Research groups

GA11 Learning and Artificial Neural Networks

Our research group, Learning and Artificial Neural Networks (AYRNA) (TIC-148, "Junta de Andalucía"), was founded in 1994 by a small group of researchers interested in the field of Artificial Neural Networks. During the last years, the group has diversified its interest areas by searching solutions to different problems through machine learning and soft computing techniques (artificial neural networks, kernel methods, evolutionary algorithms, among others).

Currently, our group consists of 15 doctoral and 3 pre-doctoral researchers, and is led by Professor César Hervás Martínez as the main researcher of the group. Our group has published 328 works, including 148 international journal papers, and has been involved in 11 research projects, including one project with the European Space Agency. Finally, 28 PhD thesis have been supervised in AYRNA. Additional information can be found at AYRNA’s website. With respect to biomedicine, we propose to tackle problems such as donor-receptor matching in liver transplants, medical imaging analysis for melanoma detection and Parkinson disease detection including different severity degrees, and assignment of treatments for HIV/HCV patients.

The long-term objectives of the group are to contribute to the scientific community all the advances we achieve in the field of machine learning. As far as possible, we want to apply our techniques and algorithms to real societal problems, as we are already doing with dermatology, liver transplants, Parkinson's Disease, climate change and renewable energies, among others.

Among our achievements, we highlight the importance of the research carried out by our group in the field of liver transplantation, which has been awarded on many occasions by different entities both nationally and internationally.

We collaborate with different companies who may be interested in the application of our computational solutions. Among them are General Electric, Iberdrola, Astellas Pharma, among others.

Research Lines

New decision support system which leads to making the correct decision about receptor choice based on efficient and impartial criteria (principles of justice, efficiency and equity). A Rule-based system that aids medical experts in making decisions about the allocation of liver transplants.


    DAMA network (TIN2015-70308-REDT)

    MAPAS network (TIN2017-90567-REDT)


  • Ordinal classification
  • artificial neural networks
  • processing of biomedical images
  • time series segmentation
  • regression models
  • ordinal regression
  • evolutionary computation

Additional Information

Our website: AYRNA

GA11 Learning and Artificial Neural Networks

Principal Investigator
César Hervás Martínez

Pedro Antonio Gutiérrez Peña
Juan Carlos Fernandez-Caballero
Francisco José Martínez-Estudillo
Mariano Carbonero-Ruz
Carlos García-Alonso
Alfonso Carlos Martínez-Estudillo
Mercedes Torres-Jiménez
Sancho Salcedo-Sanz
David Becerra-Alonso
Francisco Fernandez-Navarro
Javier Sánchez-Monedero
María Pérez-Ortiz
Mónica de la Paz Marín
Mª Luisa Rodero Cosano
José Alberto Salinas Pérez
Manuel Dorado-Moreno
Antonio M Durán Rosal

Pre-Doctoral Researchers
Javier  Barbero Gómez
David Guijo Rubio
Víctor Vargas Yún

Nursing, Technical, and Administrative Staff
Julio Camacho Cañamón


Active in 2019


Hervás-Martínez C and Gutiérrez PA. Hybrid Algorithms combining Machine-Learning and meta-hEurisTics for ordinal classification and prediction (HAMLET). Funding Agency: Ministry of Economy and Competitiveness (MINECO). Reference: TIN2017-85887-C2-1-P.

Figueiras-Vidal A and Gutiérrez PA. Learning Machines for Singular Problems and Applications (MAPAS). Funding Agency: Ministry of Economy and Competitiveness (MINECO). Reference: TIN2017-90567-REDT.

Finished projects

Hervás-Martínez C, Gutiérrez PA, Camacho-Cañamón J. Clasificación y evaluación automática de los grados de párkinson. III Plan Propio GALILEO de Innovación y Transferencia (2017) Modalidad IV. UCO-SOCIAL-INNOVA.

Hervás-Martínez C. Ordinal classification and prediction algorithms in renewable energy, Orca-Re. Funding Agency: Ministry of Economy and Competitiveness (MINECO). Reference: TIN2014-54583-C2-1-R.



