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This paper introduces an Optimal Transport (OT)-based framework for anomaly detection, designed to detect deviations from normal behaviour in time-series sensor data, that requires minimal user input and adapts to real-time data without the need for labelled training data.
This chapter explores how machine learning (ML) is transforming aquaculture, with a particular focus on enhancing decision-making processes and improving operational efficiency. The chapter is structured to first introduce the challenges in aquaculture and the role of AI and then provide an overview of ML techniques in...
An automated evaluation framework that uses three key metrics to analyze architectural trade-offs between the Agent-As-Tool and Plan-Execute paradigms, along with a systematic procedure for the automated discovery of emerging failure modes is introduced.
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