A Novel Joint Time-Frequency Spectrum Resources Sustainable Risk Prediction Algorithm Based on TFBRL Network for the Electromagnetic Environment
Round 1
Reviewer 1 Report
The article is interesting and relevant. The processing of information depends on the skills of intellectual analysis. I recommend the article for publication!
Author Response
Please see the attachment.
Author Response File: 
Author Response.pdf
Reviewer 2 Report
The manuscript entitled “A novel Joint Time-frequency Spectrum Resources Sustainable Risk Prediction Algorithm based on TFBRL Network for Electromagnetic environment
“ has been investigated in detail.
The paper’s subject could be interesting for readers of journal. Therefore, I recommend this paper for publication in this journal but before that, I have a few comments on the text that should be addressed before publication:
Comments:
1) Some abbreviations haven’t been described for the first time in the text. Please explain all in whole of the manuscript.
2)Which software has been used in this work to export the charts and diagrams in this work? For instance, software like SigmaPlot or SmartDraw are used to export and depict charts. Mentioning used software would be helpful to future researches and studies in the field of this article.
3)how did the authors evaluate precision of network training process?
4)Which software is used in this article to model and analyze data? Moreover, which software and indices are used to compare proposed model results with other existing models? Mentioning the used software would be really useful for future works.
For instance, MATLAB and Python are highly utilized by the users to model and analyze data. Of Course there is extensive range of similar ones and it is optional to use them.
5) There are no numerical results in the Abstarct section. Please add results to this section.
6) There are lots of grammatical mistakes in the manuscript. Please correct them.
7)Since recently it has been proved that computational techniques, specifically machine learning has a numerous applications in all of engineering fields, I highly recommend the authors to add some references in this manuscript in this regard. It would be useful for the readers of journal to get familiar with the application of computational techniques in other engineering fields. I recommend the others to add all the following references, which are the newest references in this field
[1] "Iranmanesh, R., Pourahmad, A., Faress, F., Tutunchian, S., Ariana, M. A., Sadeqi, H., ... & Aghel, B. (2022). Introducing a Linear Empirical Correlation for Predicting the Mass Heat Capacity of Biomaterials. Molecules, 27(19), 6540.".
[2] Hosseini, S., Taylan, O., Abusurrah, M., Akilan, T., Nazemi, E., Eftekhari-Zadeh, E., ... & Roshani, G. H. (2021). Application of Wavelet Feature Extraction and Artificial Neural Networks for Improving the Performance of Gas–Liquid Two-Phase Flow Meters Used in Oil and Petrochemical Industries. Polymers, 13(21), 3647
[3] "Nabizadeh, M., & Jamali, S. (2021). Life and death of colloidal bonds control the rate-dependent rheology of gels. Nature
Communications, 12(1), 1-9.".
Author Response
Please see the attachment.
Author Response File: Author Response.pdf
Reviewer 3 Report
The paper is well written and well expressed. But I have one suggestion, that improve literature review by adding comparison table.
The table will compare your work with literature work in term of methodology and various results. Prove the novelty of your proposed work with literature work by comparison table.
Also make clear in abstract what is novel and what you achieve that is not gone before.
Improve the literature review by adding recent works. Cross check the grammatical mistakes and improve English of your paper.
Author Response
Please see the attachment.
Author Response File: Author Response.pdf
Round 2
Reviewer 2 Report
All comments have been addressed correctly
Reviewer 3 Report
All of my questions are answered.
