Dr Mario Gongora

Job: Principal Lecturer

Faculty: Technology

School/department: School of Computer Science and Informatics

Research group(s): Centre for Computational Intelligence

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

T: +44 (0)116 207 8226

E: mgongora@dmu.ac.uk

W: www.cci.dmu.ac.uk/home

 

Personal profile

Dr. Gongora is a Principal Lecturer in the Department of Informatics, Faculty of Technology at De Montfort University.  He got his MSc and PhD from the University of Warwick (UK).  He is part of the Centre for Computational Intelligence (CCI), and his research includes the application of Artificial Intelligence techniques to the identification, modelling, simulation and control of complex systems.  His expertise is mainly in using evolutionary computing and biologically inspired methods for this purpose.  He has ongoing projects in this area, mainly applied to analysis and modelling of complex systems and behaviour-based processes as well as in the control and decision making for autonomous systems.

Dr. Gongora also works in close contact with industry, applying his research results in the analysis of consumer behaviour and other complex industrial processes.  He has an active role in the Faculty's consultancy activities, taking the expertise from the University to Industry. 

Research group affiliations

Centre for Computational Intelligence (CCI) (Deputy Director).

Publications and outputs 

  • A fuzzy ELECTRE structure methodology to assess big data maturity in healthcare SMEs
    A fuzzy ELECTRE structure methodology to assess big data maturity in healthcare SMEs Pena, Alejandro; Bonet, Isis; Lochmuller, Christian; Tabares, Marta S.; Piedrahita, Carlos C.; Sánchez, Carmen C.; Giraldo, Liliana M.; Gongora, Mario Augusto; Chiclana, Francisco Advances in technology and an increase in the amount and complexity of data that is generated in healthcare have led to an indispensable revolution in this sector related to big data. Analytics of information based on multimodal clinical data sources require big data projects. When starting big data projects in the healthcare sector, it is often necessary to assess the maturity of an organization with respect to big data, i.e. its capacity in managing big data. The assessment of the maturity of an organization requires multi criteria decision making as there is no single criterion or dimension that defines the maturity level regarding big data but an entire set of them. Based on the ISO 15504, this article proposes a fuzzy ELECTRE structure methodology to assess the maturity level of small and medium-sized enterprises in the healthcare sector (SMEHs). The obtained experimental results provide evidence that this methodology helps to determine and compare maturity levels in big data management of organizations or the evolution of maturity over time. This is also useful in terms of diagnosing the readiness of an organization before starting to implement big data initiatives or technologies. The file attached to this record is the author's final peer reviewed version.
  • A fuzzy credibility model to estimate the operational value at risk using internal and external data of risk events
    A fuzzy credibility model to estimate the operational value at risk using internal and external data of risk events Pena, Alejandro; Bonet, Isis; Lochmuller, Christian; Patino, Hector Alejandro; Chiclana, Francisco; Gongora, Mario Augusto Operational Risk (OpR) refers to the possibility of suffering losses resulting from inadequate or failure of processes and/or technology, inadequate behaviour of people or external events. OpR was one of the main risks that led to the 2008 global financial crisis. Limitations of the analytical models that are applied in estimating this risk surface when qualitative information, frequently associated with OpR events, is used. To determine the magnitude of OpR in financial organisations, qualitative data and also historical data from risk events can be used. Current research trends that focus on the development of analytical models, by using different databases, to estimate the Operational Value at Risk (OpVaR) still lack models based on qualitative information, risk management profiles and the ability to integrate different databases of OpR events. In this paper we present a fuzzy model to estimate the OpVaR of an organisation by working with two different databases that contain internal available data and external or observed data. The proposed model considers: (1) the intrinsic properties of the data as fuzzy sets related to the linguistic variables of the observed data (external) and the data from available databases (internal), and (2) a series of management profiles to mitigate the effect that external data usually causes in estimating the OpVaR of an organisation. The results obtained with the proposed model allow an organisation to estimate and determine the behaviour of the OpVaR over time by using different risk profiles. The integration of qualitative information, different risk profiles (ranging from weak to strong risk management), and internal and external databases contributes to the advancement of estimating the OpVaR in risk management. 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.
  • A fuzzy credibility model to estimate the operational value at risk using internal and external data of risk events
    A fuzzy credibility model to estimate the operational value at risk using internal and external data of risk events Pena, Alejandro; Bonet, Isis; Lochmuller, Christian; Patiño, Héctor Alejandro; Chiclana, Francisco; Gongora, Mario Augusto Operational Risk (OpR) refers to the possibility of suffering losses resulting from inadequate or failure of processes and/or technology, inadequate behaviour of people or external events. OpR was one of the main risks that led to the 2008 global financial crisis. Limitations of the analytical models that are applied in estimating this risk surface when qualitative information, frequently associated with OpR events, is used. To determine the magnitude of OpR in financial organisations, qualitative datainnd also historical data from risk events can be used. Current research trends that focus on the development of analytical models, by using different databases, to estimate the Operational Value at Risk (OpVaR) still lack models based on qualitative information, risk management profiles and the ability to integrate different databases of OpR events. In this paper we present a fuzzy model to estimate the OpVaR of an organisation by working with two different databases that contain internal available data and external or observed data. The proposed model considers: (1) the intrinsic properties of the data as fuzzy sets related to the linguistic variables of the observed data (external) and the data from available databases (internal), and (2) a series of management profiles to mitigate the effect that external data usually causes in estimating the OpVaR of an organisation. The results obtained with the proposed model allow an organisation to estimate and determine the behaviour of the OpVaR over time by using different risk profiles. The integration of qualitative information, different risk profiles (ranging from weak to strong risk management), and internal and external databases contributes to the advancement of estimating the OpVaR in risk management. 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.
