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Mnemosyne is a human-ai context management system that provides transparency and human feedback levers to effectively manage context in non-linear conversations.
In this project we propose a method for detecting sensor anomalies in time-series data based on the difference between the predicted and actual output of a sensor
Mobile menu scanner application that harnesses nutritional data to optimize meals based on athlete's preferences