ANTONIO BERNARDO SANCHEZ-rekin lankidetzan egindako argitalpenak (11)

2024

  1. 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. 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

2021

  1. 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

  2. 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

2020

  1. A hybrid predictive approach for chromium layer thickness in the hard chromium plating process based on the differential evolution/gradient boosted regression tree methodology

    Mathematics, Vol. 8, Núm. 6

  2. Chrome Layer Thickness Modelling in a Hard Chromium Plating Process Using a Hybrid PSO/ RBF–SVM–Based Model

    IJIMAI, Vol. 6, Núm. 4, pp. 39-48

2019

  1. Predictive modelling of the higher heating value in biomass torrefaction for the energy treatment process using machine-learning techniques

    Neural Computing and Applications, Vol. 31, Núm. 12, pp. 8823-8836

2018

  1. Air Quality Modeling Using the PSO-SVM-Based Approach, MLP Neural Network, and M5 Model Tree in the Metropolitan Area of Oviedo (Northern Spain)

    Environmental Modeling and Assessment, Vol. 23, Núm. 3, pp. 229-247

2016

  1. A new predictive model based on the abc optimized multivariate adaptive regression splines approach for predicting the remaining useful life in aircraft engines

    Energies, Vol. 9, Núm. 6

  2. Hard-rock stability analysis for span design in entry-type excavations with learning classifiers

    Materials, Vol. 9, Núm. 7

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

    Materials, Vol. 9, Núm. 2