PONE-D-24-17883An Anomaly Detection Scheme for data stream in Cold Chain LogisticsPLOS ONE

Dear Dr. xie,

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Zhihong (Arry) Yao, Ph.D.

Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

Reviewer #2: Yes

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3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: COMMENTS

1. The abstract should provide specific numerical results from the experiments, such as precision, recall, F1 score, and AUC, to substantiate the claims of performance improvement

2. Provide more context on the challenges and limitations of current anomaly detection techniques in cold chain logistics, specifically detailing why existing methods are inadequate.

3. Expand the literature review to include recent advances and a more comprehensive analysis of existing algorithms used in cold chain logistics.

4. Provide a clearer and more detailed explanation of the improved isolated forest algorithm and the modifications made (subsampling and cross factor).

5. Ensure that all mathematical notations and equations are clearly defined and explained, with real-world examples if possible.

6. Elaborate on the derivation and significance of the correlation coefficient and how it is used to detect anomalies.

7. Describe the dataset in more detail, including its origin, size, type of data, and how it represents cold chain logistics.

8. Include a comparative analysis with more baseline algorithms and provide statistical significance testing (e.g., t-tests, p-values).

9. dd more visualizations, such as ROC curves or precision-recall curves, to illustrate the algorithm's performance across different thresholds.

10. Discuss the implications of the results in the context of real-world cold chain logistics scenarios. Include potential limitations of the proposed method and areas for future research.

11. Summarize the key contributions and explicitly state the practical impact and potential applications of the findings.

12. Review and refine the language for clarity, coherence, and conciseness. Ensure that technical terms are used consistently throughout the paper.

Reviewer #2: The paper is appropriately organized and presented.

The research method is effective in anomaly detection for data stream of Cold Chain Logistics. The experimental data is sufficiently detailed.

There are some Innovation in anomaly detection for data streams.

However, there some minor symtax problems in Lines 114,140, 154, 205, 236, 251, 471, and so on. I hope the paper could be properly revised and make it understood easily.

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Reviewer #1: No

Reviewer #2: No

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An Anomaly Detection Scheme for data stream in Cold Chain Logistics

PONE-D-24-17883R1

Dear Dr. xie,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Zhihong (Arry) Yao, Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The authors addressed all my comments. So I am satisfied with the revised manuscript. Now its in publishable form

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7. PLOS authors have the option to publish the peer review history of their article (what does this mean? ). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy .

Reviewer #1: No

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