Review History


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Summary

  • The initial submission of this article was received on July 12th, 2021 and was peer-reviewed by 3 reviewers and the Academic Editor.
  • The Academic Editor made their initial decision on August 5th, 2021.
  • The first revision was submitted on August 28th, 2021 and was reviewed by the Academic Editor.
  • The article was Accepted by the Academic Editor on August 30th, 2021.

Version 0.2 (accepted)

· Aug 30, 2021 · Academic Editor

Accept

Good work carried out by the authors

[# PeerJ Staff Note - this decision was reviewed and approved by Monika Mortimer, a PeerJ Section Editor covering this Section #]

Version 0.1 (original submission)

· Aug 5, 2021 · Academic Editor

Minor Revisions

Good effort. As per the reviewers' suggestions do the needful to make the article is effective.

Reviewer 1 has requested that you cite specific references. You may add them if you believe they are especially relevant. However, I do not expect you to include these citations, and if you do not include them, this will not influence my decision.

[# PeerJ Staff Note: It is PeerJ policy that additional references suggested during the peer-review process should only be included if the authors are in agreement that they are relevant and useful #]

[# 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.  It is a common mistake to address reviewer questions in the response letter but not in the revised manuscript. If a reviewer raised a question then your readers will probably have the same question so you should ensure that the manuscript can stand alone without the response letter.  Directions on how to prepare a response letter can be found at: https://peerj.com/benefits/academic-rebuttal-letters/ #]

Reviewer 1 ·

Basic reporting

In this paper, the topology of the neural network is determined using principal component analysis (PCA) and K-Means clustering. Some important comments
1. Major contributions of the paper must be represented point-wise.
2. The last paragraph of the Introduction must be the Structure of the paper.
3. Why topology is important?
4. Why PCA is used. Mention properly.
5. How the dataset is chosen? Whether it is genuine? justify.
6. How variance is calculated?

Experimental design

1. Why RMSE is chosen for evaluating topology?
2. How the number of neurons is calculated?
3. Why training is conducted using 70% of the data from the dataset, while testing is done using 30% of the data from the dataset?
4. DIscuss the experimental environment.

Validity of the findings

1. Technical discussion on results must be mentioned.
2. The authors must use the proper software to draw graphs.

Additional comments

1. The English language must be improved.
2. All the key terms of the equations must be defined.
3. Draw a flowchart for a better understanding of the proposed scheme.
4. Add section number.
5. Try to give the figures and tables in the appropriate places.
6. Include the following references to improve the reference section:
Feature recognition of abstract art painting multilevel based convolutional ancient recognition neural network method”, Journal of Interconnection Networks, 2021. DOI: https://doi.org/10.1142/S0219265921410036
“Efficient algorithm for big data clustering on single machine,” CAAI Transactions on Intelligence Technology, vol. 5, no. 1, pp. 9-14, 2020.
“Nonlinear neural network based forecasting model for predicting COVID-19 cases”, Neural Processing Letters, 2021. DOI: 10.1007/s11063-021-10495-w
“Ensemble algorithm using transfer learning for sheep breed classification”, Proc. of the 2021 IEEE 15th International Symposium on Applied Computational Intelligence and Informatics (SACI), IEEE, Timisoara, Romania, pp. 199-204, 2021. DOI: 10.1109/SACI51354.2021.9465609
“Advances on QoS-aware web service selection and composition with nature-inspired computing,” CAAI Transactions on Intelligence Technology, vol. 4, no. 3, pp. 159-174, 2019
“Survey on cloud model based similarity measure of uncertain concepts,” CAAI Transactions on Intelligence Technology, vol. 4, no. 4, pp. 223-230, 2019.
“Fast and secure data accessing by using DNA computing for the cloud environment”, IEEE Transactions on Services Computing, 2020. DOI: 10.1109/TSC.2020.3046471
“Securing multimedia by using DNA based encryption in the cloud computing environment”, ACM Transactions on Multimedia Computing, Communications, and Applications, vol. 16, no. 3s, 2020. DOI: https://doi.org/10.1145/3392665

·

Basic reporting

Organization of the content good used professional language. Used state of art for solving problem.

Experimental design

Data collection and algorithms used for processing are good to find hidden patterns in data for wind speed prediction using ANN.

Validity of the findings

Impressive results with high accuracy RMSE value depicting how model learns from data.

Additional comments

Novel work with different hybrid framework

Reviewer 3 ·

Basic reporting

The main aim of the paper is the determination of topology of neural network whose objective function is regression.

The authors made a systematic contribution to the research literature in this area, however the relevance to objective function is uncertain.

Experimental design

The manuscript is well written. The introduction part is relevant and sufficient information about the previous study findings is presented.

The main concern here is the provision of rationale for the use of PCA and K-means clustering to determine the topology should be provided.

Validity of the findings

Overall, the results are clear and compelling with two possible exceptions.
1. The use of performance comparison with the mentioned author methods. Are the methods benchmark methods to compare??
2. Also, are the methods used for comparison using the same objective function proposed in the research.

Additional comments

The authors can also better explain whether the performance measure RMSE sufficient to prove the efficiency of PCA and K-means in the proposed statement.

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