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Dr Tianning Li

Dr Tianning Li
Name Tianning Li
Position Lecturer (Computing)
Section School of Mathematics, Physics and Computing
Office D218
Location Toowoomba Campus
Phone +61 7 4631 2130
Extension 2130
Email
Qualifications BISM Nanjing , MAccFin Adelaide , PhD USQ

Research interests
Machine Learning Algorithms, Signal Processing, EEG Research.

Currently teaching courses/programs
CSC8003 Machine Learning
CSC2410 Computational Thinking with Python
CSC2407 Introduction to Software Engineering
CSC5020 Foundations of Programming
CSC1401 Foundation Programming
CSC1402 Foundation Computing



Publications in ePrints

Ra, Jee Sook and Li, TianningORCID: https://orcid.org/0000-0001-5142-8654 and Li, YanORCID: https://orcid.org/0000-0002-4694-4926 (2021) A novel permutation entropy-based EEG channel selection for improving epileptic seizure prediction. Sensors, 21 (23):7972. ISSN 1424-8220

Ra, Jee Sook and Li, TianningORCID: https://orcid.org/0000-0001-5142-8654 and Li, YanORCID: https://orcid.org/0000-0002-4694-4926 (2021) A novel spectral entropy-based index for assessing the depth of anaesthesia. Brain Informatics, 8 (1):10. ISSN 2198-4026

Li, Tianning ORCID: https://orcid.org/0000-0001-5142-8654 and Sivakumar, Prashanth and Tao, XiaohuiORCID: https://orcid.org/0000-0002-0020-077X (2019) Anesthesia assessment based on ICA permutation entropy analysis of two-channel EEG signals. In: 12th International Conference on Brain Informatics (BI 2019), 13-15 Dec 2019, Haikou, Hainan, China.

Diykh, Mohammed and Li, YanORCID: https://orcid.org/0000-0002-4694-4926 and Wen, PengORCID: https://orcid.org/0000-0003-0939-9145 and Li, TianningORCID: https://orcid.org/0000-0001-5142-8654 (2018) Complex networks approach for depth of anesthesia assessment. Measurement, 119. pp. 178-189. ISSN 0263-2241

Li, Tianning ORCID: https://orcid.org/0000-0001-5142-8654 and Wen, PengORCID: https://orcid.org/0000-0003-0939-9145 (2017) Depth of anaesthesia assessment using interval second-order difference plot and permutation entropy techniques. IET Signal Processing, 11 (2). pp. 221-227. ISSN 1751-9675

View full listing in ePrints