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Outputs (13)

Two-layer ensemble of deep learning models for medical image segmentation. (2024)
Journal Article
DANG, T., NGUYEN, T.T., MCCALL, J., ELYAN, E. and MORENO-GARCÍA, C.F. 2024. Two-layer ensemble of deep learning models for medical image segmentation. Cognitive computation [online], In Press. Available from: https://doi.org/10.1007/s12559-024-10257-5

One of the most important areas in medical image analysis is segmentation, in which raw image data is partitioned into structured and meaningful regions to gain further insights. By using Deep Neural Networks (DNN), AI-based automated segmentation al... Read More about Two-layer ensemble of deep learning models for medical image segmentation..

DEFEG: deep ensemble with weighted feature generation. (2023)
Journal Article
LUONG, A.V., NGUYEN, T.T., HAN, K., VU, T.H., MCCALL, J. and LIEW, A.W.-C. 2023. DEFEG: deep ensemble with weighted feature generation. Knowledge-based systems [online], 275, article 110691. Available from: https://doi.org/10.1016/j.knosys.2023.110691

With the significant breakthrough of Deep Neural Networks in recent years, multi-layer architecture has influenced other sub-fields of machine learning including ensemble learning. In 2017, Zhou and Feng introduced a deep random forest called gcFores... Read More about DEFEG: deep ensemble with weighted feature generation..

On the elusivity of dynamic optimisation problems. (2023)
Journal Article
ALZA, J., BARTLETT, M., CEBERIO, J. and MCCALL, J. 2023. On the elusivity of dynamic optimisation problems. Swarm and evolutionary computation [online], 78, article 101289. Available from: https://doi.org/10.1016/j.swevo.2023.101289

The field of dynamic optimisation continuously designs and compares algorithms with adaptation abilities that deal with changing problems during their search process. However, restarting the search algorithm after a detected change is sometimes a bet... Read More about On the elusivity of dynamic optimisation problems..

Unsupervised change detection in hyperspectral images using principal components space data clustering. (2022)
Journal Article
LI, Y., REN, J., YAN, Y., LIU, Q., PETROVSKI, A. and MCCALL, J. 2022. Unsupervised change detection in hyperspectral images using principal components space data clustering. Journal of physics: conference series [online], 2278: proceedings of the 6th International conference on machine vision and information technology (CMVIT 2022), 25 February 2022, [virtual event], article number 012021. Available from: https://doi.org/10.1088/1742-6596/2278/1/012021

Change detection of hyperspectral images is a very important subject in the field of remote sensing application. Due to the large number of bands and the high correlation between adjacent bands in the hyperspectral image cube, information redundancy... Read More about Unsupervised change detection in hyperspectral images using principal components space data clustering..

Programming heterogeneous parallel machines using refactoring and Monte–Carlo tree search. (2020)
Journal Article
BROWN, C. JANJIC, V., GOLI, M. and MCCALL, J. 2020. Programming heterogeneous parallel machines using refactoring and Monte–Carlo tree search. International journal of parallel programming [online], 48(4): high level parallel programming, pages 583-602. Available from: https://doi.org/10.1007/s10766-020-00665-z

This paper presents a new technique for introducing and tuning parallelism for heterogeneous shared-memory systems (comprising a mixture of CPUs and GPUs), using a combination of algorithmic skeletons (such as farms and pipelines), Monte–Carlo tree s... Read More about Programming heterogeneous parallel machines using refactoring and Monte–Carlo tree search..

Evolving interval-based representation for multiple classifier fusion. (2020)
Journal Article
NGUYEN, T.T., DANG,M.T., BAGHEL, V.A., LUONG, A.V., MCCALL, J. and LIEW, A.W.-C. 2020 Evolving interval-based representation for multiple classifier fusion. Knowledge-based systems [online], 201-202, article ID 106034. Available from: https://doi.org/10.1016/j.knosys.2020.106034

Designing an ensemble of classifiers is one of the popular research topics in machine learning since it can give better results than using each constituent member. Furthermore, the performance of ensemble can be improved using selection or adaptation... Read More about Evolving interval-based representation for multiple classifier fusion..

Deep heterogeneous ensemble. (2019)
Journal Article
NGUYEN, T.T., DANG, M.T., PHAM, T.D., DAO, L.P., LUONG, A.V., MCCALL, J. and LIEW, A.W.C. 2019. Deep heterogeneous ensemble. Australian journal of intelligent information processing systems [online], 16(1): special issue on neural information processing: proceedings of the 26th International conference on neural information processing (ICONIP 2019), 12-15 December 2019, Sydney, Australia, pages 1-9. Available from: http://ajiips.com.au/papers/V16.1/v16n1_5-13.pdf

In recent years, deep neural networks (DNNs) have emerged as a powerful technique in many areas of machine learning. Although DNNs have achieved great breakthrough in processing images, video, audio and text, it also has some limitations... Read More about Deep heterogeneous ensemble..

Ensemble selection based on classifier prediction confidence. (2019)
Journal Article
NGUYEN, T.T., LUONG, A.V., DANG, M.T., LIEW, A.W.-C. and MCCALL, J. 2020. Ensemble selection based on classifier prediction confidence. Pattern recognition [online], 100, article ID 107104. Available from: https://doi.org/10.1016/j.patcog.2019.107104

Ensemble selection is one of the most studied topics in ensemble learning because a selected subset of base classifiers may perform better than the whole ensemble system. In recent years, a great many ensemble selection methods have been introduced.... Read More about Ensemble selection based on classifier prediction confidence..

Multi-label classification via incremental clustering on an evolving data stream. (2019)
Journal Article
NGUYEN, T.T., DANG, M.T., LUONG, A.V., LIEW, A. W.-C., LIANG, T. and MCCALL, J. 2019. Multi-label classification via incremental clustering on an evolving data stream. Pattern recognition [online], 95, pages 96-113. Available from: https://doi.org/10.1016/j.patcog.2019.06.001

With the advancement of storage and processing technology, an enormous amount of data is collected on a daily basis in many applications. Nowadays, advanced data analytics have been used to mine the collected data for useful information and make pred... Read More about Multi-label classification via incremental clustering on an evolving data stream..

Truck and trailer scheduling in a real world, dynamic and heterogeneous context. (2016)
Journal Article
REGNIER-COUDERT, O., MCCALL, J., AYODELE, M. and ANDERSON, S. 2016. Truck and trailer scheduling in a real world, dynamic and heterogeneous context. Transportation research, part E: logistics and transportation review [online], 93, pages 389-408. Available from: https://doi.org/10.1016/j.tre.2016.06.010

We present a new variant of the Vehicle Routing Problem based on a real industrial scenario. This VRP is dynamic and heavily constrained and uses time-windows, a heterogeneous vehicle fleet and multiple types of job. A constructive solver is develope... Read More about Truck and trailer scheduling in a real world, dynamic and heterogeneous context..