Improving Our Understanding of the Behavior of Bees Through Anomaly Detection Techniques
Author(s)
Gama, Fernando
Arruda, Helder M
Carvalho, Hanna V
de Souza, Paulo
Pessin, Gustavo
Griffith University Author(s)
Year published
2017
Metadata
Show full item recordAbstract
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 ...
View more >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.
View less >
View more >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.
View less >
Conference Title
Lecture Notes in Computer Science
Volume
10614
Subject
Artificial Intelligence and Image Processing
Science & Technology
Computer Science, Theory & Methods