Multi-level lag scheme significantly improves training efficiency in deep learning: a case study in air quality alert service over sub-tropical area

Benedito Chi Man Tam; Su Kit Tang; Alberto Cardoso

doi:10.1186/s40537-024-01043-z

Multi-level lag scheme significantly improves training efficiency in deep learning: a case study in air quality alert service over sub-tropical area

Benedito Chi Man Tam
, Su Kit Tang
, Alberto Cardoso

Faculty of Applied Sciences

研究成果: Article › 同行評審

7 引文斯高帕斯（Scopus）

摘要

An adaptively formulated Multi-level Lag Scheme can significantly. Improve the training process efficiently, and can be applied in mostly ANN-type deep learning model, with a practical case of Air Quality Alert Service in a city of sub-tropical area.

原文	English
文章編號	3
期刊	Journal of Big Data
卷	12
發行號	1
DOIs	https://doi.org/10.1186/s40537-024-01043-z
出版狀態	Published - 12月 2025

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10.1186/s40537-024-01043-z

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abstract = "An adaptively formulated Multi-level Lag Scheme can significantly. Improve the training process efficiently, and can be applied in mostly ANN-type deep learning model, with a practical case of Air Quality Alert Service in a city of sub-tropical area.",

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KW - Multivariate

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Multi-level lag scheme significantly improves training efficiency in deep learning: a case study in air quality alert service over sub-tropical area

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