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Juan Carlos Fernández 0001
Person information
- affiliation: University of Córdoba, Department of Computer Science and Numerical Analysis, Spain
Other persons with the same name
- Juan Carlos Fernández 0002
— Jaume I University, Castellón, Spain
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2020 – today
- 2024
- [j15]Francisco Bérchez-Moreno
Fusion of standard and ordinal dropout techniques to regularise deep models. Inf. Fusion 106: 102299 (2024) - [i1]Francisco Bérchez-Moreno, Víctor Manuel Vargas, Rafael Ayllón-Gavilán, David Guijo-Rubio, César Hervás-Martínez, Juan Carlos Fernández, Pedro Antonio Gutiérrez:
dlordinal: a Python package for deep ordinal classification. CoRR abs/2407.17163 (2024) - 2023
- [j14]David Guijo-Rubio
An Evolutionary Artificial Neural Network approach for spatio-temporal wave height time series reconstruction. Appl. Soft Comput. 146: 110647 (2023) - 2022
- [c23]Antonio Manuel Durán-Rosal, David Guijo-Rubio, Víctor Manuel Vargas, Antonio M. Gómez-Orellana
Gamifying the Classroom for the Acquisition of Skills Associated with Machine Learning: A Two-Year Case Study. CISIS-ICEUTE 2022: 224-235 - 2020
- [j13]David Guijo-Rubio
Prediction of convective clouds formation using evolutionary neural computation techniques. Neural Comput. Appl. 32(17): 13917-13929 (2020)
2010 – 2019
- 2019
- [j12]Juan Carlos Fernández
Multi-objective evolutionary optimization using the relationship between F 1 and accuracy metrics in classification tasks. Appl. Intell. 49(9): 3447-3463 (2019) - 2018
- [j11]Antonio Manuel Durán-Rosal
Efficient fog prediction with multi-objective evolutionary neural networks. Appl. Soft Comput. 70: 347-358 (2018) - [j10]Juan Carlos Fernández
Sensitivity versus accuracy in ensemble models of Artificial Neural Networks from Multi-objective Evolutionary Algorithms. Neural Comput. Appl. 30(1): 289-305 (2018) - 2016
- [c22]Antonio Manuel Durán-Rosal
Hybridization of neural network models for the prediction of Extreme Significant Wave Height segments. SSCI 2016: 1-8 - 2015
- [j9]Juan Carlos Fernández
Significant wave height and energy flux range forecast with machine learning classifiers. Eng. Appl. Artif. Intell. 43: 44-53 (2015) - [c21]Pedro Antonio Gutiérrez
Energy Flux Range Classification by Using a Dynamic Window Autoregressive Model. IWANN (2) 2015: 92-102 - 2014
- [j8]Carlos R. García-Alonso
Multiobjective evolutionary algorithms to identify highly autocorrelated areas: the case of spatial distribution in financially compromised farms. Ann. Oper. Res. 219(1): 187-202 (2014) - 2013
- [j7]Manuel Cruz-Ramírez
Predicting patient survival after liver transplantation using evolutionary multi-objective artificial neural networks. Artif. Intell. Medicine 58(1): 37-49 (2013) - [c20]María Pérez-Ortiz
Can Machine Learning Techniques Help to Improve the Common Fisheries Policy? IWANN (2) 2013: 278-286 - 2012
- [j6]Manuel Cruz-Ramírez
Multi-objective evolutionary algorithm for donor-recipient decision system in liver transplants. Eur. J. Oper. Res. 222(2): 317-327 (2012) - [c19]M. Pérez-Ortiz
Hybrid Multi-objective Machine Learning Classification in Liver Transplantation. HAIS (1) 2012: 397-408 - [c18]Manuel Cruz-Ramírez
Multiobjective Pareto Ordinal Classification for Predictive Microbiology. SOCO 2012: 153-162 - [c17]Pedro Antonio Gutiérrez
Approaching System Administration as a Group Project in Computer Engineering Higher Education. CISIS/ICEUTE/SOCO Special Sessions 2012: 331-340 - 2011
- [j5]Juan Carlos Fernández
Memetic Pareto Evolutionary Artificial Neural Networks to determine growth/no-growth in predictive microbiology. Appl. Soft Comput. 11(1): 534-550 (2011) - [c16]Manuel Cruz-Ramírez
