Australians are at high risk of malignant keratinocyte cancers and eye disease due to solar ultraviolet (UV) radiation. Implementing sun-protection to mitigate health risk of erythemally-effective UV is a strategic initiative of World Health Organisation. UV forecasting apps used as a skin health decision support system to predict UV Index can mitigate the risk of skin cancer and eye disease.

Predictive models inform real-time sun-protection behaviour in humans and advocate for better public health risk reduction. In this project students learn about artificial intelligence (AI) models and learn to build explainable and interpretable AI systems (denoted as xAI models) that have the skill for real-time simulation of UV index. They will integrate innovative mathematical tools such as deep Taylor decomposition, layer wise relevance propagation (LRP), Local Interpretable Model-agnostic Explanations (LIME), sensitivity analysis and feature heat maps to analyse sky images. The xAI approach will offer the explanation of machine decisions and predictions to justify their reliability. The models will also provide greater interpretability, which often means we would better understand the mechanism underlying the algorithms when building an xAI model for UV forecasting. Some of the other well-known methods such as wavelet and Bayesian method will also be used using satellite data to yield high performance. Overall, this project will contribute to the mitigation of health-risk (e.g. skin cancer and eye disease minimisation efforts).

The project aims to enhance the science of solar UV index while developing forecasting capabilities for real-time advice to mitigate risk of solar UV-exposure-related disease. It will suit students with some background in health, solar science, computing, climatology, meteorology, mathematics, statistics, environmental or atmospheric physics. Student will learn Python, r or MATLAB programming, extensive data analysis and explainable AI modelling tool.

The research project is strongly centred on AI and Health Technologies.

This project is suitable for PhD, Research or Coursework Masters Thesis. It provides opportunity to publish in high quality Q1 journals. The research student will be part of the Advanced Data Analytics Research Group under Prof Ravinesh Deo.

For more details see https://eportfolio.usq.edu.au/view/view.php?id=116719.

Professor Ravinesh Deo

• Assoc Prof Nathan Downs
• Professor Alfio Parisi
• Professor Jeffrey Soar

• Centre for Health Sciences Research
• Institute for Agriculture and the Environment
• School of Agricultural and Environmental Sciences

• Applied Mathematics
• Artificial Intelligence and Image Processing
• Atmospheric Sciences
• Environmental Science and Management
• Public Health and Health Services
• Statistics

• Doctor of Philosophy (DPHD)
• Doctor of Philosophy (DPHD)
• Master of Research (MRES)
• Master of Research (MRES)