All Outputs (5)

A weighted ensemble of regression methods for gross error identification problem. (2023)
Conference Proceeding
DOBOS, D., DANG, T., NGUYEN, T.T., MCCALL, J., WILSON, A., CORBETT, H. and STOCKTON, P. 2023. A weighted ensemble of regression methods for gross error identification problem. In Proceedings of the 2023 IEEE (Institute of Electrical and Electronics Engineers) Symposium series on computational intelligence (SSCI 2023), 5-8 December 2023, Mexico City, Mexico. Piscataway: IEEE [online], pages 413-420. Available from: https://doi.org/10.1109/SSCI52147.2023.10371882

In this study, we proposed a new ensemble method to predict the magnitude of gross errors (GEs) on measurement data obtained from the hydrocarbon and stream processing industries. Our proposed model consists of an ensemble of regressors (EoR) obtaine... Read More about A weighted ensemble of regression methods for gross error identification problem..

Ensemble learning based on classifier prediction confidence and comprehensive learning particle swarm optimisation for medical image segmentation. (2022)
Conference Proceeding
DANG, T., NGUYEN, T.T., MCCALL, J. and LIEW, A.W.-C. 2022. Ensemble learning based on classifier prediction confidence and comprehensive learning particle swarm optimisation for medical image segmentation. In Ishibuchi, H., Kwoh, C.-K., Tan, A.-H., Srinivasan, D., Miao, C., Trivedi, A. and Crockett, K. (eds.) Proceedings of the 2022 IEEE Symposium series on computational intelligence (SSCI 2022), 4-7 December 2022, Singapore. Piscataway: IEEE [online], pages 269-276. Available from: https://doi.org/10.1109/SSCI51031.2022.10022114

Segmentation, a process of partitioning an image into multiple segments to locate objects and boundaries, is considered one of the most essential medical imaging process. In recent years, Deep Neural Networks (DNN) have achieved many notable successe... Read More about Ensemble learning based on classifier prediction confidence and comprehensive learning particle swarm optimisation for medical image segmentation..

Ensemble of deep learning models with surrogate-based optimization for medical image segmentation. (2022)
Conference Proceeding
DANG, T., LUONG, A.V., LIEW, A.W.C., MCCALL, J. and NGUYEN, T.T. 2022. Ensemble of deep learning models with surrogate-based optimization for medical image segmentation. In 2022 IEEE (Institute of Electrical and Electronics Engineers) Congress on evolutionary computation (CEC 2022), co-located with 2022 IEEE International joint conferences on neural networks (IJCNN 2022), 2022 IEEE International conference on fuzzy systems (FUZZ-IEEE 2022), 18-23 July 2022, Padua, Italy. Piscataway: IEEE (online), article #1030. Available from: https://doi.org/10.1109/CEC55065.2022.9870389

Deep Neural Networks (DNNs) have created a breakthrough in medical image analysis in recent years. Because clinical applications of automated medical analysis are required to be reliable, robust and accurate, it is necessary to devise effective DNNs... Read More about Ensemble of deep learning models with surrogate-based optimization for medical image segmentation..

Weighted ensemble of deep learning models based on comprehensive learning particle swarm optimization for medical image segmentation. (2021)
Conference Proceeding
DANG, T., NGUYEN, T.T., MORENO-GARCIA, C.F., ELYAN, E. and MCCALL, J. 2021. Weighted ensemble of deep learning models based on comprehensive learning particle swarm optimization for medical image segmentation. In Proceeding of 2021 IEEE (Institute of electrical and electronics engineers) Congress on evolutionary computation (CEC 2021), 28 June - 1 July 2021, Kraków, Poland : [virtual conference]. Piscataway: IEEE [online], pages 744-751. Available from: https://doi.org/10.1109/CEC45853.2021.9504929

In recent years, deep learning has rapidly become a method of choice for segmentation of medical images. Deep neural architectures such as UNet and FPN have achieved high performances on many medical datasets. However, medical image analysis algorith... Read More about Weighted ensemble of deep learning models based on comprehensive learning particle swarm optimization for medical image segmentation..

VEGAS: a variable length-based genetic algorithm for ensemble selection in deep ensemble learning. (2021)
Conference Proceeding
HAN, K., PHAM, T., VU, T.H., DANG, T., MCCALL, J. and NGUYEN, T.T. 2021. VEGAS: a variable length-based genetic algorithm for ensemble selection in deep ensemble learning. In Nguyen, N.T., Chittayasothorn, S., Niyato, D. and Trawiński, B. (eds.) Intelligent information and database systems: proceedings of the 13th Asian conference on intelligent information and database systems 2021 (ACCIIDS 2021), 7-10 April 2021, [virtual conference]. Lecture Notes in Computer Science, 12672. Cham: Springer [online], pages 168–180. Available from: https://doi.org/10.1007/978-3-030-73280-6_14

In this study, we introduce an ensemble selection method for deep ensemble systems called VEGAS. The deep ensemble models include multiple layers of the ensemble of classifiers (EoC). At each layer, we train the EoC and generates training data for th... Read More about VEGAS: a variable length-based genetic algorithm for ensemble selection in deep ensemble learning..