Quality of Service Generalization using Parallel Turing Integration Paradigm to Support Machine Learning

Round 1

Reviewer 1 Report

The manuscript entitled "Quality of Service Generalization using Parallel Turing Integration Paradigm to Support Machine Learning" has been investigated in detail. The topic addressed in the manuscript is potentially interesting and the manuscript contains some practical meanings, however, there are few issues which should be addressed by the authors:

There are no numerical results in the Abstarct section. Please add numerical results to this section.

Figure 14 is unclear and needs more clarification. It should be modified. The authors can use color items instead of used symbols.

Usage of D-plot instead of Matlab for plotting figures 12 and 13 are recommended.

Author Response

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Author Response File: Author Response.pdf

Reviewer 2 Report

This paper proposes the parallel turing integration paradigm, PTIP, to enhance classifier learning, 3-D cube logic, and balance the engineering process of paradigms. Though the experimental results of the testing show that the proposed model increases accuracy, manages random mistakes, and offers improved QoS generalization. However, there are still several problems that exist in the paper.

1. The paper presents a very complete solution, but there seems to be so much design that it is difficult to discuss on the details.

2. How to expand the proposed parallel turing integration paradigm to the popular machine learning senarios. It is sugegstion that an actual case study to be present too.

3. The 3-D cube logic need further description.

4. Moreover, the serial numbers of the headings in section four and five (experiments and conclusion) including the subheadings, appear to be incorrect.

Author Response

Please see the attachment

Author Response File: Author Response.pdf