the limitations of these datasets affect detection models. This review article highlights the most significant components of the fake news detection model and the main challenges it faces. Data augmentation, feature extraction, and data fusion are some of the approaches explored in this review to improve detection accuracy. Moreover, it discusses the most prominent techniques used in detection models and their main advantages and disadvantages. This review aims to help other researchers improve fake news detection models.
Classification of Holy Quran translation using Neural Network technique
Suhaib Kh. Hamed, M. J. Aziz
Journal of Engineering and Applied Sciences, 2018
A question answering system on Holy Quran translation based on question expansion technique and Neural Network classification
Suhaib Kh. Hamed, Mohd Juzaiddin Ab Aziz
Journal of Computer Science, 2016
In spite of great efforts that have been made to present systems that support the user's need of the answers from the Holy Quran, the current systems of English translation of Quran still need to do more investigation in order to develop the process of retrieving the accurate verse based on user's question. The Islamic terms are different from one document to another and might be undefined for the user. Thus, the need emerged for a Question Answering System (QAS) that retrieves the exact verse based on a semantic search of the Holy Quran. The main objective of this research is to develop the efficiency of the information retrieval from the Holy Quran based on QAS and retrieving an accurate answer to the user's question through classifying the verses using the Neural Network (NN) technique depending on the purpose of the verses' contents, in order to match between questions and verses. This research has used the most popular English translation of the Quran of Abdullah Yusuf Ali as the data set. In that respect, the QAS will tackle these problems by expanding the question, using WordNet and benefitting from the collection of Islamic terms in order to avoid differences in the terms of translations and question. In addition, this QAS classifies the Al-Baqarah surah into two classes, which are Fasting and Pilgrimage based on the NN classifier, to reduce the retrieval of irrelevant verses since the user's questions are asking for Fasting and Pilgrimage. Hence, this QAS retrieves the relevant verses to the question based on the N-gram technique, then ranking the retrieved verses based on the highest score of similarity to satisfy the desire of the user. According to F-measure, the evaluation of classification by using NN has shown an approximately 90% level and the evaluation of the proposed approach of this research based on the entire QAS has shown an approximately 87% level. This demonstrates that the QAS succeeded in providing a promising outcome in this critical field.

RECENT SCHOLAR PUBLICATIONS

Enhanced feature representation for multimodal fake news detection using localized fine-tuning of improved BERT and VGG-19 models
SK Hamed, MJ Ab Aziz, MR Yaakub
Arabian Journal for Science and Engineering 50 (10), 7423-7439 , 2025
2025
Citations: 11
A data augmentation approach based on various GAN models to address class imbalance in fine-grained multimodal fake news datasets
SK Hamed, MJ Ab Aziz, MR Yaakub
Computing 107 (1), 52 , 2025
2025
Citations: 8
Improving data fusion for fake news detection: a hybrid fusion approach for unimodal and multimodal data
SK Hamed, MJ Ab Aziz, MR Yaakub
IEEE Access 12, 112412-112425 , 2024
2024
Citations: 19
A review of fake news detection approaches: A critical analysis of relevant studies and highlighting key challenges associated with the dataset, feature representation, and …
SK Hamed, MJ Ab Aziz, MR Yaakub
Heliyon 9 (10), 1-21 , 2023
2023
Citations: 121
Disinformation Detection about Islamic Issues on Social Media Using Deep Learning Techniques
SK Hamed, MJA Aziz, MR Yaakub
Malaysian Journal of Computer Science 36 (3), 242–270 , 2023
2023
Citations: 18
A Review of Fake News Detection Models: Highlighting the Factors Affecting Model Performance and the Prominent Techniques Used
SK Hamed, MJA Aziz, MR Yaakub
International Journal of Advanced Computer Science and Applications 14 (7 … , 2023
2023
Citations: 11
Fake news detection model on social media by leveraging sentiment analysis of news content and emotion analysis of users’ comments
SK Hamed, MJ Ab Aziz, MR Yaakub
Sensors 23 (4), 1748 , 2023
2023
Citations: 114
Question Expansion Technique on The Different Translations of The Holy Quran By Using WordNet and Islamic Synonyms
SKH Mohd Juzaiddin Ab Aziz
International Journal of Advanced Trends in Computer Science and Engineering … , 2021
2021
Classification of Holy Quran Translation Using Neural Network Technique
SK Hamed, MJ Ab Aziz
Journal of Engineering and Applied Sciences 13 (12), 4468-4475 , 2018
2018
Citations: 13
A Question Answering System on Holy Quran translation based on Question Expansion technique and Neural Network classification
SK Hamed, MJ Ab Aziz
Journal of Computer Sciences 12 (3), 169-177 , 2016
2016
Citations: 78

MOST CITED SCHOLAR PUBLICATIONS

A review of fake news detection approaches: A critical analysis of relevant studies and highlighting key challenges associated with the dataset, feature representation, and …
SK Hamed, MJ Ab Aziz, MR Yaakub
Heliyon 9 (10), 1-21 , 2023
2023
Citations: 121
Fake news detection model on social media by leveraging sentiment analysis of news content and emotion analysis of users’ comments
SK Hamed, MJ Ab Aziz, MR Yaakub
Sensors 23 (4), 1748 , 2023
2023
Citations: 114
A Question Answering System on Holy Quran translation based on Question Expansion technique and Neural Network classification
SK Hamed, MJ Ab Aziz
Journal of Computer Sciences 12 (3), 169-177 , 2016
2016
Citations: 78
Improving data fusion for fake news detection: a hybrid fusion approach for unimodal and multimodal data
SK Hamed, MJ Ab Aziz, MR Yaakub
IEEE Access 12, 112412-112425 , 2024
2024
Citations: 19
Disinformation Detection about Islamic Issues on Social Media Using Deep Learning Techniques
SK Hamed, MJA Aziz, MR Yaakub
Malaysian Journal of Computer Science 36 (3), 242–270 , 2023
2023
Citations: 18
Classification of Holy Quran Translation Using Neural Network Technique
SK Hamed, MJ Ab Aziz
Journal of Engineering and Applied Sciences 13 (12), 4468-4475 , 2018
2018
Citations: 13
Enhanced feature representation for multimodal fake news detection using localized fine-tuning of improved BERT and VGG-19 models
SK Hamed, MJ Ab Aziz, MR Yaakub
Arabian Journal for Science and Engineering 50 (10), 7423-7439 , 2025
2025
Citations: 11
A Review of Fake News Detection Models: Highlighting the Factors Affecting Model Performance and the Prominent Techniques Used
SK Hamed, MJA Aziz, MR Yaakub
International Journal of Advanced Computer Science and Applications 14 (7 … , 2023
2023
Citations: 11
A data augmentation approach based on various GAN models to address class imbalance in fine-grained multimodal fake news datasets
SK Hamed, MJ Ab Aziz, MR Yaakub
Computing 107 (1), 52 , 2025
2025
Citations: 8
Question Expansion Technique on The Different Translations of The Holy Quran By Using WordNet and Islamic Synonyms
SKH Mohd Juzaiddin Ab Aziz
International Journal of Advanced Trends in Computer Science and Engineering … , 2021
2021