Cai L, Yidan Yu, Zhang S, Song Y, Xiong Z, Zhou T (2020a) A sample-rebalanced outlier-rejected k-nearest neighbour regression model for short-term traffic flow forecasting. IEEE Access 8:22686–22696. https://doi.org/10.1109/ACCESS.2020.2970250
Cai W, Yang J, Yidan Yu, Song Y, Zhou T, Qin J (2020b) PSO-ELM: a hybrid learning model for short-term traffic flow forecasting. IEEE Access 8:6505–6514. https://doi.org/10.1109/ACCESS.2019.2963784
Cai L, Lei M, Zhang S, Yidan Yu, Zhou T, Qin J (2020c) A noise-immune LSTM network for short-term traffic flow forecasting. Chaos 30(2):023135. https://doi.org/10.1063/1.5120502
Chai W, Zheng Y, Tian L, Qin J, Zhou T (2023) GA-KELM: genetic-algorithm-improved kernel extreme learning machine for traffic flow forecasting. Mathematics 11(16):3574 Article Google Scholar
Chan KY, Dillon TS, Singh J, Chang E (2011) Neural-network-based models for short-term traffic flow forecasting using a hybrid exponential smoothing and Levenberg-Marquardt algorithm. IEEE Trans Intell Transp Syst 13(2):644–654. https://doi.org/10.1109/TITS.2011.2174051 Article Google Scholar
Chen L-C, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2017) DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans Pattern Anal Mach Intell 40(4):834–848. https://doi.org/10.1109/TPAMI.2017.2699184 ArticlePubMed Google Scholar
Chen W, An J, Li R, Li F, Guoqi Xie Md, Bhuiyan ZA, Li K (2018) A novel fuzzy deep-learning approach to traffic flow prediction with uncertain spatial-temporal data features. Future Gener Comput Syst 89:78–88. https://doi.org/10.1016/j.future.2018.06.021 Article Google Scholar
Chen B, Zheng Y, Ren P (2021) Error loss networks. arXiv preprint arXiv:2106.03722
Chen J, Kao S, He H, Zhuo W, Wen S, Lee C-H, Chan S-HG (2023) Run, don’t walk: chasing higher flops for faster neural networks. arXiv preprint arXiv:2303.03667
Cui Z, Huang B, Dou H, Cheng Y, Guan J, Zhou T (2022a) A two stages hybrid extreme learning model for short-term traffic flow forecasting. Mathematics. https://doi.org/10.3390/math10122087
Cui Z, Huang B, Dou H, Tan G, Zheng S, Zhou T (2022b) GSA-ELM: a hybrid learning model for short-term traffic flow forecasting. IET Intell Transp Syst 16(1):41–52. https://doi.org/10.1049/itr2.12127
Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S et al (2020) An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929
Fang W, Zhuo W, Yan J, Song Y, Jiang D, Zhou T (2022) Attention meets long short-term memory: a deep learning network for traffic flow forecasting. Physica A Stat Mech Appl 587:126485. https://doi.org/10.1016/j.physa.2021.126485
Feng X, Ling X, Zheng H, Chen Z, Yiwen X (2018) Adaptive multi-kernel SVM with spatial-temporal correlation for short-term traffic flow prediction. IEEE Trans Intell Transp Syst 20(6):2001–2013. https://doi.org/10.1109/TITS.2018.2854913 Article Google Scholar
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778
Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H (2017) MobileNets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861
Huang G, Liu Z, Van Der ML, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4700–4708
Huang H, Zheng S, Yang Z, Wu Y, Li Y, Qiu J, Cheng Y, Lin P, Lin Y, Ji Guan, Mikulis DJ, Zhou T, Wu R (2022) Voxel-based morphometry and a deep learning model for the diagnosis of early Alzheimer’s disease based on cerebral gray matter changes. Cereb Cortex. https://doi.org/10.1093/cercor/bhac099 ArticlePubMedPubMed Central Google Scholar
Lippi M, Bertini M, Frasconi P (2013) Short-term traffic flow forecasting: an experimental comparison of time-series analysis and supervised learning. IEEE Trans Intell Transp Syst 14(2):871–882. https://doi.org/10.1109/TITS.2013.2247040 Article Google Scholar
Luo Y, Wei M, Li S, Ling J, Xie G, Yao S (2023a) An effective co-support guided analysis model for multi-contrast MRI reconstruction. IEEE J Biomed Health Inform 27:2477–2488
Luo Y, Huang Q, Ling J, Lin K, Zhou T (2023b) Local and global knowledge distillation with direction-enhanced contrastive learning for single-image deraining. Knowledge-Based Systems, pp 1–10
Qiu J, Tan G, Lin Y, Guan J, Dai Z, Wang F, Zhuang C, Wilman AH, Huang H, Cao Z et al (2022) Automated detection of intracranial artery stenosis and occlusion in magnetic resonance angiography: a preliminary study based on deep learning. Magn Reson Imaging 94:105–111. https://doi.org/10.1016/j.mri.2022.09.006 ArticlePubMed Google Scholar
Tan M, Le Q (2019) EfficientNet: rethinking model scaling for convolutional neural networks. In: International conference on machine learning. PMLR, pp 6105–6114
Tan G, Zheng S, Huang B, Cui Z, Dou H, Yang X, Zhou T (2021) Hybrid GA-SVR: an effective way to predict short-term traffic flow. In: 21st International conference on algorithms and architectures for parallel processing (ICA3PP 2021), pp 1–11. https://doi.org/10.1007/978-3-030-95388-1_4
Tan G, Huang B, Cui Z, Dou H, Zheng S, Zhou T (2022) A noise-immune reinforcement learning method for early diagnosis of neuropsychiatric systemic lupus erythematosus. Math Biosci Eng 19(3):2219–2239. https://doi.org/10.3934/mbe.2022104 ArticlePubMed Google Scholar
Wang Z, Oates T (2015) Encoding time series as images for visual inspection and classification using tiled convolutional neural networks. In: Workshops at the twenty-ninth AAAI conference on artificial intelligence
Woo S, Park J, Lee J-Y, Kweon IS (2018) Cbam: convolutional block attention module. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19
Xu Y, Kong Q-J, Liu Y (2013) Short-term traffic volume prediction using classification and regression trees. In: 2013 IEEE intelligent vehicles symposium (IV). IEEE, pp 493–498 https://doi.org/10.1109/IVS.2013.6629516
Yang H-F, Dillon TS, Chang E, Chen Y-PP (2018) Optimized configuration of exponential smoothing and extreme learning machine for traffic flow forecasting. IEEE Trans Ind Inform 15(1):23–34. https://doi.org/10.1109/TII.2018.2876907 Article Google Scholar
Yao R, Zhang W, Zhang L (2020) Hybrid methods for short-term traffic flow prediction based on ARIMA-GARCH model and wavelet neural network. J Transp Eng Part A Syst 146(8):04020086. https://doi.org/10.1061/JTEPBS.0000388 Article Google Scholar
Yuanli G, Wenqi L, Xinyue X, Qin L, Shao Z, Zhang H (2019) An improved bayesian combination model for short-term traffic prediction with deep learning. IEEE Trans Intell Transp Syst 21(3):1332–1342. https://doi.org/10.1109/TITS.2019.2939290 Article Google Scholar
Zheng H, Lin F, Feng X, Chen Y (2021) A hybrid deep learning model with attention-based Conv-LSTM networks for short-term traffic flow prediction. IEEE Trans Intell Transp Syst 22(11):6910–6920. https://doi.org/10.1109/TITS.2020.2997352 Article Google Scholar
Zhou T, Han G, Xuemiao X, Lin Z, Han C, Huang Y, Qin J (2017) \(\delta \)-agree adaboost stacked autoencoder for short-term traffic flow forecasting. Neurocomputing 247(4):31–38. https://doi.org/10.1016/j.neucom.2017.03.049
Zhou T, Dou H, Tan J, Song Y, Wang F, Wang J (2022) Small dataset solves big problem: an outlier-insensitive binary classifier for inhibitory potency prediction. Knowl Based Syst. https://doi.org/10.1016/j.knosys.2022.109242
Zhu JZ, Cao JX, Zhu Y (2014) Traffic volume forecasting based on radial basis function neural network with the consideration of traffic flows at the adjacent intersections. Transp Res Part C Emerg Technol 47:139–154. https://doi.org/10.1016/j.trc.2014.06.011 Article Google Scholar