Innovative Region Convolutional Neural Network Algorithm for Object Identification

Round 1

Reviewer 1 Report

This paper summarizes the development process of object detection algorithm, and the author needs to make some revisions before the article is published.

(1) As far as I know, the object detection algorithms include both region based series (R-CNN), and YOLO series (https://doi.org/10.3390/app11020813), which is obviously not mentioned by the author. As an alternative, the author may also consider revising the title to “Region-based Convolutional Neural Network Algorithm for Object Identification”.

(2) More discussion on CNN application should be added in the introduction. Some research like:

1. EEG-based seizure prediction via Transformer guided CNN

2. Structural damage detection based on convolutional neural networks and population of bridges

(3) English language needs further improvement.

Author Response

Dear Reviewer

I have attached the revision below according to the reviewer notes:

1. The title has been revised accordingly
2. The paper has been through proofreading for grammar mistakes

Please kindly check the revision and feel free to reach out if there are more revisions.

Author Response File: Author Response.pdf

Reviewer 2 Report

Review for

The level of originality of the paper is high. The literature review and proposed methodology are properly discussed and compared to the previous studies.

In this paper, authors used only 37 sources, containing both historical and fundamental works, as well as the latest scientific research on this topic. But the literature review can be structured. The papers discussed many points of this study. Please, discuss these papers too:

Tao, Y., Jun, Z., Zhi-hao, Z. et al. Fault detection of train mechanical parts using multi-mode aggregation feature enhanced convolution neural network. Int. J. Mach. Learn. & Cyber. 13, 1781–1794 (2022). https://doi.org/10.1007/s13042-021-01488-1

Mikhaylov, A., Tarakanov, S. (2020). Development of Levenberg-Marquardt theoretical approach for electric network. Journal of Physics: Conference Series, 1515, 052006. https://doi.org/10.1088/1742-6596/1515/5/052006

 

An, J., Mikhaylov, A., Kim, K. (2020) Machine Learning Approach in Heterogeneous Group of Algorithms for Transport Safety-Critical System. Applied Sciences, 10(8), 2670. https://doi.org/10.3390/app10082670

The introduction section has benefit from having a clearer structure of what to expect in the paper. Furthermore, the author(s) would benefit from being more concise in their writing, as much of the content was redundant and overemphasized. While it is good practice to assume the reader has no prior knowledge of the content, a topic and/or discussion does not need to be explained over and over again if it is stated both adequately and appropriately once.

Some conclusions contribute to the study of the problem. The author does not formulate the problem itself – it makes impossible to analyse the contribution of the paper. The aim or the question of the paper (or even the hypothesis of the author) are formulated.

Overall, it is very clear to grasp understanding of the manuscript and content in its current state. I strongly advise using hypothesis points to articulate and/or express material in scientific writing. Publication of this piece seems likely in any reputable scientific periodical after a correction in the writing of the manuscript.

Table 1 should be discussed more properly.

Authors need to add more details on the range of simulation considered in this work should be clearly outlined within the abstract. The current statements are vague and too general to get an idea of the work that have been accomplished.

The paper possesses a proper form of well-structured and readable technical language of the field and represents the expected knowledge of the journal`s readership.

There are minor errors in English, but this does not affect the general nature of the work. The current study brings many new to the existing literature or field. For one, the author(s) seem to have a good grasp of the current literature on their topic area (i.e., recent literature and seminal texts relevant to their study is not cited/referenced).

 

 

 

Author Response

Dear Reviewer

I have attached the revision below according to the reviewer notes:

1. regarding the first paper (https://doi.org/10.1007/s13042-021-01488-1). I'm unable to find the corresponding paper. so I didn't include it in the paper
2. The introduction has been revised accordingly
3. The problem in the conclusion has been formulated in the abstract to support the conclusions itself. The abstract has also been revised
4. Additional explanation has been added for Table 1.

Please kindly check the revision and feel free to reach out if there are more revisions.

Author Response File: Author Response.pdf