Master’s Degree Research Titles – Information and Communications Technology / Computer Science

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Faculty Title / Summary Supervisor
Faculty of Computing and Informatics, Cyberjaya Campus Joint Prediction of Technical and Aesthetics Image Quality

Both IQA and IAA share some common underlying factors that affect user judgments, but existing works ignore this correlation in predicting image quality. In this research, the objective is to study the correlation between IQA and IAA, and design a novel machine learning model to jointly predict the technical and aesthetics quality of an image. the AI model of this research has various potential applications including image retrieval, image restoration, in-camera guide, robot photography, and image cropping or thumbnailing.

Note: The scope of the project can be widened to that of a Ph.D. degree.

Dr. Wong Lai Kuan

Faculty of Computing and Informatics, Cyberjaya Campus Context-Aware Image Emotion Prediction

An image can invoke various emotions, depending on the visual features and semantic content of the image. Notably, similar image content in different contexts might induce different emotions. Eg. If we see a famous football player crying on his knees, the audience may feel sad; but if this is after winning a game, the audience may feel excited. However, most of the existing image emotion prediction models ignore the context of the image or did not focus much on extracting the image semantics that can help to improve the prediction of image emotion. In this research, the key objective is to build a model that perform context-aware emotion prediction from images using deep learning techniques. The developed algorithm can be very useful for many applications, including image retrieval, intelligent billboards, media content curation, web design, scene-aware music synthesis, and sentiment analysis.

Note: The scope of the project can be widened to that of a Ph.D. degree.

Dr. Wong Lai Kuan

Faculty of Computing and Informatics, Cyberjaya Campus A Scalable, Extensible AI Radiology Framework for Improving Resilience During Covid-19 Pandemic and Beyond

While AI Radiology offers promise for prediction of COVID-19 infection, studies find that existing works resulted in large generalization gaps, leading to weak clinical performance. To reduce this generalization gap, we build an AI Radiology Framework for diagnosis and prognosis of Covid-19 using a multimodal, federated learning approach, that aggregates multi-institutional data for training the prediction model, without violating data privacy. This assistive AI tool can accelerate treatment and improve effectiveness of healthcare management. This framework is scalable and extensible to support development of AI models for new respiratory-related diseases, increasing the preparedness in the advent of future pandemics.

Note: The scope of the project can be widened to that of a Ph.D. degree.

Dr. Wong Lai Kuan

Faculty of Information Science and Technology (FIST), Melaka Campus Medical Prescription Extraction and Analysis using Deep Learning Approach

In Malaysia, medical records in the clinics are entered by the doctors according to their style and preference. Some write the prescription using short-form other may add additional notes and diagnostics information. If these data can be cleaned, extracted and store in a systematic and structured manner, then these data can be used to generate meaningful insight for pharmacy, hospital and doctors to know the effectiveness of the drugs given to the patients and understand the demand of the drugs according to location and area. The idea of this project is to conduct study and perform analysis on a large prescription data provided by clinics, develop algorithms using deep learning approach to extract and structured the prescription data so that it can be used to generate meaningful insight. The developed algorithm is required to compare against the existing solutions.

Ts. Dr. Michael Goh

Faculty of Information Science and Technology (FIST), Melaka Campus A Machine Learning Approach for Sentiment Analysis

Sentiment analysis (also known as opinion mining) refers to the use of natural language processing and text analysis to identify and extract subjective information in source materials. Sentiment analysis is widely applied to reviews and social media for a variety of applications, ranging from marketing to customer service. Sentiment analysis aims to determine the attitude of a speaker or a writer with respect to some topic or the overall contextual polarity of a document. The attitude may be his or her judgment or evaluation, affective state (the emotional state of the author when writing), or the intended emotional communication (the emotional effect the author wishes to have on the reader). The rise of social media such as blogs and social networks has fueled interest in sentiment analysis. With the proliferation of reviews, ratings, recommendations and other forms of online expression, online opinion has turned into a kind of virtual currency for businesses looking to market their products, identify new opportunities and manage their reputations. Companies look to automate the process of filtering out the noise, understanding the conversations, identifying the relevant content, and actioning it appropriately. This project aims at employing Machine Learning algorithms to automatically detect sentiment in user reviews of interested online business websites.

Assoc. Prof. Dr. Md Shohel Sayeed

Faculty of Informatics and Computing (FCI), Cyberjaya Campus Formulation of High Discriminative Discrete Krawtchouk Moment Invariants with Deep Neural Network Learning Model in Plant Condition Assessment

Translation, rotation and scale invariants (TRSI) of Krawtchouk moments proposed are well-known local feature extractors. However, existing invariants are computationally intensive and failed to resolve spatial deformations on weight function in the invariant function. As such, invariants generated by present algorithms are non-orthogonal, which compromise its discriminative power. This project aims to simplify and improve the computational efficiency of the TRSI algorithm for Krawtchouk moments. New normalization schemes with adaptive weight function will be formulated to produce high discriminative local features.

