Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: COVID-NETS: DEEP CNN ARCHITECTURES FOR DETECTING COVID-19 USING CHEST CT SCANS
Review round: 1
Reviewer: 3

Output:
Basic reporting: no comment
Experimental design: no comment
Validity of the findings: no comment
Additional comments: The authors proposed two novel deep CNN architectures (CovidResNet and CovidDenseNet) for automated COVID-19 detection using chest CT scans. The models have been developed and validated for the multi-class and binary classification tasks to differentiate COVID-19 patients from non-COVID-19 viral infections as well as healthy subjects. The SARS-CoV-2 CT-scan dataset has been used to validate the proposed models. Promising performances have been achieved.

The paper is well-structured and easy to read. The proposed models are different from most of the state-of-the-art methods which apply baseline models and being more efficient.  However, I have some concerns and questions:

1-	Table 3 shows that the dataset is not balanced. How did the proposed method deal with this problem in order to avoid biased experimental results? Is this problem handled in the augmentation steps?
2-	The authors reported: “We choose 59.5% of the subjects for training and 40.5% for testing, such that the amount of training images is 60% and 40%, respectively”. In this sentence, did the author mean “training and test images is 60% and 40%, respectively “?
3-	Why the authors didn’t apply the 10 fold-cross validation strategy to better evaluate the system instead of the previous split and to compare it with other state-of-the-art methods following the same protocol?
4-	In the transfer learning stage, the authors report that “In CovidDenseNet the adapter layers and the last layer are randomly initialized “. Could the authors explain why a random initialization is carried out with this model, contrarily to CovidResNet model?
5-	Why do the proposed architectures (CovidDenseNet and CovidResNet) need more epochs to converge (150 epochs vs 100 epochs for the baseline).
6-	The authors should provide a comparison between the baseline and the proposed COVID-Nets when dealing with the same number of epochs (100 and 150 epochs) to get a fair comparison and also to give more detail about the convergence area of each model.
7-	The authors added two different ensemble combinations for improving the overall diagnostic performance. However, there is no explanation about the type of the applied ensemble model (e.g. majority voting, weighted averaging, or other).
8-	The paper presents a comparative study of the proposed models with two baselines (fine-tuned ResNet and DensNet models). Nevertheless, the comparative study needs to be extended to other methods from the literature applying different pre-trained models that achieved very high accuracies using transfer learning strategy (i.e. VGG-16, Inception-ResNetV2).


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: COVID-NETS: DEEP CNN ARCHITECTURES FOR DETECTING COVID-19 USING CHEST CT SCANS
Review round: 2
Reviewer: 1

Output:
Basic reporting: Number of dataset is very significant in machine learning and has to be taken into account.
Experimental design: After discussing about the evaluation performance formula. Authors should add a table on confusion matrix
Validity of the findings: The number of images has to be increased
Additional comments: Comments
Authors have addressed all the comments but the article can be upgraded with the comments below

To compare state of art with currents studies, authors need to show the number of images use for each article and make realistic comparison. Number of dataset is very significant in machine learning. A model that train using 1000 images cannot be compare with a model that is train using 5000 images. This is very important.

After discussing about the evaluation performance formula. Authors should add a table on confusion matrix


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: COVID-NETS: DEEP CNN ARCHITECTURES FOR DETECTING COVID-19 USING CHEST CT SCANS
Review round: 2
Reviewer: 2

Output:
Basic reporting: Authors have solved my issues.
Experimental design: Authors have solved my issues.
Validity of the findings: Authors have solved my issues.
Additional comments: Authors have solved my issues.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: COVID-NETS: DEEP CNN ARCHITECTURES FOR DETECTING COVID-19 USING CHEST CT SCANS
Review round: 2
Reviewer: 3

Output:
Basic reporting: no comment
Experimental design: no comment
Validity of the findings: no comment
Additional comments: The authors have addressed all of my concerns.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: COVID-NETS: DEEP CNN ARCHITECTURES FOR DETECTING COVID-19 USING CHEST CT SCANS
Review round: 3
Reviewer: 1

Output:
Basic reporting: The manuscript is acceptable since all corrections have been made.
Experimental design: The article is acceptable since all corrections have been made.
Validity of the findings: The article is acceptable since all corrections have been made.
Additional comments: No additional comments


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: FRAMEAXIS: CHARACTERIZING MICROFRAME BIAS AND INTENSITY WITH WORD EMBEDDING
Review round: 1
Reviewer: 1

Output:
Basic reporting: Please see below
Experimental design: Please see below
Validity of the findings: Please see below
Additional comments: My review here is based on the conceptualization and operationalization of framing. While the effort and intention of designing a new framing method are impressive, I don’t think that the approach suggested by the authors is methodologically sound. Framing is a complicated process and inductively identifying a frame using manual coding is in itself a sophisticated endeavor. Gamson and Modigliani defined frame as a ‘central organizing idea” which “suggests “what the controversy is about, the essence of an issue” (1987). Also, Van Gorp (2010, pp. 91-92) mentions the need to identify reasoning devices like appeals to principles and causal analysis. By relying on microframes, we are ignoring the larger picture which can reduce the meaning of the text by relying on words alone. The authors claim that each microframe is operationalized by an antonym pair, but what about neutral frames? What about generic frames like human interest or economic frames? In the literature, we also have macro frames that are supposed to be the broader overreaching ideas that can be very difficult to automatedly identify.

This approach, unfortunately, will overlook so much meaning. The same argument applies to capturing framing bias. This is again one of the most complicated intellectual tasks for many reasons including the fact that bias is a relative term and operationalizing it using automated methods is almost impossible. What I think the authors are attempting to measure is the sentiments more than the cognitive concepts as they mention the following: “Framing bias in FrameAxis is analogous to positive or negative sentiment in the sentiment analysis, and framing intensity is to polarity, which is the strength of the expressed sentiment”.

In terms of operationalization of the predefined microframes, the authors mentioned that they extracted 1,828 adjective antonym pairs fromWordNetMiller. However, inductive framing analysis is far much complicated than looking at pairs of adjectives. Tankard, for example, referred to 11 framing elements including: 1. Photos, 2. photo captions, 3. Leads, 4. source selection, 5. quotes selection, 6. pull quotes, 7. Logo, 8. statistics and charts, 9. concluding statements and paragraphs, 10. Headlines, 11. Subheads (2001, p. 101). Also, Van Gorp (2010) offers a longer list of framing devices that are similar to the one above, and adjectives are not even amongst them.

To sum up, I think the approach suggested by the authors is interesting because it mixes between sentiment analysis and in a way topic modelling, but it is certainly not related to identifying frames.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: FRAMEAXIS: CHARACTERIZING MICROFRAME BIAS AND INTENSITY WITH WORD EMBEDDING
Review round: 1
Reviewer: 2

Output:
Basic reporting: The paper satisfies all the requirements.
Experimental design: The method section in the paper is missing some minor details which I point out in the general comments to authors. Again, these are all minor details and can easily be attended to.
Validity of the findings: The work, to the best of my knowledge is novel and the conclusions are well stated.
Additional comments: I would request the authors to explain framing in a bit more detail. To someone coming from Computer science background like me, it was initially a bit difficult to understand what it is. Explaining it with a real world example (may be even from the datasets that you have used) would be helpful. This would certainly improve the readability of the paper.

You mention that while computing framing bias and intensity, you do not consider the topical words. How do you decide which are the topical words? Is it done manually? How many such words did you consider?

The authors also calculate perplexity scores to determine the relevance of a microframe to a given topic. I was not sure how the language model was created. Did you just use a statistical language model and calculate bi-gram probabilities? I would like the authors to elaborate on this a bit.

The captions could be made a bit more informative by adding what one should conclude from them. Since they are all put together at the end of the manuscript, it would be helpful to the reader. The quality of the figures could be improved as well. Caption of figure 4 contains latex symbols.

The references to the figures in discussion section are missing.

Overall, I think it is a nicely written paper and I would be willing to accept it given the above mentioned modifications are made.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: FRAMEAXIS: CHARACTERIZING MICROFRAME BIAS AND INTENSITY WITH WORD EMBEDDING
Review round: 1
Reviewer: 3

Output:
Basic reporting: This paper proposes a simple approach to detecting prominent aspects of a text corpus, which the authors refer to as "microframes". The method is as follows: i) get a set of antonyms pairs and pretrained word vectors; ii) compute the difference between the word vectors corresponding to each word in a pair, and call this difference a microframe; iii) compute the cosine similarity between each word in the corpus, and report the mean and variance of the cosine similarities for a microframe over all tokens in the corpus.

This is essentially the same idea as one of the earliest papers in bias in word embeddings (Bolukbasi et al., 2016), except that difference vectors are being compared to individual words, rather than other differences, aggregated over tokens.

This is valid way to characterize a corpus, but it is unclear what the ultimate purpose of this sort of approach is. In addition, some of the choices made seem puzzling, and there isn't a very convincing effort to show that this method has advantages over others. Overall, I worry about the usefulness and the robustness of this approach, and exactly what claims the authors are trying to make.

