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All Outputs (67)

A Method for Evaluating Options for Motif Detection in Electricity Meter Data (2018)
Journal Article
Dent, I., Craig, T., Aickelin, U., & Rodden, T. (2018). A Method for Evaluating Options for Motif Detection in Electricity Meter Data. International Journal of Data Science, 16(1), 1-28. https://doi.org/10.6339/jds.201801_16%281%29.0001

Investigation of household electricity usage patterns, and matching the patterns to behaviours, is an important area of research given the centrality of such patterns in addressing the needs of the electricity industry. Additional knowledge of househ... Read More about A Method for Evaluating Options for Motif Detection in Electricity Meter Data.

Using simulation to incorporate dynamic criteria into multiple criteria decision making (2017)
Journal Article
Aickelin, U., Reps, J. M., Siebers, P., & Li, P. (2018). Using simulation to incorporate dynamic criteria into multiple criteria decision making. Journal of the Operational Research Society, 69(7), 1021-1032. https://doi.org/10.1080/01605682.2017.1410010

In this paper we present a case study demonstrating how dynamic and uncertain criteria can be incorporated into a multi-criteria analysis with the help of discrete event simulation. The simulation guided multi-criteria analysis can include both monet... Read More about Using simulation to incorporate dynamic criteria into multiple criteria decision making.

Modelling cyber-security experts' decision making processes using aggregation operators (2016)
Journal Article
Miller, S., Wagner, C., Aickelin, U., & Garibaldi, J. M. (2016). Modelling cyber-security experts' decision making processes using aggregation operators. Computers and Security, 62, 229-245. https://doi.org/10.1016/j.cose.2016.08.001

An important role carried out by cyber-security experts is the assessment of proposed computer systems, during their design stage. This task is fraught with difficulties and uncertainty, making the knowledge provided by human experts essential for su... Read More about Modelling cyber-security experts' decision making processes using aggregation operators.

Optimising rule-based classification in temporal data (2016)
Journal Article
Fattah, P., Aickelin, U., & Wagner, C. (2016). Optimising rule-based classification in temporal data. Zanco Journal of Pure and Applied Sciences, 28(2),

This study optimises manually derived rule-based expert system classification of objects according to changes in their properties over time. One of the key challenges that this study tries to address is how to classify objects that exhibit changes in... Read More about Optimising rule-based classification in temporal data.

Simulating user learning in authoritative technology adoption: an agent based model for council-led smart meter deployment planning in the UK (2016)
Journal Article
Zhang, T., Siebers, P., & Aickelin, U. (2016). Simulating user learning in authoritative technology adoption: an agent based model for council-led smart meter deployment planning in the UK. Technological Forecasting and Social Change, 106, https://doi.org/10.1016/j.techfore.2016.02.009

How do technology users effectively transit from having zero knowledge about a technology to making the best use of it after an authoritative technology adoption? This post-adoption user learning has received little research attention in technology m... Read More about Simulating user learning in authoritative technology adoption: an agent based model for council-led smart meter deployment planning in the UK.

Supervised anomaly detection in uncertain pseudoperiodic data streams (2016)
Journal Article
Ma, J., Sun, L., Wang, H., Zhang, Y., & Aickelin, U. (2016). Supervised anomaly detection in uncertain pseudoperiodic data streams. ACM Transactions on Internet Technology, 16(1), https://doi.org/10.1145/2806890

Uncertain data streams have been widely generated in many Web applications. The uncertainty in data streams makes anomaly detection from sensor data streams far more challenging. In this paper, we present a novel framework that supports anomaly detec... Read More about Supervised anomaly detection in uncertain pseudoperiodic data streams.

Refining adverse drug reaction signals by incorporating interaction variables identified using emergent pattern mining (2015)
Journal Article
Reps, J. M., Aickelin, U., & Hubbard, R. B. (2016). Refining adverse drug reaction signals by incorporating interaction variables identified using emergent pattern mining. Computers in Biology and Medicine, 69, https://doi.org/10.1016/j.compbiomed.2015.11.014

Purpose: To develop a framework for identifying and incorporating candidate confounding interaction terms into a regularised cox regression analysis to refine adverse drug reaction signals obtained via longitudinal observational data. Methods: We co... Read More about Refining adverse drug reaction signals by incorporating interaction variables identified using emergent pattern mining.

