Professor Mario Gongora

Job: Professor in Applied Intelligent Systems
Faculty Enterprise Lead

Faculty: Computing, Engineering and Media

School/department: School of Computer Science and Informatics

Research group(s): Research in Societal Enhacement (RiSE),
Institute of Artificial Intelligence (IAI)

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

T: +44 (0)116 207 8226

E: mgongora@dmu.ac.uk

W: www.tech.dmu.ac.uk/~mgongora/

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Personal profile

Professor Mario Gongora received his MSc and PhD from the University of Warwick (UK). He is currently Professor in Applied Intelligent Systems at the School of Computer Science and Informatics, De Montfort University (DMU), he is the Faculty Enterprise Lead and a senior member of the Research and Innovation Institute of Digital Research, Communications and Responsible Innovation at the Faculty of Computing, Engineering and Media.

He is currently focusing in the application of Artificial Intelligence techniques into the fields of Data Analysis of large and complex datasets and modelling Natural Phenomena, including identification, simulation and optimisation.

Prof. Gongora has extensive experience in the analysis and modelling of natural inspired systems and behaviours using computational tools. His experience involves the use of evolutionary computing and machine learning techniques to identify systems from large/incomplete datasets, modelling and emergence of complex behaviour in artificial systems and environments; and use of the models to simulate, predict or optimise the performance of systems in an on-going automated learning cycle.

He runs a spinout company to commercialise applications of his research into robust behaviour simulation and optimisation systems for customers in large Venues and complex processes. This enterprise has additionally benefited from Prof. Gongora’s close contacts with industrial partners.

Prof. Gongora works in close contact with external partners, taking the expertise from the University to Industry and Society.

Research group affiliations

Dr Gongora leads the Research in Societal Enhacement (RiSE) team.

Senior member of the Research and Innovation Institute of Digital Research, Communications and Responsible Innovation, and mentor to the Innovation of Artificial Intelligence (IAI) cluster.

