Dr Xinhui Ma

HFL-GAN: scalable hierarchical federated learning GAN for high quantity heterogeneous clients

Petch, L., Moustafa, A., Ma, X., & Yasser, M. (2025). HFL-GAN: scalable hierarchical federated learning GAN for high quantity heterogeneous clients. Applied Intelligence, 55(2), Article 170. https://doi.org/10.1007/s10489-024-05924-x

Patient specific training: development of a CT-based mixed reality fibreoptic intubation simulator

Wright, D., Ma, X., Atkin, W., Wang, L., Fagan, M., & Wellbelove, Z. (2022). Patient specific training: development of a CT-based mixed reality fibreoptic intubation simulator. International Journal of Healthcare Simulation, 2(1), A86. https://doi.org/10.54531/QOJS8275

Redefining Digital Twins - A Wind Energy Operations and Maintenance Perspective

Tuton, E., Ma, X., & Dethlefs, N. (2024, May). Redefining Digital Twins - A Wind Energy Operations and Maintenance Perspective. Presented at The Science of Making Torque from Wind (TORQUE 2024), Florence, Italy

Intelligent digital twin - machine learning system for real-time wind turbine wind speed and power generation forecasting

Tuton, E., Ma, X., & Dethlefs, N. (2023, August). Intelligent digital twin - machine learning system for real-time wind turbine wind speed and power generation forecasting. Presented at The 6th International Conference on Renewable Energy and Environment Engineering REEE 2023, Brest , France

A Multi-Modal Deep Learning Approach to the Early Prediction of Mild Cognitive Impairment Conversion to Alzheimer's Disease

Rana, S. S., Ma, X., Pang, W., & Wolverson, E. (2020, December). A Multi-Modal Deep Learning Approach to the Early Prediction of Mild Cognitive Impairment Conversion to Alzheimer's Disease. Presented at 2020 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT), Leicester, United Kingdom