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• W3011692016 abstract "<abstract> <p>Monitor and classify behavioral activities in cows is a helpful support solution for livestock based on the analysis of data from sensors attached to the animal. Accelerometers are particularly suited for monitoring cow behaviors due to small size, lightweight and high accuracy. Nevertheless, the interpretation of the data collected by such sensors when characterizing the type of behaviors still brings major challenges to developers, related to activity complexity (i.e., certain behaviors contain similar gestures). This paper presents a new design of cows' behavior classifier based on acceleration data and proposed feature set. Analysis of cow acceleration data is used to extract features for classification using machine learning algorithms. We found that with 5 features (mean, standard deviation, root mean square, median, range) and 16-second window of data (1 sample/second), classification of seven cow behaviors (including feeding, lying, standing, lying down, standing up, normal walking, active walking) achieved the overall highest performance. We validated the results with acceleration data from a public source. Performance of our proposed classifier was evaluated and compared to existing ones in terms of the sensitivity, the accuracy, the positive predictive value, and the negative predictive value.</p> </abstract>" @default.
• W3011692016 created "2020-03-23" @default.
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• W3011692016 date "2020-01-01" @default.
• W3011692016 modified "2023-10-16" @default.
• W3011692016 title "The new design of cows' behavior classifier based on acceleration data and proposed feature set" @default.
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• W3011692016 doi "https://doi.org/10.3934/mbe.2020151" @default.
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