Practice-based Systematized Nomenclature of Medicine (SNOMED) concept learning for drug-disease precaution early detection and refinement. The outcome of the Systematized Nomenclature of Medicine (SNOMED) concept learning system will help mitigate the impact of Adverse Drug Events hence directly contribute to the National Research Priority promoting and maintaining good health. It will tailor SNOMED knowledge to different clinical settings and provide evidence-based preventative health care. Th ....Practice-based Systematized Nomenclature of Medicine (SNOMED) concept learning for drug-disease precaution early detection and refinement. The outcome of the Systematized Nomenclature of Medicine (SNOMED) concept learning system will help mitigate the impact of Adverse Drug Events hence directly contribute to the National Research Priority promoting and maintaining good health. It will tailor SNOMED knowledge to different clinical settings and provide evidence-based preventative health care. The enabling methodology from this project for building computerised cognitive learning systems will be a frontier technology to enhance smart information use in clinical decision support. It will also contribute to the development of knowledge-based systems. A network version of the developed system will assist doctors working in rural and remote areas with their clinical decision making and prescribing practice.Read moreRead less
Using shape change for object perception: human and artificial vision. This project aims to examine the steps taken by the visual system to code the shape of objects, including those that change shape over time. The project seeks to employ experiments assessing human vision and machine learning techniques to examine these codes and, in particular, focus on the advantages of a system that exaggerates shape change over time. Expected outcomes include an improved shape code based on superior human ....Using shape change for object perception: human and artificial vision. This project aims to examine the steps taken by the visual system to code the shape of objects, including those that change shape over time. The project seeks to employ experiments assessing human vision and machine learning techniques to examine these codes and, in particular, focus on the advantages of a system that exaggerates shape change over time. Expected outcomes include an improved shape code based on superior human performance that can have many applications in automated visual systems. This project can directly benefit the animation industries where the creation of realistic movement of humans and animals remains a computationally intensive challenge.Read moreRead less
Design of adaptive learning visual sensor networks for crowd modelling in high-density and occluded scenarios. Partnering University of Melbourne researchers, with video surveillance experts SenSen, engineering consultants ARUP and the Melbourne Cricket Club, the project addresses research enabling a system-integrating, existing surveillance, infrastructure to model crowd behaviour and exit strategies, providing real-time analysis, prediction and response capabilities for venue managers and emer ....Design of adaptive learning visual sensor networks for crowd modelling in high-density and occluded scenarios. Partnering University of Melbourne researchers, with video surveillance experts SenSen, engineering consultants ARUP and the Melbourne Cricket Club, the project addresses research enabling a system-integrating, existing surveillance, infrastructure to model crowd behaviour and exit strategies, providing real-time analysis, prediction and response capabilities for venue managers and emergency services. This new capability enhances utilisation of security resources to prevent injury and fatalities in evacuation scenarios, applicable to existing venues and influencing the development of new facilities around the country. The project delivers researcher training, global clientele for local technology and a platform for local industry growth.Read moreRead less