Use of artificial neural networks to determine the daily number of meals served by a university cafeteria

Detalhes bibliográficos
Autor(a) principal: ROCHA, José Celso
Data de Publicação: 2023
Outros Autores: MATOS, Felipe Delestro, FREI, Fernando
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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spelling 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

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