Artificial intelligence (AI) is strongly impacting the development of new software systems, particularly natural language processing, computer vision, signal processing, and deep learning-based reasoning used in process automation and various semi- and fully autonomous systems, where it is filling a gap not previously achievable by other traditional computational and mathematical approaches.
AI is also being implemented in the decision support systems (e.g., control room). There is a significant scope for the application of the cutting-edge AI methods to improve HSE as well as risk management.
Artificial intelligence for health, safety and environmental risk management program has been developed to provide the industry with tailor-made software and hardware solutions as well as scientific guidance that will support achieving transformation in HSE, risk management and productivity optimization which new AI methods promise.
To accomplish these goals the program looks at research questions throughout multiple cross-cutting areas, including:
- Machine learning for human-systems interaction
- Natural language processing for knowledge extraction, management, visualization and reporting
- Complex systems applications in modern information, processing, and energy industries
- Deep learning-based data reconstruction
- Intelligent digital twins
- AI-based systems risk analysis and deployment management
Research lead
Dr Nikodem Ryback
View Nikodem Ryback's research profile
Research team
Professor Maureen Hassall
View Maureen Hassall's research profile
Professor Robin Burgess-Limerick
View Robin Burgess-Limerick's research profile
Adjunct Professor Mathew Hancock
Dr Sara Pazell
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- Artificial intelligence-based system for automatic extraction, classification, and reporting of risks from safety-related text and data information for a better decision support (NLP)
- Artificial intelligence-based 3D impact zone management (computer vision and data reconstruction)
- Knowledge extraction and deep learning for risk management in resourcing decarbonization (deep learning-based reasoning and complex systems theory)
- Knowledge graphs for AI-based digital twins (next generation digital twins and data visualisation)
Sample of research outputs
Publications
Rybak, Nikodem, and Maureen Hassall. "Artificial Intelligence Applications for Workplace Safety: An In-Depth Examination." Encyclopedia of Information Science and Technology, Sixth Edition, edited by Mehdi Khosrow-Pour, D.B.A., IGI Global, 14 Aug 2024. IGI Global, doi.org/10.4018/978-1-6684-7366-5.ch085.
Harker, C., Hassall, M., Lant, P., Rybak, N., & Dargusch, P. (2023). Machine Learning-enhanced Text Mining as a Support Tool for Research on Climate Change: Theoretical and Technical Considerations. 5G, Artificial Intelligence, and Next Generation Internet of Things: Digital Innovation for Green and Sustainable Economies. DOI: 10.4018/978-1-6684-8634-4
Harker, C., Hassall, M., Lant, P., Rybak, N., & Dargusch, P. (2022). What Can Machine Learning Teach Us about Australian Climate Risk Disclosures?. Sustainability, 14(16), 10000.
Rybak, N., & Hassall, M. (2022). Machine Learning–Enhanced Decision-Making. Handbook of Smart Materials, Technologies, and Devices: Applications of Industry 4.0, 477.
Rybak, N., & Hassall, M. (2021). Deep Learning Unsupervised Text-Based Detection of Anomalies in US Chemical Safety and Hazard Investigation Board Reports. In 2021 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME) (pp. 1-7). IEEE.
Rybak, N., Hassall, M., Parsa, K., & Angus, D. J. (2017). New real-time methods for operator situational awareness retrieval and higher process safety in the control room. In 2017 IEEE International Systems Engineering Symposium (ISSE) (pp. 1-7). IEEE.
Software
Knowledge Graphs and Knowledge Extraction: https://youtu.be/8cUloFnTlh4?t=2498
Video-based Safety Management and Hazard Prevention Machine Learning Solutions: https://youtu.be/WfZaPJNKcWs
Deep Learning Unsupervised Text-Based Detection of Anomalies in Safety Incidents Reports: https://youtu.be/NH-ltg5znVk
Contact us
Get in touch to learn more about our research.
Dr Nikodem Rybak
Research Fellow, Minerals Industry Safety and Health Centre
The Artificial Intelligence for Health, Safety and Environmental Risk Management Program sits within the Minerals Industry Safety and Health Centre (MISHC).