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This project involves model-based depth of anaesthesia monitoring using autoregressive moving average modelling and neural mass and neural field modelling of the electroencephalographic (EEG) signal
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, decision-making processes, and even democratic institutions. Large language models (LLMs) have shown tremendous potential in natural language understanding and generation. This research aims to harness
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study the underlying theory, using the framework of Evolutionary Game Theory and build models for concrete applications based on this theory [2]. The ultimate goal of this project is to develop new
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The primary objective of this project is to enhance Large Language Models (LLMs) by incorporating software knowledge documentation. Our approach involves utilizing existing LLMs and refining them
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Australians, it’s not surprising how hard it is to have access to dermatologist expertise. Solution: We aim to develop AI model that is equipped with specialist expertise to help improve diagnosis accuracy
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on game theoretical modelling, usually employing populations of software agents to emulate the behaviour of human populations. Researchers construct models, usually based on known games, and empirical data
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migrating birds steer their flocks, how fish schools hunt, and how ants swarm when they forage. Ants are indeed a prototypical model system for the study of self-organised used behaviour. An ant colony must
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of Machine Learning (ML) models across large-scale distributed systems. Leveraging advanced AI and distributed computing strategies, this project focuses on deploying ML models on real-world distributed
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on-device ML, models must be deployed at the local mobile devices, thereby creating a new attack surface inevitably. Commercial ML models are now stored on mobile devices, which is completely out