Publications and outputs

Assessment of Dataset Scalability for Classification of Black Sigatoka in Banana Crops Using UAV-Based Multispectral Images and Deep Learning Techniques
dc.title: Assessment of Dataset Scalability for Classification of Black Sigatoka in Banana Crops Using UAV-Based Multispectral Images and Deep Learning Techniques dc.contributor.author: Gongora, Mario Augusto; Linero-Ramos, R.; Parra-Rodríguez, C.; Espinosa-Valdez, A.; Gómez-Rojas, J. dc.description.abstract: This paper presents an evaluation of different convolutional neural network (CNN) architectures using false-colour images obtained by multispectral sensors on drones for the detection of Black Sigatoka in banana crops. The objective is to use drones to improve the accuracy and efficiency of Black Sigatoka detection to reduce its impact on banana production and improve the sustainable management of banana crops, one of the most produced, traded, and important fruits for food security consumed worldwide. This study aims to improve the precision and accuracy in analysing the images and detecting the presence of the disease using deep learning algorithms. Moreover, we are using drones, multispectral images, and different CNNs, supported by transfer learning, to enhance and scale up the current approach using RGB images obtained by conventional cameras and even smartphone cameras, available in open datasets. The innovation of this study, compared to existing technologies for disease detection in crops, lies in the advantages offered by using drones for image acquisition of crops, in this case, constructing and testing our own datasets, which allows us to save time and resources in the identification of crop diseases in a highly scalable manner. The CNNs used are a type of artificial neural network widely utilised for machine training; they contain several specialised layers interconnected with each other in which the initial layers can detect lines and curves, and gradually become specialised until reaching deeper layers that recognise complex shapes. We use multispectral sensors to create false-colour images around the red colour spectra to distinguish infected leaves. Relevant results of this study include the construction of a dataset with 505 original drone images. By subdividing and converting them into false-colour images using the UAV’s multispectral sensors, we obtained 2706 objects of diseased leaves, 3102 objects of healthy leaves, and an additional 1192 objects of non-leaves to train classification algorithms. Additionally, 3640 labels of Black Sigatoka were generated by phytopathology experts, ideal for training algorithms to detect this disease in banana crops. In classification, we achieved a performance of 86.5% using false-colour images with red, red edge, and near-infrared composition through MobileNetV2 for three classes (healthy leaves, diseased leaves, and non-leaf extras). We obtained better results in identifying Black Sigatoka disease in banana crops using the classification approach with MobileNetV2 as well as our own datasets. dc.description: open access article This research was pursued in collaboration with the Pontificia Universidad Javeriana and the Universidad del Magdalena, Colombia.
A Multispectral Image Classification Framework for Estimating the Operational Risk of Lethal Wilt in Oil Palm Crops
dc.title: A Multispectral Image Classification Framework for Estimating the Operational Risk of Lethal Wilt in Oil Palm Crops dc.contributor.author: Pena, Alejandro; Puerta, Alejandro; Bonet, Isis; Caraffini, Fabio; Ochoa, Ivan; Gongora, Mario Augusto dc.description.abstract: Operational risk is the risk associated with business operations in an organisation. With respect to agricultural crops, in particular, operational risk is a fundamental concept to establish differentiated coverage and to seek protection against different risks. For cultivation, these risks are related to the agricultural business process and to external risk events. An operational risk assessment allows one to identify the limits of environmental and financial sustainability. Specifically, in oil palm cultivation, the characterisation of the associated risk remains a challenge from a technological perspective. To advance in this direction, researchers have used different technologies, including spectral aerial images, unmanned aerial vehicles to construct a vegetation index, intelligent augmented platforms for real-time monitoring, and adaptive fuzzy models to estimate operational risk. In line with these technological developments, in this article we propose a framework for the estimation of the risk assessment associated with the disease of Lethal Wilt (LW) in oil palm plantations. Although our purpose is not to predict lethal wilt, since the framework starts from the result of a prediction model, a model to detect LW in an early stage is used for the demonstration. For the implementation of the prediction model, we use a novel deep learning system based on two neural networks. This refers to a case study conducted at UNIPALMAS. We show that the suitability of our system aims to evaluate operational risks of LW with a confidence level of 99.9% and for a period of 6 months. dc.description: Under its remit as a delivery partner of the Newton Fund, the Royal Academy of Engineering has partnered with Promigas, Ecopetrol and Ruta N to enhance engineering teaching, research and innovation outcomes in Colombian universities by building bilateral industry-academia links. One of the projects funded through this scheme seeks to develop an intelligence system to improve the sustainability of oil palm crops through the construction of forecasting maps that integrate adaptive vegetation indices from multispectral aerial views. It brings together researchers from EIA University in Colombia and De Montfort University in the UK in collaboration with Unipalma S.A., a Colombian agricultural company that specialises in the cultivation of oil palm crops.
‘Insight Unlocked’: Applying a Collective Intelligence approach to engage employers in informing Local Skills Improvement Planning
