TMCS-ENN: A Temporal Feature-Correlation Cuckoo Search-Elman Neural Network for Sugar Raw Materials Demands Prediction

Round 1

Reviewer 1 Report

Have a someone with a good knowledge of English read the paper and mark grammatical errors.

Author Response

Dear reviewer, Thank you very much for your valuable suggestions. I have replied to your question in the word document. Please check the document.Thank you again for your review of this article. I wish you success in your work and a happy life.

Author Response File: Author Response.pdf

Reviewer 2 Report

The authors present the article entitled “TMCS-ENN: A Temporal Feature-correlation Cuckoo Search-Elman Neural Network for Sugar Raw Materials Demands Prediction”.

This paper aims at exploring deep learning architectures for combining time-series and temporal feature-correlation data for sugar raw materials demand prediction. 

The article presents the following concerns:

• Avoid using apostrophes.

• Lines 92-95: Please discuss in detail the research of [14] and [15].

• Please justify the use of the sigmoid function.

• Figures must be vectorized.

• Add a little introduction between points 3 and 3.1.

• Line 203: “a little bit” please be more specific and objective.

• Include a table which compares the findings of the work vs the already reported in the stat of the art.

• Please add a nomenclature table to define variables and acronyms

• Editing of English language and style required

• Line 133 could be justified with the following up-to-date works regarding metaheuristic algorithms: Metaheuristic algorithm-based vibration response model for a gas microturbine; Self-tuning control using an online-trained neural network to position a linear actuator; Non-linear regression models with vibration amplitude optimization algorithms in a microturbine; Parameter identification of motors by cuckoo search using steady-state relations.

• The text must be written in the 3rd person or passive voice.

• The text presents coincidences with the next article and isn't referenced: 

• Song, K., Lv, S., Hu, D., & He, P. (2021). Software Defect Prediction Based on Elman Neural Network and Cuckoo Search Algorithm. Mathematical Problems in Engineering, 2021.

 

The article presents the following concerns:

 

1. lines 1-2: “to the modern industries. 1 Currently, most studies are based on the tralditional regression…” Should be rewritten as “ to modern industries. Most studies are based on traditional regression…” Check the correct form to use the articles.

2. line 5: “According to this,  this study proposed…” Should be rewritten as “This study proposed…”

3. line 8: “ achieved the better…” should be rewritten as “ achieved better…”

4. line 9-10: “improving the product efficiency and automation level, as well as for reducing the costs…”  Should be rewritten as “improving the production efficiency and automation level, as well as reducing costs…”

5. line 21: “thus to maintain the balance of production, reduce the overall cost…” Should be rewritten as “thus maintaining the balance of production and reducing the overall cost of the company…”

6. line 46: “man-power…” Should be rewritten as “manpower…” 

7. line 47: “prob-lem…” Should be rewritten as “problem…” 

8. line 72: “Eng-lish…” Should be rewritten as “English…”

9. line 81: “re-search…” Should be rewritten as “research…”

10. line 82: “fore-casting…” Should be rewritten as “forecasting…”

11. line 86: “pro-posed…” Should be rewritten as “proposed…”

12. line 88: “forecast-ing…” Should be rewritten as “forecasting…”

13. line 100: “hydro-logical…” Should be rewritten as “hydrological…”

14. line 134: The use of “and so on” in formal writing is generally frowned upon. Consider rewriting the sentence.

15. line 185: “upgrad-ing…” Should be rewritten as “upgrading…”

16. line 212: “oscillation though…” Should be rewritten as “oscillation through…”

17. line 239: “nests harder…” Should be rewritten as “nests are harder…”

18. line 282: “to solve…” Should be rewritten as “of solving…”

19. line 283: “may be…” Should be rewritten as “maybe…”

20. line 347: “sugar industry which has…”sugar industry that has…”

21. line 452: “net-work…” Should be rewritten as “network…”

22. line 463: “at considering…” Should be rewritten as “to consider…”

23. line 465: “con-sumes…” Should be rewritten as “consumes…”

Author Response

Dear reviewer, Thank you very much for your valuable suggestions. I have replied to your question in the word document. Please check the document. I have saved the manuscript of the article in PDF format to facilitate your review of the manuscript. Thank you again for your review of this article. I wish you success in your work and a happy life.

