Dr Jethro Shell

Job: VC2020 Senior Lecturer

Faculty: Technology

School/department: School of Computer Science and Informatics

Research group(s): Centre for Computational Intelligence (CCI)

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

T: 44 (0)116 207 8520

E: jethros@dmu.ac.uk

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Personal profile

Whilst working for Panasonic Computer Products (Europe), Dr Shell completed a part time MSc in Information Technology at De Montfort University, finishing his studies in 2008. Following a 10 year career with Panasonic, he left industry to return to academia completing a PhD at De Montfort University. The focus of his PhD was on sparse data environments where the ability to gain labelled data is initially either extremely difficult or impossible. Environments such as extremely remote locations, ad-hoc structures or user groups that are very specific. These environments often also produce uncertain and vague data. To address these issues he constructed a framework that combined a fuzzy logic representation of the data with a transductive transfer learning approach.

Dr Shell has been a Principal Investigator (PI) on a number of internal and externally funded (including Horizon2020) projects that involve the application of Computational Intelligence (CI) and gaming applications.

The research interests of Dr Shell are focussed around the application of Computational Intelligence, specifically fuzzy logic and transfer learning across and within three main areas: gaming, healthcare and HCI.

He is currently a senior lecturer and researcher in Games and Information Systems at De Montfort University

Research group affiliations

Centre for Computational Intelligence (CCI)

De Montfort University Games And interactive MEdia Research (DMU:GAMER)

