Skip to main content

Research Repository

Advanced Search

All Outputs (32)

A pattern-based algorithm with fuzzy logic bin selector for online bin packing problem (2024)
Journal Article
Lin, B., Li, J., Cui, T., Jin, H., Bai, R., Qu, R., & Garibaldi, J. (2024). A pattern-based algorithm with fuzzy logic bin selector for online bin packing problem. Expert Systems with Applications, 249, Article 123515. https://doi.org/10.1016/j.eswa.2024.123515

The online bin packing problem is a well-known optimization challenge that finds application in a wide range of real-world scenarios. In the paper, we propose a novel algorithm called FuzzyPatternPack(FPP), which leverages fuzzy inference and pattern... Read More about A pattern-based algorithm with fuzzy logic bin selector for online bin packing problem.

LMISA: A Lightweight Multi-modality Image Segmentation Network via Domain Adaptation using Gradient Magnitude and Shape Constraint (2022)
Journal Article
Jafari, M., Francis, S., Garibaldi, J. M., & Chen, X. (2022). LMISA: A Lightweight Multi-modality Image Segmentation Network via Domain Adaptation using Gradient Magnitude and Shape Constraint. Medical Image Analysis, 81, Article 102536. https://doi.org/10.1016/j.media.2022.102536

In medical image segmentation, supervised machine learning models trained using one image modality (e.g. computed tomography (CT)) are often prone to failure when applied to another image modality (e.g. magnetic resonance imaging (MRI)) even for the... Read More about LMISA: A Lightweight Multi-modality Image Segmentation Network via Domain Adaptation using Gradient Magnitude and Shape Constraint.

Lessons learned from the COVID-19 pandemic about sample access for research in the UK (2022)
Journal Article
Mai Sims, J., Lawrence, E., Glazer, K., Gander, A., Fuller, B., Garibaldi, J., …Quinlan, P. R. (2022). Lessons learned from the COVID-19 pandemic about sample access for research in the UK. BMJ Open, 12(4), Article e047309. https://doi.org/10.1136/bmjopen-2020-047309

Objective Annotated clinical samples taken from patients are a foundation of translational medical research and give mechanistic insight into drug trials. Prior research by the Tissue Directory and Coordination Centre (TDCC) indicated that researcher... Read More about Lessons learned from the COVID-19 pandemic about sample access for research in the UK.

Machine learning can predict disease manifestations and outcomes in lymphangioleiomyomatosis (2020)
Journal Article
Chernbumroong, S., Johnson, J., Gupta, N., Miller, S., Mccormack, F. X., Garibaldi, J. M., & Johnson, S. R. (2021). Machine learning can predict disease manifestations and outcomes in lymphangioleiomyomatosis. European Respiratory Journal, 57(6), Article 2003036. https://doi.org/10.1183/13993003.03036-2020

Background: Lymphangioleiomyomatosis (LAM) is a rare multisystem disease with variable clinical manifestations and differing rates of progression that make management decisions and giving prognostic advice difficult. We used machine learning to ident... Read More about Machine learning can predict disease manifestations and outcomes in lymphangioleiomyomatosis.

A Fast Inference and Type-Reduction Process for Constrained Interval Type-2 Fuzzy Systems (2020)
Journal Article
D'Alterio, P., Garibaldi, J. M., John, R. I., & Wagner, C. (2021). A Fast Inference and Type-Reduction Process for Constrained Interval Type-2 Fuzzy Systems. IEEE Transactions on Fuzzy Systems, 29(11), 3323-3333. https://doi.org/10.1109/TFUZZ.2020.3018379

Constrained interval type-2 (CIT2) fuzzy sets have been introduced to preserve interpretability when moving from type-1 to interval type-2 (IT2) membership functions. Although they can be used to produce type-2 fuzzy systems with enhanced explainabil... Read More about A Fast Inference and Type-Reduction Process for Constrained Interval Type-2 Fuzzy Systems.

End-to-End Fovea Localisation in Colour Fundus Images with a Hierarchical Deep Regression Network (2020)
Journal Article
Xie, R., Liu, J., Cao, R., Qiu, C. S., Duan, J., Garibaldi, J., & Qiu, G. (2020). End-to-End Fovea Localisation in Colour Fundus Images with a Hierarchical Deep Regression Network. IEEE Transactions on Medical Imaging, 40(1), 116-128. https://doi.org/10.1109/TMI.2020.3023254

Accurately locating the fovea is a prerequisite for developing computer aided diagnosis (CAD) of retinal diseases. In colour fundus images of the retina, the fovea is a fuzzy region lacking prominent visual features and this makes it difficult to dir... Read More about End-to-End Fovea Localisation in Colour Fundus Images with a Hierarchical Deep Regression Network.