The authors position their approach in comparison to analyses of "framing" in the social sciences. The literature of framing of course is not a monolith. Researchers are often interested in getting deep insight into the arguments that are made in a set of documents, which is why the effort is devoted to developing a codebook and carefully reading the text. It is unclear that the outputs of this system would be of any value to such researchers. Perhaps the authors have a different set of users in mind, but if not social scientists, then whom? (And if social scientists, then more extensive use cases with examples of how this might work in practice would be very helpful.) Stating how users would benefit from this system, and demonstrating that it achieves that goal in comparison to alternative would make this paper more suitable for scholarly publication.

In terms of the reliability of the method, the fact that word vector similarity captures semantic similarity is now well established. The novelty here seems to be partly the use of antonyms from WordNet for coming up with around 1600 potential "microframes". However, I am concerned that many of these will be highly questionable and not informative.

I replicated the authors proposed way of generating such a list, and generated a set of five random pairs, which were:
(quantitative, qualitative)
(incongruent, congruent)
(inessential, essential)
(crosswise, lengthwise)
(agitated, unagitated)

Some of these might be relevant to framing, but it seems like many will not. Moreover, because the authors propose using only two words to establish the direction corresponding to the "microframe", some of these will likely be quite unstable. For example, using the same word vectors as the authors, the most similar words to the qualitative - quantitative direction appear to be: quantitative, Quantitative, QE, QE3, Fed, Bernanke, Treasury.

Presumably texts having to do with "quantiative easing" have given rise to the similarity to (Ben) Bernake and the fed, but this is not related to the qualitative quantitative distinction. More thorough investigation of the potential problems or limitations of this approach would be useful.

A few issues related to the presentation should also be mentioned. There are a few disfluencies, e.g., "the common procedure of the framing research", "corpora" instead of "corpus" when referring to a single corpus, "microaframes" on page 5, "are worth to examine" on page 8, "leveraging word embedding"). There are also improperly compiled references to Figures, such as "Figure ??(E)" on page 10. One extremely annoying problem is that the in-line citations are not formatted properly. They are missing brackets, making them difficult to distinguish from the text.

I am also concerned that Figure A is somewhat misleading. They show cases of high framing intensity as being those when words are similar to the individual poles of the antonym pair. However, because the cosine similarity is being computed with respect to the difference between the pair of vectors, the words that represent the highest intensities should be those that are parallel to the difference between the two. (i.e., starting at the origin, but pointing in the same direction as the difference of vectors). In some cases, the words close to the polls will tend to have high cosine similarity to this vector, but that is more of a property of the word vector space.

As an example, taking a pair the authors highlight -- the (clean, dirty) pair -- the highest intensity words appear to be clean, Clean, ensure, maintain, Excellent, ensure, CLEAN.  Clearly some of these are closely aligned with clean (e.g., clean, Clean, CLEAN), but others are not aligned with either clean or dirty individually (ensure, maintain, etc.)

The raw data for one experiment is shared, but not for all. In particular, the full raw results from the human evaluation should also be made available, complete with anonymized worker IDs.

Some terminology is not defined. On page 2 the authors define c_f^w in terms of similarity, but this should be "cosine similarity" to be specific. More importantly, the authors introduce "topic" words on page 3, but do not define what a topic word is, or how they are selected. Beyond that, it is unclear why a topic word would not be relevant to framing, and why they are removed.
Experimental design: The primary experiments in this paper include a Mechanical Turk study, and several visualizations of examples. Although the visualizations are useful for illustrating the kinds of things the authors see this as being used for, they do not do a good job of convincing the read reader that this method is useful.

On the human evaluation, it seems the purpose is to see if the top microframes are more relevant than random ones to the aspect of a restaurant review, but this seems like a very low bar, given that so many of the micorframes appear to be highly niche. (Indeed, I am surprised the results are not stronger here, which makes me wonder about the quality of the annotations). In terms of selecting the significant frames to present, the use of a significance threshold of 0.05, without any correction for multiple comparisons, is surprising, given that there are 1000s of pairs being tested.

The idea of testing the contextual relevance of microframes to a given corpus seems like a poor fit to the rest of the paper, and is not well developed. It is not clear that there is any experimental evaluation of this, and many details are absent. Saying "a language-model based approach" is almost completely uninformative and could refer to a huge range of possibilities. The choice to use particular constructions (A is B) is not well motivated or tested.

Mostly the results consist of displaying lists of microframes that seem to have have face validity (e.g., we assume that hospitable and inhospitable) is relevant to "service", but there is no real evaluation of whether or not this is the "correct" inference for this data or not.
Validity of the findings: As noted above, the usefulness of this method has not been rigorously evaluated. All findings reported seem valid and legitimate, but it is not clear they answer the questions of what is this method useful for, and is it better than the alternatives. Giving more though to the specific scientific claims that the authors want to make would help to determine what experiments are relevant to evaluating those claims. Presenting examples is useful, but not enough on their own.

As also noted above, some raw data have been shared, but not all.

It is unclear what the conclusions of this paper are, in part due to the lack of a clear research question. Although I commend the authors for developing this idea and releasing a system for using it, a research paper based on this idea should have a stronger set of claims being made and well-designed experiments to evaluate those claims.
Additional comments: No additional comments


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: FRAMEAXIS: CHARACTERIZING MICROFRAME BIAS AND INTENSITY WITH WORD EMBEDDING
Review round: 2
Reviewer: 1

Output:
Basic reporting: No comment
Experimental design: No comment
Validity of the findings: No comment
Additional comments: The authors have satisfactorily answered all my questions from the first round of reviews. I have no issues in accepting the paper.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: FRAMEAXIS: CHARACTERIZING MICROFRAME BIAS AND INTENSITY WITH WORD EMBEDDING
Review round: 2
Reviewer: 2

Output:
Basic reporting: The revised submission of "FrameAxis: Characterizing Microframe Bias and Intensity with Word Embedding" is much improved, and I think it is acceptable for publication.

The key improvements that have been made are as follows:
- reframing of the contribution so as to make it clear that this is a proposed framework for unsupervised exploration of a text corpus, not a way of doing traditional framing analysis
- correction of typos and formatting issues
- improved figures
- clarification of terms like "topic words" and "similarity"
- additional details about certain methodological components, like the use of GPT
- some additional data included with submission
Experimental design: Unlike a traditional computer science paper, the experiments here are much closer to case studies or illustrations, plus sanity checks to show that the method is okay (i.e., the MTurk studies). Everything presented in these case studies seems valid, so by the stated standards of PeerJ, I believe these are acceptable. It is still not obvious that there is a single clear research question, but rather a proposal for a new way to do unsupervised exploration of a textual corpus, with some illustrations of how the more abstract idea of (measuring bias and intensity based on antonym pairs) might be deployed in practice (complete with the many somewhat arbitrary choices that need to be made along the way, like selection of topic words). As such, this is not the kind of paper that contains rigorous experiments demonstrating evidence of new knowledge, nor does it contain quite enough detail to fully replicate everything (for example, precise details on how GPT is being used), but I think they are sufficient to convince people that this approach is something they may want to try for themselves, and enough details and suggestions to help them get started. It is in the nature of this kind of unsupervised exploration that every application is somewhat different, and so it is simply not possible to provide a comprehensive set of parameters and experiments that will work for all settings.
Validity of the findings: The revised paper makes it more clear that the point of the paper is to introduce a new framework for unsupervised exploration of a text corpus, and the case studies show that it has the potential to be useful, in a way that I believe is sound.

Almost all data have been provided. Although the authors did include the MTurk responses with this revision, which is appreciated, they unfortunately did not include the corresponding questions, limiting its usefulness. This answers are still a good thing to share, as it allows others to verify the analysis of these responses, but I would encourage the authors to eventually release the full MTurk data, including questions (either as part of this submission or elsewhere).

The conclusions (discussion) have been updated data, and now more accurately reflect what is illustrated by the paper.
Additional comments: This is a difficult sort of paper to review, because it is aiming at the type of application where individual users will necessarily have to make somewhat arbitrary choices. I do not, for example, think that this paper would meet the standards of a Human-Computer Interaction venue, where there would be a much higher expectation of demonstrating that this system actually helps real users to accomplish some task.

Rather, this paper is more about formalizing an idea that is now well known (using vector similarity based on antonym pairs), and wrapping it in choices that try to turn it into a more general tool that could be used in text analysis. Unfortunately this sort of approach will necessarily involve many somewhat arbitrary choices, such as what counts as a topic word, what antonym pairs should be used, what language model templates are appropriate to use, etc. However, this is likely unavoidable, and I think the current paper does a fair job of illustrating how this method will require these sorts of choices, and of providing reasonable examples of what choices made be made. Again, the correctness of each of these choices is not assessed in this paper, but that is not really the point, since there is not necessarily a "correct" choice in any setting, just a set of choices that will influence the outcome, and which users must be aware of.