Author Response File: Author Response.pdf

Reviewer 3 Report

The demand prediction for sugar raw materials using a deep learning approach by combining time-series and temporal feature-correlation data was discussed in the manuscript. The manuscript has several grammatical errors as well as typos. Following are some suggestions and comments:

1- Abstract needs revision to better show the importance of the study along with the key findings.

2- Please introduce the abbreviation at first appearance, for instance, MP and BP on lines 87 and 89.

3- There are some typos such as lines 47, 72 86, 109, .... Please revise them.

4- In equation 3, which one is correct, x(k-1) or u(k-1)? 

5- Line 147, TMCS-ENN is correct. Please revise it.

6- Please add references to arguments in lines 194 to 200.

7- The quality of figures especially 5 and 6 is low.

8- Captions for Tables 1 to 3 need to be rewritten in small characters. Moreover, the title for section 5-2 is wrong.

9- Please clarify what are the inputs of the network specifically? Also, please mention how you considered the effect of holidays on your model? Try to use graphical context.

Author Response

Dear reviewer, Thank you very much for your valuable suggestions. I have replied to your question in the word document. Please check the document. I have saved the manuscript of the article in PDF format to facilitate your review of the manuscript. Thank you again for your review of this article. I wish you success in your work and a happy life.

Author Response File: Author Response.pdf

Reviewer 4 Report

Manuscript “TMCS-ENN: A Temporal Feature-correlation Cuckoo Search-Elman Neural Network for Sugar Raw Materials Demands Prediction”

 

This manuscript will be interesting for sellers of table sugar, which are represented on the Chinese market, because it will allow them, using the developed TMCS-ENN model, to forecast sugar sales. However, TMCS-ENN model, which takes into account the increase in the purchase of table sugar during holidays in Chine, is unsuitable for forecasting the purchase of sugar raw materials by sugar producers. Since the processing of sugar raw materials is seasonal and depends on the harvest period of the sugar beets in a certain area of the world and the sugar beet harvest and does not directly depend on the needs for sugar in the country's market.

 

In order to improve the manuscript, I suggest the following corrections:

1. The article states: “The mathematician Yule proposed time series analysis to predict future market changes.” (p. 2, lines 61-62). It is necessary to indicate the source where this information can be verified. Also, different models proposed by Walker (p. 2, line 72), McCulloch and Pitts (p. 3, lines 86-87) are mentioned in the article. It is necessary to indicate the source from which such information can be verified.

2. In the Fig. 1, where the structure of Elman neural network is presented, one of the layers is marked as Content Layer. But, in the text (p. 3, line 117) this layer is marked as Context Layer. It needs to be corrected.

3. If the flowchart of Cuckoo search algorithm (Fig. 2) was not proposed by the authors of the article, the resource from which it was taken should be indicated.

4. The impact of traditional Chinese customs and holidays on the purchase of sugar raw materials (Section 4) is analyzed in the article. In the article, it is not clear what the authors mean by sugar raw materials. For beet sugar, the main raw material is sucrose extracted from beets. But the article most likely refers to table sugar, since sugar beet is a seasonal raw material and its processing usually lasts several months a year. Therefore, appropriate clarifications or explanations must be made in the article text and in Fig. 3.

5. It is necessary to clarify the data on the graphs (Fig. 3a and Fig. 3b). If we add the data (Fig. 3 b) for the first half month (≈ 20500) and second half month (≈ 27000), then for the first month together it will be ≈ 47500. But, in the Fig. 3a, we can see the value for the first month is ≈ 56000. In addition, it is necessary to indicate the source of the data, which are shown in the Fig. 3.

6. In the Fig. 5, the unit of the iteration time must be specified.

7. If the actual data (data that need to be clarified), which are presented in Fig. 3a and Fig. 3b, were compared with the data generated by the developed TMCS-ENN model, then this had an impact on the assessment of the accuracy of the data predicted by the model.

Comments for author File: Comments.pdf

Author Response

Dear reviewer, Thank you very much for your valuable suggestions. I have replied to your question in the word document. Please check the document. I have saved the manuscript of the article in PDF format to facilitate your review of the manuscript. Thank you again for your review of this article. I wish you success in your work and a happy life.

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

The manuscript can be accepted

Reviewer 4 Report

The authors took into account all recommendations for improving the quality of the manuscript. The manuscript can be accepted for publication in its present form.