Predictive Models To Design And Develop New Antibiotics Derived From The Community For Open Antimicrobial Drug Discovery
Funder
National Health and Medical Research Council
Funding Amount
$977,427.00
Summary
With the rise of infections from multidrug-resistant bacteria, and limited antibiotics in the development pipeline, new strategies are required to generate novel antibiotics. This project will apply artificial intelligence methods to study a unique dataset generated over five years with the help of over 300 academic groups around the world. It will produce predictive models that will then be applied to design new antibiotics, which will be synthesized and tested for antimicrobial activity.
DEEP LEARNING AND PHYSIOLOGY BASED APPROACH TO DERIVE AND LINK OBSTRUCTIVE SLEEP APNOEA PHENOTYPES AND SYMPTOMATOLOGY
Funder
National Health and Medical Research Council
Funding Amount
$402,978.00
Summary
Obstructive sleep apnoea (OSA) is a highly prevalent nocturnal breathing disorder strongly related to daytime sleepiness, accident risk and reduced quality of life. However, the current severity index, the apnoea-hypopnoea index, poorly predicts daytime sleepiness and vigilance. In this project we elegantly combine physiological insight and artificial intelligence to develop and evaluate novel clinically applicable computational tools for detailed quantification of OSA severity and its symptoms.
Computational Intelligence Methods for Financial Applications. Complex financial problems can be better addressed with software that can learn from available data and adapt to environmental changes. It is therefore essential to develop technologies that enable prediction and optimisation in constrained and dynamic environments. There are currently some limitations in existing business decision support systems despite their ubiquity providing an opportunity for Australia to be at the forefront as ....Computational Intelligence Methods for Financial Applications. Complex financial problems can be better addressed with software that can learn from available data and adapt to environmental changes. It is therefore essential to develop technologies that enable prediction and optimisation in constrained and dynamic environments. There are currently some limitations in existing business decision support systems despite their ubiquity providing an opportunity for Australia to be at the forefront as new standards in the field are developed. Furthermore, the fund management industry (particularly superannuation) is significant to the Australian economy and development of this technology has the potential to enhance its performance and reputation.Read moreRead less
Ischaemic Stroke And Atrial Fibrillation: Development Of A Novel Artificial Intelligence System Using Magnetic Resonance Imaging
Funder
National Health and Medical Research Council
Funding Amount
$92,335.00
Summary
Atrial fibrillation (AF) is an abnormal heart rhythm which is a common cause of stroke. AF can often go unrecognized as patients may have no symptoms. This project aims to develop computer software which can automatically detect underlying AF based on MRI brain scan pattern, in patients who have had an acute stroke. This project has the potential to offer several benefits, including reduced need for costly investigations, improved AF detection and a larger pool of patients being treated for AF.
Electrical Stimulation Of The Brain For Restoring Vision
Funder
National Health and Medical Research Council
Funding Amount
$1,555,864.00
Summary
This project is focused on the clinical demonstration of the Monash Vision Group’s ‘Gennaris’ cortical prosthesis or bionic eye. The clinical work will demonstrate the use of the Gennaris as a viable medical device that provides useful vision to people with adult-onset profound vision loss in their everyday living environments. This will place MVG in a position to attract funding from investors or commercial partners to perform multi-site clinical trials and obtain regulatory approval.
A Novel Framework for Optimised Ensemble Classifier. The project aims to develop a novel framework for creating an optimised ensemble classifier that will improve data analysis and the accuracy of many real-world applications such as document analysis, robotics and medical diagnosis. The project plans to develop and investigate novel methods for generating diverse training environment layers, base classifiers and fusion of classifiers. It also plans to design a multi-objective evolutionary algor ....A Novel Framework for Optimised Ensemble Classifier. The project aims to develop a novel framework for creating an optimised ensemble classifier that will improve data analysis and the accuracy of many real-world applications such as document analysis, robotics and medical diagnosis. The project plans to develop and investigate novel methods for generating diverse training environment layers, base classifiers and fusion of classifiers. It also plans to design a multi-objective evolutionary algorithm-based search obtain the optimal number of layers, clusters and base classifiers. The expected outcomes of the proposed framework are advances in classifier learning. The final outcome may be novel methods which will bring in diversity during the learning of the base classifiers and provide an optimal ensemble classifier for real-world applications.Read moreRead less
An automated system for the analysis of road safety and conditions. This project aims to develop an automated system for the analysis of road safety and conditions. Digital video road data is collected over every state road in Queensland annually, and has the potential to provide a range of value-added products for safer roads. This project will develop deep learning based neural network techniques which can learn and classify roadside objects so that video data can be automatically analysed all ....An automated system for the analysis of road safety and conditions. This project aims to develop an automated system for the analysis of road safety and conditions. Digital video road data is collected over every state road in Queensland annually, and has the potential to provide a range of value-added products for safer roads. This project will develop deep learning based neural network techniques which can learn and classify roadside objects so that video data can be automatically analysed allowing the estimation of the proximity of objects for road safety and rating. The expected outcome will be new identification techniques and software which can be incorporated with road data collection systems.Read moreRead less
Deep Learning Architecture with Context Adaptive Features for Image Parsing. This project aims to develop a novel deep learning network architecture with contextual adaptive features for image parsing that can improve the object detection accuracy in real-world applications. A number of innovative methods for deep learning, contextual features and network parameter selection will be developed and investigated. The impact of the proposed architecture and features will be improved object-detection ....Deep Learning Architecture with Context Adaptive Features for Image Parsing. This project aims to develop a novel deep learning network architecture with contextual adaptive features for image parsing that can improve the object detection accuracy in real-world applications. A number of innovative methods for deep learning, contextual features and network parameter selection will be developed and investigated. The impact of the proposed architecture and features will be improved object-detection accuracy and advances in deep learning network architecture for image parsing. The intended outcomes are deep learning network architecture, contextual feature extraction techniques and network parameter optimisation techniques for image parsing.Read moreRead less