Dr Shahadate Rezvy
| Name | Dr Shahadate Rezvy |
|---|---|
| Job title | Hourly Academic |
| Research institute | |
| Primary appointment | Computer Science |
| Email address | shahadate2@mdx.ac.uk |
| Contact category | Academic staff (past) |
Research outputs
An explainable AI-based intrusion detection system for DNS over HTTPS (DoH) attacks
Zebin, T., Rezvy, S. and Luo, Y. 2022. An explainable AI-based intrusion detection system for DNS over HTTPS (DoH) attacks. IEEE Transactions on Information Forensics and Security. 17, pp. 2339-2349. https://doi.org/10.1109/TIFS.2022.3183390Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy
Ali, S., Dmitrieva, M., Ghatwary, N., Bano, S., Polat, G., Temizel, A., Krenzer, A., Hekalo, A., Guo, Y., Matuszewski, B., Gridach, M., Voiculescu, I., Yoganand, V., Chavan, A., Raj, A., Nguyen, N., Tran, D., Huynh, L., Boutry, N., Rezvy, S., Chen, H., Choi, Y., Subramanian, A., Balasubramanian, V., Gao, X., Hu, H., Liao, Y., Stoyanov, D., Daul, C., Realdon, S., Cannizzaro, R., Lamarque, D., Tran-Nguyen, T., Bailey, A., Braden, B., East, J. and Rittscher, J. 2021. Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy. Medical Image Analysis. 70. https://doi.org/10.1016/j.media.2021.102002COVID-19 detection and disease progression visualization: Deep learning on chest X-rays for classification and coarse localization
Zebin, T. and Rezvy, S. 2021. COVID-19 detection and disease progression visualization: Deep learning on chest X-rays for classification and coarse localization. Applied Intelligence. 51 (2), pp. 1010-1021. https://doi.org/10.1007/s10489-020-01867-1COVID-19 detection and disease progression visualization: Deep learning on chest X-rays for classification and coarse localization
Zebin, T., Rezvy, S. and Pang, W. 2020. COVID-19 detection and disease progression visualization: Deep learning on chest X-rays for classification and coarse localization. https://doi.org/10.21203/rs.3.rs-34534/v1Transfer learning for endoscopy disease detection and segmentation with mask-RCNN benchmark architecture
Rezvy, S., Zebin, T., Pang, W., Taylor, S. and Gao, X. 2020. Transfer learning for endoscopy disease detection and segmentation with mask-RCNN benchmark architecture. 2nd International Workshop and Challenge on Computer Vision in Endoscopy. Iowa City, United States 03 Apr 2020 pp. 68-72A deep learning approach for length of stay prediction in clinical settings from medical records
Zebin, T., Rezvy, S. and Chaussalet, T. 2019. A deep learning approach for length of stay prediction in clinical settings from medical records. 2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). Siena, Italy 09 - 10 Jul 2019 IEEE. pp. 1-5 https://doi.org/10.1109/CIBCB.2019.8791477An efficient deep learning model for intrusion classification and prediction in 5G and IoT networks
Rezvy, S., Luo, Y., Petridis, M., Lasebae, A. and Zebin, T. 2019. An efficient deep learning model for intrusion classification and prediction in 5G and IoT networks. 2019 53rd Annual Conference on Information Sciences and Systems (CISS). Baltimore, MD, USA, USA 20 - 22 Mar 2019 IEEE. pp. 1-6 https://doi.org/10.1109/CISS.2019.8693059Intrusion detection and classification with autoencoded deep neural network
Rezvy, S., Petridis, M., Lasebae, A. and Zebin, T. 2019. Intrusion detection and classification with autoencoded deep neural network. Lanet, J. and Toma, C. (ed.) SecITC 2018: International Conference on Security for Information Technology and Communications. Bucharest, Romania 08 - 09 Nov 2018 Switzerland Springer. pp. 142-156 https://doi.org/10.1007/978-3-030-12942-2_12System capacity Improvement by on request channel allocation in LTE cellular network
Lasebae, A., Rahman, S. and Rezvy, S. 2014. System capacity Improvement by on request channel allocation in LTE cellular network. The 15th IEEE International Conference on a World of Wireless, Mobile and Multimedia Networks. Sydney, Australia 16 - 19 Jun 2014Instant channel allocation technique to improve system throughput in joint LTE network
Rezvy, S., Rahman, S., Lasebae, A. and Loo, J. 2014. Instant channel allocation technique to improve system throughput in joint LTE network. The 28th IEEE International Conference on Advanced Information Networking and Applications. Victoria, BC, Canada 03 - 16 May 2014 IEEE. pp. 900-904 https://doi.org/10.1109/WAINA.2014.198Instant channel allocation technique to improve system throughput in joint LTE cellular network
Rezvy, S., Rahman, S., Lasebae, A. and Loo, J. 2014. Instant channel allocation technique to improve system throughput in joint LTE cellular network. Advanced Information Networking and Applications Workshops (WAINA 2014). Victoria, Canada 13 - 16 May 2014 Institute of Electrical and Electronics Engineers (IEEE). pp. 900-904 https://doi.org/10.1109/WAINA.2014.198System capacity improvement by on request channel allocation in LTE cellular network
Rezvy, S., Rahman, S., Lasebae, A. and Loo, J. 2014. System capacity improvement by on request channel allocation in LTE cellular network. 48th Annual Conference on Information Sciences and Systems (CISS-2014). Princeton, New Jersey, USA 19 - 21 Mar 2014 Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/CISS.2014.6814105Downlink femto-macro ICI cancellation by on request channel allocation in LTE network
Rezvy, S., Rahman, S., Lasebae, A. and Loo, J. 2014. Downlink femto-macro ICI cancellation by on request channel allocation in LTE network. 48th Annual Conference on Information Sciences and Systems (CISS-2014). Princeton University, New Jersey, USA 19 - 21 Mar 2014On demand based frequency allocation to mitigate interference in femto-macro LTE cellular network
Rezvy, S., Rahman, S., Lasebae, A. and Loo, J. 2013. On demand based frequency allocation to mitigate interference in femto-macro LTE cellular network. Second International Conference on Future Generation Communication Technologies (FGCT- 2013). London, UK 12 - 14 Nov 2013 Institute of Electrical and Electronics Engineers (IEEE). pp. 213-218288
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