Dr Santosh Tirunagari
| Name | Dr Santosh Tirunagari |
|---|---|
| Job title | Research Fellow in Machine Learning |
| Research institute | |
| Primary appointment | Computer Science |
| Email address | s.x.tirunagari@mdx.ac.uk |
| Contact category | Academic staff (past) |
Research outputs
Margin-aware active learning for user-adaptive text classification
Tirunagari, S., Dhami, M. and Windridge, D. 2026. Margin-aware active learning for user-adaptive text classification. Unger, H. and Meesad, P. (ed.) 9th International Conference on Natural Language Processing and Information Retrieval. Kyushu University, Fukuoka, Japan 12 - 14 Dec 2025 Springer. pp. 447-461 https://doi.org/10.1007/978-3-032-20897-2_33The application of pre-trained transformer models to UK Court of Appeal legal judgments
Abbas, W., Zia, T., Tirunagari, S., Chennareddy, V., Dhami, M. and Windridge, D. 2025. The application of pre-trained transformer models to UK Court of Appeal legal judgments. Quan, T.T., Sombattheera, C., Pham, H.-A. and Tran, N.T. (ed.) 18th International Conference on Multi-disciplinary Trends in Artificial Intelligence. Ho Chi Minh City, Vietnam 03 - 05 Dec 2025 Singapore Springer. pp. 252-264 https://doi.org/10.1007/978-981-95-4963-4_21A latent diffusion approach to visual attribution in medical imaging
Siddiqui, A.A., Tirunagari, S., Zia, T. and Windridge, D. 2025. A latent diffusion approach to visual attribution in medical imaging. Scientific Reports. 15 (1). https://doi.org/10.1038/s41598-024-81646-xInterpretable machine learning for predicting multiple sclerosis conversion from clinically isolated syndrome
Daniel, E.C., Tirunagari, S., Batth, K., Windridge, D. and Balla, Y. 2024. Interpretable machine learning for predicting multiple sclerosis conversion from clinically isolated syndrome. https://doi.org/10.1101/2024.07.18.24310578VALD-MD: visual attribution via latent diffusion for medical diagnostics
Siddiqui, A., Tirunagari, S., Zia, T. and Windridge, D. 2024. VALD-MD: visual attribution via latent diffusion for medical diagnostics. https://doi.org/10.48550/arxiv.2401.01414Visual attribution using Adversarial Latent Transformations
Zia, T., Wahab, A., Windridge, D., Tirunagari, S. and Bhatti, N. 2023. Visual attribution using Adversarial Latent Transformations. Computers in Biology and Medicine. 166. https://doi.org/10.1016/j.compbiomed.2023.107521Pediatrics in artificial intelligence era: a systematic review on challenges, opportunities, and explainability
Balla, Y., Tirunagari, S. and Windridge, D. 2023. Pediatrics in artificial intelligence era: a systematic review on challenges, opportunities, and explainability. Indian Pediatrics. 60 (7), pp. 561-569. https://doi.org/10.1007/s13312-023-2936-8Interpretable chronic kidney disease risk prediction from clinical data using machine learning
Chennareddy, V., Tirunagari, S., Mohan, S., Windridge, D. and Balla, Y. 2023. Interpretable chronic kidney disease risk prediction from clinical data using machine learning. 16th Multi-Disciplinary International Conference on Artificial Intelligence (MIWAI 2023). Hyderabad, India 21 2023 - 22 Jul 2024 Springer. https://doi.org/10.1007/978-3-031-36402-0_63Addressing challenges in healthcare big data analytics
Tirunagari, S., Mohan, S., Windridge, D. and Balla, Y. 2023. Addressing challenges in healthcare big data analytics. 16th Multi-Disciplinary International Conference on Artificial Intelligence (MIWAI 2023). Hyderabad, India 21 2023 - 22 Jul 2024 Springer. https://doi.org/10.1007/978-3-031-36402-0_70A protocol for conducting review on the challenges, opportunities of machine learning in pediatric healthcare
Balla, Y., Tirunagari, S. and Windridge, D. 2023. A protocol for conducting review on the challenges, opportunities of machine learning in pediatric healthcare. INPLASY. https://doi.org/10.37766/inplasy2023.5.0045A convex selective segmentation model based on a piece-wise constant metric guided edge detector function
Khan, M., Ali, H., Zakarya, M., Tirunagari, S., Khan, A., Khan, R., Ahmed, A. and Rada, L. 2022. A convex selective segmentation model based on a piece-wise constant metric guided edge detector function. https://doi.org/10.21203/rs.3.rs-2391118/v1A novel kernel based approach to arbitrary length symbolic data with application to type 2 diabetes risk
