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Resource-Constrained Devices Bayesian Generative AI Explainability and Compact representation of K-MDPs Creating a 21st Century Helpline for Enhanced Support and Continuity of Care Authorised by: Marketing
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. Butler, C. Goncu, and L. Holloway. Tactile presentation of network data: Text, matrix or diagram? In CHI2020, pages 1–12, 2020. I. Zukerman et al.˙Exploratory Interaction with a Bayesian Argumentation
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interpretability is by using integrative approaches to analyse and predict target classes based on the context of prior biological knowledge. This study aims to propose accurate and interpretable cancer prediction
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of the neurons responsible for specific behaviours in these disorders may eventually lead to the development of better pharmacological therapies, which target these neurons. Finally, the student may be able
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are commonly targeted at experts in data analysis in search of insights from unfamiliar datasets. The premise is that most of current learning analytics tools are not designed as explanatory interfaces. This is
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aspect relates to using existing PET algorithms to address a real-life problem that can be of a significant value for the society. There is effectively no limit to what social problem can be targeted
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optimisation queries. A few sources of the target optimisation problems will be considered. To mention a few, the project will focus on (1) combining abstraction refinement and optimisation, (2) dealing with
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trusted societies. This is driven by a wide range of technologies - from targeted observation, through more generalised surveillance via the networked IOT, to accessing data held on public and private