Building an Expert System through Machine Learning for Predicting the Quality of a Website Based on Its Completion

Round 1

Reviewer 1 Report

1. In this study, it is necessary to display specific equations accompanied by their corresponding equation numbers. For example, the equation that signifies the Quality of URL/WEB can be expressed as Qwp = Quality of URL/WEB.

2. This study utilized a multi-layer perceptron neural network to represent the expert model. The authors are required to provide a detailed description of the multi-layer perceptron neural network and include an illustration of its structure.

3. How should the training and testing datasets be defined? Additionally, what is the process for defining the structure of the multi-layer perceptron neural network?

4. What the meaing of Weight Code and Weight Value in Table5 ?

5. The results should be further elaborated upon. Many aspects are currently only presented as outcomes. It is advisable to provide more comparisons and descriptions based on the observed results.

6.  What the meaing of the number in Table 4 ? How many categories does the quality of the website have?

Author Response

Respected Reviewer,

Thanks for your effort and Remarkable review. We have carefully reviewed all your comments and observations and made the required changes.  Please let us know if any issue is to be further attended to.

Thanks, and regards.

Authors

Author Response File: Author Response.pdf

Reviewer 2 Report

In the discussion section, further elaboration on the implications of the findings could be useful. How do the high accuracy rates and comparisons with other methods impact the field of website quality assessment? The conclusion is concise, but it would benefit from a more comprehensive summary of the study's key findings. Additionally, potential avenues for future research in this area, such as expanding the model to consider additional factors perhaps would helpful.

Author Response

Respected Reviewer,

Thanks for your effort and Remarkable review. We have carefully reviewed all your comments and observations and made the required changes.  Please let us know if any issue is to be further attended to.

Thanks, and regards.

Authors

Author Response File: Author Response.pdf

Reviewer 3 Report

The paper deals with very interesting topic and the proposed approach is applicable. But there are few major issues which must be improved:

1. Authors uses several models for creating expert system, but they are not described in detail - type of fuzzy linguistic variables, fuzzy methodst used for computation, etc.

2. The dataset od 100 websites is poorly described - missing types of websites, URL, how many images, PDFs the have, etc.

3. Results must be extended with details about resulting level of quality of tested websites

4. Conclusion is very short and must be extended with practical implications and deep discussion about results.

Author Response

Respected Reviewer,

Thanks for your effort and Remarkable review. We have carefully reviewed all your comments and observations and made the required changes.  Please let us know if any issue is to be further attended to.

Thanks, and regards.

Authors

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

The authors have revised the comments based on the feedback provided by the reviewers.

Reviewer 3 Report

The authors extend the paper based on my remarks. The paper can be accepted.