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vast amounts of rich, complex data, unlocking their insights requires cutting-edge AI and machine learning (ML) techniques. Meanwhile, although artificial neural networks (ANNs) have powered recent AI
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residents to adopt more green mobility strategies, and especially the residents of newly built redevelopment housing complexes, to utilise the public transit system with affordable monthly passes, to cycle
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knowledge through attention mechanisms within transformer networks. Traditional data-driven models struggle with convergence and generalisation due to limited contextual understanding. Earth observation data
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identity can effectively adopt and apply MI tools. Through qualitative research, including in-depth interviews, firm observations, and engagement with industry networks, the project will: *Map how NI
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visual inspection. The research will address several challenges: Complex Surfaces: Developing robust algorithms (leveraging Convolutional Neural Networks and Transformers) capable of identifying tiny