| TAnoGAN: Time series anomaly detection with generative adversarial networks MA Bashar, R Nayak 2020 IEEE Symposium Series on Computational Intelligence (SSCI), 1778-1785, 2020 | 134 | 2020 |
| Regularising LSTM classifier by transfer learning for detecting misogynistic tweets with small training set MA Bashar, R Nayak, N Suzor Knowledge and Information Systems, 2020 | 45 | 2020 |
| Multi-layer manifold learning for deep non-negative matrix factorization-based multi-view clustering K Luong, R Nayak, T Balasubramaniam, MA Bashar Pattern Recognition 131, 108815, 2022 | 44 | 2022 |
| QutNocturnal@ HASOC'19: CNN for hate speech and offensive content identification in Hindi language MA Bashar, R Nayak arXiv preprint arXiv:2008.12448, 2020 | 39 | 2020 |
| Misogynistic tweet detection: Modelling CNN with small datasets MA Bashar, R Nayak, NP Suzor, B Weir Australasian Data Mining Conference, 2018 | 31 | 2018 |
| Progressive domain adaptation for detecting hate speech on social media with small training set and its application to COVID-19 concerned posts MA Bashar, R Nayak, K Luong, T Balasubramaniam Social Network Analysis and Mining 11 (1), 69, 2021 | 27 | 2021 |
| Identifying Covid-19 misinformation tweets and learning their spatio-temporal topic dynamics using Nonnegative Coupled Matrix Tensor Factorization T Balasubramaniam, R Nayak, K Luong, MA Bashar Social Network Analysis and Mining 11 (1), 57, 2021 | 21 | 2021 |
| Active learning for effectively fine-tuning transfer learning to downstream task MA Bashar, R Nayak ACM Transactions on Intelligent Systems and Technology (TIST) 12 (2), 1-24, 2021 | 18 | 2021 |
| A review and prospects of quantum teleportation MA Bashar, MA Chowdhury, R Islam, MS Rahman, SK Das 2009 International Conference on Computer and Automation Engineering, 213-217, 2009 | 17 | 2009 |
| Finding semantically valid and relevant topics by association-based topic selection model Y Gao, Y Li, RYK Lau, Y Xu, MA Bashar ACM Transactions on Intelligent Systems and Technology (TIST) 9 (1), 1-22, 2017 | 16 | 2017 |
| Understanding the spatio-temporal topic dynamics of covid-19 using nonnegative tensor factorization: a case study T Balasubramaniam, R Nayak, MA Bashar 2020 IEEE symposium series on computational intelligence (SSCI), 1218-1225, 2020 | 15 | 2020 |
| Deep learning based topic and sentiment analysis: COVID19 information seeking on social media MA Bashar, R Nayak, T Balasubramaniam Social Network Analysis and Mining 12 (1), 90, 2022 | 14 | 2022 |
| First, do no harm: automated detection of abusive comments in student evaluation of teaching surveys S Cunningham, M Laundon, A Cathcart, MA Bashar, R Nayak Assessment & Evaluation in Higher Education 48 (3), 377-389, 2023 | 11 | 2023 |
| Interpretation of text patterns MA Bashar, Y Li Data Mining and Knowledge Discovery 32, 849-884, 2018 | 11 | 2018 |
| Exploring fusion strategies in deep learning models for multi-modal classification D Zhang, R Nayak, MA Bashar Australasian Conference on Data Mining, 102-117, 2021 | 10 | 2021 |
| Text-independent speaker identification system using average pitch and formant analysis MA Bashar, MT Ahmed, M Syduzzaman, PJ Ray, AT Islam International Journal on Information Theory (IJIT) 3 (3), 23-30, 2014 | 10 | 2014 |
| A framework for automatic personalised ontology learning MA Bashar, Y Li, Y Gao 2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI), 105-112, 2016 | 9 | 2016 |
| Topic, sentiment and impact analysis: Covid19 information seeking on social media MA Bashar, R Nayak, T Balasubramaniam arXiv preprint arXiv:2008.12435, 2020 | 7 | 2020 |
| Exploring topic models to discern cyber threats on Twitter: A case study on Log4Shell Y Wang, MA Bashar, M Chandramohan, R Nayak Intelligent Systems with Applications 20, 200280, 2023 | 6 | 2023 |
| Deep learning for bias detection: from inception to deployment MA Bashar, R Nayak, A Kothare, V Sharma, K Kandadai Data Mining: 19th Australasian Conference on Data Mining, AusDM 2021 …, 2021 | 6 | 2021 |
Richi NayakProfessor, Queensland University of Technology在 qut.edu.au 的电子邮件经过验证