A Comprehensive Study of the Efficiency of Type-Reduction Algorithms (2020)
Journal Article
Chen, C., Wu, D., Garibaldi, J. M., John, R. I., Twycross, J., & Mendel, J. M. (2021). A Comprehensive Study of the Efficiency of Type-Reduction Algorithms. IEEE Transactions on Fuzzy Systems, 29(6), 1556 -1566. https://doi.org/10.1109/tfuzz.2020.2981002

Improving the efficiency of type-reduction algorithms continues to attract research interest. Recently, there have been some new type-reduction approaches claiming that they are more efficient than the well-known algorithms such as the enhanced Karni... Read More about A Comprehensive Study of the Efficiency of Type-Reduction Algorithms.

Constrained Interval Type-2 Fuzzy Sets (2020)
Journal Article
Dalterio, P., Garibaldi, J. M., John, R., & Pourabdollah, A. (2021). Constrained Interval Type-2 Fuzzy Sets. IEEE Transactions on Fuzzy Systems, 29(5), 1212-1225. https://doi.org/10.1109/tfuzz.2020.2970911

In many contexts, type-2 fuzzy sets are obtained from a type-1 fuzzy set to which we wish to add uncertainty. However, in the current type-2 representation there is no restriction on the shape of the footprint of uncertainty and the embedded sets tha... Read More about Constrained Interval Type-2 Fuzzy Sets.

Towards a Framework for Capturing Interpretability of Hierarchical Fuzzy Systems - A Participatory Design Approach (2020)
Journal Article
Soria, D., Razak, T. R., Garibaldi, J. M., Pourabdollah, A., & Wagner, C. (2021). Towards a Framework for Capturing Interpretability of Hierarchical Fuzzy Systems - A Participatory Design Approach. IEEE Transactions on Fuzzy Systems, 29(5), 1160-1172. https://doi.org/10.1109/tfuzz.2020.2969901

Hierarchical fuzzy systems (HFSs) have been shown to have the potential to improve the interpretability of fuzzy logic systems (FLSs). However, challenges remain, such as: "How can we measure their interpretability?", "How can we make an informed ass... Read More about Towards a Framework for Capturing Interpretability of Hierarchical Fuzzy Systems - A Participatory Design Approach.

Deep Fuzzy Tree for Large-Scale Hierarchical Visual Classification (2019)
Journal Article
Wang, Y., Hu, Q., Zhu, P., Li, L., Lu, B., Garibaldi, J. M., & Li, X. (2020). Deep Fuzzy Tree for Large-Scale Hierarchical Visual Classification. IEEE Transactions on Fuzzy Systems, 28(7), 1395-1406. https://doi.org/10.1109/tfuzz.2019.2936801

Deep learning models often use a flat softmax layer to classify samples after feature extraction in visual classification tasks. However, it is hard to make a single decision of finding the true label from massive classes. In this scenario, hierarchi... Read More about Deep Fuzzy Tree for Large-Scale Hierarchical Visual Classification.

ADONiS - Adaptive Online Non-Singleton Fuzzy Logic Systems (2019)
Journal Article
Pekaslan, D., Wagner, C., & Garibaldi, J. M. (2020). ADONiS - Adaptive Online Non-Singleton Fuzzy Logic Systems. IEEE Transactions on Fuzzy Systems, 28(10), 2302-2312. https://doi.org/10.1109/tfuzz.2019.2933787

Non-Singleton Fuzzy Logic Systems (NSFLSs) have the potential to capture and handle input noise within the design of input fuzzy sets. In this paper, we propose an online learning method which utilises a sequence of observations to continuously updat... Read More about ADONiS - Adaptive Online Non-Singleton Fuzzy Logic Systems.

An end-to-end deep learning histochemical scoring system for breast cancer TMA (2018)
Journal Article
Liu, J., Xu, B., Zheng, C., Gong, Y., Garibaldi, J., Soria, D., …Qiu, G. (2019). An end-to-end deep learning histochemical scoring system for breast cancer TMA. IEEE Transactions on Medical Imaging, 38(2), 617-628. https://doi.org/10.1109/TMI.2018.2868333

One of the methods for stratifying different molecular classes of breast cancer is the Nottingham prognostic index plus, which uses breast cancer relevant biomarkers to stain tumor tissues prepared on tissue microarray (TMA). To determine the molecul... Read More about An end-to-end deep learning histochemical scoring system for breast cancer TMA.

A comment on "A direct approach for determining the switch points in the Karnik-Mendel algorithm" (2018)
Journal Article
Chen, C., Wu, D., Garibaldi, J. M., John, R., Twycross, J., & Mendel, J. M. (2018). A comment on "A direct approach for determining the switch points in the Karnik-Mendel algorithm". IEEE Transactions on Fuzzy Systems, 26(6), 3905-3907. https://doi.org/10.1109/tfuzz.2018.2865134

This letter is a supplement to the previous paper “A Direct Approach for Determining the Switch Points in the Karnik-Mendel Algorithm”. In the previous paper, the enhanced iterative algorithm with stop condition (EIASC) was shown to be the most ineff... Read More about A comment on "A direct approach for determining the switch points in the Karnik-Mendel algorithm".

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.

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 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.

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.