Professor David Elizondo

Job: Professor in Intelligent Transport

Faculty: Computing, Engineering and Media

School/department: School of Computer Science and Informatics

Research group(s): The De Montfort University Interdisciplinary Group in Intelligent Transport Systems (DIGITS)

Address: De Montfort University, The Gateway, Leicester, LE1 9BH, United Kingdom

T: +44 (0)116 207 8471

E: Elizondo@dmu.ac.uk

W: https://www.dmu.ac.uk/digits

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Personal profile

Dr. David Elizondo is a Principal Lecturer in the Department of Computer Technology at De Montfort University. After completing his BA in Computer Science from Knox College , Galesbourg, Illinois, USA, he worked as a software engineer/lab manager for a latinoamerican agronomical research and teaching institute based in Costa Rica ( CATIE ). This institute, through a Swiss project, sponsored him to do a MS in Artificial Intelligence at the Department of Artificial Intelligence and Cognitive Computing of the University of Georgia, Athens, Georgia, USA. After this he obtained a PhD in computer science from the University of Strasbourg , France in cooperation with the Swiss Dalle Molle Institute for Perceptual Artificial Intelligence (IDIAP). He then worked for Neuvoice, formerly Neural Systems, a spin off company of the University of Plymouth , UK. As a senior researcher he worked in the development of an intelligent monitoring system for the petroleum industry. This system was based on neural network techniques. Later, he worked as a software architect for ACTERNA, an international company which supplies software/hardware solutions to telecom companies. He was part of the team developing QMS, a quality of service management system for leased lines. In parallel to this work, he was a part time lecturer at the University of Plymouth where he taught database, and data structures and algorithms.

Research group affiliations

The De Montfort University Interdisciplinary Group in Intelligent Transport Systems (DIGITS)

I am also an active member of the following research groups:
(1) The Cyber Security Centre
(2) The Centre for Computational Intelligence (CCI).

I am the research leader of the CCI Neural Network subgroup, which is particularly well known internationally for the research work conducted in the area of Constructive Neural Networks and Linear Separability as evidenced by my on-going list of high quality publications in these two fields of research.