I would like to thank the authors for being so receptive to the concerns raised by this and other reviewers, and I too think the paper has been improved considerably via this process.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: RESEARCH ON IMAGE CLASSIFICATION METHOD BASED ON IMPROVED MULTI-SCALE RELATIONAL NETWORK
Review round: 1
Reviewer: 1

Output:
Basic reporting: This paper proposes a new approach of multi-scale meta-relational network as a classification approach based on optimized initialization representation for image with few samples.  The idea of META-SGD is adopted and model-independent meta-learning algorithm is introduced to find the optimal parameters of the model.  The contents and target of the paper take interests of readers.
Experimental design: Experiments suggests the performance according to the theoretical aspect discussed in the paper.  Experimental parts satisfies the necessary comparison between models and tasks.
Validity of the findings: The proposed approach of meta-learning is very important topic in the recent deep learning research since the principle is based on the idea “how to learn by using previous experience” with the small number of sample images.
Additional comments: Meta-learning of multi-scale meta-relational network proposed in this paper has advantage to have the highest accuracy as the meta-validation set is recorded.  Paper suggests that MAML and Meta-SGD need fine-tuning on new tasks while multi-scale relational network based on metric learning can achieve good generalization performance on new tasks without fine-tuning.
  Experiments suggests the performance according to the theoretical aspect discussed in the paper.  Detailed descriptions are provided and the paper is well written in both of theoretical aspect and experimental part.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: RESEARCH ON IMAGE CLASSIFICATION METHOD BASED ON IMPROVED MULTI-SCALE RELATIONAL NETWORK
Review round: 1
Reviewer: 2

Output:
Basic reporting: The title does not reflect the contribution of this paper.
New learning and classification methods are introduced and not research on mage classification method
The paper proposes a new classification approach addressing small dataset problems.  The derivations of the proposed method is provided.  The approach is evaluated with standard datasets and measures.

Suggestions for improvements on the paper for the presentation issues are given below.

1.	Chronology is important.  However, it is redundant to keep mentioning the years as citations include publication year.-- Example. In 2016, .... (citation, 2016)

2.	Quite often though capital letters and small letters are used inconsistently.
ARTIFICIAL intelligence
deep Learning
This method USES the idea of…..

3.	Consistencies in citations convention is needed in the presentation

4.	The last reference on the reference list is incomplete ….
Experimental design: Yes, a clear comparative evaluation was provided
Validity of the findings: All datasets and measures were clearly provided.
Additional comments: All my comments largely on the presentation was given above.

The following sections of the paper needs further clarification

1. Sentence 219 n the multi-scale meta-relational network, we hope to find a set of characterization (thetha)  that can make fine adjustments efficiently according to a small number of samples. Where, is composed of feature extractor parameter and metric learner parameter . These two parameters are mentioned but not explained.  Elaboration on these are needed

2. Sentence 517 at the end --- a better way of yuan learning
-	Is this referring to the ----MAML learning Yu Zaiyuan task distribution of …
-	Elaboration on yuan learning is needed


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: RESEARCH ON IMAGE CLASSIFICATION METHOD BASED ON IMPROVED MULTI-SCALE RELATIONAL NETWORK
Review round: 1
Reviewer: 3

Output:
Basic reporting: the processing of iterative is clear, but the convergence and convergence speed are not involved.
Experimental design: no comment
Validity of the findings: no comment
Additional comments: to classify image  based on small sample, a novel parameters iteration method to improve multi-scale meta-relational network is  presented in this paper.  the classification results illustrate the superiority of the method. but there are still some problem existed.
1. there are some mistakes in serial number of formula. For example：  line 237:   Equation (3-6) and (3-7) should be (6) and (7)；
2. the processing of iterative is clear, but the convergence and convergence speed are not involved.
3. the results is better than existing methods， are the differences significant enough？ will the gap depend on the sample？


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: RESEARCH ON IMAGE CLASSIFICATION METHOD BASED ON IMPROVED MULTI-SCALE RELATIONAL NETWORK
Review round: 2
Reviewer: 1

Output:
Basic reporting: no comment
Experimental design: no comment
Validity of the findings: no comment
Additional comments: Authors have made suitable modifications to the suggestions


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: NATURAL LANGUAGE UNDERSTANDING OF MAP NAVIGATION QUERIES IN ROMAN URDU BY JOINT ENTITY AND INTENT DETERMINATION
Review round: 1
Reviewer: 1

Output:
Basic reporting: 1.	Please double-check the grammar and language to make sure the delivery is clear, such as the sentence at line 43 – 45 needs to rearrange.
2.	Please double-check the grammar, lots of Singular nouns are missing their articles.
3.	Check the line number; the line numbers are break after line 122.
Experimental design: a.	The dataset introduction should be in the method part, which would give the audience a clearer idea of how the experiment is designed.
b.	Please give justifications for the selection of the datasets. Is it the only available one, or is there a reason that this specific dataset is selected? Is it based on the property of the dataset or based on the method?
c.	If you are using F-score as an estimation for the performance, please explain how they work and the possible merit and drawback of using F-score as an estimation method other than other methods?
d.	Please give a more detailed description and comparison of results from different methods and from different epochs. Is this outcome your prediction, and what are the possible causes for the difference between results? It is not presented clearly in this paper how the different methods would impact the F1 Score.
Validity of the findings: This data and results in this paper are robust and sound. However, the justification of why this dataset is brought into use needs to be clearer.
Additional comments: No additional comments


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: NATURAL LANGUAGE UNDERSTANDING OF MAP NAVIGATION QUERIES IN ROMAN URDU BY JOINT ENTITY AND INTENT DETERMINATION
Review round: 1
Reviewer: 2

Output:
Basic reporting: The idea of the paper is sound and need be investigated more however few suggestions are there .
1 abstract needs to be rewritten with respect to findings
2 add latest literature in this area
3 evaluation of proposed method is not clear therefore authors need to provide confusion matrix to understand the recognition rate of the system.
Therefore I recommend to accept the paper after incorporating above comments
Experimental design: Experiments need to be explained more
Validity of the findings: Need to justify the results with latest literature
Additional comments: Authors are suggested to proof read from native English speaker


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: NATURAL LANGUAGE UNDERSTANDING OF MAP NAVIGATION QUERIES IN ROMAN URDU BY JOINT ENTITY AND INTENT DETERMINATION
Review round: 2
Reviewer: 1

Output:
Basic reporting: It can be seen that after the modification, the language level and quality of this paper have been improved.But in our opinion, this paper still has some small shortcomings that need to be revised.
1. The "Abstract" part of the paper needs to be condensed.Too much technical background.
2. This paper has introduced many references in the "Introduction" section, but I think what should be introduced is the relationship between these literatures and your research, rather than simply listing the work done by these literatures.
3. The quality of Figure 5 needs to be improved.It is suggested that the meaning of each curve be intuitively expressed in the picture.
I hope the author can seriously modify the article and polish the language in the article. After the quality is further improved, it can be considered for publication.
Experimental design: no comment
Validity of the findings: no comment
Additional comments: no comment


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: NATURAL LANGUAGE UNDERSTANDING OF MAP NAVIGATION QUERIES IN ROMAN URDU BY JOINT ENTITY AND INTENT DETERMINATION
Review round: 3
Reviewer: 1

Output:
Basic reporting: The authors have pay attention to all the advice. Suggest to publish.
Experimental design: The paper is original and fitful for the journal. The author has respond to all the advices.
Validity of the findings: This paper is validity and provide novel contributions to the field. The authors have made all the adjustment required.
Additional comments: The author has respond to all the advices. Suggest to publish.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: ADVANCED METHODS FOR MISSING VALUES IMPUTATION BASED ON SIMILARITY LEARNING
Review round: 1
Reviewer: 1

Output:
Basic reporting: - In the abstract, please add the stands for all of the abbreviations.

- The conclusion should present summarize instead of repeating the detail of the proposed approach
Experimental design: Please specify what is parameters of the dataset should be categorized as large-scale and small datasets, also there should provide a comparison of the difference if the method is used in the small and large data sets.
Validity of the findings: - It is not clear the correlation between KI and FCKI, please consider adding the general diagram that describes the proposed architecture in brief.

- Please provide an ablation study to explain the details for each component in the proposed approach
Additional comments: The problem is clearly defined in this paper with two proposed approached called KI and FCKI. Note from me related to the proposed approach is the correlation between KI and FCKI should be present more clearly.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: ADVANCED METHODS FOR MISSING VALUES IMPUTATION BASED ON SIMILARITY LEARNING
Review round: 1
Reviewer: 2

Output:
Basic reporting: This paper describes two similarity-based methods to impute missing data. The text is well structured and reports promising results. Yet, some issues must be pointed out:

General comments

- Text needs proofreading. There are some minor grammatical mistakes, typos and incomplete sentences. Please check the comments in the document for examples of such problems.

- There are several sentences that should be supported by references. There are highlighted examples of these sentences in the document.
- Section 3 also encompasses related work. Why is it apart from Section 2?

- Placing figures and tables at the end of the manuscript severely hinders the reviewing process. It demands many ups and downs. Please, put them near their citations.

