Classify ecuadorian receipes with convolutional neural networks
| 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.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.identifier.uri | https://link.springer.com/chapter/10.1007/978-3-030-40690-5_22 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14809/3400 | |
| 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 |