Publications dans lesquelles il/elle collabore avec PAULINO JOSE GARCIA NIETO (66)

2024

  1. Estimation of the coal higher heating value for energy systems relied on ultimate analysis with machine learning techniques

    Fuel, Vol. 357

  2. Forecast of the outlet turbidity and filtered volume in different microirrigation filters and filtration media by using machine learning techniques

    Journal of Computational and Applied Mathematics, Vol. 439

  3. Hybrid DE optimised kernel SVR–relied techniques to forecast the outlet turbidity and outlet dissolved oxygen in distinct filtration media and micro-irrigation filters

    Biosystems Engineering, Vol. 243, pp. 42-56

  4. Hybrid DE-Optimized GPR and NARX/SVR Models for Forecasting Gold Spot Prices: A Case Study of the Global Commodities Market

    Mathematics, Vol. 12, Núm. 7

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

    Alexandria Engineering Journal, Vol. 86, pp. 144-156

2023

  1. A support vector regression model for time series forecasting of the COMEX copper spot price

    Logic Journal of the IGPL, Vol. 31, Núm. 4, pp. 775-784

  2. Data-driven state-of-charge prediction of a storage cell using ABC/GBRT, ABC/MLP and LASSO machine learning techniques

    Journal of Computational and Applied Mathematics, Vol. 433

  3. Method for the Detection of Functional Outliers Applied to Quality Monitoring Samples in the Vicinity of El Musel Seaport in the Metropolitan Area of Gijón (Northern Spain)

    Mathematics, Vol. 11, Núm. 12

  4. Modelling energy performance of residential dwellings by using the MARS technique, SVM-based approach, MLP neural network and M5 model tree

    Applied Energy, Vol. 341

  5. Modelling hydrogen production from biomass pyrolysis for energy systems using machine learning techniques

    Environmental Science and Pollution Research, Vol. 30, Núm. 31, pp. 76977-76991

2022

  1. Análisis de la bibliografía disponible para el tema de aritmética finita y teoría de errores de la asignatura de Computación Numérica del Grado en Ingeniería Informática del Software y recomendaciones para su uso

    Avances y desafíos para la transformación educativa

  2. Forecast of the higher heating value based on proximate analysis by using support vector machines and multilayer perceptron in bioenergy resources

    Fuel, Vol. 317

  3. Modeling eutrophication risks in Tanes reservoir by using a hybrid WOA optimized SVR-relied technique along with feature selection based on the MARS approximation

    Stochastic Environmental Research and Risk Assessment, Vol. 36, Núm. 9, pp. 2561-2580

2021

  1. A Multivariate Approach to Time Series Forecasting of Copper Prices with the Help of Multiple Imputation by Chained Equations and Multivariate Adaptive Regression Splines

    Advances in Intelligent Systems and Computing

  2. A new hybrid model to foretell thermal power efficiency from energy performance certificates at residential dwellings applying a Gaussian process regression

    Neural Computing and Applications, Vol. 33, Núm. 12, pp. 6627-6640

  3. Modeling algal atypical proliferation in La Barca reservoir using L-SHADE optimized gradient boosted regression trees: a case study

    Neural Computing and Applications, Vol. 33, Núm. 13, pp. 7821-7838

  4. Modelling energy performance using a new hybrid DE/MARS–based approach for fossil-fuel thermal power stations

    Environmental Science and Pollution Research, Vol. 28, Núm. 4, pp. 4417-4429

  5. Prediction of the critical temperature of a superconductor by using the WOA/MARS, Ridge, Lasso and Elastic-net machine learning techniques

    Neural Computing and Applications, Vol. 33, Núm. 24, pp. 17131-17145

  6. Time Series Analysis for the COMEX Copper Spot Price by Using Support Vector Regression

    Advances in Intelligent Systems and Computing

2020

  1. A New Predictive Model for Evaluating Chlorophyll-a Concentration in Tanes Reservoir by Using a Gaussian Process Regression

    Water Resources Management, Vol. 34, Núm. 15, pp. 4921-4941