• Boletín diario de información científica N° 55

  Asociación Peruana de Bibliotecas Académicas ALTAMIRA (Asociación Peruana de Bibliotecas Académicas ALTAMIRA, 2020-07-03)

  Boletín que incluye información científica sobre el COVID-19, incluye artículos científicos y artículos preprint actualizados al 03 de Julio de 2020.

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  Method for Collecting Relevant Topics from Twitter supported by Big Data

  Silva, Jesús; Senior Naveda, Alexa; Gamboa Suarez, Ramiro; Hernández Palma, Hugo; Niebles Núẽz, William (Institute of Physics Publishing, 2020-01-07)

  There is a fast increase of information and data generation in virtual environments due to microblogging sites such as Twitter, a social network that produces an average of 8, 000 tweets per second, and up to 550 million tweets per day. That's why this and many other social networks are overloaded with content, making it difficult for users to identify information topics because of the large number of tweets related to different issues. Due to the uncertainty that harms users who created the content, this study proposes a method for inferring the most representative topics that occurred in a time period of 1 day through the selection of user profiles who are experts in sports and politics. It is calculated considering the number of times this topic was mentioned by experts in their timelines. This experiment included a dataset extracted from Twitter, which contains 10, 750 tweets related to sports and 8, 758 tweets related to politics. All tweets were obtained from user timelines selected by the researchers, who were considered experts in their respective subjects due to the content of their tweets. The results show that the effective selection of users, together with the index of relevance implemented for the topics, can help to more easily find important topics in both sport and politics.
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  Time Series Decomposition using Automatic Learning Techniques for Predictive Models

  Silva, Jesús; Hernández Palma, Hugo; Niebles Núẽz, William; Ovallos-Gazabon, David; Varela, Noel (Institute of Physics Publishing, 2020-01-07)

  This paper proposes an innovative way to address real cases of production prediction. This approach consists in the decomposition of original time series into time sub-series according to a group of factors in order to generate a predictive model from the partial predictive models of the sub-series. The adjustment of the models is carried out by means of a set of statistic techniques and Automatic Learning. This method was compared to an intuitive method consisting of a direct prediction of time series. The results show that this approach achieves better predictive performance than the direct way, so applying a decomposition method is more appropriate for this problem than non-decomposition.

  Acceso abierto
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  Neural Networks for the Web Services Classification

  Silva, Jesús; Senior Naveda, Alexa; Solórzano Movilla, José; Niebles Núẽz, William; Hernández Palma, Hugo (Institute of Physics Publishing, 2020-01-07)

  This article introduces a n-gram-based approach to automatic classification of Web services using a multilayer perceptron-type artificial neural network. Web services contain information that is useful for achieving a classification based on its functionality. The approach relies on word n-grams extracted from the web service description to determine its membership in a category. The experimentation carried out shows promising results, achieving a classification with a measure F=0.995 using unigrams (2-grams) of words (characteristics composed of a lexical unit) and a TF-IDF weight.

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  Forecasting Electric Load Demand through Advanced Statistical Techniques

  Silva, Jesús; Senior Naveda, Alexa; García Guliany, Jesús; Niebles Núẽz, William; Hernández Palma, Hugo (Institute of Physics Publishing, 2020-01-07)

  Traditional forecasting models have been widely used for decision-making in production, finance and energy. Such is the case of the ARIMA models, developed in the 1970s by George Box and Gwilym Jenkins [1], which incorporate characteristics of the past models of the same series, according to their autocorrelation. This work compares advanced statistical methods for determining the demand for electricity in Colombia, including the SARIMA, econometric and Bayesian methods.

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