Research on Binary Mixed VOCs Gas Identification Method Based on Multi-Task Learning DOI Creative Commons
Haixia Mei,

Ruiming Yang,

Jingyi Peng

и другие.

Sensors, Год журнала: 2025, Номер 25(8), С. 2355 - 2355

Опубликована: Апрель 8, 2025

Traditional volatile organic compounds (VOCs) detection models separate component identification and concentration prediction, leading to low feature utilization limited learning in small-sample scenarios. Here, we realize a Residual Fusion Network based on multi-task (MTL-RCANet) implement prediction of VOCs. The model integrates channel attention mechanisms cross-fusion modules enhance extraction capabilities task synergy. To further balance the tasks, dynamic weighted loss function is incorporated adjust weights dynamically according training progress each task, thereby enhancing overall performance model. proposed network achieves an accuracy 94.86% R2 score 0.95. Comparative experiments reveal that using only 35% total data length as input yields excellent performance. Moreover, effectively information across significantly improving efficiency compared single-task learning.

Язык: Английский

Household cleaning agents impact on pediatric asthma: a systematic review and meta-analysis DOI
Muhammad Imran Arif, Zhen Wang, Liang Ru

и другие.

International Journal of Environmental Health Research, Год журнала: 2025, Номер unknown, С. 1 - 13

Опубликована: Фев. 19, 2025

Background Household cleaning agents promote hygiene along with causing respiratory effects, especially pediatric asthma. This systematic review quantified the association between exposure to household and

Язык: Английский

Процитировано

0
Research on Binary Mixed VOCs Gas Identification Method Based on Multi-Task Learning DOI Creative Commons
Haixia Mei,

Ruiming Yang,

Jingyi Peng

и другие.

Sensors, Год журнала: 2025, Номер 25(8), С. 2355 - 2355

Опубликована: Апрель 8, 2025

Traditional volatile organic compounds (VOCs) detection models separate component identification and concentration prediction, leading to low feature utilization limited learning in small-sample scenarios. Here, we realize a Residual Fusion Network based on multi-task (MTL-RCANet) implement prediction of VOCs. The model integrates channel attention mechanisms cross-fusion modules enhance extraction capabilities task synergy. To further balance the tasks, dynamic weighted loss function is incorporated adjust weights dynamically according training progress each task, thereby enhancing overall performance model. proposed network achieves an accuracy 94.86% R2 score 0.95. Comparative experiments reveal that using only 35% total data length as input yields excellent performance. Moreover, effectively information across significantly improving efficiency compared single-task learning.

Язык: Английский

Процитировано

0