John Panneerselvam
| Name | John Panneerselvam |
|---|---|
| Job title | Associate Lecturer |
| Research institute | College of Science and Engineering |
Research outputs
Modeling and Analyzing Logic Vulnerabilities of E-Commerce Systems at the Design Phase
Wangyang Yu, Lu Liu, Xiaoming Wang, Ovidiu Bagdasar and John Panneerselvam 2023. Modeling and Analyzing Logic Vulnerabilities of E-Commerce Systems at the Design Phase. IEEE Transactions on Systems, Man, and Cybernetics: Systems. https://doi.org/10.1109/tsmc.2023.3299605Efficient resampling for fraud detection during anonymised credit card transactions with unbalanced datasets
Mrozek, Petr, Panneerselvam, J. and Bagdasar, Ovidiu 2020. Efficient resampling for fraud detection during anonymised credit card transactions with unbalanced datasets. IEEE. https://doi.org/10.1109/ucc48980.2020.00067Exploring network embedding for efficient message routing in opportunistic mobile social networks
Yuan, Bo, Anjum, Ashiq, Panneerselvam, J. and Liu, Lu 2020. Exploring network embedding for efficient message routing in opportunistic mobile social networks. IEEE. https://doi.org/10.1109/ICDMW.2019.00077A GRU-based prediction framework for intelligent resource management at cloud data centres in the age of 5G
Lu, Yao, Liu, Lu, Panneerselvam, J., Yuan, Bo, Gu, Jiayan and Antonopoulos, Nick 2019. A GRU-based prediction framework for intelligent resource management at cloud data centres in the age of 5G. IEEE Transactions on Cognitive Communications and Networking. 6 (2), pp. 486-498. https://doi.org/10.1109/tccn.2019.2954388An inductive content-augmented network embedding model for edge artificial intelligence
Yuan, Bo, Panneerselvam, J., Liu, Lu, Antonopoulos, Nick and Lu, Yao 2019. An inductive content-augmented network embedding model for edge artificial intelligence. IEEE Transactions on Industrial Informatics. 15 (7), pp. 4295-4305. https://doi.org/10.1109/TII.2019.2902877An efficient indexing model for the fog layer of industrial Internet of Things.
Miao, Dejun, Liu, Lu, Xu, Rongyan, Panneerselvam, J., Wu, Yan and Xu, Wei 2018. An efficient indexing model for the fog layer of industrial Internet of Things. IEEE Transactions on Industrial Informatics. https://doi.org/10.1109/TII.2018.2799598An approach to optimise resource provision with energy-awareness in datacentres by combating task heterogeneity.
Liu, Lu, Antonopoulos, Nikolaos and Panneerselvam, J. 2018. An approach to optimise resource provision with energy-awareness in datacentres by combating task heterogeneity. IEEE Transactions on Emerging Topics in Computing. https://doi.org/10.1109/TETC.2018.2794328An investigation into the impacts of task-level behavioural heterogeneity upon energy efficiency in Cloud datacentres.
Panneerselvam, J., Liu, Lu, Lu, Yao and Antonopoulos, Nikolaos 2018. An investigation into the impacts of task-level behavioural heterogeneity upon energy efficiency in Cloud datacentres. Future Generation Computer Systems. https://doi.org/10.1016/j.future.2017.12.064Collaborative actuation of wireless sensor and actuator networks for the agriculture industry.
Bai, Xingzhen, Liu, Lu, Cao, Maoyong, Panneerselvam, J., Sun, Qiao and Wang, Haixia 2017. Collaborative actuation of wireless sensor and actuator networks for the agriculture industry. IEEE Access. https://doi.org/10.1109/ACCESS.2017.2725342InOt-RePCoN: Forecasting user behavioural trend in large-scale cloud environments.
Panneerselvam, J., Liu, Lu and Antonopoulos, Nikolaos 2017. InOt-RePCoN: Forecasting user behavioural trend in large-scale cloud environments. Future Generation Computer Systems. https://doi.org/10.1016/j.future.2017.05.022Mobilouds: An energy efficient MCC collaborative framework with extended mobile participation for next generation networks
Panneerselvam, J., Hardy, J., Liu, Lu, Yuan, Bo and Antonopoulos, Nikolaos 2017. Mobilouds: An energy efficient MCC collaborative framework with extended mobile participation for next generation networks. IEEE Access. https://doi.org/10.1109/ACCESS.2016.2602321CCLBR: Congestion control-based load balanced routing in unstructured P2P systems
Shen, Xiang-Jun, Chang, Qing, Liu, Lu, Panneerselvam, J. and Zha, Zheng-Jun 2016. CCLBR: Congestion control-based load balanced routing in unstructured P2P systems. IEEE Systems Journal. https://doi.org/10.1109/JSYST.2016.2558515RVLBPNN: A workload forecasting model for smart cloud computing
Lu, Yao, Panneerselvam, J., Liu, Lu and Wu, Yan 2016. RVLBPNN: A workload forecasting model for smart cloud computing. Scientific Programming. https://doi.org/10.1155/2016/5635673A critical review of the routing protocols in opportunistic networks
Panneerselvam, J., Atojoko, Anthony, Smith, K., Liu, Lu and Antonopoulos, Nikolaos 2014. A critical review of the routing protocols in opportunistic networks. EAI Endorsed Transactions on Industrial Networks and Intelligent Systems. https://doi.org/10.4108/inis.1.1.e6iMIG: Toward an adaptive live migration method for KVM virtual machines
Li, Jianxin, Zhao, Jieyu Zhao, Li, Yi, Cui, Lei, Li, Bo, Liu, Lu and Panneerselvam, J. 2014. iMIG: Toward an adaptive live migration method for KVM virtual machines. The Computer Journal. https://doi.org/10.1093/comjnl/bxu065Achieving dynamic load balancing through mobile agents in small world P2P networks
Shen, Xiang-Jun, Liu, Lu, Zha, Zheng-Jun, Gu, Pei-Ying, Jiang, Zhong-Qiu, Chen, Ji-Ming and Panneerselvam, J. 2014. Achieving dynamic load balancing through mobile agents in small world P2P networks. Computer Networks. https://doi.org/10.1016/j.comnet.2014.05.003919
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