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Research Topic : pattern recognition
Field of Research : Simulation and Modelling
Status : Closed
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  • Researchers (31)
  • Funded Activities (6)
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  • Funded Activity

    Discovery Projects - Grant ID: DP150102442

    Funder
    Australian Research Council
    Funding Amount
    $384,700.00
    Summary
    Coupling Learning in Big Data. Big data features complex coupling relationships within and between diverse entities in various forms and layers. This fundamentally challenges existing learning theories, which usually assume that data is independent and identically distributed (IID). This indicates that such IID tools may either be inapplicable for big data or capture an incomplete or even biased picture of the ground truth in big data. Hence, this project aims to invent breakthrough theories and .... Coupling Learning in Big Data. Big data features complex coupling relationships within and between diverse entities in various forms and layers. This fundamentally challenges existing learning theories, which usually assume that data is independent and identically distributed (IID). This indicates that such IID tools may either be inapplicable for big data or capture an incomplete or even biased picture of the ground truth in big data. Hence, this project aims to invent breakthrough theories and effective tools for systematically modelling and learning sophisticated couplings embedded in big data applications. The outcomes are expected to enhance Australia's leading role in data science research and lift data intelligence-driven productivity and economic growth in a changing world.
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    Funded Activity

    Linkage Projects - Grant ID: LP140100937

    Funder
    Australian Research Council
    Funding Amount
    $360,000.00
    Summary
    Active Management of Complex Non-self-finalising Behaviours through Deep Analytics. This project aims to build theoretical breakthroughs and novel tools for deep analytics and active management of non-self-finalising (NSF) individual and business behaviours, which are sophisticated and increasingly seen in public sectors such as taxation and business including banking and insurance. The challenging economic environment continues to make managing NSF behaviours difficult. To date, there are no su .... Active Management of Complex Non-self-finalising Behaviours through Deep Analytics. This project aims to build theoretical breakthroughs and novel tools for deep analytics and active management of non-self-finalising (NSF) individual and business behaviours, which are sophisticated and increasingly seen in public sectors such as taxation and business including banking and insurance. The challenging economic environment continues to make managing NSF behaviours difficult. To date, there are no sufficient theories or effective systems in data mining and behavioural science to systematically learn the intent, impact and patterns of NSF behaviours, and to suggest cost-effective responses to these behaviours. This project aims to ensure Australia’s leading role in innovation for evidence-driven enterprise behaviour analytics and management.
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    Funded Activity

    Linkage Projects - Grant ID: LP100200774

    Funder
    Australian Research Council
    Funding Amount
    $300,000.00
    Summary
    Detecting significant changes in organisation-customer interactions leading to non-compliance. The instant detection of risky customer and/or group dynamics and business policy and/or process changes dispersed in normal interactions can avoid immense losses and inconsistent policies for Government and industries, such as preventing Centrelink customer debt. This project will deliver novel analytical techniques and smart information use to effectively detect the above-mentioned changes leading to .... Detecting significant changes in organisation-customer interactions leading to non-compliance. The instant detection of risky customer and/or group dynamics and business policy and/or process changes dispersed in normal interactions can avoid immense losses and inconsistent policies for Government and industries, such as preventing Centrelink customer debt. This project will deliver novel analytical techniques and smart information use to effectively detect the above-mentioned changes leading to non-compliance. It will enhance service quality, compliance, payment accuracy and policy design for the Australian Government and industries such as Centrelink, the Department of Families, Housing, Community Services and Indigenous Affairs (FaHCSIA), banking and insurance. The resulting systems, the researchers trained and resulting publications will significantly enhance Australia's leading role in tackling change-driven non-compliance.
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    Funded Activity

    Discovery Projects - Grant ID: DP130102691

    Funder
    Australian Research Council
    Funding Amount
    $360,000.00
    Summary
    Modelling and discovering complex interaction relations hidden in group behaviours in businesses, online and social communities. This project addresses the shortage in current behavior analysis by inventing innovative theories and algorithms for analysing complex relations and interactions in group behaviours. The outcomes of this project will enable effective detection of suspicious large groups, contributing to safer businesses and society and improved compliance in online and social communiti .... Modelling and discovering complex interaction relations hidden in group behaviours in businesses, online and social communities. This project addresses the shortage in current behavior analysis by inventing innovative theories and algorithms for analysing complex relations and interactions in group behaviours. The outcomes of this project will enable effective detection of suspicious large groups, contributing to safer businesses and society and improved compliance in online and social communities.
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    Funded Activity

    Linkage Projects - Grant ID: LP170100891

    Funder
    Australian Research Council
    Funding Amount
    $415,893.00
    Summary
    Reshaping superannuation practice in Australia using big data analytics. This project aims to reform superannuation investment practices in Australia. Using sophisticated data analytics and machine-learning techniques, combined with economic modelling and quantitative finance. The project will try to understand the broad characteristics of Australian superannuation investors and their practice from a ‘big data’ perspective. The expected outcomes of this project are the identification of key dete .... Reshaping superannuation practice in Australia using big data analytics. This project aims to reform superannuation investment practices in Australia. Using sophisticated data analytics and machine-learning techniques, combined with economic modelling and quantitative finance. The project will try to understand the broad characteristics of Australian superannuation investors and their practice from a ‘big data’ perspective. The expected outcomes of this project are the identification of key determinants for successful superannuation behaviour to inform decision-making for better superannuation practices and policies. It is expected that the insights arising from this project will contribute to safeguarding the future of Australia’s superannuation schemes, and to better financial security at retirement.
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    Funded Activity

    ARC Future Fellowships - Grant ID: FT100100345

    Funder
    Australian Research Council
    Funding Amount
    $542,552.00
    Summary
    A networked robotic telescope array for coincident detection of transient phenomena in the optical, gravitational wave, neutrino and radio spectra. An international collaboration of scientists will employ a global network of rapid response robotic telescopes and detectors to study exotic transient phenomena in the early Universe. Potential spin-offs include the application of novel image analysis techniques for identifying and tracking dangerous space junk.
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    Showing 1-6 of 6 Funded Activites

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