Faculty of Science and Technology

Dr Sunil Aryal - Research

Current research projects

  • Data-dependent (dis)similarity measures
  • Learning from small subsamples of data
  • Random and Ensemble-based data mining methods

Research publications/conferences

Book chapter:

  1. Neupane,   A., Soar, J., Vaidya, K., and Aryal,   S. 2017. Application of E-Government Principles in Anti-Corruption   Framework. In Digital   Governance and E-Government Principles Applied to Public Procurement, eds Rajesh Kumar Shakya, pp. 56-74, , IGI Global,   Hershey, Pennsylvania USA
  2. Neupane,   A., Soar, J., Vaidya, K., and Aryal,   S. 2014. The potential of ICT tools to promote public participation in   fighting corruption. In Human Rights   and the Impact of ICT in the Public Sphere: Participation, Democracy, and Political   Autonomy, eds Christina M. Akrivopoulou and N. Garipidis, pp.175-191, IGI   Global, Hershey, Pennsylvania USA

Journal articles:

  1. Aryal, S., Ting, K. M., Washio, T.   and Haffari, G. 2017. Data-dependent dissimilarity measure: an effective   alternative to geometric distance measures. Knowledge and Information Systems, vol. 53, issue 2, Springer, pp.479-506
  2. Ting,   K.M., Washio, T., Wells, J.R. and Aryal,   S. 2017. Defying the gravity of learning   curve: a characteristic of nearest neighbour anomaly detectors. Machine Learning,  vol. 106, issue 1, Springer, pp.55-91
  3. Aryal, S. and Ting, K.M. 2016. A   generic ensemble approach to estimate multi-dimensional likelihood in   Bayesian classifier learning. Computational Intelligence, vol. 32, issue 3, Wiley, pp.458-479
  4. Ting,   K.M., Washio, T., Wells, J.R., Liu, F.T. and Aryal, S. 2013. DEMass: A new density estimator for big data. Knowledge   and Information Systems, vol. 35, issue 3, Springer UK, United Kingdom,   pp. 493-524

Conference papers:

  1. Aryal, S.,   Ting, K. M. and Haffari, G. 2016. Revisiting Attribute Independence   Assumption in Probabilistic Unsupervised Anomaly Detection. In Proceedings of   the 2016 Pacific-Asia Workshop on Intelligence and Security Informatics   (PAISI), pp.73-86
  2. Aryal,   S., Ting, K. M., Haffari, G. and Washio, T. 2015. Beyond tf-idf and   cosine distance in documents dissimilarity measure. In Proceedings of the 11th Conference of   Asia Information Retrieval Societies (AIRS), pp. 400-406
  3. Aryal,   S., Ting, K. M., Haffari, G. and Washio, T. 2014. Mp- dissimilarity:   A data dependent dissimilarity measure. In Proceedings of the 2014 IEEE International Conference on Data Mining   (ICDM), pp. 707-711
  4. Aryal,   S., Ting, K.M., Wells, J.R. and Washio, T. 2014. Improving iForest   with Relative Mass. In Proceedings of the 18th Pacific-Asia   Conference on Knowledge Discovery and Data Mining (PAKDD), pp. 510-521
  5. Aryal,   S. and Ting, K.M. 2013. MassBayes: a new generative classifier with   multi-dimensional likelihood estimation. In Proceedings of the 17th Pacific-Asia   Conference on Knowledge Discovery and Data Mining (PAKDD), pp. 136-148
  6. Schietgat, L., Aryal, S. and Ramon, J. 2010. Predicting protein function with   the relative backbone position kernel. In Proceedings of the 9th European on Computational Biology (ECCB), pp. 39-39 (extended abstract)

Professional memberships and associations

  • Australian Computer Society
  • IEEE Computer Society
  • IEEE Young Professionals

Potential honours and PhD projects

  • Simple probabilistic anomaly detection
  • Application of data-dependent dissimilarity measures in different data mining tasks
  • Inter-document similarity measurement
  • Effect of data size in learning (with Prof. Kai Ming Ting)

Internal/external web links