Publications and outputs

2022 Index IEEE Transactions on Artificial Intelligence Vol. 3
dc.title: 2022 Index IEEE Transactions on Artificial Intelligence Vol. 3 dc.contributor.author: Aafaq, N.; Elizondo, David
Improved Flow Recovery from Packet Data
dc.title: Improved Flow Recovery from Packet Data dc.contributor.author: Kenyon, Anthony; Elizondo, David; Deka, Lipika dc.description.abstract: Typical event datasets such as those used in network intrusion detection comprise hundreds of thousands, sometimes millions, of discrete packet events. These datasets tend to be high dimensional, stateful, and time-series in nature, holding complex local and temporal feature associations. Packet data can be abstracted into lower dimensional summary data, such as packet flow records, where some of the temporal complexities of packet data can be mitigated, and smaller well-engineered feature subsets can be created. This data can be invaluable as training data for machine learning and cyber threat detection techniques. Data can be collected in real-time, or from historical packet trace archives. In this paper we focus on how flow records and summary metadata can be extracted from packet data with high accuracy and robustness. We identify limitations in current methods, how they may impact datasets, and how these flaws may impact learning models. Finally, we propose methods to improve the state of the art and introduce proof of concept tools to support this work.
Neural network models for predicting flowering and physiological maturity of soybean
dc.title: Neural network models for predicting flowering and physiological maturity of soybean dc.contributor.author: Elizondo, David; McClendon, R. W.; Hoogenboom, G. dc.description.abstract: It is important for farmers to know when various plant development stages occur for making appropriate and timely crop management decisions. Although computer simulation models have been developed to simulate plant growth and development, these models have not always been very accurate in predicting plant development for a wide range of environmental conditions. The objective of this study was to develop a neural network model to predict flowering and physiological maturity for soybean (Glycine max L. Merr.). An artificial neural network is a computer software system consisting of various simple and highly interconnected processing elements similar to the neuron structure found in the human brain. A neural network model was used because it has the capabilities to identify relationships between variables of rather large and complex data bases. For this study, field-observed flowering dates for the cultivar Bragg from experimental studies conducted in Gainesville and Quincy, Florida, and Clayton, North Carolina, were used. Inputs considered for the neural network model were daily maximum and minimum air temperature, photoperiod, and days after planting or days after flowering. The data sets were split into training sets to develop the models and independent data sets to test the models. The average relative error of the test data sets for date of flowering prediction was+0.143 days (n = 21, R2 = 0.987) and for date of physiological maturity prediction was +2.19 days (n = 21, R2 = 0.950). It can be concluded from this study that the use of neural network models to predict flowering and physiological maturity dates is promising and needs to be explored further.
A permutation entropy-based EMD--ANN forecasting ensemble approach for wind speed prediction
dc.title: A permutation entropy-based EMD--ANN forecasting ensemble approach for wind speed prediction dc.contributor.author: Ruiz-Aguilar, Juan Jesus; Turias, Ignacio; Gonzalez-Enrique, Javier; Urda, Daniel; Elizondo, David dc.description.abstract: Accurate wind speed prediction is critical for many tasks, especially for air pollution modelling. Data-driven approaches are particularly interesting but the stochastic nature of wind renders prediction tasks difficult. Therefore, a combination of methods could be useful to obtain better results. To overcome this difficulty, a hybrid wind speed forecasting approach is proposed in this work. The Bay of Algeciras, Spain, was used as a case study, and the database was collected from a weather monitoring station. The study consists of combining a pre-processing method, the empirical mode decomposition (EMD), an information-based method, the permutation entropy (PE), and a machine learning technique (artificial neural networks, ANNs), using an ensemble learning methodology. Different prediction horizons were considered: ph-hours (ph = 1, 2, 8, 24) ahead and 8-h and 24-h average. The introduction of PE significantly reduces the computational cost and the predictive risk in comparison with traditional EMD methodology, by reducing the number of the decomposed components to be predicted. Moreover, the experimental results demonstrated that the EMD–PE–ANN approach outperforms the prediction performance of the single ANN models in all the prediction horizons tested. The EMD–PE–ANN model is capable to achieve a correlation coefficient of 0.981 and 0.807 for short-term (1 h) and medium-term (24 h) predictions, respectively, significantly overcoming those obtained by a single ANN model (0.929 and 0.503). These results show that the proposed model reaches significant improvements when the prediction horizon increases, where forecasting models tend to worsen their prediction performance. Therefore, the proposed EMD–PE–ANN approach may become a powerful tool for wind speed forecasting.
Improving uncertainty estimation with semi-supervised deep learning for COVID-19 detection using chest X-ray images