- Numbering the steps described in Figures 1 and 2 would help the reader a lot.

- The text references appendices and tables that are not available. Please, check the comments about these flaws in the document.

Content/technical comments

Abstract

- I think that there is one important gap in the abstract (and in the rest of the paper):
The text does not state the problem clearly. There are several studies about missing data imputation that are based on data similarity. What are the limitations of those studies that the methods proposed by this paper overcome? The abstract (and the rest of the paper) must highlight it clearly. I believe line 644 may contain a glance of what must be clarified.
- In accordance with the previous comment, the statement “The primary existing techniques provide low efficiency and effectiveness results for imputing an incomplete large-scale dataset.” should be better justified.

Section 1

- Line 48: A dataset is not trained.
- Line 100 (and several others): I think the word "phase" is not appropriate, once it suggests some kind of temporal ordering which does not seem to be the case. I believe the proposed methods can be applied independently.
- KI seems to follow a classical hot-deck approach to impute data based on data similarity (by the way, this approach was never mentioned in any part of the text – it indicates a several flaw in the literature reviewing process). How does KI differ from KNNI, for example? What is the hypothesis (ie., the rationale) that makes the authors believe that this method can overcome the state-of-the-art ones? This is a very important point and must be clarified.

- The same question holds for FCKI.
- Another question concerning FCKI: If the dataset is big, the clustering process may have high computational demand. This point must be better justified.
- Do the proposed methods consider datasets with missing values in multiple attributes? Please, clearly state that.

Sections 2 and 3

- These sections do not clearly state the limitation(s) of the existing imputation methods that is (are) tackled by the proposed methods.
- In my opinion, related work should be presented and analyzed under the problem’s point of view. Please, explicitly (and exclusively) mention related work’s limitations concerning the problem’s point of view. Limitations that are not treated by the article should not be mentioned.
- What about hot-deck-based data imputation methods?
- Here go other studies about missing data imputation methods. They may be included as related work:

* Christopher, S. Z., Siswantining, T., Sarwinda, D., and Bustaman, A. (2019). Missing value analysis of numerical data using fractional hot-deck imputation. In 2019 3rd International Conference on Informatics and Computational Sciences (ICICoS), pages 1–6. IEEE.

* Ford, B. L. (1983). An overview of hot-deck procedures. Incomplete data in sample
surveys, 2(Part IV):185–207.
* Graham, J. W. (2012). Missing data: Analysis and design. Springer Science & Business
Media.

* Graham, J. W., Cumsille, P. E., and Shevock, A. E. (2012). Methods for handling missing
data. Handbook of Psychology, Second Edition, 2.
* Hassan, M. M., Atiya, A. F., El-Gayar, N., and El-Fouly, R. (2007). Regression in the
presence missing data using ensemble methods. In 2007 International Joint Conference
on Neural Networks, pages 1261–1265. IEEE.

* Karanikola, A. and Kotsiantis, S. (2019). A hybrid method for missing value imputation. In
Proceedings of the 23rd Pan-Hellenic Conference on Informatics, pages 74–79.
* Kumar, S., Pandey, M. K., Nath, A., and Subbiah, K. (2016). Performance analysis of
ensemble supervised machine learning algorithms for missing value imputation. In
2016 2nd International Conference on Computational Intelligence and Networks (CINE),
pages 160–165. IEEE.
* Lu, X., Si, J., Pan, L., and Zhao, Y. (2011). Imputation of missing data using ensemble
algorithms. In 2011 Eighth International Conference on Fuzzy Systems and Knowledge
Discovery (FSKD), volume 2, pages 1312–1315. IEEE.

* Madhu, G., Bharadwaj, B. L., Nagachandrika, G., and Vardhan, K. S. (2019). A novel
algorithm for missing data imputation on machine learning. In 2019 International
Conference on Smart Systems and Inventive Technology (ICSSIT), pages 173–177.
IEEE.

* Magnani, M. (2004). Techniques for dealing with missing data in knowledge discovery
tasks. Obtido http://magnanim. web. cs. unibo. it/index. html, 15(01):2007.
* Poulos, J. and Valle, R. (2018). Missing data imputation for supervised learning. Applied
Artificial Intelligence, 32(2):186–196.
* Rubin, D. B. (1988). An overview of multiple imputation. In Proceedings of the survey
research methods section of the American statistical association, pages 79–84. Citeseer.
* Suresh, M., Taib, R., Zhao, Y., and Jin, W. (2019). Sharpening the blade: Missing data
imputation using supervised machine learning. In Australasian Joint Conference on
Artificial Intelligence, pages 215–227. Springer.

* Templ, M., Kowarik, A., and Filzmoser, P. (2011). Iterative stepwise regression imputation
using standard and robust methods. Computational Statistics & Data Analysis,
55(10):2793–2806.
Experimental design: Section 5
- Line 322: missing record or missing value?
- Suggestion: Present the definitions in a separate table before the explanations given in lines 341- 367. It would help a lot.
- Be careful with some formalisms. E.g.:
* Is R_i the set of records returned by KNN for record r_i? Or R_i=r_i? There seems to be a notation flaw here.
* What is a k-NN record?
* kNN=k-NN?
* Line 347: Consider removing "{" and "}". Moreover, R_i is a record, not a set... This problem occurs in other points. See highlighted comments in the document.
- Line 357 - Suggestion: Use "N_p - 1 RMSE values" instead of "values of N_p - 1 RMSE"
- Is there a loop to consider multiple non-missing value attributes in R_i? What about step 11 in this case?
- Algorithm 4: What happens if there are many values for z?
- Considering FCKI: What happens when a data record belongs to two or more clusters? According to the fuzzy set theory, it is possible and, hence, must be taken into consideration.
- Line 414: Does it consider the membership degree of each record to the corresponding cluster?
Validity of the findings: Section 6
- Table 1: None of these datasets actually contains "big" data. 100000 records (in PPS - the biggest dataset) cannot be considered a high value when compared to most contemporary real-world scenarios. Hence, experiments with real big datasets should be executed.
- Table 1: Although the values of the features in the Zoo dataset have numerical representation, they are, in fact, boolean (i.e., categorical) values. This should be better explained. Moreover, the other datasets must be checked concerning this subtle characteristic.
- Line 498: Why sonar?
- Table 2: What about the standard deviation?
- Table 2: Please, highlight the best value for each dataset.
- What about a detailed analysis of the boxplot of Figure 9?
- Is there a reason that justifies KI and FCKI's superiority?
- What about a comparison between KI and FCKI?
- The authors should comment on NRMSE and MAE values.
- I suggest that the authors run the Friedman Test before presenting the results of the Nemenyi Test. By the way, what of the confidence value used in the test? What about the values of the statistic?
- Lines 635-639: Are those future works? If so, I suggest placing them at the end of Section 7.
- Table 5: Does this time encompass finding the best c value and the clustering process? Please clarify that in the text.
Additional comments: No additional comments


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: ADVANCED METHODS FOR MISSING VALUES IMPUTATION BASED ON SIMILARITY LEARNING
Review round: 1
Reviewer: 3

Output:
Basic reporting: -> Clear and unambiguous, professional English used throughout.
Generally, the article uses clear, adequate technical language. It respects the style of scientific communications and has an easy reading.

However, the excessive use of the verb "to tackle", especially in the first part of the text, makes reading truncated. Synonyms can quickly solve this situation.

There is also an overuse of passive voice in the text, which can be tiring for the reader. More direct communication becomes useful in its purposes. Finally, there is an excessive repetition of words. Make more use of synonyms.

-> Literature references, sufficient field background/context provided.
The core of the proposal omitted a vital concept: both proposed implement hot-deck imputation, proposed by Ford in 1983 (Ford, BL. An overview of hot-deck procedures. In: Madow, WG .; Olkin, I .; Rubin, DB., Editors. Incomplete Data in Sample Surveys. Vol. 2. New York: Academic Press; 1983. p. 185-207).

The authors' proposals reveal that they adopt the hot-deck imputation. It becomes apparent in the abstract, in lines 19 to 21: "The best set of nearest neighbors for each missing record is discovered through the records similarity by using the KNN algorithm."

The work uses the k-NN and fuzzy c-means algorithms as predecessors of imputation. However, they are clustering algorithms! So, we see the hot-deck imputation there.

It is the most severe structural failure that the article presents since it ignores 37 years of research that used the term hot-deck to reference algorithms that group related data, reducing the original data set before inputting them to provide better accuracy in filling in missing data.

From this demand, there is a critical need to review the literature, including hot-deck approaches. It affects the entire literature review decisively.

The entire article should be revised based on this concept. Several papers approach this paradigm.

-> Professional article structure, figures, tables. Raw data shared.
The structure of the article follows the standard expected for scientific publications. The sections have, in general, a well-defined function in the context of the work. Figures and tables contribute to the understanding of the proposal and the analysis of results.

However, Section 3 ("Imputation Methods") seems out of context when reading the article sequentially. After completing the work's reading, its function presents characteristics of the ten methods chosen for comparison with the authors' proposal. However, this looks more like a hybrid section of theoretical foundation with the works that proposed the methods that serve as the analysis target. The name "Imputation Methods" does not reveal the semantics of the section.

