Dr Sarah Monazam Erfani
- Big Data (Scalable Learning, Data Integration, Data Analysis)
- Computer Security & Privacy (Cybersecurity, Data Privacy)
- Data Mining
- Machine Learning
Sarah Erfani is a lecturer in the Department of Computing and Information Systems at The University of Melbourne. Research interests: - Machine Learning - Large-scale Data Mining - Cyber Security - Data Privacy
- Cheng W, Monazam Erfani S, Zhang R, Kotagiri R. Accurate recognition of the current activity in the presence of multiple activities. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. 2017, Vol. 10235 LNAI.
- Iredale TB, Monazam Erfani S, Leckie C. An efficient visual assessment of cluster tendency tool for large-scale time series data sets. IEEE International Conference on Fuzzy Systems. Institute of Electrical and Electronics Engineers. 2017.
- Rathore P, Bezdek J, Monazam Erfani S, Rajasegarar S, Palaniswami M. Ensemble Fuzzy Clustering using Cumulative Aggregation on Random Projections. IEEE Transactions on Fuzzy Systems. IEE Institute of Electronic Engineers. 2017.
- Moshtaghi M, Monazam Erfani S, Leckie C, Bezdek J. Exponentially Weighted Ellipsoidal Model for Anomaly Detection. INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS. John Wiley & Sons. 2017, Vol. 32, Issue 9.
- Monazam Erfani S, Baktashmotlagh M, Moshtaghi M, Nguyen V, Leckie C, Bailey J, Ramamohanarao K. From shared subspaces to shared landmarks: A robust multi-source classification approach. 31st AAAI Conference on Artificial Intelligence (AAAI). 2017.
- Fahiman F, Bezdek JC, Monazam Erfani S, Palaniswami M, Leckie C. Fuzzy c-Shape: A new algorithm for clustering finite time series waveforms. IEEE International Conference on Fuzzy Systems. Institute of Electrical and Electronics Engineers. 2017.
- Fahiman F, Monazam Erfani S, Rajasegarar S, Palaniswami M, Leckie C. Improving load forecasting based on deep learning and K-shape clustering. Proceedings of the International Joint Conference on Neural Networks. 2017, Vol. 2017-May.
- Lyu L, Law YW, Monazam Erfani S, Leckie C, Palaniswami M. An Improved Scheme for Privacy-Preserving Collaborative Anomaly Detection. 2016 IEEE INTERNATIONAL CONFERENCE ON PERVASIVE COMPUTING AND COMMUNICATION WORKSHOPS (PERCOM WORKSHOPS). IEEE. 2016.
- Ghafoori Z, Monazam Erfani S, Rajasegarar S, Karunasekera S, Leckie C. Anomaly Detection in Non-stationary Data: Ensemble based Self-Adaptive OCSVM. 2016 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN). IEEE. 2016, Vol. 2016-October.
- Monazam Erfani S, Rajasegarar S, Karunasekera S, Leckie C. High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning. PATTERN RECOGNITION. Pergamon-Elsevier Science. 2016, Vol. 58.
- Glennan T, Leckie C, Monazam Erfani S. Improved classification of known and unknown network traffic flows using semi-supervised machine learning. 21st Australasian Conference on Information Security and Privacy (ACISP). Springer Verlag. 2016, Vol. 9723.
- Monazam Erfani S, Baktashmotlagh M, Rajasegarar S, Nguyen V, Leckie C, Bailey J, Kotagiri R. R1STM: One-class support tensor machine with randomised kernel. 16th SIAM International Conference on Data Mining 2016, SDM 2016. 2016.
- Monazam Erfani S, Baktashmotlagh M, Moshtaghi M, Nguyen V, Leckie C, Bailey J, Kotagiri R. Robust domain generalisation by enforcing distribution invariance. 25th International Joint Conference on Artificial Intelligence (IJCAI). AAAI Press. 2016, Vol. 2016-January.
- Amsaleg L, Bailey J, Monazam Erfani S, Furon T, Houle ME, RadovanoviĆ M, Nguyen X. The vulnerability of learning to adversarial perturbation increases with intrinsic dimensionality. NII Technical Reports. 2016, Issue 5.
- Nguyen X, Monazam Erfani S, Paisitkriangkrai S, Bailey J, Leckie C, Kotagiri R. Training Robust Models Using Random Projection. 2016 23RD INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR). I EEE Xplore. 2016.
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