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Dear authors,
Thank you for clearly addressing all of the reviewers' comments. Your article is accepted for publication after the last revision.
Best wishes,
[# PeerJ Staff Note - this decision was reviewed and approved by Claudio Ardagna, a PeerJ Section Editor covering this Section #]
The changing and replacements are done nicely
The updates regarding the comments are in good structure
The enhancements as a whole are in acceptable form
I am satisfied with the revised version and there is no further comment from my side.
I am satisfied with the revised version and there is no further comment from my side.
I am satisfied with the revised version and there is no further comment from my side.
I am satisfied with the revised version and there is no further comment from my side.
Dear authors,
Your article has not been recommended for publication in its current form. However, we do encourage you to address the concerns and criticisms of the reviewers and resubmit your article once you have updated it accordingly. Furthermore, it is important to address the concerns and suggestions listed below.
Best wishes,
1. The research gaps and contributions should be clearly summarized in Introduction section. You just described the related works that the researchers have done, but you did not evaluate the advantages and disadvantages of the related works. Please evaluate that how your study is different from others? What do you have where others do not? You should discuss the literature review more deeply and clearly. Try to make the paragraphs in the introduction section more comprehensive, it is short. Authoritative synthesis assessing the current state-of-the-art is absent. The current introduction is simple and misses many contents related to the problem formulation.
2. Please correct the writing style and formatting error.
3. References should be written according to the Peerj Computer Science journal referencing style.
4. You should clarify the pros and cons of the methods. What are the limitation(s) methodology(ies) adopted in this work? Please indicate practical advantages, and discuss research limitations.
5. The conclusion section is indicative, but it might be strengthened to highlight the importance and applicability of the work done with some more in-depth considerations, to summarize the findings, and to give readers a point of reference. Additional comments about the reached results should be included.
6. Encoding type and fitness function of the used metaheuristic algorithm for focused problem are not clearly described.
7. Important data are missing in the experimental results section so that the experiments can be reproduced, and even so that conclusions can be drawn from the reported results. For example, basic questions as the number of runs that have been carried out for each experiment are not mentioned in this section or in the rest of the paper.
8. Add further details on how simulations were conducted. Similarly, system and resource characteristics could be added to Tables for clarity. The paper lacks the running environment, including software and hardware. The analysis and configurations of experiments should be presented in detail for reproducibility. It is convenient for other researchers to redo your experiments and this makes your work easy acceptance. A table with parameter setting for experimental results and analysis should be included in order to clearly describe them.
9. Many of the equations are part of the related sentences. Attention is needed for correct sentence formation.
10. Some mathematical notations are not rigorous enough to correctly understand the contents of the paper. The authors are requested to recheck all the definition of variables and further clarify these equations. Definitions of all variables and their intervals should be given.
11. All of the values for the parameters of all algorithms selected for comparison should be given.
**PeerJ Staff Note:** Please ensure that all review and editorial comments are addressed in a response letter and 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 copyediting@peerj.com 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 paper presents a novel commercial crime identification model based on modularity optimization. By computing transaction weights, it effectively captures the capital transaction relationships between accounts in the commercial network, facilitating precise identification of commercial crimes. The model exhibits outstanding training performance, enabling the accurate detection of criminal gangs within the commercial transaction network and providing valuable legal insights. This research is instrumental in addressing the escalating challenges of comprehensive social governance and the growing prominence of commercial crimes. Nevertheless, certain improvements are needed in the writing aspect:
In the introduction, there appears to be an overlap with the literature review in section 2. I suggest the author reorganize the citations in both sections to ensure a coherent writing flow.
The citation of Reference (15) seems inappropriate, as it focuses on assessing commercial crime risk and pertains to crime detection in social media data. The authors should reconsider this citation.
The introduction of data sets requires more attention. If necessary, the authors should include details about the data preprocessing process or methods, which would facilitate other scholars in referencing and learning from this study.
Section 2 includes a compilation of various researchers' work on crime prediction models. However, there are issues with improper citation and inadequate summarization. It is advisable for the author to thoroughly revise this section.
While analyzing the optimization process of modularity in Section 3 (Commercial Crime Identification Model Based on Modularity Optimized LM), the fundamentals of the LM algorithm are not adequately explained. The authors should provide a clear explanation of the LM algorithm.
It is essential for the author to carefully review and provide explanations for the formulas used in the paper, such as Formula (3), which currently lacks corresponding explanations.
In implementing the community discovery algorithm (Section 3.2), the focus on step 2 requires clarification. The author should specify the particular application of this step in the context of commercial crime identification.
The number of references in the paper is insufficient. I recommend adding more relevant and recent references both in the reference list and within the main text. Consider incorporating references from reputable journal papers to strengthen the credibility of the research.
Combined with the relatively objective, fair, and efficient characteristics of
intelligent algorithms in automatic decision-making, this study designed a commercial
crime identification model based on module degree optimization and built a legally
assisted decision model based on CNN to reduce the risk of public security.
The following aspects should be improved for the revised version of the paper. Authors should revise the article following the comments indicated below to increase the quality of the research
justification, contributions, originality, and findings:
1. Add some quantitative results about the findings of the present research in the abstract section.
2. The necessity of this study should be emphasized more clearly. Authors should present compelling arguments highlighting the originality and value of the proposed model. This should be explicitly stated in the final paragraphs of both the introduction and conclusion sections.
3. In Figure 2's introduction (algorithm step), it is important to address the effect of the order of traversal nodes during the execution of Step (2) on community
clustering. This consideration may impact the computation of the community node.
4. Prior to constructing the commercial crime identification model, provide an introduction to the basic concepts and application scenarios of the LM algorithm. This will aid in better explaining your perspective.
5. Strengthen the justification for the chosen method and compare it with similar approaches in the research area. Increase the number of references to other similar studies and consider using a well-developed table to enhance this section.
6. When introducing the SCA algorithm for optimizing hyperparameters, ensure that the full name is spelled out where it first appears, followed by the abbreviation in parentheses.
7. Include more results beyond just accuracy as a performance measure. Consider incorporating other relevant performance measures to provide a comprehensive evaluation of the model.
8. In the discussion section, compare the results obtained in the present study in detail with previously published results to highlight the research's contributions and advancements.
9. Incorporate the future scope of the research in the conclusion part to indicate
potential directions for further investigation and development.
Please see the comments in basic reporting.
Please see the comments in basic reporting.
Please see the comments in basic reporting.
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