Classify ecuadorian receipes with convolutional neural networks

Please use this identifier to cite or link to this item: https://repositorio.uti.edu.ec//handle/123456789/3400

Title:	Classify ecuadorian receipes with convolutional neural networks
Authors:	Soria, Luis Jiménez-Cadena, Gabriela Martínez, Carlos Castillo-Salazar, David
Issue Date:	2020
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
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.
URI:	https://link.springer.com/chapter/10.1007/978-3-030-40690-5_22 http://repositorio.uti.edu.ec//handle/123456789/3400
Appears in Collections:	Artículos Científicos Indexados

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