World's Best Scientists 2026 revealed!
Sutharshan Rajasegarar

Sutharshan Rajasegarar

D-Index & Metrics

Computer Science

D-Index
36
Citations
7326
World Ranking
11083
National Ranking
335

Overview

Sutharshan Rajasegarar is affiliated with Deakin University in Australia and has contributed extensively to research spanning multiple areas within computer science and engineering. Their work prominently intersects artificial intelligence, plant science, computer vision and pattern recognition, computer networks and communications, and electrical and electronic engineering.

The primary research topics covered in their publications include:

  • Anomaly Detection Techniques and Applications
  • Network Security and Intrusion Detection
  • Smart Agriculture and AI
  • Advanced Malware Detection Techniques
  • Plant Disease Management Techniques
  • Emotion and Mood Recognition
  • Face and Expression Recognition

Key recent papers authored or coauthored by Rajasegarar include:

  • "Deep Metric Learning Based Citrus Disease Classification With Sparse Data," 2020, IEEE Access
  • "LGAttNet: Automatic micro-expression detection using dual-stream local and global attentions," 2020, Knowledge-Based Systems
  • "Quantum deep learning-based anomaly detection for enhanced network security," 2024, Quantum Machine Intelligence
  • "Multi-attention graph neural networks for city-wide bus travel time estimation using limited data," 2022, Expert Systems with Applications
  • "Deep Continual Learning for Emerging Emotion Recognition," 2021, IEEE Transactions on Multimedia

Frequent collaborators in Rajasegarar's work include John Yearwood, Selvarajah Thuseethan, Lei Pan, Maia Angelova, and Sivasubramaniam Janarthan.

Rajasegarar's publications have appeared in several venues, notably:

  • IEEE Access
  • arXiv (Cornell University)
  • Expert Systems with Applications
  • PLoS ONE
  • IEEE Transactions on AgriFood Electronics

Their research output indicates contributions to areas where advanced machine learning techniques are applied to practical domains such as disease classification in plants, micro-expression detection, network security enhancement, travel time estimation using graph neural networks, and emotion recognition through continual learning frameworks.

Best Publications

  • High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning

    Sarah M. Erfani;Sutharshan Rajasegarar;Shanika Karunasekera;Christopher Leckie

  • Non-Intrusive Load Monitoring Approaches for Disaggregated Energy Sensing: A Survey

    Ahmed Zoha;Alexander Gluhak;Muhammad Ali Imran;Sutharshan Rajasegarar

  • Anomaly detection in wireless sensor networks

    S. Rajasegarar;C. Leckie;M. Palaniswami

  • Distributed Anomaly Detection in Wireless Sensor Networks

    S. Rajasegarar;C. Leckie;M. Palaniswami;J.C. Bezdek

  • Parking availability prediction for sensor-enabled car parks in smart cities

    Yanxu Zheng;Sutharshan Rajasegarar;Christopher Leckie

  • Quarter Sphere Based Distributed Anomaly Detection in Wireless Sensor Networks

    S. Rajasegarar;C. Leckie;M. Palaniswami;J.C. Bezdek

  • Centered Hyperspherical and Hyperellipsoidal One-Class Support Vector Machines for Anomaly Detection in Sensor Networks

    S Rajasegarar;C Leckie;J C Bezdek;M Palaniswami

  • Labelled data collection for anomaly detection in wireless sensor networks

    Shan Suthaharan;Mohammed Alzahrani;Sutharshan Rajasegarar;Christopher Leckie

  • Anomaly detection in wireless sensor networks in a non-stationary environment

    Colin O'Reilly;Alexander Gluhak;Muhammad Ali Imran;Sutharshan Rajasegarar

  • A Hybrid Approach to Clustering in Big Data

    Dheeraj Kumar;James C. Bezdek;Marimuthu Palaniswami;Sutharshan Rajasegarar

  • Fog-Empowered Anomaly Detection in IoT Using Hyperellipsoidal Clustering

    Lingjuan Lyu;Jiong Jin;Sutharshan Rajasegarar;Xuanli He

  • Hyperspherical cluster based distributed anomaly detection in wireless sensor networks

    Sutharshan Rajasegarar;Christopher Leckie;Marimuthu Palaniswami

  • Clustering ellipses for anomaly detection

    Masud Moshtaghi;Timothy C. Havens;James C. Bezdek;Laurence Park

  • Bus travel time prediction with real-time traffic information

    Jiaman Ma;Jeffrey Chan;Goce Ristanoski;Sutharshan Rajasegarar

  • Elliptical anomalies in wireless sensor networks

    Sutharshan Rajasegarar;James C. Bezdek;Christopher Leckie;Marimuthu Palaniswami

  • Deep metric learning based citrus disease classification with sparse data

    Sivasubramaniam Janarthan;Selvarajah Thuseethan;Sutharshan Rajasegarar;Qiang Lyu

  • Improving load forecasting based on deep learning and K-shape clustering

    Fateme Fahiman;Sarah M. Erfani;Sutharshan Rajasegarar;Marimuthu Palaniswami

  • Anomaly detection by clustering ellipsoids in wireless sensor networks

    Masud Moshtaghi;Sutharshan Rajasegarar;Christopher Leckie;Shanika Karunasekera

  • Efficient Unsupervised Parameter Estimation for One-Class Support Vector Machines

    Zahra Ghafoori;Sarah M. Erfani;Sutharshan Rajasegarar;James C. Bezdek

  • A Rapid Hybrid Clustering Algorithm for Large Volumes of High Dimensional Data

    Punit Rathore;Dheeraj Kumar;James C. Bezdek;Sutharshan Rajasegarar

  • Real-Time Urban Microclimate Analysis Using Internet of Things

    Punit Rathore;Aravinda S. Rao;Sutharshan Rajasegarar;Elena Vanz

  • A Scalable Framework for Trajectory Prediction

    Punit Rathore;Dheeraj Kumar;Sutharshan Rajasegarar;Marimuthu Palaniswami

Frequent Co-Authors

Christopher Leckie
Christopher Leckie University of Melbourne
Marimuthu Palaniswami
Marimuthu Palaniswami University of Melbourne
James C. Bezdek
James C. Bezdek University of Melbourne
Shanika Karunasekera
Shanika Karunasekera University of Melbourne
John Yearwood
John Yearwood Deakin University
Jemal H. Abawajy
Jemal H. Abawajy Deakin University
James Bailey
James Bailey University of Melbourne
Kotagiri Ramamohanarao
Kotagiri Ramamohanarao University of Melbourne
James M. Keller
James M. Keller University of Missouri
Xi Zheng
Xi Zheng Macquarie University

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Exploring computer science in the USA doesn’t have to follow a single pathway. Many students are turning to online learning options for their flexibility and accessibility. For those looking to quickly upgrade their qualifications, the shortest online masters degree programs can help you earn a relevant credential in as little as one year. These accelerate the transition into advanced tech roles.

Choosing the right program is also critical. Consider the what masters program should i do guide to discover which graduate degrees are most valued in today’s job market. This can be a smart move for aligning your studies with strong career demand.

If you’re just getting started, an online associate's degree offers an entry-level pathway into IT and computer science fields, often at a lower price and with faster completion times than traditional degrees.

Affordability is another key consideration. Research your options with the affordable online colleges guide to find programs that fit your budget, helping you avoid excessive student debt while starting a rewarding tech career.

Best Scientists Citing Sutharshan Rajasegarar

Trending Scientists