Zobrazeno 1 - 10
of 89
pro vyhledávání: '"Odemir M. Bruno"'
Publikováno v:
Epidemics, Vol 28, Iss , Pp - (2019)
Seasonal influenza is a worldwide public health concern. Forecasting its dynamics can improve the management of public health regulations, resources and infrastructure, and eventually reduce mortality and the costs induced by influenza-related absent
Externí odkaz:
https://doaj.org/article/4ca8e48b841047ca8ee415a459aa44dc
Publikováno v:
Plants, Vol 9, Iss 11, p 1613 (2020)
The CO2 and water vapor exchange between leaf and atmosphere are relevant for plant physiology. This process is done through the stomata. These structures are fundamental in the study of plants since their properties are linked to the evolutionary pr
Externí odkaz:
https://doaj.org/article/b2c113f634294644b1ccd0bffe8874b8
Autor:
Mayra Z Rodriguez, Cesar H Comin, Dalcimar Casanova, Odemir M Bruno, Diego R Amancio, Luciano da F Costa, Francisco A Rodrigues
Publikováno v:
PLoS ONE, Vol 14, Iss 1, p e0210236 (2019)
Many real-world systems can be studied in terms of pattern recognition tasks, so that proper use (and understanding) of machine learning methods in practical applications becomes essential. While many classification methods have been proposed, there
Externí odkaz:
https://doaj.org/article/5f718bfdd11d4d6eb66192da1e5788bc
BackgroundCOVID-19 (coronavirus disease 2019) is a disease caused by infection with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), affecting millions of people worldwide, with a high rate of deaths. The present study aims to evalua
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::14c348b81549cacd95e8d09f9dd42378
https://doi.org/10.1101/2022.11.21.517338
https://doi.org/10.1101/2022.11.21.517338
Publikováno v:
PLoS ONE, Vol 13, Iss 5, p e0195843 (2018)
Modeling the basic structure of metabolic machinery is a challenge for modern biology. Some models based on complex networks have provided important information regarding this machinery. In this paper, we constructed metabolic networks of 17 plants c
Externí odkaz:
https://doaj.org/article/90b9251de1db47798d91118b62bb70f0
Publikováno v:
PLoS ONE, Vol 13, Iss 3, p e0193703 (2018)
The authorship attribution is a problem of considerable practical and technical interest. Several methods have been designed to infer the authorship of disputed documents in multiple contexts. While traditional statistical methods based solely on wor
Externí odkaz:
https://doaj.org/article/ba9cb3ae3b894757a3f6ed6a73cde1b7
Autor:
João V.B.S. Merenda, Odemir M. Bruno
Publikováno v:
Physica A: Statistical Mechanics and its Applications. 621:128713
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8738e9c32af451fce67fa4a42b3ebf41
https://doi.org/10.1016/j.physa.2023.128713
https://doi.org/10.1016/j.physa.2023.128713
Publikováno v:
2022 Eleventh International Conference on Image Processing Theory, Tools and Applications (IPTA).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bedadc19e9c9effaf15524deb86ca41b
https://doi.org/10.1109/ipta54936.2022.9784123
https://doi.org/10.1109/ipta54936.2022.9784123
Publikováno v:
2022 Eleventh International Conference on Image Processing Theory, Tools and Applications (IPTA).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::184ba5e5a0906c5a18d7feca2036f43d
https://doi.org/10.1109/ipta54936.2022.9784114
https://doi.org/10.1109/ipta54936.2022.9784114
Autor:
Leonardo F.S. Scabini, Odemir M. Bruno
Publikováno v:
Physica A: Statistical Mechanics and its Applications. 615:128585
Understanding the behavior of Artificial Neural Networks is one of the main topics in the field recently, as black-box approaches have become usual since the widespread of deep learning. Such high-dimensional models may manifest instabilities and wei
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a56c768ddbcaada6081b5699127499ca
https://doi.org/10.1016/j.physa.2023.128585
https://doi.org/10.1016/j.physa.2023.128585