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Pineda, Omar (3)
García Guiliany, Jesús Enrique (2)Hernandez-P, Hugo (2)Viloria, Amelec (2)Barrantes, Fanny (1)CABAS VASQUEZ, LUIS CARLOS (1)Jiménez González, Roberto (1)Larios, Omar (1)Manotas, Alberto (1)Orellano Llinás, Nataly (1)... View MoreSubject
Data mining (3)
Concave programming (1)Data analysis (1)Decision trees (1)Electric billing (1)Electric service billing (1)Estimation (1)Forecasts (1)Prediction of facts (1)Public data (1)... View MoreDate Issued2020 (3)Has File(s)Yes (3)

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Forecast of operational data in electric energy plants using adaptive algorithm 

Viloria, Amelec; García Guiliany, Jesús Enrique; Hernandez-P, Hugo; CABAS VASQUEZ, LUIS CARLOS; Pineda, Omar (Corporación Universidad de la Costa, 2020)
Traditional time series methods offer models whose parameters remain constant over time. However, industrial supply and demand processes require timely decisions based on a dynamic reality. A change in configuration, turning ...
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Algorithms for crime prediction in smart cities through data mining 

Silva, Jesús; Romero Marin, Ligia Cielo; Jiménez González, Roberto; Larios, Omar; Barrantes, Fanny; Pineda, Omar; Manotas, Alberto (Corporación Universidad de la Costa, 2020)
The concentration of police resources in conflict zones contributes to the reduction of crime in the region and the optimization of those resources. This paper presents the use of regression techniques to predict the number ...
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Selecting electrical billing attributes: big data preprocessing improvements 

Viloria, Amelec; García Guiliany, Jesús Enrique; Orellano Llinás, Nataly; Hernandez-P, Hugo; Steffens Sanabria, Ernesto; Pineda, Omar (Corporación Universidad de la Costa, 2020)
The attribute selection is a very relevant activity of data preprocessing when discovering knowledge on databases. Its main objective is to eliminate irrelevant and/or redundant attributes to obtain computationally treatable ...

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