Publications and outputs

INNATE: Intelligent Non-invasive Nocturnal epilepsy Assistive TEchnology
INNATE: Intelligent Non-invasive Nocturnal epilepsy Assistive TEchnology Malekmohamadi, Hossein; Shell, Jethro; Coupland, Simon Epilepsy is a neurological disease that affects the brain and is characterised by repeated seizures. Generalised, focal and unknown are three major types of seizures. Each type has several subgroups. For this reason, seizure detection and classification are expensive and erroneous. Other factors can also affect the detection. For example, patients can have a combination of different seizures or start with one type and finish with another. Nocturnal epilepsy can be prominent in many sufferers of this disease. This displays seizures that occur during the sleep cycle. The nature of such seizures makes the gathering of data and the subsequent detection and classification complex and costly. The current standard for seizure detection is the invasive use of electroencephalogram (EEG) monitoring. Both medical and research communities have expressed a large interest in the detection and classification of seizures automatically and non-invasively. This project proposes the use of 3D computer vision and pattern recognition techniques to detect seizures non-invasively.
Application of Artificial Neural Network and Support Vector Regression in Cognitive Radio Networks for RF Power Prediction Using Compact Differential Evolution Algorithm
Application of Artificial Neural Network and Support Vector Regression in Cognitive Radio Networks for RF Power Prediction Using Compact Differential Evolution Algorithm Iliya, Sunday; Gongora, Mario Augusto; Goodyer, E. N.; Gow, J. A.; Shell, Jethro Cognitive radio (CR) technology has emerged as a promising solution to many wireless communication problems including spectrum scarcity and underutilization. To enhance the selection of channel with less noise among the white spaces (idle channels), the a priory knowledge of Radio Frequency (RF) power is very important. Computational Intelligence (CI) techniques cans be applied to these scenarios to predict the required RF power in the available channels to achieve optimum Quality of Service (QoS). In this paper, we developed a time domain based optimized Artificial Neural Network (ANN) and Support Vector Regression (SVR) models for the prediction of real world RF power within the GSM 900, Very High Frequency (VHF) and Ultra High Frequency (UHF) FM and TV bands. Sensitivity analysis was used to reduce the input vector of the prediction models. The inputs of the ANN and SVR consist of only time domain data and past RF power without using any RF power related parameters, thus forming a nonlinear time series prediction model. The application of the models produced was found to increase the robustness of CR applications, specifically where the CR had no prior knowledge of the RF power related parameters such as signal to noise ratio, bandwidth and bit error rate. Since CR are embedded communication devices with memory constrain limitation, the models used, implemented a novel and innovative initial weight optimization of the ANN’s through the use of compact differential evolutionary (cDE) algorithm variants which are memory efficient. This was found to enhance the accuracy and generalization of the ANN model
Optimized Neural Network Using Differential Evolutionary and Swarm Intelligence Optimization Algorithms for RF Power Prediction in Cognitive Radio Network: A Comparative study
Optimized Neural Network Using Differential Evolutionary and Swarm Intelligence Optimization Algorithms for RF Power Prediction in Cognitive Radio Network: A Comparative study Iliya, Sunday; Goodyer, E. N.; Shell, Jethro; Gow, J. A.; Gongora, Mario Augusto Cognitive radio (CR) technology has emerged as a promising solution to many wireless communication problems including spectrum scarcity and underutilization. The a priory knowledge of Radio Frequency (RF) power (primary signals and/ or interfering signals plus noise) in the channels to be exploited by CR is of paramount importance. This will enable the selection of channel with less noise among idle (free) channels. Computational Intelligence (CI) techniques can be applied to these scenarios to predict the required RF power in the available channels to achieve optimum Quality of Service (QoS). In this paper, we developed a time domain based optimized Artificial Neural Network (ANN) model for the prediction of real world RF power within the GSM 900, Very High Frequency (VHF) and Ultra High Frequency (UHF) TV bands. The application of the models produced was found to increase the robustness of CR applications, specifically where the CR had no prior knowledge of the RF power related parameters such as signal to noise ratio, bandwidth and bit error rate. The models used, implemented a novel and innovative initial weight optimization of the ANN’s through the use of differential evolutionary and swarm intelligence algorithms. This was found to enhance the accuracy and generalization of the ANN model. For this problem, DE/best/1/bin was found to yield a better performance as compared with the other algorithms implemented.
Fuzzy Transfer Learning: Methodology and application
Fuzzy Transfer Learning: Methodology and application Shell, Jethro; Coupland, Simon Producing a methodology that is able to predict output using a model is a well studied area in Computational Intelligence (CI). However, a number of real-world applications require a model but have little or no data available of the specific environment. Predominantly, standard machine learning approaches focus on a need for training data for such models to come from the same domain as the target task. Such restrictions can severely reduce the data acquisition making it extremely costly, or in certain situations, impossible. This impedes the ability of these approaches to model such environments. It is on this particular problem that this paper is focussed. In this paper two concepts, Transfer Learning (TL) and Fuzzy Logic (FL) are combined in a framework, Fuzzy Transfer Learning (FuzzyTL), to address the problem of learning tasks that have no prior direct contextual knowledge. Through the use of a FL based learning method, uncertainty that is evident in dynamic environments is represented. By applying a TL approach through the combining of labelled data from a contextually related source task, and little or no unlabelled data from a target task, the framework is shown to be able to accomplish predictive tasks using models learned from contextually different data. 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.
Optimized Artificial Neural Network Using Differential Evolution for Prediction of RF Power in VHF/UHF TV and GSM 900 Bands for Cognitive Radio Networks
Optimized Artificial Neural Network Using Differential Evolution for Prediction of RF Power in VHF/UHF TV and GSM 900 Bands for Cognitive Radio Networks Iliya, Sunday; Goodyer, E. N.; Gongora, Mario Augusto; Gow, J. A.; Shell, Jethro Cognitive radio (CR) technology has emerged as a promising solution to many wireless communication problems including spectrum scarcity and underutilization. The knowledge of Radio Frequency (RF) power (primary signals and/ or interfering signals plus noise) in the channels to be exploited by CR is of paramount importance, not just the existence or absence of primary users. If a channel is known to be noisy, even in the absence of primary users, using such channels will demand large quantities of radio resources (transmission power, bandwidth, etc) in order to deliver an acceptable quality of service to users. Computational Intelligence (CI) techniques can be applied to these scenarios to predict the required RF power in the available channels to achieve optimum Quality of Service (QoS). While most of the prediction schemes are based on the determination of spectrum holes, those designed for power prediction use known radio parameters such as signal to noise ratio (SNR), bandwidth, and bit error rate. Some of these parameters may not be available or known to cognitive users. In this paper, we developed a time domain based optimized Artificial Neural Network (ANN) model for the prediction of real world RF power within the GSM 900, Very High Frequency (VHF) and Ultra High Frequency (UHF) TV bands. The application of the models produced was found to increase the robustness of CR applications, specifically where the CR had no prior knowledge of the RF power related parameters. The models used implemented a novel and innovative initial weight optimization of the ANN’s through the use of differential evolutionary algorithms. This was found to enhance the accuracy and generalization of the approach
