Francisco Martínez Álvarez-rekin lankidetzan egindako argitalpenak (36)
2021
-
A Preliminary Study on Deep Transfer Learning Applied to Image Classification for Small Datasets
Advances in Intelligent Systems and Computing
-
A Preliminary Study on Deep Transfer Learning Applied to Image Classification for Small Datasets
15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020): Burgos, Spain ; September 2020
-
On the Performance of Deep Learning Models for Time Series Classification in Streaming
Advances in Intelligent Systems and Computing
-
On the Performance of Deep Learning Models for Time Series Classification in Streaming
15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020): Burgos, Spain ; September 2020
2020
-
Coronavirus Optimization Algorithm: A Bioinspired Metaheuristic Based on the COVID-19 Propagation Model
Big data, Vol. 8, Núm. 4, pp. 308-322
2019
-
A novel ensemble method for electric vehicle power consumption forecasting: Application to the Spanish system
IEEE Access, Vol. 7, pp. 120840-120856
-
Impact of Auto-evaluation Tests as Part of the Continuous Evaluation in Programming Courses
Advances in Intelligent Systems and Computing
2018
-
A novel approach to forecast urban surface-level ozone considering heterogeneous locations and limited information
Environmental Modelling and Software, pp. 52-61
-
Big data analytics for discovering electricity consumption patterns in smart cities
Energies, Vol. 11, Núm. 3
-
Data science and big data in energy forecasting
Energies, Vol. 11, Núm. 11
-
SmartFD: A real big data application for electrical fraud detection
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
2017
-
Applications of Computational Intelligence in Time Series
Computational Intelligence and Neuroscience
-
Recent advances in energy time series forecasting
Energies
2016
-
Improving a multi-objective evolutionary algorithm to discover quantitative association rules
Knowledge and Information Systems, Vol. 49, Núm. 2, pp. 481-509
-
Obtaining optimal quality measures for quantitative association rules
Neurocomputing, Vol. 176, pp. 36-47
2015
-
A comparison of machine learning regression techniques for LiDAR-derived estimation of forest variables
Neurocomputing, Vol. 167, pp. 24-31
-
A survey on data mining techniques applied to electricity-related time series forecasting
Energies, Vol. 8, Núm. 11, pp. 13162-13193
2014
-
A comparative study of machine learning regression methods on LiDAR data: A case study
Advances in Intelligent Systems and Computing
-
Selecting the best measures to discover quantitative association rules
Neurocomputing, Vol. 126, pp. 3-14
-
TriGen: A genetic algorithm to mine triclusters in temporal gene expression data
Neurocomputing, Vol. 132, pp. 42-53