| [1] |
卢宇红, 宋佳丽, 王萌 ,等. 基于深度神经网络融合稀疏分组 lasso 的预测模型研究[J]. 中国卫生统计, 2021,38(6): 821-827.
|
|
LU Y H , SONG J L , WANG M ,et al. The study on the prediction model based on deep neural network together with sparse group lasso[J]. Chinese Journal of Health Statistics, 2021,38(6): 821-827.
|
| [2] |
顾亦然, 王雨, 杨海根 . 基于用户行为序列的短视频用户多行为点击预测模型[J]. 电子与信息学报, 202310.11999/JEIT211458.
|
|
GU Y R , WANG Y , YANG H G . Multi-action click prediction model for short video users based on user’s behavior sequence[J]. Journal of Electronics ＆ Information Technology, 202310.11999/JEIT211458.
|
| [3] |
CAO W C , WANG K , GAN H C ,et al. User online purchase behavior prediction based on fusion model of CatBoost and Logit[J]. Journal of Physics:Conference Series, 2021,2003(1): 012011.
|
| [4] |
LI H R , LIN F Q , LU X ,et al. Systematic analysis of fine-grained mobility prediction with on-device contextual data[J]. IEEE Transactions on Mobile Computing, 2022,21(3): 1096-1109.
|
| [5] |
QIAO S B , PANG S C , WANG M ,et al. Online video popularity regression prediction model with multichannel dynamic scheduling based on user behavior[J]. Chinese Journal of Electronics, 2021,30(5): 876-884.
|
| [6] |
NIU B , SUI L , TANG J R ,et al. Prediction of microblog users’ forwarding behavior based on interactive and active information[C]// Proceedings of the 2020 International Conference on Aviation Safety and Information Technology. New York:ACM Press, 2020: 554-559.
|
| [7] |
XIAO Y P , LI J H , ZHU Y F ,et al. User behavior prediction of social hotspots based on multimessage interaction and neural network[J]. IEEE Transactions on Computational Social Systems, 2020,7(2): 536-545.
|
| [8] |
HU G Y , ZHOU Z J , HU C H ,et al. Hidden behavior prediction of complex system based on time-delay belief rule base forecasting model[J]. Knowledge-Based Systems, 2020,203: 106147.
|
| [9] |
SUDAN B , CANSIZ S , OGRETICI E ,et al. Prediction of success and complex event processing in E-learning[C]// Proceedings of 2020 International Conference on Electrical,Communication,and Computer Engineering (ICECCE). Piscataway:IEEE Press, 2020: 1-6.
|
| [10] |
SOLTANI N Y , . Online learning of sparse Gaussian conditional random fields with application to prediction of energy consumers behavior[C]// Proceedings of 2021 IEEE Statistical Signal Processing Workshop (SSP). Piscataway:IEEE Press, 2021: 486-490.
|
| [11] |
SUN L T , GAO S W , WANG L . An automatic test sequence generation method based on Markov chain model[C]// Proceedings of 2021 World Conference on Computing and Communication Technologies (WCCCT). Piscataway:IEEE Press, 2021: 91-96.
|
| [12] |
DENNIS L A , FU Y , SLAVKOVIK M . Markov chain model representation of information diffusion in social networks[J]. Journal of Logic and Computation, 2022,32(6): 1195-1211.
|
| [13] |
PENG L , WEN L , QIANG L ,et al. Research on complexity model of important product traceability efficiency based on Markov chain[J]. Procedia Computer Science, 2020,166: 456-462.
|
| [14] |
HAN C , CHEN J , TAN M K ,et al. A tensor-based Markov chain model for heterogeneous information network collective classification[J]. IEEE Transactions on Knowledge and Data Engineering, 2022,34(9): 4063-4076.
|
| [15] |
CRUZ I R , LINDSTR?M J ,, TROFFAES M C M ,et al. Iterative importance sampling with Markov chain Monte Carlo sampling in robust Bayesian analysis[J]. Computational Statistics ＆ Data Analysis, 2022,176: 107558.
|
| [16] |
ALAMOUDI A , LIU M L , PAYANI A ,et al. Predicting mobile users traffic and access-time behavior using recurrent neural networks[C]// Proceedings of 2021 IEEE Wireless Communications and Networking Conference (WCNC). Piscataway:IEEE Press, 2021: 1-6.
|
| [17] |
LIU K , TATINATI S , KHONG A W H . A weighted feature extraction technique based on temporal accumulation of learner behavior features for early prediction of dropouts[C]// Proceedings of 2020 IEEE International Conference on Teaching,Assessment,and Learning for Engineering (TALE). Piscataway:IEEE Press, 2021: 295-302.
|
| [18] |
SETIA S , JYOTI V , DUHAN N . HPM:a hybrid model for user’s behavior prediction based on N-gram parsing and access logs[J]. Scientific Programming, 2020: 1-18.
|
| [19] |
CHEN L Y , WANG L H , ZHOU Y X . Research on data mining combination model analysis and performance prediction based on students’ behavior characteristics[J]. Mathematical Problems in Engineering, 2022: 1-10.
|
| [20] |
RASOULI A , ROHANI M , LUO J . Bifold and semantic reasoning for pedestrian behavior prediction[C]// Proceedings of 2021 IEEE/CVF International Conference on Computer Vision (ICCV). Piscataway:IEEE Press, 2022: 15580-15590.
|
| [21] |
ZHOU H , YU K M , CHEN Y C ,et al. A hybrid feature selection method RFSTL for manufacturing quality prediction based on a high dimensional imbalanced dataset[J]. IEEE Access, 2021,9: 29719-29735.
|
| [22] |
JIANG L , LIU H , JIANG H ,et al. Heuristic and neural network based prediction of project-specific API member access[J]. IEEE Transactions on Software Engineering, 2022,48(4): 1249-1267.
|
