Improving human reasoning with causal Bayes networks: a multimodal approach. This project aims to improve human causal and probabilistic reasoning about complex systems by taking a user-centric, multimodal, interactive approach. The project will explore new integrated visual and verbal ways of explaining a causal probabilistic model and its reasoning, to reduce known human reasoning difficulties, and investigate how to reduce cognitive load by prioritising the most useful user- and context-speci ....Improving human reasoning with causal Bayes networks: a multimodal approach. This project aims to improve human causal and probabilistic reasoning about complex systems by taking a user-centric, multimodal, interactive approach. The project will explore new integrated visual and verbal ways of explaining a causal probabilistic model and its reasoning, to reduce known human reasoning difficulties, and investigate how to reduce cognitive load by prioritising the most useful user- and context-specific information. Expected outcomes include novel AI methods that empower users to drive the reasoning process and strengthen trust in the system’s reasoning. Performance will be assessed in medical and legal domains, with significant potential benefits to end users from better, more transparent reasoning and decision making.Read moreRead less
Classification and Prediction Modelling for Financial Distress, Tax Debt and Insolvency for ATO Clients. The Australian Taxation Office (ATO) has clients who are not able to meet their taxation debts, resulting in revenue shortfalls for both the State and Federal Governments. Through this project, we will develop predictive models and techniques which identify client classes and clusters in the ATO client population and the defining attributes of these collections - especially those which are a ....Classification and Prediction Modelling for Financial Distress, Tax Debt and Insolvency for ATO Clients. The Australian Taxation Office (ATO) has clients who are not able to meet their taxation debts, resulting in revenue shortfalls for both the State and Federal Governments. Through this project, we will develop predictive models and techniques which identify client classes and clusters in the ATO client population and the defining attributes of these collections - especially those which are at high risk of incurring debt and defaulting on paying taxes. In turn, the early identification of clients in financial distress will allow the ATO to give them assistance so that they can reduce their debts and meet their financial obligations.Read moreRead less
Intelligent Decision Support for Neonatal Analysis and Trend Detection. Nearly five percent of newborn babies require intensive care after birth. Several electronic instruments monitor a baby's vital signs such as blood oxygen, blood pressure and heart rate. A major limitation in neonatal clinical management is that the physiological data generated by the monitors is not extracted to provide an integrated picture of the baby's condition or to enable detection of trends and patterns in clinical a ....Intelligent Decision Support for Neonatal Analysis and Trend Detection. Nearly five percent of newborn babies require intensive care after birth. Several electronic instruments monitor a baby's vital signs such as blood oxygen, blood pressure and heart rate. A major limitation in neonatal clinical management is that the physiological data generated by the monitors is not extracted to provide an integrated picture of the baby's condition or to enable detection of trends and patterns in clinical and real-time physiological data. This project will develop a methodology and technology that supports neonatal analysis incorporating a framework to mine data for trend detection, resulting in higher survival rates.Read moreRead less
Intelligent Design Advisor for Manufacturing Process Knowledge within Concurrent Engineering in the Aerospace Industry. At present the design of engineering components in the aerospace industry is accomplished by experts from design and manufacturing either sequentially or in collaboration. If performed in sequence then time and quality is jeopardised. If performed in collaboration then more manpower than is necessary is expended. The aim of this project is to develop an intelligent design advis ....Intelligent Design Advisor for Manufacturing Process Knowledge within Concurrent Engineering in the Aerospace Industry. At present the design of engineering components in the aerospace industry is accomplished by experts from design and manufacturing either sequentially or in collaboration. If performed in sequence then time and quality is jeopardised. If performed in collaboration then more manpower than is necessary is expended. The aim of this project is to develop an intelligent design advisor for Manufacturing Process Knowledge that will provide this expert knowledge to the design engineer in order to speed up the design process while reducing costs and still maintaining the high standard of quality necessary in the Aerospace industry.Read moreRead less
Multi-dimensional Temporal Abstraction to Support Neonatal Clinical Research. Each year, the death of a baby causes grief for thousands of Australian parents, contributes to depression and considerable anxiety in the population. In this work we propose procedures that will significantly reduce this unhappy scenario. The availability of a complex trend and pattern analysis will give Neonatologists access to predictive clinical analysis that has not previously been available locally or internation ....Multi-dimensional Temporal Abstraction to Support Neonatal Clinical Research. Each year, the death of a baby causes grief for thousands of Australian parents, contributes to depression and considerable anxiety in the population. In this work we propose procedures that will significantly reduce this unhappy scenario. The availability of a complex trend and pattern analysis will give Neonatologists access to predictive clinical analysis that has not previously been available locally or internationally. Thus, significant benefits in terms of lower mortality rates and lower long-term disability rates among babies requiring special care is possible. This research will provide the basis for future projects that will support regional hospitals.Read moreRead less
High Frequency Data Stream Event Correlation for Complex Neonatal Medical Alerts. Nearly twenty percent of newborn babies require intensive care after birth. Several electronic instruments monitor a baby's vital signs such as blood oxygen, blood pressure and heart rate. A major limitation in neonatal clinical management is that the data generated by these monitors is not integrated to enable the alerting of condition deterioration or early warning of possible condition onset. This project will d ....High Frequency Data Stream Event Correlation for Complex Neonatal Medical Alerts. Nearly twenty percent of newborn babies require intensive care after birth. Several electronic instruments monitor a baby's vital signs such as blood oxygen, blood pressure and heart rate. A major limitation in neonatal clinical management is that the data generated by these monitors is not integrated to enable the alerting of condition deterioration or early warning of possible condition onset. This project will develop a methodology and technology that supports the cross correlation of neonatal clinical and physiological data for complex neonatal medical alerts, through the use of agents within an event stream processor, resulting in higher survival rates.Read moreRead less
Temporal and spatial Bayesian network modelling for improved fog forecasting. This project aims to improve the accuracy of fog forecasting by explicitly modelling the spatial and temporal uncertainties surrounding fog formation. It is expected weather forecast services will adopt our approach to improve their predictions of fog, which will in turn help transport companies save costs, cut emissions and improve safety.
Fuzzy Transfer Learning for Prediction in Data-Shortage and Rapidly-Changing Environments. Collecting sufficient up-to-date data to train a learning model for data analysis and prediction is difficult and expensive. This project will develop a Fuzzy Transfer Learning methodology, using Information Granularity theory, that exploits data with different features and/or distributions available in other, similar systems, to provide accurate learning-based prediction for current problems. It will esta ....Fuzzy Transfer Learning for Prediction in Data-Shortage and Rapidly-Changing Environments. Collecting sufficient up-to-date data to train a learning model for data analysis and prediction is difficult and expensive. This project will develop a Fuzzy Transfer Learning methodology, using Information Granularity theory, that exploits data with different features and/or distributions available in other, similar systems, to provide accurate learning-based prediction for current problems. It will establish a new research direction, Fuzzy Transfer Learning for Prediction, and the outcomes will enable government and industry to better use past experience to make more accurate predictions and decisions. Highly significant benefits will also accrue in the data analytics, business intelligence and decision making research fields.Read moreRead less