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Found 13 matching student topics

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Data mining for eResearch – environmental acoustic analysis

While huge amount of data has been collected, processing and mining those data is challenging.The QUT EcoAcoustic research group focuses on utilising information technology to aid in scientific research. A sensor network for environmental monitoring has been established.

Study level
PhD, Master of Philosophy, Honours
Faculty
Science and Engineering Faculty
Lead unit
School of Electrical Engineering and Computer Science

Machine learning from big data for root cause analysis of power system faults

Power systems are fundamental infrastructure world-wide and their safe and reliable operations are critical. However, faults and failures are inevitable in power systems. Nevertheless, these should be avoided as much as possible.When a fault or failure occurs, the real-time supervisory control and data acquisition system (SCADA) usually presents this information as alarms or indications at the control centre. Quite often, the resulting alarm data are reviewed manually with the help of some automated system components to identify the contributing factors …

Study level
PhD
Faculty
Science and Engineering Faculty
Lead unit
School of Electrical Engineering and Computer Science

Process data analytics: quality-informed and responsible process mining

Modern organisations consider data to be the lifeblood of their organisations. Technological advances in the fields of Business Intelligence (BI) and Data Science empower organisations to become ‘data-driven’ by applying new techniques to analyse and visualise large amounts of data. The potential benefits include a better understanding of business performance and more-informed decision making for business growth.A key road block to this vision is the lack of transparency surrounding the quality of data. Significant data quality issues persist and hinder …

Study level
PhD, Master of Philosophy, Honours, Vacation research experience scheme
Faculty
Science and Engineering Faculty
Lead unit
School of Information Systems

Image/audio analysis and retrieval for environment monitoring

Sensor networks bring ornithologists and pattern recognition researchers together to make some applications possible.These applications include unobtrusive observations (where the presence of humans changes some animal behaviours) and the studies of spatial and temporal variation in biological processes.

Study level
PhD, Master of Philosophy, Honours
Faculty
Science and Engineering Faculty
Lead unit
School of Electrical Engineering and Computer Science

Hybrid Intelligence - Big data visual analytics

The impact of climate change on human and fauna has led to advances in sensing technologies which have enabled environmental monitoring over a large area and a long period of time.Multiple networks have been set up around the world to monitor biodiversity using terrestrial acoustic sensors. Timely and appropriate analysis of the big archive of environmental sound becomes a great challenge.The research question that will be looked at is: What are good ways to support human interaction with big data? …

Study level
PhD, Master of Philosophy, Honours, Vacation research experience scheme
Faculty
Science and Engineering Faculty
Lead unit
School of Electrical Engineering and Computer Science

Guided process analytics for actionable improvement recommendations

Process analytics mainly comprises of data-driven analysis of business processes using the massive amount of event log data captured by information systems in the organisations. Various analytical techniques have been developed to help extract insights about the actual business processes with the ultimate goal of process improvement.However, in the ‘big data’ era, we often face the problem that there is too much data to analyse. Without a clear objective for analysis, the tasks such as extracting the relevant data, analysing …

Study level
PhD, Master of Philosophy, Honours, Vacation research experience scheme
Faculty
Science and Engineering Faculty
Lead unit
School of Information Systems

Discovering organisational knowledge from process event logs

Process mining enables data-driven process analysis using the massive amount of event log data captured by information systems in today's organisations. Various techniques have been developed to help extract insights about the actual business processes with the ultimate goal to improve process performance as well as the organisations' business performance.As an important sub-field of process mining, organisational mining focuses on discovering organisational knowledge, including e.g. organisational structures and human resources relevant to the performance of a business process, from event …

Study level
PhD, Master of Philosophy, Honours, Vacation research experience scheme
Faculty
Science and Engineering Faculty
Lead unit
School of Information Systems

Map-reduce databases for process mining: a good match?

Process mining is a specialised form of analytics of growing practical significance. It is well known for its ability to mine datasets, resulting from the execution of processes, to identify inefficiencies, and to reveal insights into resource behaviour, activity dependencies, and process compliance, among others.As such, it is instrumental to effective decision making and streamlining of business processes. Process mining can also operate in tandem with process automation to fine tune on-the-fly decision making and predicting resource utilisation.A challenge that …

Study level
Honours, Vacation research experience scheme
Faculty
Science and Engineering Faculty
Lead unit
School of Information Systems

Comparison of models for multivariate data analysis

Determining useful models for multivariate data still remains a challenge. There are several classes of models now available, and it is currently unclear as to which class of models tend to provide a better fit to real data in practice.This project will explore several classes of multivariate models, implement them on real data sets and compare the fits.This project would be suitable for extending into an Honours thesis.

Study level
Honours
Faculty
Science and Engineering Faculty
Lead unit
School of Mathematical Sciences

Data augmentation for training deep learning networks to detect brain lesions from MRI

Detecting lesions from medical imaging is a difficult tasks for radiologists that is time-consuming and subjective. Artificial intelligence (AI) techniques could outperform humans but there is a lack of well-characterised, large datasets available for training purposes.This PhD project will focus on data augmentation techniques using synthetic approaches, as well as weekly supervised learning.This project is part of a large team of researchers involving startups, CSIRO, QUT faculties and several postdoc and PhD students. …

Study level
PhD
Faculty
Science and Engineering Faculty
Lead unit
School of Electrical Engineering and Computer Science

Time series modelling of athlete performance using wearable technologies

With elite sports becoming ever more competitive, coaches, athletes and sports scientists are looking to use data to maximise training outcomes for greater competitive performance.For the 2018 Commonwealth Games on the Gold Coast, a series of projects are being offered towards the development of new statistical and machine learning tools in cross-disciplinary collaboration with sports scientists and end users. The projects involve QUT, the ARC Centre of Excellence in Mathematical and Statistical frontiers (ACEMS) and the Queensland Academy of Sport …

Study level
Honours
Faculty
Science and Engineering Faculty
Lead unit
School of Mathematical Sciences

Benchmarking elite athletes using multilevel models

With elite sports becoming ever more competitive, coaches, athletes and sports scientists are looking to use data to maximise training outcomes for greater competitive performance.For the 2018 Commonwealth Games on the Gold Coast, a series of projects are being offered towards the development of new statistical and machine learning tools in cross-disciplinary collaboration with sports scientists and end users. The projects involve QUT, the ARC Centre of Excellence in Mathematical and Statistical frontiers (ACEMS) and the Queensland Academy of Sport …

Study level
Honours
Faculty
Science and Engineering Faculty
Lead unit
School of Mathematical Sciences

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