PONE-D-24-31209Adoption of K-Means Clustering Algorithm in Smart City Security: Analysis and Mythical Experience Analysis of Urban ImagePLOS ONE

Dear Dr. Han,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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Yirui Wang

Academic Editor

PLOS ONE

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Additional Editor Comments:

Please revise your manuscript point by point according to reviewers' comments. Especially in experiments, evaluation metrics and dataset need to be enriched to improve the effectiveness and reliability of results and support conclusions. Besides, contributions of this manuscript need to be elaborated.

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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

Reviewer #3: Partly

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

Reviewer #1: Yes

Reviewer #2: N/A

Reviewer #3: I Don't Know

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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

Reviewer #3: 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: No

Reviewer #2: Yes

Reviewer #3: 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: The comments are as follows:

1) The manuscript compares the proposed model with Naive Bayes (NB) and Logistic Regression (LR) models. Why were more advanced machine learning models, such as Random Forests, Support Vector Machines (SVM), or Gradient Boosting Machines (GBM), not included in the comparison? How might the results differ if these more sophisticated models were considered?

2) The study uses ROC curves as a performance metric, which can be misleading in cases of imbalanced datasets. How does the proposed model address potential class imbalance in the data, and why were other metrics, such as precision-recall curves or the F2 score, not considered?

3) The study uses data from 15 smart cities to validate the model. Is this sample size sufficient to generalize the findings to other smart cities globally, especially given the diverse nature of smart city implementations? How was the sample size determined, and what statistical methods were used to ensure it is representative?

4) Introduction is not focused and literature can be reorganised to strengthen literature review following contributions and discuss few relevant works i.e.,

a) Deep learning models for intelligent healthcare: implementation and challenges

b) Unsupervised pre-trained filter learning approach for efficient convolution neural network

c) CSFL: A novel unsupervised convolution neural network approach for visual pattern classification

d) Optimization of CNN through novel training strategy for visual classification problems

e) Face recognition: A novel un-supervised convolutional neural network method

5) The results indicate that cities with the same attributes exhibit minor differences in risk levels, while cities with different attributes show significant variations. Can the authors provide a more detailed explanation of what these differences in attributes entail and how they specifically contribute to variations in risk levels?

Reviewer #2: I reviewed the manuscript titled 'Adoption of K-Means Clustering Algorithm in Smart City Security: Analysis and Mythical Experience Analysis of Urban Image'. After a careful examination, I found that the manuscript is complete and well-written. The methodology used in the study is highly accurate and valuable. The research questions have been addressed effectively. The content presented in the manuscript has been meticulously collected. Based on these observations, it appears that the study can be accepted in its current form."

Reviewer #3: This paper evaluates information security in smart cities (SC) through a combined K-Means clustering and C4.5 decision tree model, examining not only security assessments but also residents' mythological experiences in these cities. The following review highlights the paper’s strengths and areas for improvement:

Strengths

Innovative Methodology: The use of a combined K-Means clustering and C4.5 decision tree for information security assessment in smart cities is a novel approach that could offer new insights into security evaluations.

Strong Results and Model Comparison: The paper reports high accuracy and an AUC greater than 0.9 for the combined model. Comparisons with other methods, such as Naïve Bayes (NB) and Logistic Regression (LR), further support the effectiveness of the combined model.

Cultural and Mythological Analysis: Integrating an analysis of cultural and mythological experiences with urban security is an original approach that adds a unique dimension, highlighting the potential relationship between culture and security in urban spaces.

Weaknesses

Lack of Clear Link Between Security and Culture: While the paper attempts to connect security and cultural experiences in smart cities, this relationship remains somewhat unclear. More detailed explanations on how information security is related to mythological experiences would add clarity.

Insufficient Details in Methodology: The methodology section is somewhat brief. For instance, further explanation on the Delphi method, the selection of experts, and other assessment steps would enhance the reader's understanding and confidence in the findings.

