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machine learning, Reliability Engineering & System Safety, Volume 264, Part A, December 2025, 111368 L. Deng, C. Shi, H. Li, M. Wan, F. Ren, Y. Hou, et al. Prediction of energy mass loss rate for biodiesel
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intelligence (AI) and machine learning (ML) to investigate the role of folate in breast cancer prevention across three interconnected aims spanning epidemiology, clinical biomarkers, and molecular analysis. 1
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interactions, and mobility are collected). Machine Learning models will be trained to infer fatigue in real time, triggering adaptive prompts, such as suggesting micro-breaks. Expected outcomes include a sensing
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system resilience under changing climatic and economic conditions. Traditional predictive models, such as regression and standard machine learning, capture correlations but not causal mechanisms—how
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al. (2024) Photonics for Neuromorphic Computing: Fundamentals, Devices, and Opportunities. Advanced Materials. doi: 10.1002/adma.202312825. [6] K. Lee et al., “Secure Machine Learning Hardware
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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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-aligned investment, integrating sustainability metrics within transparent machine learning models. “An AI-Driven Connected Health System to Support Movement and Wellbeing during Preconception, Pregnancy and
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to the learning outcomes of an Honours degree in lieu of academic qualifications. Research proposal of 2000 words detailing aims, objectives, milestones and methodology of the project Equal Opportunities
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intelligence and machine learning methods to improve pregnancy outcomes. Briefings in Bioinformatics, 22(5), bbaa369. De Vivo, M., & Mills, H. (2019). “They turn to you first for everything”: insights
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vision systems addressing an urgent industrial challenge with immediate, large-scale impact. The project will take existing knowledge in computer vision and deep learning and apply it directly to a