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
Data mining complex transactional and criminal networks. Money laundering, if undetected, poses a major concern for governments and communities. The software system platform for detecting money laundering networks from this project will be the first that can assist intelligence data analysts to detect unknown money laundering networks faster and more accurately, helping fight crimes more efficiently.
Discovery Early Career Researcher Award - Grant ID: DE180101268
Funder
Australian Research Council
Funding Amount
$367,446.00
Summary
Inference and resilient control of complex cyber-physical networks. This project aims to establish a fundamental framework to efficiently analyse and control critical, modern infrastructure networks such as power grids and the Internet. The project expects to bridge the gap between cyber-physical network theory and network resilience engineering through developing a body of knowledge about cyber-physical systems, security analysis and emergence of network behaviours. The project will develop des ....Inference and resilient control of complex cyber-physical networks. This project aims to establish a fundamental framework to efficiently analyse and control critical, modern infrastructure networks such as power grids and the Internet. The project expects to bridge the gap between cyber-physical network theory and network resilience engineering through developing a body of knowledge about cyber-physical systems, security analysis and emergence of network behaviours. The project will develop design methodologies to improve the resilience of these networks against internal faults and external attacks. This should improve the robustness and invulnerability of Australian power grids and the Internet against random failures and malicious cyber-physical attacks.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
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
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