Ghent University Academic Bibliography

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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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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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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) 2021 5TH INTERNATIONAL CONFERENCE ON INNOVATION IN ARTIFICIAL INTELLIGENCE (ICIAI 2021). 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
- C1
- open access
Context-based analysis of urban air quality using an opportunistic mobile sensor network

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

(2020) Science and Technologies for Smart Cities : 5th EAI International Summit, SmartCity360, Proceedings. In Lecture notes of the institute for computer sciences 323. p.285-300

Academic Bibliography

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

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

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

Context-based analysis of urban air quality using an opportunistic mobile sensor network

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