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Research Topic : NEURAL NETWORK
Socio-Economic Objective : Application packages
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  • Funded Activity

    Discovery Projects - Grant ID: DP0878061

    Funder
    Australian Research Council
    Funding Amount
    $235,944.00
    Summary
    Translucent Optical Networks: Architecture, Design, Operation, and Survivability. Australia is a geographically disperse country. Long-haul optical transport networks are key communication infrastructure between metropolises. Our research aims to discover cost-effective network architecture and planning and operational strategies for optical transport networks, which is beneficial to the deployment and operation of Australian telecommunication networks. In addition, viewing the sparse connectivi .... Translucent Optical Networks: Architecture, Design, Operation, and Survivability. Australia is a geographically disperse country. Long-haul optical transport networks are key communication infrastructure between metropolises. Our research aims to discover cost-effective network architecture and planning and operational strategies for optical transport networks, which is beneficial to the deployment and operation of Australian telecommunication networks. In addition, viewing the sparse connectivity of Australian backbone transport networks, the research on network survivability will provide effective strategies for Australian transport networks to survive any network failures due to natural disasters or terror attacks, thereby providing reliable network services to Australian.
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    Funded Activity

    Discovery Projects - Grant ID: DP1096053

    Funder
    Australian Research Council
    Funding Amount
    $165,000.00
    Summary
    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.
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    Funded Activity

    Discovery Projects - Grant ID: DP0773584

    Funder
    Australian Research Council
    Funding Amount
    $210,000.00
    Summary
    Automatic detection of the circle of Willis in neuro-images using multi-scale gradient calculation and knowledge-based genetic algorithms. Stroke is the third most common cause of death and a major contributor to long term disability in Australia. The most efficient way of preventing stroke from happening is to detect related symptoms early. The group of cerebral blood vessels that closely related to strokes is the circle of Willis (CoW). We build a system that can automatically detect and quan .... Automatic detection of the circle of Willis in neuro-images using multi-scale gradient calculation and knowledge-based genetic algorithms. Stroke is the third most common cause of death and a major contributor to long term disability in Australia. The most efficient way of preventing stroke from happening is to detect related symptoms early. The group of cerebral blood vessels that closely related to strokes is the circle of Willis (CoW). We build a system that can automatically detect and quantify CoW in neuroimages, providing ways of preventing strokes from happening. The project will enhance Australia¡¯s leading position in promoting and maintaining good health, especially in preventive healthcare.
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    Funded Activity

    Linkage Projects - Grant ID: LP0882413

    Funder
    Australian Research Council
    Funding Amount
    $235,944.00
    Summary
    Development of an Integrated Platform for Performance-Based Safety Assessment. Building fires are the cause of major fatalities and injuries amongst all types of accidental fires in Australia. With significant advancement in numerical simulation technique, computer models are becoming acceptable tools for fire safety designs. Nonetheless, these methods still suffer from large amount of manual inputs and lengthy computational times. This research project will address the prevalent deficiency in m .... Development of an Integrated Platform for Performance-Based Safety Assessment. Building fires are the cause of major fatalities and injuries amongst all types of accidental fires in Australia. With significant advancement in numerical simulation technique, computer models are becoming acceptable tools for fire safety designs. Nonetheless, these methods still suffer from large amount of manual inputs and lengthy computational times. This research project will address the prevalent deficiency in many of these computer models and develop a novel and innovative design methodology. The success of this project will revolutionize the computational tools for the industry realizing a convenient, robust and time-saving integrated platform catering for increasing future market demand.
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    Funded Activity

    Discovery Projects - Grant ID: DP0343346

    Funder
    Australian Research Council
    Funding Amount
    $159,264.00
    Summary
    Analysing Iterative Machine Learning Algorithms with Information Geometric Methods. Online machine learning problems arise from situations where data is provided a point at a time. There are many classical algorithms for solving such problems based on the principle of stochastic gradient descent. Recent research by the CIs and others have thrown up interesting but diverse geometric connections that offer new insights. The proposed research aims to integrate the understanding of these algori .... Analysing Iterative Machine Learning Algorithms with Information Geometric Methods. Online machine learning problems arise from situations where data is provided a point at a time. There are many classical algorithms for solving such problems based on the principle of stochastic gradient descent. Recent research by the CIs and others have thrown up interesting but diverse geometric connections that offer new insights. The proposed research aims to integrate the understanding of these algorithms with the aim of designing algorithms better able to exploit prior knowledge, and to extend existing algorithms to new problem domains thus offering well principled and well understood algorithms for solving a variety of novel online problems.
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    Funded Activity

