Sutharshan Rajasegarar

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Christopher LeckieProfessor, Computing and Information Systems, The University of MelbourneVerified email at unimelb.edu.au
M. PalaniswamiProfessor of Electrical EngineeringVerified email at unimelb.edu.au
James C. BezdekUniversity of Melbourne, Computer and Information SystemsVerified email at unimelb.edu.au
Shanika KarunasekeraProfessor, School of Computing and Information Systems, University of MelbourneVerified email at unimelb.edu.au
Dheeraj KumarAssistant Professor, Indian Institute of technology (IIT) RoorkeeVerified email at iitr.ac.in
Muhammad Ali Imran, FIEEE, FRSE, FI...Professor of Communication Systems - University of GlasgowVerified email at glasgow.ac.uk
Alexander GluhakDigital CatapultVerified email at digicatapult.org.uk
John YearwoodDeakin UniversityVerified email at deakin.edu.au
Timothy C. HavensMichigan Technological UniversityVerified email at mtu.edu
Jeffrey ChanSchool of Computing Technologies, RMIT UniversityVerified email at rmit.edu.au
Jayavardhana GubbiTCS Research, Tata Consultancy ServicesVerified email at tcs.com
Michele NatiWSENSE SRLVerified email at wsense.it
Maia AngelovaProfessor of AI in Health Aston UniversityVerified email at aston.ac.uk
James M KellerProfessor of Electrical and Computer EngineeringVerified email at missouri.edu
Laurence A. F. ParkSchool of Computer, Data and Mathematical Sciences, Western Sydney UniversityVerified email at scem.uws.edu.au
Subhash ChallaSenSen NetworksVerified email at sensennetworks.com
James BaileyProfessor, School of Computing and Information Systems, University of MelbourneVerified email at unimelb.edu.au
Ian M AtkinsonJames Cook UniversityVerified email at jcu.edu.au
Stuart KininmonthThe University of QueenslandVerified email at uq.edu.au
Ron JohnstoneSchool of Earth and Environmental Sciences, University of QueenslandVerified email at uq.edu.au

Sutharshan Rajasegarar

Associate Professor, School of Information Technology, Deakin University

Verified email at unimelb.edu.au

Anomaly detection Machine learning Artificial Intelligence (AI)Computer vision IoT and Health


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High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning SM Erfani, S Rajasegarar, S Karunasekera, C Leckie Pattern Recognition 58, 121-134, 2016	1367	2016
Non-intrusive load monitoring approaches for disaggregated energy sensing: A survey A Zoha, A Gluhak, MA Imran, S Rajasegarar Sensors 12 (12), 16838-16866, 2012	1238	2012
Anomaly detection in wireless sensor networks S Rajasegarar, C Leckie, M Palaniswami IEEE Wireless Communications 15 (4), 34-40, 2008	397	2008
Distributed anomaly detection in wireless sensor networks S Rajasegarar, C Leckie, M Palaniswami, JC Bezdek 2006 10th IEEE Singapore international conference on communication systems, 1-5, 2006	378	2006
Parking availability prediction for sensor-enabled car parks in smart cities Y Zheng, S Rajasegarar, C Leckie 2015 IEEE tenth international conference on intelligent sensors, sensor …, 2015	322	2015
Quarter sphere based distributed anomaly detection in wireless sensor networks S Rajasegarar, C Leckie, M Palaniswami, JC Bezdek 2007 IEEE International Conference on Communications, 3864-3869, 2007	263	2007
Centered hyperspherical and hyperellipsoidal one-class support vector machines for anomaly detection in sensor networks S Rajasegarar, C Leckie, JC Bezdek, M Palaniswami IEEE Transactions on Information Forensics and Security 5 (3), 518-533, 2010	192	2010
Labelled data collection for anomaly detection in wireless sensor networks S Suthaharan, M Alzahrani, S Rajasegarar, C Leckie, M Palaniswami 2010 sixth international conference on intelligent sensors, sensor networks …, 2010	175	2010
A Hybrid Approach to Clustering in Big Data D Kumar, JC Bezdek, M Palaniswami, S Rajasegarar, C Leckie, ... IEEE Transactions on Cybernetics, 2015	158*	2015
Anomaly detection in wireless sensor networks in a non-stationary environment C O'Reilly, A Gluhak, MA Imran, S Rajasegarar IEEE Communications Surveys & Tutorials 16 (3), 1413-1432, 2014	154	2014
Fog-empowered anomaly detection in IoT using hyperellipsoidal clustering L Lyu, J Jin, S Rajasegarar, X He, M Palaniswami IEEE Internet of Things Journal 4 (5), 1174-1184, 2017	133	2017
Hyperspherical cluster based distributed anomaly detection in wireless sensor networks S Rajasegarar, C Leckie, M Palaniswami Journal of Parallel and Distributed Computing 74 (1), 1833-1847, 2014	131	2014
Bus travel time prediction with real-time traffic information J Ma, J Chan, G Ristanoski, S Rajasegarar, C Leckie Transportation Research Part C: Emerging Technologies 105, 536-549, 2019	118	2019
Clustering ellipses for anomaly detection M Moshtaghi, TC Havens, JC Bezdek, L Park, C Leckie, S Rajasegarar, ... Pattern Recognition 44 (1), 55-69, 2011	112	2011
Improving load forecasting based on deep learning and K-shape clustering F Fahiman, SM Erfani, S Rajasegarar, M Palaniswami, C Leckie 2017 international joint conference on neural networks (IJCNN), 4134-4141, 2017	105	2017
Elliptical anomalies in wireless sensor networks S Rajasegarar, JC Bezdek, C Leckie, M Palaniswami ACM Transactions on Sensor Networks (TOSN) 6 (1), 1-28, 2010	88	2010
Deep Metric Learning Based Citrus Disease Classification With Sparse Data S JANARTHAN, S THUSEETHAN, S RAJASEGARAR, Q LYU, Y ZHENG, ... IEEE Access 8, 162588-162600, 2020	84	2020
A scalable framework for trajectory prediction P Rathore, D Kumar, S Rajasegarar, M Palaniswami, JC Bezdek IEEE Transactions on Intelligent Transportation Systems 20 (10), 3860-3874, 2019	79	2019
Efficient unsupervised parameter estimation for one-class support vector machines Z Ghafoori, SM Erfani, S Rajasegarar, JC Bezdek, S Karunasekera, ... IEEE transactions on neural networks and learning systems 29 (10), 5057-5070, 2018	77	2018
A rapid hybrid clustering algorithm for large volumes of high dimensional data P Rathore, D Kumar, JC Bezdek, S Rajasegarar, M Palaniswami IEEE Transactions on Knowledge and Data Engineering 31 (4), 641-654, 2018	76	2018

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