Publications

Journal articles

• A. A. Baniya, T-K. Lee, P. W. Eklund and S. Aryal, 2023, Omnidirectional video super-resolution using deep learning, IEEE Transactions on Multimedia, (Accepted on 09 April 2023)
• D. Samariya, J. Ma, S. Aryal and X. Zhao, 2023, Detection and explanation of anomalies in healthcare data, Health Information Science and Systems, 11, Art. No. 20, doi:10.1007/s13755-023-00221-2
• Z. Rasool*, S. Aryal*, M. R. Bouadjenek and R. Dazeley, 2023, Overcoming Weaknesses of Density Peak Clustering Using a Data-dependent Similarity Measure, Pattern Recognition, 137, Art. No. 109287, doi:10.1016/j.patcog.2022.109287 * Contributed equally
• R. K. Halder, M. N. Uddin, M. A. Uddin, S. Aryal, M. A. Islam, F. Hossain, N. Jahan, A. Khraisat and A. Alazab, 2023, A Grid Search-Based Multilayer Dynamic Ensemble System to Identify DNA N4-Methylcytosine Using Deep Learning Approach, Genes, 14(3), Art. No. 582, doi:10.3390/genes14030582
• T. T. Nguyen, M. Abdelrazek, D. T. Nguyen, S. Aryal, D. T. Nguyen, S. Reddy, Q. V. H. Nguyen, A. Khatami, T. T. Nguyen, E. B. Hsu and S. Yang, 2022, Origin of novel coronavirus causing COVID-19: A computational biology study using artificial intelligence, doi:10.1016/j.mlwa.2022.100328
• K.C. Santosh, N. Rasmussen, M. Mamun, and S. Aryal, 2022, A systematic review on cough sound analysis for Covid-19 diagnosis and screening: is my cough sound COVID-19?, PeerJ Computer Science, 8:e958 doi:10.7717/peerj-cs.958
• J. Aryal, C. Sitaula and S. Aryal, 2022, NDVI Threshold-Based Urban Green Space Mapping from Sentinel-2A at the Local Governmental Area (LGA) Level of Victoria, Australia. Land, 11(3), 351, doi:10.3390/land11030351
• C. Sitaula, T. B. Shahi, S. Aryal and F. Marzbanrad, 2021, Fusion of multi-scale bag of deep visual words features of chest X-ray images to detect COVID-19 infection. Scientific Report , 11, Article No. 23914, doi:10.1038/s41598-021-03287-8
• C. Sitaula, S. Aryal, Y. Xiang, A. Basnet and X. Lu, 2021, Content and context features for scene image representation. Knowledge-Based Systems, doi:10.1016/j.knosys.2021.107470
• X. Song, S. Aryal, K. Ting, Z. Liu and B. He, 2021, Spectral-Spatial Anomaly Detection of Hyperspectral Data Based on Improved Isolation Forest. EEE Transactions on Geoscience and Remote Sensing, doi:10.1109/tgrs.2021.3104998
• C. Sitaula, Y. Xiang, S. Aryal and X. Lu, 2021, Scene image representation by foreground, background and hybrid features. Expert Systems with Applications, 182 pp 115285
• R. Dazeley, P. Vamplew, C. Foale, C. Young, S. Aryal and F. Cruz, 2021, Levels of Explainable Artificial Intelligence for Human-Aligned Conversational Explanations, Artificial Intelligence, 299, pp 103525 doi:j.artint.2021.103525
• C. Sitaula and S. Aryal, 2021, New Bag of Deep Visual Words based Features to Classify Chest X-ray Images for COVID-19 diagnosis, Health Information Science and Systems, (Accepted on 16 April 2021)
• C. Sitaula, A. Basnet and S. Aryal, 2021, Vector representation based on a supervised codebook for Nepali documents classification, PeerJ Computer Science, 7:e412 doi: 10.7717/peerj-cs.412
• S. Aryal, KC Santosh and R. Dazeley, 2021, usfAD: A robust anomaly detector based on unsupervised stochastic forest, International journal of machine learning and cybernetics, 12 pp 1137–1150
• C. Sitaula and S. Aryal, 2020, Fusion of whole and part features for the classification of histopathological image of breast tissue, Health Information Science and Systems, 8 (Article#38), doi: 10.1007/s13755-020-00131-7
• J. R. Wells, S. Aryal and K. M. Ting, 2020, Simple supervised dissimilarity measure: Bolstering iForest-induced similarity with class information without learning, Knowledge and Information Systems, 62(8) pp 3203-3216
• S. Aryal, K. M. Ting, T. Washio and G. Haffari, 2020, A comparative study of data-dependent approaches without learning in measuring similarities of data objects, Data Mining and Knowledge Discovery 34(1) pp 124-162
• C. Sitaula, Y. Xiang, Y. Zhang, X. Lu, S. Aryal, 2019, Indoor Image Representation by High-Level Semantic Features. IEEE Access 7 pp 84967-84979
• S. Aryal, K. M. Ting, T. Washio and G. Haffari, 2017, Data dependent dissimilarity measure: An effective alternative to geometric distance measures, Knowledge and Information Systems 53(2) pp 479–506
• K. M. Ting, T. Washio, J. R. Wells and S. Aryal, 2017, Defying the gravity of learning curve: a characteristic of nearest neighbour anomaly detectors, Machine Learning 106(1) pp 55–91
• S. Aryal and K. M. Ting, 2016, A generic ensemble approach to estimate multi-dimensional likelihood in Bayesian classifier learning, Computational Intelligence 32(3) pp 458-479
• K. M. Ting, T. Washio, J. R. Wells, F. T. Liu and S. Aryal, 2013, DEMass: A new density estimator for big data, Knowledge and Information Systems 35(3) pp 493-524

