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
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