Need for More Comprehensive Data: The study focuses on only 15 cities, which may be insufficient for drawing broader conclusions on urban information security and cultural experiences. Expanding security assessment indicators and including additional variables would strengthen the model.

Suggestions for Improvement

Expand Methodology Details: Providing further details on data collection, standardization, and the rationale for selecting the combined K-Means and C4.5 model would give readers a clearer understanding of the model's effectiveness.

Increase Sample Size and Data Variety: Expanding the study to cover more cities and examine additional indicators would enhance the generalizability of the results.

Clarify the Link Between Security and Culture: Clearer explanations on how information security impacts cultural and spiritual experiences of city residents could enhance the paper’s value and originality.

Conclusion

Overall, the paper is logically structured and presents an innovative approach, yet it could benefit from additional detail and a stronger conceptual link between security and culture. Addressing these areas would allow the paper to become a valuable resource in assessing the relationship between information security and cultural experience in smart cities.

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Reviewer #1: No

Reviewer #2: Yes

Reviewer #3: No

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PONE-D-24-31209R1Adoption of K-Means Clustering Algorithm in Smart City Security: Analysis and Mythical Experience Analysis of Urban ImagePLOS ONE

Dear Dr. Han,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Feb 20 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

• A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
• A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
• An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols . Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols .

We look forward to receiving your revised manuscript.

Kind regards,

Yirui Wang

Academic Editor

PLOS ONE

Additional Editor Comments:

Please further improve your manuscript according to new reviewer's comments.

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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 #2: All comments have been addressed

Reviewer #4: 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 #2: Yes

Reviewer #4: Yes

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

Reviewer #2: Yes

Reviewer #4: Yes

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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 #2: Yes

Reviewer #4: 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 #2: Yes

Reviewer #4: Yes

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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 #2: I checked the revised version. I noticed that you answered all the revisions correctly. Now it seems that this manuscript has reached an acceptable level in terms of quality.

Reviewer #4: Comments on the manuscript entitled

“Adoption of K-Means Clustering Algorithm in Smart City Security: Analysis and Mythical Experience Analysis of Urban Image”

The manuscript presents a well-structured and coherent proposal for a hybrid model, demonstrating an improvement in accuracy over traditional models such as logistic regression and Naive Bayes. The authors have adequately addressed the reviewers' queries, indicating a thorough understanding of the research domain. In my opinion, it can be published in the journal after addressing the following comments/suggestions.

Some Comments/ Recommendations

1. While the model's performance is superior to simpler models e.g. LR and NB, it does not exhibit a significant improvement over more complex models like SVM and Random Forest. It would be beneficial for the author(s) to explore and discuss the possible level of interpretability of their proposed model in relation to those of counterparts mentioned.

2. First, there is no explanation on how the data has been splitted to train/test sets. Then, given the relatively small number of observations, it's essential to assess whether the models, particularly the new one, suffer from overfitting or underfitting or not.

Additional Remarks

• The formulae, specifically (1), (2), and (7), could be rewritten for clarity and consistency, potentially following the format of (8).

• Line 315: Simplify the notation to "F1-F20" for better readability.

• Eq (4): distance

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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 #2: Yes:

Reviewer #4: Yes: 

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[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/ . PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org . Please note that Supporting Information files do not need this step.

Adoption of K-Means Clustering Algorithm in Smart City Security: Analysis and Mythical Experience Analysis of Urban Image

PONE-D-24-31209R2

Dear Dr. Han,

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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If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Yirui Wang

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 #2: (No Response)

Reviewer #4: 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 #2: (No Response)

Reviewer #4: Yes

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

Reviewer #2: (No Response)

Reviewer #4: Yes

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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 #2: (No Response)

Reviewer #4: 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 #2: (No Response)

Reviewer #4: Yes

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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 #2: With these revisions, the quality of the manuscript has been enhanced, and it is now accepted.

Reviewer #4: (No Response)

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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 #2: Yes

Reviewer #4: No

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