Theory and applications in data analytics of time series became popular in the past few years due to the availability of data in various sources. This project aims to investigate and generalise Hybrid and Neural Network methods in time series to develop forecast algorithms. The methodology will be developed as a theoretical construct together with wide variety of applications.
Honours and Minor Thesis projects
Displaying 171 - 180 of 216 honours projects.
Primary supervisor: David Dowe
Primary supervisor: David Dowe
DNA or RNA motif discovery is a popular biological method to identify over-represented DNA or RNA sequences in next generation sequencing experiments. These motifs represent the binding site of transcription factors or RNA-binding proteins. DNA or RNA binding sites are often variable. However, all motif discovery tools report redundant motifs that poorly represent the biological variability of the same motif, hence renders the identification of the binding protein difficult.
Primary supervisor: Yuan-Fang Li
Develop NLP tools to track politicians’ campaign promises on traditional and social media: With applications to Australian, Indian and/or US politics.
Primary supervisor: Vincent Lee
Issues and solutions exist on different aspects of the management of real-time data, such as persistence, visualisation, and online processing. This project is a research project to identify the significant issues of real-time data management in structural health monitoring (SHM), particularly for bridges, and implement an integrated software solution for enterprise usage. This project involves time series database design, visualisation and online processing of time series, and service-oriented and web-based software development.
Primary supervisor: Bioinformatics
This project focuses on the locomotion pattern of freely moving animals. The model organism we used is C. elegans, a transparent nematode about 1 mm, which displays a sinusoidal movement on the plates.
Primary supervisor: Bioinformatics
A major challenge in cancer therapeutics is to kill tumour cells without harming normal cells in the body. Traditional chemotherapy tries to do this by killing cells that are fast dividing, a characteristic hallmark of cancer cells, however as many other cells in the body are also fast dividing – such as those in the hair and the gut – chemotherapy typically results in undesirable side effects. Newer targeted therapies are designed to specifically target cancer cells, by exploiting the genetic changes that distinguish tumour cells from normal cells.…
Primary supervisor: Bioinformatics
Despite enormous progress in research, cancer remains a devastating disease worldwide. Since generally not all patients will respond to a specific therapy, a great challenge in cancer treatment is the ability to predict which patients would benefit (or not) to a therapy of choice. This helps improve treatment efficacy and minimise unnecessary sufferings by non-responders. There is thus a pressing need to identify robust biomarkers (i.e. genes/proteins) that can accurately predict the right patients for the right drugs.…
Primary supervisor: Bioinformatics
Antimicrobial resistance (AMR) continues to evolve as a major threat to human health and new strategies are required for the treatment of AMR infections. Bacteriophages (phages) that kill bacterial pathogens are being identified for use in phage therapies, with the intention to apply these bactericidal viruses directly into the infection sites in bespoke phage cocktails. Using such a biological agent for infection control requires deep understanding of the phage.
Primary supervisor: Bioinformatics
Multidrug resistance (MDR) poses critical challenges to global health. In 2017 the World Health Organization identified Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacteriaceae as the top-priority pathogens that urgently require development of novel therapeutic options. Recently, bacteriophage therapy has attracted extensive attention owing to its potential of being used as novel antimicrobials to combat MDR pathogens.
Primary supervisor: Yuan-Fang Li
Neural module networks (NMNs) [1] support explainable question answering over text [2] by parsing a natural-language question into a program. Such a program consists of a number of differentiable neural modules that can be executed on text in a soft way, operating over attention scores. As a result, NMNs learn to jointly program and execute these programs in an end-to-end way.