Another significant observation is related to the quality of figures 1 and 2, the authors' proposals heart. Understandably, there is a spatial challenge to be faced (the page size). However, given the images' complexity, understanding the proposal just looking for the figures was a challenging task. Perhaps the authors should divide the two figures of the proposals into functional blocks and adding new figures. The pollution of current visual and textual elements will not make the article easy to read for the future audience.

Besides, the images' physical disposition, far from the text's natural flow, greatly affected this analysis. The comings and goings between the text that detailed the algorithm and the figures, coupled with the overload of each figure's visual elements, made it very difficult to assess.

-> Self-contained with relevant results to hypotheses.
The presented proposal can be considered self-contained. It contextualizes the problem, defines the terms, algorithms, and metrics it uses. However, the text does not present the hypothesis, the addressed research problem. We start with the proposal of two new imputation presented methods as more efficient. However, the problem itself is not formally explained.

-> Formal results should include clear definitions of all terms and theorems and detailed proofs.
The results presented use concepts and definitions that were previously formally defined, and the algorithms used to generate missing data.
Experimental design: -> Original primary research within Aims and Scope of the journal.
The article serves the journal's objectives and scopes well. It can be categorized correctly under the theme "Data Mining and Machine Learning." Regarding originality, the article makes an alternative proposal for data imputation using two new methods. After a previous search, these methods were, in fact, not previously proposed in the manner presented, which guarantees the originality of the work.

-> Research question well defined, relevant & meaningful. It is stated how research fills an identified knowledge gap.
The research question was not clearly defined. This inference comes from the proposal, but there is no clear presentation of a hypothesis – a situation that is easy to solve but needs attention.

-> Rigorous investigation performed to a high technical & ethical standard.
The work has a satisfactory performance in technical and ethical rigors, without showing signs of disappointment in this regard.

-> Methods described with sufficient detail & information to replicate.
The influential contributions of the proposal revolve around the choice of the best k value in the first proposed method (KI), and the second in the use of fuzzy c-means in the FCKI method, which allows the same occurrence (tuple) to participate in several groups of the later phase for the use of k-NN.

The first contribution appears between lines 665 and 671 ("By comparing with the original k-means for imputing the missing data, there are some benefits for fuzzy clustering. Fuzzy clustering is more naturalistic than hard clustering in many situations. When records are not well-separated, it provides a better description method, as is the case for missing data problems. Besides, suppose the initial points are not correctly selected. In that case, the original k-means algorithm may be stuck in the minimum local status. Continuous membership values in the fuzzy clustering, on the other hand, provide the resulting algorithms less sensitive to get stuck at a local minimum"). This conclusion comes from the analysis of Figure 1, from this highlighted section, and from line 644 ("A suitable k value is estimated automatically for the k-NN"). I had to deduce that after several rounds of reading. We need to improve this presentation.

The second contribution was evident in the section between lines 385 and 389 ("In the case of fuzzy clustering techniques, records on the frontiers between multiple clusters are not compelled to assign to one of the clusters completely. The records can belong to multiple clusters at the same time (Sefidian & Daneshpour, 2019). Each record has a membership degree between 0 and 1, showing its partial membership"). It seems that the ideas could be better presented in the article. It should be a consequence of the lack of definition of a research question, a hypothesis. The proposed methods present a set of questionable choices. Some highlighted points:

- Why did the absence percentages vary between 1% and 20%? Is there a significant difference in the method's performance between 1% and 2%, or between 19% and 20%? Moreover, do the proposed methods work well with higher percentages of absence, where the base's semantic loss is accentuated (which can influence the imputed data quality)?

- The authors chose ten methods for comparison (mean, kNNI, SoftImpute, SVDimpute, Iterative Imputation, EMI, DMI, KDMI, KEMI, and KEMI+), but did not adequately justify this choice. Between lines 129 and 135, the methods are presented and referenced, but there is no mention of the selection criteria. Between lines 269 and 271, the authors reinforce that the chosen methods as competitors. The impression that remains is that the choice of comparison algorithms left out a more straightforward analysis – especially when it comes to performance evaluation. Why didn't they compare KI and FCKI with hot-deck imputation using a clustering algorithm like k-means, for example?

- the article generates an expectation of discussion regarding computational time, which proved to be reasonably deflated. In lines 576 and 577, they affirm that "The experiments investigate the scalability of the missing data imputation methods by assessing runtimes for all datasets with different missing ratios [...]". However, what kind of time is measured? Wall time? Processor time? When discussing performance, there is a need at least to refer to speedup issues, which did not appear. Given that the authors pointed the discussion in this direction, it deserves more outstanding care.

- Presenting missing value generation algorithms MCAR, MAR, and NMAR is exciting. Most papers that simulate them do not present the way it works. So, the authors' initiative, who based on the work of Garciarena and Santana (2017), referenced in the text, should be praised. However, some issues need clarification regarding this point: (i) I was unable to identify the adaptation mentioned (line 286: "These algorithms are derived from Garciarena & Santana (2017)"). It seems that it is only a detailed form of them, presented in the form of pseudocode. Thus, there are two paths to follow: either the authors present the adaptations made to the algorithm that makes a difference or mention them and initially present them. Garciarena and Santana's article shows the algorithms in a cleaner, intelligible form. The one presented by the authors may appear in an appendix, but not in the main text, under pain of tiring the reader; (ii) on lines 298 and 299, the authors say that "The algorithm presumes a single attribute as causative." As presented, it seems to be a proposal from them, the authors, and not from Garciarena and Santana's paper. So, it is necessary to complement the information; (iii) in the MNAR algorithm (Algorithm 3), the authors ignore the discussion made by Garciarena and Santana, who share the absence mechanism in MIV and MuOV. Algorithm 3 implements which of the two sub-options?

- lines 29 and 32: "Therefore, proposed imputation techniques are compared with other missing data imputation methods using three measures; root mean square error (RMSE), the normalized root mean square error (NRMSE), and the mean absolute error (MAE)." This statement was mentioned six more times in the text (lines 136 to 138, lines 272 to 274, lines 487 to 489, lines 521 to 523, lines 550 to 552, and lines 675 and 676). Despite so many mentions, the whole discussion of accuracy revolved around RMSE, the preferred metric for imputation articles. What, after all, why calculate NRMSE and MAE metrics? The only use I was able to notice was in the generation of boxplots

- on lines 664 and 665, the authors say: "Therefore, it can enhance imputation effectiveness and efficiency." What differentiates the two concepts in the article? It certainly is a critical discussion.

- on line 567, the authors indicate the use of the Nemenyi non-parametric test. Why didn't they choose the Friedman test, which is also non-parametric and more popular? Nemenyi is a test by nature conservative. There is at least an explanation of the reason for the choice.
Validity of the findings: -> Impact and novelty not assessed. Negative/inconclusive results accepted. Meaningful replication encouraged where rationale & benefit to literature is clearly stated.
The reproduction of the experiments follows the pattern used in the research field on missing data. Furthermore, considering that there is no benchmark to reference, which would mention previously known results of baseline algorithms' execution, each experiment's re-execution is necessary.

-> All underlying data have been provided; they are robust, statistically sound, & controlled.
The authors attached the databases they used in the article. Fifteen files in the CSV format were available on the downloadable platform and the Python code that implements the missing data generator algorithms. Every file has no flaws. Table 1 offers the attached databases' essential characteristics, except for the files' size. It is also essential data to figure out in the table. This information (size of each dataset) could appear as a fourth column instead of the "Number of categorical features" column, equal to zero for all cases.

The most extensive dataset is 25 MB; the one with the most records, 100,000 tuples; the one with the most columns has 90 features.

I missed a more consistent justification regarding the choice of these bases. In subsection 6.2, presenting the selection criteria for the bases lines 471 and 472 only indicate "The evaluation of the proposed methods to impute missing data, KI and FCKI, using fifteen datasets, shown in Table 1". Why were these bases chosen? What repository did they come from? Is there any basis that justifies that they fit well with the objectives of what they want to demonstrate? Were they used in previous works that use the ten algorithms chosen to compare? The only explanation was found on line 479 was in the excerpt "The selected databases are commonly used." Commonly under what conditions? Where are the references of the communal use of these datasets? It is not a real justification. The absence of a methodological approach to choosing the bases may generate sampling bias.

In the excerpt "The attained results show that the proposed imputation techniques, KI and FCKI, outperform other competitors in terms of imputation accuracy," a more consistent discussion on the choice of bases would give more significant support to this statement.

The study of imputation techniques on small datasets is essential and does not invalidate the results obtained. Besides, the use of small databases is a widespread feature in imputation works. However, it must not need a more careful analysis not to jeopardize the research's contributions. It is a challenge that the work in the area needs to solve in general.

Furthermore, there was an expectation that the databases used were indeed large, given the excerpt "In the analysis and mining of large-scale data, large missing values present formidable challenges" presented in lines 45, which was confirmed neither in the presentation of the work nor in the datasets. It was evident looking for the computing environment was described in section 6 – a machine with a 2x4 core Intel i7-7500U processor with 8 GB of RAM. These are characteristics of personal computer desktops or notebooks, revealing the lack of demand for greater computational power equipment.

This expectation matches the text between lines 110 and 113: "In the second phase, an enhanced hybrid missing data imputation method is proposed, called FCKI, an extension of KI. It integrates fuzzy c-means, k-nearest neighbors, and iterative imputation method for imputing the missing data for a large-scale dataset".

The visualization of datasets in sonar charts is an exciting differential of the article, not seen in previous works. It gives a good view of the patterns of absence, restricted, however, pinning in an absence percentage (10%). Nevertheless, why were this value chosen? How it works on others missing values percentages? How is the generation of these graphs reproduced?

-> Conclusions are well stated, linked to original research question & limited to supporting results.
Conclusions follow what is expected: they review the motivations, consolidate the results, and point to future work.

The authors were shy when presenting future works. There are several possibilities to explore.

-> Speculation is welcome but should be identified as such.
There are some strong speculations not identified as such in the article:

1. In lines 13 and 14, the authors state that "The main challenge of mining datasets is the existence14 of missing values". This is a strong statement that needs to be referenced or proved.

2. In lines 15 and 16, the statement "The primary existing techniques provide low efficiency and effectiveness results for imputing an incomplete large-scale dataset" also needs to be supported, as it disappoints an entire history of related works in the area.
Additional comments: 1. I did not understand the excerpt "A method of missing data imputation is considered efficient. It means that it does not have to rely on the entire dataset for imputing missing records" between lines 97 and 99. It seems lost in the paragraph.

2. Describing the same algorithm twice is tiring for the reader and generates an increase in the article's size that is not interesting. It happens with algorithms 4 and 5.

3. In this same section, step 1 mentions the use of the elbow method. Why is it not described and referenced? Is it a prerequisite for the reader to know?

4. The excerpt "The missing values are generated into the selected datasets using various algorithms, which generate various missing patterns" in lines 480 and 481 induces a deconstruction presented in Section 4. I believe the whole job considered the algorithms presented. So, it is essential always to emphasize them.

5. The sentence between lines 490 and 491 should appear in the previous paragraph.

6. The paragraph between lines 633 and 639 is misplaced. The first part (justifying k-NN) should appear in the proposal section and the second part in future works.

7. The term "global correlation" used in line 647 conflicts with the idea of global imputation enshrined in the literature. This considers the use of the entire dataset suggests reviewing the term's use.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: ADVANCED METHODS FOR MISSING VALUES IMPUTATION BASED ON SIMILARITY LEARNING
Review round: 2
Reviewer: 1

Output:
Basic reporting: - The author has already explained the comment on the first revision. Please check again and make sure if the writing style of the algorithm name is consistent, e.g., "k nearest neighbor imputation" should written as "k-nearest neighbor," and please consider that "k" is the name of a variable.

- The author has already modified the conclusion that summarizes the proposed approach and its results.
Experimental design: - "Large-scale data can be associated with data that grows to a huge size over time. Small data is data in a volume and format that makes it accessible, informative, and actionable." The quoted statement is obtained from the rebuttal letter should be mentioned in the manuscript to clarify the different small and large scale datasets.
Validity of the findings: the author already re-create the figure to make a clearer representation of the correlation of FI and FCKI
Additional comments: In general, the response from the author is addressing the reviewer's question. In some points, the author should be considered some advice to be reviewed for the better manuscript.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: ADVANCED METHODS FOR MISSING VALUES IMPUTATION BASED ON SIMILARITY LEARNING
Review round: 2
Reviewer: 2

Output:
Basic reporting: All the answers presented fully comply with the points previously raised.
Experimental design: - Why did the absence percentages vary between 1% and 20%? Is there a significant difference in the method's performance between 1% and 2%, or between 19% and 20%? Moreover, do the proposed methods work well with higher percentages of absence, where the base's semantic loss is accentuated (which can influence the imputed data quality)?

Response: As shown in the results, the performance varies through different missing ratios from 1% to 20% for the three types of missing data; MCAR, MAR, MNAR. These results show the proposed method's performance by applying twelve different datasets generated by using three types of missing data; MCAR, MAR, MNAR for four different missing ratios; 1%, 5%, 10%, and 20%. The missing data imputation review shows that the familiar and significantly different missing ratios used to apply the imputation methods are from 1% to 20%.

Rejoinder: It is understandable the author's choices. Nevertheless, it is essential that at least one comment touch this question fits in future works. The algorithm's behavior needs to be evaluated for data absence rates greater than 20% since the semantic loss of the dataset can affect the results obtained.

- The authors chose ten methods for comparison (mean, kNNI, SoftImpute, SVDimpute, Iterative Imputation, EMI, DMI, KDMI, KEMI, and KEMI+), but did not adequately justify this choice. Between lines 129 and 135, the methods are presented and referenced, but there is no mention of the selection criteria. Between lines 269 and 271, the authors reinforce that the chosen methods as competitors. The impression that remains is that the choice of comparison algorithms left out a more straightforward analysis – especially when it comes to performance evaluation. Why didn't they compare KI and FCKI with hot-deck imputation using a clustering algorithm like k-means, for example?

Response: The hot-deck imputation involves replacing missing values with values from a "similar" responding unit. Therefore, KNNI and KI demonstrate an essential hot-deck imputation to tackle missing data based on data similarity. We think that hot-deck imputation is found implicitly in our comparison. We have also included other hot deck-based imputation methods in the related work section.

Rejoinder: The highlighted question specifically touches less on the question more about the justification for using the ten mentioned algorithms. It is comprehensible that it is essential to explain why this work had chosen each of these algorithms and not others. A well-founded explanation strengthens the work presented, providing guidance for future research in the area.
Validity of the findings: - The study of imputation techniques on small datasets is essential and does not invalidate the results obtained. Besides, the use of small databases is a widespread feature in imputation works. However, it must not need a more careful analysis not to jeopardize the research's contributions. It is a challenge that the work in the area needs to solve in general.
Furthermore, there was an expectation that the databases used were indeed large, given the excerpt "In the analysis and mining of large-scale data, large missing values present formidable challenges" presented in lines 45, which was confirmed neither in the presentation of the work nor in the datasets. It was evident that looking for the computing environment was described in section 6 – a machine with a 2x4 core Intel i7-7500U processor with 8 GB of RAM. These are characteristics of personal computer desktops or notebooks, revealing the lack of demand for greater computational power equipment.
This expectation matches the text between lines 110 and 113: "In the second phase, an enhanced hybrid missing data imputation method is proposed, called FCKI, an extension of KI. It integrates fuzzy c-means, k-nearest neighbors, and iterative imputation method for imputing the missing data for a large-scale dataset".

Response: Concerning the first observation, we used the algorithm to validate the model with different dataset sizes.
We fully agree that the application on large real datasets is helpful to compare the exactness of different data sets. We collected some real datasets, but we could not access them at our laboratories due to the COVID-19 lockdown. We hope to understand the critical circumstances worldwide and how most laboratories are locked because of social distance restrictions.
We will take your valuable comment into consideration as a future direction to extend our work on actual big data. In the section on Conclusions and Future Work, we also mentioned that point.
Concerning the second observation, a 2x4 core Intel Processor I7-7500U with 8 GB RAM was used. We agree entirely that the computer should be used better for quick computing on other high-performance computers. At Benha University, we already have a fully equipped computer intelligence lab with high-performance computers and fast GPUs. However, due to COVID-19 situations and limited government rules, we could not use the laboratory.
We will look at your valuable point for future work and hope that the situation will improve in the next period to achieve all our goals.

Rejoinder: I really understand the exceptional situation that we are experiencing. It imposes difficulties that we would not usually have. However, the text still gives the impression that it claims to work with large datasets when that is not the situation at hand. Thus, I believe that it is interesting that the authors reevaluate the use of the term "large" together with datasets to resolve doubts such as those I present.
Additional comments: All the answers presented fully comply with the points previously raised.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: ADVANCED METHODS FOR MISSING VALUES IMPUTATION BASED ON SIMILARITY LEARNING
Review round: 3
Reviewer: 1

Output:
Basic reporting: no comment
Experimental design: no comment
Validity of the findings: no comment
Additional comments: No additional comments


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: ADVANCED METHODS FOR MISSING VALUES IMPUTATION BASED ON SIMILARITY LEARNING
Review round: 3
Reviewer: 2

Output:
Basic reporting: All the answers presented fully comply with the points previously raised.
Experimental design: - The authors chose ten methods for comparison (mean, kNNI, SoftImpute, SVDimpute, Iterative Imputation, EMI, DMI, KDMI, KEMI, and KEMI+), but did not adequately justify this choice. Between lines 129 and 135, the methods are presented and referenced, but there is no mention of the selection criteria. Between lines 269 and 271, the authors reinforce that the chosen methods as competitors. The impression that remains is that the choice of comparison algorithms left out a more straightforward analysis – especially when it comes to performance evaluation. Why didn't they compare KI and FCKI with hot-deck imputation using a clustering algorithm like k-means, for example?

