The tennis tour is a series of tennis tournaments played globally over a calendar year, where professional tennis players compete for prize money and ranking points. The structure of the tennis tour is organised into different tiers for both men and women, including grand slam tournaments and ATP/WTA tour events. In this project we use stochastic processes to model and simulate the tour under different experimental rules.
Honours and Masters project
Displaying 121 - 130 of 246 honours projects.
Deep Learning-Assisted Brain Tumor Segmentation in MRI Imaging
Description:
Magnetic Resonance Imaging (MRI) stands as a cornerstone in medical imaging, providing non-invasive, high-resolution images of the human body's internal structures. Brain tumor segmentation from MRI scans is essential for precise diagnosis and treatment planning. MRI provides detailed views of brain structures and abnormalities, but challenges like image noise, contrast imperfections and tumor variations can make segmentation difficult.
Anomaly Detection in MRI Scans through Deep Learning: A Healthy Cohort Training Approach
Description:
The early detection of neurological abnormalities through Magnetic Resonance Imaging (MRI) is crucial in the medical field, potentially leading to timely interventions and better patient outcomes. However, the traditional diagnostic process is often time-consuming and subject to human error. This project seeks to improve this aspect by employing deep learning for anomaly detection in MRI scans, exclusively using images from healthy participants for model training [1].
Using AI for landmark detection for facial reconstruction from images of skulls
Problem Statement: The forensic identification of human remains is a critical legal process, culminating in the issuance of a death certificate by the appropriate authority. It is a multifaceted procedure that integrates scientific evidence—from antemortem records to advanced DNA analysis, forensic odontology, and anthropology—to match unidentified remains with missing persons. This holistic approach is foundational to resolving cases of unlawful killings, which bear significant implications for public health, legal resolution of civil affairs, and community well-being.
Intellectual Property Violations in Generative Machine Learning
This project will explore different aspects of intellectual property violations in generative machine learning . There is sufficient research work in this project for at least two students. However, individual sub-topics are rather independent and don't necessarily require group work.
Topics can be summarised as follows:
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[Malaysia] An application of machine learning regression to feature selection: a study of logistics performance and megatrend attributes
This project will apply feature selection techniques for identifying features that can effectively predict the Logistics Performance Index (LPI), building upon our previously published work [1].
[Malaysia] Analyzing Twitter for Noncommunicable Disease Information
This project aims to analyse the comments of Twitter on non-communicable diseases. Students are expected to carry out Aspects Detection to identify the specific aspects discussed in the tweets e.g., causes, transmission and symptoms. Subsequently, students are expected to conduct sentiment analysis utilizing tools like TextBlob or VADER, while also taking into account the importance of considering emojis to enhance classification accuracy. Students also need to provide solutions to the problem of ironic tweets (e.g., seem positive but actually negative) and misinformation.
Continuous-time Automated Decision Making with Mathematical Optimisation
SCIPPlan is a mathematical optimisation based automated planner for domains with i) mixed (i.e., real and/or discrete valued) state and action spaces, ii) nonlinear state transitions that are functions of time, and iii) general reward functions. SCIPPlan iteratively i) finds violated constraints (i.e., zero-crossings) by simulating the state transitions, and ii) adds the violated constraints back to its underlying optimization model, until a valid plan is found. The purpose of this project is to improve the performance of SCIPPlan.
[Malaysia] AI for Cybersecurity
Cybersecurity researchers are contemplating how to best use the currently trending AI techniques to aid cybersecurity, beyond just for classification.
The aim of this Honours project is to get the student to work with the supervisors on the latest AI techniques to adapt them over for cybersecurity, building first on baseline approaches for which code is available.
The student is free to discuss with the supervisor on any specific aspects of his/her choice and interest.
High Precision Arithmetic for Cryptographic Applications
Cryptographic applications require a careful implementation to avoid side-channel attacks that reveal secret information to an attacker (e.g. via run-time measurements). In particular, for floating point arithmetic it is known that the timing of some basic arithmetic operations and functions on some CPUs depends on the input values [1], and thus the timing may leak secret information when the input contains secret values. Constant-time implementation tries to mitigate such run-time timing leakage on typical devices.