dc.title: Improving uncertainty estimation with semi-supervised deep learning for COVID-19 detection using chest X-ray images dc.contributor.author: Calderon-Ramirez, Saul; Yang, Shengxiang; Moemeni, Armaghan; Colreavy-Donnelly, Simon; Elizondo, David; Oala, Luis; Rodriguez-Capitan, Jorge; Jimenez-Navarro, Manuel; Lopez-Rubio, Ezequiel; Molina-Cabello, Miguel A. dc.description.abstract: In this work we implement a COVID-19 infection detection system based on chest X-ray images with uncertainty estimation. Uncertainty estimation is vital for safe usage of computer aided diagnosis tools in medical applications. Model estimations with high uncertainty should be carefully analyzed by a trained radiologist. We aim to improve uncertainty estimations using unlabelled data through the MixMatch semi-supervised framework. We test popular uncertainty estimation approaches, comprising Softmax scores, Monte-Carlo dropout and deterministic uncertainty quantification. To compare the reliability of the uncertainty estimates, we propose the usage of the Jensen-Shannon distance between the uncertainty distributions of correct and incorrect estimations. This metric is statistically relevant, unlike most previously used metrics, which often ignore the distribution of the uncertainty estimations. Our test results show a significant improvement in uncertainty estimates when using unlabelled data. The best results are obtained with the use of the Monte Carlo dropout method. dc.description: open access article
E2PAMEA: un algoritmo evolutivo para la extraccion eficiente de patrones emergentes difusos en entornos big data
dc.title: E2PAMEA: un algoritmo evolutivo para la extraccion eficiente de patrones emergentes difusos en entornos big data dc.contributor.author: Garcia-Vico, A. M.; Elizondo, David; Charte, F.; Gonzalez, P.; Carmona, C. J.
A preliminary study on crop classification with unsupervised algorithms for time series on images with olive trees and cereal crops
dc.title: A preliminary study on crop classification with unsupervised algorithms for time series on images with olive trees and cereal crops dc.contributor.author: Rivera, Antonio Jesus; Perez-Godoy, Maria Dolores; Elizondo, David; Deka, Lipika; del Jesus, Maria Jose
Skin lesion classification by ensembles of deep convolutional networks and regularly spaced shifting
dc.title: Skin lesion classification by ensembles of deep convolutional networks and regularly spaced shifting dc.contributor.author: Thurnhofer-Hemsi, Karl; Lopez-Rubio, Ezequiel; Dominguez, Enrique; Elizondo, David dc.description.abstract: Skin lesions are caused due to multiple factors, like allergies, infections, exposition to the sun, etc. These skin diseases have become a challenge in medical diagnosis due to visual similarities, where image classification is an essential task to achieve an adequate diagnostic of different lesions. Melanoma is one of the best-known types of skin lesions due to the vast majority of skin cancer deaths. In this work, we propose an ensemble of improved convolutional neural networks combined with a test-time regularly spaced shifting technique for skin lesion classification. The shifting technique builds several versions of the test input image, which are shifted by displacement vectors that lie on a regular lattice in the plane of possible shifts. These shifted versions of the test image are subsequently passed on to each of the classifiers of an ensemble. Finally, all the outputs from the classifiers are combined to yield the final result. Experiment results show a significant improvement on the well-known HAM10000 dataset in terms of accuracy and F-score. In particular, it is demonstrated that our combination of ensembles with test-time regularly spaced shifting yields better performance than any of the two methods when applied alone. dc.description: open access article
Urinary Metabolic Distinction of Niemann--Pick Class 1 Disease through the Use of Subgroup Discovery
dc.title: Urinary Metabolic Distinction of Niemann--Pick Class 1 Disease through the Use of Subgroup Discovery dc.contributor.author: Carmona, C. J.; German-Morales, M.; Elizondo, David; Ruiz-Rodado, V.; Grootveld, M. dc.description.abstract: In this investigation, we outline the applications of a data mining technique known as Subgroup Discovery (SD) to the analysis of a sample size-limited metabolomics-based dataset. The SD technique utilized a supervised learning strategy, which lies midway between classificational and descriptive criteria, in which given the descriptive property of a dataset (i.e., the response target variable of interest), the primary objective was to discover subgroups with behaviours that are distinguishable from those of the complete set (albeit with a differential statistical distribution). These approaches have, for the first time, been successfully employed for the analysis of aromatic metabolite patterns within an NMR-based urinary dataset collected from a small cohort of patients with the lysosomal storage disorder Niemann–Pick class 1 (NPC1) disease (n = 12) and utilized to distinguish these from a larger number of heterozygous (parental) control participants. These subgroup discovery strategies discovered two different NPC1 disease-specific metabolically sequential rules which permitted the reliable identification of NPC1 patients; the first of these involved ‘normal’ (intermediate) urinary concentrations of xanthurenate, 4-aminobenzoate, hippurate and quinaldate, and disease-downregulated levels of nicotinate and trigonelline, whereas the second comprised ‘normal’ 4-aminobenzoate, indoxyl sulphate, hippurate, 3-methylhistidine and quinaldate concentrations, and again downregulated nicotinate and trigonelline levels. Correspondingly, a series of five subgroup rules were generated for the heterozygous carrier control group, and ‘biomarkers’ featured in these included low histidine, 1-methylnicotinamide and 4-aminobenzoate concentrations, together with ‘normal’ levels of hippurate, hypoxanthine, quinolinate and hypoxanthine. These significant disease group-specific rules were consistent with imbalances in the combined tryptophan–nicotinamide, tryptophan, kynurenine and tyrosine metabolic pathways, along with dysregulations in those featuring histidine, 3-methylhistidine and 4-hydroxybenzoate. In principle, the novel subgroup discovery approach employed here should also be readily applicable to solving metabolomics-type problems of this nature which feature rare disease classification groupings with only limited patient participant and sample sizes available. dc.description: open access article
Computational approaches to Explainable Artificial Intelligence: Advances in theory, applications and trends
dc.title: Computational approaches to Explainable Artificial Intelligence: Advances in theory, applications and trends dc.contributor.author: Gorriz, J. M.; Elizondo, David dc.description.abstract: Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9 International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications. dc.description: open access article