Duran-Rosal AM, Gutierrez PA, Carmona-Poyato A, Heryas-Martinez C; A hybrid dynamic exploitation barebones particle swarm optimisation algorithm for time series segmentation. NEUROCOMPUTING. 2019 353;45-55. doi: 10.1016/j.neucom.2018.05.129
IF: 4,072
Q: 1

Comino F, Guijo-Rubio D, de Adana MR, Hervas-Martinez C; Validation of multitask artificial neural networks to model desiccant wheels activated at low temperature. INTERNATIONAL JOURNAL OF REFRIGERATION-REVUE INTERNATIONALE DU FROID. 2019, 100;434-442. doi: 10.1016/j.ijrefrig.2019.02.002.
IF: 3,177
Q: 1
Fernandez JC, Carbonero M, Gutierrez PA, Hervas-Martinez C; Multi-objective evolutionary optimization using the relationship between F-1 and accuracy metrics in classification tasks. APPLIED INTELLIGENCE. 2019. 49;9(3447-3463). doi: 10.1007/s10489-019-01447-y. 
IF: 2,882
Q: 2

Sanchez-Monedero J, Gutierrez PA, Perez-Ortiz M; ORCA: A Matlab/Octave Toolbox for Ordinal Regression. JOURNAL OF MACHINE LEARNING RESEARCH. 2019. 20;1-5. 
IF: 4,091
Q: 1


Redel-Macias MD, Hervas-Martinez C, Gutierrez PA, Pinzi S, Cubero-Atienza AJ, Dorado MP. Computational models to predict noise emissions of a diesel engine fueled with saturated and monounsaturated fatty acid methyl esters. ENERGY. 2018; 144:110-119.
IF: 4,968
Q: 1  D: 1

Duran-Rosal AM, Gutierrez PA, Martinez-Estudillo FJ, Hervas-Martinez C. Simultaneous optimisation of clustering quality and approximation error for time series segmentation. INFORMATION SCIENCES. 2018; 442:186-201.
IF: 4,305
Q: 1  D: 1

Fernandez JC, Cruz-Ramirez M, Hervas-Martinez C. Sensitivity versus accuracy in ensemble models of Artificial Neural Networks from Multi-objective Evolutionary Algorithms. NEURAL COMPUTING & APPLICATIONS. 2018; 30(1):289-305.
IF: 4,213
Q: 1

Fernandez-Navarro F, de la Cruz MA, Gutierrez PA, Castano A, Hervas-Martinez C. Time series forecasting by recurrent product unit neural networks. NEURAL COMPUTING & APPLICATIONS. 2018; 29(3):779-791.
IF: 4,213
Q: 1

Duran-Rosal AM, Gutierrez PA, Salcedo-Sanz S, Hervas-Martinez C. A statistically-driven Coral Reef Optimization algorithm for optimal size reduction of time series. APPLIED SOFT COMPUTING. 2018; 63:139-153.
IF: 3,907
Q: 1

Duran-Rosal AM, Fernandez JC, Casanova-Mateo C, Sanz-Justo J, Salcedo-Sanz S, Hervas-Martinez C. Efficient fog prediction with multi-objective evolutionary neural networks. APPLIED SOFT COMPUTING. 2018; 70:347-358.
IF: 3,907
Q: 1

Sanchez-Monedero J, Perez-Ortiz M, Saez A, Gutierrez PA, Hervas-Martinez C. Partial order label decomposition approaches for melanoma diagnosis. APPLIED SOFT COMPUTING. 2018; 64:341-355.
IF: 3,907
Q: 1

Guijo-Rubio D, Gutierrez PA, Casanova-Mateo C, Sanz-Justo J, Salcedo-Sanz S, Hervas-Martinez C. Prediction of low-visibility events due to fog using ordinal classification. ATMOSPHERIC RESEARCH. 2018; 214:64-73.
IF: 3,817
Q: 1

Dorado-Moreno M, Gutierrez PA, Cornejo-Bueno L, Prieto L, Salcedo-Sanz S, Hervas-Martinez. Ordinal multi-class architecture for predicting wind power ramp events based on reservoir computing. NEURAL PROCESSING LETTERS.  2018. doi: 10.1007/s11063-018-9922-5.
IF: 2,591
Q: 2

