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Dear Author,
Your paper has been accepted for publication in PEERJ Computer Science. Thank you for your fine contribution.
[# PeerJ Staff Note - this decision was reviewed and approved by Mehmet Cunkas, a PeerJ Section Editor covering this Section #]
Dear Author
Your paper has been revised. It needs minor revisions before being accepted for publication in PEERJ Computer Science. In particular, you must carefully follow the journal template and ensure the English language is used correctly.
**Language Note:** The Academic Editor 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
The authors made the requested revisions. I'm happy to see the revised version in this good presentation. The paper can be accepted for publication after carefully following the journal template and the English language.
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**PeerJ Staff Note:** Please ensure that all review, editorial, and staff 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.
No comment
It need some improvement as attached.
Please see the attached file.
Please see the attached file.
Paper Title: AI-enhanced diagnostics for pediatric asthma and respiratory irregularities using deep learning and wearable sensors
The paper contributed the following:
This research develops a sophisticated learning-based diagnostic system using CNNs and RNNs to identify respiratory anomalies and pediatric asthma with high accuracy (>95%), enabling early intervention and reducing the need for emergency room visits.
• The presented study offers real-time physiological indicator monitoring with non-invasive wearable biosensors by AI-PARI. By constant data analysis, the aim is to enhance asthma control.
• Using federated learning, the proposed system might enhance customized models while safeguarding patient data, thereby allowing safe and flexible AI-driven respiratory health monitoring.
However, some areas for improvement have been highlighted below:
The AI on the paper title should be written in full.
The equations in the paper are too many and confusing because of they lack detailed explanations of the parameters
(1 to 20). The authors should focus on few equations and explain the parameters for each equation in detail.
The authors should mention some of the features of the Pulmonary Sound Dataset on Kaggle, used for the study.
Table 1 is not necessary, it should be removed.
Figure 1 to 5, should be streamlined. The current number of figures are too many and confusing. Use only the relevant figure that will communicate and align with the research objective. You may present only the AI-driven pediatric respiratory wellness diagnostic framework and elaborate on the components.
Figure 6, 7 and 8 should be compressed into a single figure. Alternatively, use only one of the figures. The too many figures in the paper are making the paper to look like a book chapter. The paper contents are too many and repetitive in some cases.
Algorithms 1 and 2 are okay, however, select only the figures, tables and equations that aligns with Algorithms 1 and 2.
Reference number 29 is incomplete
Reference number 22 is incomplete and inconsistent with the other references. It should be small letters.
Towards the end of Results and Discussion, the authors may compare (with citations) the statement/results with the results of previous similar study, and form an opinion:
Results: The AI model demonstrated high diagnostic accuracy, achieving over 95% precision in detecting asthma exacerbations and respiratory irregularities when validated against benchmark clinical datasets.
See (1) above
See (1) above
See (1) above
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