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Thanks for undertaking a thorough revision of the paper.
[# PeerJ Staff Note - this decision was reviewed and approved by Xiangjie Kong, a PeerJ Section Editor covering this Section #]
the paper has been greatly improved
the paper has been greatly improved
the paper has been greatly improved
the paper has been greatly improved
After going through your paper and reviewing the comments made by the reviewers on your paper, i agree with the comments made by the reviewers, that the paper need a lot of improvement. In particular you need to pay attention to the language related changes. Please have somebody read and edit the language for you. The reviewers also provide you a detailed suggestion on how to improve the paper. The paper needs more work, but it doable. Based on the reviewers' comments, if you choose to modify, please undertake the revision and submit it as a revised paper.
**PeerJ Staff Note:** Please ensure that all review and editorial comments are addressed in a response letter and that any edits or clarifications mentioned in the letter are also inserted into the revised manuscript where appropriate.
**Language Note:** The review process has identified that the English language must be improved. PeerJ can provide language editing services - please contact us at [email protected] for pricing (be sure to provide your manuscript number and title). Alternatively, you should make your own arrangements to improve the language quality and provide details in your response letter. – PeerJ Staff
This paper proposes a novel prediction model that enhances the reliability and lifetime testing processes of digital microfluidic systems. Traditional methods have shortcomings due to their heavy reliance on real-time monitoring data, resulting in delays and inefficiencies in evaluating complex droplet behaviors. To address these issues, the authors introduce an integrated model that combines Attention-based Bidirectional Long Short-Term Memory (BiLSTM) with eXtreme Gradient Boosting (XGBoost) in a Stacking approach. This model aims to provide an efficient identification of health states and accurate predictions of failure times for digital microfluidic devices. I would suggest revisions as below:
1. How does the performance of the proposed BiLSTM-XGBoost model quantitatively compare to other advanced machine learning models beyond those mentioned (like BiGRU), particularly in diverse operational conditions of digital microfluidic systems? Are there scenarios where it may underperform?
2. some references about xgboost and explainable AI to enhance the litterature review: Explainable AI models for predicting drop coalescence in microfluidics device; Explainable AI model for predicting equivalent viscous damping in dual frame-wall resilient system
3. Is the BiLSTM-XGBoost model adaptable to other types of microfluidic systems or different domains altogether? How might the model's structure change if applied to other fields, and what limitations might arise?
4. Can the authors provide more detail on how the attention mechanism impacts the model's performance? Specifically, what features or aspects of the data does the model prioritize, and how was this selectivity validated?
see above
see above
n/a
detailed report is attached.
detailed report is attached.
detailed report is attached.
detailed report is attached.
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