All Outputs (121)

A comparison of non-stationary, type-2 and dual surface fuzzy control
Presentation / Conference Contribution
Benatar, N., Aickelin, U., & Garibaldi, J. M. (in press). A comparison of non-stationary, type-2 and dual surface fuzzy control.

Type-1 fuzzy logic has frequently been used in control systems. However this method is sometimes shown to be too restrictive and unable to adapt in the presence of uncertainty. In this paper we compare type-1 fuzzy control with several other fuzzy ap... Read More about A comparison of non-stationary, type-2 and dual surface fuzzy control.

Clustering breast cancer data by consensus of different validity indices
Presentation / Conference Contribution
Soria, D., Garibaldi, J. M., Ambrogi, F., Lisboa, P. J., Boracchi, P., & Biganzoli, E. M. Clustering breast cancer data by consensus of different validity indices.

Clustering algorithms will, in general, either partition a given data set into a pre-specified number of clusters or will produce a hierarchy of clusters. In this paper we analyse several different clustering techniques and apply them to a particular... Read More about Clustering breast cancer data by consensus of different validity indices.

Performance measurement under increasing environmental uncertainty in the context of interval type-2 fuzzy logic based robotic sailing
Presentation / Conference Contribution
Benatar, N., Aickelin, U., & Garibaldi, J. M. Performance measurement under increasing environmental uncertainty in the context of interval type-2 fuzzy logic based robotic sailing.

Performance measurement of robotic controllers based on fuzzy logic, operating under uncertainty, is a subject area which has been somewhat ignored in the current literature. In this paper standard measures such as RMSE are shown to be inappropriate... Read More about Performance measurement under increasing environmental uncertainty in the context of interval type-2 fuzzy logic based robotic sailing.

Using clustering to extract personality information from socio economic data
Presentation / Conference Contribution
Ladas, A., Aickelin, U., Garibaldi, J. M., & Ferguson, E. Using clustering to extract personality information from socio economic data.

It has become apparent that models that have been applied widely in economics, including Machine Learning techniques and Data Mining methods, should take into consideration principles that derive from the theories of Personality Psychology in order t... Read More about Using clustering to extract personality information from socio economic data.

Comparing data-mining algorithms developed for longitudinal observational databases
Presentation / Conference Contribution
Reps, J., Garibaldi, J. M., Aickelin, U., Soria, D., Gibson, J. E., & Hubbard, R. B. Comparing data-mining algorithms developed for longitudinal observational databases.

Longitudinal observational databases have become
a recent interest in the post marketing drug surveillance community due to their ability of presenting a new perspective for detecting negative side effects. Algorithms mining longitudinal observation... Read More about Comparing data-mining algorithms developed for longitudinal observational databases.

Examining the classification accuracy of TSVMs with feature selection in comparison with the GLAD algorithm
Presentation / Conference Contribution
Helmi, H., Garibaldi, J. M., & Aickelin, U. Examining the classification accuracy of TSVMs with feature selection in comparison with the GLAD algorithm.

Gene expression data sets are used to classify and predict patient diagnostic categories. As we know, it is extremely difficult and expensive to obtain gene expression labelled examples. Moreover, conventional supervised approaches cannot function pr... Read More about Examining the classification accuracy of TSVMs with feature selection in comparison with the GLAD algorithm.

Investigating the detection of adverse drug events in a UK general practice electronic health-care database
Presentation / Conference Contribution
Reps, J., Feyereisl, J., Garibaldi, J. M., Aickelin, U., Gibson, J. E., & Hubbard, R. B. Investigating the detection of adverse drug events in a UK general practice electronic health-care database.

Data-mining techniques have frequently been developed
for Spontaneous reporting databases. These techniques
aim to find adverse drug events accurately and efficiently. Spontaneous reporting databases are prone to missing information,under reporting... Read More about Investigating the detection of adverse drug events in a UK general practice electronic health-care database.

