Ghent University Academic Bibliography

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Explaining graph neural networks with topology-aware node selection : application in air quality inference

Esther Rodrigo Bonet, Tien Huu Do, Xuening Qin (UGent) , Jelle Hofman, Valerio Panzica La Manna, Wilfried Philips (UGent) and Nikos Deligiannis

(2022) IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS. 8. p.499-513
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- Journal Article
- A1
- open access
Fine-grained urban air quality mapping from sparse mobile air pollution measurements and dense traffic density

Xuening Qin (UGent) , Tien Huu Do, Jelle Hofman, Esther Rodrigo Bonet, Valerio Panzica La Manna, Nikos Deligiannis and Wilfried Philips (UGent)

(2022) REMOTE SENSING. 14(11).
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Spatiotemporal air quality inference of low-cost sensor data : evidence from multiple sensor testbeds

Jelle Hofman, Tien Huu Do, Xuening Qin (UGent) , Esther Rodrigo Bonet, Wilfried Philips (UGent) , Nikos Deligiannis and Valerio Panzica La Manna

(2022) ENVIRONMENTAL MODELLING & SOFTWARE. 149.
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Street-level air quality inference based on geographically context-aware random forest using opportunistic mobile sensor network

Xuening Qin (UGent) , Tien Huu Do, Jelle Hofman, Esther Rodrigo, Valerio Panzica La Manna, Nikos Deligiannis and Wilfried Philips (UGent)

(2021) ICIAI 2021: 2021 the 5th International Conference on Innovation in Artificial Intelligence. p.221-227
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Spatiotemporal air quality inference of low-cost sensor data : application on a cycling monitoring network

Jelle Hofman, Tien Huu Do, Xuening Qin (UGent) , Esther Rodrigo, Marta E. Nikolaou, Wilfried Philips (UGent) , Nikos Deligiannis and Valerio Panzica La Manna

(2021) Pattern recognition, ICPR international workshops and challenges, Proceedings, Part VI. In Lecture notes in computer science 12666. p.139-147
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- Conference Paper
- C3
Combining mobile air quality sensor data and machine learning for more fine-grained air quality assessments in urban areas

Jelle Hofman, Mania Nikolaou, Do Huu Tien, Xuening Qin (UGent) , Rodrigo Bonet Esther, Wilfried Philips (UGent) , Nikos Deligiannis and Panzica La Manna Valerio

(2021) Meteorology and Climate : Modeling for Air Quality Conference, Abstracts.
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- Conference Paper
- C1
- open access
Multi-disciplinary sensing for personal exposure assessments : quantifying the impact of traffic interventions and meteorological variability

Luc Dekoninck (UGent) and Jelle Hofman

(2020) PROCEEDINGS OF FORUM ACUSTICUM. p.3085-3090
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Graph-deep-learning-based inference of fine-grained air quality from mobile IoT sensors

Tien Huu Do, Evaggelia Tsiligianni, Xuening Qin (UGent) , Jelle Hofman, Valerio Panzica La Manna, Wilfried Philips (UGent) and Nikos Deligiannis

(2020) IEEE INTERNET OF THINGS JOURNAL. 7(9). p.8943-8955
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Mapping air quality in IoT cities : cloud calibration and air quality inference of sensor data

Jelle Hofman, Martha E. Nikolaou, Tien Huu Do, Xuening Qin (UGent) , Esther Rodrigo, Wilfried Philips (UGent) , Nikos Deligiannis and Valerio Panzica La Manna

(2020) 2020 IEEE SENSORS. In IEEE Sensors
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- Conference Paper
- C3
- open access
Multi-disciplinary sensing in the city of the future : simultaneous noise and air pollution monitoring to quantify the impact of traffic interventions on personal exposure

Luc Dekoninck (UGent) and Jelle Hofman

(2019) Monitoring Ambient Air 2019, Abstracts.

Academic Bibliography

Explaining graph neural networks with topology-aware node selection : application in air quality inference

Fine-grained urban air quality mapping from sparse mobile air pollution measurements and dense traffic density

Spatiotemporal air quality inference of low-cost sensor data : evidence from multiple sensor testbeds

Street-level air quality inference based on geographically context-aware random forest using opportunistic mobile sensor network

Spatiotemporal air quality inference of low-cost sensor data : application on a cycling monitoring network

Combining mobile air quality sensor data and machine learning for more fine-grained air quality assessments in urban areas

Multi-disciplinary sensing for personal exposure assessments : quantifying the impact of traffic interventions and meteorological variability

Graph-deep-learning-based inference of fine-grained air quality from mobile IoT sensors

Mapping air quality in IoT cities : cloud calibration and air quality inference of sensor data

Multi-disciplinary sensing in the city of the future : simultaneous noise and air pollution monitoring to quantify the impact of traffic interventions on personal exposure

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