Year |
Citation |
Score |
2022 |
Bashar MA, Nayak R, Balasubramaniam T. Deep learning based topic and sentiment analysis: COVID19 information seeking on social media. Social Network Analysis and Mining. 12: 90. PMID 35911483 DOI: 10.1007/s13278-022-00917-5 |
0.565 |
|
2022 |
Balasubramaniam T, Warne DJ, Nayak R, Mengersen K. Explainability of the COVID-19 epidemiological model with nonnegative tensor factorization. International Journal of Data Science and Analytics. 1-14. PMID 35528806 DOI: 10.1007/s41060-022-00324-1 |
0.53 |
|
2022 |
White NM, Balasubramaniam T, Nayak R, Barnett AG. An observational analysis of the trope "A p-value of < 0.05 was considered statistically significant" and other cut-and-paste statistical methods. Plos One. 17: e0264360. PMID 35263374 DOI: 10.1371/journal.pone.0264360 |
0.532 |
|
2021 |
Bashar MA, Nayak R, Luong K, Balasubramaniam T. Progressive domain adaptation for detecting hate speech on social media with small training set and its application to COVID-19 concerned posts. Social Network Analysis and Mining. 11: 69. PMID 34341673 DOI: 10.1007/s13278-021-00780-w |
0.567 |
|
2021 |
Balasubramaniam T, Nayak R, Luong K, Bashar MA. Identifying Covid-19 misinformation tweets and learning their spatio-temporal topic dynamics using Nonnegative Coupled Matrix Tensor Factorization. Social Network Analysis and Mining. 11: 57. PMID 34149960 DOI: 10.1007/s13278-021-00767-7 |
0.598 |
|
2020 |
Balasubramaniam T, Nayak R, Yuen C, Kang U. Efficient Nonnegative Tensor Factorization via Saturating Coordinate Descent Acm Transactions On Knowledge Discovery From Data. 14: 1-28. DOI: 10.1145/3385654 |
0.588 |
|
2020 |
Balasubramaniam T, Nayak R, Yuen C, Tian Y. Column-wise Element Selection for Computationally Efficient Nonnegative Coupled Matrix Tensor Factorization Ieee Transactions On Knowledge and Data Engineering. 1-1. DOI: 10.1109/Tkde.2020.2967045 |
0.577 |
|
2019 |
Marakkalage SH, Sarica S, Lau BPL, Viswanath SK, Balasubramaniam T, Yuen C, Yuen B, Luo J, Nayak R. Understanding the Lifestyle of Older Population: Mobile Crowdsensing Approach Ieee Transactions On Computational Social Systems. 6: 82-95. DOI: 10.1109/Tcss.2018.2883691 |
0.551 |
|
Show low-probability matches. |