Review History


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Summary

  • The initial submission of this article was received on June 3rd, 2023 and was peer-reviewed by 2 reviewers and the Academic Editor.
  • The Academic Editor made their initial decision on June 27th, 2023.
  • The first revision was submitted on August 29th, 2023 and was reviewed by 2 reviewers and the Academic Editor.
  • The article was Accepted by the Academic Editor on August 31st, 2023.

Version 0.2 (accepted)

· Aug 31, 2023 · Academic Editor

Accept

As per comments from both original reviewers, I would like to accept this revised paper now.

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

Reviewer 1 ·

Basic reporting

no comment

Experimental design

no comment

Validity of the findings

no comment

·

Basic reporting

Appropriate

Experimental design

Appropriate

Validity of the findings

Appropriate

Additional comments

This paper is very interesting and challenging. Moreover, this paper is well-organized and well-written. In my point of view, the paper deserves publication in the Journal.

Version 0.1 (original submission)

· Jun 27, 2023 · Academic Editor

Major Revisions

As per the comments from the two reviewers, I suggest the authors should make a major revision to make this manuscript better. In the revised version, please also mark all changes in a separate document.

In addition, Reviewer 2 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 #]

Reviewer 1 ·

Basic reporting

Please see the additional comments.

Experimental design

Please see the additional comments.

Validity of the findings

Please see the additional comments.

Additional comments

1. Keywords should be ordered alphabetically.
2. The authors should summarize their contributions in the introduction section using some bullets or numbers briefly.
3. “Related work” or “literature review” section should be added after the introduction section as section 2.
4. Place figures and their captions at the top or bottom of columns. Avoid placing them in the middle of columns, e.g., Fig. , etc.
5. References should be provided for the equations borrowed from the literature.
6. The symbols in an equation should be defined before or immediately following the equation, e.g., what are V_i, X_t, and m in (1). There is no dependency on t in the right side of the equation (1)!. So, why there is X_t?
7. The symbols in an equation should be defined before or immediately following the equation, e.g., all parameters in (4) and dot product in (4). Defining symbols and operators, e.g., element wise product etc. must be done for all equations in the paper.
8. Based on the paragraph before equation (1), definition of this equation is not clear. A reference should be provided for recommendation systems.
9. To calculate the probability of the user clicking on the candidate clip x_n, why recommendation system should take {X_{n-t},…, x_n} instead of { X_{n-t},…, X_{n-1}}. What are X_t’s? What is the difference between X_{n-t} and x_n?
10. Define abbreviations and acronyms the first time they are used in the text, even after they have been defined in the abstract, e.g., DAN, LSTM, PCNN, etc.
11. References should be given for deep learning models discussed in this paper such as LSTM, CNN, PCNN, ARNN, etc. to explain their theoretical background, fundamentals, structures, and their back-propagation formula.
12. In the paragraph after equation (1), LSTM is not mentioned, while its input gate is explained. So, LSTM should be mentioned in this paragraph.
13. The sentence after equation (4) is about CNN while previous formulas and sentences are about LSTM. Each part of the section should be organized in a way that the reader can easily follow and understand each part and relation between each part. Different parts can be put in different subsections with titles.
14. Citations in the reference section should follow the IEEE format if it is not specified.
15. The “future work” should be explained briefly in the conclusion section or added as a separate section after the conclusion.
16. Figures which show structure of LSTM, CNN, RNN, PCNN, PCNN, ARNN should be added to the content of the paper.

·

Basic reporting

This paper investigates the development and connection between the state of development of the teaching model of multimedia integration. A framework for teaching English with multimedia integration is built. The components of the hardware system framework and software system architecture are analyzed. And for the problem that the English corpus in the process of university English translation teaching is numerous and updated at length, the corpus is screened and analyzed by using big data technology.

Experimental design

To address the problem that traditional recommendation algorithms ignore the temporal sequence of teachers' and students' browsing behaviors, a recurrent neural network algorithm based on an ant colony optimization algorithm is proposed, using recurrent neural networks to model users' time-series behaviors and convolutional neural network to mine users' potential preference features.

Validity of the findings

The results listed in the paper in the form of formulas, algorithms, figures, and analysis are seeming true and correct. The paper is well written, and it is written in a truly sporty manner.

Additional comments

* The contents of the paper are good and contain new ideas. Anyhow, I would like to see the following modifications, whether minor or major, in the revised version, which would increase the strength of the paper and increase its potential readers, as well as improve the current work.
1. Explain how each of the parameters influences the performance of the proposed approach.
2. Describe the following in detail:
- Big data technology.
- Recurrent neural network.
- High-performance computing.
3. The listed results especially formulas, figures, and analyses should be revised to avoid any errors.
4. The derivation of Equation (5) should be given in detail.
5. More descriptions of Equation (8) should be given.
6. Define the used symbols clearly and numerate all equations that appear. Further, reformulate the keywords and the conclusion to reflect the contents of the paper.
7. The documentation of the paper is poor as seen from the references. Thus, the authors should mention this work more carefully and should update some of the listed references in their paper to add power to the paper. Anyhow, the following references should be cited in the paper: (https://doi.org/10.1016/j.ins.2014.03.128), (https://doi.org/10.1155/2013/831657), (https://doi.org/10.1155/2014/401696), and (https://doi.org/10.1155/2012/205391)
8. What are the rules of multimedia integration?
9. English is generally good; I think it needs to be polished further and some typos need to be revised. Further punctuation marks should be checked throughout the paper, especially after the equations and at the end of the statements.

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