Alpha V. Pernía-Espinoza

Orcid: 0000-0001-6227-075X

According to our database1, Alpha V. Pernía-Espinoza authored at least 23 papers between 2005 and 2023.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of five.

Timeline

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Bibliography

2023
HYB-PARSIMONY: A hybrid approach combining Particle Swarm Optimization and Genetic Algorithms to find parsimonious models in high-dimensional datasets.
Neurocomputing, December, 2023

PSO-PARSIMONY: A method for finding parsimonious and accurate machine learning models with particle swarm optimization. Application for predicting force-displacement curves in T-stub steel connections.
Neurocomputing, 2023

Hybrid Intelligent Parsimony Search in Small High-Dimensional Datasets.
Proceedings of the Hybrid Artificial Intelligent Systems - 18th International Conference, 2023

2022
Work-in-Progress: Building Up Employability Skills and Social Responsibility in the University of La Rioja Industrial Engineering Degrees.
Proceedings of the Learning in the Age of Digital and Green Transition, 2022

New Hybrid Methodology Based on Particle Swarm Optimization with Genetic Algorithms to Improve the Search of Parsimonious Models in High-Dimensional Databases.
Proceedings of the Hybrid Artificial Intelligent Systems - 17th International Conference, 2022

2021
A comparative study of six model complexity metrics to search for parsimonious models with GAparsimony R Package.
Neurocomputing, 2021

PSO-PARSIMONY: A New Methodology for Searching for Accurate and Parsimonious Models with Particle Swarm Optimization. Application for Predicting the Force-Displacement Curve in T-stub Steel Connections.
Proceedings of the Hybrid Artificial Intelligent Systems - 16th International Conference, 2021

Active learning methodologies in STEM degrees jeopardized by COVID19.
Proceedings of the IEEE Global Engineering Education Conference, 2021

2020
Technical projects with social commitment for teaching-learning intervention in STEM students.
Proceedings of the 2020 IEEE Global Engineering Education Conference, 2020

2018
Stacking ensemble with parsimonious base models to improve generalization capability in the characterization of steel bolted components.
Appl. Soft Comput., 2018

GAparsimony: An R Package for Searching Parsimonious Models by Combining Hyperparameter Optimization and Feature Selection.
Proceedings of the Hybrid Artificial Intelligent Systems - 13th International Conference, 2018

2017
Improving hotel room demand forecasting with a hybrid GA-SVR methodology based on skewed data transformation, feature selection and parsimony tuning.
Log. J. IGPL, 2017

Single and Blended Models for Day-Ahead Photovoltaic Power Forecasting.
Proceedings of the Hybrid Artificial Intelligent Systems - 12th International Conference, 2017

2016
Hotel Reservation Forecasting Using Flexible Soft Computing Techniques: A Case of Study in a Spanish Hotel.
Int. J. Inf. Technol. Decis. Mak., 2016

Searching Parsimonious Solutions with GA-PARSIMONY and XGBoost in High-Dimensional Databases.
Proceedings of the International Joint Conference SOCO'16-CISIS'16-ICEUTE'16, 2016

2015
GA-PARSIMONY: A GA-SVR approach with feature selection and parameter optimization to obtain parsimonious solutions for predicting temperature settings in a continuous annealing furnace.
Appl. Soft Comput., 2015

A Straightforward Implementation of a GPU-accelerated ELM in R with NVIDIA Graphic Cards.
Proceedings of the Hybrid Artificial Intelligent Systems - 10th International Conference, 2015

2013
Towards Improving the Applicability of Non-parametric Multiple Comparisons to Select the Best Soft Computing Models in Rubber Extrusion Industry.
Proceedings of the International Joint Conference SOCO'13-CISIS'13-ICEUTE'13, 2013

2012
Combining genetic algorithms and the finite element method to improve steel industrial processes.
J. Appl. Log., 2012

2011
Improving Steel Industrial Processes Using Genetic Algorithms and Finite Element Method.
Proceedings of the Soft Computing Models in Industrial and Environmental Applications, 2011

Genetic Algorithms Combined with the Finite Elements Method as an Efficient Methodology for the Design of Tapered Roller Bearings.
Proceedings of the Soft Computing Models in Industrial and Environmental Applications, 2011

2007
A neural network-based approach for optimising rubber extrusion lines.
Int. J. Comput. Integr. Manuf., 2007

2005
TAO-robust backpropagation learning algorithm.
Neural Networks, 2005


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