Exploring the Advancements and Future Research Directions of Artificial Neural Networks: A Text Mining Approach

Round 1

Reviewer 1 Report

The authors analyze 10,661 papers from six journals in order to identify current and future research directions. Unsurprisingly, they find that there is a high interest in topics related to machine learning, deep learning, and artificial neural networks (ANNs).

The following observations should be addressed:

1. First of all, the most important research done in the field of ANNs is published in conferences, and not in journals. Moreover, many of these conferences are not even indexed in Web of Science. The authors should use more general research databases, like Google Scholar, to select the papers.

2. The journals selected by the authors are far from being the most important, as the majority of papers in the field do not have the words “neural networks” in their title. The authors should vastly extend the journal list.

3. The results of the research could have been obtained very easily by anyone doing an advanced search on the Web of Science platform. As such, the contribution of the paper is rather minor.

4. In Table 2, why are the odd lines blacked out?

5. The clusters obtained show that some of the papers included in the study are not even about artificial neural networks, but rather about biological neural networks. The authors should exclude papers that are about biological neural networks.

6. In general, the article should be reviewed to correct spelling errors, and to improve the English language.

Author Response

Thank you very much for offering us the opportunity to revise our submission. Please, find attached our answers to the reviewer’s comments.

Author Response File: Author Response.docx

Reviewer 2 Report

1. This is a large-scale literature review. Doing a good job of category classification will attract everyone's citations.

2. Is it possible to add a section of application fields for the ANN-based approach?

3. Is it possible to make a summary table to present the advantages, disadvantages, and limitations of the ANN-based approach?

4. In Table 2, the contents with odd numbers in the Rank column are all black, is this normal?

5. Figures 6, 7, and 8 need more explanation.    

 

6. Is it possible to evaluate the effectiveness and efficiency of the applications of the ANN methods? 

Author Response

Thank you very much for offering us the opportunity to revise our submission. Please, find attached our answers to the reviewer’s comments.

Author Response File: Author Response.docx

Reviewer 3 Report

The overall manuscript quality is low and not suitable for publication

 

Author Response

Thank you very much for offering us the opportunity to revise our submission. Please, find attached our answers to the reviewer’s comments.

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

The authors have addressed the majority of the issues raised by the reviewers. The paper can be accepted in its current form.

Author Response

Please refer to the attached file.

Reviewer 2 Report

1. The words in the keyword column are connected together in Table 3.

2. The authors have answered all of my questions.

Author Response

Please refer to the attached file.

Author Response File: Author Response.docx

Reviewer 3 Report

The authors must not use citations in abstract

The abstract is not well defined 

No summary of the tables are given from the exsisting work

There is lot of work gone into this filed which is at the advance level related to ML and Text Mining

Diagrams representation and explanation is not well defined and inadequate

Not suitable for publication since the article lacks more investigation and paper representation 

Conclusion must be improved

 

 

 

Author Response

We have considered the reviewer's comments and answered each of them. Please refer to the attached file.

Author Response File: Author Response.docx

Round 3

Reviewer 3 Report

The authors have carried out the previous corrections.

Also the authors are requested to expand the literature survey and cite the following paper

Md, A. Q., Kapoor, S., AV, C. J., Sivaraman, A. K., Tee, K. F., Sabireen, H., & Janakiraman, N. (2023). Novel optimization approach for stock price forecasting using multi-layered sequential LSTM. Applied Soft Computing, 134, 109830.
Chicago

 

Author Response

We thank the reviewer for acknowledging the corrections made based on his recommendations. 

We have considered the reference suggested by the reviewer in the revised manuscript. Kindly, refer to the text in blue in section 2.