Conference & workshop papers

• V. V. Malgi*, S. Aryal*, Z. Rasool and D. Tay, 2023, Data-dependent and scale-invariant kernel for Support Vector Machine classification, Accepted to be published In Proceedings of the 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2021) (Accepted on 21 Feb 2023) *Contributed equally
• Z. Imran, E. Grooby, V. Malgi, C. Situala, S. Aryal and F. Marzbanrad, 2022, A Fusion of Handcrafted Feature-Based and Deep Learning Classifiers for Heart Murmur Detection, In Proceedings of the 49th Conference on Computing in Cardiology (CinC 2022) doi:10.22489/CinC.2022.310 (Third place winner in the outcomes category of the George B. Moody PhysioNet 2022 Challenge)
• D. Samariya, J. Ma and S. Aryal, 2022, sGrid++ : Revising Simple Grid based Density Estimator for Mining Outlying Aspect, Accepted to be published In Proceedings of the International Conference on Web Information Systems Engineering (WISE 2022), pp. 194-208
• A. A. Baniya, T. K. Lee, P. Eklund, and S. Aryal, 2022, STIFS: Spatio-Temporal Input Frame Selection for Learning-based Video Super-Resolution Models, In Proceedings of the 19th International Conference on Signal Processing and Multimedia Applications (SIGMAP 2022) pp. 48-85
• S. Aryal and J. R. Wells, 2021, Ensemble of Local Decision Trees for Anomaly Detection in Mixed Data, In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD 2021) pp. 687-702
• S. Aryal, A. A. Baniya, I. Razzak and K.C. Santosh, 2021, SPAD+: An Improved Probabilistic Anomaly Detector based on One-dimensional Histograms, In Proceedings of the International Joint Conference on Neural Networks (IJCNN 2021) pp. 1-7
• D. Samariya, S. Aryal, K. M. Ting and J. Ma, 2020, A new effective and efficient measure for outlying aspect mining, In Proceedings of the 21st International Conference on Web Information Systems Engineering (WISE 2020) pp. 463-474
• C. Sitaula, Y. Xiang, A. Basnet, S. Aryal and X. Lu, 2020, HDF: Hybrid Deep Features for Scene Image Representation, In Proceedings of the International Joint Conference on Neural Networks (IJCNN 2020) pp. 1-8
• C. Sitaula, Y. Xiang, A. Basnet, S. Aryal and X. Lu, 2019, Tag-Based Semantic Features for Scene Image Classification, In Proceedings of the International Conference on Neural Information Processing (ICONIP) pp. 90-102.
• A. A. Baniya, S. Aryal and K. C. Santosh, 2019, A novel data pre-processing technique robust to units and scales of measurement, In Proceedings of the International Conference on Neural Information Processing (ICONIP) as a special issue of the Australian Journal of Intelligent Information Processing Systems 16(3) pp. 1-8.
• C. Sitaula, Y. Xiang, S. Aryal and X. Lu, 2019, Unsupervised Deep Features for Privacy Image Classification, In Proceedings of the Pacific-Rim Symposium on Image and Video Technology (PSIVT) pp. 404-415.
• H. Shojanazeri, D. Zhang, S. Wei Teng, S. Aryal and G. Lu, 2018, A Novel Perceptual Dissimilarity Measure for Image Retrieval, In Proceedings of the International Conference on Image and Vision Computing New Zealand (IVCNZ) pp. 1-6.
• H. Shojanazeri, S. Aryal, S. W. Teng, D. Zhang and G. Lu, 2018, Image Clustering Using a Similarity Measure Incorporating Human Perception, In Proceedings of the International Conference on Image and Vision Computing New Zealand (IVCNZ) pp. 1-6
• M. Santhanagopalan, M. Chetty, C. Foale, S. Aryal and B. Klein, 2018, Relevance of frequency of heart-rate peaks as indicator of biological stress level, In Proceedings of the 25th International Conference on Neural Information Processing (ICONIP 2018) pp 598-609
• S. Aryal, 2018, Anomaly detection technique robust to units and scales of measurement, In Proceedings of the 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) pp 589-601
• M. Santhanagopalan, M. Chetty, C. Foale, S. Aryal and B. Klein, 2018, Modelling neurocognitive reaction time with Gamma distribution, In Proceedings of the 2018 Australasian Computer Science Week (ACSW) Multiconference pp 28:1–28:10
• S. Aryal, K. M. Ting, and G. Haffari, 2016, Revisiting Attribute Independence Assumption in Probabilistic Unsupervised Anomaly Detection, In Proceedings of the 11th Pacific Asia Workshop on Intelligence and Security Informatics (PAISI) pp 73-86
• S. Aryal, K. M. Ting, G. Haffari and T. Washio, 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
• S. Aryal, K. M. Ting, G. Haffari and T. Washio, 2014, Mp-dissimilarity: A data dependent dissimilarity measure, In Proceedings of the IEEE International Conference on Data Mining (ICDM) pp 707-712
• S. Aryal, K. M. Ting, J. R. Wells and T. Washio, 2014, Improving iForest with Relative Mass, In Proceedings of the 18th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) pp 510-521
• S. Aryal and K. M. Ting, 2013, MassBayes: A new generative classifier with multidimensional likelihood estimation, In Proceedings of the 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) pp 136-148