The transfer of evolved artificial immune system behaviours between small and large scale robotic platforms
Book Chapter
Whitbrook, A., Aickelin, U., & Garibaldi, J. M. The transfer of evolved artificial immune system behaviours between small and large scale robotic platforms. In P. Collet, N. Monmarché, P. Legrand, M. Schoenauer, & E. Lutton (Eds.), Artificial evolution: 9th International Conference = Evolution Artificielle, EA 2009: Strasbourg, France, October 26-28, 2009: revised selected papers. Springer

This paper demonstrates that a set of behaviours evolved in
simulation on a miniature robot (epuck) can be transferred to a much larger scale platform (a virtual Pioneer P3-DX) that also differs in shape, sensor type, sensor configuration and progra... Read More about The transfer of evolved artificial immune system behaviours between small and large scale robotic platforms.

Two-timescale learning using idiotypic behaviour mediation for a navigating mobile robot
Journal Article
Whitbrook, A., Aickelin, U., & Garibaldi, J. M. Two-timescale learning using idiotypic behaviour mediation for a navigating mobile robot. Applied Soft Computing, 10(3), https://doi.org/10.1016/j.asoc.2009.10.005

A combined short-term learning (STL) and long-term learning (LTL) approach to solving mobile-robot
navigation problems is presented and tested in both the real and virtual domains. The LTL phase consists
of rapid simulations that use a genetic algo... Read More about Two-timescale learning using idiotypic behaviour mediation for a navigating mobile robot.

An idiotypic immune network as a short-term learning architecture for mobile robots
Book Chapter
Whitbrook, A., Aickelin, U., & Garibaldi, J. M. An idiotypic immune network as a short-term learning architecture for mobile robots. In P. Bentley, D. Lee, & S. Jung (Eds.), Artificial immune systems: 7th international conference, ICARIS 2008, Phuket, Thailand, August 10-13, 2008: proceedings. Springer

A combined Short-Term Learning (STL) and Long-Term Learning (LTL) approach to solving mobile robot navigation problems is presented and tested in both real and simulated environments. The LTL consists of rapid simulations
that use a Genetic Algorith... Read More about An idiotypic immune network as a short-term learning architecture for mobile robots.

Genetic algorithm seeding of idiotypic networks for mobile-robot navigation
Presentation / Conference Contribution
Whitbrook, A., Aickelin, U., & Garibaldi, J. M. Genetic algorithm seeding of idiotypic networks for mobile-robot navigation.

Robot-control designers have begun to exploit the properties of the human immune system in order to
produce dynamic systems that can adapt to complex, varying, real-world tasks. Jerne’s idiotypic-network theory has proved the most popular artificial... Read More about Genetic algorithm seeding of idiotypic networks for mobile-robot navigation.

On the Concept of Meaningfulness in Constrained Type-2 Fuzzy Sets
Presentation / Conference Contribution
D'Alterio, P., Garibaldi, J. M., & John, R. (2019, June). On the Concept of Meaningfulness in Constrained Type-2 Fuzzy Sets. Presented at International Conference on Fuzzy Systems (FUZZ-IEEE 2019), New Orleans, LA, USA

Constrained type-2 fuzzy sets have been proposed as a tool to model type-2 fuzzy sets starting from a type-1 generator set with uncertainty. This constrained representation only defines as acceptable the embedded sets that have the same shape as the... Read More about On the Concept of Meaningfulness in Constrained Type-2 Fuzzy Sets.

Augmented Neural Networks for modelling consumer indebtness
Presentation / Conference Contribution
Ladas, A., M. Garibaldi, J., Scarpel, R., & Aickelin, U. (2014, July). Augmented Neural Networks for modelling consumer indebtness. Presented at 2014 International Joint Conference on Neural Networks (IJCNN), Beijing, China

Consumer Debt has risen to be an important problem of modern societies, generating a lot of research in order to understand the nature of consumer indebtness, which so far its modelling has been carried out by statistical models. In this work we show... Read More about Augmented Neural Networks for modelling consumer indebtness.

A similarity-based inference engine for non-singleton fuzzy logic systems
Presentation / Conference Contribution
Wagner, C., Pourabdollah, A., McCulloch, J., John, R., & Garibaldi, J. M. (2016, July). A similarity-based inference engine for non-singleton fuzzy logic systems. Presented at 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2016), Vancouver, BC, Canada

In non-singleton fuzzy logic systems (NSFLSs) input uncertainties are modelled with input fuzzy sets in order to capture input uncertainty such as sensor noise. The performance of NSFLSs in handling such uncertainties depends both on the actual input... Read More about A similarity-based inference engine for non-singleton fuzzy logic systems.

