Research Titles – ICT

Master’s Degree

Faculty Title / Summary Supervisor
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



Faculty Title / Summary Supervisor
Faculty of Informatics and Computing (FCI), Cyberjaya Campus Event Storyboarding

The advancement of digital broadcasting and Internet technology has encouraged the publishing of news articles online. Having different versions of same news incident published by different news agencies are common encountered these days. Browsing and reading different versions of same incident reported online may be time and effort consuming. However, the essence of the same incident can be observed by the Named-Entities (NEs) mentioned throughout the different versions (Goh et al, 2015). Event extraction is still a rather challenging task, as events are with different structures and components; while natural languages are often with semantic ambiguities and discourse styles.
Hence, this gives rise to the essential need of automatic identification and indexing of NEs within news articles for detecting same incident across different versions broadcasted online (Goh et al, 2015), and illustrated in the chronological and summary manner.

Hui-Ngo Goh, Lai-Ki Soon, Su-Cheng Haw, Automatic discovery of person-related named entity in news artcicles based on verb analysis, Multimedia Tools Application, 2015

 Dr. Goh Hui Ngo

Faculty of Engineering and Technology (FET), Melaka Campus Intelligent Data Quality framework for IoT Systems

High volume of data available due to sensors’ ubiquity and pervasiveness need to be processed and analyzed to extract meaningful or actionable information to recommend appropriate changes in IoT systems. Due to the sheer volume of the data from these IoT systems, any errors from user entry, data corruption, data accumulation, data integration, or data processing can be snowball, causing massive errors that can detrimentally affect the decision-making process. It is found from the literature review that incomplete (missing values), duplicate, inconsistency and inaccurate are the four common dimensions of dirty data exists. Consequently, there needs to be a clear understanding of the challenges associated with data quality and a way to evaluate and ensure that data quality in Iot Data Analytics (DA) is maintained for different applications. Although several studies have made various proposals on managing data quality (DQ), there seems to be a lack of methodologies that are general enough to assist developers in managing the quality of the data for the IoT systems. Therefore, there is a need for a complete data quality framework that will clean intelligently. Machine Learning (ML) technique will be used to overcome the issues.

Dr. Md. Jakir Hossen

Faculty of Information Science and Technology (FIST), Melaka Campus A Permissioned Blockchain-based Device Identity Management Framework for Internet-of-Things (IoT) – Cloud Network

The recent proliferation of the Internet of Things (IoT) has enabled seamless integration of interconnected sensors, actuators, and any other computational devices, in the form of distributed computing network. The establishment of such network requires a form of identification mechanism for all the devices to be connected. Potential threats exist, such as rogue device that can masquerade authentic IoT device, enabling it to take control over the entire network. Existing implementation relies on a trusted third-party authentication and identification management that resides over the cloud or within the distributed network. The deployment of such auspicious solution has faced many challenges since the centralization of the trust and connectivity of the IoT devices forces network to become a single point of failure that may disrupt the entire IoT operations. A decentralized approach offers an elegant solution to solve this problem. Hence, this work proposes a permissioned blockchain-based identity management framework for interconnected IoT devices within a distributed network infrastructure. Unlike existing approach where the identification and authentication of devices are handled by a single entity, this formulated framework utilizes the immutable blockchain network as a decentralized identity management authority and repository through a combination of distributed ledger scheme and smart contracts.

Ts. Dr. Nazrul Muhaimin Bin Ahmad

Faculty of Information Science and Technology (FIST), Melaka Campus Blockchain-based Framework for IoT Computation and Secure Communication

This project proposes a permissioned blockchain-based framework for the IoT devices to elastically offload its critical and intensive computation task to nearby cloud and to communicate securely between them without authorization, authentication and identification by the cloud. Unlike existing IoT-cloud framework where IoT to IoT communication is practically impossible, this framework augments the possibilities of cooperation, sharing and messaging between the IoT devices that unleash the realization of totally new applications.

Ts. Dr. Nazrul Muhaimin Bin Ahmad

Faculty of Information Science and Technology (FIST), Melaka Campus Privacy Preserving Distributed Architecture using Deep Learning and Blockchain Technology

This project proposes to develop a privacy preserving decentralised architecture using deep learning and blockchain technology. The proposed architecture will provide data confidentiality and other important security properties to ensure confidence and wider adoption of distributed deep learning in disciplines such as healthcare where data privacy is of utmost importance.

Prof. Ts. Dr. Heng Swee Huay

Faculty of Information Science and Technology (FIST), Melaka Campus Design and Analysis of Secure Searchable Encryption

In order to provide secure message communication within mobile networks, encryption scheme with keyword search schemes that enable searching of keywords within encrypted message are desirable. These schemes protect the confidentiality of encrypted messages but at the same time allow intermediary gateways to search encrypted messages for any keywords as instructed by the receiving mobile nodes. The main objective of this research is to design and analyse a secure and efficient searchable encryption scheme with additional features which could be operated using different platforms.

Prof. Ts. Dr. Heng Swee Huay

Faculty of Information Science and Technology (FIST), Melaka Campus Design and Analysis of Data Sharing Protocol for Cyber-Physical Cloud Environment

Despite the popularity of mobile cloud and cyber-physical cloud systems, security issues in untrusted cloud environment and physical devices remain major concerns. Data sharing protocol is a potential cryptographic solution that can be used to facilitate secure data sharing in a cyber-physical cloud environment. The protocol is designed to achieve authentication between a physical device and the cloud controller, and provide a secure end-to-end secure communication in the cloud. This research focuses on the design and analysis of secure data sharing protocol via mathematical approach.

Prof. Ts. Dr. Heng Swee Huay

Faculty of Engineering (FOE), Cyberjaya Campus Computer-Aided Histoscoring of HER2-SISH Predictive Marker for Breast Cancer Evaluation and Treatment

The project aims to automate the scoring of HER2 tumor biomarker from SISH imagery using machine learning, and develop a computer-aided system for tumor biomarkers expression.

Assoc. Prof. Dr. Mohammad Faizal Bin Ahmad Fauzi