  • A fuzzy credibility model to estimate the operational value at risk using internal and external data of risk events
    A fuzzy credibility model to estimate the operational value at risk using internal and external data of risk events Pena, Alejandro; Bonet, Isis; Lochmuller, Christian; Patino, Hector Alejandro; Chiclana, Francisco; Gongora, Mario Augusto Operational Risk (OpR) refers to the possibility of suffering losses resulting from inadequate or failure of processes and/or technology, inadequate behaviour of people or external events. OpR was one of the main risks that led to the 2008 global financial crisis. Limitations of the analytical models that are applied in estimating this risk surface when qualitative information, frequently associated with OpR events, is used. To determine the magnitude of OpR in financial organisations, qualitative data and also historical data from risk events can be used. Current research trends that focus on the development of analytical models, by using different databases, to estimate the Operational Value at Risk (OpVaR) still lack models based on qualitative information, risk management profiles and the ability to integrate different databases of OpR events. In this paper we present a fuzzy model to estimate the OpVaR of an organisation by working with two different databases that contain internal available data and external or observed data. The proposed model considers: (1) the intrinsic properties of the data as fuzzy sets related to the linguistic variables of the observed data (external) and the data from available databases (internal), and (2) a series of management profiles to mitigate the effect that external data usually causes in estimating the OpVaR of an organisation. The results obtained with the proposed model allow an organisation to estimate and determine the behaviour of the OpVaR over time by using different risk profiles. The integration of qualitative information, different risk profiles (ranging from weak to strong risk management), and internal and external databases contributes to the advancement of estimating the OpVaR in risk management . 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.
  • Ant colony stream clustering: A fast density clustering algorithm for dynamic data streams
    Ant colony stream clustering: A fast density clustering algorithm for dynamic data streams Fahy, Conor; Yang, Shengxiang; Gongora, Mario Augusto A data stream is a continuously arriving sequence of data and clustering data streams requires additional considerations to traditional clustering. A stream is potentially unbounded, data points arrive on-line and each data point can be examined only once. This imposes limitations on available memory and processing time. Furthermore, streams can be noisy and the number of clusters in the data and their statistical properties can change over time. This paper presents an on-line, bio-inspired approach to clustering dynamic data streams. The proposed Ant-Colony Stream Clustering (ACSC) algorithm is a density based clustering algorithm, whereby clusters are identified as high-density areas of the feature space separated by low-density areas. ACSC identifies clusters as groups of micro-clusters. The tumbling window model is used to read a stream and rough clusters are incrementally formed during a single pass of a window. A stochastic method is employed to find these rough clusters, this is shown to significantly speed the algorithm with only a minor cost to performance, as compared to a deterministic approach. The rough clusters are then refined using a method inspired by the observed sorting behaviour of ants. Ants pick-up and drop items based on the similarity with the surrounding items. Artificial ants sort clusters by probabilistically picking and dropping micro-clusters based on local density and local similarity. Clusters are summarised using their constituent micro-clusters and these summary statistics are stored offline. Experimental results show that the clustering quality of ACSC is scalable, robust to noise and favourable to leading ant-clustering and stream-clustering algorithms. It also requires fewer parameters and less computational time.
  • An Integrated Inverse Adaptive Neural Fuzzy System with Monte-Carlo Sampling Method for Operational Risk Management
    An Integrated Inverse Adaptive Neural Fuzzy System with Monte-Carlo Sampling Method for Operational Risk Management Chiclana, Francisco; Gongora, Mario Augusto; Pena, Alejandro; Bonet, Isis; Lochmuller, Christian Operational risk refers to deficiencies in processes, systems, people or external events, which may generate losses for an organization. The Basel Committee on Banking Supervision has defined different possibilities for the measurement of operational risk, although financial institutions are allowed to develop their own models to quantify operational risk. The advanced measurement approach, which is a risk-sensitive method for measuring operational risk, is the financial institutions preferred approach, among the available ones, in the expectation of having to hold less regulatory capital for covering operational risk with this approach than with alternative approaches. The advanced measurement approach includes the loss distribution approach as one way to assess operational risk. The loss distribution approach models loss distributions for business-line-risk combinations, with the regulatory capital being calculated as the 99,9% operational value at risk, a percentile of the distribution for the next year annual loss. One of the most important issues when estimating operational value at risk is related to the structure (type of distribution) and shape (long tail) of the loss distribution. The estimation of the loss distribution, in many cases, does not allow to integrate risk management and the evolution of risk; consequently, the assessment of the effects of risk impact management on loss distribution can take a long time. For this reason, this paper proposes a flexible integrated inverse adaptive fuzzy inference model, which is characterized by a Monte-Carlo behavior, that integrates the estimation of loss distribution and different risk profiles. This new model allows to see how the management of risk of an organization can evolve over time and it effects on the loss distribution used to estimate the operational value at risk. The experimental study results, reported in this paper, show the flexibility of the model in identifying (1) the structure and shape of the fuzzy input sets that represent the frequency and severity of risk; and (2) the risk profile of an organization. Therefore, the proposed model allows organizations or financial entities to assess the evolution of their risk impact management and its effect on loss distribution and operational value at risk in real time. The file attached to this record is the author's final peer reviewed version.
