اصلاحات مقاله - 112005

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شرح پروژه

سلام و عرض ادب. یه مقاله دارم که داور یه سری کامنت روش گذاشته. براتون بفرستم می تونید برام ایرادات رو رفع کنید

Dear Editor,

In response to the requirements for the rigorous scientific evaluation of the manuscript, we have addressed the following critical methodological and analytical points. 

- Data Validity and Market Efficiency: We have integrated rigorous data validity assessments into the model. Notably, the efficiency conditions of the Iranian market have been incorporated as a primary control variable to account for local structural nuances.

- Data Presentation and Schematics: A comprehensive audit of the data output, the main data schematic, and the variable definition table has been conducted. Furthermore, a Research Synthesis Table has been added to provide a clear bridge between the theoretical framework and the empirical results.

- Research Rationale and Gap Analysis: The fundamental rationale for this study has been re-evaluated to precisely extract the research gap. This ensures the study addresses a specific, unfulfilled niche in the current literature.

- Innovation and Comparative Contribution: Given the extensive volume of existing work in this field, we have explicitly highlighted the original contribution and innovations* of this research. We have clearly delineated the differences between this study and previous publications to underscore its unique value-add to the scientific community.

We believe these refinements significantly strengthen the manuscript's methodology and its contribution to the field.

Sincerely,

Dear Editor,

I have carefully reviewed the manuscript titled "Development of an Intelligent Auditing System Based on Deep Learning for Financial Fraud Detection". The study presents a robust and timely investigation into the application of advanced artificial intelligence within the Iranian auditing context, specifically targeting companies listed on the Tehran Stock Exchange.

The manuscript addresses a significant gap in the existing literature by proposing a localized hybrid CNN-LSTM architecture that accounts for both spatial and temporal dependencies in financial data. Furthermore, the integration of Explainable AI (XAI) tools, namely LIME and SHAP, effectively mitigates the "black-box" nature of deep learning models, making the findings practically relevant for professional auditors.

Evaluation of Key Contributions:

Methodological Rigor: The combination of SMOTE and Semi-Supervised learning to address severe class imbalance (1:13 ratio) is technically sound and led to a substantial improvement in the model's recall (from 45% to 87%).Empirical Validation: The use of a substantial dataset (45,000 transactions) and comparison against traditional models like Logistic Regression and Decision Trees provide strong evidence of the hybrid model's superiority.Practical Utility: The reported 60% reduction in decision-making time and the 100% coverage of transactions highlight the transformative potential of this framework for the auditing profession.

Recommendation:

Based on the high scientific quality and practical significance of the work, I recommend Minor Revision. While the study is well-executed, the following points should be addressed to further enhance the manuscript:

Simulation Transparency: Provide more technical details regarding the "advanced simulation algorithm" used for transactional data to ensure reproducibility and validate its alignment with real-world market noise.Architectural Justification: Briefly discuss the rationale for selecting the CNN-LSTM architecture over more recent Transformer-based models in the context of financial time-series analysis.Deployment Feasibility: Expand on the economic implications for small-to-medium auditing firms, considering the hardware requirements (e.g., NVIDIA Tesla T4/RTX 3090) mentioned in the findings.

In conclusion, this paper provides a significant contribution to digital transformation in auditing and is suitable for publication after these minor enhancements.

Sincerely,

این دوتا هم یادداشت های داور دوم و سوم هست.

اصلاحات را  اصلاح کنید و یه جدول درست کنید  یک ستون  نظر داور و ستون جلوش  پاسخ به آن  یا  توضیح اصلاحی که صورت گرفته

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