Logan's run: Lane optimisation using genetic algorithms based on nsga-ii
Logan's run: Lane optimisation using genetic algorithms based on nsga-ii Witheridge, S.; Passow, Benjamin N.; Shell, Jethro Whilst bus lanes are an important tool to ensure bus time reliability their presence can be detrimental to urban traffic. In this paper a Non-dominated Sorting Genetic Algorithm (NSGA-II) has been adopted to study the effect of bus lanes on urban traffic in terms of location and time of operation. Due to the complex nature of this problem traditional search would not be feasible. An artificial arterial route has been modelled from real data to evaluate candidate solutions. The results confirm this methodology for the purpose of studying and identifying bus lane locations and times of operation. Additionally it is shown that bus lanes can exist on an arterial link without exclusively occupying a continuous lane for large periods of time. Furthermore results indicate a use for this methodology over a larger scale and potential near real-time operation.
Where Are the Pictures? Linking Photographic Records across Collections Using Fuzzy Logic.
Where Are the Pictures? Linking Photographic Records across Collections Using Fuzzy Logic. Brown, Stephen C.; Croft, David; Coupland, Simon; Shell, Jethro; von Lünen, A. This paper describes a novel approach to interrogating different online collections to identify potential matches between them, using fuzzy logic based data mining algorithms. Potentially, information about objects from one collection could be used to enrich records in another where there are overlaps. But although there is a considerable amount of bibliographic and other kinds of data on the Web that share similar information, a standardized way of structuring such data in a way that makes it easy to identify significant relationships does not yet exist. In the case of historical photographs, the challenge is further exacerbated by the enormous breadth of subjects depicted and the fact that surviving records are not always complete, accurate or consistent and the amount of text available per record is very small. Fuzzy matching algorithms and sematic similarity techniques offer a way of finding potential matches between such items when standard ontology and corpus based approaches are inadequate, in this case helping researchers to match photographs held in different archives to historical exhibition catalogue records for the first time. This was a collaborative project with the Centre for Computational Intelligence
Fuzzy Transfer Learning
Fuzzy Transfer Learning Shell, Jethro The use of machine learning to predict output from data, using a model, is a well studied area. There are, however, a number of real-world applications that require a model to be produced but have little or no data available of the specific environment. These situations are prominent in Intelligent Environments (IEs). The sparsity of the data can be a result of the physical nature of the implementation, such as sensors placed into disaster recovery scenarios, or where the focus of the data acquisition is on very defined user groups, in the case of disabled individuals. Standard machine learning approaches focus on a need for training data to come from the same domain. The restrictions of the physical nature of these environments can severely reduce data acquisition making it extremely costly, or in certain situations, impossible. This impedes the ability of these approaches to model the environments. It is this problem, in the area of IEs, that this thesis is focussed. To address complex and uncertain environments, humans have learnt to use previously acquired information to reason and understand their surroundings. Knowledge from different but related domains can be used to aid the ability to learn. For example, the ability to ride a road bicycle can help when acquiring the more sophisticated skills of mountain biking. This humanistic approach to learning can be used to tackle real-world problems where a-priori labelled training data is either difficult or not possible to gain. The transferral of knowledge from a related, but differing context can allow for the reuse and repurpose of known information. In this thesis, a novel composition of methods are brought together that are broadly based on a humanist approach to learning. Two concepts, Transfer Learning (TL) and Fuzzy Logic (FL) are combined in a framework, Fuzzy Transfer Learning (FuzzyTL), to address the problem of learning tasks that have no prior direct contextual knowledge. Through the use of a FL based learning method, uncertainty that is evident in dynamic environments is represented. By combining labelled data from a contextually related source task, and little or no unlabelled data from a target task, the framework is shown to be able to accomplish predictive tasks using models learned from contextually different data. The framework incorporates an additional novel five stage online adaptation process. By adapting the underlying fuzzy structure through the use of previous labelled knowledge and new unlabelled information, an increase in predictive performance is shown. The framework outlined is applied to two differing real-world IEs to demonstrate its ability to predict in uncertain and dynamic environments. Through a series of experiments, it is shown that the framework is capable of predicting output using differing contextual data.
A fast and efficient semantic short text similarity metric
A fast and efficient semantic short text similarity metric Croft, D.; Coupland, Simon; Shell, Jethro; Brown, S.
Towards fuzzy transfer learning for intelligent environments
Towards fuzzy transfer learning for intelligent environments Shell, Jethro; Coupland, Simon By their very nature, Intelligent Environments (IE’s) are infused with complexity, unreliability and uncertainty due to a combination of sensor noise and the human element. The quantity, type and availability of data to model these applications can be a major issue. Each situation is contextually different and constantly changing. The dynamic nature of the implementations present a challenging problem when attempting to model or learn a model of the environment. Training data to construct the model must be within the same feature space and have the same distribution as the target task data, however this is often highly costly and time consuming. There can even be occurrences were a complete lack of labelled target data occurs. It is within these situations that our study is focussed. In this paper we propose a framework to dynamically model IE’s through the use of data sets from differing feature spaces and domains. The framework is constructed using a novel Fuzzy Transfer Learning (FuzzyTL) process. The use of a FuzzyTL algorithm allows for a source of labelled data to improve the learning of an alternative context task. We will demonstrate the application of an Fuzzy Inference System (FIS) to produce a model from a source Intelligent Environment (IE) which can provide the knowledge for a differing target context. We will investigate the use of FuzzyTL within differing contextual distributions through the use of temporal and spatial alternative domains.

