My Team

Ongoing PhD student

Graduated PhD student

Project Name:

The Bystander Contagion in Enhancing Cyberbully Modelling

Haifa’s project aims to enhance cyberbullying detection by exploring the causative factor of cyberbullying spreading, known as Bystander Contagion. This often-overlooked aspect plays a crucial role in the escalation of cyberbullying behavior. Haifa plans to investigate the detection of cyberbullying through the evaluation of users’ profiles, activities, and social media history, with a specific focus on bystander contagion. The goal is to identify influential cyberbullies or bystanders, enabling the suspension of their accounts to effectively reduce cyberbullying incidents.

In developing the cyberbullying detection model, Haifa will address key points, including the influence and characteristics of bystanders, measurement of bystanders’ contagiousness in cyberbullying diffusion, and identification of the most influential users capable of influencing others. The project will utilize datasets from the Twitter platform, with a focus on Arabic language tweets, acknowledging the unique challenges posed by the complex nature of cyberbullying in Arabic. Haifa’s methodology involves labeling and annotating the data, preprocessing procedures, and applying natural language processing, sentiment analysis, and machine learning with feature extraction for classification purposes.

Ongoing

Project Name:

Multimodal Trust Modelling at Zero-acquaintance (co-supervision with Assoc. Prof. Dr. Fernando Fernandez)

Christina Luna Jimenez delved into the intricate concept of trustworthiness during her research attachment. The focus was on annotating emotional videos at zero acquaintance in a cross-cultural context, with the intention of translating these annotations into a machine-readable format using machine learning tools.

Christina, alongside researchers from Spain, Malaysia, and Hungary, gathered annotations from diverse cultural backgrounds. However, the final experiments exclusively utilized annotations from Malaysian and Hungarian contributors. The findings revealed that perceived characteristics such as eloquence, attractiveness, and authenticity significantly influenced trustworthiness annotations, displaying high correlation values. Notably, these influences varied across cultures; for instance, kindness played a more crucial role for Hungarians than for Malaysians in determining trustworthiness.

Graduated

Project Name:

Stress Modelling Using Multimodal Cues (co-supervision with Dr. Sufril)

Mustafa Al Qudah is engaged in a project centered on human stress and emotion detection. The focus of this endeavor is the detection of stress using multimodal cues, specifically facial expression and temperature. To achieve this, four thermal/visible cameras will be strategically placed in a room to capture participants’ faces as they move. Additionally, a remote photoplethysmography (PPG) camera-based technique will measure participants’ heart rates during the experiment. The experimental setup involves the use of acoustic stimuli to gradually induce stress in participants. User states will be recorded both before and after exposure to the stimuli. The classification of stress and other affective states will rely on deep learning algorithms, showcasing their effectiveness in classifying thermal images. The project aims to include a comparison with alternative classification approaches to provide a comprehensive evaluation of the proposed methods.

Ongoing

Project Name:

Elastic-bunch Graph Matching-based Models to Recognize Faces Exposed to Occlusion, Expression and Illumination

Badr’s research interest lies in biometrics: particularly the study focuses on unconstrained facial recognition – occluded images with additional challenges such as expression, variations and single sample per subject. Badr has attempted to study occluded images coupled with expression, a new challenge few research in this area considers.

Graduated

Project Name:

Understanding Bribery Practices from a Neuroscience Cognitive Perspective

Aisyahtul was a graduated Master student under the supervision of Dr. Syaheerah and is continuing with her PhD. Her Masters project focuses on data visualization embedded with time series prediction regarding the attrition and promotion issues among the permanent USM staff. The dashboard that describes the attrition pattern and promotion structure that happened in USM has been developed successfully using PowerBI. In order to touch the prediction element of data science, the attrition trend using the time series prediction models has been built using Python and embedded into PowerBI for the best visual. For her PhD, she is exploring multimodal bribery tendency prediction using eye-tracking and survey questions.

Ongoing

Project Name:

Membrane-Inspired Bat Algorithm and Feature Fusion to Recognize Faces in the Wild

Bisan Alsalibi received her first degree from the Islamic University of Gaza, Computer Engineering Department, Palestine in 2009 and her master degree from the Computer Science School in USM, Malaysia, 2013. She worked as a research assistant in USM in 2013. She is currently a PhD candidate at USM funded by MIS scholarship. Her general research interest is bio-inspired computing, membrane computing, graph theory and computer vision. In short, she is interested in recognizing objects under uncertainty which is being a challenge to even humans by means of modeling biologically motivated approaches. Bisan graduated in 2017 and is now a teaching fellow in SOCS USM.

Graduated

Graduated Master Student

Project Name:

Modelling Speaker Behavioural Profile for an Enhanced Context in Speech using Deep Learning

Under the guidance of Dr. Syaheerah, Nahvin Muthusamy leads a pioneering speech analysis research endeavor. Beyond conventional transcription, Nahvin explores linguistic nuances, cultural elements, emotions, personal traits, cognition, and speaker reliability.

