My Team

Graduated Master Student

Graduated Undergraduate Student

Project Name:

Facial Image Recognition of Boko Haram Terrorists using Transfer Learning and Convolutional Neural Network

Motivated by a personal encounter with the devastating impact of Boko Haram’s insurgency in Maiduguri, where Ibrahim’s home was attacked and razed in December 2021, this research addresses the urgent need for enhanced security measures. Leveraging images from the Nigerian Army’s wanted list and other online sources, the proposed Convolutional Neural Network (CNN) employs transfer learning from the ImageNet dataset to discern Boko Haram members from the community.

Emphasizing that many Boko Haram faces are familiar due to recruitment from the local area, the model focuses on precise facial image recognition. Through careful data collection, including images from known Boko Haram individuals, and fine-tuning the VGG16 architecture pretrained on ImageNet, the model achieves a remarkable 95% accuracy. This work not only advances counter-terrorism strategies but also stands as a personal commitment to fortifying security in regions affected by extremist activities, where community-specific knowledge is pivotal for effective identification of the boko haram terrorist group.

Graduated

Project Name:

Enhancing Cyberbully Detecting using Images

Ryyan Rashid is a committed undergraduate student majoring in computer science with a specialization in intelligent computing at the University of Sains Malaysia. Possessing a robust background in computer science, Ryyan has demonstrated proficiency in developing fully functional web applications and a successful history of constructing various neural networks, including RNN, LSTM, and Transformers, for Natural Language Processing projects.

Fueled by a passion for innovation and technology, Ryyan is motivated to contribute to the future of the field. During a six-month tenure as a researcher at Zhang Lab – Harvard Medical School, he actively applied his expertise in Machine Learning, Artificial Intelligence, Image Processing, and Neural Networks, resulting in meaningful advancements.

Presently, Ryyan is engaged in a project focused on leveraging image data in conjunction with textual data to detect cyberbullying and explore the role bystanders play in such scenarios.

Graduated

Project Name:

Prediction of Interestingness in Movie Trailers

MSc. Research

Graduated

Project Name:

Mental Health Dashboard (MENDA)

Ammar has developed MENDA (Mental Health Dashboard), a tool tailored for educational institutions with the primary goal of offering a comprehensive overview of students’ mental health. In response to the escalating mental health concerns among students at SMK Datuk Hj. Mohamed Nor Ahmad, Ammar’s MENDA seamlessly integrates yearly student survey data, including GAD-7 and PHQ-9 surveys. This information is presented in a user-friendly dashboard powered by PowerBI.

MENDA’s dashboard provides visualizations, group comparisons, and the capability to identify students exhibiting depressive and anxiety symptoms. Additionally, Ammar has incorporated a machine learning model for early case identification. By utilizing various features from student surveys, this proactive approach aims to predict potential mental health issues, not only supporting students with existing symptoms but also emphasizing prevention.

Teachers can leverage MENDA to gain profound insights into their students’ mental health, enabling them to effectively provide the necessary support and intervention.

Graduated

Project Name:

Mining Public Sentiment From Twitter for COVID-19 Relief and Using Machine Learning Techniques.

MSc. Mix Mode

Graduated

Project Name:

Automated Meme Classification Model

Cheam Yu Chein, a third-year student from USM, is set to embark on a research internship in Madrid, Spain, facilitated by the Erasmus+ Program. With the program’s support, Cheam aims to develop an automated classification model during the internship, focusing on categorizing memes as either hateful or not hateful. The research will explore the model’s performance with different inputs, such as text, image, or a combination of both.

The overarching goal of Cheam’s research is to determine the effectiveness of the classification model in various input scenarios. The anticipated outcomes of this investigation hold potential significance in advancing the development of a predictive model for evaluating trustworthiness. The research aligns with broader efforts to contribute valuable insights to the ongoing progress in trustworthiness prediction models.

