Livable bathrooms for older people: designing out dependence in activities of daily living. This project aims to establish the design fundamentals needed for the development of more flexible, innovative and safer bathroom fixtures and domestic bathroom environments for older Australians. It provides an understanding of bathroom features and characteristics that function well for older persons and those that diminish their wellbeing.
Cross-domain knowledge transfer for data-driven decision making. This project aims to develop a set of cross-domain knowledge transfer methodologies to support Data-Driven Decision-Making (D3M) systems. D3M is essential in business, particularly for ever-changing environments in today’s big data era, but D3Ms for solving new problems may face in-domain data insufficiency. The challenge is to effectively transfer knowledge from multiple heterogeneous source domains. The outcomes are expected to t ....Cross-domain knowledge transfer for data-driven decision making. This project aims to develop a set of cross-domain knowledge transfer methodologies to support Data-Driven Decision-Making (D3M) systems. D3M is essential in business, particularly for ever-changing environments in today’s big data era, but D3Ms for solving new problems may face in-domain data insufficiency. The challenge is to effectively transfer knowledge from multiple heterogeneous source domains. The outcomes are expected to transfer implicit and explicit knowledge, handle discrete and continuous outputs, and support business decision-making, which should advance the discipline of transfer learning and data-driven DSS in dynamically changing environments.Read moreRead less
Intelligent CRM through Conjoint Data Mining of Heterogeneous Sources. This project aims to investigate and develop techniques to improve customer relationship management (CRM) for public and private organisations. It aims to develop an intelligent framework to assist in adaptive marketing and management of customers. The framework is designed to manage multiple information resources for information sharing, and to synthesise knowledge through visualisation. Intended outcomes are standardised XM ....Intelligent CRM through Conjoint Data Mining of Heterogeneous Sources. This project aims to investigate and develop techniques to improve customer relationship management (CRM) for public and private organisations. It aims to develop an intelligent framework to assist in adaptive marketing and management of customers. The framework is designed to manage multiple information resources for information sharing, and to synthesise knowledge through visualisation. Intended outcomes are standardised XML profiles for the different data sets and business processes, novel techniques for conjoint mining of structured and semi-structured data, and adaptive business intelligence techniques. The results will be validated using large real-world data sets provided by the partner organisation.Read moreRead less
Linkage Infrastructure, Equipment And Facilities - Grant ID: LE110100023
Funder
Australian Research Council
Funding Amount
$500,000.00
Summary
Integrated command and control facility for large-scale critical infrastructure management. This is a test bed facility for achieving sustainable operation of Australia's critical infrastructure, particularly at airports. The facility will enable an integrated and coordinated strategy to increase operational resilience while not losing sight of the complex nature and dynamic requirements of critical infrastructure management.
Fuzzy Transfer Learning for Prediction in Data-Shortage and Rapidly-Changing Environments. Collecting sufficient up-to-date data to train a learning model for data analysis and prediction is difficult and expensive. This project will develop a Fuzzy Transfer Learning methodology, using Information Granularity theory, that exploits data with different features and/or distributions available in other, similar systems, to provide accurate learning-based prediction for current problems. It will esta ....Fuzzy Transfer Learning for Prediction in Data-Shortage and Rapidly-Changing Environments. Collecting sufficient up-to-date data to train a learning model for data analysis and prediction is difficult and expensive. This project will develop a Fuzzy Transfer Learning methodology, using Information Granularity theory, that exploits data with different features and/or distributions available in other, similar systems, to provide accurate learning-based prediction for current problems. It will establish a new research direction, Fuzzy Transfer Learning for Prediction, and the outcomes will enable government and industry to better use past experience to make more accurate predictions and decisions. Highly significant benefits will also accrue in the data analytics, business intelligence and decision making research fields.Read moreRead less
Transfer Learning for Genome Analysis and Personalised Recommendation. This project aims to improve the accuracy, adaptability, and comprehensiveness of health characteristic predictions and provide personalised recommendations for healthcare service and disease prevention. The deliverables include uncertainty learning and multi-source transfer learning methodologies for predictions based on genome analysis that distils and transfers useful knowledge from multiple sources into an Australian geno ....Transfer Learning for Genome Analysis and Personalised Recommendation. This project aims to improve the accuracy, adaptability, and comprehensiveness of health characteristic predictions and provide personalised recommendations for healthcare service and disease prevention. The deliverables include uncertainty learning and multi-source transfer learning methodologies for predictions based on genome analysis that distils and transfers useful knowledge from multiple sources into an Australian genome analysis model. A federated cross-domain recommender system will be developed to profile individuals and generate personalised recommendations. The outcomes are expected to create a paradigm shift in learning-based prediction and personalised recommendations to support healthcare services in complex environments. Read moreRead less
