The Novel Combination of Nano Vector Network Analyzer and Machine Learning for Fruit Identification and Ripeness Grading
and Nhan Le-Thanh 1,4
Round 1
Reviewer 1 Report
Please see the attachment.
Comments for author File: 
Comments.pdf
Author Response
We would like to thank the reviewers and editors for their valuable comments that help to improve the manuscript's quality.
You will find in the attached document a detailed answer to all reviewer comments and a new version of the manuscript with changes highlighted in blue.
Author Response File: Author Response.pdf
Reviewer 2 Report
In this work, the authors proposed a machine learning approach to classify fruits. The results are promising. However, there are several concerns to address before publication of the paper:
1. Why k-NN and Neural Net? There lack enough motivations or experiments to choose these two models. For example, why not decision tree/random forest or support vector machine? The authors should provide more explanation or numerical comparison to justify their choice.
2. Hyper-parameter tuning. The authors only provide a set of parameters of neural network for their modeling. Is it the only one? Why not try different hyper-parameters?
3. The authors should make their dataset and trained models openly available for other researchers to repeat their works.
Author Response
We would like to thank the reviewers and editors for their valuable comments that help to improve the manuscript's quality.
You will find in the attached document a detailed answer to all reviewer comments and a new version of the manuscript with changes highlighted in blue.
Author Response File: Author Response.pdf
Round 2
Reviewer 1 Report
Comments are mostly well addressed. No further comments.
Reviewer 2 Report
The authors have addressed my concerns. I recommend publication.
