Skip to content

Professor David Elizondo

Job: Professor in Intelligent Transport

Faculty: Technology

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: http://www.dmu.ac.uk/digits

 

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 

  • Dealing with scarce labelled data: Semi-supervised deep learning with mix match for Covid-19 detection using chest X-ray images
    Dealing with scarce labelled data: Semi-supervised deep learning with mix match for Covid-19 detection using chest X-ray images Calderon-Ramirez, Saul; Giri, Raghvendra; Yang, Shengxiang; Moemeni, Armaghan; Umana, Mario; Elizondo, David; Torrents-Barrena, Jordina; Molina-Cabello, Miguel A. Coronavirus (Covid-19) is spreading fast, infecting people through contact in various forms including droplets from sneezing and coughing. Therefore, the detection of infected subjects in an early, quick and cheap manner is urgent. Currently available tests are scarce and limited to people in danger of serious illness. The application of deep learning to chest X-ray images for Covid-19 detection is an attractive approach. However, this technology usually relies on the availability of large labelled datasets, a requirement hard to meet in the context of a virus outbreak. To overcome this challenge, a semi-supervised deep learning model using both labelled and unlabelled data is proposed. We develop and test a semi-supervised deep learning framework based on the Mix Match architecture to classify chest X-rays into Covid-19, pneumonia and healthy cases. The presented approach was calibrated using two publicly available datasets. The results show an accuracy increase of around 15% under low labelled / unlabelled data ratio. This indicates that our semi-supervised framework can help improve performance levels towards Covid-19 detection when the amount of high-quality labelled data is scarce. Also, we introduce a semi-supervised deep learning boost coefficient which is meant to ease the scalability of our approach and performance comparison. The file attached to this record is the author's final peer reviewed version.
  • Are Public Intrusion Datasets Fit for Purpose: Characterising the State of the Art in Intrusion Event Datasets
    Are Public Intrusion Datasets Fit for Purpose: Characterising the State of the Art in Intrusion Event Datasets Kenyon, Anthony; Deka, Lipika; Elizondo, David In recent years cybersecurity attacks have caused major disruption and information loss for online organisations, with high profile incidents in the news. One of the key challenges in advancing the state of the art in intrusion detection is the lack of representative datasets. These datasets typically contain millions of time-ordered events (e.g. network packet traces, flow summaries, log entries); subsequently analysed to identify abnormal behavior and specific attacks [1]. Generating realistic datasets has historically required expensive networked assets, specialised traffic generators, and considerable design preparation. Even with advances in virtualisation it remains challenging to create and maintain a representative environment. Major improvements are needed in the design, quality and availability of datasets, to assist researchers in developing advanced detection techniques. With the emergence of new technology paradigms, such as intelligent transport and autonomous vehicles, it is also likely that new classes of threat will emerge [2]. Given the rate of change in threat behavior [3] datasets become quickly obsolete, and some of the most widely cited datasets date back over two decades. Older datasets have limited value: often heavily filtered and anonymised, with unrealistic event distributions, and opaque design methodology. The relative scarcity of (Intrusion Detection System) IDS datasets is compounded by the lack of a central registry, and inconsistent information on provenance. Researchers may also find it hard to locate datasets or understand their relative merits. In addition, many datasets rely on simulation, originating from academic or government institutions. The publication process itself often creates conflicts, with the need to de-identify sensitive information in order to meet regulations such as General Data Protection Act (GDPR) [4]. Another final issue for researchers is the lack of standardised metrics with which to compare dataset quality. In this paper we attempt to classify the most widely used public intrusion datasets, providing references to archives and associated literature. We illustrate their relative utility and scope, highlighting the threat composition, formats, special features, and associated limitations. We identify best practice in dataset design, and describe potential pitfalls of designing anomaly detection techniques based on data that may be either inappropriate, or compromised due to unrealistic threat coverage. Such contributions as made in this paper is expected to facilitate continuous research and development for effectively combating the constantly evolving cyber threat landscape. The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
  • Heavy Duty Vehicle Fuel Consumption Modelling Using Artificial Neural Networks.
