The Sustainable Rural Industrial Development under Entrepreneurship and Deep Learning from Digital Empowerment
Round 1
Reviewer 1 Report
Dear authors,
Be aware of the following recommendations:
1 - In the introduction, you must identify the literature lacks, based on previous studies, that lead you to the development of your study.
2 - The theoretical framework should be organized according to the topics of your study (SRID, DL, DE, ...), with fresh references (last 3 years).
3 - From this, you should derive the research hypotheses and the research model. Strange the fact that your study has no research hypotheses!
4 - Create a specific section regarding the Methodology adopted.
5 - Provide a section for Results.
6 - In the Discussion section you should confront your results with those of other authors. Consider the results of the research hypotheses to be included.
7 - Conclusions should (1) answer the study objectives, (2) state the theoretical and practical implications, (3) limitations, and (4) future lines of research.
Best of luck with the review to be conducted.
Regards
Author Response
1 - In the introduction, you must identify the literature lacks, based on previous studies, that lead you to the development of your study.
Reply: Thank you for your suggestion. Based on your suggestions, we have revised the introduction section, summarized and discussed the current literature, and proposed the specific shortcomings of the current study and the specific research directions of this study.
2 - The theoretical framework should be organized according to the topics of your study (SRID, DL, DE, ...), with fresh references (last 3 years).
Reply: Thank you for your comments. The theoretical framework have been reorganized according to your comments (including SRID, DL, DE, etc.) and the latest references (published in the last 3 years) have been cited.
3 - From this, you should derive the research hypotheses and the research model. Strange the fact that your study has no research hypotheses!
Reply: Thank you for your comment. The content of Section 3 has been revised in response to your comment and the discussion of the research model has been fully optimized and the research hypotheses have been provided in this paper. This paper hypothesizes that the GA-NN model has a positive impact on the development of digitally empowered villages and develops a synthesis of the model's utility.
4 - Create a specific section regarding the Methodology adopted.
Reply: Thank you for your valuable suggestions. Based on your suggestion, section 2.4 has been revised to 3. Research methodology and research model design section, thus providing a reasonable research methodology and model design section for this paper.
5 - Provide a section for Results.
Reply: Thank you for your reminder. In response to your reminder, sections 3.1 and 3.2 have been revised to section 4. Study results, thus providing a separate results section for this paper.
6 - In the Discussion section you should confront your results with those of other authors. Consider the results of the research hypotheses to be included.
Reply: Thank you for your guidance. We have revised the discussion section according to your guidance, and in it, we have compared this study with other more advanced studies, thus highlighting the strengths of this study and also illustrating the final results of the hypotheses of this study. In comparison with previous studies, it is found that this study not only achieves an innovative technical model design, but also provides a comprehensive assessment of the application and development of the model.
7 - Conclusions should (1) answer the study objectives, (2) state the theoretical and practical implications, (3) limitations, and (4) future lines of research.
Reply: Thank you for your comment. Based on your comment, the conclusion distribution has been revised to include (1) an answer to the research objectives, (2) a description of the theoretical and practical implications, (3) limitations, and (4) future research directions.
Reviewer 2 Report
The topic is interesting and the manuscript is well written.
The authors could improve Discussion section trying to introduce the limits of the research recovering and extending the concepts mentioned in the conclusion.
Author Response
The topic is interesting and the manuscript is well written.
The authors could improve Discussion section trying to introduce the limits of the research recovering and extending the concepts mentioned in the conclusion.
Reply: Thank you for your valuable comments. Based on your comments, we have revised the Discussion section, in which we have fully discussed the limitations of this paper and provided specific suggestions for future development, and we have also revised the Conclusion section to provide specific directions for future research.
Reviewer 3 Report
1. Please re-emphasize the theory of this study on rural development, and explain the importance of this study to the development of rural leisure agriculture in a specific area of China. And enhance research motivation to attract readers.
