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predictive accuracy and prohibitively long computational times, making them unsuitable for real-time process control. Artificial intelligence (AI) models present a promising alternative by addressing
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: The project will adopt a mixed-methods approach: - Data collection: Deploy camera traps and artificial flower attractants across urban and agricultural sites to capture pollinator activity. - Model development
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convert relevant measurement data into actionable information, such as the health condition, and/or the remaining useful life of critical assets. Currently, Artificial Intelligence (AI) based big data
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overlook the impact of liquid metal convection within the molten pool. Although using an artificial compensation through calibration with experiments can improve the temperature prediction, the predictive
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, embedded intelligence, and adaptive cyber-physical systems that operate safely under uncertainty and dynamic conditions. This PhD at Cranfield University explores the development of resilient, AI-enabled
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, intelligent monitoring systems and predictive technologies have become essential competitive advantages. This project sits at the intersection of data science, engineering, and design innovation, addressing
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this issue and we could use obtain data-driven models using machine learning algorithms such as artificial neural networks, reinforcement learning, and deep learning. A typical caveat of data-driven modelling