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Extreme learning machine for iris-based diabetes detection
| dc.contributor.author | Fernandez-Grandon, Carlos | |
| dc.contributor.author | Soto, Ismael | |
| dc.contributor.author | Zabala-Blanco, David | |
| dc.date.accessioned | 2024-05-07T15:58:38Z | |
| dc.date.available | 2024-05-07T15:58:38Z | |
| dc.date.issued | 2023 | |
| dc.identifier.uri | http://repositorio.ucm.cl/handle/ucm/5374 | |
| dc.description.abstract | This study proposes a methodology for iris-based diabetes detection in 130 subjects, in which geometric transformations and changes in brightness and contrast were applied to increase to 1300 images, and a selection of 10% of the pixels were selected, and 13 principal components were used to feed an Extreme Learning Machine with the Adam optimization algorithm, a learning rate of 0.01, 256 neurons in the hidden layer, and a batch size of 128. After performing five-fold cross-validation, the results demonstrated balanced performance, with a mean accuracy of 0.9992, mean F1-score of 0.9988, and mean AUC of 0.9999 for diabetes detection. | es_CL |
| dc.language.iso | en | es_CL |
| dc.rights | Atribución-NoComercial-SinDerivadas 3.0 Chile | * |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/cl/ | * |
| dc.source | IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON), Valdivia, Chile, 1-6 | es_CL |
| dc.subject | Extreme learning machines | es_CL |
| dc.subject | Neurons | es_CL |
| dc.subject | Diabetes | es_CL |
| dc.subject | Information and communication technology | es_CL |
| dc.subject | Feeds | es_CL |
| dc.subject | Optimization | es_CL |
| dc.subject | Principal component analysis | es_CL |
| dc.title | Extreme learning machine for iris-based diabetes detection | es_CL |
| dc.type | Article | es_CL |
| dc.ucm.facultad | Facultad de Ciencias de la Ingeniería | es_CL |
| dc.ucm.indexacion | Scopus | es_CL |
| dc.ucm.uri | ieeexplore.ieee.org/document/10418742 | es_CL |
| dc.ucm.doi | doi.org/10.1109/CHILECON60335.2023.10418742 | es_CL |
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