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              <p>Dairy cow activity data is collected hourly to detect behavioural anomalies that could be precursors to health problems. This data is supplemented by tags indicating the cow's health status. These are collected daily by the farmer. Cows behave differently when they are sick. However, due to the possibility of labelling errors, it is difficult to assess the robustness of an anomaly detection algorithm. We propose to build a simulation model of dairy cow behaviour using Markov chains. The aim is to create a synthetic dataset close to reality on which it will be possible to test different anomaly detection algorithms. Fuzzy rules will also be introduced to represent a data set where the transition from one health state to another is gradual.</p>
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              <p>Des données d'activité de vaches laitières sont collectées chaque heure afin de détecter des anomalies comportementales possiblement précurseurs de problèmes de santé. À ces données s'ajoutent des étiquettes spécifiant l'état de santé de la vache. Celles-ci sont relevées quotidiennement par l'éleveur. Le comportement de la vache est modifié quand elle est malade. Cependant, dû aux possibilités d'erreurs d'étiquetage, il est difficile d'évaluer la robustesse d'un algorithme de détection d'anomalies. Nous proposons de mettre en place un modèle de simulation de comportement de vaches laitières à l'aide des chaînes de Markov. L'objectif est de créer un jeu de données synthétique proche de la réalité, sur lequel il sera possible de tester différents algorithmes de détection d'anomalie. Des règles floues sont également introduites afin de présenter un jeu de données dans lequel le passage d'un état de santé à un autre se fasse de manière progressive.</p>
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