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Data mining techniques and multivariate analysis to discover Patterns in university final researches

amelec, viloria; Rodríguez López, Jorge; García Leyva, Diana Margarita; Vargas Mercado, Carlos; Hernández-Palma, Hugo; ORELLANO LLINAS, NATALY; Arrozola David, Mónica; Velasquez Rodriguez, Javier (Procedia Computer Science, 2019)

The aim of this study is to extract knowledge from the final researches of the Mumbai University Science Faculty. Five classification models were applied: Vector Support Machines, Neural Networks, Decision Tree, Random ...
Models of multivariate regression for labor accidents in different production sectors: comparative study

Bonerge Pineda Lezama, Omar; Varela Izquierdo, Noel; Pérez Fernández, Damayse; Gómez Dorta, Rafael Luciano; Viloria Silva, Amelec Jesus; Romero Marín, Ligia (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018)

The present article shows the results of an investigation carried out on the use of alternatives to carry out work accident studies in an objective manner in three production sectors that are of high risk: the electric ...
Prediction rules in e-learning systems using genetic programming

Amelec, Viloria; Gaitan Angulo, Mercedes; J. Kamatkar, Sadhana; De la Hoz Hernández, Juan David; García Guiliany, Jesús Enrique; Redondo Bilbao, Osman Enrique; Hernandez-P, Hugo (Universidad de la Costa, 2019)

This paper describes the use of Data Mining Techniques to improve teaching–learning processes in the linear programming course offered at the Engineering Faculty at Mumbai University, India. The proposed approach seeks to ...

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Data mining techniques and multivariate analysis to discover Patterns in university final researches
...
amelec, viloria | 2019

The aim of this study is to extract knowledge from the final researches of the Mumbai University Science Faculty. Five classification models were applied: Vector Support Machines, Neural Networks, Decision Tree, Random Forest and Powering; considering the Experiment Design and Multivariate Analysis Lines. Results showed that for the Experiment Design line, the most accurate model was Random Forest with 71.48% predictions that are correct respecting to the total. Regarding the Multivariate Analysis line, there was no significant difference in overall accuracy, fluctuating by 97%.

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Models of multivariate regression for labor accidents in different production sectors: comparative study
...
Bonerge Pineda Lezama, Omar | 2018

The present article shows the results of an investigation carried out on the use of alternatives to carry out work accident studies in an objective manner in three production sectors that are of high risk: the electric power production sector, cement production and oil refining sector, so the main objective is focused on identifying the influential variables and the regression model that best explains the accident in each of these sectors and perform a comparative analysis between them. Among the techniques and tools used (data mining) are those related to multivariate statistics and generalized linear models and through the Akaike information criterion and Bayeciano criterion, it was possible to determine that the best regression model that explains the accident rate in two of the sectors studied is the negative binomial (cement and petroleum refining), while in the electric power sector, the best fit model resulted in Logistic Regression. In turn, for the three sectors in general, the variables that have the most significant impact are related to aspects such as: management commitment, occupational safety climate, safety training, psychosocial aspects and ergonomic sources, this result was corroborated by means of an accident analysis carried out in these three sectors.

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Prediction rules in e-learning systems using genetic programming
...
Amelec, Viloria | 2019

This paper describes the use of Data Mining Techniques to improve teaching–learning processes in the linear programming course offered at the Engineering Faculty at Mumbai University, India. The proposed approach seeks to model the student’s interaction with the study material using prediction rules whose interpretation will allow to detect the weaknesses of the educational process and evaluate the quality of the study material. The proposed rule discovery method is the Evolutionary Algorithms and particularly the Grammar-Based Genetic Programming (GB-GP), which is compared to association rules and decision tree construction for discovering prediction rules.

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ListarArtículos científicos por tema "Data mining techniques"

Data mining techniques and multivariate analysis to discover Patterns in university final researches ﻿

Models of multivariate regression for labor accidents in different production sectors: comparative study ﻿

Prediction rules in e-learning systems using genetic programming ﻿

Envíos recientes

Data mining techniques and multivariate analysis to discover Patterns in university final researches

Models of multivariate regression for labor accidents in different production sectors: comparative study

Prediction rules in e-learning systems using genetic programming

Data mining techniques and multivariate analysis to discover Patterns in university final researches

Models of multivariate regression for labor accidents in different production sectors: comparative study

Prediction rules in e-learning systems using genetic programming