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COVIDGR dataset and COVID-SDNet methodology for predicting COVID-19 based on Chest X-Ray images

Autor: Tabik, S., Gómez-Ríos, A., Martín-Rodríguez, J. L., Sevillano-García, I., Rey-Area, M., Charte, D., Guirado, E., Suárez, J. L., Luengo, J., Valero-González, M. A., García-Villanova, P., Olmedo-Sánchez, E., Herrera, F.

Currently, Coronavirus disease (COVID-19), one of the most infectious diseases in the 21st century, is diagnosed using RT-PCR testing, CT scans and/or Chest X-Ray (CXR) images. CT (Computed Tomography) scanners and RT-PCR testing are not available in

Externí odkaz: http://arxiv.org/abs/2006.01409

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A data relocation approach for terrain surface analysis on multi-GPU systems: a case study on the total viewshed problem

Autor: Sanchez-Fernandez, A. J., Romero, L. F., Bandera, G., Tabik, S.

Digital Elevation Models (DEMs) are important datasets for modelling the line of sight, such as radio signals, sound waves and human vision. These are commonly analyzed using rotational sweep algorithms. However, such algorithms require large numbers

Externí odkaz: http://arxiv.org/abs/2003.02200

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MNIST-NET10: A heterogeneous deep networks fusion based on the degree of certainty to reach 0.1 error rate. Ensembles overview and proposal

Autor: Tabik, S., Alvear-Sandoval, R. F., Ruiz, M. M., Sancho-Gómez, J. L., Figueiras-Vidal, A. R., Herrera, F.

Ensemble methods have been widely used for improving the results of the best single classificationmodel. A large body of works have achieved better performance mainly by applying one specific ensemble method. However, very few works have explored com

Externí odkaz: http://arxiv.org/abs/2001.11486

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