Attention-based BiLSTM-XGBoost model for reliability assessment and lifetime prediction of digital microfluidic systems
Traditional methods for reliability and lifetime testing of digital microfluidic systems heavily rely on real-time monitoring data. This often leads to evaluation lag and limits their application, especially for complex droplets. To address these issues, this study proposes a novel prediction model...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
PeerJ Inc.
2025-07-01
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| Series: | PeerJ Computer Science |
| Subjects: | |
| Online Access: | https://peerj.com/articles/cs-3037.pdf |
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