All Outputs (56)

Input uncertainty sensitivity enhanced non-singleton fuzzy logic controllers for long-term navigation of quadrotor UAVs (2018)
Journal Article
Fu, C., Sarabakha, A., Kayacan, E., Wagner, C., John, R., & Garibaldi, J. M. (2018). Input uncertainty sensitivity enhanced non-singleton fuzzy logic controllers for long-term navigation of quadrotor UAVs. IEEE/ASME Transactions on Mechatronics, 23(2), 725-734. https://doi.org/10.1109/TMECH.2018.2810947

Input uncertainty, e.g., noise on the on-board camera and inertial measurement unit, in vision-based control of unmanned aerial vehicles (UAVs) is an inevitable problem. In order to handle input uncertainties as well as further analyze the interactio... Read More about Input uncertainty sensitivity enhanced non-singleton fuzzy logic controllers for long-term navigation of quadrotor UAVs.

A fast community detection method in bipartite networks by distance dynamics (2017)
Journal Article
Sun, H., Ch'ng, E., Yong, X., Garibaldi, J. M., See, S., & Chen, D. (2018). A fast community detection method in bipartite networks by distance dynamics. Physica A: Statistical Mechanics and its Applications, 496, https://doi.org/10.1016/j.physa.2017.12.099

Many real bipartite networks are found to be divided into two-mode communities. In this paper, we formulate a new two-mode community detection algorithm BiAttractor. It is based on distance dynamics model Attractor proposed by Shao et al. with extens... Read More about A fast community detection method in bipartite networks by distance dynamics.

Positive mood on the day of influenza vaccination predicts vaccine effectiveness: A prospective observational cohort study (2017)
Journal Article
Ayling, K., Fairclough, L., Tighe, P., Todd, I., Halliday, V., Garibaldi, J., …Vedhara, K. (2018). Positive mood on the day of influenza vaccination predicts vaccine effectiveness: A prospective observational cohort study. Brain, Behavior, and Immunity, 67, 314-323. https://doi.org/10.1016/j.bbi.2017.09.008

© 2017 Influenza vaccination is estimated to only be effective in 17–53% of older adults. Multiple patient behaviors and psychological factors have been shown to act as ‘immune modulators’ sufficient to influence vaccination outcomes. However, the re... Read More about Positive mood on the day of influenza vaccination predicts vaccine effectiveness: A prospective observational cohort study.

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.

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.

A Direct Approach for Determining the Switch Points in the Karnik–Mendel Algorithm (2017)
Journal Article
Chen, C., John, R., Twycross, J., & Garibaldi, J. M. (2018). A Direct Approach for Determining the Switch Points in the Karnik–Mendel Algorithm. IEEE Transactions on Fuzzy Systems, 26(2), 1079-1085. https://doi.org/10.1109/tfuzz.2017.2699168

The Karnik-Mendel algorithm is used to compute the centroid of interval type-2 fuzzy sets, determining the switch points needed for the lower and upper bounds of the centroid, through an iterative process. It is commonly acknowledged that there is n... Read More about A Direct Approach for Determining the Switch Points in the Karnik–Mendel Algorithm.

Can machine-learning improve cardiovascular risk prediction using routine clinical data (2017)
Journal Article
Weng, S. F., Reps, J. M., Kai, J., Garibaldi, J. M., & Quereshi, N. (2017). Can machine-learning improve cardiovascular risk prediction using routine clinical data. PLoS ONE, 12(4), Article e0174944. https://doi.org/10.1371/journal.pone.0174944

Background Current approaches to predict cardiovascular risk fail to identify many people who would benefit from preventive treatment, while others receive unnecessary intervention. Machine-learning offers opportunity to improve accuracy by exploiti... Read More about Can machine-learning improve cardiovascular risk prediction using routine clinical data.

A new accuracy measure based on bounded relative error for time series forecasting (2017)
Journal Article
Chen, C., Twycross, J., & Garibaldi, J. M. (2017). A new accuracy measure based on bounded relative error for time series forecasting. PLoS ONE, 12(3), Article e0174202. https://doi.org/10.1371/journal.pone.0174202

Many accuracy measures have been proposed in the past for time series forecasting comparisons. However, many of these measures suffer from one or more issues such as poor resistance to outliers and scale dependence. In this paper, while summarising c... Read More about A new accuracy measure based on bounded relative error for time series forecasting.