2. The research results developed by previous researchers should be discussed again intensified. Especially in the part of leisure agricultural tourism and rural sustainability. In addition, the part about deep learning is also lack of in-depth discussion and analysis.
3. The methodology part is weak, and the important methodology of this research must be further strengthened.
4. The part of research limitations is lacking. The authors are invited to add research limitations and suggestions for future research.
5. The subject of this study - Sustainable Rural Industrial Development under Entrepreneurship and Deep Learning from Digital Empowerment. In the discussion part, it seems that there is no in-depth analysis of the rural sustainable part and suggestions. In my opinion, I suggest that the authors can add the practical implications of rural transformation and sustainable development brought about by this research to the discussion part.
Author Response
1. Please re-emphasize the theory of this study on rural development, and explain the importance of this study to the development of rural leisure agriculture in a specific area of China. And enhance research motivation to attract readers.
Reply: Thank you for your comment. The content of section 2.1 has been revised based on your comment, in which the theory of rural development has been fully expanded and the importance of this research for future agricultural development has been reflected through the discussion of the role of technology models, which has increased the attractiveness of this research.
2. The research results developed by previous researchers should be discussed again intensified. Especially in the part of leisure agricultural tourism and rural sustainability. In addition, the part about deep learning is also lack of in-depth discussion and analysis.
Reply: Thank you for your guidance. The second part of the introduction has been revised according to your guidance, in which the specific results and shortcomings of the current research are fully summarized and discussed, and the theoretical basis for this paper to promote sustainable rural development based on deep learning techniques has been provided.
3. The methodology part is weak, and the important methodology of this research must be further strengthened.
Reply: Thank you for your suggestion. Based on your suggestion, we have revised section 2.4 to section 3. Research methodology and research model design, in which we have fully discussed the specific methodology and model design process of this paper, and proposed the research hypothesis, thus effectively enhancing the value of the research methodology section of this paper.
4. The part of research limitations is lacking. The authors are invited to add research limitations and suggestions for future research.
Reply: Thank you for your careful reading. The conclusion section has been revised based on your suggestions, in which the limitations of this paper are fully discussed and specific directions for future research are provided. The limitation of this paper is that rural digitalization is still in the research stage, and if the weights of each indicator are clarified, the model can be upgraded to a simple evaluation software that can be easily used in practice. Future research directions will focus on abstracting the multi-objective operational optimization model into concrete mathematical numerical problems.
5. The subject of this study - Sustainable Rural Industrial Development under Entrepreneurship and Deep Learning from Digital Empowerment. In the discussion part, it seems that there is no in-depth analysis of the rural sustainable part and suggestions. In my opinion, I suggest that the authors can add the practical implications of rural transformation and sustainable development brought about by this research to the discussion part.
Reply: Thank you for your comments. Based on your comments, we have revised the discussion section to include an in-depth analysis of the rural sustainability section and provide specific recommendations. Through the study, it is found that deep learning technology can provide many new development opportunities for sustainable rural development. Thus, it can create a richer development space for rural areas, realize the joint improvement of technology and economy, and inject new momentum into sustainable rural development.
Reviewer 4 Report
The article aims to identify the important factors involved in rural urban development. The authors extracts various parameters of rural urban development along with the structures of resource development. NN and GA are used to identify the various sectors which needs to developed rigorously. However, there some things which the author needs to clarify:
1) The source of the data and its authenticity needs to outlined by the author along with its structure. In which other studies has the same data source been used?
2) Usually NN and other machine models are used to extract a decision. In this case the authors use it to extract weights. Weights on the other may not always represent the significance of the input. The author should clearly describe how he has tweaked the model to obtain meaningful weights.
3) Some validation of results is required. Validation is a proof in a scientific process that the proposed technique really works. The results produced by the model can not be excepted as trustworthy unless validated by some validation technique. I suggest that the author incorporates some validation technique to make his findings more reliable.
