An Amazon stingless bee foraging activity predicted using recurrent artificial neural networks and attribute selection
Author(s)
Gomes, Pedro A. B.; Suhara, Yoshihiko; Nunes-Silva, Patrícia; Costa, Luciano; Arruda, Helder; Venturieri, Giorgio; Imperatriz-Fonseca, Vera Lucia; Pentland, Alex; de Souza, Paulo; Pessin, Gustavo; ... Show more Show less
DownloadAn Amazon stingless bee foraging activity predicted using recurrent artificial neural networks and attribute selection.pdf (2.307Mb)
Terms of use
Metadata
Show full item recordAbstract
Bees play a key role in pollination of crops and in diverse ecosystems. There have been multiple reports in recent years illustrating bee population declines worldwide. The search for more accurate forecast models can aid both in the understanding of the regular behavior and the adverse situations that may occur with the bees. It also may lead to better management and utilization of bees as pollinators. We address an investigation with Recurrent Neural Networks in the task of forecasting bees’ level of activity taking into account previous values of level of activity and environmental data such as temperature, solar irradiance and barometric pressure. We also show how different input time windows, algorithms of attribute selection and correlation analysis can help improve the accuracy of our model.
Date issued
2020-01-08Department
MIT Connection Science (Research institute)Publisher
Scientific Reports
Citation
Gomes, P. A., Suhara, Y., Nunes-Silva, P., Costa, L., Arruda, H., Venturieri, G., ... & Pessin, G. (2020). An Amazon stingless bee foraging activity predicted using recurrent artificial neural networks and attribute selection. Scientific reports, 10(1), 1-12.
Collections
The following license files are associated with this item: