Mr Jinran Wu
PhD Student
Queensland University of Technology
I am a PhD student working on statistical machine learning and optimization with applications. In the theoritical field, I am interested in incorporating statistical modelling in machine learning and developing some robust machine learning frameworks. In practice areas, I focus on the forecasting applications of statistical modelling and machine learning in engineeringm business, and bioinformatics.
Research Interests:
Forecasting
Parameter estimation
Robust regression
Publications
Book Chapters
Wu, J., & Ding Z.
(2020). Improved Grey Model by Dragonfly Algorithm for Chinese Tourism Demand Forecasting..
(H., F., P. F-V., M. A., & J. S., Ed.).Lecture Notes in Computer Science: Trends in Artificial Intelligence Theory and Applications. Artificial Intelligence Practices.. 12144, 199 - 209. doi: 10.1007/978-3-030-55789-8_18
Invited talks, refereed proceedings and other conference outputs
Zhang, B., Yang Y., Zhao D., & Wu J.
(2020). A robust decomposition-ensemble framework for wind speed forecasting.
020 16th International Conference on Control, Automation, Robotics and Vision (ICARCV). 9305351. doi: 10.1109/ICARCV50220.2020.9305351
Gao, Y., Qian C., Tao Z., Zhou H., Wu J., & Yang Y.
(2020). Improved Whale Optimization Algorithm via Cellular Automata.
2020 IEEE International Conference on Progress in Informatics and Computing (PIC)2020 IEEE International Conference on Progress in Informatics and Computing (PIC). 34 - 39. doi: 10.1109/PIC50277.2020.9350796
Cui, Z., Hou X., Zhou H., Lian W., & Wu J.
(2020). Modified Slime Mould Algorithm via Levy Flight.
2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). 1109 - 1113. doi: 10.1109/CISP-BMEI51763.2020.9263669
Journal Articles
Cui, Z., Wu J., Ding Z., Duan Q., Lian W., Yang Y., et al.
(2021). A hybrid rolling grey framework for short time series modelling.
Neural Computing and Applications. doi: 10.1007/s00521-020-05658-0
Yang, Y., Fan X., Xu C., Wu J., & Sun B.
(2021). State Consensus Cooperative Control for a Class of Nonlinear Multi-Agent Systems with Output Constraints via ADP Approach.
Neurocomputing. 458, 284-296. doi: 10.1016/j.neucom.2021.05.046
Yang, Y., Tao Z., Qian C., Gao Y., Zhou H., Ding Z., et al.
(2021). A hybrid robust system considering outliers for electric load series forecasting.
Applied Intelligence. doi: 10.1007/s10489-021-02473-5
Zhu, M., Wu J., & Wang Y‐G.
(2021). Multi‐horizon accommodation demand forecasting: A New Zealand case study.
International Journal of Tourism Research. 23(3), 442-453. doi: 10.1002/jtr.2416
Zhang, S., Wu J., Jia Y., Wang Y-G., Zhang Y., & Duan Q.
(2021). A temporal LASSO regression model for the emergency forecasting of the suspended sediment concentrations in coastal oceans: Accuracy and interpretability.
Engineering Applications of Artificial Intelligence. 100, 104206. doi: 10.1016/j.engappai.2021.104206
Ouyang, Z., Zhai X., Wu J., Yang J., Yue D., Dou C., et al.
(2021). A Cloud Endpoint Coordinating CAPTCHA based on Multi-view Stacking Ensemble.
Computers & Security. 103, 102178. doi: 10.1016/j.cose.2021.102178
Wu, J., Wang Y-G., Tian Y-C., Burrage K., & Cao T.
(2021). Support vector regression with asymmetric loss for optimal electric load forecasting.
Energy. 223, 119969. doi: 10.1016/j.energy.2021.119969
Wu, J., Wang Y-G., Burrage K., Tian Y-C., Lawson B., & Ding Z.
(2020). An improved firefly algorithm for global continuous optimization problems.
Expert Systems with Applications. 149, 113340. doi: 10.1016/j.eswa.2020.113340
Yang, Y., Ge J., Yue D., Meng Q., & Wu J.
(2020). Adaptive Resilient Control of a Class of Nonlinear Systems Based on Event-Triggered Mechanism.
Neurocomputing. 403, 304-313. doi: 10.1016/j.neucom.2020.04.061
Gao, S., Wu J., Stiller J., Zheng Z., Zhou M., Wang Y-G., et al.
(2020). Identifying barley pan-genome sequence anchors using genetic mapping and machine learning.
Theoretical and Applied Genetics. 133(9), 2535 - 2544. doi: 10.1007/s00122-020-03615-y
Wu, J., Cui Z., Chen Y., Kong D., & Wang Y-G.
(2019). A new hybrid model to predict the electrical load in five states of Australia.
Energy. 166, 598 - 609. doi: 10.1016/j.energy.2018.10.076
