Charles Sturt University
Charles Sturt University

Publications

DSRU Publications Image

Image sourced from https://researchoutput.csu.edu.au/en/organisations/data-science-research-unit/publications/

Some recent quality research outputs are listed below.  A comprehensive list of all DSRU publications can be accessed from here.

  1. Huda, S., Islam, R., Abawajy, J., Yearwood, J., Hassan, M.M., & Fortino, G. (2018). A hybrid-multi filter-wrapper framework to identify run-time behavior for fast malware detection. Future Generation Computer Systems83, 193-207. https://doi.org/10.1016/j.future.2017.12.037
  2. Liu, Y., Liu, A., Liu, X. & Huang, X. (2019). A statistical approach to participant selection in location-based social networks for offline event marketing. Information Sciences 480, 90-108. https://doi.org/10.1016/j.ins.2018.12.028
  3. Rahman, M.G. & Islam, M.Z. (2021). Adaptive Decision Forest: An Incremental Machine Learning Framework. Pattern Recognition, 122, 108345. https://doi.org/10.1016/j.patcog.2021.108345
  4. Rahman, M.A. & Islam, M.Z. (2018). Application of a density based clustering technique on the biomedical datasets. Applied Soft Computing 73, 623-634. https://doi.org/10.1016/j.asoc.2018.09.012
  5. Adnan, M.N., Ip, R.H.L., Bewong, M. & Islam, M.Z. (2021). BDF: A new decision forest algorithm. Information Sciences 569, 687-705. https://doi.org/10.1016/j.ins.2021.05.017
  6. Ip, R.H.L., Ang, L.M., Seng, K.P., Broster, J.C. & Pratley, J.E. (2018). Big data and machine learning for crop protection. Computers and Electronics in Agriculture 151, 367-383. https://doi.org/10.1016/j.compag.2018.06.008
  7. Rahman, M.A., Ang, L.M. & Seng, K.P. (2020). Clustering biomedical and gene expression datasets with kernel density and unique neighborhood set based vein detection. Information Systems 91, 101490. https://doi.org/10.1016/j.is.2020.101490
  8. Fletcher, S. & Islam, M.Z. (2019). Decision tree classification with differential privacy: A survey. ACM Computing Surveys 52, 83, 1-33. https://doi.org/10.1145/3337064
  9. Huda, S., Miah, S., Hassan, M.M., Islam, R., Yearwood, J., Alrubaian, M., & Almogren, A. (2017). Defending unknown attacks on cyber-physical systems by semi-supervised approach and available unlabeled data. Information Sciences 379, 211-228. https://doi.org/10.1016/j.ins.2016.09.041
  10. Bhuiyan, M.S.I., Rahman, A., Kim, G.W., Das, S., & Kim, P.J. (2021). Eco-friendly yield-scaled global warming potential assists to determine the right rate of nitrogen in rice system: A systematic literature review. Environmental Pollution 271, 116386. https://doi.org/10.1016/j.envpol.2020.116386
  11. Yates, D. & Islam, M.Z. (2021). FastForest: Increasing random forest processing speed while maintaining accuracy. Information Sciences 557, 130-152. https://doi.org/10.1016/j.ins.2020.12.067
  12. Alnabulsi, H., Islam, R., & Talukder, M. (2018). GMSA: Gathering multiple signatures approach to defend against code injection attacks. IEEE Access 6, 77829-77840. https://doi.org/10.1109/ACCESS.2018.2884201
  13. Li, J., Xing, Z. & Kabir, M.A. (2021). Leveraging official content and social Context to recommend software documentation. IEEE Transactions on Services Computing 14, 472–486. https://doi.org/10.1109/TSC.2018.2812729
  14. Kabir, M.A., Rahman, S.S., Islam, M.M., Ahmed, S. & Laird, C (2021) Mobile apps for foot measurement in pedorthic practice: Scoping review. JMIR mHealth and uHealth 9, e24202. https://doi.org/10.2196/24202
  15. Yao, S., You, R., Wang, S., Xiong, Y., Huang, X. & Zhu, S. (2021) NetGO 2.0: improving large-scale protein function prediction with massive sequence, text, domain, family and network information. Nucleic Acids Research, 49, W469-W475. https://doi.org/10.1093/nar/gkab398
  16. Ip, R.H.L. & Li, W.K. (2017). On some Matérn covariance functions for spatio-temporal random fields. Statistica Sinica 27, 805-822. https://doi.org/10.5705/ss.202015.0037
  17. Wang, Y., Lin, X., Wu, L., Zhang, W., Zhang, Q. & Huang, X. (2015). Robust subspace clustering for multi-view data by exploiting correlation consensus. IEEE Transactions on Image Processing 24, 3939-3949. https://doi.org/10.1109/TIP.2015.2457339
  18. Rahman, A., Harding, A., Tanton, R. & Liu, S. (2013). Simulating the characteristics of populations at the small area level: New validation techniques for a spatial microsimulation model in Australia. Computational Statistics and Data Analysis 57, 149-165. https://doi.org/10.1016/j.csda.2012.06.018
  19. Siers, M. & Islam, M.Z. (2015). Software defect prediction using a cost sensitive decision forest and voting, and a potential solution to the class imbalance problem. Information Systems 51, 62-71. https://doi.org/10.1016/j.is.2015.02.006
  20. Rahman, A. (2019). Statistics-based data preprocessing methods and machine learning algorithms for big data analysis. International Journal of Artificial Intelligence 17, 44-65. http://www.ceser.in/ceserp/index.php/ijai/article/view/6253
  21. Liang, B. & Zheng, L. (2017). Specificity and latent correlation learning for action recognition using synthetic multi-view data from depth maps. IEEE Transactions on Image Processing 26, 5560-5574. https://doi.org/10.1109/TIP.2017.2740122
  22. Kabir, M.A., Han, J., Hossain, M.A. & Versteeg, S. (2021). SpecMiner: Heuristic-based mining of service behavioral models from interaction traces. Future Generation Computer Systems 117, 59-71. https://doi.org/10.1016/j.future.2020.10.033
  23. Soomro, T.A., Afifi, A.J.., Gao, J., Hellwich, O., Zheng, L. & Paul, M. (2019). Strided fully convolutional neural network for boosting the sensitivity of retinal blood vessels segmentation. Expert Systems with Applications 134, 36-52. https://doi.org/10.1016/j.eswa.2019.05.029
  24. Rahman, M.A., Ang, L.M. & Seng, K.P. (2018). Unique neighborhood set parameter independent density-based clustering with outlier detection, IEEE Access 6, 44707-44717. https://doi.org/10.1109/ACCESS.2018.2857834
  25. Wang, B., Zheng, L., Liu, D.L., Ji, F., Clark, A. & Yu, Q. (2018). Using multi‐model ensembles of CMIP5 global climate models to reproduce observed monthly rainfall and temperature with machine learning methods in Australia. International Journal of Climatology 38, 4891-4902. https://doi.org/10.1002/joc.5705