Click here to view a full listing of David Elizondo's publications and outputs.

Research interests/expertise

My research interests include both work in the theory and application of Neural Networks. Application areas include transport related problems that led to the development of DIGITS (iTRAQ project).

Areas of teaching

Artificial Neural Networks and Prolog programming.

Qualifications

French Qualification: University Full Professor Qualification by the Conseille National des Universites (CNU). Artificial Neural Networks, Theory and Applications - 2008.
French Qualification: Senior Lecturer/Principal Lecturer (Maitre de Conferences) Qualification by the Conseille National des Universites (CNU) - 2003.
PhD in Computer Science from the University Louis Pasteur, Strasbourg, France and IDIAP, Martigny, Switzerland. The Recursive Deterministic Perceptron and some Strategies for Topology Reduction on Neural Networks -1998.
DEA in Computer Science from the University of Montpellier, Montpellier, France, Application of Neural Networks to a control process in a dynamic environment - 1993.
Master of Science in Artificial Intelligence from the University of Georgia, Athens, Georgia, USA, Neural Network Models to Predict Solar Radiation and Plant Phenology - 1992.
Bachelor of Science in Computer Science from Knox College, Galesburg, Illinois, USA - 1986.

Courses taught

Artificial Neural Networks and Prolog programming.

Membership of external committees

Workshop Organizer for The British Computer Society Specialist Group on Artificial Intelligence (SGAI) International Conference in Artificial Intelligence for 2010.
UK Computational Intelligence workshop (UKCI).
IEEE International Conference in Artificial Neural Networks (2004,2005, 2006,2007, 2008, 2009).

Membership of professional associations and societies

IEEE Senior Member.

Conference attendance

Organiser and chairman of the following special conference sessions:

IEEE-WCCI-2012, Brisbane, Australia. Special session on Computational Intelligence for Privacy. (http://www.ieee-wcci2012.org/)
IEEE-WCCI-2010, Barcelona, Spain. Special session on Computational Intelligence for Privacy, Security, Forensics. (http://www.wcci2010.org/)
IEEE-ICANN-2008 Prague, Czech Republic. Special session on Constructive Neural Network Algorithms (http://www.icann2008.org/ssession.php). Contacted by Springer to produce a book of extended versions of these papers. The book will be published by January 2009.). Contacted by Springer to produce a book of extended versions of these papers. The book will be published by January 2009.
IEEE-ICANN-2005 Warsaw, Poland. Special session on Knowledge Extraction (http://www.ibspan.waw.pl/ICANN-2005/SpecialSession9.pdf)

National Conference Chairman

Programme Chair Workshop on Computational Intelligence (UKCI), De Montfort University, Leicester, Sept 10-12 2008 (http://www.cci.dmu.ac.uk/ukci2008/).

Consultancy work

Large International Banana producer Company. Banana hand cut optimization using Artificial Intelligence Techniques.

Current research students

2010-2013 John North. Associating Cause and Effect: Applying Computational Intelligence to Post-Incident Security Data. De Montfort University, Symantec.

2010-2014 Harold Kimball. Adaptive Security for Mobile Devices.

2013-2016 Simon Witheridge. Integrated Traffic Management and Air Quality Control.

Externally funded research grants information

“TITLE”, SPONSOR	ROLE	AMOUNT	PERIOD
“Banana Hand cut optimization using Computational Intelligence Techniques”, Chiquita Brands International Inc., USA.	PI	£12000	June 2010
“Travel Grant, WCCI-2010, Barcelona, Spain”, Royal Academy of Engineering.	PI	£600	2010
“Dynamic Traffic Management and Passenger Guidance to Meet the Carbon Challenge”, Transport iNet HECF.	PI	£45K	2009−2010
“Travel Grant, IJCNN-2009, Atlanta, Georgia”, Royal Academy of Engineering.	PI	£800	2009
“Travel Grant, ICANN-2008, Prague, Czek Republic”, Royal Academy of Engineering.	PI	£800	2008
“Travel Grant, ICANN-2007, Porto Portugal”, Royal Academy of Engineering.	PI	£800	2007
“Design of constructive methods on neural computing systems and its application to data mining in oncology”, Spanish Research Council.	CI	£225K	2008−2012
“New strategies in the design of neurocomputing systems. Application to the process of oncology data”, Spanish Research Council.	CI	£90K	2008−2010
“Integrated Traffic Management and Air Quality Control Using Downstream Space Services”, European Space Agency.	PI	e500K (£160K for DMU)	2011
“Innovation Fellowship with the School of Pharmacy”, EMDA, UK.	PI	£15K	2011

Internally funded research project information

“TITLE”, SPONSOR	ROLE	AMOUNT	PERIOD
“Associating Cause and Effect: Applying Computational Intelligence to Post-Incident Security Data”, DMU Research Scholarship, DMU, UK, Symantec, UK.	PI	£50K	2011−2014
“Intelligent Transport Systems: Integrated Traffic Management Control”, DMU Research Scholarship, DMU, UK.	CI	£50K	2012−2015
“De Montfort Interest Group in Transport Systems (DIGITS)”, DMU RIF.	CI	£10K	Jan−Apr 2012

Professional esteem indicators

Associate editor for the IEEE Transactions on Neural Networks and Learning Systems Journal (2.95 Impact Factor and in position 12 out of 111 according to the impact factor in the area of Artificial Intelligence)
Reviewer of European FP7 research projects (2009)
Referee for the Swiss National Science Foundation (2010)
Industrial Liaison for the IEEE Computational Intelligence Society (CSI), UKRI Chapter
Workshop Organizer for The British Computer Society Specialist Group on Artificial Intelligence (SGAI)
International Conference in Artificial Intelligence for 2010
Senior Member of the IEEE
Industrial Liaison for the IEEE Computational Intelligence Society (CSI), UKRI Chapter.