Francisco Zayas-Gato

Professor

Universidade da Coruña

Spain

Research Interests

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About

Francisco Zayas-Gato is a Professor at the University of A Coruña in Spain. His research focuses on advanced methodologies in robotics, geothermal energy, and wastewater treatment, as evidenced by his recent publications. Notable areas of his work include software anomaly detection, predictive modeling for geothermal systems, and enhanced traceability in naval construction. He is also involved in the development of innovative teaching methods for STEAM education.

Articles (18)

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2025

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2022

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2022

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2022

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2021

Collaborators (15)

Nuño Basurto

Assistant Professor

Universidad de Burgos

SPAIN

Francisco Zayas-Gato

Research Interests

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About

Articles (18)

A new deep learning-based approach for predicting the geothermal heat pump’s thermal power of a real bioclimatic house

Detecting Software Anomalies in Robots by Means of One-class Classifiers

Defect Detection for Enhanced Traceability in Naval Construction

An Intelligent Regression-Based Approach for Predicting a Geothermal Heat Exchanger’s Behavior in a Bioclimatic House Context

Novel adaptive approach for anomaly detection in nonlinear and time-varying industrial systems

Clustering techniques performance comparison for predicting the battery state of charge: A hybrid model approach

Beta Hebbian Learning for intrusion detection in networks with MQTT Protocols for IoT devices

Optimizing wastewater treatment plants with advanced feature selection and sensor technologies

Virtual laboratories for distance learning in STEAM degrees

A novel method for anomaly detection using beta Hebbian learning and principal component analysis

Gasificación de biomasa

Intelligent One-Class Classifiers for the Development of an Intrusion Detection System: The MQTT Case Study

Experiencia blended learning apoyada en un laboratorio virtual para educación de materias STEM

Intelligent Model for Power Cells State of Charge Forecasting in EV

A distributed topology for identifying anomalies in an industrial environment

A hybrid one‐class approach for detecting anomalies in industrial systems

Intelligent model for active power prediction of a small wind turbine

Hybrid Intelligent Model to Predict the Remifentanil Infusion Rate in Patients under General Anesthesia

Collaborators (15)

Nuño Basurto

Jose Antonio Aveleira Mata

José-Luis Casteleiro-Roca

Jose Luis Calvo-Rolle

Carlos Cambra

Jose M. Gonzalez-Cava

Hector Alaiz Moreton

Paulo Novais

Antonio Díaz-Longueira

Francisco Javier Pérez Castelo

José A. Reboso

Maria Elena Arce

Héctor Quintián

Francisco Javier de Cos Juez

Andrés Suárez-García

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