dc.contributor.author | Gomes, Pedro AB | |
dc.contributor.author | Suhara, Yoshihiko | |
dc.contributor.author | Nunes-Silva, Patricia | |
dc.contributor.author | Costa, Luciano | |
dc.contributor.author | Arruda, Helder | |
dc.contributor.author | Venturieri, Giorgio | |
dc.contributor.author | Imperatriz-Fonseca, Vera Lucia | |
dc.contributor.author | Pentland, Alex | |
dc.contributor.author | de Souza, Paulo | |
dc.contributor.author | Pessin, Gustavo | |
dc.date.accessioned | 2020-01-13T05:04:10Z | |
dc.date.available | 2020-01-13T05:04:10Z | |
dc.date.issued | 2020 | |
dc.identifier.issn | 2045-2322 | |
dc.identifier.doi | 10.1038/s41598-019-56352-8 | |
dc.identifier.uri | http://hdl.handle.net/10072/390165 | |
dc.description.abstract | 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. | |
dc.description.peerreviewed | Yes | |
dc.language | English | |
dc.language.iso | eng | |
dc.publisher | Nature Publishing Group | |
dc.relation.ispartofpagefrom | 2020:1 | |
dc.relation.ispartofpageto | 2020:12 | |
dc.relation.ispartofissue | 1 | |
dc.relation.ispartofjournal | Scientific Reports | |
dc.relation.ispartofvolume | 10 | |
dc.subject.fieldofresearch | Ecology | |
dc.subject.fieldofresearchcode | 3103 | |
dc.title | An Amazon stingless bee foraging activity predicted using recurrent artificial neural networks and attribute selection | |
dc.type | Journal article | |
dc.type.description | C1 - Articles | |
dcterms.bibliographicCitation | Gomes, PAB; Suhara, Y; Nunes-Silva, P; Costa, L; Arruda, H; Venturieri, G; Imperatriz-Fonseca, VL; Pentland, A; Souza, PD; Pessin, G, An Amazon stingless bee foraging activity predicted using recurrent artificial neural networks and attribute selection, Scientific Reports, 2020, 10 (1), pp. 2020:1-2020:12 | |
dcterms.license | http://creativecommons.org/licenses/by/4.0/ | |
dc.date.updated | 2020-01-09T04:02:03Z | |
dc.description.version | Version of Record (VoR) | |
gro.rights.copyright | © The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. | |
gro.hasfulltext | Full Text | |
gro.griffith.author | De Souza Junior, Paulo A. | |