Theses

• S. Aryal, 2017, A data-dependent dissimilarity measure: An effective alternative to distance measures, PhD Thesis, Clayton School of Information Technology, Monash University
• S. Aryal, 2012, New generative classifiers with mass-based likelihood estimation, Master’s Thesis, Gippsland School of Information Technology, Monash University

Books chapters

• A. Neupane, J. Soar, K. Vaidya, S. Aryal, 2017, Development and evaluation of an anti-corruption framework to address the public procurement corruption, In R. Shakya (ed.) Digital Governance and E-Government Principles Applied to Public Procurement, IGI Global pp 56-74
• A. Neupane, J. Soar, K. Vaidya, S. Aryal, 2014, The Potential For ICT Tools to Promote Public Participation in Fighting Corruption, In Christina M. Akrivopoulou and N. Garipidis (eds.) Human Rights and the Impact of ICT in the Public Sphere: Participation, Democracy, and Political Autonomy, IGI Global pp 175-191

Abstracts, posters, talks & miscellaneous

• S. Aryal, 2020, Robust Data Mining for not to be misled by Numbers, The 3rd National Workshop on Machine Learning & Data Science, Kathmandu, Nepal [ Keynote presentation (Remotely via Zoom)]
• S. Aryal, 2019, Are you aware that data can be misleading?, PACIFIC 2019 Pitchfest, Sydney, Austalia [ Selected to pitch resaerch idea]
• K. M. Ting and S. Aryal and T. Washio 2018, Which Outlier Detector should I use?, In the IEEE ICDM 2018, Singapore [One-day long tutorial]
• K. M. Ting and S. Aryal, 2017, The impact of sample size and dissimilarity on learning in dynamic and uncertain environments, In the US-AUS Robotics & Autonomy Workshop, Adelaide, Australia [Invited talk to pitch research idea]
• S. Aryal, K. M. Ting, G. Haffari and T. Washio, 2015, Beyond tf-idf and cosine distance in documents dissimilarity measure, In the 11th Conference of Asia Information Retrieval Societies (AIRS), Brisbane, Australia [Poster]
• S. Aryal, K. M. Ting, G. Haffari and T. Washio, 2014, Mp-dissimilarity: A data dependent dissimilarity measure, In the IEEE International Conference on Data Mining (ICDM), Shenzhen, China [Talk]
• S. Aryal and K. M. Ting, 2014, MassBayes: A new generative classifier with multidimensional likelihood estimation, In the Monash University Faculty of IT higher degrees by research conference [Poster]
• L. Schietgat, S. Aryal, Ramon J., 2010, Predicting protein function with the relative backbone position kernel, In Proceedings of the 9th European Conference on Computational Biology (ECCB) pp 39 [Extended abstract and poster]
• S. Aryal, 2002, Prospects and Challenges of IT in Nepal, In the Kathmandu Don Bosco College Newsletter pp 3 [Newspaper column]

See my google scholar page here

See my DBLP page here