Modeling and control of operator functional state in a unified framework of fuzzy inference petri nets (2017)
Journal Article
Zhang, J., Xia, J., Garibaldi, J. M., Groumpos, P. P., & Wang, R. (2017). Modeling and control of operator functional state in a unified framework of fuzzy inference petri nets. Computer Methods and Programs in Biomedicine, 144, https://doi.org/10.1016/j.cmpb.2017.03.016

Background and objective: In human-machine (HM) hybrid control systems, human operator and machine cooperate to achieve the control objectives. To enhance the overall HM system performance, the discrete manual control task-load by the operator must b... Read More about Modeling and control of operator functional state in a unified framework of fuzzy inference petri nets.

On Using Genetic Algorithm for Initialising Semi-supervised Fuzzy c-Means Clustering (2016)
Journal Article
Lai, D. T. C., & Garibaldi, J. M. (2017). On Using Genetic Algorithm for Initialising Semi-supervised Fuzzy c-Means Clustering. Advances in Intelligent Systems and Computing, 532, 3-14. https://doi.org/10.1007/978-3-319-48517-1_1

In a previous work, suitable initialisation techniques were incorporated with semi-supervised Fuzzy c-Means clustering (ssFCM) to improve clustering results on a trial and error basis. In this work, we present a single fully-automatic version of an e... Read More about On Using Genetic Algorithm for Initialising Semi-supervised Fuzzy c-Means Clustering.

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.

KI67 and DLX2 predict increased risk of metastasis formation in prostate cancer: a targeted molecular approach (2016)
Journal Article
Green, W. J., Ball, G., Hulman, G., Johnson, C., Van Schalwyk, G., Ratan, H. L., …Powe, D. G. (in press). KI67 and DLX2 predict increased risk of metastasis formation in prostate cancer: a targeted molecular approach. British Journal of Cancer, https://doi.org/10.1038/bjc.2016.169

Background:There remains a need to identify and validate biomarkers for predicting prostate cancer (CaP) outcomes using robust and routinely available pathology techniques to identify men at most risk of premature death due to prostate cancer. Previo... Read More about KI67 and DLX2 predict increased risk of metastasis formation in prostate cancer: a targeted molecular approach.

Improved Uncertainty Capture for Nonsingleton Fuzzy Systems (2016)
Journal Article
Pourabdollah, A., Wagner, C., Aladi, J. H., & Garibaldi, J. M. (2016). Improved Uncertainty Capture for Nonsingleton Fuzzy Systems. IEEE Transactions on Fuzzy Systems, 24(6), 1513-1524. https://doi.org/10.1109/TFUZZ.2016.2540065

© 2016 IEEE. In nonsingleton fuzzy logic systems (NSFLSs), input uncertainties are modeled with input fuzzy sets in order to capture input uncertainty (e.g., sensor noise). The performance of NSFLSs in handling such uncertainties depends on both the... Read More about Improved Uncertainty Capture for Nonsingleton Fuzzy Systems.

A multi-cycled sequential memetic computing approach for constrained optimisation (2016)
Journal Article
Sun, J., Garibaldi, J. M., Zhang, Y., & Al-Shawabkeh, A. (2016). A multi-cycled sequential memetic computing approach for constrained optimisation. Information Sciences, 340-341, 175-190. https://doi.org/10.1016/j.ins.2016.01.003

In this paper, we propose a multi-cycled sequential memetic computing structure for constrained optimisation. The structure is composed of multiple evolutionary cycles. At each cycle, an evolutionary algorithm is considered as an operator, and connec... Read More about A multi-cycled sequential memetic computing approach for constrained optimisation.

Automatic detection of protected health information from clinic narratives (2015)
Journal Article
Yang, H., & Garibaldi, J. M. (2015). Automatic detection of protected health information from clinic narratives. Journal of Biomedical Informatics, 58(Suppl.), S30-S38. https://doi.org/10.1016/j.jbi.2015.06.015

This paper presents a natural language processing (NLP) system that was designed to participate in the 2014 i2b2 de-identification challenge. The challenge task aims to identify and classify seven main Protected Health Information (PHI) categories an... Read More about Automatic detection of protected health information from clinic narratives.

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.

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.

From Interval-Valued Data to General Type-2 Fuzzy Sets (2014)
Journal Article
Wagner, C., Miller, S., Garibaldi, J. M., Anderson, D. T., & Havens, T. C. (2015). From Interval-Valued Data to General Type-2 Fuzzy Sets. IEEE Transactions on Fuzzy Systems, 23(2), 248-269. https://doi.org/10.1109/tfuzz.2014.2310734

In this paper, a new approach is presented to model interval-based data using fuzzy sets (FSs). Specifically, we show how both crisp and uncertain intervals (where there is uncertainty about the endpoints of intervals) collected from individual or mu... Read More about From Interval-Valued Data to General Type-2 Fuzzy Sets.