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
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
Smart micro learning with open education resources. This project aims to enhance personalised learning systems for mobile device users . Open online education is gaining in popularity with its ease of use. The project tackles the problems in relation to more and more popular mobile and ‘micro learning’, where people learn on the move and within small units of time. Ontology and machine learning technologies used in this project will help to optimise the offering of open education resources, by p ....Smart micro learning with open education resources. This project aims to enhance personalised learning systems for mobile device users . Open online education is gaining in popularity with its ease of use. The project tackles the problems in relation to more and more popular mobile and ‘micro learning’, where people learn on the move and within small units of time. Ontology and machine learning technologies used in this project will help to optimise the offering of open education resources, by providing solutions meeting each individual learner’s needs. The main outcome will consolidate a cloud based micro learning framework through integrating a group of novel algorithms.Read moreRead less
Drift learning for decision-making in dynamic multi-stream environments. This project aims to provide application-ready real-time decision support systems for big data situations. Real-time support for organisational decisions is crucial in fast-changing environments that are highly dependent on data from multiple large streams. Unforeseen changes in data distribution (drift) are inevitable. The ability to learn drift in dynamic environments with multiple large data streams will benefit innovati ....Drift learning for decision-making in dynamic multi-stream environments. This project aims to provide application-ready real-time decision support systems for big data situations. Real-time support for organisational decisions is crucial in fast-changing environments that are highly dependent on data from multiple large streams. Unforeseen changes in data distribution (drift) are inevitable. The ability to learn drift in dynamic environments with multiple large data streams will benefit innovation and decision quality in challenging data situations. The project will have wide applications, such as in cybersecurity, telecommunications, bushfire control and logistics. The project will advance machine learning knowledge, providing a foundation and technologies to support real-time decision-making in big data environments.Read moreRead less
Concept Drift Detection and Reaction for Data-driven Decision Making. Unforeseeable changes to patterns that underlie data (concept drift) occur in all organisational data, and in unstructured data, making subsequent data-driven prediction less accurate as time passes, which leads to poor decision outcomes. To solve these problems, this project aims to develop novel fuzzy competence models to reflect concept drift, with methods to detect and react to changes, and integrate them into Decision Sup ....Concept Drift Detection and Reaction for Data-driven Decision Making. Unforeseeable changes to patterns that underlie data (concept drift) occur in all organisational data, and in unstructured data, making subsequent data-driven prediction less accurate as time passes, which leads to poor decision outcomes. To solve these problems, this project aims to develop novel fuzzy competence models to reflect concept drift, with methods to detect and react to changes, and integrate them into Decision Support Systems (DSS) to provide adaptivity for ever-changing environments. These cutting-edge results are intended to be directly used to enhance organisational real-time data analytics and dynamic decision making, and are expected to significantly contribute to information science by introducing a new research field, adaptive data-driven DSS.Read moreRead less
Visual analytics for high volume multi attribute financial data streams. While our ability to accumulate data (such as financial data) is increasing, our capability to analyse them is still inadequate despite technological improvements. The new Visual Analytics methods will allow processing of the massive and time-varying data so that the time-critical decisions can be made with minimum effort.