Graduated

Project Name:

Trust Modelling based on Facial Expression using Deep Learning

K. Sarmla investigated trust detection using facial expression towards developing a trust prediction model, for her Masters. In general, behavioral judgement of an individual and decisions on whether to interact or not are highly influenced by trust. Many trust and reputation models were developed in the past but based only on numeric paradigm. The desire to trust is subjective to each individual and is found to be highly affected by one’s facial features. However, do expressions play a role? This serve as the motivation for her to study the effect of human’s facial expressions on trustworthiness of a person by using deep learning based algorithm – Convolutional Neural Network (CNN).

Graduated

Project:

The aim of Belén’s FYP is to reduce cyberbullying on Twitter by taking into account bystander roles and the effect of their contagiousness.

Graduated

Michael Kong (Malaysia)

Project Name:

Classification of Oscillatoriales Cyanobacteria Using Multimodal Features

Michael Kong received his Bachelor of Computer Science with distinction from the University of Wollongong in 2016 and his Master of Science (Computer Science) from Universiti Sains Malaysia in 2018. His research for his masters thesis focused on image recognition in the area of microbiology, particularly for cyanobacteria recognition. In his free time, he enjoys learning about new and upcoming web technologies as well as photography.

Graduated

Project Name:

Web-based Data Extraction System

Nur Syahirah has developed a Web-based Data Extraction System. This system is designed to collect data from the Twitter platform based on researchers’ specifications and filters. It serves as a valuable resource for researchers, especially those focusing on cyberbullying, providing access to social media data that includes information about onlookers and bystanders. Users have the ability to filter data according to their requirements, including specified dates and times. The system offers outputs in two formats: CSV and HTML tables, enhancing flexibility for researchers in obtaining desired data. Nur Syahirah’s system contributes to the efficiency of gathering and analyzing social media data, supporting research endeavors in the domain of cyberbullying and related studies.

Graduated

Project Name:

Prediction Models of Extraversion and Neuroticism of Malaysian Facebook Users

Zaaba is a Masters candidate who has worked on a research in-line with the trending of Social Media era where he predicted personality traits through the usage behaviour of social media. His study was to demonstrate how people’s behaviour in social media can be used to predict user personality traits. He has been inspired to work in this area as social media has become universal and important platform for networking and content sharing. Zaaba is now teaching in a local public University.

Graduated

Project Name:

The Effect of Vocal Cues on Trust at Zero Acquaintance

Undergraduate Research

Graduated

Project Name:

A Computational Model for Detection Learning

Najlaa Sadiq Mokhtar is a MSc. candidate who embarks on an interesting research in the field of Human Computer Interactions, focusing in Affective Computing (personality, behaviour and emotion analysis) in learning, which. In short, she attempts to investigate the effects of emotions in learning. She hopes to build an affect-sensitive synthetic tutor that could detect and respond appropriately to learner’s state. The emotions involved would be those that accompany learning. Najlaa is now a senior software engineer in a private company.

Graduated

Project Name:

Automatic Person-independent Detection of 3D Facial Expressions using Optimizition Algorithm Based on Conformal Mapping and Differential Evolution

Amal Aziz’s research interests lay in the field of computer vision, image processing and pattern recognition. Particularly, she is passionate about human emotion recognition towards enhancing human-computer interaction. Her Master’s research is on automatic facial expression recognition in 3D faces. Her intentions are to work in analyzing the verbal and non-verbal human emotion cues to build a multi-model emotion recognition system. Applying such a system in a particular application, such as: gaming, tutoring, etc, to improve the human emotional experience with machines is her ultimate goal. Amal successfully completed her Masters (by research) and is currently embarking PhD. She investigates facial expression recognition impairments in drug users. She would move on to investigate the contribution of gaze in this study. Amal is currently undergoing PhD in Australia with a full scholarship.

Graduated

Project Name:

The Impact of Age in Social Media Selection and Emotional Sharing Among Chinese People in Mainland China

Laura Ren’s interests lie within the area of Social Informatics, which is the study of social phenomena, behaviours and structure through the use of technology. Her work focuses in interaction in the Chinese cultural context, using social media features. Currently she is a MSc. Informatics candidate at PPSKOMP, USM. The idea of making machines understand people’s language and emotion has attracted her attention. She is eager to explor

Graduated