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. A. Ahmmed, M. Murshed, M. Paul, and D. Taubman (2021), “A Commonality Modeling Framework for Enhanced Video Coding Leveraging on the Cuboidal Partitioning Based Representation of Frames,” IEEE Transactions on Multimedia, [CORE A*, SJR and JCR Q1]
  2. F.  Afsana, M. A. Kabir, N. Hassan, and M. Paul (2021), “Automatically assessing quality of online health articles,” IEEE Journal of Biomedical and Health Informatics, doi: 10.1109/JBHI.2020.3032479, [CORE A*, JCR Q1]
  3. M. R. Islam and M Paul (2021), “Improved Video Rain Streaks Removal by Combining Data-Driven and Feature-Based Models,” Sensors, [JCR and SJR Q1]
  4. Al-Saggaf, Y. & O'Donnell, S. B. (2019). Phubbing: Perceptions, reasons behind, predictors, and impacts. Human Behavior and Emerging Technologies1(2), 132-140. https://doi.org/10.1002/hbe2.137
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. 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
  11. 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
  12. 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
  13. Al-Saggaf, Y. & Ceric, A. (2017). Bullying in the Australian ICT workplace: the views of Australian ICT professionals. Australasian Journal of Information Systems, 21. DOI: http://dx.doi.org/10.3127/ajis.v21i0.1322
  14. 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
  15. Al-Saggaf, Y. & Nielsen, S. (2014). Self-disclosure on Facebook among female users and its relationship to feelings of loneliness. Computers in Human Behavior, 36, 460-468. http://dx.doi.org/10.1016/j.chb.2014.04.014
  16. 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
  17. Camtepe, Seyit, Jarek Duda, Arash Mahboubi, Paweł Morawiecki, Surya Nepal, Marcin Pawłowski, and Josef Pieprzyk. "Compcrypt–lightweight ANS-based compression and encryption." IEEE Transactions on Information Forensics and Security 16 (2021): 3859-3873
  18. 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
  19. 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
  20. 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
  21. 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
  22. 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
  23. 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
  24. 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
  25. 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
  26. 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
  27. 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
  28. 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
  29. 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
  30. 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
  31. 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
  32. 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