Nwegbu, N., Tirunagari, S. and Windridge, D. 2022. A novel kernel based approach to arbitrary length symbolic data with application to type 2 diabetes risk. Scientific Reports. 12 (1), pp. 1-16. https://doi.org/10.1038/s41598-022-08757-1Data-forensic determination of the accuracy of international COVID-19 reporting: using Zipf's law for pandemic investigation
Iorliam, A., Ho, A.T.S., Tirunagari, S., Windridge, D., Adeyelu, A., Otor, S. and Akumba, B.O. 2021. Data-forensic determination of the accuracy of international COVID-19 reporting: using Zipf's law for pandemic investigation. International Journal of Advanced Computer Science and Applications. 12 (1), pp. 42-49. https://doi.org/10.14569/IJACSA.2021.0120106Epileptic seizure detection using constrained singular spectrum analysis and 1D-local binary patterns
Ramanna, S., Tirunagari, S. and Windridge, D. 2020. Epileptic seizure detection using constrained singular spectrum analysis and 1D-local binary patterns. Health and Technology. 10 (3), pp. 699-709. https://doi.org/10.1007/s12553-019-00395-4Automatic delineation of kidney region in DCE-MRI
Tirunagari, S., Poh, N., Wells, K., Bober, M., Gorden, I. and Windridge, D. 2019. Automatic delineation of kidney region in DCE-MRI.Movement correction in DCE-MRI through windowed and reconstruction dynamic mode decomposition
Tirunagari, S., Poh, N., Wells, K., Bober, M., Gorden, I. and Windridge, D. 2017. Movement correction in DCE-MRI through windowed and reconstruction dynamic mode decomposition. Machine Vision and Applications. 28 (3-4), pp. 393-407. https://doi.org/10.1007/s00138-017-0835-5Dynamic mode decomposition for univariate time series: analysing trends and forecasting
Tirunagari, S., Kouchaki, S., Poh, N., Bober, M. and Windridge, D. 2017. Dynamic mode decomposition for univariate time series: analysing trends and forecasting.Can DMD obtain a scene background in color?
Tirunagari, S., Poh, N., Bober, M. and Windridge, D. 2016. Can DMD obtain a scene background in color? 2016 International Conference on Image, Vision and Computing (ICIVC). Portsmouth, UK 03 - 05 Aug 2016 pp. 46-50 https://doi.org/10.1109/ICIVC.2016.7571272Windowed DMD as a microtexture descriptor for finger vein counter-spoofing in biometrics
Tirunagari, S., Poh, N., Bober, M. and Windridge, D. 2015. Windowed DMD as a microtexture descriptor for finger vein counter-spoofing in biometrics. 2015 IEEE International Workshop on Information Forensics and Security (WIFS). Rome, Italy 16 - 19 Nov 2015 Institute of Electrical and Electronics Engineers (IEEE). pp. 1-6 https://doi.org/10.1109/WIFS.2015.7368599Identifying similar patients using self-organising maps: a case study on type-1 diabetes self-care survey responses
Tirunagari, S., Poh, N., Hu, G. and Windridge, D. 2015. Identifying similar patients using self-organising maps: a case study on type-1 diabetes self-care survey responses. https://doi.org/10.48550/arXiv.1503.06316Breast cancer data analytics with missing values: a study on ethnic, age and income groups
Tirunagari, S., Poh, N., Abdulrahman, H., Nemmour, N. and Windridge, D. 2015. Breast cancer data analytics with missing values: a study on ethnic, age and income groups. https://doi.org/10.48550/arXiv.1503.03680Detection of face spoofing using visual dynamics
Tirunagari, S., Poh, N., Windridge, D., Iorliam, A., Suki, N. and Ho, A. 2015. Detection of face spoofing using visual dynamics. IEEE Transactions on Information Forensics and Security. 10 (4), pp. 762-777. https://doi.org/10.1109/TIFS.2015.2406533Challenges in designing an online healthcare platform for personalised patient analytics
Poh, N., Tirunagari, S. and Windridge, D. 2014. Challenges in designing an online healthcare platform for personalised patient analytics. 2014 IEEE Symposium on Computational Intelligence in Big Data (CIBD). Orlando, FL., USA 09 - 12 Dec 2014 IEEE. pp. 1-6 https://doi.org/10.1109/CIBD.2014.7011526Patient level analytics using self-organising maps: a case study on type-1 diabetes self-care survey responses
Tirunagari, S., Poh, N., Aliabadi, K., Windridge, D. and Cooke, D. 2014. Patient level analytics using self-organising maps: a case study on type-1 diabetes self-care survey responses. 2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM). Orlando, FL., USA 09 - 12 Dec 2014 Institute of Electrical and Electronics Engineers (IEEE). pp. 304-309 https://doi.org/10.1109/CIDM.2014.70086823310
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