Use of artificial neural networks to determine the daily number of meals served by a university cafeteria
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Revista de Nutrição |
Texto Completo: | https://periodicos.puc-campinas.edu.br/nutricao/article/view/9853 |
Resumo: | ObjectiveThis study aimed to build an artificial neural network to help the managers of university cafeterias to predict the number of daily meals.MethodsThis study was based on a survey of eight variables that influence the number of daily meals served by a university cafeteria. Backpropagation training algorithm was used and the results obtained by the network are compared with results of the studied series and the results estimated by simple arithmetic average.ResultsThe proposed network follows the numerous changes that occur in the number of daily meals of the university cafeteria. In 73% of the analyzed days, the artificial neural networks method presented a greater success rate than the simple arithmetic average method.ConclusionArtificial neural network predicted the number of meals better than the simple average method or than decisions made subjectively |
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Revista de Nutrição |
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Use of artificial neural networks to determine the daily number of meals served by a university cafeteriaUtilização de redes neurais artificiais para a determinação do número de refeições diárias de um restaurante universitárioFood wasterfoulnessArtificial neural networksFood servicesDesperdícios de alimentosRedes neurais artificiaisServiços de alimentaçãoObjectiveThis study aimed to build an artificial neural network to help the managers of university cafeterias to predict the number of daily meals.MethodsThis study was based on a survey of eight variables that influence the number of daily meals served by a university cafeteria. Backpropagation training algorithm was used and the results obtained by the network are compared with results of the studied series and the results estimated by simple arithmetic average.ResultsThe proposed network follows the numerous changes that occur in the number of daily meals of the university cafeteria. In 73% of the analyzed days, the artificial neural networks method presented a greater success rate than the simple arithmetic average method.ConclusionArtificial neural network predicted the number of meals better than the simple average method or than decisions made subjectivelyObjetivoConstruir uma rede neural artificial para auxiliar os gestores de restaurantes universitários na previsão de refeições diárias.MétodosO estudo foi desenvolvido a partir do levantamento de oito variáveis que influenciam o número de refeições diárias servidas no restaurante universitário. Utiliza-se o algoritmo de treinamento Backpropagation. Os resultados por meio da rede são comparados com os da série estudada e com resultados da estimação por média aritmética simples.ResultadosA rede proposta acompanha as inúmeras alterações que ocorrem no número de refeições diárias do restaurante universitário. Em 73% dos dias analisados, o método das redes neurais artificiais apresenta uma taxa de acerto maior do que o método da média aritmética simples.ConclusãoA rede neural artificial mostrou-se mais adequada para a previsão do número de refeições do que a metodologia de média simples ou quando a decisão do número de refeições é feita de forma subjetiva, sem critérios científicos.Núcleo de Editoração – PUC-Campinas2023-09-27info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://periodicos.puc-campinas.edu.br/nutricao/article/view/9853Brazilian Journal of Nutrition; Vol. 24 No. 5 (2011): Revista de NutriçãoRevista de Nutrição; Vol. 24 Núm. 5 (2011): Revista de NutriçãoRevista de Nutrição; v. 24 n. 5 (2011): Revista de Nutrição1678-9865reponame:Revista de Nutriçãoinstname:Pontifícia Universidade Católica de Campinas (PUC-CAMPINAS)instacron:PUC_CAMPporhttps://periodicos.puc-campinas.edu.br/nutricao/article/view/9853/7191Copyright (c) 2023 José Celso ROCHA, Felipe Delestro MATOS, Fernando FREIhttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessROCHA, José Celso MATOS, Felipe Delestro FREI, Fernando2023-12-05T17:10:49Zoai:ojs.periodicos.puc-campinas.edu.br:article/9853Revistahttp://www.scielo.br/rnPRIhttps://periodicos.puc-campinas.edu.br/nutricao/oai||sbi.submissionrn@puc-campinas.edu.br1678-98651415-5273opendoar:2023-12-05T17:10:49Revista de Nutrição - Pontifícia Universidade Católica de Campinas (PUC-CAMPINAS)false |
