Information Technology
Adrian Shatte
- Senior Lecturer, Information Technology
- adrian.shatte@jcu.edu.au
Asha Joseph
- Adjunct Research Fellow
- asha.joseph1@jcu.edu.au
FLARE – Federated Learning for AI-Based RRR Engagement
FLARE – Federated Learning for AI-Based RRR Engagement, will develop a smartphone-based AI system for mental health support in RRR communities. FLARE will enable adaptive, privacy-preserving mental health support (screening, diagnosis, and treatment) on mobile devices, reducing barriers to access, stigma, and service availability in the regions JCU serves.
Development of Artificial Intelligence for Predicting Diabetic Foot Ulcers in Regional, Rural and Remote Areas: A Pilot Study
People living in regional, rural, and remote areas are many times more likely to have diabetic foot ulcer (DFU) due largely to late detection of the condition. We propose to develop an artificial intelligence (AI) model using clinical data and images to detect early DFU. Two hundred and fifty diabetes subjects aged >18 years with or without DFUs living in Townsville and Northwest HHSs will be studied in collaboration with JCU IT experts. The AI applications will be trained to select the strongest predictors to determine early occurrence of DFUs which will lead to possible reduction of limb amputations.
Roster Modelling for FIFO in the mining, oil and gas sector using genetic algorithms and neural networks
Australia currently has approximately 130,000 Fly-In/Fly-Out (FIFO) mining workers and 70,000 construction workers nationally as at 2011 (Australian Government report, 2011). The vast majority of FIFOs are on two week rosters and 45% time at work, which equates to 10.4 million plane trips (i.e., 5.2 million return trips) per year, approximately 84,000 room nights, 250,000 meals per day and other associated supply logistics such as cleaning and administration. Currently, the systems that manage the rostering of FIFO worker's travel, accommodation, meals, cleaning and administration, have limited ability to assist with determining the most efficient roster model. A 3% waste across transport and accommodation equates to millions of dollars (e.g., approximately 300,000 flights, 2,500 rooms and 7,500 meals). Any reduction in waste would represent substantial saving. The ability to efficiently schedule resources and maintain sufficient staff coverage, rest periods etc. has the capacity to improve the productivity of FIFO enterprises. The aim of this research is to explore the use of artificial intelligence to build an optimised travel, accommodation and roster model based on varying sets of seed data and supervised and unsupervised neural network training. Neural networks can be used to optimise rosters, transport and accommodation using a subset of all required parameters. Two approaches will be investigated to determine the most effective and efficient AI model. These
Please provide a brief outline of the proposed project and deliverables
Researchers in Business Application Form v4
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include neural networks with 1) supervised training using cloud based historical data or 2) unsupervised training using Genetic algorithms. The outcome of this project will be an artificially intelligent solution to optimisation modelling of workforce logistics in the mining, oil and gas sector. The product will be an intuitive, automated roster modelling tool that will benefit Osmotion's current and future clients by offering waste reduction and economic value through optimising the FIFO roster model.
Wildlife in Virtual Reality - a New Way to Educate and Engage (Old ID 22306)
This project will harness rapidly evolving technologies to create an exciting new product for raising community awareness of conservation issues. Specifically, the project will use the latest computer wizardry to allow people (locals, visitors, students) to have the unique experience of getting up close and personal with a Lumholtz's tree-kangaroo through virtual reality.
Develop the ResPax (digital) Passbook (Old ID 27496)
To develop digital tools for collecting tourism data in the region to enable better decision-making through various data mining approaches.
Kimberley - Wet Tropics Wildlife on Virtual Reality (Old ID 23592)
This project will further develop 'Kimberley' the 3D virtual reality tree-kangaroo to the stage where the 'experience' can go live to the public at the Malanda Falls Visitor Centre. This project further extends 3D tree-Kangaroo with Virtual Reality and artificial intelligence.
ResPax Digital Passbook (Phase II) (Old ID 29015)
To continue the Phase I of project to further develop digital tools for collecting tourism data in the region to enable better decisionmaking.
Applying Artificial Intelligence to Business Management Advice (Old ID 24802)
Develop methods of analysing large volumes of accounting data to provide valuable insights for business owners and actionable advice that will help them improve their business practices. Mentoring fee for guidance provided to Graduate Student employed by Calxa Pty Ltd.
Leveraging digital technology to reduce the prevalence and severity of eating disorders in Australia (WIRED project)
Inter-linked studies exploring efficacy, user experience and cost-effectiveness of app based delivery of eating disorder interventions. The project also tackles influence of social media conversations on treatment seeking and disordered eating behaviours
Carbon accounting for sustainable design and manufacturing in the signage industry
Industry partnership with Diadem Pty. Ltd.
Urban crime analysis through areal categorized multivariate association mining
- 2008
- Taylor & Francis
- Researchers:Ickjai Lee
Modified Simpson O(n3) algorithm for the full sibship reconstruction problem
- 2005
- Oxford University Press
- Researchers:Dmitry Konovalov
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ACM-SIGSPATIAL
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