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All Outputs (221)

Vehicle incident hot spots identification: An approach for big data (2017)
Conference Proceeding
Triguero, I., Figueredo, G. P., Mesgarpour, M., Garibaldi, J. M., & John, R. (2017). Vehicle incident hot spots identification: An approach for big data. In Proceedings - 16th IEEE International Conference on Trust, Security and Privacy in Computing and Communications; 11th IEEE International Conference on Big Data Science and Engineering (IEEE BigDataSE); and 14th IEEE International Conference on Embedded Software and Systems, (901-908). https://doi.org/10.1109/Trustcom/BigDataSE/ICESS.2017.329

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.

An improved game-theoretic approach to uncover overlapping communities (2017)
Journal Article
Sun, H., Ch'ng, E., Yong, X., Garibaldi, J. M., See, S., & Chen, D. (2017). An improved game-theoretic approach to uncover overlapping communities. International Journal of Modern Physics C, 28(8), Article 1750112. https://doi.org/10.1142/S0129183117501121

How can we uncover overlapping communities from complex networks to understand the inherent structures and functions? Chen et al. firstly proposed a community game (Game) to study this problem, and the overlapping communities have been discovered whe... Read More about An improved game-theoretic approach to uncover overlapping communities.

Machine learning and statistical approaches to classification – a case study (2017)
Conference Proceeding
Eyoh, I., & John, R. (2017). Machine learning and statistical approaches to classification – a case study.

The advent of information technology has led to the proliferation of data in disparate databases. Organisations have become data rich but knowledge poor. Users need efficient analysis tools to help them understand their data, predict future trends an... Read More about Machine learning and statistical approaches to classification – a case study.

Automated generation of constructive ordering heuristics for educational timetabling (2017)
Journal Article
Pillay, N., & Özcan, E. (2017). Automated generation of constructive ordering heuristics for educational timetabling. Annals of Operations Research, 275, 181-208. https://doi.org/10.1007/s10479-017-2625-x

Construction heuristics play an important role in solving combinatorial optimization problems. These heuristics are usually used to create an initial solution to the problem which is improved using optimization techniques such as metaheuristics. For... Read More about Automated generation of constructive ordering heuristics for educational timetabling.

Efficient deformable motion correction for 3-D abdominal MRI using manifold regression (2017)
Conference Proceeding
Chen, X., Balfour, D. R., Marsden, P. K., Reader, A. J., Prieto, C., & King, A. P. (in press). Efficient deformable motion correction for 3-D abdominal MRI using manifold regression.

We present a novel framework for efficient retrospective respiratory motion correction of 3-D abdominal MRI using manifold regression. K-space data are continuously acquired under free breathing using the stack-of-stars radial gold-en-angle trajector... Read More about Efficient deformable motion correction for 3-D abdominal MRI using manifold regression.

A review of electrostatic monitoring technology: The state of the art and future research directions (2017)
Journal Article
Wen, Z., Hou, J., & Atkin, J. (2017). A review of electrostatic monitoring technology: The state of the art and future research directions. Progress in Aerospace Sciences, 94, https://doi.org/10.1016/j.paerosci.2017.07.003

Electrostatic monitoring technology is a useful tool for monitoring and detecting component faults and degradation, which is necessary for system health management. It encompasses three key research areas: sensor technology; signal detection, process... Read More about A review of electrostatic monitoring technology: The state of the art and future research directions.

Testing and debugging functional reactive programming (2017)
Journal Article
Perez, I., & Nilsson, H. (2017). Testing and debugging functional reactive programming. Proceedings of the ACM on Programming Languages, 1(1), Article 2. https://doi.org/10.1145/3110246

Many types of interactive applications, including video games, raise particular challenges when it comes to testing and debugging. Reasons include de-facto lack of reproducibility and difficulties of automatically generating suitable test data. This... Read More about Testing and debugging functional reactive programming.

An Immune-Inspired Technique to Identify Heavy Goods Vehicles Incident Hot Spots (2017)
Journal Article
Figueredo, G. P., Triguero, I., Mesgarpour, M., Maciel Guerra, A., Garibaldi, J. M., & John, R. (2017). An Immune-Inspired Technique to Identify Heavy Goods Vehicles Incident Hot Spots. IEEE Transactions on Emerging Topics in Computational Intelligence, 1(4), 248-258. https://doi.org/10.1109/TETCI.2017.2721960

We report on the adaptation of an immune-inspired instance selection technique to solve a real-world big data problem of determining vehicle incident hot spots. The technique, which is inspired by the Immune System self-regulation mechanism, was orig... Read More about An Immune-Inspired Technique to Identify Heavy Goods Vehicles Incident Hot Spots.