Chung YJ, Salvador-Carulla L, Salinas-Perez JA, Uriarte-Uriarte JJ, Iruin-Sanz A, Garcia-Alonso CR. Use of the self-organising map network (SOMNet) as a decision support system for regional mental health planning. HEALTH RESEARCH POLICY AND SYSTEMS. 2018; 16:Núm artículo 35
IF: 2,179
Q: 2

Fernandez-Arias D, Lopez-Martin M, Montero-Romero T, Martinez-Estudillo F, Fernandez-Navarro F. Financial Soundness Prediction Using a Multi-classification Model: Evidence from Current Financial Crisis in OECD Banks. COMPUTATIONAL ECONOMICS. 2018; 52(1):275-297.
IF: 1,038
Q: 3

Perez-Barea JJ, Fernandez-Navarro F, Montero-Simo MJ, Araque-Padilla R. A socially responsible consumption index based on non-linear dimensionality reduction and global sensitivity analysis. APPLIED SOFT COMPUTING. 2018; 69:599-609.
IF: 3,907
Q: 1

Ayllon MD, Ciria R, Cruz-Ramirez M, Perez-Ortiz M, Gomez I, Valente R, O'Grady J, de la Mata M, Hervas-Martinez C, Heaton ND, Briceno J. Validation of artificial neural networks as a methodology for donor-recipient matching for liver transplantation. LIVER TRANSPLANTATION. 2018; 24(2):192-203.
IF: 3,752
Q: 1

Del Pozo-Antunez JJ, Ariza-Montes A, Fernandez-Navarro F, Molina-Sanchez H. Effect of a Job Demand-Control-Social Support Model on Accounting Professionals' Health Perception. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH. 2018; 15(11):Núm artículo 2437
IF: 2,145
Q: 2


Carbonero-Ruz, M, Martinez-Estudillo, FJ, Fernandez-Navarro, F, Becerra-Alonso, D, Martinez-Estudillo, AC. A two dimensional accuracy-based measure for classification performance. INFORMATION SCIENCES. 2017. 82-383. 60-80.
IF: 4,832
Q: 1

Perez-Ortiz M, Gutierrez PA, Ayllon-Teran MD, Heaton N, Ciria R, Briceno J, Hervas-Martinez C. Synthetic semi-supervised learning in imbalanced domains: Constructing a model for donor-recipient matching in liver transplantation. KNOWLEDGE-BASED SYSTEMS. 2017.123:75-87.
IF: 4,529
Q: 1 

Dorado-Moreno M, Cornejo-Bueno L, Gutierrez PA, Prieto L, Hervas-Martinez C, Salcedo-Sanz S. Robust estimation of wind power ramp events with reservoir computing. RENEWABLE ENERGY. 2017.111:428-437.
IF: 4,357
Q: 1 

Dorado-Moreno M, Perez-Ortiz M, Gutierrez PA, Ciria R, Briceno J, Hervas-Martinez C. Dynamically weighted evolutionary ordinal neural network for solving an imbalanced liver transplantation problem. ARTIFICIAL INTELLIGENCE IN MEDICINE. 2017.77():1-11.
IF: 2,009
Q: 2 

Duran-Rosal AM, Fernandez JC, Gutierrez PA, Hervas-Martinez C. Detection and prediction of segments containing extreme significant wave heights. OCEAN ENGINEERING. 2017.142:268-279.
IF: 1,894
Q: 1 

Duran-Rosal AM, de la Paz-Marin M, Gutierrez PA, Hervas-Martinez C. Identifying Market Behaviours Using European Stock Index Time Series by a Hybrid Segmentation Algorithm. NEURAL PROCESSING LETTERS. 2017.46(3):767-790.
IF: 1,62
Q: 3

Durán-Rosal AM, Dorado-Moreno M, Gutiérrez PA, Hervás-Martínez C. Identification of extreme wave heights with an evolutionary algorithm in combination with a likelihood-based segmentation. PROGRESS IN ARTIFICIAL INTELLIGENCE. 2017. 6 (1); 59-66.


Dr. César Hervás Martínez

Principal Investigator