    Heavy Duty Vehicle Fuel Consumption Modelling Using Artificial Neural Networks. Wysocki, Oskar; Deka, Lipika; Elizondo, David In this paper an artificial neural network (ANN) approach to modelling fuel consumption of heavy duty vehicles is presented. The proposed method uses easy accessible data collected via CAN bus of the truck. As a benchmark a conventional method, which is based on polynomial regression model, is used. The fuel consumption is measured in two different tests, performed by using a unique test bench to apply the load to the engine. Firstly, a transient state test was performed, in order to evaluate the polynomial regression and 25 ANN models with different parameters. Based on the results, the best ANN model was chosen. Then, validation test was conducted using real duty cycle loads for model comparison. The neural network model outperformed the conventional method and represents fuel consumption of the engine operating in transient states significantly better. The presented method can be applied in order to reduce fuel consumption in utility vehicles delivering accurate fuel economy model of truck engines, in particular in low engine speed and torque range. The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
  • Use of Bayesian Inference Method to Model Vehicular Air Pollution in Local Urban Areas
    Use of Bayesian Inference Method to Model Vehicular Air Pollution in Local Urban Areas Orun, A.; Elizondo, David; Goodyer, E.; Paluszczyszyn, D. Traffic Related Air Pollution (TRAP) studies are usually investigated using different categories such as air pollution exposure for health impacts, urban transportation network design to mitigate pollution, environmental impacts of pollution, etc. All of these subfields often rely on a robust air pollution model, which also necessitates an accurate prediction of future pollutants. As is widely accepted by the heath authorities, TRAP is considered to be the major health issue in urban areas, and it is difficult to keep pollution at harmless levels if the time sequenced dynamic pollution and traffic parameters are not identified and modelled efficiently. In our work here, artificial intelligence techniques, such as Bayesian Networks with an optimized configuration, are used to deliver a probabilistic traffic data analysis and predictive modelling for air pollution (SO2, NO2 and CO) at very local scale of an urban region with up to 85% accuracy. The main challenge for traditional data analysis is a lack of capability to reveal the hidden links between distant data attributes (e.g. pollution sources, dynamic traffic parameters, etc.), whereas some subtle effects of these parameters or events may play an important role in pollution on a long-term basis. This study focuses on the optimisation of Bayesian Networks to unveil hidden links and to increase the prediction accuracy of TRAP considering its further association with a predictive GIS system The file attached to this record is the author's final peer reviewed version.
  • An Intelligent traffic network optimisation by use of Bayesian inference methods to combat air pollution
    An Intelligent traffic network optimisation by use of Bayesian inference methods to combat air pollution Elizondo, David; Orun, A. Traffic flow related air pollution is one of the major problems in urban areas, and is often difficult to avoid it if the time sequenced dynamic pollution and traffic parameters are not identified and modelled efficiently. In our introduced work here, an artificial intelligence technique such as Bayesian networks are used for a robust traffic data analysis and modelling. The most common challenge in traditional data analysis is a lack of capability of unveiling the hidden links between the distant data attributes (e.g. pollution sources, dynamic traffic parameters, geographic location characteristics, etc.), whereas some subtle effects of these parameters or events may play an important role in pollution on a long term basis. CCI Group has contributed to the research
  • Supervised Descriptive Rule Discovery: A Survey of the State-of-the-Art
    Supervised Descriptive Rule Discovery: A Survey of the State-of-the-Art Carmona, C. J.; Elizondo, David The supervised descriptive rule discovery concept groups a set of data mining techniques whose objective is to describe data with respect to a property of interest. Among the techniques within this concept are the subgroup discovery, emerging patterns and contrast sets. This contribution presents the supervised descriptive rule discovery concept within the data mining literature. Specifically, it is important to remark the main di erence with respect to other existing techniques within classification or description. In addition, a a survey of the state-of-the-art about the different techniques within supervised descriptive rule discovery throughout the literature can be observed. The paper allows to the experts to analyse the compatibilities between terms and heuristics of the different data mining tasks within this concept.
  • Modelling the spectral irradiance distribution in sunny inland locations using an ANN-based methodology
