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Dear Authors,
Your paper has been revised. It 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 #]
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After analyzing the revised version of the article, together with the comments made by the reviewers and the detailed response from the authors, it is clear that all observations have been addressed in a clear, precise, and comprehensive manner.
The article presents a solid theoretical and methodological foundation, aspects that have been strengthened in this new version. Likewise, the analysis of the results is presented in a lucid and coherent manner, leading to robust and well-founded conclusions that are logically connected to the data presented.
The modifications implemented not only address the requested revisions but also substantially enrich the quality and rigor of the manuscript. There is a notable improvement in the structure and clarity of each section, which facilitates understanding of the central argument and the contribution of the work to the field of study.
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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.
**Language Note:** When you prepare your next revision, please either (i) have a colleague who is proficient in English and familiar with the subject matter review your manuscript, or (ii) contact a professional editing service to review your manuscript. PeerJ can provide language editing services - you can contact us at [email protected] for pricing (be sure to provide your manuscript number and title). – PeerJ Staff
It is recommended to expand the introduction of the document by incorporating one or two paragraphs that provide a deeper discussion of the relevance of the study. This section should clearly and concisely present the motivation behind the current work, contextualizing the addressed problem and emphasizing its importance within the corresponding field.
Additionally, it is suggested to review and enhance the discussion and analysis presented between lines 48 and 124. The aim of this improvement is to offer the reader a more robust justification for the study by clearly identifying the existing research gap. This argumentation should help demonstrate the relevance and contribution of the proposed work with greater clarity.
To further enrich the content, it is recommended to include a comparative table summarizing the studies analyzed in the literature review. This table should include, at a minimum, the following elements: the objective of each study, the method used, the dataset employed along with its most relevant characteristics (e.g., size, classes), the main strengths and limitations identified, and the opportunities highlighted in each work that served as a foundation or motivation for the development of the present study.
It is also advised to revise and modify the content between lines 219 and 223 to provide a more detailed and precise description of the proposed security approach. This section should offer the reader a clear and comprehensive understanding of the components, mechanisms, and rationale of the proposed approach. Moreover, it is recommended that the description be coherently aligned with the corresponding figure so that the visual representation serves as an explanatory complement that reinforces the understanding of the proposed model.
The methods section does not provide sufficient detail to enable replication of the experiment by other researchers. Although the hardware used to run the experiments is specified, a more comprehensive description of the key parameters defined for each of the applied models is required. Additionally, the programming language used, as well as the libraries and frameworks employed in the experimental development, should be clearly indicated.
Section 3 should offer a more in-depth analysis of the experimental results obtained. It is essential to include a critical discussion that not only reports the results but also provides sound arguments explaining the causes behind them. Furthermore, the section should clearly identify which of the evaluated models demonstrates the best performance according to the defined criteria, justifying why this model best fits the objectives of the study. Lastly, the impact of this model’s results on the addressed problem, as well as its practical implications and potential benefits, should be thoroughly discussed.
The paper addresses a highly relevant topic; however, the approach taken is rather weak. The introduction lacks a strong justification that highlights the importance of the subject and its relevance in the current context. Moreover, the results require a more in-depth analysis and discussion, including a critical examination of the findings and well-founded arguments to explain the outcomes observed in the experiments.
1. Abstract (line 28): Please clarify the phrase “complex interfering objects” as it feels vague. Providing a specific example or clearer wording would help.
2. Lines 57-58: Consider changing "face vectors’ values" to "facial feature vectors" to improve clarity.
3. Lines 86-91: This section would benefit from more precise language. Terms like “tested area” and “recognized positions” are unclear. The use of "Conversely" seems out of place in the current context and could be rephrased for better logical flow.
4. Introduction: It would be helpful to introduce the motivation for integrating security measures into disability detection more clearly and earlier. Explaining why secure transmission of medical images is practically important would add weight to the work.
1. While the encryption and hashing techniques are described, it’s not entirely clear how these are integrated into the model pipeline. Are they applied during data preprocessing, training, or inference? Clarifying this, along with whether they impact model performance, would be useful.
2. You’ve done a good job listing dataset sources, but a short discussion on any limitations or biases in using publicly available datasets would strengthen transparency.
3. Including training details like the number of epochs, early stopping criteria, and loss functions used would enhance the reproducibility of your study.
1. A discussion of potential limitations and ethical considerations would add more balance. For instance, you could mention concerns around data privacy or the risks of misclassification in real-world clinical settings.
2. The mention of future directions is appreciated, but it could be expanded to include how these methods might be integrated into actual clinical workflows, including technical and regulatory challenges.
3. The evaluation metrics are appropriate, but briefly explaining why each was chosen in the context of disability detection would give your results more impact.
4. Outlining how others could replicate or build upon your work would be valuable. For example, is the code or dataset accessible? Sharing how this work could benefit real-world medical image analysis or diagnostics would round out the contribution.
1. Consider briefly mentioning how your proposed framework compares with existing commercial or widely adopted medical imaging solutions, if applicable, to better contextualize your study within current practices.
2. Including a discussion about computational costs and how scalable your approach might be in real-world clinical settings would further enhance the manuscript’s practical relevance and applicability.
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