Dr Jingjing Zhang

2021

• Daniel, R., Zhang, J. and Farewell, D. 2021. Making apples from oranges: Comparing noncollapsible effect estimators and their standard errors after adjustment for different covariate sets. Biometrical Journal 63(3), pp. 528-557. (10.1002/bimj.201900297)

2019

• Gadalla, A. A. M. et al. 2019. Identification of clinical and urine biomarkers for uncomplicated urinary tract infection using machine learning algorithms. Scientific Reports 9(1), article number: 19694. (10.1038/s41598-019-55523-x)

2017

• Zhang, J. et al. 2017. Machine-learning algorithms define pathogen-specific local immune fingerprints in peritoneal dialysis patients with bacterial infections. Kidney International 92(1), pp. 179-191. (10.1016/j.kint.2017.01.017)

2016

• Liuzzi, A. R. et al. 2016. Unconventional human T cells accumulate at the site of infection in response to microbial ligands and induce local tissue remodeling. Journal of Immunology 197(6), pp. 2195-2207. (10.4049/jimmunol.1600990)

2015

• Zhao, W., Zhang, J. and Li, K. 2015. An efficient LS-SVM based method for fuzzy system construction. IEEE Transactions on Fuzzy Systems 23(3), pp. 627-643. (10.1109/TFUZZ.2014.2321594)
• Zhang, J., Zhang, J., Coats, M., Li, W., Carey, F. and McKenna, S. 2015. Multi-scale analysis of the surface morphology of colorectal polyps from optical tomography. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 5(5), pp. 318-328. (10.1080/21681163.2015.1035404)

2014

• Zhang, J., McKenna, S., Zhang, J., Coats, M. and Carey, F. A. 2014. Analysing the surface morphology of colorectal polyps: differential geometry and pit pattern prediction. Presented at: 18th Annual Conference in Medical Image Understanding and Analysis, London, United Kingdom.
• Zhang, J., Li, K., Zhao, W., Fei, M. and Wang, Y. 2014. A systematic fire detection approach based on sparse least-squares SVMs. Presented at: International Conference on Intelligent Computing for Sustainable Energy and Environment and International Conference on Life System Modeling and Simulation, Shanghai, China, 20 -23 September 2014 Presented at Fei, M. et al. eds.Computational Intelligence, Networked Systems and Their Applications, Vol. 462. Communications in Computer and Information Science Vol. 462. Berlin: Springer pp. 351-362., (10.1007/978-3-662-45261-5_37)

2012

• Zhang, J. and Li, K. 2012. Heuristic based model selection for a new least-squares SVM solution. Presented at: 26th European Simulation and Modelling Conference-ESM'2012, Essen, Germany, 22-24 October 2012.
• Zhang, J., Li, K., Irwin, G. W. and Zhao, W. 2012. A regression approach to LS-SVM and sparse realization based on fast subset selection. Presented at: 10th World Congress on Intelligent Control and Automation (WCICA), Beijing, China, 6-8 July 2012 Presented at Cheng, D. ed.Proceedings of the 10th World Congress on Intelligent Control and Automation July 6-8, 2012, Beijing, China. Piscataway, NJ: Institute of Electrical and Electronics Engineers pp. 612-617., (10.1109/WCICA.2012.6357952)

2011

• Zhang, J., Niu, Q., Li, K. and Irwin, G. W. 2011. Model selection in SVMs using differential evolution. IFAC Proceedings Volumes 44(1), pp. 14717-14722. (10.3182/20110828-6-IT-1002.00584)