Classify ecuadorian receipes with convolutional neural networks

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Campo DC	Valor	Lengua/Idioma
dc.contributor.author	Soria, Luis	-
dc.contributor.author	Jiménez-Cadena, Gabriela	-
dc.contributor.author	Martínez, Carlos	-
dc.contributor.author	Castillo-Salazar, David	-
dc.date.accessioned	2022-06-29T15:24:00Z	-
dc.date.available	2022-06-29T15:24:00Z	-
dc.date.issued	2020	-
dc.identifier.uri	https://link.springer.com/chapter/10.1007/978-3-030-40690-5_22	-
dc.identifier.uri	http://repositorio.uti.edu.ec//handle/123456789/3400	-
dc.description.abstract	This 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.iso	eng	es
dc.publisher	Advances 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 2020	es
dc.rights	openAccess	es
dc.rights.uri	https://creativecommons.org/licenses/by/4.0/	es
dc.title	Classify ecuadorian receipes with convolutional neural networks	es
dc.type	article	es
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