All Outputs (16)

A fast community detection method in bipartite networks by distance dynamics (2017)
Journal Article
Sun, H.-L., Ch'ng, E., Yong, X., Garibaldi, J. M., See, S., & Chen, D.-B. (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.

Visual landmark sequence-based indoor localization (2017)
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.

Determining firing strengths through a novel similarity measure to enhance uncertainty handling in non-singleton fuzzy logic systems (2017)
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.

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., Royal, S., Hamed, A., Buchanan, H., & 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.

Vehicle incident hot spots identification: An approach for big data (2017)
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.

An improved game-theoretic approach to uncover overlapping communities (2017)
Journal Article
Sun, H.-L., Ch'ng, E., Yong, X., Garibaldi, J. M., See, S., & Chen, D.-B. (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., McDermott, E. M., Drewe, E., Powell, R. J., Bainbridge, S., Hamed, M., Crouch, S., Garibaldi, J., St-Gallay, S., Fairclough, L. C., & 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.

Exploring the use of type-2 fuzzy sets in multi-criteria decision making based on TOPSIS (2017)
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.

Type-1 and interval type-2 ANFIS: a comparison (2017)
Presentation / Conference Contribution
Chen, C., John, R., Twycross, J., & Garibaldi, J. M. Type-1 and interval type-2 ANFIS: a comparison. Presented at 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2017)

In a previous paper, we proposed an extended ANFIS architecture and showed that interval type-2 ANFIS produced larger errors than type-1 ANFIS on the well-known IRIS classification problem. In this paper, more experiments on both synthetic and real-w... Read More about Type-1 and interval type-2 ANFIS: a comparison.

A new dynamic approach for non-singleton fuzzification in noisy time-series prediction (2017)
Presentation / Conference Contribution
Pourabdollah, A., John, R., & Garibaldi, J. M. A new dynamic approach for non-singleton fuzzification in noisy time-series prediction. Presented at 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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.

Similarity-based non-singleton fuzzy logic control for improved performance in UAVs (2017)
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.

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.-H., Xia, J.-J., Garibaldi, J. M., Groumpos, P. P., & Wang, R.-B. (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.