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dc.contributor.authorSoria, Luis-
dc.contributor.authorJiménez-Cadena, Gabriela-
dc.contributor.authorMartínez, Carlos-
dc.contributor.authorCastillo-Salazar, David-
dc.date.accessioned2022-06-29T15:24:00Z-
dc.date.available2022-06-29T15:24:00Z-
dc.date.issued2020-
dc.identifier.urihttps://link.springer.com/chapter/10.1007/978-3-030-40690-5_22-
dc.identifier.urihttp://repositorio.uti.edu.ec//handle/123456789/3400-
dc.description.abstractThis work is a proposal to resolve the problem of identification plates of food through photographs. It involves using a large set of pictures which are processed by convolutional neural networks and parallel processing TensorFlow. The results show a 90% greater accuracy in training and between 63% and 80% in the test. The reason is that Ecuadorian dishes are very similar in the images of some recipes. © Springer Nature Switzerland AG 2020.es
dc.language.isoenges
dc.publisherAdvances in Intelligent Systems and Computing. Volume 1137 AISC, Pages 223 - 229. International Conference on Information Technology and Systems, ICITS 2020. Bogota. 5 February 2020 through 7 February 2020es
dc.rightsopenAccesses
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/es
dc.titleClassify ecuadorian receipes with convolutional neural networkses
dc.typearticlees
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