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.
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
A Novel Automatic Neural Network Feature Extractor. This project aims to study feature extraction abilities of convolutional as well as traditional neural networks and develop a generic feature extractor which can be applied to wide variety of real-world image and non-image data. New concepts for automatic feature extraction, feature explanation, hybrid evolutionary algorithms and non-iterative ensemble learning will be introduced and evaluated. The expected outcomes are a generic feature extrac ....A Novel Automatic Neural Network Feature Extractor. This project aims to study feature extraction abilities of convolutional as well as traditional neural networks and develop a generic feature extractor which can be applied to wide variety of real-world image and non-image data. New concepts for automatic feature extraction, feature explanation, hybrid evolutionary algorithms and non-iterative ensemble learning will be introduced and evaluated. The expected outcomes are a generic feature extractor for automatically extracting features, an optimiser for finding optimal parameters and non-iterative ensemble learning technique for classification of features into classes. The impact of this project will be automatic feature extractors and classifiers for real-world applications.Read moreRead less
Computational Modeling of RNA Control Networks. One of the most exciting new ideas for understanding the regulation of gene expression involves the contribution of intronic and other non-protein coding RNAs to regulatory networks within cells. This novel role for intronic RNA is currently making headlines within the molecular biology community but has not yet been modelled computationally. The network of genetic regulatory interactions forms a complex system which is amenable to computational ....Computational Modeling of RNA Control Networks. One of the most exciting new ideas for understanding the regulation of gene expression involves the contribution of intronic and other non-protein coding RNAs to regulatory networks within cells. This novel role for intronic RNA is currently making headlines within the molecular biology community but has not yet been modelled computationally. The network of genetic regulatory interactions forms a complex system which is amenable to computational analysis. This project aims to extend current models to incorporate intronic RNA feedback control, complementing parallel studies in vivo, and computationally testing ideas essential to the theoretical understanding of the basis of life.Read moreRead less
Smart Information Processing for Roadside Fire Risk Assessment Using Computational Intelligence and Pattern Recognition. This project proposes a novel approach for identifying roadside fire risks using pattern recognition and computational intelligence techniques. The video 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 aims to develop new techniques for identification of roadside obje ....Smart Information Processing for Roadside Fire Risk Assessment Using Computational Intelligence and Pattern Recognition. This project proposes a novel approach for identifying roadside fire risks using pattern recognition and computational intelligence techniques. The video 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 aims to develop new techniques for identification of roadside objects so that the data can be automatically analysed allowing the estimation of fire risk factors. The final outcome intends to be techniques for segmentation and classification of roadside objects and estimation of fire risk factors.Read moreRead less
Special Research Initiatives - Grant ID: SR0567109
Funder
Australian Research Council
Funding Amount
$69,438.00
Summary
Smart astronomy: using computational science to understand distant radio galaxies. Radio galaxies are among the largest galaxies in the universe with their copious radio emission powered by massive black holes. Australian radio telescopes are very effective at tracing these massive galaxies back in time so we can measure how black holes formed and developed. These measurements depend on reliable identification of the radio sources with our optical telescopes to make vital measurements of their d ....Smart astronomy: using computational science to understand distant radio galaxies. Radio galaxies are among the largest galaxies in the universe with their copious radio emission powered by massive black holes. Australian radio telescopes are very effective at tracing these massive galaxies back in time so we can measure how black holes formed and developed. These measurements depend on reliable identification of the radio sources with our optical telescopes to make vital measurements of their distances. Until now this identification process has been straightforward, but the next generation of studies will look so far back in time that the identification will become ambiguous. Our project will develop a software tool that applies techniques from computational science to overcome the ambiguity in this matching problem.Read moreRead less
Talking with Robots: Evolving Grounded Language for Embodied Agents. The coming personal robot revolution will be built on robots that have real-world intelligence, with an ability to understand and communicate about the world in the way we humans do. This project extends a previous ARC project, which developed robot-friendly languages for naming places in the world. This new project will develop the robots' abilities and language to understand a comprehensive range of real world objects, places ....Talking with Robots: Evolving Grounded Language for Embodied Agents. The coming personal robot revolution will be built on robots that have real-world intelligence, with an ability to understand and communicate about the world in the way we humans do. This project extends a previous ARC project, which developed robot-friendly languages for naming places in the world. This new project will develop the robots' abilities and language to understand a comprehensive range of real world objects, places, actions, attributes and relationships. This project represents a major advance for Australia in the new and fast growing personal robot industry.Read moreRead less
Enhancing Intelligent Robot Navigation with the Evolution of a Robot-Friendly Language. Personal robots are set to become as popular as personal computers. The key ingredient that has been missing is intelligence - not the kind of intelligence that plays chess, but the kind that allows robots to understand the world in the way that we humans do. This project represents a major advance in that kind of intelligence, giving robots the ability to understand the world and the ability to communicate a ....Enhancing Intelligent Robot Navigation with the Evolution of a Robot-Friendly Language. Personal robots are set to become as popular as personal computers. The key ingredient that has been missing is intelligence - not the kind of intelligence that plays chess, but the kind that allows robots to understand the world in the way that we humans do. This project represents a major advance in that kind of intelligence, giving robots the ability to understand the world and the ability to communicate about their experiences. Armed with this new technology, Australia will have a competitive edge in the new and fast growing personal robot industry.Read moreRead less