Zobrazeno 1 - 10
of 81
pro vyhledávání: '"Cristiano Leite de Castro"'
Publikováno v:
Information Sciences. 625:578-592
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d78dcab6c6f5662852f5a557a08def70
https://doi.org/10.1016/j.ins.2022.12.085
https://doi.org/10.1016/j.ins.2022.12.085
Publikováno v:
Learning and Nonlinear Models. 20:31-46
Dealing with missingness in time series data is a very important, but oftentimes overlooked, step in data analysis. In this paper, the nature of time series data and missingness mechanisms are described to help identify which imputation method should
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b28904e4ce721cf5bb50fe88fba1a0d4
https://doi.org/10.21528/lnlm-vol20-no1-art3
https://doi.org/10.21528/lnlm-vol20-no1-art3
Publikováno v:
Journal of Control, Automation and Electrical Systems. 33:1457-1465
Due to Big Data and the Internet of Things, Machine Learning algorithms targeted specifically to model evolving data streams had gained attention from both academia and industry. Many Incremental Learning models had been successful in doing so, but m
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5fab054bd0fd65f3fd39e8508042cbb1
https://doi.org/10.1007/s40313-021-00882-y
https://doi.org/10.1007/s40313-021-00882-y
Autor:
Yuri Sousa Aurelio, Gustavo Matheus de Almeida, Cristiano Leite de Castro, Antonio Padua Braga
Publikováno v:
Neural Processing Letters. 54:3097-3114
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f8966ac4be0d44cbfce3c7f5a9179c35
https://doi.org/10.1007/s11063-022-10756-2
https://doi.org/10.1007/s11063-022-10756-2
Publikováno v:
IEEE Geoscience and Remote Sensing Letters. 19:1-5
Recent advances and applications of aerial image semantic segmentation have yielded an increase in their use in day-to-day tasks. However, state-of-art algorithms, composed mostly of deep semantic segmentation networks (DSSNs), may not be suitable fo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2d92e1611a8d99c5e2b27f9094a487c7
https://doi.org/10.1109/lgrs.2021.3113878
https://doi.org/10.1109/lgrs.2021.3113878
Autor:
Augusto Mafra, Janier Arias-Garcia, Luiz C. B. Torres, Antônio de Pádua Braga, Frederico Coelho, Liliane Reis Gade, Cristiano Leite de Castro
Publikováno v:
IEEE Transactions on Industrial Informatics. 17:1186-1196
It is well known that there is an increasing interest in edge computing to reduce the distance between cloud and end devices, especially for machine learning (ML) methods. However, when related to latency-sensitive applications, little work can be fo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::35e704b12f64362663c387fabeed993e
https://doi.org/10.1109/tii.2020.2987329
https://doi.org/10.1109/tii.2020.2987329
Autor:
Jose Guilherme S. Maia, Frederico Gadelha Guimarães, André Paim Lemos, Carlos Alberto Severiano Junior, Juan Camilo Fonseca Galindo, Miri Weiss Cohen, Cristiano Leite de Castro
Publikováno v:
Future Generation Computer Systems. 106:672-684
Many applications have been producing streaming data nowadays, which motivates techniques to extract knowledge from such sources. In this sense, the development of data stream clustering algorithms has gained an increasing interest. However, the appl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6d6c259b3dd84e523cd1a0d0b760052d
https://doi.org/10.1016/j.future.2020.01.017
https://doi.org/10.1016/j.future.2020.01.017
Publikováno v:
2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b45ca092634f494f575acaedcac10e71
https://doi.org/10.1109/la-cci48322.2021.9769791
https://doi.org/10.1109/la-cci48322.2021.9769791
Autor:
Antônio de Pádua Braga, Cristiano Leite de Castro, Yuri Sousa Aurelio, Gustavo Matheus de Almeida
Publikováno v:
Neural Processing Letters. 50:1937-1949
This paper presents a novel approach to deal with the imbalanced data set problem in neural networks by incorporating prior probabilities into a cost-sensitive cross-entropy error function. Several classical benchmarks were tested for performance eva
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ed4631b482d8143938d8914d41a7b3d9
https://doi.org/10.1007/s11063-018-09977-1
https://doi.org/10.1007/s11063-018-09977-1
Autor:
Liliana Otero, RAFAEL GONZALEZ-OTERO, Cristiano Leite de Castro, Carlos Silva, Michel Bessani
Publikováno v:
Expert Systems with Applications. 200:116955
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::527cc4ec39c2abf560aa30ea0500754d
https://doi.org/10.1016/j.eswa.2022.116955
https://doi.org/10.1016/j.eswa.2022.116955