Response: The hot-deck imputation involves replacing missing values with values from a "similar" responding unit. Therefore, KNNI and KI demonstrate an essential hot-deck imputation to tackle missing data based on data similarity. We think that hot-deck imputation is found implicitly in our comparison. We have also included other hot deck-based imputation methods in the related work section.

Rejoinder: The highlighted question specifically touches less on the question more about the justification for using the ten mentioned algorithms. It is comprehensible that it is essential to explain why this work had chosen each of these algorithms and not others. A well-founded explanation strengthens the work presented, providing guidance for future research in the area.
Response: Thanks for your valuable comment. We used the existing methods because the literature review mentioned that there is a need to be applied for many applications with different sizes of datasets and they have been shown to be effective. Some recent research relied on these methods for comparison. Moreover, most of these methods rely on data similarity, which is the basis of our proposed methods. These methods gradually vary from simple to advanced. In this revised version, we have clarified this point in lines 555-560.

Rejoinder: To complete this explanation, it is important that the authors indicate articles that implement these techniques after the sentence "Some recent research relied on these methods for comparison" (lines 557-558). These references must touch the listed algorithms.
Validity of the findings: All the answers presented fully comply with the points previously raised.
Additional comments: All the answers presented fully comply with the points previously raised.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: USING OF N-GRAMS FROM MORPHOLOGICAL TAGS FOR FAKE NEWS CLASSIFICATION
Review round: 1
Reviewer: 1

Output:
Basic reporting: The paper describes the Using of N-Grams from Morphological Tags for Fake News Classification. The paper is well written and presented well. Although, there are some of the comments, which need to be considered.
In abstract, author didn’t mention about the rationale behind the proposed system. Also, abstract has vague information, which need to be replaced with the important information. Also, it is necessary to add simulation parameters in the same section.
Introduction is too generic and is lacking important information about the N-Grams from Morphological Tags and proposed scheme. Why the author proposed such scheme is missing in the introduction. Also, it is necessary to add contribution and advantages of the proposed scheme at the end of the proposed scheme.
Background and related work is missing drawbacks of the existing scheme. Also, author of the paper has done some old survey. It is highly recommended to add latest references from reputable journals.
Proposed scheme section is missing an overview of the proposed scheme and also it is necessary to add overflow diagram in the same section. In addition, figures are not very clear. Please re-draw figures and add important information because you have mentioned about various systems but you didn’t include it in the explanation of the proposed figure diagram.
Results and evaluation is missing various terminologies. It is highly recommended to add the working of the implementation in algorithm. In addition, which parameters are considered and why they are considered is necessary to be added in this section.
Author should compare their scheme with existing schemes.
Also, results are not enough to defend the proposed scheme. Please add more results and discussion.
Conclusion need to revise as it looks like the summary of the paper. Also, please add latest references.
Experimental design: Results and evaluation is missing various terminologies. It is highly recommended to add the working of the implementation in algorithm. In addition, which parameters are considered and why they are considered is necessary to be added in this section.
Author should compare their scheme with existing schemes.
Also, results are not enough to defend the proposed scheme. Please add more results and discussion.
Conclusion need to revise as it looks like the summary of the paper. Also, please add latest references.
Validity of the findings: no comment
Additional comments: The paper describes the Using of N-Grams from Morphological Tags for Fake News Classification. The paper is well written and presented well. Although, there are some of comments, which need to be considered.
In abstract, author didn’t mention about the rationale behind the proposed system. Also, abstract has vague information, which need to be replaced with the important information. Also, it is necessary to add simulation parameters in the same section.
Introduction is too generic and is lacking important information about the N-Grams from Morphological Tags and proposed scheme. Why the author proposed such scheme is missing in the introduction. Also, it is necessary to add contribution and advantages of the proposed scheme at the end of the proposed scheme.
Background and related work is missing drawbacks of the existing scheme. Also, author of the paper has done some old survey. It is highly recommended to add latest references from reputable journals.
Proposed scheme section is missing an overview of the proposed scheme and also it is necessary to add overflow diagram in the same section. In addition, figures are not very clear. Please re-draw figures and add important information because you have mentioned about various systems but you didn’t include it in the explanation of the proposed figure diagram.
Results and evaluation is missing various terminologies. It is highly recommended to add the working of the implementation in algorithm. In addition, which parameters are considered and why they are considered is necessary to be added in this section.
Author should compare their scheme with existing schemes.
Also, results are not enough to defend the proposed scheme. Please add more results and discussion.
Conclusion need to revise as it looks like the summary of the paper. Also, please add latest references.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: USING OF N-GRAMS FROM MORPHOLOGICAL TAGS FOR FAKE NEWS CLASSIFICATION
Review round: 1
Reviewer: 2

Output:
Basic reporting: The paper experimentally evaluates the potential of the common use of n-grams and POS tags for the correct classification of fake and true news. Continuous sequences of n items from a given sample of POS tags (n-grams) were analyzed. The techniques based on POS tags were proposed and used in order to meet this aim.  These techniques were compared with the standardized reference TF-IDF technique to evaluate their main performance characteristics. Simultaneously, the question, whether the TF-IDF technique can be improved using POS tags, was researched in detail. All techniques have been applied in the pre-processing phase on different groups of n-grams. The resulted datasets have been analyzed using decision tree classifiers.  The paper is well written and here I will suggest some points to improve the quality of the paper.
Experimental design: The flow of the experiment is not clear author should write concisely but make it clear and easy to understand.

Training and testing records shown be mention in a table for each target class.

Validation should be done by authors
Validity of the findings: Results are significant
Additional comments: The author has preset work with quality and the suggested point can improve the quality of the manuscript.

Typos and grammar needed to be check throughout the paper.
is there any results validation technique used by the author?
The quality of the figures should be improved.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: USING OF N-GRAMS FROM MORPHOLOGICAL TAGS FOR FAKE NEWS CLASSIFICATION
Review round: 2
Reviewer: 1

Output:
Basic reporting: N/A
Experimental design: N/A
Validity of the findings: N/A
Additional comments: Upload the updated response to the reviewer report with section number, page number, and line number that should clearly show the reflected changes.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: A GAME THEORETIC POWER CONTROL AND SPECTRUM SHARING APPROACH USING COST DOMINANCE IN COGNITIVE RADIO NETWORKS
Review round: 1
Reviewer: 1

Output:
Basic reporting: This paper has studied a game theoretic cost dominant approach for cognitive radio networks. In general, this paper is interesting and good but could be improved. Some typos should be corrected. For example, in line 218, there should be a space between 'benefit' and 'Wang. Line 266: 'Figure2'->'Figure 2'.The title and abstract may be rephrased. Related work section is very long and could be more concise.
Experimental design: Experimental design is OK but could be improved. Some figure could be made clearer, for example, Fig. 2, 5, 6 are difficult to read.
Validity of the findings: The power control in cognitive radio has been studied extensively in the past. The authors should state clear what the contribution their papers has brought compared to the existing state-of-the-art. The authors are also suggested to compare their findings with the existing solutions in the literature and show the improvement.
Also, it is better you can analyze more on the convergence and complexity theoretically of the algorithms.
Additional comments: It is better the authors can show how the proposed solution can be applied in the practical system. It seems just the theoretical analysis conducted in the current version.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: A GAME THEORETIC POWER CONTROL AND SPECTRUM SHARING APPROACH USING COST DOMINANCE IN COGNITIVE RADIO NETWORKS
Review round: 1
Reviewer: 2

Output:
Basic reporting: English grammar and sentence structure need to be rechecked. There are many irregularities in writing, such as:
1) The first sentence in abstract, "In wireless networks, poor communication, and congestion results from an increase in
demand of already scares bandwidth resources."
2) The third sentence in abstract, "While the CR paradigm is an attractive choice, the CRs selfishly compete to
acquire and utilize available bandwidth that may ultimately results in power allocation,
causing degradation in Quality of Service of the network"
3) Page 5, line 34, "In cognitive radio networks the PU"---->“In cognitive radio networks, the PU”, line 41, what is "Law et al.."? The same problem appears on line 46.
4) Line 190, "In this paper we propose..."-->"In this paper, we propose..."
Experimental design: 1) The reasearch question is defined. The numerical results and relevant code are provided. However, more clearly and prominently statements should be given on how reasearch is different from or outperforms the existing research works such as the papers "A. Al Talabani, A. Nallanathan and H. X. Nguyen, "A Novel Chaos Based Cost Function for Power Control of Cognitive Radio Networks," in IEEE Communications Letters, vol. 19, no. 4, pp. 657-660, April 2015, doi: 10.1109/LCOMM.2014.2385068", "Z. Xiao et al., "Spectrum Resource Sharing in Heterogeneous Vehicular Networks: A Noncooperative Game-Theoretic Approach With Correlated Equilibrium," in IEEE Transactions on Vehicular Technology, vol. 67, no. 10, pp. 9449-9458, Oct. 2018, doi: 10.1109/TVT.2018.2855683."

