Hierarchical Recovery of Missing Air Pollution Data via Improved Long-short Term Context Encoder Network-Reference-Cited by-同舟云学术

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Hierarchical Recovery of Missing Air Pollution Data via Improved Long-short Term Context Encoder Network

Published:2021 Issue: Volume: Page:1-1
ISSN:2332-7790
Container-title:IEEE Transactions on Big Data
language:
Short-container-title:IEEE Trans. Big Data

Author:

Yu Yangwen,Li Victor OK,Lam Jacqueline C.K.

Funder

Theme-based Research Scheme of the Research Grants Council of Hong Kong SAR Government

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Information Systems and Management,Information Systems

Link

http://xplorestaging.ieee.org/ielx7/6687317/7153538/09594444.pdf?arnumber=9594444

Cited by 8 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Distributed personalized imputation based on Gaussian mixture model for missing data;Neural Computing and Applications;2024-05-06

2. Apict:Air Pollution Epidemiology Using Green AQI Prediction During Winter Seasons in India;IEEE Transactions on Sustainable Computing;2024-05

3. Distributed Multi-kernel Learning Based on Gaussian Mixture Model with Missing Data;Proceedings of the 2nd International Conference on Signal Processing, Computer Networks and Communications;2023-12-08

4. GCN-ST-MDIR: Graph Convolutional Network-Based Spatial-Temporal Missing Air Pollution Data Pattern Identification and Recovery;IEEE Transactions on Big Data;2023-10

5. Deep learning approach to forecast air pollution based on novel hourly index;Physica Scripta;2023-08-24

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