Publicacións nas que colabora con PAULINO JOSE GARCIA NIETO (35)

2020

  1. A methodology for detecting relevant single nucleotide polymorphism in prostate cancer with multivariate adaptive regression splines and backpropagation artificial neural networks

    Neural Computing and Applications, Vol. 32, Núm. 5, pp. 1231-1238

  2. Detection of outliers in pollutant emissions from the Soto de Ribera coal-fired power plant using functional data analysis: a case study in northern Spain

    Environmental Science and Pollution Research, Vol. 27, Núm. 1, pp. 8-20

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

    Scientific Reports, Vol. 10, Núm. 1

  4. Missing Data Imputation for Continuous Variables Based on Multivariate Adaptive Regression Splines

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

2018

  1. Cyanotoxin level prediction in a reservoir using gradient boosted regression trees: a case study

    Environmental Science and Pollution Research, Vol. 25, Núm. 23, pp. 22658-22671

  2. Estimation of PM10 concentration from air quality data in the vicinity of a major steelworks site in the metropolitan area of Avilés (Northern Spain) using machine learning techniques

    Stochastic Environmental Research and Risk Assessment, Vol. 32, Núm. 11, pp. 3287-3298

  3. PM10 concentration forecasting in the metropolitan area of Oviedo (Northern Spain) using models based on SVM, MLP, VARMA and ARIMA: A case study

    Science of the Total Environment, Vol. 621, pp. 753-761

2016

  1. Hybrid ABC optimized MARS-based modeling of the milling tool wear from milling run experimental data

    Materials, Vol. 9, Núm. 2

2015

  1. A hybrid PCA-CART-MARS-based prognostic approach of the remaining useful life for aircraft engines

    Sensors (Switzerland), Vol. 15, Núm. 3, pp. 7062-7083

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

    Reliability Engineering and System Safety, Vol. 138, pp. 219-231

2014

  1. Evolutionary support vector regression algorithm applied to the prediction of the thickness of the chromium layer in a hard chromium plating process

    Applied Mathematics and Computation, Vol. 227, pp. 164-170

  2. Mathematical models for predicting the cyanotoxins presence in several reservoirs in the Cantabrian Basin (Northern Spain)

    Cyanobacteria: Ecological Importance, Biotechnological Uses and Risk Management (Nova Science Publishers, Inc.), pp. 61-78

  3. Prediction of cyanotoxin production along with cyanobacteria presence using genetic algorithms and multivariate adaptive regression splines

    Advances in Machine Learning Research (Nova Science Publishers, Inc.), pp. 113-134

2013

  1. Battery state-of-charge estimator using the MARS technique

    IEEE Transactions on Power Electronics, Vol. 28, Núm. 8, pp. 3798-3805

  2. Battery state-of-charge estimator using the SVM technique

    Applied Mathematical Modelling, Vol. 37, Núm. 9, pp. 6244-6253

  3. Forecasting the cyanotoxins presence in fresh waters: A new model based on genetic algorithms combined with the MARS technique

    Ecological Engineering, Vol. 53, pp. 68-78

  4. Hybrid modelling based on support vector regression with genetic algorithms in forecasting the cyanotoxins presence in the Trasona reservoir (Northern Spain)

    Environmental Research, Vol. 122, pp. 1-10

  5. Support Vector Machines and Multilayer Perceptron Networks Used to Evaluate the Cyanotoxins Presence from Experimental Cyanobacteria Concentrations in the Trasona Reservoir (Northern Spain)

    Water Resources Management, Vol. 27, Núm. 9, pp. 3457-3476

2012

  1. A new improved study of cyanotoxins presence from experimental cyanobacteria concentrations in the Trasona reservoir (Northern Spain) using the MARS technique

    Science of the Total Environment, Vol. 430, pp. 88-92

  2. Study of the influence of forest canopies on the accuracy of GPS measurements by using genetic algorithms

    Handbook of Genetic Algorithms: New Research (Nova Science Publishers, Inc.), pp. 229-240