Fuzzy Neural Network with Ordered Fuzzy Numbers for Life Quality Technologies

Round 1

Reviewer 1 Report

The paper present the idea of using fuzzy logic and Artificial Neural network and its application to improve the quality of life. 

The structure of the paper needs to be improve this is not a unique approach as the authors claimed, there are a lots of approaches  used this before , the author must have a literature review, he can compare his approach with ANFIS algorithm.

The figures 5 to 14 are not clear , the mathematics equation not numbered and not in described so it very difficult to understand and go through it

The error percentage not clear how it was calculated.

Overall the paper needs a a lot of work

Author Response

Dear Reviwer,

Thanks a lot for Your review, we try to improve our paper.

We have add a literaturę review and compare out approach with ANFIS algorithm. We summarize the results, add more conlusion which provide the main contribution of out paper. We summarize why our approach is unique  - becaouse noone is using Ordered Fuzzy Numbers in neural network weights. So the overal layout of the paper was changed.

The figures 5 to 14 was improved. The mathematics equations are numberd and summarized. The description of error calculations was added in the test section. We add a section with the study of the art according to fuzzy networks. Then, more results discussion was added and finally more conclusion wich summarize the main contribution fo our paper. Then the paper was checked by the professional English translator.

Best Regards

Authors

Reviewer 2 Report

The submitted manuscript applied artificial neural network with ordered fuzzy numbers to assess the overall life satisfaction. This study is interesting and matches the scope of the SI. However, the theoretical/scientific contribution of the study is weak and not enough for the journal. All techniques are readily available and directly used. No new methods were proposed. Furthermore, most of the manuscript is devoted to introduction of existing methods. It is not clear what the authors did. Last but not the least, the English expression of the manuscript is very jerky and needs to be greatly improved.

Author Response

Dear Reviewer,

many thanks for the review. We try to improve our paper according to Your sugestion.

We have add a literaturę review and compare out approach with ANFIS algorithm. We summarize the results, add more conlusion which provide the main contribution of out paper. We summarize why our approach is unique  - becaouse noone is using Ordered Fuzzy Numbers in neural network weights. So the overal layout of the paper was changed.

The figures 5 to 14 was improved. The mathematics equations are numberd and summarized. The description of error calculations was added in the test section. We add a section with the study of the art according to fuzzy networks. Then, more results discussion was added and finally more conclusion wich summarize the main contribution fo our paper. Then the paper was checked by the professional English translator.

Best Regards

Authors

Reviewer 3 Report

Summary of the work:

The present study proposes neural network with ordered fuzzy numbers for quality of life. It is suitable for detection of attack on a computer network, the anticipation of servers load, management of multiplexing of data transmission paths, or transmission error rate forecasting.

General comments:

The topic of the paper is interesting. However, the paper needs to be improved on the basis of the following comments and suggestions. Authors are suggested to pay full attention to all of them in further improving the quality of your paper.

Detailed comments and suggestions:

1. The title is ‘fuzy’ or ‘fuzzy’? Please check it.

2. The motivation of the paper could be enhanced. Why are artificial neural networks with ordered fuzzy numbers used for life quality technologies? What are the research gaps? How can you fill them? Compare the existing models in the literature and why you think your model is merit in the literature? Please work on improving the clarity of your paper.

3. Based on the above suggestions, I would suggest the authors to add some reference from the last 3 years. There is many existing research on fuzzy numbers. Some closely related ones are suggested here: Incomplete Pythagorean fuzzy preference relation for subway station safety management during COVID-19 pandemic; Zero-carbon measure prioritization for sustainable freight transport using interval 2 tuple linguistic decision approaches.

4. The comparison analysis and in-depth discussions are presented in this paper. Please enhance them to demonstrate the reliability of your advocated model and its application. How to verify the effectiveness of the developed method for quality and life.

5. In particular, the future applications of the paper might be included in the conclusion section. I think the developed neural network is suitable for solving energy consumption reduction techniques. For example, Rail train operation energy-saving optimization based on improved brute-force search; Energy consumption optimization of tramway operation based on improved PSO algorithm.