2) New technolgy is developped recently, for example the Intelligent Reflecting Surface (IRS), the CR throughput can be further improved with assistance of IRS. The authors should mention relevant research work as the latest research background, such as the paper "Lei Zhang et al., Intelligent Reflecting Surface Aided MIMO Cognitive Radio Systems, in IEEE Transactions on Vehicular Technology, vol. 69, no. 10, pp. 11445-11457, Oct. 2020, doi: 10.1109/TVT.2020.3011308".
Validity of the findings: no comment
Additional comments: A congestion game model is proposed in this paper to mitigate interference and congestion in CR network. However, some places that may confuse the reader require careful explanation by the author.
1) The result of the game is equivalent to minimize the inverse SIR, why not directly aim to maximize the SIR? It seems that they have a same Nash equilibrium.
2) In equations (6) and (7), the first terms after the equal signs, which is the benefit of minimum correlation with other players, should be positive (without "-") from the perspective of formula 3. Why do you add "-"?
3) Please explain the unstabel state when the number of iterations is 100 in Figure 2.
4) The first occurrence of mathematical symbols should be explained, such as "B_i()" and "C_i()" in equation (2).


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: A GAME THEORETIC POWER CONTROL AND SPECTRUM SHARING APPROACH USING COST DOMINANCE IN COGNITIVE RADIO NETWORKS
Review round: 2
Reviewer: 1

Output:
Basic reporting: The format of the paper should be improved.

The figures are not clear.

Introduction should be Section I?

In Simulation section, section 5, second paragraph, there is ''Figure. ??''
Experimental design: Simulation part could be improved. The figures are difficult to read.

In Simulation section, section 5, second paragraph, there is ''Figure. ??''
Validity of the findings: Please improve your simulation part. Some figures are not clear and it is difficult to read.
Additional comments: The paper should be future improved. For example, the figures are not clear.  Introduction Section should be Section I? There are several typos in the paper.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: A GAME THEORETIC POWER CONTROL AND SPECTRUM SHARING APPROACH USING COST DOMINANCE IN COGNITIVE RADIO NETWORKS
Review round: 2
Reviewer: 2

Output:
Basic reporting: My questions have been answered appropriately.  The quality of the revised paper has been improved. However, the grammar and typos should be checked again through the whole article. For example, in the third sentence of the abstract, "....that may ultimately results in inappropriate...." should be "...that may ultimately result in inappropriate...".
Experimental design: no comment
Validity of the findings: no comment
Additional comments: No additional comments


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: DYNAMIC SAMPLING OF IMAGES FROM VARIOUS CATEGORIES FOR CLASSIFICATION BASED INCREMENTAL DEEP LEARNING IN FOG COMPUTING
Review round: 1
Reviewer: 1

Output:
Basic reporting: I am not sure what kind of habit you have while you were doing your literature review.
e.g.
This has led to state-of-the-art developments (Choi et al., 2019; Gepperth & Karaoguz, 2016; Hayes et al., 2019; Kemker & Kanan, 2018; Lesort et al., 2020; Nallaperuma et al., 2019; Parisi et al., 2018; Rebuffi et al., 2017; Rusu et al., 2016; Wu et al., 2018).
e.g.
A lot of research has been done in this area that discards training data 128 during training (Alain et al., 2015; Gopal, 2016; Katharopoulos & Fleuret, 2018; Loshchilov & 129 Hutter, 2015; Needell et al., 2016)
e.g.
achieving state-of-the-art classification accuracies (Castro et al., 2018; Kemker &  Kanan, 2018; Rebuffi et al., 2017; Rusu et al., 2016; Wu et al., 2019)

a general statement + a list of people's names and years... it does not give the reader anything but just shows off that you seem to read some papers... yet who knows whether you actually read those...

a bad habit... if you actually went thru those, please summarise and present them!
Experimental design: 1) there are figures of general knowledge on fog computing and fog device to the central server. but what is your design? figure 2 is too general, where CNN happens? where "transfer learning" happens?
2) i would like to know how you implement transfer learning.
3) the novelty might be the Sample Elimination Algorithm, but why you need it?  what is/are its purpose? why do you want to discard samples in a class?
Validity of the findings: if it is a research of computer vision/image processing. at least show some qualitative result of your study.
Additional comments: No additional comments


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: DYNAMIC SAMPLING OF IMAGES FROM VARIOUS CATEGORIES FOR CLASSIFICATION BASED INCREMENTAL DEEP LEARNING IN FOG COMPUTING
Review round: 1
Reviewer: 2

Output:
Basic reporting: In this article the author present few introduction in abstract which doesn't provide a link to proposed methodology. If possible few points may be added in abstract.

Literature was well organised.

Content of the paper is good.
Experimental design: Need more data samplings

Result present is not sufficient.

Axes parameters are not clear in Graphical representation.
Validity of the findings: Novel approach can be seen in this paper.
Additional comments: Provide the list of contributions in the Introduction

Most of the formulae in this article are considered from the published articles, recommended to cite them.

There are several grammatical errors and typos. It should be rectified.

Tables should be presented in understandable way. It look complicated.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: DYNAMIC SAMPLING OF IMAGES FROM VARIOUS CATEGORIES FOR CLASSIFICATION BASED INCREMENTAL DEEP LEARNING IN FOG COMPUTING
Review round: 1
Reviewer: 3

Output:
Basic reporting: Authors should explain the scenarios (e.g. live face recognition, healthcare etc.) where images are sent to fog devices and then forwarded to servers to provide readers a clear understanding of why fog computing is being employed here.

Some grammatical mistakes.
Experimental design: In fog computing many fog devices are connected to a server. The author should explain how incremental learning is performed by the server on receiving samples from the various connected fog devices.

The proposed sampling method is compared with 4 old statistical sampling methods. Authors may provide comparison of their method with some non-statistical, heuristic based selective sampling methods to ensure its effectiveness.
Validity of the findings: No comment
Additional comments: The technical details are well presented in the paper.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: IMPROVING PATIENT REHABILITATION PERFORMANCE IN EXERCISE GAMES USING COLLABORATIVE FILTERING APPROACH
Review round: 1
Reviewer: 1

Output:
Basic reporting: The paper provides clear definition of concepts, terms and methods (Collaborative Filtering, Kmeans algorithm, etc.).
Sufficient context is provided to understand: 1) why exergames are useful for patients; 2) why rec sys were needed for the MIRA platform,  why exergames are useful for patients.
The overall aim of the paper is clear: analysis of patients' movements and use of rec sys to make suggestions on the most appropriate settings for patients' movements.
The structure of the paper is clear; raw data and figures are shared.

However, the paper needs a professional English review (e.g. line 43 "who having" > "who are having"; the overuse of which statements in line 487 and 488, and again in line 69 and 70; in line 47 "where is a" > "where there is a", etc.).

The innovative contributions for MIRA platform are clear (no rec sys before), however the paper needs further work to demonstrate and articulate why MIRA with a rec sys system is innovative compared to the available research ("to the best of the authors' knowledge, addressing this problem has been neglected in previous work" is based on trust, not evidence - we need evidence. Please add references of other works and how your work is innovative compared to others).
Experimental design: As mentioned before: The overall aim of the paper is clear: analysis of patients' movements and use of rec sys to make suggestions on the most appropriate settings for patients' movements.

Research methods are clear both in the abstract and in the Materials and Methods section. Benchmarks are specified, as well as Dataset and equations used.

As mentioned above, it's needed some work to state how the research fills an identified knowledge gap, as there are probably other exergames  systems developed by others as well.
Validity of the findings: As mentioned above, the impact is clear for the MIRA platform, but not clear compared to the state of the art of exergames. If the novelty is provided by the use of recommender systems in an unexplored field (exergames), a clear statement is needed. More extensive state of the art research works in exergames are needed. It's also recommended to add in the conclusions a clear statement of how the usage of rec sys in the MIRA platform would benefit the field of exergames and provide a value compared to other works (it's already clear what results were obtained, just add the overall impact compared to the existing literature).

The rationale looks clear: 3 approaches were explored and tested for the recommender system. Each approach used a combination of different methods (knn, Kmeans, bfoa). RMSE was used to measure the accuracy.

Suggestion:  line 609 "the experimental results demonstrated that the recoms+ approach appeared better compared to the recoms approach" " please further develop this sentence specifying why (it's mentioned in the discussion of the results, but add a clear conclusive statement here).  Also, "it appeared better" is a vague statement -better in which way?. Same for line 610 about Recoms++.

In the conclusion, there is a reference of Recoms+ having "better" experimental results than recoms, and recoms++ providing better accuracy performance than recoms+. Does this means recoms++ is the best approach because the accuracy is better, and is therefore the best approach for MIRA? If yes, please assert so. If not, please discuss why, in the evaluation, accuracy is not the only relevant parameter for identifying the best approach.

Some observations are described in the Discussion section, just make clear conclusive statements on the Conclusion section.
Additional comments: No additional comments