Displaying 11 - 20 of 307 honours projects.
1. Background
Skin cancer, including melanoma, basal cell carcinoma (BCC), and squamous cell carcinoma (SCC), is a significant health concern in Australia. Early detection and accurate risk assessment can support timely clinical assessment and treatment.
2. Research Aim
This research aims to develop an AI and machine-learning framework for early detection, risk classification, clinical decision support, and forecasting of skin cancer.
3. Data and Methods
Lung cancer prediction research in computer science focuses on overcoming critical data engineering and algorithmic challenges—such as severe class imbalance in Electronic Health Records (EHR), high-dimensional tabular feature interactions, and the opacity of deep neural networks.
To address these AI-specific bottlenecks, this research proposes an end-to-end Predictive Artificial Intelligence pipeline designed for early risk scoring and multi-class stage prediction.
1. Research Objectives & Predictive AI Architecture
Brain cancer—particularly High-Grade Gliomas (HGG) like Glioblastoma—remains one of the most lethal central nervous system malignancies, primarily due to non-specific early symptoms, rapid infiltrative growth, and late-stage clinical detection. Standard diagnostic pathways rely heavily on invasive biopsies and complex neuroimaging (e.g., MRI), which can delay intervention and lead to poor overall survival outcomes.
This project uses explainable machine learning, predictive analytics, and statistical risk modelling to predict ADHD symptoms in children using publicly available or clinical healthcare datasets. Students will clean and analyse pediatric health and behavioral data, apply classification and risk stratification algorithms such as Logistic Regression, Random Forest, XGBoost, or multimodal deep learning, and identify key diagnostic risk factors using explainable AI methods.
The security threat by quantum computing to almost all currently used digital signatures was triggered by the discovery of Shor’s quantum algorithm, which efficiently breaks the two problems underlying the security of these schemes, namely integer factoring, and elliptic curve discrete logarithms (ECDLP). When quantum computers become widespread, all security for the current digital signatures that are widely used to secure a wide range of systems is lost.
Digital provenance refers to the process of verifying and tracing the origins, lifecycle, and integrity of digital content. C2PA (Coalition for Content Provenance and Authenticity) is an initiative founded by multiple technology and media companies, aiming to address the increasing concern of misleading information and disinformation on the internet in the age of AI. The main objective of C2PA is to establish a standardized approach for digital content provenance, which essentially means tracing the origin and verifying the integrity of digital content.
Australia’s cybersecurity infrastructure, particularly in IoT networks, must be strengthened to meet evolving standards set by international bodies like NIST and the NSA. This project will support Australian organisations in adapting to quantum-safe standards, ensuring the protection of sensitive data and critical system
Digital signatures are asymmetric cryptographic schemes used to validate the authenticity and integrity of digital messages or documents. The signer uses their private key to generate a signature on a message. Then, this signature can be validated by any verifier who knows the signer’s corresponding public key. Sometimes a digital message might require signatures from a group of signers. The naïve method to achieve this goal is collecting distinct signatures from all signers.
Climate change will affect us all, and we have to do everything we can to minimize the magnitude of change. Investments in renewable generation help to reduce the impact of energy usage on the supply side, but that will not get us all the way there, especially in the near term. Consumers will also have to become much more efficient with their energy use.
Discussion of dangers of artificial intelligence (or artificial superintelligence, ASI) being an ambitious subordinate and ultimately taking over control from humans goes back at least as far as R J Solomonoff (1967). R J Solomonoff (1985) gives an approximate time-frame in which this might occur.