A supervised adverse drug reaction signalling framework imitating Bradford Hill’s causality considerations (2015)
Journal Article
Reps, J. M., Garibaldi, J. M., Aickelin, U., Gibson, J. E., & Hubbard, R. B. (2015). A supervised adverse drug reaction signalling framework imitating Bradford Hill’s causality considerations. Journal of Biomedical Informatics, 56, https://doi.org/10.1016/j.jbi.2015.06.011

Big longitudinal observational medical data potentially hold a wealth of information and have been recognised as potential sources for gaining new drug safety knowledge. Unfortunately there are many complexities and underlying issues when analysing l... Read More about A supervised adverse drug reaction signalling framework imitating Bradford Hill’s causality considerations.

Juxtaposition of System Dynamics and Agent-Based Simulation for a Case Study in Immunosenescence (2015)
Journal Article
Figueredo, G. P., Siebers, P., Aickelin, U., Whitbrook, A., & Garibaldi, J. M. (2015). Juxtaposition of System Dynamics and Agent-Based Simulation for a Case Study in Immunosenescence. PLoS ONE, 10(3), Article e0118359. https://doi.org/10.1371/journal.pone.0118359

Advances in healthcare and in the quality of life significantly increase human life expectancy. With the aging of populations, new un-faced challenges are brought to science. The human body is naturally selected to be well-functioning until the age o... Read More about Juxtaposition of System Dynamics and Agent-Based Simulation for a Case Study in Immunosenescence.

Indebted households profiling: a knowledge discovery from database approach (2015)
Journal Article
Scarpel, R., Ladas, A., & Aickelin, U. (2015). Indebted households profiling: a knowledge discovery from database approach. Annals of Data Science, 2(1), https://doi.org/10.1007/s40745-015-0031-2

A major challenge in consumer credit risk portfolio management is to classify households according to their risk profile. In order to build such risk profiles it is necessary to employ an approach that analyses data systematically in order to detect... Read More about Indebted households profiling: a knowledge discovery from database approach.

Feature selection in detection of adverse drug reactions from the Health Improvement Network (THIN) database (2015)
Journal Article
Liu, Y., & Aickelin, U. (2015). Feature selection in detection of adverse drug reactions from the Health Improvement Network (THIN) database. https://doi.org/10.5815/ijitcs.2015.03.10

Adverse drug reaction (ADR) is widely concerned for public health issue. ADRs are one of most common causes to withdraw some drugs from market. Prescription event monitoring (PEM) is an important approach to detect the adverse drug reactions. The mai... Read More about Feature selection in detection of adverse drug reactions from the Health Improvement Network (THIN) database.

Augmented Neural Networks for modelling consumer indebtness (2014)
Journal Article
Ladas, A., M. Garibaldi, J., Scarpel, R., & Aickelin, U. (2014). Augmented Neural Networks for modelling consumer indebtness. Proceedings of International Joint Conference on Neural Networks, 3086-3093. https://doi.org/10.1109/IJCNN.2014.6889760

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.

Data classification using the Dempster-Shafer method (2014)
Journal Article
Chen, Q., Whitbrook, A., Aickelin, U., & Roadknight, C. (2014). Data classification using the Dempster-Shafer method. Journal of Experimental and Theoretical Artificial Intelligence, https://doi.org/10.1080/0952813X.2014.886301

In this paper, the Dempster-Shafer method is employed as the theoretical basis for creating data classification systems. Testing is carried out using three popular (multiple attribute) benchmark datasets that have two, three and four classes. In each... Read More about Data classification using the Dempster-Shafer method.

Signalling Paediatric Side Effects using an Ensemble of Simple Study Designs (2014)
Journal Article
Reps, J., M. Garibaldi, J., Aickelin, U., Soria, D., E. Gibson, J., & B. Hubbard, R. (2014). Signalling Paediatric Side Effects using an Ensemble of Simple Study Designs. Drug Safety, 37(3), 163-170. https://doi.org/10.1007/s40264-014-0137-z

Background: Children are frequently prescribed medication `o-label', meaning there has not been sucient testing of the medication to determine its safety or eectiveness. The main reason this safety knowledge is lacking is due to ethical restrictions... Read More about Signalling Paediatric Side Effects using an Ensemble of Simple Study Designs.