Predictive analytics from at home telemonitoring of vital signs. Predictive analytics from at home telemonitoring of vital signs. This project aims to reduce unscheduled admissions to hospital, by developing statistical models of people’s health using longitudinal measurements of vital signs and questionnaires. Hospital costs are becoming unsustainable and will overwhelm state budgets within thirty years. Telehealth monitoring to manage chronic disease is becoming increasingly routine internatio ....Predictive analytics from at home telemonitoring of vital signs. Predictive analytics from at home telemonitoring of vital signs. This project aims to reduce unscheduled admissions to hospital, by developing statistical models of people’s health using longitudinal measurements of vital signs and questionnaires. Hospital costs are becoming unsustainable and will overwhelm state budgets within thirty years. Telehealth monitoring to manage chronic disease is becoming increasingly routine internationally and should reduce unnecessary hospital admissions and health service costs. To scale up telehealth services nationally, automated means of assessing changes in an individual health status are needed. This project’s automated risk assessment models are expected to identify exacerbations and orchestrate an optimal response from health services to reduce unscheduled admissions to hospital.Read moreRead less
A safety-preserving ecosystem for autonomous driving. In this project, Macquarie University will collaborate with UTS and SilverQuest to develop an innovative safety-preserving ecosystem for autonomous driving. This system will not only be adopted by SilverQuest’s customers (automotive companies) to secure their latest autonomous driving models, but also be commercialised as a toolset that can be plugged into existing autonomous vehicles to detect and prevent malicious attacks on autonomous driv ....A safety-preserving ecosystem for autonomous driving. In this project, Macquarie University will collaborate with UTS and SilverQuest to develop an innovative safety-preserving ecosystem for autonomous driving. This system will not only be adopted by SilverQuest’s customers (automotive companies) to secure their latest autonomous driving models, but also be commercialised as a toolset that can be plugged into existing autonomous vehicles to detect and prevent malicious attacks on autonomous driving models. The project will lead to two innovations: in theory design an attack detection and prevention ecosystem for autonomous driving and in application implement a safety analysis toolset for industry-scale autonomous systems.Read moreRead less
Sequential decision-making in dynamic and uncertain environments. Current machine learning and optimisation methods cannot well support sequential prediction and decision-making due to the dynamic nature and pervasive presence of big data. This project aims to create a foundation and technology for sequence and uncertainty learning, sequential and dynamic optimisation, and their integration. It is expected to improve robustness and mitigate the vulnerabilities of machine learning algorithms, to ....Sequential decision-making in dynamic and uncertain environments. Current machine learning and optimisation methods cannot well support sequential prediction and decision-making due to the dynamic nature and pervasive presence of big data. This project aims to create a foundation and technology for sequence and uncertainty learning, sequential and dynamic optimisation, and their integration. It is expected to improve robustness and mitigate the vulnerabilities of machine learning algorithms, to increase prediction accuracy and reliability in dynamic sequences, and to support decision-making in complex situations to achieve robust and adaptive results. Anticipated outcomes can help data scientists with state-of-the-art skills to manage sequential data and benefit data-enabled innovation in Australia.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE220101075
Funder
Australian Research Council
Funding Amount
$415,820.00
Summary
Fuzzy transfer learning for real-time decision making under uncertainty. This project’s objective is to build new tools for the next generation of real-time decision making. As the datasphere grows more complex, meaningful decision support already requires a strong capacity for knowledge transfer, substantial robustness to uncertainty, and real-time analytics. Today’s methods are struggling to meet these challenges. The new schema to be devised combines fuzzy logic, transfer learning, reinforcem ....Fuzzy transfer learning for real-time decision making under uncertainty. This project’s objective is to build new tools for the next generation of real-time decision making. As the datasphere grows more complex, meaningful decision support already requires a strong capacity for knowledge transfer, substantial robustness to uncertainty, and real-time analytics. Today’s methods are struggling to meet these challenges. The new schema to be devised combines fuzzy logic, transfer learning, reinforcement learning and deep neural networks. These integrations will lay the foundations for real-time decision-making solutions over the next decade and will advance machine learning under uncertainty. Immediate applications include structural health monitoring, climate prediction and telecommunications maintenance. Read moreRead less