| Inclusion of features derived from a mixture of time window sizes improved classification accuracy of machine learning algorithms for sheep grazing behaviours S Hu, A Ingham, S Schmoelzl, J McNally, B Little, D Smith, ... Computers and Electronics in Agriculture 179, 105857, 2020 | 23 | 2020 |
| Robust regression with data-dependent regularization parameters and autoregressive temporal correlations N Wang, YG Wang, S Hu, ZH Hu, J Xu, H Tang, G Jin Environmental Modeling & Assessment 23, 779-786, 2018 | 10 | 2018 |
| Energy-efficient virtual machine placement in data centres via an accelerated Genetic Algorithm with improved fitness computation E Hormozi, S Hu, Z Ding, YC Tian, YG Wang, ZG Yu, W Zhang Energy 252, 123884, 2022 | 8 | 2022 |
| Software-Defined Networking for Real-Time Network Systems B Goswami, S Hu, Y Feng Goswami B., Hu S., Feng Y. (2022) Software-Defined Networking for Real-Time …, 2022 | 8 | 2022 |
| Predictions of machine learning with mixed-effects in analyzing longitudinal data under model misspecification S Hu, YG Wang, C Drovandi, T Cao Statistical Methods & Applications 32 (2), 681-711, 2023 | 5 | 2023 |
| Predicting bite rate of grazing cattle from accelerometry data via semi-supervised regression S Hu, R Arablouei, GJ Bishop-Hurley, A Reverter, A Ingham Smart Agricultural Technology 5, 100256, 2023 | 4 | 2023 |
| A Cost-Efficient Resource Provisioning and Scheduling Approach for Deadline-Sensitive MapReduce Computations in Cloud Environment A Jabbari, F Masoumiyan, S Hu, M Tang, YC Tian 2021 IEEE 14th International Conference on Cloud Computing (CLOUD), 600-608, 2021 | 3 | 2021 |
| An efficient and robust inference method based on empirical likelihood in longitudinal data analysis S Hu, J Xu Communications in Statistics-Theory and Methods, 1-17, 2020 | 2 | 2020 |
| Inferring Circadian Gene Regulatory Relationships from Gene Expression Data with a Hybrid Framework S Hu, Y Jing, T Li, YG Wang, Z Liu, J Gao, YC Tian BMC Bioinformatics 24 (362), 2023 | 1 | 2023 |
| Statistical modeling and machine learning in longitudinal data analysis S Hu Queensland University of Technology, 2021 | 1 | 2021 |
| Transcriptomic insights into shared responses to Fusarium crown rot infection and drought stresses in bread wheat (Triticum aestivum L.) Z Su, S Gao, Z Zheng, J Stiller, S Hu, MD McNeil, S Shabala, M Zhou, ... Theoretical and Applied Genetics 137 (2), 34, 2024 | | 2024 |
| Analyzing Cattle Activity Patterns with Ear Tag Accelerometer Data S Hu, A Reverter, R Arablouei, G Bishop-Hurley, J McNally, F Alvarenga, ... Animals 14 (2), 301, 2024 | | 2024 |
| Robust penalized empirical likelihood in high dimensional longitudinal data analysis L Fu, S Hu, J Li Journal of Statistical Planning and Inference 228, 11-22, 2024 | | 2024 |
| Comparing genomic prediction accuracies for commercial cows' reproductive performance using GA2CAT and two machine learning methods. Y Li, S Hu, L Porto-Neto, R McCulloch, S McWilliam, P Alexandre, ... | | 2023 |
| Performance of variance estimators in the analysis of longitudinal data with a large cluster size S Hu, YG Wang, L Fu Journal of Statistical Computation and Simulation 92 (1), 1-18, 2022 | | 2022 |
| Application of Principal Component Analysis and Factor Analysis in China's Stock Evaluation Market S Hu, J Xu Journal of Chongqing University of Technology (Natural Science) 31 (05), 192-202, 2017 | | 2017 |
| Application of Robust Empirical Likelihood Estimation Method in Longitudinal Data S Hu Chongqing University, 2017 | | 2017 |
