UTS Digital Thesis Collection
This Community contains theses written by research students at UTS. It contains PhD, Masters and Honours theses.
Search or browse the UTS Digital Thesis Collection to locate theses. More information and submission guidelines are available from the Graduate Research School.
UTS higher degree theses from this collection may also be made available via subscription or open access products, for example, the ProQuest Dissertations and Theses Global and the ProQuest Open Access Platform. This is intended to increase the exposure of UTS research. If you wish to remove your thesis from these products please email us at lib-adt@uts.edu.au.If you would like to contact the Library, please contact us at lib-adt@uts.edu.au.
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Information systems (IS) research on the management of gender bias in AI and its negative repercussions, including techniques for reducing gender bias in AI, is lacking despite the information syst...
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Robots have already increased the productivity of industries such as manufacturing, mining, and logistics by taking over many dangerous, dirty or dull tasks and freeing humans to focus on more inte...
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Natural organic matter (NOM) are ubiquitous in the aquatic environments. NOM are considered to be problematic in the perspective of providing safe drinking water and treatment operation. Natural or...
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This research is aimed to identify design and process engineering requirements for the implementation of an efficient and effective e-health-based personalised diabetes management in Saudi Arabia. ...
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Consensus views in finance must be continuously challenged and re-evaluated. This thesis uses new techniques and modern perspectives to challenge commonly held beliefs, both new and old, in financi...
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Deep neural networks (DNNs) have revolutionized various fields with their superior performance on particular tasks. However, these tasks in real-world applications are often interrelated, raising t...
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The effective management of Artificial Intelligence (AI) technologies is a crucial aspect that accompanies the adoption of AI within organisations. As AI’s rapid and widespread integration continue...
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Modern machine learning systems, particularly deep neural networks, have driven advancements in many artificial intelligence domains. Their success largely depends on ample, high-quality labeled da...
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Deep neural networks (DNNs) have transformed computer vision, advancing object recognition, scene understanding, and image synthesis. However, a critical challenge remains in their ability to gener...
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Traditional vision is focused on the perception of visible light but limits us to only detecting surface features. Near-infrared light can penetrate certain materials, so to capture this light, hyp...