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Graph-Deep-Learning-Based Inference of Fine-Grained Air Quality From Mobile IoT Sensors

  • Published:2020-09 Issue:9 Volume:7 Page:8943-8955
  • ISSN:2327-4662
  • Container-title:IEEE Internet of Things Journal
  • language:
  • Short-container-title:IEEE Internet Things J.

Author:

Do Tien HuuORCID,Tsiligianni EvaggeliaORCID,Qin XueningORCID,Hofman JelleORCID,La Manna Valerio Panzica,Philips Wilfried,Deligiannis NikosORCID

Funder

imec

VUB SRP Project

FWO

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Computer Science Applications,Hardware and Architecture,Information Systems,Signal Processing

Cited by 28 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. An Overview of Predictive Analysis for Air Quality Measurements and Visualization;2024 International Conference on Computing and Data Science (ICCDS);2024-04-26

2. Physics-Guided Variational Graph Autoencoder For Air Quality Inference;ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2024-04-14

3. GF-LRP: A Method for Explaining Predictions Made by Variational Graph Auto-Encoders;IEEE Transactions on Emerging Topics in Computational Intelligence;2024

4. Phy-Apmr: A Physics-Informed Air Pollution Map Reconstruction Approach with Mobile Crwodsensing for Fine-Grained Measurement;2024

5. Opportunistic mobile air quality mapping using sensors on postal service vehicles: from point clouds to actionable insights;Frontiers in Environmental Health;2023-09-28
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