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dc.contributor.authorGomes, Pedro AB
dc.contributor.authorSuhara, Yoshihiko
dc.contributor.authorNunes-Silva, Patricia
dc.contributor.authorCosta, Luciano
dc.contributor.authorArruda, Helder
dc.contributor.authorVenturieri, Giorgio
dc.contributor.authorImperatriz-Fonseca, Vera Lucia
dc.contributor.authorPentland, Alex
dc.contributor.authorde Souza, Paulo
dc.contributor.authorPessin, Gustavo
dc.date.accessioned2020-01-13T05:04:10Z
dc.date.available2020-01-13T05:04:10Z
dc.date.issued2020
dc.identifier.issn2045-2322
dc.identifier.doi10.1038/s41598-019-56352-8
dc.identifier.urihttp://hdl.handle.net/10072/390165
dc.description.abstractBees 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.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherNature Publishing Group
dc.relation.ispartofpagefrom2020:1
dc.relation.ispartofpageto2020:12
dc.relation.ispartofissue1
dc.relation.ispartofjournalScientific Reports
dc.relation.ispartofvolume10
dc.subject.fieldofresearchEcology
dc.subject.fieldofresearchcode3103
dc.titleAn Amazon stingless bee foraging activity predicted using recurrent artificial neural networks and attribute selection
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationGomes, 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.licensehttp://creativecommons.org/licenses/by/4.0/
dc.date.updated2020-01-09T04:02:03Z
dc.description.versionVersion 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.hasfulltextFull Text
gro.griffith.authorDe Souza Junior, Paulo A.


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