Employing a diverse dataset and a survey-based approach, Nahvin annotates and timestamps speech data. His research questions the effectiveness of an AI system in analyzing multiple factors, addressing challenges in dataset collection, and benchmarking against existing techniques, utilizing a CNN-based approach on spectrogram representations.

Nahvin’s project contributes significantly to advancing speech analysis techniques, fostering natural human-machine interaction, and enabling innovative applications in various domains. The project’s vision is to revolutionize technology, communication, and our understanding of human behavior through comprehensive speech analysis.

Ongoing

Project Name:

Culturally Responsive Zero-Trust Model

Meet Bader Zyoud, an insightful Information Security Engineer based in the UAE. As he nears completion of his Masters, Bader has immersed himself in pioneering research at the intersection of cybersecurity and organizational culture. Beyond being a proficient Information Security Engineer, Bader is a driven professional with a keen interest in understanding how information security culture influences the implementation of the zero-trust model within organizations.

In his ongoing research, Bader investigates the intricate dynamics between information security culture and the zero-trust paradigm, unraveling the nuanced ways in which these elements intersect to fortify an organization’s cyber defenses. His commitment to academic excellence, coupled with real-world experience, positions Bader as a thought leader poised to shape the future of cybersecurity in the UAE. Stay tuned for his insightful contributions to the evolving landscape of information security and the zero-trust model.

Graduated

Project Name:

Multimodal detection of Human Trustworthiness

Under the guidance of Dr. Syaheerah, Li Dongliang ‘s research focuses on multimodal trust detection. Traditional methods typically use statistical analysis to assess trust, but current studies tend to measure trustworthiness through multimodal detection accuracy.

However, accuracy is not the same as trustworthiness. By integrating trust perception in multimodal analysis, new insights can be gained in the field of trust detection. For deception detection, which currently utilizes techniques such as 3D point cloud and image compression, research into trust perception could give AI human-like discrimination. In artificial general intelligence (AGI) research, creators remix film and TV characters and dialogue to achieve high levels of trust without discomforting the viewer.
This study is important because it introduces the concept of trust perception ability. By incorporating trust perception into the analysis, the research can more fully assess people’s trustworthiness and avoid the limitations that come with relying solely on accuracy measures. It provides a new perspective and method for trust detection.

Ongoing

Project Name:

An Interactive International Mobility Forecasting Dashboard Using Power BI

Eugene is working on a project for International Mobility & Collaboration Centre (IMCC), USM.
His project consists of developing a dashboard for the mobility department by using the inbound mobility data and outbound mobility data. Besides that, he has to make enrollment predictions for the enhance resource planning for the IMCC.
The first benefit for his client is that visualization of data allows management to make informed decision and secondly, it enhance resource planning.

Graduated

Project Name:

Deepfake Detection

I am Assad H. Thary Al-Ghrairi, currently a PhD student in the School of Computer Sciences at USM. I am also a member of the teaching staff at the College of Science at Al-Nahrain University, Iraq, Baghdad. I am interested in research in computer science, pattern recognition, image processing, artificial intelligence, multimedia, remote sensing, and satellite image classification. Deepfake technology has advanced significantly, posing challenges in distinguishing real from manipulated content, particularly in facial images and videos. Artificial intelligence (AI) plays a crucial role in detecting deepfakes through techniques such as Generative Adversarial Networks (GANs) and Convolutional Neural Networks (CNNs), which analyze facial features and patterns for inconsistencies. Key indicators of deepfake content include unnatural facial movements, artifacts in skin tone, and audio-visual synchronization issues. However, the evolving nature of deepfake creation tools makes detection increasingly difficult, necessitating continuous adaptation of detection methods. Effective AI models require extensive datasets of both real and deepfake content to improve accuracy. As this technology progresses, ongoing research and development in detection strategies are essential to mitigate the risks associated with manipulated media.

Ongoing

Project Name:

Prediction of Pin Failure in Test Interface Unit Using Machine Learning Approach

Muhamad Aizat has developed a machine learning project that will detect a pin failure from testing product data that focused on enhancing current processes which is manual effort of debugging why the pin was fail and replace the pin. The process is lengthy where during the events of products fail in testing, a technician who is debugging the TIU will first address the issue by manually locate the failure pins. The criteria of the pin failure will be determined by test program, and it’s embedded into software that the machine runs. While testing, if the scenario happens, the software will notify user to check the pin of TIU if there is any pin failure. After that the technician will debug the pin and replace it. To improve this process, the project embedded machine learning to analyze the data by predicting the failure before it actually fails. It will tells the user that certain criteria of data will likely fail the testing and notifies the location of pin that going to cause the failure

Graduated