Click here to view a full listing of Jethro Shell's publications and outputs.

Research interests/expertise

Computational intelligence
Computer game AI
Fuzzy logic
Transfer learning
Dynamic content generation
Medical applications of CI
Human computer interface
Sensors
Virtual reality

Areas of teaching

Game design
Mobile games
Fuzzy logic
Games projects
UML
Game AI
Physics

Qualifications

Courses taught

IMAT1606: Game Architecture, Design and Development
IMAT2608: Mobile Games
IMAT2800: Mechanics and Artificial Intelligence For Simulation
IMAT3406: Fuzzy Logic and Knowledge Based Systems
IMAT3451: Computing Project

Externally funded research grants information

3D-TuneIn: H2020 project to produce digital games applied to hearing aids to address social inclusion, 01 June 2015 to 31 May 2018, Principal Investigator (Imperial College, University of Nottingham, Vianet, Reactify, University of Malaga)

Internally funded research project information

INNATE: HEIF Project to detect and predict nocturnal seizures using non-invasive techniques, 6 Jan - 01 June 2016, Principal Investigator.

Professional esteem indicators

IEEE Transactions on Neural Networks and Learning Systems, Jan 2014, current, reviewer
Sensors, March 2014, current, reviewer
IEEE Transactions on Computational Intelligence and AI in Games, Oct 2016, current, reviewer

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