dc.title: ‘Insight Unlocked’: Applying a Collective Intelligence approach to engage employers in informing Local Skills Improvement Planning dc.contributor.author: Rae, David; Cartwright, Edward; Gongora, Mario Augusto; Hobson, Chris; Shah, Harsh dc.description.abstract: This paper demonstrates how the innovative application of a Collective Intelligence approach enhanced Local Skills Planning information for employers, education and skills training organisations and regional economic policy organisations. This took place within a Knowledge Transfer Partnership between a Chamber of Commerce and a University. This aimed to develop and deploy regional business intelligence for enhanced policy and decision-making in enterprise and economic development. The project converged knowledge from several research centres including economics, entrepreneurship and innovation, data science, and Artificial Intelligence. The paper presents a project case study which provides two contributions to applied knowledge. Firstly, it demonstrates how a Collective Intelligence (CI) approach can be applied to achieve rapid results in resolving the real-world problem of local skills information availability. Useful real-time data was gathered from employers in three sectors on skills requirements, supply and training. This was analysed using Artificial Intelligence tools, then shared publicly via an automated Internet portal, providing a scalable model for wider use. Secondly, it explores and evaluates how the knowledge exchange (KE) process can function effectively and quickly in applying CI-based innovation in practical ways which create new value, within a Knowledge Transfer Partnership between a University and Chamber of Commerce. environment. dc.description: 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. The project for which the paper provides a case study was a Knowledge Transfer Partnership between De Montfort University and East Midlands Chamber, funded by UKRI.
The Arquive of Tatuoca Magnetic Observatory Brazil: from paper to intelligent bytes
dc.title: The Arquive of Tatuoca Magnetic Observatory Brazil: from paper to intelligent bytes dc.contributor.author: Berrío-Zapata, Cristian; Ferreira da Silva, Ester; Costa Pinheiro, Mayara; Carvalho de Abreu, Vinicius Augusto; Mendel Martins, Cristiano; Gongora, Mario Augusto; Dunman, Kelso dc.description.abstract: The Magnetic Observatory of Tatuoca (TTB) was installed by Observatório Nacional (ON) in 1957, near Belém city in the state of Pará, Brazilian Amazon. Its history goes back to 1933, when a Danish mission used this location to collect data, due to its privileged position near the terrestrial equator. Between 1957 and 2007, TTB produced 18,000 magnetograms on paper using photographic variometers, and other associated documents like absolute value forms and yearbooks. Data was obtained manually from these graphs with rulers and grids, taking 24 average readings per day, that is, one per hour. In 2017, the Federal University of Pará (UFPA in the Portuguese acronym) and ON collaborated to rescue this physical archive. In 2022 UFPA took a step forward and proposed not only digitizing the documents but also developing an intelligent agent capable of reading and extracting the information of the curves with a resolution better than an hour, being this the central goal of the project. If the project succeeds, it will rescue 50 years of data imprisoned in paper, increasing measurement sensitivity far beyond what these sources used to give. This will also open the possibility of applying the same AI to similar documents in other observatories or disciplines like seismography. This article recaps the project, and the complex challenges faced in articulating Archival Science principles with AI and Geoscience.
An Evolutionary Intelligent Control System for a Flexible Joints Robot
dc.title: An Evolutionary Intelligent Control System for a Flexible Joints Robot dc.contributor.author: Peña, Alejandro; Tejada, Juan C.; Gonzalez, Juan; Sepulveda-Cano, Lina Maria; Chiclana, Francisco; Caraffini, Fabio; Gongora, Mario Augusto dc.description.abstract: In this paper, we present a model for a serial robotic system with flexible joints (RFJ) using Euler–Lagrange equations, which integrates the oscillatory dynamics generated by the flexible joints at specific operating points, using a pseudo-Ornstein-Uhlembeck process with reversion to the mean. We also propose a Stochastic Flexible - Adaptive Neural Integrated System (SF-ANFIS) to identify and control the RFJ with two degrees of freedom. For the configuration of the model, we use two adaptive strategies. One strategy is based on the Generalised Delta Rule (GDR). In contrast, a second strategy is based on the EDA-MAGO algorithm (Estimation Distribution Algorithms - Multi-dynamics Algorithm for Global Optimisation), improving online learning. We considered three stages for analysing and validating the proposed SF-ANFIS model: a first identification stage, a second stage defined by the adaptive control process, and a final stage or cancellation of oscillations. Results show that, for the identification stage, the SF-ANFIS model showed better statistical indices than the MADALINE model in control for the second joint, which presents the greatest oscillations; among those that stand out, the IOA (0.9955), VG (1.0012) and UAPC2 (-0.0003). For the control stage, The SF-ANFIS model showed, in a general way, the best behaviour in the system’s control for both joints, thanks to the capacity to identify and cancel oscillations based on the advanced sampling that defines the EDA algorithm. For the cancellation of the oscillations stage, the SF-ANFIS achieved the best behaviour, followed by the MADALINE model, where it is highlighted the UAPC2 (0.9525) value. dc.description: 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
Deep Learning to Improve the Sustainability of Agricultural Crops Affected by Phytosanitary Events: A Financial-Risk Approach
dc.title: Deep Learning to Improve the Sustainability of Agricultural Crops Affected by Phytosanitary Events: A Financial-Risk Approach dc.contributor.author: Tejada, Juan C.; Gonzalez-Ruiz, Juan David; Gongora, Mario Augusto; Pena, Alejandro dc.description.abstract: Given the challenges in reducing greenhouse gases (GHG), one of the sectors that have attracted the most attention in the Sustainable Development Agenda 2030 (SDA-2030) is the agricultural sector. In this context, one of the crops that has had the most remarkable development worldwide has been oil-palm cultivation, thanks to its high productive potential and being one of the most efficient sources of palmitic acid production. However, despite the significant presence of oil palm in the food sector, oil-palm crops have not been exempt from criticism, as its cultivation has developed mainly in areas of ecological conservation around the world. This criticism has been extended to other crops in the context of the Sustainable Development Goals (SDG) due to insecticides and fertilisers required to treat phytosanitary events in the field. To reduce this problem, researchers have used unmanned aerial vehicles (UAVs) to