4) Since the work makes use of Machine learning models therefore the author must mention some recent work like:
Malebary, S. J., & Khan, Y. D. (2021). Evaluating machine learning methodologies for identification of cancer driver genes. Scientific reports, 11(1), 1-13.
Naseer, S., Ali, R. F., Khan, Y. D., & Dominic, P. D. D. (2022). iGluK-Deep: computational identification of lysine glutarylation sites using deep neural networks with general pseudo amino acid compositions. Journal of Biomolecular Structure and Dynamics, 40(22), 11691-11704.
Feng, X., Zhang, H., Lin, H., & Long, H. (2023). Single-cell RNA-seq data analysis based on directed graph neural network. Methods, 211, 48-60.
5) Additionally, it will be interesting to see if any other machine learning model could provide the same or better results.
Author Response
The article aims to identify the important factors involved in rural urban development. The authors extracts various parameters of rural urban development along with the structures of resource development. NN and GA are used to identify the various sectors which needs to developed rigorously. However, there some things which the author needs to clarify:
1) The source of the data and its authenticity needs to outlined by the author along with its structure. In which other studies has the same data source been used?
Reply: Thank you for your suggestion. The current section 4 has been revised in accordance with your suggestion, and the data sources and the process used in this study have been fully explained in it, thus proving the authenticity of the data sources for this study. The python environment integrated with Anaconda was used to build the virtual environment. The DL algorithm model library, Keras, was installed and used to perform simulations. The data collected covered the period from 2009 to 2022.
2) Usually NN and other machine models are used to extract a decision. In this case the authors use it to extract weights. Weights on the other may not always represent the significance of the input. The author should clearly describe how he has tweaked the model to obtain meaningful weights.
Reply: Thank you for your comments. The current section 4 has been revised based on your comments, in which it is fully explained that the model is optimized using Gaussian fuzzy algorithm in order to obtain meaningful weights for the model.
3) Some validation of results is required. Validation is a proof in a scientific process that the proposed technique really works. The results produced by the model can not be excepted as trustworthy unless validated by some validation technique. I suggest that the author incorporates some validation technique to make his findings more reliable.
Reply: Thank you for your valuable comments. The experimental design section has been revised based on your comments, in which the specific experimental validation methods used in this study have been fully explained, thus improving the reliability of this study. In order to solve the problem of model underfitting, this paper adopts the method of replicating 100 sample data into the model for training in the training process of NN. Gaussian noise was added to the samples to solve the overfitting problem of the model.
4) Since the work makes use of Machine learning models therefore the author must mention some recent work like:
Malebary, S. J., & Khan, Y. D. (2021). Evaluating machine learning methodologies for identification of cancer driver genes. Scientific reports, 11(1), 1-13.
Naseer, S., Ali, R. F., Khan, Y. D., & Dominic, P. D. D. (2022). iGluK-Deep: computational identification of lysine glutarylation sites using deep neural networks with general pseudo amino acid compositions. Journal of Biomolecular Structure and Dynamics, 40(22), 11691-11704.
Feng, X., Zhang, H., Lin, H., & Long, H. (2023). Single-cell RNA-seq data analysis based on directed graph neural network. Methods, 211, 48-60.
Reply: This is a good reminder. Based on your reminder, the above-mentioned references have been cited and compared with this study, thus effectively highlighting the specific strengths of this study.
5) Additionally, it will be interesting to see if any other machine learning model could provide the same or better results.
Reply: Thank you for your suggestion. We have drilled down into more machine learning technology models based on your suggestions and have revised the discussion section to provide the types of machine learning technologies that can be used in the future at the end of it. These models include decision tree algorithms, support vector machine algorithms, neural network algorithms, integrated learning algorithms, and natural language processing algorithms.
Round 2
Reviewer 3 Report
Thanks to the authors for accepting my suggestions and making corrections.
Reviewer 4 Report
All the issues pointed out in the previous review have been satisfactorily addressed.
