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Neural Computing and Applications

Mifanet: multi-scale information fusion attention network for determining hatching eggs activity via detecting PPG signals

  • 16-08-2023
  • Original Article
Published in:
Authors
Quan Guo
Lei Geng
Zhitao Xiao
Fang Zhang
Yanbei Liu
Published in
Neural Computing and Applications | Issue 30/2023

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Abstract

The article introduces Mifanet, a multi-scale information fusion attention network designed to classify PPG signals for determining hatching eggs activity. It addresses the limitations of current heartbeat signal classification methods by combining local and global feature extraction through LANet and GANet, respectively. Mifanet uses a multi-head fusion attention (MHFA) module to calibrate local features based on global information, leading to more accurate classifications. The method is validated through extensive experiments on a large dataset, demonstrating superior performance compared to state-of-the-art techniques. The article also highlights the importance of PPG technology in monitoring physiological parameters and its application in the agricultural sector, particularly in vaccine manufacturing.
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previous article Cross-region building counting in satellite imagery using counting consistency
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Title
Mifanet: multi-scale information fusion attention network for determining hatching eggs activity via detecting PPG signals
Authors
Quan Guo
Lei Geng
Zhitao Xiao
Fang Zhang
Yanbei Liu
Publication date
16-08-2023
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 30/2023
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-023-08798-1
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