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dc.contributor.authorIparraguirre-Villanueva, Orlandoes_ES
dc.contributor.authorGuevara-Ponce, Victores_ES
dc.contributor.authorRuiz-Alvarado, Danieles_ES
dc.contributor.authorBeltozarClemente, Saules_ES
dc.contributor.authorSierra-Liñan, Fernandoes_ES
dc.contributor.authorZapata-Paulini, Joselynes_ES
dc.contributor.authorCabanillas-Carbonell, Michaeles_ES
dc.date.accessioned2023-03-13T19:36:32Z
dc.date.available2023-03-13T19:36:32Z
dc.date.issued2022-10-29
dc.identifier.urihttps://hdl.handle.net/20.500.13053/8063
dc.description.abstract“Unit short-term memory (LSTM) is a type of recurrent neural network (RNN) whose sequence-based models are being used in text generation and/or prediction tasks, question answering, and classification systems due to their ability to learn long-term dependencies. The present research integrates the LSTM network and dropout technique to generate a text from a corpus as input, a model is developed to find the best way to extract the words from the context. For training the model, the poem ““La Ciudad y los perros““ which is composed of 128,600 words is used as input data. The poem was divided into two data sets, 38.88% for training and the remaining 61.12% for testing the model. The proposed model was tested in two variants: word importance and context. The results were evaluated in terms of the semantic proximity of the generated text to the given context.“es_ES
dc.formatapplication/pdfes_ES
dc.language.isoenges_ES
dc.publisherInstitute of Advanced Engineering and Sciencees_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/es_ES
dc.subject"Dropout Prediction Recurrent neural network Text Unit short-term memory"es_ES
dc.titleText prediction recurrent neural networks using long shortterm memory-dropoutes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.identifier.doi10.11591/ijeecs.v29.i3.pp1758-1768es_ES
dc.type.versioninfo:eu-repo/semantics/publishedVersiones_ES
dc.publisher.countryIDes_ES
dc.subject.ocdehttp://purl.org/pe-repo/ocde/ford#1.02.00es_ES


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