Zobrazeno 1 - 10
of 16
pro vyhledávání: '"Josenildo Costa da Silva"'
Publikováno v:
Revista Brasileira de Computação Aplicada, Vol 11, Iss 3, Pp 1-11 (2019)
Unconstrained numerical problems are common in solving practical applications that, due to its nature, are usually devised by several design variables, narrowing the kind of technique or algorithm that can deal with them. An interesting way of tackli
Externí odkaz:
https://doaj.org/article/3f0e427ade104f55b745d4c8c785d469
Publikováno v:
Revista Brasileira de Computação Aplicada, Vol 10, Iss 3, Pp 11-20 (2018)
Este trabalho mostra que é possível extrair conhecimento útil de dados puros sobre os estudantes de graduação IFMA, de modo a tentar entender os problemas de evasão do referido instituto. Neste artigo, o conhecimento foi modelado como um classi
Externí odkaz:
https://doaj.org/article/1af8ac93b860434d83ec3cefa81c7180
Autor:
Francisco da Conceição Silva, Josenildo Costa da Silva, Arthur Mota França, George Sanders Carvalho Araújo, Emerson Elias Moraes
Publikováno v:
Anais da X Escola Regional de Computação do Ceará, Maranhão e Piauí (ERCEMAPI 2022).
Neste trabalho, propõe-se uma investigação acerca da identificação de indicadores de evasão de alunos em cursos de uma instituição de ensino técnico e tecnológico por meio de técnicas de mineração de dados, para auxiliar as
Publikováno v:
ICMLA
The clustering of genes with similar temporal profiles is an important task in gene expression data analysis. Current approaches to the clustering of sparse gene expression data with temporal information suffer from their at least quadratic complexit
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dbc8e65445683481935b155309f416d8
http://hdl.handle.net/11585/624553
http://hdl.handle.net/11585/624553
Autor:
Josenildo Costa da Silva, Luciano Reis Coutinho, Gustavo H. B. S. Oliveira, Ivan Pereira Pinto, Ariel Soares Teles, Davi Viana dos Santos, Júlia M. S. Ferreira, Francisco José da Silva e Silva
Publikováno v:
Expert Systems with Applications. 151:113331
In this paper, we propose a novel method for automatic k-complex (KC) detection in human sleep EEG, named MT-KCD. KCs are slow oscillations in the EEG signal characterized by a well-delineated, negative, sharp waves immediately followed by a positive
Publikováno v:
Anais do 11. Congresso Brasileiro de Inteligência Computacional.
Evolutionary Algorithms (EAs) are able to find out solutions in many fields and complex disciplines. Parallel Evolutionary Algorithms (PEAs) solve many kinds of problems, as well; moreover it overcomes problems with run time constraints when the prob
Autor:
Omar Andres Carmona Cortes, Bruno Alberth Silva Barros, Rafael Fernandes Lopes, Josenildo Costa da Silva, Pedro Felipe do Prado
Publikováno v:
Anais do 11. Congresso Brasileiro de Inteligência Computacional.
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783319463483
IDA
IDA
Several privacy measures have been proposed in the privacy preserving data mining literature. However, privacy measures either assume centralized data source or that no insider is going to try to infer some information. This paper presents distribute
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b4ce4b520332eb1954f4db6ec676055f
https://doi.org/10.1007/978-3-319-46349-0_23
https://doi.org/10.1007/978-3-319-46349-0_23
Autor:
Luís Carlos Fonseca, Reinaldo Gomes da Silva, Francisco José da Silva e Silva, Josenildo Costa da Silva
Publikováno v:
Anais do XXVI Simpósio Brasileiro de Informática na Educação (SBIE 2015).
This paper presents the results of a survey that is to create a predictive model of data mining in a Virtual Learning Environment (VLE). The obtained model aims to make the diagnosis of students evasion, based on their interactions in discussion foru
Publikováno v:
Engineering Applications of Artificial Intelligence. 19:363-369
In this paper we address confidentiality issues in distributed data clustering, particularly the inference problem. We present KDEC-S algorithm for distributed data clustering, which is shown to provide mining results while preserving confidentiality