Biography

Dr. Herna Viktor
University of Ottawa, Canada

Email: hlviktor@site.uottawa.ca

Qualifications
1999 Ph.D., University of Stellenbosch, South Africa
1991 M.Sc., University of Stellenbosch, South Africa
1987 B.Sc., University of Stellenbosch, South Africa

Publications (selected)
  1. Olorunnimbe, K., & Viktor, H. (2024). Ensemble of temporal Transformers for financial time series. Journal of Intelligent Information Systems, 1-25.
  2. Hunter, J., Soleymani, F., Viktor, H., Michalowski, W., Poitras, S., & Beaulé, P. E. (2024). Using unsupervised machine learning to predict quality of life after total knee arthroplasty. The Journal of Arthroplasty, 39(3), 677-682.
  3. Crothers, E. N., Japkowicz, N., & Viktor, H. L. (2023). Machine-generated text: A comprehensive survey of threat models and detection methods. IEEE Access, 11, 70977-71002.
  4. Olorunnimbe, K., & Viktor, H. (2023). Deep learning in the stock market—a systematic survey of practice, backtesting, and applications. Artificial Intelligence Review, 56(3), 2057-2109.
  5. Soleymani, F., Paquet, E., Viktor, H. L., Michalowski, W., & Spinello, D. (2023). ProtInteract: A deep learning framework for predicting protein–protein interactions. Computational and Structural Biotechnology Journal, 21, 1324-1348.
  6. Crothers, E., Japkowicz, N., Viktor, H., & Branco, P. (2022, July). Adversarial robustness of neural-statistical features in detection of generative transformers. In 2022 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE.
  7. Soleymani, F., Paquet, E., Viktor, H., Michalowski, W., & Spinello, D. (2022). Protein–protein interaction prediction with deep learning: A comprehensive review. Computational and Structural Biotechnology Journal, 20, 5316-5341.
  8. Sadeghi, F., & Viktor, H. L. (2021, September). Online-MC-queue: Learning from imbalanced multi-class streams. In Third International Workshop on Learning with Imbalanced Domains: Theory and Applications (pp. 21-34). PMLR.
  9. Alabdulrahman, R., & Viktor, H. (2021). Catering for unique tastes: Targeting grey-sheep users recommender systems through one-class machine learning. Expert systems with applications, 166, 114061.
  10. Vafaie, P., Viktor, H., & Michalowski, W. (2020, November). Multi-class imbalanced semi-supervised learning from streams through online ensembles. In 2020 International Conference on Data Mining Workshops (ICDMW) (pp. 867-874). IEEE.
  11. Abdulrahman, R., & Viktor, H. L. (2020). Personalised Recommendation Systems and the Impact of COVID-19: Perspectives, Opportunities and Challenges. KDIR, 295-301.
  12. Moulton, R. H., Viktor, H. L., Japkowicz, N., & Gama, J. (2019). Clustering in the presence of concept drift. In Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2018, Dublin, Ireland, September 10–14, 2018, Proceedings, Part I 18 (pp. 339-355). Springer International Publishing.
  13. Pesaranghader, A., Viktor, H., & Paquet, E. (2018). Reservoir of diverse adaptive learners and stacking fast hoeffding drift detection methods for evolving data streams. Machine Learning, 107(11), 1711-1743. 
  14. Pesaranghader, A., Viktor, H. L., & Paquet, E. (2018, July). McDiarmid drift detection methods for evolving data streams. In 2018 International joint conference on neural networks (IJCNN) (pp. 1-9). IEEE.
  15. J Doyle, HL Viktor and E Paquet, “A Metadata Framework for Long Term Digital Preservation of 3D Data”, International Journal of Information Studies, 1 (3), 165-171, 2009.
  16. J. Doyle, HL Viktor and E. Paquet, “Long Term Digital Preservation – Preserving Authenticity and Usability of 3D Data”, International Journal on Digital Libraries, Springer Publishers, Volume 10, pp.33-48, 2009.
  17. H Guo and HL Viktor, Learning from skewed class multi-relational databases, Fundamenta Informaticae, Volume 89, pp.69-94, 2008.
  18. H Guo and HL Viktor, Multirelational classification: A multiple view approach, Knowledge and Information Systems: An International Journal, Springer Publishers, 17, pp.287–312, 2008.
  19. HL Viktor and E Paquet, Calibration of Virtual Mannequins Through Anthropometric Measurements, Cluster Analysis, and Content-based Retrieval of 3-D Body Scans, the IEEE Transactions on Instrumentation and Measurements, IEEE Press, Vol 56 (5), pp.1924-1929, October 2007.
  20. E Paquet and HL Viktor, On the application of 3-D technologies to the framework of cultural heritage, Annales des télécommunications, Hermes Science Publications, Paris: France, 60, pp.1311-1325, December 2005.
  21. Abdali, HL Viktor, E Paquet and M Rioux, Exploring anthropometric data through cluster analysis, SAE 2004 Transactions Journal of Aerospace, Volume V113-1, pp.241-246, 2004. Judged to be among the most outstanding SAE technical papers of 2004.
  22. H Guo and HL Viktor, Learning from imbalanced data sets with boosting and data generation: The DataBoost-IM approach, ACM SIGKDD Explorations, pp.30-39, June 2004.


Profile Details:

https://www.uottawa.ca/faculty-engineering/school-electrical-engineering-computer-science/directory/herna-viktor
https://scholar.google.com/citations?hl=en&user=9KeZgpQAAAAJ
https://www.researchgate.net/profile/Herna-Viktor
https://orcid.org/0000-0003-1914-5077
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