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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Michael Cabanillas-Carbonell
Universidad Privada Norbert Wiener

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Text prediction recurrent neural networks using long short-term memory-dropout Orlando Iparraguirre-Villanueva; Victor Guevara-Ponce; Daniel Ruiz-Alvarado; Saul Beltozar-Clemente; Fernando Sierra-Liñan; Joselyn Zapata-Paulini; Michael Cabanillas-Carbonell
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 3: March 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i3.pp1758-1768

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.
Search and classify topics in a corpus of text using the latent dirichlet allocation model Orlando Iparraguirre-Villanueva; Fernando Sierra-Liñan; Jose Luis Herrera Salazar; Saul Beltozar-Clemente; Félix Pucuhuayla-Revatta; Joselyn Zapata-Paulini; Michael Cabanillas-Carbonell
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 1: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i1.pp246-256

Abstract

This work aims at discovering topics in a text corpus and classifying the most relevant terms for each of the discovered topics. The process was performed in four steps: first, document extraction and data processing; second, labeling and training of the data; third, labeling of the unseen data; and fourth, evaluation of the model performance. For processing, a total of 10,322 "curriculum" documents related to data science were collected from the web during 2018-2022. The latent dirichlet allocation (LDA) model was used for the analysis and structure of the subjects. After processing, 12 themes were generated, which allowed ranking the most relevant terms to identify the skills of each of the candidates. This work concludes that candidates interested in data science must have skills in the following topics: first, they must be technical, they must have mastery of structured query language, mastery of programming languages such as R, Python, java, and data management, among other tools associated with the technology.
Co-Authors Daniel Ruiz-Alvarado Félix Pucuhuayla-Revatta Fernando Sierra-Liñan Jose Luis Herrera Salazar Joselyn Zapata-Paulini Orlando Iparraguirre-Villanueva Saul Beltozar-Clemente Victor Guevara-Ponce