    Modelling the spectral irradiance distribution in sunny inland locations using an ANN-based methodology Torres-Ramirez, M.; Elizondo, David; Garcia-Domingo, B.; Nofuentes, G.; Talavera, D. L. This work is aimed at verifying that in sunny inland locations artificial intelligence techniques may provide an estimation of the spectral irradiance with adequate accuracy for photovoltaic applications. An ANN (artificial neural network) based method was developed, trained and tested to model the spectral distributions between wavelengths ranging from 350 to 1050 nm. Only commonly available input data such as geographical information regarding location, specific date and time together with horizontal global irradiance and ambient temperature are required. Historical information from a 24-month experimental campaign carried out in Jae n (Spain) provided the necessary data to train and test the ANN tool. A Kohonen self-organized map was used as innovative technique to classify the whole input dataset and build a small and representative training dataset. The shape of the spectral irradiance dis- tribution, the in-plane global irradiance (GT) and irradiation (HT) and the APE (average photon energy) values obtained through the ANN method were statistically compared to the experimental ones. In terms of shape distribution fitting, the mean relative deformation error stays below 4.81%. The root mean square percentage error is around 6.89% and 0.45% when estimating GT and APE, respectively. Regarding HT, errors lie below 3.18% in all cases. N/A
  • A fuzzy genetic programming-based algorithm for subgroup discovery and the application to one problem of pathogenesis of acute sore throat conditions in humans
    A fuzzy genetic programming-based algorithm for subgroup discovery and the application to one problem of pathogenesis of acute sore throat conditions in humans Carmona, C. J.; Ruiz-Rodado, Victor; del Jesus, M. J.; Weber, A.; Grootveld, M.; Gonzalez, P.; Elizondo, David
  • CPV module electric characterisation by artificial neural networks.
    CPV module electric characterisation by artificial neural networks. Garcia-Domingo, Beatriz; Piliougine, M.; Aguilar, Jorge; Elizondo, David Concentrating photovoltaic is a new technology with promising future expectations. However, it is in an early stage of development and it has much room for improvement. In order to gain knowledge about concentrating photovoltaic technology, real outdoor measurements are necessary to adjust models and to study the influence of the atmospheric conditions on the modules performance. The current-voltage curve of a module characterises its behaviour under specific meteorological conditions. In this work, multilayer perceptron models are applied to generate these characteristic curves using the influential atmospheric variables as inputs of the network. To train these networks an experimental campaign with real measures of the electric performance of concentrating photovoltaic modules as well as atmospheric conditions was carried out in Jaén from July 2011 to June 2012. In addition to a model based on I-V curves expressed as a list of points in Cartesian coordinates, we present an alternative model trained with curves defined by points in polar coordinates. A previous selection of the most representative samples from the initial dataset was performed to train the multilayer perceptron models using a Kohonen self-organizing map. This procedure improves the simulation of the curves under non frequent atmospheric conditions. Using the proposed models, it is possible to obtain the characteristic curve of a concentrating photovoltaic module with a high accuracy and fidelity. Since there is not any standard algebraic procedure to obtain I-V curves of this type of modules under different meteorological conditions, the proposed models are very interesting tools when estimating their electric performance.
  • Encouraging Active Commuting through monitoring and analysis of commuter travel method habits
    Encouraging Active Commuting through monitoring and analysis of commuter travel method habits Hasshu, Salim; Chiclana, Francisco; Passow, Benjamin N.; Elizondo, David The aim of this research is to understand and encourage healthier commuter travel method habits. Commuters who choose healthier travel, such as; walking, cycling or public transport methods, are known as Active Commuters (AC). However current literature suggests private car use is still the dominant method of transport. Additionally, there are very few AC monitoring and analysis applications for mobile devices, which lead to the following research question; “If commuters are able to monitor and analyse their travel habits, would this encourage them to choose AC methods?” In this work we propose a novel methodology that investigates this question. This technique was implemented and tested as an Android mobile application, giving valuable insights into AC habits. The Active Commute Tracker (ACT) mobile application was developed to include the following three components: (1) Commute Transport Method Calculation component, (2) Health component and (3) Sharing component. ACT allows users to monitor and record their commute method, distance travelled in total and commute health analysis. A basic version of this data can be shared on Facebook. Users tested the application for a set number of days and provided feedback of functionality, but more importantly whether or not it encouraged AC. Feedback from users confirmed that there is a demand for an application of this nature. No user was discouraged as a direct result of ACT.

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

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.
 David

Search Who's Who

 

 
News target area image
News

DMU is a dynamic university, read about what we have been up to in our latest news section.

Events at DMU
Events

At DMU there is always something to do or see, check out our events for yourself.

Mission and vision target area image
Mission and vision

Read about our mission and vision and how these create a supportive and exciting learning environment.