Memetic evolutionary multi-objective neural network classifier to predict graft survival in liver transplant patients. GECCO (Companion) 2011: 479-486 - [c15]Manuel Cruz-Ramírez
Selecting the best artificial neural network model from a multi-objective Differential Evolution Pareto front. SDE 2011: 96-103 - 2010
- [j4]Manuel Cruz-Ramírez
Memetic pareto differential evolutionary artificial neural networks to determine growth multi-classes in predictive microbiology. Evol. Intell. 3(3-4): 187-199 (2010) - [j3]Juan Carlos Fernández Caballero
Sensitivity versus accuracy in multiclass problems using memetic Pareto evolutionary neural networks. IEEE Trans. Neural Networks 21(5): 750-770 (2010) - [c14]Manuel Cruz-Ramírez
Hybrid Pareto Differential Evolutionary Artificial Neural Networks to Determined Growth Multi-classes in Predictive Microbiology. IEA/AIE (3) 2010: 646-655 - [c13]Manuel Cruz-Ramírez
Learning Artificial Neural Networks multiclassifiers by evolutionary multiobjective differential evolution guided by statistical distributions. IJCNN 2010: 1-8 - [c12]Javier Sánchez-Monedero
On the suitability of Extreme Learning Machine for gene classification using feature selection. ISDA 2010: 507-512 - [c11]Manuel Cruz-Ramírez
Ensemble determination using the TOPSIS decision support system in multi-objective evolutionary neural network classifiers. ISDA 2010: 513-518
2000 – 2009
- 2009
- [j2]Pedro Antonio Gutiérrez
Combined projection and kernel basis functions for classification in evolutionary neural networks. Neurocomputing 72(13-15): 2731-2742 (2009) - [j1]Jesús Alcalá-Fdez
KEEL: a software tool to assess evolutionary algorithms for data mining problems. Soft Comput. 13(3): 307-318 (2009) - [c10]Pedro Antonio Gutiérrez
Hybrid Multilogistic Regression by Means of Evolutionary Radial Basis Functions: Application to Precision Agriculture. HAIS 2009: 244-251 - [c9]Juan Carlos Fernández
Memetic Pareto Differential Evolution for Designing Artificial Neural Networks in Multiclassification Problems Using Cross-Entropy Versus Sensitivity. HAIS 2009: 433-441 - [c8]Pedro Antonio Gutiérrez
MultiLogistic Regression using Initial and Radial Basis Function covariates. IJCNN 2009: 1067-1074 - [c7]César Hervás-Martínez, Pedro Antonio Gutiérrez
Hyperbolic Tangent Basis Function Neural Networks Training by Hybrid Evolutionary Programming for Accurate Short-Term Wind Speed Prediction. ISDA 2009: 193-198 - [c6]Juan Carlos Fernández
Design of Artificial Neural Networks Using a Memetic Pareto Evolutionary Algorithm Using as Objectives Entropy versus Variation Coefficient. ISDA 2009: 408-413 - [c5]Francisco Fernández-Navarro
A Sensitivity Clustering Method for Hybrid Evolutionary Algorithms. IWINAC (1) 2009: 245-254 - 2008
- [c4]Francisco J. Martínez-Estudillo, Pedro Antonio Gutiérrez
Evolutionary learning by a sensitivity-accuracy approach for multi-class problems. IEEE Congress on Evolutionary Computation 2008: 1581-1588 - [c3]Pedro Antonio Gutiérrez
Feature Selection for Hybrid Neuro-Logistic Regression Applied to Classification of Remote Sensed Data. HIS 2008: 625-630 - [c2]Juan Carlos Fernández
Memetic Pareto Evolutionary Artificial Neural Networks for the Determination of Growth Limits of Listeria Monocytogenes. HIS 2008: 631-636 - [p1]Pedro Antonio Gutiérrez
Combined Projection and Kernel Basis Functions for Classification in Evolutionary Neural Networks. Innovations in Hybrid Intelligent Systems 2008: 88-95 - 2007
- [c1]César Hervás, Francisco José Martínez, Mariano Carbonero-Ruz, Cristóbal Romero
Evolutionary Combining of Basis Function Neural Networks for Classification. IWINAC (1) 2007: 447-456
Coauthor Index
aka: César Hervás
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