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.
Honours and Masters project
Displaying 121 - 130 of 243 honours projects.
[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.
[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].
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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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.
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].
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.
Modelling the tennis tour with stochastic processes
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.
Asymmetric games between journals and scientists
This project is based on the paper "Academic Journals, Incentives, and the Quality of Peer Review: A Model", in which we analyse strategic interactions between scientists and science journals. Our results shed light on how different objectives for journals shape the strategies that scientists adopt when aiming to publish their work.
Privacy-preserving Machine Learning
Machine learning (ML) training and evaluation usually involve large-scale datasets and complicated computation. To process data efficiently, a promising solution is to outsource the processes to cloud platforms. However, traditional approaches of collecting users' data at cloud platforms are vulnerable to data breaches.