#artificial intelligence

AI-driven automatic translation of blueprints into construction instructions 

This project develops AI models to address the critical gap in automated interpretation of blueprints in construction domain. The expected outcome is an AI system that translates complex blueprints into plain language actional construction instructions. The project outcome will reduce errors, delays and cost in construction industry, enhancing productivity, safety, and sustainability. 

Identifying serum biomarkers in PFAS serum concentration using metabolomics

This project investigates the health impacts of PFAS exposure in firefighters using advanced metabolomic techniques to identify biomarkers. The expected outcome is the development of new biomarkers for PFAS exposure, enhancing health diagnostics and preventive measures. This research will potentially lead to improved health risk assessments, better regulatory policies, and enhanced safety for workers exposed to hazardous substances.

AI-empowered visual recognition system for dairy cow identification, health and behaviour monitoring

This project develops an AI-driven system to identify individual cows, and monitor and detect their health and behaviour, through the use of facial recognition, posture analysis, and thermal imaging. The expected outcome is early detection of illness and abnormal activity of cows. This project will support productivity, reduce losses, and improve animal welfare in the dairy industry.

Building responsible AI: Co-designing knowledge transfer solutions using generative AI

This project will bridge the gap between the principles of responsible AI and their measurable practice, by developing AI knowledge tools to empower end-user community groups. The expected outcomes are to build insights and develop AI tools for information dissemination and knowledge transfer. The potential benefit is to produce responsible AI solutions to help disability groups connect, communicate, share and learn.

Enhancing cybersecurity with AI and Large Language Models 

This project will explore the integration of artificial intelligence (AI) and large language models (LLMs) to predict organisational cybersecurity risks and mitigate threats in advance. The expected outcomes are an enhanced cybersecurity framework, better threat intelligence techniques and user-centric designs, and an adaptable solution. This may help businesses to identify cyber risks and prevent cyber incidents prior to happening and avoid financial losses and brand damage.