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: 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 

  • 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.
  • 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.
  • 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
  • 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.
  • 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
    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 Ruiz-Rodado, Victor; Luque-Baena, R. M.; te Vruchte, D. J.; Probert, Fay; Lachmann, R. H.; Hendriksz, Christian J.; Wraith, James E.; Imrie, Jackie; Elizondo, David; Sillence, Daniel J.; Clayton, P.; Platt, Frances M.; Grootveld, M. Niemann-Pick Class 1 (NPC1) disease is a rare, debilitating neurodegenerative lysosomal storage disease; however, urinary biomarkers available for it and its prognosis are currently limited. In order to identify and establish such biomarkers, we employed high-resolution 1H NMR analysis coupled to a range of multivariate (MV) analysis approaches, i.e. PLS-DA, RFs and uniquely the cross-validated correlated component regression (CCR) strategy in order to discern differences between the urinary metabolic profiles of 13 untreated NPC1 disease and 47 heterozygous (parental) carrier control participants. Novel computational intelligence techniques (CITs) involving genetic algorithms (GAs) were also employed for this purpose
  • Range extended for electric vehicle based on driver behaviour
    Range extended for electric vehicle based on driver behaviour Al-Doori, Moath; Paluszczyszyn, D.; Elizondo, David; Passow, Benjamin N.; Goodyer, E. N. Driver behaviour has been considered one of the main factors that contribute to increase fuel consumption, CO2 emissions, traffic accidents and causalities. Thus, the concept of detecting and classifying driver behaviour i s vital when tackling these challenges. Recognition of the driver behaviour is a difficult task as in the real-world, the driving behaviour is effected by many factors e.g. traffic, road conditions, duration of the journey etc. Many approaches have considered the use of Computational Intelligence techniques, to develop a driver behaviour detection system. In this paper we concentrate on the impact of driver behaviour on the energy consumption and thereby on the range of electric vehicles. A new architecture is proposed to show how computational intelligence techniques could interact with the contextual information collected from the vehicle, the driver and external environment. A neural network model is used to classify the driver behaviour, and then this classification is used in a fuzzy logic controller to make balanced managements to the range extender operation.
  • Land Usage Classification: A Hierarchical Neural Network Approach
    Land Usage Classification: A Hierarchical Neural Network Approach Palomo, E. J.; Elizondo, David; Brunschwig, Gilles The classification of land usage in grassland mountain bovine areas is important for the management of forage production and grazing in grass-based livestock systems. This article proposes a novel, hierarchical neural network based, approach towards the classification of land usage in these areas. A survey of 72 farms was conducted in the Massif Central (France). Information was gathered on geographical characteristics and cutting and/or grazing practices on three general groups of fields: cut only, cut and grazed, and grazed only fields. To classify the land usage, the data was clustered and visualised in a hierarchical fashion. This was done by using a novel method for the analysis and classification of data based on growing hierarchical self- organising maps (GHSOM). Self-organising maps have shown to be successful for the analysis of highly dimensional input data in data mining applications as well as for data visualisation. Moreover, the hierarchical architecture of the GHSOM is more flexible than a single SOM in the adaptation process to input data, capturing inherent hierarchical relationships among them. Experimental results show the utility of this approach.

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:

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 target area image
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.