Comparing stochastic differential equations and agent-based modelling and simulation for early-stage cancer (2014)
Journal Article
Figueredo, G. P., Siebers, P., Owen, M. R., Reps, J., & Aickelin, U. (2014). Comparing stochastic differential equations and agent-based modelling and simulation for early-stage cancer. PLoS ONE, 9(4), Article e95150. https://doi.org/10.1371/journal.pone.0095150

There is great potential to be explored regarding the use of agent-based modelling and simulation as an alternative paradigm to investigate early-stage cancer interactions with the immune system. It does not suffer from some limitations of ordinary d... Read More about Comparing stochastic differential equations and agent-based modelling and simulation for early-stage cancer.

Comparison of algorithms that detect drug side effects using electronic healthcare databases (2013)
Journal Article
Reps, J. M., Garibaldi, J. M., Aickelin, U., Soria, D., Gibson, J. E., & Hubbard, R. B. (2013). Comparison of algorithms that detect drug side effects using electronic healthcare databases. Soft Computing, 17(12), https://doi.org/10.1007/s00500-013-1097-4

The electronic healthcare databases are starting to become more readily available and are thought to have excellent potential for generating adverse drug reaction signals. The Health Improvement Network (THIN) database is an electronic healthcare dat... Read More about Comparison of algorithms that detect drug side effects using electronic healthcare databases.

A Novel Semisupervised Algorithm for Rare Prescription Side Effect Discovery (2013)
Journal Article
Reps, J. M., Garibaldi, J. M., Aickelin, U., Soria, D., Gibson, J. E., & Hubbard, R. B. (2014). A Novel Semisupervised Algorithm for Rare Prescription Side Effect Discovery. IEEE Journal of Biomedical and Health Informatics, 18(2), 537-547. https://doi.org/10.1109/JBHI.2013.2281505

Drugs are frequently prescribed to patients with the aim of improving each patient's medical state, but an unfortunate consequence of most prescription drugs is the occurrence of undesirable side effects. Side effects that occur in more than one in a... Read More about A Novel Semisupervised Algorithm for Rare Prescription Side Effect Discovery.

Investigating mathematical models of immuno-interactions with early-stage cancer under an agent-based modelling perspective (2013)
Journal Article
Figueredo, G. P., Siebers, P., & Aickelin, U. (2013). Investigating mathematical models of immuno-interactions with early-stage cancer under an agent-based modelling perspective. BMC Bioinformatics, 14(Spl. 6), Article S6. https://doi.org/10.1186/1471-2105-14-S6-S6

Many advances in research regarding immuno-interactions with cancer were developed with the help of ordinary differential equation (ODE) models. These models, however, are not effectively capable of representing problems involving individual localisa... Read More about Investigating mathematical models of immuno-interactions with early-stage cancer under an agent-based modelling perspective.

Theoretical formulation and analysis of the deterministic dendritic cell algorithm (2013)
Journal Article
Gu, F., Greensmith, J., & Aickelin, U. (2013). Theoretical formulation and analysis of the deterministic dendritic cell algorithm. BioSystems, 111(2), 127-135. https://doi.org/10.1016/j.biosystems.2013.01.001

As one of the emerging algorithms in the field of artificial immune systems (AIS), the dendritic cell algorithm (DCA) has been successfully applied to a number of challenging real-world problems. However, one criticism is the lack of a formal definit... Read More about Theoretical formulation and analysis of the deterministic dendritic cell algorithm.

Wavelet feature extraction and genetic algorithm for biomarker detection in colorectal cancer data (2013)
Journal Article
Liu, Y., Aickelin, U., Feyereisl, J., & Durrant, L. G. (2013). Wavelet feature extraction and genetic algorithm for biomarker detection in colorectal cancer data. Knowledge-Based Systems, 37, https://doi.org/10.1016/j.knosys.2012.09.011

Biomarkers which predict patient’s survival can play an important role in medical diagnosis and treatment. How to select the significant biomarkers from hundreds of protein markers is a key step in survival analysis. In this paper a novel method is... Read More about Wavelet feature extraction and genetic algorithm for biomarker detection in colorectal cancer data.