6. The conclusion should be improved to summarize clearly the main contributions of the paper and future research efforts. It will increase the impact of the paper if the authors try to indicate this explicitly in the manuscript. Critical limitations in the proposed framework should be offered. Extensions and applications of the proposal in other fields could be exemplified in the Conclusion section. 

8. Language should be further improved. Please carefully revise and improve it. 

9. The layout of the paper should be improved.

Author Response

Dear Reviewer,

Thanks a lot for Your review, we try to improve our paper.

Summary of the work:

The present study proposes neural network with ordered fuzzy numbers for quality of life. It is suitable for detection of attack on a computer network, the anticipation of servers load, management of multiplexing of data transmission paths, or transmission error rate forecasting. 

General comments:

The topic of the paper is interesting. However, the paper needs to be improved on the basis of the following comments and suggestions. Authors are suggested to pay full attention to all of them in further improving the quality of your paper.

Detailed comments and suggestions

1. The title is ‘fuzy’ or ‘fuzzy’? Please check it.

There was a mistake, we change it to ‘fuzzy’ of course.

2. The motivation of the paper could be enhanced. Why are artificial neural networks with ordered fuzzy numbers used for life quality technologies? What are the research gaps? How can you fill them? Compare the existing models in the literature and why you think your model is merit in the literature? Please work on improving the clarity of your paper.

There is a new literature study added with the relevant papers from the last 3 years. The lackage of existing method is provided, the gap according to the quality and life was added with some section how it is filled by the proposed method.

3. Based on the above suggestions, I would suggest the authors to add some reference from the last 3 years. There is many existing research on fuzzy numbers. Some closely related ones are suggested here:Incomplete Pythagorean fuzzy preference relation for subway station safety management during COVID-19 pandemic; Zero-carbon measure prioritization for sustainable freight transport using interval 2 tuple linguistic decision approaches.

There is a new literature study added with the relevant papers from the last 3 years.

4. The comparison analysis and in-depth discussions are presented in this paper. Please enhance them to demonstrate the reliability of your advocated model and its application. How to verify the effectiveness of the developed method for quality and life.

There is a new paragraph in which the achieved results are discussed and the effectiveness of the developed method for quality and life is described.

5. In particular, the future applications of the paper might be included in the conclusion section. I think the developed neural network is suitable for solving energy consumption reduction techniques. For example, Rail train operation energy-saving optimization based on improved brute-force search; Energy consumption optimization of tramway operation based on improved PSO algorithm.

There is a section of future use of proposed network added. Also some possible place in which the solutuon could be used are described.

6. The conclusion should be improved to summarize clearly the main contributions of the paper and future research efforts. It will increase the impact of the paper if the authors try to indicate this explicitly in the manuscript. Critical limitations in the proposed framework should be offered. Extensions and applications of the proposal in other fields could be exemplified in the Conclusion section. 

The conclusion section was improved, there are clear summary and main contributions described. Also some critical limitations and future work are described.

8. Language should be further improved. Please carefully revise and improve it. 

The language of the paper was improved.

9. The layout of the paper should be improved.

The layout of the paper was improved, there are also new section added.

To summarize: We have add a literaturę review and compare out approach with ANFIS algorithm. We summarize the results, add more conlusion which provide the main contribution of out paper. We summarize why our approach is unique  - becaouse noone is using Ordered Fuzzy Numbers in neural network weights. So the overal layout of the paper was changed. The figures 5 to 14 was improved. The mathematics equations are numberd and summarized. The description of error calculations was added in the test section. We add a section with the study of the art according to fuzzy networks. Then, more results discussion was added and finally more conclusion wich summarize the main contribution fo our paper. Then the paper was checked by the professional English translator.

Best Regards

Authors

Round 2

Reviewer 2 Report

No further comments.

Reviewer 3 Report

all thec have been responded. I suggest to accept it.