D. Elizondo

发表

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS Publication Information

Michael J. Watts, Haibo He, Giorgio Battistelli, 2020, IEEE Transactions on Neural Networks and Learning Systems.

Development of a neural network model to predict daily solar radiation

G. Hoogenboom, D. Elizondo, R. McClendon, 1994 .

Authentication of Individuals using Hand Geometry Biometrics: A Neural Network Approach

Miguel Angel Ferrer-Ballester, Marcos Faúndez-Zanuy, David A. Elizondo, 2007, Neural Processing Letters.

The linear separability problem: some testing methods

David A. Elizondo, D. Elizondo, 2006, IEEE Transactions on Neural Networks.

Searching for linearly separable subsets using the class of linear separability method

D. Elizondo, 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

Multilayer perceptron applied to the estimation of the influence of the solar spectral distribution on thin-film photovoltaic modules

D. Elizondo, M. Piliougine, L. Mora-López, 2013 .

Neural Network Models for Predicting Flowering and Physiological Maturity of Soybean

Gerrit Hoogenboom, R. W. McClendon, David Elizondo, 1994 .

Current Trends on Knowledge Extraction and Neural Networks

David A. Elizondo, Mario A. Góngora, D. Elizondo, 2005, ICANN.

Modelling the spectral irradiance distribution in sunny inland locations using an ANN-based methodology

D. L. Talavera, D. Elizondo, G. Nofuentes, 2015 .

Skin Lesion Classification by Ensembles of Deep Convolutional Networks and Regularly Spaced Shifting

D. Elizondo, Karl Thurnhofer-Hemsi, Ezequiel López-Rubio, 2021, IEEE Access.

A permutation entropy-based EMD–ANN forecasting ensemble approach for wind speed prediction

D. Urda, J. J. Ruiz-Aguilar, I. Turias, 2020, Neural Computing and Applications.

Infering Air Quality from Traffic Data Using Transferable Neural Network Models

Miguel A. Molina-Cabello, David A. Elizondo, Enrique Domínguez, 2019, IWANN.

A Preliminary Study on Crop Classification with Unsupervised Algorithms for Time Series on Images with Olive Trees and Cereal Crops

Antonio J. Rivera, María José del Jesus, David Elizondo, 2020, SOCO.

A Constructive Neural Network for Evolving a Machine Controller in Real-Time

David A. Elizondo, Andreas Huemer, Mario A. Góngora, 2009, Constructive Neural Networks.

Analysis and test of efficient methods for building recursive deterministic perceptron neural networks

Éric D. Taillard, David A. Elizondo, Mario A. Góngora, 2007, Neural Networks.

Application of Fuzzy Logic in Computer Security and Forensics

Sulaiman Al Amro, David A. Elizondo, Francisco Chiclana, 2012, Computational Intelligence for Privacy and Security.

Improving Uncertainty Estimation With Semi-Supervised Deep Learning for COVID-19 Detection Using Chest X-Ray Images

Miguel A. Molina-Cabello, D. Elizondo, M. Jiménez-Navarro, 2021, IEEE Access.

A fuzzy genetic programming-based algorithm for subgroup discovery and the application to one problem of pathogenesis of acute sore throat conditions in humans

María José del Jesús, David A. Elizondo, Cristóbal J. Carmona, 2015, Inf. Sci..

Intelligence Techniques in Computer Security and Forensics: At the Boundaries of Ethics and Law

Yingqin Zheng, Bernd Carsten Stahl, David A. Elizondo, 2012, Computational Intelligence for Privacy and Security.

Ethical and legal issues of the use of computational intelligence techniques in computer security and computer forensics

Yingqin Zheng, Bernd Carsten Stahl, David A. Elizondo, 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

Correcting data imbalance for semi-supervised COVID-19 detection using X-ray chest images pdf

Miguel A. Molina-Cabello, Simon Colreavy-Donnelly, David Elizondo, 2020, Applied Soft Computing.

Dimensionality Reduction and Microarray Data

Andreas Huemer, Benjamin N. Passow, David Elizondo, 2008 .

1 Dimensionality Reduction and Microarray data

Benjamin N. Passow, D. Elizondo, Ralph Birkenhead, 2007 .

An Introduction to the Use of Neural Networks for Network Intrusion Detection

David A. Elizondo, Tim Watson, John North, 2012, Computational Intelligence for Privacy and Security.

Evolutionary Computation in Computer Security and Forensics: An Overview

Sulaiman Al Amro, Agusti Solanas, David A. Elizondo, 2012, Computational Intelligence for Privacy and Security.

E2PAMEA: A fast evolutionary algorithm for extracting fuzzy emerging patterns in big data environments

David A. Elizondo, Ángel Miguel García-Vico, Francisco Charte, 2020, Neurocomputing.

Supervised Descriptive Rule Discovery: A Survey of the State-of-the-Art

Cristóbal J. Carmona, David Elizondo, C. J. Carmona, 2016 .

Subgroup Discovery: Real-World Applications

Cristóbal J. Carmona, David Elizondo, C. J. Carmona, 2011 .

Analysing the Moodle e-learning platform through subgroup discovery algorithms based on evolutionary fuzzy systems

Cristóbal J. Carmona, David Elizondo, C. J. Carmona, 2012 .

