AYRNA

Research Group

Learning and Artificial Neural Networks

UCO · Spain

The Learning and Artificial Neural Networks (AYRNA) research group (TIC-148 of the Junta de Andalucía) was founded in 1994 by a small group of researchers led by César Hervás-Martínez, focusing on the field of Artificial Neural Networks (ANNs).

In recent years, the group has diversified its areas of expertise, addressing various problems through soft computing techniques—including shallow and deep artificial neural networks, evolutionary algorithms, and other meta-heuristics.

Team Statistics

25 Total Members
12 PhD Researchers
Prof. Pedro Antonio Gutiérrez Peña
Principal Investigator Prof. Pedro Antonio Gutiérrez Peña

Research Statistics

512 Total Publications
112 Last 5 years
246 Journal Articles
125 JCR Q1 Ranked

Research Areas

Applications - Agronomy

Application of classification models to the agronomy field

Applications - Donor-recipient matching in liver transplants

Application of classification models to the agronomy field

Applications - Economy

Application of classification models to the economy field

Applications - Engine Noise Prediction

Application of classification models to predict engine noise

Applications - Health

Application of classification models to healthcare field

Applications - Precision agriculture

Weed patches identification using remote sensing techniques and crop cover classification

Applications - Predictive microbiology

Application of classification models to predictive microbiology field

Applications - Renewable Energy

Application of machine learning and deep learning techniques to renewable energy management

Applications - Weather Forecasting

Application of classification models to weather forecasting

Fairness & Data Justice

Critical analysis of algorithmic bias, biometric systems, and the socio-political implications of datafication

Methodology - Deep Learning

Development of deep learning techniques

Methodology - Evolutionary Artificial Neural Networks

Development of evolutionary artificial neural networks techniques

Methodology - Machine Learning

Development of machine learning techniques

Methodology - Ordinal Classification

Development of ordinal classification techniques

Methodology - Teaching Innovation

Development of teaching innovation techniques related to computer science

Methodology - Time Series

Development of time series techniques