Abstract
Bees are one of the most important pollinators since they assist in plant reproduction and ensure seed and fruit production. They are important both for pollination and honey production, which benefits small and large-scale agriculturists. However, in recent years, the bee populations have declined significantly in alarming ways on a global scale. In this scenario, understanding the behavior of bees has become a matter of great concern in an attempt to find the possible causes of this situation. In this study, an anomaly detection algorithm is created for data labeling, as well as to evaluate the classification models of anomalous events in a time series obtained from RFID sensors installed in bee hives.
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Gama, F., Arruda, H.M., Carvalho, H.V., de Souza, P., Pessin, G. (2017). Improving Our Understanding of the Behavior of Bees Through Anomaly Detection Techniques. In: Lintas, A., Rovetta, S., Verschure, P., Villa, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2017. ICANN 2017. Lecture Notes in Computer Science(), vol 10614. Springer, Cham. https://doi.org/10.1007/978-3-319-68612-7_59
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DOI: https://doi.org/10.1007/978-3-319-68612-7_59
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