Bankruptcy forecasting: An empirical comparison of AdaBoost and neural networks

David A. Elizondo, Matías Gámez, Esteban Alfaro Cortés, 2008, Decis. Support Syst..

Foreground detection by ensembles of random polygonal tilings

Miguel A. Molina-Cabello, Ezequiel López-Rubio, David A. Elizondo, 2020, Expert Syst. Appl..

Data Augmentation Techniques to Improve Metabolomic Analysis in Niemann-Pick Type C Disease

Francisco J. Moreno-Barea, D. Elizondo, M. Grootveld, 2022, ICCS.

A renewed approach to serious games for cyber security

David A. Elizondo, Tim Watson, Alexis Le Compte, 2015, 2015 7th International Conference on Cyber Conflict: Architectures in Cyberspace.

1H NMR-Linked Urinary Metabolic Profiling of Niemann-Pick Class C1 (NPC1) Disease: Identification of Potential New Biomarkers using Correlated Component Regression (CCR) and Genetic Algorithm (GA) Analysis Strategies

R. M. Luque-Baena, D. Elizondo, P. Clayton, 2014 .

Constructive Neural Network Algorithms for Feedforward Architectures Suitable for Classification Tasks

Maria do Carmo Nicoletti, David A. Elizondo, Leonardo Franco, 2009, Constructive Neural Networks.

Dealing with Scarce Labelled Data: Semi-supervised Deep Learning with Mix Match for Covid-19 Detection Using Chest X-ray Images

Miguel A. Molina-Cabello, Jordina Torrents-Barrena, David Elizondo, 2020, 2020 25th International Conference on Pattern Recognition (ICPR).

Development of an RDP neural network for building energy consumption fault detection and diagnosis

Frédéric Magoulès, David Elizondo, Hai‐Xiang Zhao, 2013 .

Support Vector Regression for Electricity Consumption Prediction in a Building in Japan

Frédéric Magoulès, David Elizondo, Michel Piliougine, 2016, 2016 IEEE Intl Conference on Computational Science and Engineering (CSE) and IEEE Intl Conference on Embedded and Ubiquitous Computing (EUC) and 15th Intl Symposium on Distributed Computing and Applications for Business Engineering (DCABES).

XAIRE: An ensemble-based methodology for determining the relative importance of variables in regression tasks. Application to a hospital emergency department

M. D. Pérez-Godoy, A. J. Rivera, D. Elizondo, 2023, Artif. Intell. Medicine.

Fuzzy Logic Applied to System Monitors

Miguel A. Molina-Cabello, David A. Elizondo, Lipika Deka, 2021, IEEE Access.

Foreground object detection enhancement by adaptive super resolution for video surveillance

Miguel A. Molina-Cabello, Ezequiel López-Rubio, Rafael Marcos Luque-Baena, 2019 .

Heavy Duty Vehicle Fuel Consumption Modelling Based on Exploitation Data by Using Artificial Neural Networks

Lipika Deka, Jacek Kropiwnicki, David Elizondo, 2019, IWANN.

Use of Bayesian inference method to model vehicular air pollution in local urban areas

Daniel Paluszczyszyn, Eric Goodyer, David Elizondo, 2018, Transportation Research Part D: Transport and Environment.

Linear separability and classification complexity

David A. Elizondo, Matías Gámez, Ralph Birkenhead, 2012, Expert Syst. Appl..

Modelling photovoltaic modules with neural networks using angle of incidence and clearness index

Llanos Mora-López, Mariano Sidrach-de-Cardona, David Elizondo, 2015 .

The Generalisation of the Recursive Deterministic Perceptron

Éric D. Taillard, David A. Elizondo, Ralph Birkenhead, 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

Range extended for electric vehicle based on driver behaviour

Eric Goodyer, Daniel Paluszczyszyn, Moath Al-Doori, 2014 .

A Novel Method for Extracting Knowledge from Neural Networks with Evolving SQL Queries

David A. Elizondo, Tim Watson, Mario A. Góngora, 2005, ICANN.

Encouraging Active Commuting through monitoring and analysis of commuter travel method habits

Francisco Chiclana, Benjamin N. Passow, David Elizondo, 2015, 2015 SAI Intelligent Systems Conference (IntelliSys).

iTRAQ – An Integrated Traffic Management and Air Quality Control System Using Space Services

Eric Goodyer, Benjamin N. Passow, David Elizondo, 2012 .

Photovoltaic module simulation by neural networks using solar spectral distribution

Llanos Mora-López, David Elizondo, Michel Piliougine, 2013 .

A Novel and Efficient Method for Testing Non Linear Separability

D. Elizondo, Ralph Birkenhead, J. M. Ortiz-de-Lazcano-Lobato, 2007, ICANN.

CPV module electric characterisation by artificial neural networks

B. García-Domingo, Jorge Aguilera, David Elizondo, 2015 .

Assessment of geometric features for individual identification and verification in biometric hand systems

Ezequiel López-Rubio, Esteban J. Palomo, David A. Elizondo, 2013, Expert Syst. Appl..

A robust reinforcement based self constructing neural network

David A. Elizondo, Andreas Huemer, Mario A. Góngora, 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).