  • Flexible inverse adaptive fuzzy inference model to identify the evolution of Operational Value at Risk for improving operational risk management
    Flexible inverse adaptive fuzzy inference model to identify the evolution of Operational Value at Risk for improving operational risk management Pena, Alejandro; Bonet, Isis; Lochmuller, Christian; Chiclana, Francisco; Gongora, Mario Augusto Operational risk was one of the most important risks in the 2008 global financial crisis. This is due to limitations of the applied models in explaining and estimating this type of risk from highly qualitative information related to failures in the operations of financial organizations. A review of research literature on this area indicates an increase in the development of models for the estimation of the operational value at risk. However, there is a lack of models that use qualitative information for estimating this type of risk. Motivated by this finding, we propose a Flexible Inverse Adaptive Fuzzy Inference Model that integrates both a novel Montecarlo sampling method for the linguistic input variables of frequency and severity that allow the characterization of a risk event, the impact of risk management matrices to estimate the loss distribution and the associated operational value at risk. The methodology follows a loss distribution approach as defined by Basel II. A benefit of the proposed model is that it works with highly qualitative risk data and it also connects the risk measurement (operational value at risk) with risk management, based on risk management matrices. This way, we mitigate limitations related to a lack of available operational risk event data when assessing operational risk. We evaluate the experimental results obtained through the proposed model by using the Index of Agreement indicator. The results provide a flexible loss distribution under different risk profiles or risk management matrices that explain the evolution of operational risk in real time.
  • Considering flexibility in the evolutionary dynamic optimisation of airport security lane schedules
    Considering flexibility in the evolutionary dynamic optimisation of airport security lane schedules Chitty, Darren M.; Yang, Shengxiang; Gongora, Mario Augusto Airports face pressures to reduce costs at the security lane area by reducing lane opening hours whilst maintaining a passenger service level. Evolutionary methods have been shown to design schedules that minimise both objectives. However, by reducing lane opening hours schedules have a tendency to over-fit the expectation of passenger arrivals at security resulting in long delays with deviations from this forecast. Evolutionary dynamic re-optimisation can mitigate for this reducing passenger waiting times but the security lane problem is an example of a constrained problem in that schedules cannot be significantly altered. Consequently, this paper will investigate the consideration of flexibility when evolving initial schedules to facilitate the evolutionary dynamic re-optimization process. Several differing methods of measuring flexibility will be investigated alongside reducing security lane opening hours and passenger waiting times. Results demonstrate that considering flexibility in the initial design of schedules improves the effectiveness of evolutionary dynamic re-optimisation of schedules. 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.
  • Finding multi-density clusters in non-stationary data streams using an ant colony with adaptive parameters
    Finding multi-density clusters in non-stationary data streams using an ant colony with adaptive parameters Fahy, Conor; Yang, Shengxiang; Gongora, Mario Augusto Density based methods have been shown to be an effective approach for clustering non-stationary data streams. The number of clusters does not need to be known a priori and density methods are robust to noise and changes in the statistical properties of the data. However, most density approaches require sensitive, data dependent parameters. These parameters greatly affect the clustering performance and in a dynamic stream a good set of parameters at time t are not necessarily the best at time t+1. Furthermore, these parameters are global and so restrict the algorithm to finding clusters of the same density. In this paper, we propose a density based algorithm with adaptive parameters which are local to each discovered cluster. The algorithm, denoted Ant Colony Multi-Density Clustering (ACMDC), uses artificial ants to form nests in dense areas of the data. As the ants move between nests, their collective memory is stored in the form of pheromone trails. Clusters are identified as groups of similar nests. The proposed algorithm is evaluated across a number of synthetic data streams containing overlapping and embedded multi-density clusters. The performance of the algorithm is shown to be favourable to a leading density based stream-clustering algorithm despite requiring no tunable parameters. 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.
  • Robustness and evolutionary dynamic optimisation of airport security schedules
    Robustness and evolutionary dynamic optimisation of airport security schedules Chitty, Darren M.; Yang, Shengxiang; Gongora, Mario Augusto Reducing security lane operations whilst minimising passenger waiting times in unforseen circumstances is important for airports. Evolutionary methods can design optimised schedules but these tend to over-fit passenger arrival forecasts resulting in lengthy waiting times for unforeseen events. Dynamic re-optimisation can mitigate for this issue but security lane schedules are an example of a constrained problem due to the human element preventing major modifications. This paper postulates that for dynamic re-optimisation to be more effective in constrained circumstances consideration of schedule robustness is required. To reduce over-fitting a simple methodology for evolving more robust schedules is investigated. Random delays are introduced into forecasts of passenger arrivals to better reflect actuality and a range of these randomly perturbed forecasts are used to evaluate schedules. These steps reduced passenger waiting times for actual events for both static and dynamic policies with minimal increases in security operations.

