Esperanza García–Gonzalo

Universidad de Oviedo

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i-10 index

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Co-Authors

P.J. Garcı́a Nieto

Universidad de Oviedo

Juan Luis Fernández‐Martínez

Universidad de Oviedo

Fernando Sánchez Lasheras

Universidad de Oviedo

C. Díaz Muñiz

Ministerio de Agricultura, Pesca y Alimentación

J.R. Alonso Fernández

Boehringer Ingelheim (Spain)

Antonio Bernardo-Sánchez

Universidad de León

José P. Paredes–Sánchez

Universidad de Oviedo

Tapan Mukerji

Stanford University

Jaume Puig‐Bargués

University of Girona

Miquel Duran–Ros

University of Girona

Cooperative Institutions

Universidad de Oviedo

Nationwide Children's Hospital

The Ohio State University

Universidad de León

Weill Cornell Medical College in Qatar

University of Girona

Universidade de Vigo

Mayo Clinic in Arizona

Stanford University

University of Lisbon

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Aligned PSO for Optimization of Image Processing Methods Applied to the Face Recognition Problem

Lecture notes in computer science (2013)

Juan Luis Fernández‐Martínez Ana Cernea Esperanza García–Gonzalo Julián Velasco Bijaya Ketan Panigrahi

Identification

10.1007/978-3-319-03753-0_57

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Citations (3)

Enunciados de exámenes de análisis numérico: E.U.I.T. Informática de Oviedo

Esperanza García–Gonzalo Manuel Gutiérrez

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Citations (0)

Evolution and forecasting of PM10 concentration at the Port of Gijon (Spain)

Scientific Reports (2020)

Fernando Sánchez Lasheras P.J. Garcı́a Nieto Esperanza García–Gonzalo L. Bonavera Francisco Javier de Cos Juez

Abstract The name PM 10 refers to small particles with a diameter of less than 10 microns. The present research analyses different models capable of predicting PM 10 concentration using the previous values of PM 10 , SO 2 , NO, NO 2 , CO and O 3 as input variables. The information for model training uses data from January 2010 to December 2017. The models trained were autoregressive integrated moving average (ARIMA), vector autoregressive moving average (VARMA), multilayer perceptron neural networks (MLP), support vector machines as regressor (SVMR) and multivariate adaptive regression splines. Predictions were performed from 1 to 6 months in advance. The performance of the different models was measured in terms of root mean squared errors (RMSE). For forecasting 1 month ahead, the best results were obtained with the help of a SVMR model of six variables that gave a RMSE of 4.2649, but MLP results were very close, with a RMSE value of 4.3402. In the case of forecasts 6 months in advance, the best results correspond to an MLP model of six variables with a RMSE of 6.0873 followed by a SVMR also with six variables that gave an RMSE result of 6.1010. For forecasts both 1 and 6 months ahead, ARIMA outperformed VARMA models.

Moving average

Multilayer perceptron

Autoregressive–moving-average model

Rank (graph theory)

10.1038/s41598-020-68636-5

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Citations (18)

Time Series Forecasting of Gold Prices with the Help of Its Decomposition and Multivariate Adaptive Regression Splines

Advances in intelligent systems and computing (2021)

Fernando Sánchez Lasheras P.J. Garcı́a Nieto Esperanza García–Gonzalo Gregorio Fidalgo Valverde Alicja Krzemień

Mean absolute error

Univariate

Absolute deviation

10.1007/978-3-030-87869-6_13

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Citations (2)

A comparison of several machine learning techniques for the centerline segregation prediction in continuous cast steel slabs and evaluation of its performance

Journal of Computational and Applied Mathematics (2017)

P.J. Garcı́a Nieto Esperanza García–Gonzalo Juan Carlos Álvarez Antón Víctor M. González Ricardo Mayo Bayón

Kernel (algebra)

Perceptron

Multilayer perceptron

Hyperparameter

10.1016/j.cam.2017.02.031

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Citations (54)

PSO Advances and Application to Inverse Problems

Lecture notes in computer science (2010)

Juan Luis Fernández‐Martínez Esperanza García–Gonzalo

Swarm intelligence

10.1007/978-3-642-17563-3_18

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Citations (2)

TWO ALGORITHMS OF THE EXTENDED PSO FAMILY

International Conference on Evolutionary Computation (2010)

Juan Luis Fernández‐Martínez Esperanza García–Gonzalo

Abstract: In this paper we present two novel algorithms belonging to the extended family of PSO: the PP-GPSO and the RR-GPSO. These algorithms correspond respectively to progressive and regressive discretizations in acceleration and velocity. PP-GPSO has the same velocity update than GPSO, but the velocities used to update the trajectories are delayed one iteration, thus, PP-GPSO acts as a Jacobi system updating positions and velocities at the same time. RR-GPSO is similar to a GPSO with stochastic constriction factor. Both versions have a very different behavior from GPSO and the other family members introduced in the past: CC-GPSO and CP-GPSO. The numerical comparison of all the family members has shown that RR-GPSO has the greatest convergence rate and its good parameter sets can be calculated analytically since they are along a straight line located in the first order stability region. Conversely PP-GPSO is a more explorative version.

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Predicting the critical superconducting temperature using the random forest, MLP neural network, M5 model tree and multivariate linear regression

Alexandria Engineering Journal (2023)

P.J. Garcı́a Nieto Esperanza García–Gonzalo Luis Alfonso Menéndez García Laura Álvarez de Prado Antonio Bernardo-Sánchez

Using a random forest regression (RFR) machine learning technique, the critical temperature (Tc) of a superconductor was predicted in the context of Industry 4.0 in this study using features derived from the material's physico-chemical properties, containing atomic mass, electron affinity, atomic radius, valence, and thermal conductivity. The same experimental data were also fitted with multilayer perceptron (MLP) artificial neural networks (ANN), M5 model tree and multivariate linear regression (MLR) model for comparison. The current investigation's findings show that the proposed RFR–relied model can successfully forecast the critical temperature of a superconductor. Additionally, the Tc estimate was reached with a correlation coefficient of 0.9565 and a coefficient of determination 0.9146, when the observed dataset was used to test this unique technique. Additionally, the outcomes from the MLP, M5, and MLR models are obviously worse than those from the RFR–relied model. When it comes to fully comprehending the superconductivity, this investigation is noteworthy. Regarding forecasting effectiveness and feature reduction rate, the RFR approach has obvious advantages and generalizability, and it also demonstrates suitability for high-temperature superconductor Tc forecasting. In fact, it offers a practical and affordable approach to data-driven superconductor investigation.

Multilayer perceptron

10.1016/j.aej.2023.11.034

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Citations (6)

Hybrid PSO–SVM-based method for forecasting of the remaining useful life for aircraft engines and evaluation of its reliability

Reliability Engineering & System Safety (2015)

P.J. Garcı́a Nieto Esperanza García–Gonzalo Fernando Sánchez Lasheras Francisco Javier de Cos Juez

Kernel (algebra)

10.1016/j.ress.2015.02.001

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Citations (278)

Sulf2 is associated with podocyte pathophysiology