Outputs (46)

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..

Integrating selection-based aspect sentiment and preference knowledge for social recommender systems. (2019)
Journal Article
CHEN, Y.Y., WIRATUNGA, N. and LOTHIAN, R. 2020. Integrating selection-based aspect sentiment and preference knowledge for social recommender systems. Online information review [online], 44(2), pages 399-416. Available from: https://doi.org/10.1108/OIR-02-2017-0066

Purpose: Recommender system approaches such as collaborative and content-based filtering rely on user ratings and product descriptions to recommend products. More recently, recommender system research has focussed on exploiting knowledge from user-ge... Read More about Integrating selection-based aspect sentiment and preference knowledge for social recommender systems..

Multi-objective evolutionary design of antibiotic treatments. (2019)
Journal Article
OCHOA, G., CHRISTIE, L.A., BROWNLEE, A.E. and HOYLE, A. 2020. Multi-objective evolutionary design of antibiotic treatments. Artificial intelligence in medicine [online], 102, article number 101759. Available from: https://doi.org/10.1016/j.artmed.2019.101759

Antibiotic resistance is one of the major challenges we face in modern times. Antibiotic use, especially their overuse, is the single most important driver of antibiotic resistance. Efforts have been made to reduce unnecessary drug prescriptions, but... Read More about Multi-objective evolutionary design of antibiotic treatments..

The effects of measurement error and testing frequency on the fitness-fatigue model applied to resistance training: a simulation approach. (2019)
Journal Article
STEPHENS HEMINGWAY, B.H., BURGESS, K.E., ELYAN, E. and SWINTON, P.A. 2020. The effects of measurement error and testing frequency on the fitness-fatigue model applied to resistance training: a simulation approach. International journal of sports science and coaching [online], 15(1), pages 60-71. Available from: https://doi.org/10.1177/1747954119887721

This study investigated the effects of measurement error and testing frequency on prediction accuracy of the standard fitness-fatigue model. A simulation-based approach was used to systematically assess measurement error and frequency inputs commonly... Read More about The effects of measurement error and testing frequency on the fitness-fatigue model applied to resistance training: a simulation approach..

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..

Combining t-distributed stochastic neighbor embedding with convolutional neural networks for hyperspectral image classification. (2019)
Journal Article
GAO, L., GU, D., ZHUANG, L., REN, J., YANG, D. and ZHANG, B. 2020. Combining t-distributed stochastic neighbor embedding with convolutional neural networks for hyperspectral image classification. IEEE geoscience and remote sensing letters [online], 17(8), pages 1368-1372. Available from: https://doi.org/10.1109/LGRS.2019.2945122

Hyperspectral images (HSIs), featured by high spectral resolution over a wide range of electromagnetic spectra, have been widely used to characterize materials with subtle differences in the spectral domain. However, a large number of bands and an in... Read More about Combining t-distributed stochastic neighbor embedding with convolutional neural networks for hyperspectral image classification..

Data stream mining: methods and challenges for handling concept drift. (2019)
Journal Article
WARES, S., ISAACS, J. and ELYAN, E. 2019. Data stream mining: methods and challenges for handling concept drift. SN applied sciences [online], 1(11), article ID 1412. Available from: https://doi.org/10.1007/s42452-019-1433-0

Mining and analysing streaming data is crucial for many applications, and this area of research has gained extensive attention over the past decade. However, there are several inherent problems that continue to challenge the hardware and the state-of... Read More about Data stream mining: methods and challenges for handling concept drift..

Multiscale spatial-spectral convolutional network with image-based framework for hyperspectral imagery classification. (2019)
Journal Article
CUI, X., ZHENG, K., GAO, L., ZHANG, B., YANG, D. and REN, J. 2019. Multiscale spatial-spectral convolutional network with image-based framework for hyperspectral imagery classification. Remote sensing [online], 11(19), article 2220. Available from: https://doi.org/10.3390/rs11192220

Jointly using spatial and spectral information has been widely applied to hyperspectral image (HSI) classification. Especially, convolutional neural networks (CNN) have gained attention in recent years due to their detailed representation of features... Read More about Multiscale spatial-spectral convolutional network with image-based framework for hyperspectral imagery classification..

Neighbourhood-based undersampling approach for handling imbalanced and overlapped data. (2019)
Journal Article
VUTTIPITTAYAMONGKOL, P. and ELYAN, E. 2020. Neighbourhood-based undersampling approach for handling imbalanced and overlapped data. Information sciences [online], 509, pages 47-70. Available from: https://doi.org/10.1016/j.ins.2019.08.062

Class imbalanced datasets are common across different domains including health, security, banking and others. A typical supervised learning algorithm tends to be biased towards the majority class when dealing with imbalanced datasets. The learning ta... Read More about Neighbourhood-based undersampling approach for handling imbalanced and overlapped data..

室内3D点云模型的门窗检测. (2019)
Journal Article
SHEN, L., LI, G., XIAN, C., JIANG, Y. and XIONG, Y. 2019. 室内3D点云模型的门窗检测. Jisuanji fuzhu sheji yu tuxingxue xuebao/Journal of computer-aided design and computer graphics [online], 31(9), pages 1494-1501. Available from: https://doi.org/10.3724/SP.J.1089.2019.17575

This paper proposes a 3D-2D-3D algorithm for doors and windows detection in 3D indoor environment of point cloud data. Firstly, by setting up a virtual camera in the middle of this 3D environment, a set of pictures are taken from different angles by... Read More about 室内3D点云模型的门窗检测..