capture multi-spectral aerial images (MAIs) to assess fields’ plant vigour and detect phytosanitary events early using vegetation indices (VIs). However, detecting phytosanitary events in the early stages still suggests a technological challenge. Thus, to improve the environmental and financial sustainability of oil-palm crops, this paper proposes a hybrid deep-learning model (stacked–convolutional) for risk characterisation derived from a phytosanitary event, as suggested by lethal wilt (LW). For this purpose, the proposed model integrates a Lagrangian dispersion model of the backward-Gaussian-puff-tracking type into its convolutional structure, which allows describing the evolution of LW in the field for stages before a temporal reference scenario. The results show that the proposed model allowed the characterisation of the risk derived from a phytosanitary event, (PE) such as lethal wilt (LW), in the field, promoting improvement in agricultural environmental and financial sustainability activities through the integration of financial-risk concepts. This improved risk management will lead to lower projected losses due to a natural reduction in insecticides and fertilisers, allowing a balance between development and sustainability for this type of crop from the RSPO standards. dc.description: open access article.
Applications of Computational Intelligence-based Systems for Societal Enhancement
dc.title: Applications of Computational Intelligence-based Systems for Societal Enhancement dc.contributor.author: Caraffini, Fabio; Chiclana, Francisco; Moodley, Raymond; Gongora, Mario Augusto dc.description.abstract: Editorial of the special issue on the "Applications of Computational Intelligence based Systems for Societal Enhancement" dc.description: 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.
Classification in Dynamic Data Streams with a Scarcity of Labels
dc.title: Classification in Dynamic Data Streams with a Scarcity of Labels dc.contributor.author: Fahy, Conor; Yang, Shengxiang; Gongora, Mario Augusto dc.description.abstract: Ensemble techniques are a powerful method for recognising and reacting to changes in non-stationary data. However, most researches into dynamic classification with ensembles assume that the true class label of each incoming point is available or easily obtained. This is unrealistic in most practical applications, especially in high-velocity streams where manually labeling each point is prohibitively expensive. To address this challenge, this paper proposes an algorithm, named Clustering and One-Class Classification Ensemble Learning (COCEL), which incorporates a stream clustering algorithm and an ensemble of one-class classifiers with active learning, for classification in dynamic data streams. The method exploits the intuitive relationship between clusters and one-class classifiers to cope with a small training set (or no training set) and improve with experience, self-modifying its internal state to cope with changes in the data stream. The proposed method is evaluated on synthetic data streams exhibiting concept evolution and concept drift and a collection of high-velocity real data streams where manually labeling each incoming point is infeasible or expensive and labor intensive. Finally, a comparative evaluation with peer stream classification ensembles shows that COCEL can achieve superior or comparative accuracy while typically requiring less than 0.01% of the stream labels. dc.description: The file attached to this record is the author's final peer reviewed version.
Scarcity of labels in non-stationary data streams: A survey
dc.title: Scarcity of labels in non-stationary data streams: A survey dc.contributor.author: Fahy, Conor; Yang, Shengxiang; Gongora, Mario Augusto dc.description.abstract: In a dynamic stream there is an assumption that the underlying process generating the stream is non-stationary and that concepts within the stream will drift and change as the stream progresses. Concepts learned by a classification model are prone to change and non-adaptive models are likely to deteriorate and become ineffective over time. The challenge of recognising and reacting to change in a stream is compounded by the scarcity of labels problem. This refers to the very realistic situation in which the true class label of an incoming point is not immediately available (or might never be available) or in situations where manually annotating data points is prohibitively expensive. In a high-velocity stream it is perhaps impossible to manually label every incoming point and pursue a fully-supervised approach. In this article we formally describe the types of change which can occur in a data-stream and then catalogue the methods for dealing with change when there is limited access to labels. We present an overview of the most influential ideas in the field along with recent advancements and we highlight trends, research gaps, and future research directions. dc.description: The file attached to this record is the author's final peer reviewed version.
Applying fuzzy scenarios for the measurement of operational risk
dc.title: Applying fuzzy scenarios for the measurement of operational risk dc.contributor.author: Bonet, Isis; Pena, Alejandro; Lochmuller, Christian; Patiño, Hector Alejandro; Chiclana, Francisco; Gongora, Mario Augusto dc.description.abstract: Operational risk measurement assesses the probability to suffer financial losses in an organisation. The assessment of this risk is based primarily on the organisation’s internal data. However, other factors, such as external data and scenarios are also key elements in the assessment process. Scenarios enrich the data of operational risk events by simulating situations that still have not occurred and therefore are not part of the internal databases of an organisation but which might occur in the future or have already happened to other companies. Internal data scenarios often represent extreme risk events that increase the operational Value at Risk (OpVaR) and also the average loss. In general, OpVaR and the loss distribution are an important part of risk measurement and management. In this paper, a fuzzy method is proposed to add risk scenarios as a valuable data source to the data for operational risk measurement. We compare adding fuzzy scenarios with the possibility of adding non fuzzy or crisp scenarios. The results show that by adding fuzzy scenarios the tail of the aggregated loss distribution increases but that the effect on the expected average loss and on the OpVaR is lesser in its extent. dc.description: 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.

