<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>1316-4821</journal-id>
<journal-title><![CDATA[Universidad, Ciencia y Tecnología]]></journal-title>
<abbrev-journal-title><![CDATA[uct]]></abbrev-journal-title>
<issn>1316-4821</issn>
<publisher>
<publisher-name><![CDATA[AutanaBooks S.A.S. Revista de la Universidad Experimental Politécnica Antonio José de Sucre, Vice Rectorado Puerto Ordaz, Venezuela, gestionada en Ecuador por AutanaBooks]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1316-48212023000100051</article-id>
<article-id pub-id-type="doi">10.47460/uct.v27i118.686</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Redes neuronales artificiales: una medición de aprendizajes de pronósticos como demanda potencial]]></article-title>
<article-title xml:lang="en"><![CDATA[Artificial neural networks: a measurement of forecast learnings as potential deman]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Castañeda Sánchez]]></surname>
<given-names><![CDATA[Willy Alex]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Polo Escobar]]></surname>
<given-names><![CDATA[Benjamín Roldan]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Vega Huincho]]></surname>
<given-names><![CDATA[Fernando]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad César Vallejo  ]]></institution>
<addr-line><![CDATA[Trujillo ]]></addr-line>
<country>Peru</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Toribio Rodríguez de Mendoza  ]]></institution>
<addr-line><![CDATA[ Amazonas]]></addr-line>
<country>Perú</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Universidad César Vallejo  ]]></institution>
<addr-line><![CDATA[Trujillo ]]></addr-line>
<country>Peru</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>03</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>03</month>
<year>2023</year>
</pub-date>
<volume>27</volume>
<numero>118</numero>
<fpage>51</fpage>
<lpage>60</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://ve.scielo.org/scielo.php?script=sci_arttext&amp;pid=S1316-48212023000100051&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://ve.scielo.org/scielo.php?script=sci_abstract&amp;pid=S1316-48212023000100051&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://ve.scielo.org/scielo.php?script=sci_pdf&amp;pid=S1316-48212023000100051&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN Las redes neuronales en la actualidad se han constituido una herramienta tecnológica muy importante debido a su contribución en el desarrollo de problemas relacionados con los aprendizajes de pronósticos en la planificación de la producción para cumplir la demanda potencial. El objetivo de la investigación consiste en aplicar las redes neuronales artificiales para medir el aprendizaje profundo en pronósticos. El estudio es cuantitativo de diseño pre experimental. Se aplicó una prueba con pres test y pos test sobre lenguaje de programación Visual Net de red neuronal para cada tipo de aprendizaje programado. Los resultados basados en los promedios respecto al aprendizaje conceptual de pronóstico y el método de red neuronal fueron sustanciales, mientras con el aprendizaje automático y profundo contribuyeron al desarrollo del problema, en cuanto al aprendizaje profundo de las conductas de los alumnos es importante y los resultados de la aplicación de pronósticos ayudaron a los cambios de la red neuronal en adquirir la capacidad de aprender funciones más complejas. Concluyendo que las redes neuronales artificiales en el aprendizaje profundo de pronóstico mejoran sustancialmente los aprendizajes en conceptos, procedimientos y conductas las cuales con llevan a la empresa a minimizar los costos e incrementar utilidades por diversos conceptos.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT Neural networks today have become a very important technological tool due to their contribution in the development of problems related to forecast learning in production planning to meet potential demand. The objective of the research is to apply artificial neural networks to measure deep learning in forecasts. The study is quantitative with a pre-experimental design. A test with pre-test and post-test was applied on the Visual Net neural network programming language for each type of programmed learning. The results based on the averages regarding the predictive conceptual learning and the neural network method were substantial, while with the automatic and deep learning they contributed to the development of the problem, in terms of the deep learning of the behaviors of the students it is important and the results of the application of forecasts helped the changes of the neural network in acquiring the ability to learn more complex functions. Concluding that artificial neural networks in deep forecasting learning substantially improve learning in concepts, procedures and behaviors which lead the company to minimize costs and increase profits for various concepts.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Redes neuronales artificiales]]></kwd>
<kwd lng="es"><![CDATA[aprendizaje de pronósticos]]></kwd>
<kwd lng="es"><![CDATA[planificación]]></kwd>
<kwd lng="es"><![CDATA[producción]]></kwd>
<kwd lng="en"><![CDATA[Artificial neural networks]]></kwd>
<kwd lng="en"><![CDATA[forecast learning]]></kwd>
<kwd lng="en"><![CDATA[planning]]></kwd>
<kwd lng="en"><![CDATA[production]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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