#machine learning

Exploring solar-powered EV charging networks

This project explores strategies for developing a solar-powered EV charging network that ensures comprehensive coverage across urban and remote regions in Australia. The expected outcome is to enhance energy security, mitigate range anxiety, and protect user data, by leveraging AI-driven data analytics and privacy-preserving mechanisms. The project will potentially provide insights into the feasibility and design of a scalable and sustainable EV charging solution in Australia.

AI-driven automatic translation of blueprints into construction instructions 

This project develops AI models to address the critical gap in automated interpretation of blueprints in construction domain. The expected outcome is an AI system that translates complex blueprints into plain language actional construction instructions. The project outcome will reduce errors, delays and cost in construction industry, enhancing productivity, safety, and sustainability. 

Identifying serum biomarkers in PFAS serum concentration using metabolomics

This project investigates the health impacts of PFAS exposure in firefighters using advanced metabolomic techniques to identify biomarkers. The expected outcome is the development of new biomarkers for PFAS exposure, enhancing health diagnostics and preventive measures. This research will potentially lead to improved health risk assessments, better regulatory policies, and enhanced safety for workers exposed to hazardous substances.

Generalisation of the radiotherapy atlas contouring (TRAC) tool

This project will develop AI tools to both define and check medical image segmentations in radiotherapy clinical trials and clinical practice. The expected outcome is to develop quality assurance tools from artificial intelligence techniques and data from multiple medical imaging modalities. This project will have potential to improve patient outcomes and ensure effective implementation of advanced radiotherapy technologies and clinical trials.

Multimodal sensing intelligence for aerial robots in bushfire mitigation and pest management

This project develops advanced multimodal sensing techniques for next-generation aerial robots to improve bushfire mitigation and pest management. The expected outcome is a robotic sensing prototype for real-time monitoring. This technology will deliver customer, national, industry, and public benefits through enhanced safety, sustainability, cost-efficiency, and support for Indigenous manufacturing. 

Smartphone for triage to detect stroke

This project will focus on AI based voice analysis to assess patients in the emergency departments to assist in early detection of stroke conditions. The expected outcome is to develop interactive software and AI modelling focusing on responsible AI, overcoming gender, ethnicity and age-related biases. The potential benefit of this project is to reduce misdiagnosis that takes place in hospitals due to differences in gender, age and ethnicity.

Quantum-AI for food security and student wellbeing

This project develops quantum-AI tools to optimise urban hydroponic food systems and evaluate their therapeutic impact on student well-being. The expected outcome is to create quantum-reinforced AI algorithms for resource-efficient hydroponics and evidence-based frameworks for integrating green space exposure for improved student wellbeing. This project will potentially enhance urban food resilience, reduce resource waste, and create scalable mental health interventions, advancing national sustainability and education priorities. 

Distributed trust AI for solar energy sustainability in commercial settings

This project develops a scheme and prototype for utilising advanced AI technologies to manage and efficient utilise of solar energy. The expected outcomes is an energy sharing trustworthy framework that enables commercial customers reducing energy bills. The project will lead to a sustainable solution for SMEs and contribute to environment protection.

AI-empowered visual recognition system for dairy cow identification, health and behaviour monitoring

This project develops an AI-driven system to identify individual cows, and monitor and detect their health and behaviour, through the use of facial recognition, posture analysis, and thermal imaging. The expected outcome is early detection of illness and abnormal activity of cows. This project will support productivity, reduce losses, and improve animal welfare in the dairy industry.

Dark web analytics for ransomware threat actor profiling

This project investigates active ransomware groups and attempts to identify their key characteristics, attack signatures, victims, and stolen data. The expected outcome is development of a dataset to serve as an essential resource. The project will potentially provide more knowledge on ransomware groups, their tactics, techniques, and procedures for launching attacks.