Agriculture sector often suffers great losses due to plant diseases. Image-based classification of plant diseases holds potential for early plant disease detection. However, automatic plant condition detection faces many challenges associated with large variations of visual symptoms, background and illumination. In this work, we proposed a moment-based Deep Neural Network (DNN) that utilizes our improved, high discriminative Krawtchouk moments for improving plant condition classification.

This project begins by formulating recursive functions of affine Krawtchouk transformation for fast computation, followed by the formulation of TRSI normalization schemes and modification of the weight function for feature extraction. Next, plant images of healthy and unhealthy plants will be collected, and preprocessed. Region-of-interests (ROI) for each image will then be identified and TRSI is calculated to extract features from ROIs. Finally a DNN model is designed and trained to classify plant health conditions.

Dr. Pee Chih Yang

Faculty of Informatics and Computing (FCI), Cyberjaya Campus Features of Games on Smartphones that will Encourage Exercise Indoors

Weather and other factors (like COVID-19) often prevent people from going to exercise outside. Standard modern smartphones have step trackers that will track walking, even indoors, but most exercise gamification involves being able to go outdoors. We want to explore what game features would encourage people to keep exercising even when indoors.

Note: This is a wide open area and could be expanded to a Ph.D.

Dr. Ian Chai

Faculty of Engineering and Technology (FET), Melaka Campus Development of Anomaly Novelty Detection for Abnormal Data in Time-series using an Improved Unsupervised Learning Algorithm

Problem Statement: Time series and streaming data are generated every minute and second due to the available sensors and software in Smart Home Systems. With a combination of The Internet of Things (IoT) and Smart Home Energy Management Systems (SHEMS), it becomes a scorching topic nowadays for a better process of extracting useful knowledge. It helps for better management and visualization of electricity usage. However, there are few challenges faced by the system developed, such as data quality or data anomaly issues. These anomalies can be due to technical or non-technical faults. The non-technical fault is essential to detect, which might cause economic cost. It also becomes challenging to train models in the case of an unlabeled dataset.

Objective: The main objective is to find data abnormalities in time-series data for better monitoring and forecasting. Secondly, the system should able to distinguish between actual anomaly and seasonal anomaly.

Methodology: Here an intelligent system will be developed using unsupervised learning and integrate into the smart home system. The model will be capable of clustering data into anomalies and not. It will automatically adapt to changing values in different environments. For determining seasonal anomalies, feature engineering will be processing before model training.

Expected Outcome: Finally, it is expected that the developed anomaly detector will be able to detect all anomalies as soon as possible, triggering real alarms in real-time for time-series data’s energy consumption or electricity theft. The users will be able to interact with the intelligent model from their browser. Even the developers will be able to ingrate the mode in their system using API.

Conclusion: Detecting data anomalies will help make a better decision to reduce energy usage wasted or point out if someone is stealing electricity.

Dr. Md. Jakir Hossen

Faculty of Information Science and Technology (FIST), Melaka Campus Spatial-temporal Analysis on Dynamic Handwritten Signature

This project is about dynamic handwritten signature recognition. It is expected to propose a feature extraction approach to analyze the dynamic handwritten signature based on spatial and temporal attributes.

Assoc. Prof. Ts. Dr. Pang Ying Han

Faculty of Information Science and Technology (FIST), Melaka Campus EEG Signal Processing for Alzheimer’s Patients Recognition

This project is about processing EEG signal for Alzheimer’s patients classification. The student is expected to explore

  1. Preprocessing of EEG signals,
  2. Feature extraction of the EEG signals, and
  3. Classification on the extracted features.

Supervised based feature extraction approach is a good alternative to well describe the signal patterns.

Assoc. Prof. Ts. Dr. Pang Ying Han

Faculty of Information Science and Technology (FIST), Melaka Campus Machine Learning on Classifying Human Physical Activities

Human activity recognition is the problem of classifying sequences of inertial data recorded by specialized harnesses or smart phones into known well-defined movements.

Assoc. Prof. Ts. Dr. Pang Ying Han

Faculty of Information Science and Technology (FIST), Melaka Campus Relationships among the Security Properties of Undeniable Signature Scheme and Its Applications

Undeniable signature scheme is a special featured signature scheme whereby the validity and invalidity of an undeniable signature can only be verified with the signer’s consent by engaging in a confirmation protocol and a disavowal protocol respectively, as opposed to a digital signature which is universally verifiable. Some potential applications of undeniable signatures are such as electronic payment, electronic voting, electronic auctions, etc. This research focuses on the analysis of relationships among the security properties of undeniable signature scheme and its potential applications.

Prof. Ts. Dr. Heng Swee Huay

Faculty of Information Science and Technology (FIST), Melaka Campus Blockchain-Based Cryptographic Applications

This study focuses on the review and analysis of the existing blockchain-based cryptographic applications, and proposes new applications. The new cryptographic applications will be supported with implementation and performance and comparison analysis, and rigorous theoretical framework if applicable.

Prof. Ts. Dr. Heng Swee Huay