Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Yania Molina Souto"'
Autor:
Maria A. F. Silva Dias, Yania Molina Souto, Bruno Biazeto, Enzo Todesco, Jose A. Zuñiga Mora, Dylana Vargas Navarro, Melvin Pérez Chinchilla, Carlos Madrigal Araya, Dayanna Arce Fernández, Berny Fallas López, Jose P. Cantillano, Roberta Boscolo, Hamid Bastani
Publikováno v:
Energies, Vol 17, Iss 22, p 5575 (2024)
The energy sector relies on numerical model output forecasts for operational purposes on a short-term scale, up to 10 days ahead. Reducing model errors is crucial, particularly given that coarse resolution models often fail to account for complex top
Externí odkaz:
https://doaj.org/article/7fa33a221f1b441090689a326c4a20eb
Autor:
Rafael S. Pereira, Brian Tsan, Anderson Chaves, Eduardo Ogasawara, Artur Ziviani, Florin Rusu, Yania Molina Souto, Rocio Zorilla, Fabio Porto
Publikováno v:
SSDBM
Consider a set of black-box models – each of them independently trained on a different dataset – answering the same predictive spatio-temporal query. Being built in isolation, each model traverses its own life-cycle until it is deployed to produc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c1d31af0b7a0acc531b6666ff64637ef
https://doi.org/10.1145/3468791.3468806
https://doi.org/10.1145/3468791.3468806
Applying machine learning models to meteorological data brings many opportunities to the Geosciences field, such as predicting future weather conditions more accurately. In recent years, modeling meteorological data with deep neural networks has beco
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::be4e33ccabcc69b4f456c2f593bab845
http://arxiv.org/abs/1912.00134
http://arxiv.org/abs/1912.00134
Publikováno v:
IJCNN
This paper proposes a new ensemble method built upon a deep neural network architecture. We use a set of meteorological models for rain forecast as base predictors. Each meteorological model is provided to a channel of the network and, through a conv
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::95ac15fd58fbc13471031478b1f5ec4b
https://doi.org/10.1109/ijcnn.2018.8489693
https://doi.org/10.1109/ijcnn.2018.8489693
Autor:
Paulo F. Pires, Edward Pacheco, Fabio Porto, Bruno Costa, João Guilherme Nobre Rittmeyer, Jonas Dias, Vieira Wagner Dos Santos, Gottin Vinícius Michel, Angelo E. M. Ciarlini, Yania Molina Souto
Publikováno v:
BeyondMR@SIGMOD
Demands for large-scale data analysis and processing have led to the development and widespread adoption of computing frameworks that leverage in-memory data processing, largely outperforming disk-based processing systems. One such framework is Apach
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f0ed687a856e138e89ab90b2768a0734
https://doi.org/10.1145/3206333.3206339
https://doi.org/10.1145/3206333.3206339
Publikováno v:
Anais do Brazilian e-Science Workshop (BreSci).
Uncertain time series analysis has recently become an important research topic, particularly when searching for features of natural phenomena using similarity functions. Natural phenomena are often modeled as time series, such as in weather forecast,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fbfd420c3c46ed5b72e5e1ba26dcfd73
https://doi.org/10.5753/bresci.2015.7204
https://doi.org/10.5753/bresci.2015.7204