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The authors have addressed all of the reviewers' comments as submitted by the reviewers.
[# PeerJ Staff Note - this decision was reviewed and approved by Xiangjie Kong, a PeerJ Section Editor covering this Section #]
All things are revised as per my comments
The design is improved as per the suggestions
The findings are visually presented as per requested in my comments
I congratulate authors on the valuable addition to the research field. However there is a room from improvement. I suggest to work on more thoroughly in the future directions
The authors have incorporated all the changes listed in first round.
No further changes are required
The results are valid and novel.
The authors should prepare a major revision based on all comments of the two reviewers.
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The authors need to become more professional in reporting the data in Tabular format. Just sharing the name with citation and accuracy is little in-sufficient for the basic reporting.
Add a little bit of details in your Tabular accuracy.
I suggest to have a look of reporting in this article:
https://doi.org/10.1016/j.suscom.2023.100907
Also, in the Introduction end, you should have to discuss the organization of the article. You need to be consistent in the Introduction.
The article suddenly changes to topic of Deep Learning without any pre-requisites information for the reader.
Equations needs to be aligned
The investigation is presented in a goof visual format. However it lacks some basic block diagram for understanding the process.
The MHI signals with pose needs to be explained in more intuitive manner. However the visualizations are good.
The ResNet-18 is computationally expensive, Have you compare your proposed methods with earlier methods in terms of time and cost ?
Does your solution have the practical viability to scale up ?
The solution you have discussed about SIFT can be viable to be deployed but what about yours?
You're using a sophisticated background. What are the cases in case of clutter/occlusion/noisy background ?
Literature is clearly presented but the author needs to be little consistent. If you are reporting accuracy in the table. Make sure to highlight the gaps when writing the literature survey.
Lacking in a logical context makes it a little boring for the user. Read your Paragraph Survey again and again. You'll find a little shift from reporting methods to talking about accuracy.
However, your method is explained well but you are severely lacking in Mathematical context and equations.
Can we distribute the methods in form of chart : ML Based, DL Based & Traditional Feature Extraction Based methods
Mathematical Equations needs to be added
Dataset Reporting in Intuitive Way
Why you're superior? needs to be discussed
Clear the research questions and give answers in the paper.
Add the little bold section comparing your methods with other popular known ones.
The authors proposed a novel approach for the recognition of isolated sign language videos using Resnet and Support Vector Machines. The paper is interesting but needs major improvements.
First, the abstract does not highlight the actual contribution as SVM and Resnet are well known algorothms. I believe that utlizing these approahces in an effective manner leads to an important contribution, but abstact fails to show the actual working of proposed work.
Second, why authors didnt explored Hourglass network that are specifically designed for post estimation. Some other good allternatives are also missing in discussion such as Densenet.
The title is too confusing, i will suggest do not use abbrevaution and also avoid to lengthen it.
The proposed framewrok is missing leads to a question regarding the major contribution. There is a need of in-depth figure with detail discussion on proposed framework that should highlight the actual contribution.
Included in basic reporting
Finding seems valid but needs a detailed comparsion with recent studies.
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