Mostrando ítems 21-27 de 27

Method for collecting relevant topics from twitter supported by Big Data

silva d, jesus g; Senior Naveda, Alexa; GAMBOA SUAREZ, RAMIRO; Hernández Palma, Hugo; Niebles Nuñez, William (Journal of Physics: Conference Series, 2020)

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 ...
Neural networks for the web services classification

silva d, jesus g; Senior Naveda, Alexa; Solórzano Movilla, José; Niebles Núñez, William; Hernández Palma, Hugo (Journal of Physics: Conference Series, 2020)

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 ...
Predicting short-term electricity demand through artificial neural network

Viloria, Amelec; García Guliany, Jesús; Varela Izquierdo, Noel; Pineda, Omar; Hernández Palma, Hugo; Valero, Lesbia; Marín-González, Freddy (Corporación Universidad de la Costa, 2020)

Forecasting the consumption of electric power on a daily basis allows considerable money savings for the supplying companies, by reducing the expenses in generation and operation. Therefore, the cost of forecasting errors ...
Retraction: Electrical consumption patterns through machine learning

amelec, viloria; Senior Naveda, Alexa; Hernández Palma, Hugo; Niebles Núñez, William; Niebles Nuñez, Leonardo David (Corporación Universidad de la Costa, 2019)

This article, and others within this volume, has been retracted by IOP Publishing following clear evidence of plagiarism and citation manipulation. This work was originally published in Spanish (1) and has been translated ...
Retraction: time series decomposition using automatic learning techniques for predictive models

Silva, Jesús; Hernández Palma, Hugo; Niebles Núñez, William; Ovallos-Gazabon, David; Varela, Noel (Corporación Universidad de la Costa, 2020)

This article, and others within this volume, has been retracted by IOP Publishing following clear evidence of plagiarism and citation manipulation. This work was originally published in Spanish (1) and has been translated ...
Temporary variables for predicting electricity consumption through data mining

silva d, jesus g; Senior Naveda, Alexa; Hernández Palma, Hugo; Niebles Núñez, William; Niebles Nuñez, Leonardo David (Journal of Physics: Conference Series, 2020)

In the new global and local scenario, the advent of intelligent distribution networks or Smart Grids allows real-time collection of data on the operating status of the electricity grid. Based on this availability of data, ...
Using Big Data to determine potential dropouts in higher education

amelec, viloria; Senior Naveda, Alexa; Hernández Palma, Hugo; Niebles Nuñez, William; Niebles Nuñez, Leonardo David (Journal of Physics: Conference Series, 2020)

In higher education, student dropout is a relevant problem, not just in Latin America but also in developed countries. Although there is no consensus to measure the education quality, one of the important indicators of ...

Envíos recientes

Method for collecting relevant topics from twitter supported by Big Data
...
silva d, jesus g | 2020

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.

LEER
Neural networks for the web services classification
...
silva d, jesus g | 2020

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.

LEER
Predicting short-term electricity demand through artificial neural network
...
Viloria, Amelec | 2020

Forecasting the consumption of electric power on a daily basis allows considerable money savings for the supplying companies, by reducing the expenses in generation and operation. Therefore, the cost of forecasting errors can be of such magnitude that many studies have focused on minimizing the forecasting error, which makes this topic as an integral part of planning in many companies of various kinds and sizes, ranging from generation, transmission, and distribution to consumption, by requiring reliable forecasting systems.

LEER
Retraction: Electrical consumption patterns through machine learning
...
amelec, viloria | 2019

This article, and others within this volume, has been retracted by IOP Publishing following clear evidence of plagiarism and citation manipulation. This work was originally published in Spanish (1) and has been translated and published without permission or acknowledgement to the original authors. IOP Publishing Limited has discovered other papers within this volume that have been subjected to the same treatment. This is scientific misconduct. Misconduct investigations are ongoing at the author’s institutions. IOP Publishing Limited will update this notice if required once those investigations have concluded. IOP Publishing Limited request any citations to this article be redirected to the original work (1). Anyone with any information regarding these papers is requested to contact conferenceseries@ioppublishing.org.

LEER
Retraction: time series decomposition using automatic learning techniques for predictive models
...
Silva, Jesús | 2020

This article, and others within this volume, has been retracted by IOP Publishing following clear evidence of plagiarism and citation manipulation. This work was originally published in Spanish (1) and has been translated and published without permission or acknowledgement to the original authors. IOP Publishing Limited has discovered other papers within this volume that have been subjected to the same treatment. This is scientific misconduct. Misconduct investigations are ongoing at the author's institutions. IOP Publishing Limited will update this notice if required once those investigations have concluded. To date, there is no evidence to suggest anyone other than Amelec Viloria / Jesus Silva was directly culpable for the actions that led to retraction.

LEER
Temporary variables for predicting electricity consumption through data mining
...
silva d, jesus g | 2020

In the new global and local scenario, the advent of intelligent distribution networks or Smart Grids allows real-time collection of data on the operating status of the electricity grid. Based on this availability of data, it is feasible and convenient to predict consumption in the short term, from a few hours to a week. The hypothesis of the study is that the method used to present time variables to a prediction system of electricity consumption affects the results.

LEER
Using Big Data to determine potential dropouts in higher education
...
amelec, viloria | 2020

In higher education, student dropout is a relevant problem, not just in Latin America but also in developed countries. Although there is no consensus to measure the education quality, one of the important indicators of university success is the time to graduation (TTG), which is directly related to student dropout [1]. Global estimates put this dropout rate at 42% [2]. In the United States, this rate is around 30% and represents a loss of 9 billion dollars in the education of these students [3]. However, desertion not only affects the quality of education and the economy of a country, but also has effects on the development of society, since society demands the contributions derived from the population with higher education such as: innovation, knowledge production and scientific discovery [4]. Using basic statistical learning techniques, this paper presents a simple way to predict possible dropouts based on their demographic and academic characteristics.

LEER

Listar por autor "Hernández Palma, Hugo"

Method for collecting relevant topics from twitter supported by Big Data ﻿

Neural networks for the web services classification ﻿

Predicting short-term electricity demand through artificial neural network ﻿

Retraction: Electrical consumption patterns through machine learning ﻿

Retraction: time series decomposition using automatic learning techniques for predictive models ﻿

Temporary variables for predicting electricity consumption through data mining ﻿

Using Big Data to determine potential dropouts in higher education ﻿

Envíos recientes

Method for collecting relevant topics from twitter supported by Big Data

Neural networks for the web services classification

Predicting short-term electricity demand through artificial neural network

Retraction: Electrical consumption patterns through machine learning

Retraction: time series decomposition using automatic learning techniques for predictive models

Temporary variables for predicting electricity consumption through data mining

Using Big Data to determine potential dropouts in higher education

Method for collecting relevant topics from twitter supported by Big Data

Neural networks for the web services classification

Predicting short-term electricity demand through artificial neural network

Retraction: Electrical consumption patterns through machine learning

Retraction: time series decomposition using automatic learning techniques for predictive models

Temporary variables for predicting electricity consumption through data mining

Using Big Data to determine potential dropouts in higher education