Click here to view a full listing of Mario Gongora's publications and outputs.

Research interests/expertise

  • Identification, Modelling and simulation using Computational Intelligence
  • Intelligent Data Mining and Behaviour Identification
  • Application of Computational Intelligence to industry and real world problems
  • Evolutionary programming
  • Intelligent Mobile Robots and Bioengineering.

Areas of teaching

Artificial Intelligence

Robotics

Embedded programming

Qualifications

BSc, MSc, PhD 

Membership of external committees

Checkpoint of the Future expert group, IATA (International Air Transport Association), current member of the technology expert group.

Projects

  • On-line path optimisation using self simulation and genetic paradigm.
  • Path search for migrations of ancient populations using analysis of mDNA.
  • Analysis of Developmental Genetics Theory using Evolutionary Programming.

Consultancy work

Intelligent Data Mining

Automation and Robotics (including telemetry and instrumentation)

Computational Intelligence applications (e.g. optimisation, system identification, modelling and simulation)

Some consultancy/commercial projects worked for have for example: GSH (telemetry and automation for intelligent buildings), Rolls Royce (automation and instrumentation), Venuesim (intelligent data mining, modelling and simulation), among others.

Current research students

Currently supervising 10 research students. 

Externally funded research grants information

Has had funding from various sources (Royal Academy, EPSRC, TSB, ERDF, etc.).  Active projects:

Venuesim (spinout company created with seed/investment funding from Lachesis), commercialising research outcomes of intelligent data mining, modelling and simulation. Jan 2008 – current.

Intelligent GUI systems, KTP (TSB) funding to develop highly effective and intelligent GUI frameworks to present information from complex systems, in collaboration with Northrop Grumman. June 2012 – May 2014.

Internally funded research project information

Has had funding from various sources (PhD scholarships from EPSRC DTA, RIF, HEIF).  Active projects:

PhD student from EPSRC DTA, Oct 2009 – Sep 2012, working in Intelligent Data Mining.

Intelligent questionnaire, HEIF, developing intelligent autonomous surveying tools to support social services. May 2012 – Feb 2013.

Published patents

US Patent number 6,339,720 “Early warning apparatus for acute Myocardial Infarction in the first six hours of pain”, US government.

US Patent number 5,545,971 “AC voltage regulator”, US Government.

Professional esteem indicators

Journal reviewing: IEEE Transactions on Neural Networks, IEEE Transactions on Computational Intelligence and AI in Games, Elsevier Information Sciences, Elsevier Applied Soft Computing, Elsevier Evolving Systems, Springer Artificial Intelligence Review.

Case studies

Spinout company Venusim resulting from Dr. Gongora’s research in intelligent data mining.

Article in Airport-technology.com which is the only site focused on bringing the latest news about airport projects, trends, products and services for the global airport industry: http://www.airport-technology.com/features/featureartificial-intelligence-predictive-modelling-airport/

Invited by IATA (International Air Transport Association) to be a member in their expert group for the Checkpoint of the future, an international initiative to drive forward and contribute to aviation security science; by bringing together governments, industry and academic experts from across the world.

 

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