    Discovery Projects - Grant ID: DP0985723

    Funder
    Australian Research Council
    Funding Amount
    $281,000.00
    Summary
    The Next Step in Intelligent Decision-Support Systems (IDSS): Systems that Learn and Adapt. This project will benefit Australia's scientific knowledge and technology base in the areas of evolutionary computation, business intelligence, and decision management. The outcomes will advance Australian companies and organisations, as many common yet complex business problems can be better addressed with systems that automatically learn and adapt to environmental changes. Such complex business problems .... The Next Step in Intelligent Decision-Support Systems (IDSS): Systems that Learn and Adapt. This project will benefit Australia's scientific knowledge and technology base in the areas of evolutionary computation, business intelligence, and decision management. The outcomes will advance Australian companies and organisations, as many common yet complex business problems can be better addressed with systems that automatically learn and adapt to environmental changes. Such complex business problems include dynamic scheduling (in the manufacturing sector), resource allocation optimisation (in the defence, mining, and agriculture sectors), and network design optimisation (in the telecommunications and energy sectors).
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    Funded Activity

    Discovery Projects - Grant ID: DP0345901

    Funder
    Australian Research Council
    Funding Amount
    $165,000.00
    Summary
    Unsupervised learning of finite mixture models in data mining applications. The extraction of useful information from massively large databases is known as data mining. Its broad but vague goal is to find "interesting structure" in the data, which typically leads to breaking the data into clusters. To this end, we consider the fast, efficient, and automatic learning of finite mixture models in hugh data sets without any prior knowledge of the structure. This probabilistic approach to the discove .... Unsupervised learning of finite mixture models in data mining applications. The extraction of useful information from massively large databases is known as data mining. Its broad but vague goal is to find "interesting structure" in the data, which typically leads to breaking the data into clusters. To this end, we consider the fast, efficient, and automatic learning of finite mixture models in hugh data sets without any prior knowledge of the structure. This probabilistic approach to the discovery and validation of group structure in data mining applications will considerably enhance knowledge management and decision support in science, industry, and government.
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    Funded Activity

    Discovery Projects - Grant ID: DP0880739

    Funder
    Australian Research Council
    Funding Amount
    $191,000.00
    Summary
    A Comprehensive Platform for Dynamic Decision Support in Warning Systems through Better Management of Uncertain Information. Public and individual warning systems are installed widely in Australia for emergency situations such as fire, terrorist attack, tsunami, and financial risk. The developed platform with its uncertain information management and dynamic decision support software will directly assist Australian government agencies, industries, and professional officers responsible for public .... A Comprehensive Platform for Dynamic Decision Support in Warning Systems through Better Management of Uncertain Information. Public and individual warning systems are installed widely in Australia for emergency situations such as fire, terrorist attack, tsunami, and financial risk. The developed platform with its uncertain information management and dynamic decision support software will directly assist Australian government agencies, industries, and professional officers responsible for public warning systems by improving the reliability of generated warnings, effective design of any new warning system, and accurate decision making in responding threats. It will also greatly contribute to the processing of uncertain information in other organizational information systems, and enhance training exercise facilities in complex emergency environments.
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    Funded Activity

    Linkage Projects - Grant ID: LP0455343

    Funder
    Australian Research Council
    Funding Amount
    $70,668.00
    Summary
    DEVELOPING A SMART FIRE RISK ADVISORY SYSTEM USING FIRE MODELLING AND KNOWLEDGE-BASED SYSTEMS TECHNOLOGIES. This project aims to develop a prototype fire risk advisory system for fire prevention and protection. Intelligent knowledge-based systems technologies will be tailored to provide a standardised approach for fire risk analysis by using collective expertise of fire engineers and fire modelling. This project will lead to a significant advancement to the development of intelligent systems tec .... DEVELOPING A SMART FIRE RISK ADVISORY SYSTEM USING FIRE MODELLING AND KNOWLEDGE-BASED SYSTEMS TECHNOLOGIES. This project aims to develop a prototype fire risk advisory system for fire prevention and protection. Intelligent knowledge-based systems technologies will be tailored to provide a standardised approach for fire risk analysis by using collective expertise of fire engineers and fire modelling. This project will lead to a significant advancement to the development of intelligent systems techniques for industrial applications and provide a better fire risk analysis tool for fire protection and preventions with the benefit of reduced risk for buildings.
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