Exploring the use of type-2 fuzzy sets in multi-criteria decision making based on TOPSIS
Presentation / Conference Contribution
Madi, E., Garibaldi, J. M., & Wagner, C. (2017, July). Exploring the use of type-2 fuzzy sets in multi-criteria decision making based on TOPSIS. Presented at 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2017), Naples, Italy

Multi-criteria decision making (MCDM) problems are a well known category of decision making problem that has received much attention in the literature, with a key approach being the Technique for Order Preference by Similarity to Ideal Solution (TOPS... Read More about Exploring the use of type-2 fuzzy sets in multi-criteria decision making based on TOPSIS.

Similarity-based non-singleton fuzzy logic control for improved performance in UAVs
Presentation / Conference Contribution
Fu, C., Sarabakha, A., Kayacan, E., Wagner, C., John, R., & Garibaldi, J. M. (2017, July). Similarity-based non-singleton fuzzy logic control for improved performance in UAVs. Presented at 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Naples, Italy

© 2017 IEEE. As non-singleton fuzzy logic controllers (NSFLCs) are capable of capturing input uncertainties, they have been effectively used to control and navigate unmanned aerial vehicles (UAVs) recently. To further enhance the capability to handle... Read More about Similarity-based non-singleton fuzzy logic control for improved performance in UAVs.

Vehicle incident hot spots identification: An approach for big data
Presentation / Conference Contribution
Triguero, I., Figueredo, G. P., Mesgarpour, M., Garibaldi, J. M., & John, R. (2017, August). Vehicle incident hot spots identification: An approach for big data. Presented at 2017 IEEE Trustcom/BigDataSE/ICESS, Sydney, NSW, Australia

In this work we introduce a fast big data approach for road incident hot spot identification using Apache Spark. We implement an existing immuno-inspired mechanism, namely SeleSup, as a series of MapReduce-like operations. SeleSup is composed of a nu... Read More about Vehicle incident hot spots identification: An approach for big data.

Determining firing strengths through a novel similarity measure to enhance uncertainty handling in non-singleton fuzzy logic systems
Presentation / Conference Contribution
Pekaslan, D., Kabir, S., Wagner, C., & Garibaldi, J. M. (2017, November). Determining firing strengths through a novel similarity measure to enhance uncertainty handling in non-singleton fuzzy logic systems. Presented at International Joint Conference on Computational Intelligence (IJCCI 2017), Funchal, Madeira, Portugal

Non-singleton Fuzzy Logic Systems have the potential to tackle uncertainty within the design of fuzzy systems. The inference process has a major role in determining results, being partly based on the interaction of input and antecedent fuzzy sets (in... Read More about Determining firing strengths through a novel similarity measure to enhance uncertainty handling in non-singleton fuzzy logic systems.

Visual landmark sequence-based indoor localization
Presentation / Conference Contribution
Li, Q., Zhu, J., Liu, T., Garibaldi, J., Li, Q., & Qiu, G. (2017, November). Visual landmark sequence-based indoor localization. Presented at 1st Workshop on Artificial Intelligence and Deep Learning for Geographic Knowledge Discovery, Los Angeles, California, USA

This paper presents a method that uses common objects as landmarks for smartphone-based indoor localization and navigation. First, a topological map marking relative positions of common objects such as doors, stairs and toilets is generated from floo... Read More about Visual landmark sequence-based indoor localization.

Comparison of fuzzy integral-fuzzy measure based ensemble algorithms with the state-of-the-art ensemble algorithms
Presentation / Conference Contribution
Agrawal, U., Pinar, A. J., Wagner, C., Havens, T. C., Soria, D., & Garibaldi, J. M. (2018, June). Comparison of fuzzy integral-fuzzy measure based ensemble algorithms with the state-of-the-art ensemble algorithms. Presented at 17th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2018), Cadiz, Spain

The Fuzzy Integral (FI) is a non-linear aggregation operator which enables the fusion of information from multiple sources in respect to a Fuzzy Measure (FM) which captures the worth of both the individual sources and all their possible combinations.... Read More about Comparison of fuzzy integral-fuzzy measure based ensemble algorithms with the state-of-the-art ensemble algorithms.