A signalome screening approach in the autoinflammatory disease TNF receptor associated periodic syndrome (TRAPS) highlights the anti-inflammatory properties of drugs for repurposing (2017)
Journal Article
Figueredo, G., Todd, I., Negm, O. H., Reps, J., Radford, P., Figueredo, G. P., …Tighe, P. J. (2017). A signalome screening approach in the autoinflammatory disease TNF receptor associated periodic syndrome (TRAPS) highlights the anti-inflammatory properties of drugs for repurposing. Pharmacological Research, 125, 188-200. https://doi.org/10.1016/j.phrs.2017.08.012

© 2017 Elsevier Ltd TNF receptor associated periodic syndrome (TRAPS) is an autoinflammatory disease caused by mutations in TNF Receptor 1 (TNFR1). Current therapies for TRAPS are limited and do not target the pro-inflammatory signalling pathways tha... Read More about A signalome screening approach in the autoinflammatory disease TNF receptor associated periodic syndrome (TRAPS) highlights the anti-inflammatory properties of drugs for repurposing.

The gift of the algorithm: beyond autonomy and control (2017)
Other
Chamberlain, A., & De Roure, D. (2017). The gift of the algorithm: beyond autonomy and control. [Digital Performance]

This piece brings together, participation, algorithmic composition and augmentation (as a mechanism by which people can work together to augment and support a composer’s workflow). The performance is about understanding the ways in which composition... Read More about The gift of the algorithm: beyond autonomy and control.

Elliptic membership functions and the modeling uncertainty in type-2 fuzzy logic systems as applied to time series prediction (2017)
Conference Proceeding
Kayacan, E., Coupland, S., John, R., & Khanesar, M. A. (2017). Elliptic membership functions and the modeling uncertainty in type-2 fuzzy logic systems as applied to time series prediction. In 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (1-7). https://doi.org/10.1109/FUZZ-IEEE.2017.8015457

In this paper, our aim is to compare and contrast various ways of modeling uncertainty by using different type-2 fuzzy membership functions available in literature. In particular we focus on a novel type-2 fuzzy membership function–”Elliptic membersh... Read More about Elliptic membership functions and the modeling uncertainty in type-2 fuzzy logic systems as applied to time series prediction.

Optimization of fed-batch fermentation processes using the Backtracking Search Algorithm (2017)
Journal Article
Zain, M. Z. B. M., Kanesan, J., Kendall, G., & Chuah, J. H. (in press). Optimization of fed-batch fermentation processes using the Backtracking Search Algorithm. Expert Systems with Applications, 91, https://doi.org/10.1016/j.eswa.2017.07.034

Fed-batch fermentation has gained attention in recent years due to its beneficial impact in the economy and productivity of bioprocesses. However, the complexity of these processes requires an expert system that involves swarm intelligence-based meta... Read More about Optimization of fed-batch fermentation processes using the Backtracking Search Algorithm.

A new dynamic approach for non-singleton fuzzification in noisy time-series prediction (2017)
Conference Proceeding
Pourabdollah, A., John, R., & Garibaldi, J. M. (in press). A new dynamic approach for non-singleton fuzzification in noisy time-series prediction.

Non-singleton fuzzification is used to model uncertain (e.g. noisy) inputs within fuzzy logic systems. In the standard approach, assuming the fuzzification type is known, the observed [noisy] input is usually considered to be the core of the input fu... Read More about A new dynamic approach for non-singleton fuzzification in noisy time-series prediction.

fuzzycreator: a python-based toolkit for automatically generating and analysing data-driven fuzzy sets (2017)
Conference Proceeding
McCulloch, J. (2017). fuzzycreator: a python-based toolkit for automatically generating and analysing data-driven fuzzy sets.

This paper presents a toolkit for automatic generation and analysis of fuzzy sets (FS) from data. Toolkits are vital for the wider dissemination, accessibility and implementation of theoretic work and applications on FSs. There are currently several... Read More about fuzzycreator: a python-based toolkit for automatically generating and analysing data-driven fuzzy sets.

Time series forecasting with interval type-2 intuitionistic fuzzy logic systems (2017)
Conference Proceeding
Eyoh, I., John, R., & de Maere, G. (in press). Time series forecasting with interval type-2 intuitionistic fuzzy logic systems.

Conventional fuzzy time series approaches make use of type-1 or type-2 fuzzy models. Type-1 models with one index (membership grade) cannot fully handle the level of uncertainty inherent in many real world applications. The type-2 models with upper a... Read More about Time series forecasting with interval type-2 intuitionistic fuzzy logic systems.

Efficient modeling and representation of agreement in interval-valued data (2017)
Conference Proceeding
Havens, T. C., Wagner, C., & Anderson, D. T. (2017). Efficient modeling and representation of agreement in interval-valued data. In 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (1-6). https://doi.org/10.1109/FUZZ-IEEE.2017.8015466

Recently, there has been much research into effective representation and analysis of uncertainty in human responses, with applications in cyber-security, forest and wildlife management, and product development, to name a few. Most of this research ha... Read More about Efficient modeling and representation of agreement in interval-valued data.

Exploring the use of type-2 fuzzy sets in multi-criteria decision making based on TOPSIS (2017)
Conference Proceeding
Madi, E., Garibaldi, J. M., & Wagner, C. (2017). Exploring the use of type-2 fuzzy sets in multi-criteria decision making based on TOPSIS. In 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (1-6). https://doi.org/10.1109/FUZZ-IEEE.2017.8015664

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.