Peisong Zhang

2022

1. 

   Dual-grained directional representation for infectious disease case prediction

   Peisong Zhang, Zhijin Wang, Yaohui Huang, and 1 more author

   Knowledge-Based Systems, 2022

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   The uncertain infection transmission causes challenges in accurate disease prediction. Numerous methods have been proposed to capture the temporal pictures from past observations within equal time intervals, which are called single-grained time series. However, these methods are not suitable for capturing uncertain temporal dynamics from infectious disease time series, since the infectious diseases may propagate in the incubation period. To address this issue, this paper proposes a Dual-Grained Directional Representation (DGDR) to generate predictions, via consolidating the representations of an equal-grained time series and several fine-grained time series. Firstly, the proposed DGDR learns a transformed segmentation into three kinds of representations. And then those representations from both equal-grained data and fine-grained data are temporally consolidated to connect with outputs. Extensive experiments on two real infectious disease datasets are done to validate the proposed DGDR. Compared with the other twelve methods, MAE value is decreased by 31.5%, RMSE value is decreased by 29.9%, and R2 value is improved by 87.6%.

   @article{journal/knosys2022/Zhang,
     title = {Dual-grained directional representation for infectious disease case prediction},
     journal = {Knowledge-Based Systems},
     volume = {256},
     pages = {109806},
     year = {2022},
     issn = {0950-7051},
     publisher = {Elsevier},
     doi = {https://doi.org/10.1016/j.knosys.2022.109806},
     url = {https://www.sciencedirect.com/science/article/pii/S0950705122009194},
     author = {Zhang, Peisong and Wang, Zhijin and Huang, Yaohui and Wang, Mingzhai},
     dimensions = {true},
   }

2. 

   An Oriented Attention Model for Infectious Disease Cases Prediction

   Peisong Zhang, Zhijin Wang, Guoqing Chao, and 2 more authors

   In Advances and Trends in Artificial Intelligence. Theory and Practices in Artificial Intelligence, Jul 2022

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   Effective infectious disease prediction supports the success of infection prevention and control. Several attention-based predictive models can be applied to undertake the prediction task. However, using a single attention mechanism can only capture local information, i.e. part of the temporal dynamics from time series. In this paper, we take for the hypothesis that using multiple attention from different aspects could improve prediction accuracy. An oriented attention model (OAM) is proposed to draw temporal dynamics in several aspects, via oriented attention units and their aggregation. Firstly, time series are represented as oriented transformations. And then those representations are consolidated to connect with outputs. Intensive experiments on two real infectious disease datasets show OAM’s effectiveness.

   @inproceedings{conference/ieaaie2022/Zhang,
     author = {Zhang, Peisong and Wang, Zhijin and Chao, Guoqing and Huang, Yaohui and Yan, Jingwen},
     title = {An Oriented Attention Model for Infectious Disease Cases Prediction},
     booktitle = {Advances and Trends in Artificial Intelligence. Theory and Practices in Artificial Intelligence},
     year = {2022},
     month = jul,
     publisher = {Springer International Publishing},
     address = {Cham, Japan},
     pages = {124--136},
     dimensions = {true},
   }