dc.title.none.fl_str_mv |
Use of artificial neural networks to determine the daily number of meals served by a university cafeteria Utilização de redes neurais artificiais para a determinação do número de refeições diárias de um restaurante universitário |
title |
Use of artificial neural networks to determine the daily number of meals served by a university cafeteria |
spellingShingle |
Use of artificial neural networks to determine the daily number of meals served by a university cafeteria ROCHA, José Celso Food wasterfoulness Artificial neural networks Food services Desperdícios de alimentos Redes neurais artificiais Serviços de alimentação |
title_short |
Use of artificial neural networks to determine the daily number of meals served by a university cafeteria |
title_full |
Use of artificial neural networks to determine the daily number of meals served by a university cafeteria |
title_fullStr |
Use of artificial neural networks to determine the daily number of meals served by a university cafeteria |
title_full_unstemmed |
Use of artificial neural networks to determine the daily number of meals served by a university cafeteria |
title_sort |
Use of artificial neural networks to determine the daily number of meals served by a university cafeteria |
author |
ROCHA, José Celso |
author_facet |
ROCHA, José Celso MATOS, Felipe Delestro FREI, Fernando |
author_role |
author |
author2 |
MATOS, Felipe Delestro FREI, Fernando |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
ROCHA, José Celso MATOS, Felipe Delestro FREI, Fernando |
dc.subject.por.fl_str_mv |
Food wasterfoulness Artificial neural networks Food services Desperdícios de alimentos Redes neurais artificiais Serviços de alimentação |
topic |
Food wasterfoulness Artificial neural networks Food services Desperdícios de alimentos Redes neurais artificiais Serviços de alimentação |
description |
ObjectiveThis study aimed to build an artificial neural network to help the managers of university cafeterias to predict the number of daily meals.MethodsThis study was based on a survey of eight variables that influence the number of daily meals served by a university cafeteria. Backpropagation training algorithm was used and the results obtained by the network are compared with results of the studied series and the results estimated by simple arithmetic average.ResultsThe proposed network follows the numerous changes that occur in the number of daily meals of the university cafeteria. In 73% of the analyzed days, the artificial neural networks method presented a greater success rate than the simple arithmetic average method.ConclusionArtificial neural network predicted the number of meals better than the simple average method or than decisions made subjectively |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-09-27 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://periodicos.puc-campinas.edu.br/nutricao/article/view/9853 |
url |
https://periodicos.puc-campinas.edu.br/nutricao/article/view/9853 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
https://periodicos.puc-campinas.edu.br/nutricao/article/view/9853/7191 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2023 José Celso ROCHA, Felipe Delestro MATOS, Fernando FREI https://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2023 José Celso ROCHA, Felipe Delestro MATOS, Fernando FREI https://creativecommons.org/licenses/by/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Núcleo de Editoração – PUC-Campinas |
publisher.none.fl_str_mv |
Núcleo de Editoração – PUC-Campinas |
dc.source.none.fl_str_mv |
Brazilian Journal of Nutrition; Vol. 24 No. 5 (2011): Revista de Nutrição Revista de Nutrição; Vol. 24 Núm. 5 (2011): Revista de Nutrição Revista de Nutrição; v. 24 n. 5 (2011): Revista de Nutrição 1678-9865 reponame:Revista de Nutrição instname:Pontifícia Universidade Católica de Campinas (PUC-CAMPINAS) instacron:PUC_CAMP |
instname_str |
Pontifícia Universidade Católica de Campinas (PUC-CAMPINAS) |
instacron_str |
PUC_CAMP |
institution |
PUC_CAMP |
reponame_str |
Revista de Nutrição |
collection |
Revista de Nutrição |
repository.name.fl_str_mv |
Revista de Nutrição - Pontifícia Universidade Católica de Campinas (PUC-CAMPINAS) |
repository.mail.fl_str_mv |
||sbi.submissionrn@puc-campinas.edu.br |
_version_ |
1799126075729510400 |