[1] Ting Ke*,Yajiang Zhang, et al. An effective CNN-MHSA method for the fault diagnosis of ZPW-2000A track circuit. Transactions of the Institute of Measurement and Control. 2024;0(0). doi:10.1177/01423312241273855(SCI)
[2] Ting Ke*,Wenlong Ke, et al. An Effective Deep SVM Approach for Fault Diagnosis of 25 Hz Track Circuit. ICIC 2024, LNAI 14878, pp. 1-11. https://doi.org/10.1007/978-981-97-5672-8_12
[3] Ting Ke*, Yaozong Zheng, et al. Exploration of Railway Signal Unloading Task Based on Deep Reinforcement Learning Method. ICIC 2024, LNCS 14863, pp. 493-503, 2024.
[4] Yajiang Zhang, Ting Ke*, et al.Multi-task Online Course Recommendation Method Based on FDMA.ICIC 2024, LNAI 14880, pp. 158-170, 2024.
[5] Yibo Liu, Ting Ke*. Coronary Artery 3D/2D Registration Based on Particle Swarm Optimization of Contextual Morphological Features. ICIC 2024, LNBI 14881, pp. 219-228, 2024
[7] Ting Ke*, Xuechun Ge, et al. A general maximal margin hyper-sphere SVM for multi-class classification. Expert Systems With Applications, 2024, 237, 121647. (SCI,IF:8.5 , Top期刊,一区,CCF B类)
[8] Ting Ke*, Lidong Zhang, et al. Construct a Robust Least Squares Support Vector Machine Based on L p -norm and L ∞ -norm [J]. Engineering Applications of Artificial Intelligence. 2022, 99: 104134. (SCI,IF:7.802, Top期刊,一区,CCF B类)
[9] Ting Ke*, Ling Jing, et al. Global and Local Learning from Positive and Unlabeled Examples[J]. Applied Intelligence. 2018, 48(8): 2373-2392. (SCI, IF:5.019, 二区, 他引次数:7,CCF C类)
[10] Ting Ke*, Hui Lv, et al. A Biased Least Square Support Vector Machine Based on Mahalanobis Distance for PU Learning[J]. Physica A: Statistical Mechanics and its Applications. 2018, 509: 422-438. (SCI, IF: 3.778 二区,他引次数:11)
[11] Ting Ke*, Lidong Zhang, et al. A Robust Least Squares Support Vector Machine Based on L∞-norm [J]. Neural Processing Letters. 2020, 52: 2371-2397. (SCI,IF:2.565,三区,CCF C类)