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

Research interests/expertise

Computational Intelligence: Hybrid Optimisation Systems, Evolutionary Computing.
Intelligent Data Analytics for large, complex, disparate and incomplete data sets.
Applications of Computational Intelligence and Edge systems to the analysis of Complex or unstructured Processes and Natural systems.

Areas of teaching

Artificial Intelligence

Robotics

Embedded programming

Qualifications

BSc, MSc, PhD

Honours and awards

Award conferred "International Leaders Making an Impact in Security" in the "International Leaders in Security" category, at the 5th Edition of COLADCA International awards ceremony - 11th Dec 2021

Award conferred "International Project that Leaves a Footprint in Security" as Project Lead of "Artificial Intelligence for analysing Stop and Search and other police activities" in the "Protection of Human Rights and Individual Freedoms" category, at the 5th Edition of COLADCA International awards ceremony - 11th Dec 2021

Nominated as finalist for the ATC Global Excellence Awards ‘Industry Partnership of the Year’ category (Northrop Grumman Airport Systems, VenueSim and East Midlands Airport), March 2013. Finalists list:
•    US Airways, ACSS, FAA and EUROCONTROL partnership for NextGen/ADS-B avionics
•    AMP Corporation, DCA Low Cost Aircraft terminal (LCAT) KLIA Package A (Systems)
•    Guntermann & Drunck GmbH & EUROCONTROL - Mission-critical applications in Air Traffic Control
•    Honeywell - ITP
•    Middle East Airlines and Air Arabia - ATS and Technical Affairs
•    Northrop Grumman - Northrop Grumman Airport Systems, VenueSim and East Midlands Airport

Winner of the 8th British Computer Society prize for progress toward machine intelligence. SGAI 2009, Peterhouse College, Cambridge, UK. Project: Novel use of sound to guide an autonomous helicopter.

Membership of external committees

Member by invitation of the Digital Revolution Challenge Group at the British Chambers of Commerce (BCC), as independent academic advisor in AI policy.

Invited as observer and contributor to the ACI World Smart Sec Management Group since Jun 2021.

Advisor for Artificial Intelligence to COLADCA, the International community of Risk Management and Security Industries and experts, with over 15 member countries and over 3000 individual members and international companies. Appointed 1st Feb 2019 to date.

Member by invitation of the Expert Group to support the Smart Security Programme from IATA / ACI (International Air Transport Association & Airports Council International) from 2012 to date, continuous contribution to the Blueprints and Guidelines that are distributed to all airlines and airports in the world.

Projects

Using augmented intelligence platforms in agriculture, to help identify crops that are under strain due to climate or require intervention with fertiliser or pesticides; and analyse risk to manage financing and insurance issues
Artificial Intelligence for analysing Stop and Search and other police activities
Improving the sustainability of oil palm crops through ML and computer vision for classification of fruit in terms of quality and ripeness
Artificial intelligence to support the improvement of pupil attendance and engagement at schools

Sensor fusion using intelligent agents to enhance the effectiveness of artisan land mine detection

Consultancy work

Recent consultancy: Applying Artificial Intelligence to support the East Midlands Chamber in their Local Skills Improve Plan (LSIP) project.

Consultancy fields: Intelligent Data Mining, Automation and Robotics (including telemetry and instrumentation), Computational Intelligence applications (e.g. optimisation, system identification, modelling and simulation)

Past consultancy/commercial projects include 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 5 research (PhD) students.

Externally funded research grants information

KTP (joint inter-faculty) East Midlands Chamber of Commerce and De Montfort University, to create a Business Research & Intelligence Unit for the East Midlands region. Granted Oct 2020, April 2021 for March 2023. Highly praised and awarded KTP.

Awarded 2 Dishtiguished Internationa Associates (DIA): Prof. Alejandro Pena, Universidad EAFIT, Medellin, Colombia (2023-24), and Dr, Isis Bonet, Universidad EIA, Medellin, Colombia (2022-23)

Royal Academy of Engineering – Newton Fund, International collaboration grant (IAPP) for an “Intelligent system to improve the sustainability of oil palm crops through the construction of forecasting maps integrating adaptive vegetation indices from multispectral aerial views”, Mar 2018 – Mar 2020.

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, QRF, RIF, HEIF)

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.

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.

Impact (REF) Case Studies

REF 2020 UoA 11: Support for Operations and Security for the Global Air Transport Industry (Modelling, Forecasting and Optimisation)

This ICS present how it has enhanced the security screening process of millions of passengers travelling daily through nearly 1,200 international airports. This was achieved through the contributions of Dr Mario Gongora as international adviser, where he disseminated the relevant research outcomes supporting security; leading to an active contribution in the development of the Smart Security programme guidelines disseminated across all airports around the world by the International Air Transport Association (IATA) and Airports Council International (ACI) and steering the industry to develop suitable solutions

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