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 COMPARATIVE ANALYSIS ON QUESTION CLASSIFICATION TASK BASED ON DEEP LEARNING APPROACHES
Review round: 1
Reviewer: 2

Output:
Basic reporting: The writing needs to be substantially and thoroughly improved and proofread so that it can properly deliver the messages of this work to the reviewers and the readers. There are scattered grammar issues and typos across this manuscript in its current form.
To list a few:
Line 20: There are... have been ... -> ... that have been ...
Line 44: ... and how to ... -> ... (becomes an open question.)
Line 68: ... ... to the credible classify of the answers -> ... credible classification of ...
Line 71: After categories...
Line 96: Most articles focus on English language has ... -> ... focused on ...
Line 123: stag -> stage

The related work part is kind of out-dated, for which the authors may refer to more recent works.
Experimental design: The descriptions of the experimental settings are missing, including the data preprocessing steps, and the training policies and hyper-parameter settings of the deep learning models.
The supplementary code files can not be open properly. You may use UTF-8 for the file encoding.
The publish and organization of a Turkish dataset is definitely meaningful and positive.
Validity of the findings: When saying ``there are several unique features in Turkish languages that make NLP challenging'', please explain it with more details as it's the main motivation of this work.
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: A COMPARATIVE ANALYSIS ON QUESTION CLASSIFICATION TASK BASED ON DEEP LEARNING APPROACHES
Review round: 1
Reviewer: 3

Output:
Basic reporting: The whole manuscript should be revised because the quality of written language is very poor making it difficult to understand. It has many grammatical issues and repeated nonsensical text. Even the first sentence of the abstract is an example of this repetition: "Question classification is one of the important tasks for automatic question classification in natural language processing (NLP).". Throughout the text there are too many examples to list. Some technical terms used are also not very clear, for example, the authors say that Turkish is "strongly inflective language" but they also say that it is agglutinative, so this issue should be clarified for the readers.

I would also advise to dismiss most of the text about Word2vec and deep learning models since these are established and widely used algorithms. Instead, more examples about the problem at hand (Turkish question classification) should be given.

The manuscript also has an organization issue: "DEEP LEARNING APPROACHES" starts with an overview of deep learning models but then starts describing the proposed model. Then on "METHODOLOGY & RESULTS", the authors provide a full explanation of the word2vec algorithm, as well as experimental details (but no results since the next section is called "Experimental Results").
Experimental design: The experimental design seems to be an application of existing architectures to turkish question classification. I was unable to understand what adaptations were made to work with Turkish questions. Furthermore very few details are given about the dataset. A dataset was created by translating an english language dataset, but it was not clear how and by whom it was translated and if this dataset is or will be made publicly available. As neither the code or data used to train the models is provided, it would be difficult to replicate the results.
Validity of the findings: The authors provide a comparison between all the architectures considered and different embeddings. They claim that in comparison to english questions, they have lower results due to "an absence of effectiveness lemmatization methods for Turkish Language compared to English language.". However this is the first that they mention lemmatization so I don't know why and how this would impact the results.
Additional comments: This manuscript presents a deep learning approach to question classification applied to turkish questions. The authors compare several deep learning architectures and they mention the challenge that is doing this type of task in Turkish texts. They provide a strong related work section on this task, and describe the models used in detail, however due to language issues most of the manuscript is difficult to understand.


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 COMPARATIVE ANALYSIS ON QUESTION CLASSIFICATION TASK BASED ON DEEP LEARNING APPROACHES
Review round: 2
Reviewer: 1

Output:
Basic reporting: The quality of this manuscript has apparently improved. Kindly suggest revising the current title of this manuscript as "A comparative analysis on question classification task based on deep learning approaches".
Experimental design: I have no more questions.
Validity of the findings: The current work supports their aims.
Additional comments: Thanks for carefully addressing 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: A COMPARATIVE ANALYSIS ON QUESTION CLASSIFICATION TASK BASED ON DEEP LEARNING APPROACHES
Review round: 2
Reviewer: 2

Output:
Basic reporting: English writing has been improved, but many issues still exist.

Just to name a few randomly:
Line 267: GRU store[s] and filter[s]
Line 326: to extrcut [extract]
Line 341 we proposed [a] deep learning hybrid architecture

I'd suggest proofreading and revising the manuscript seriously.
Experimental design: It has been moderately improved.
Validity of the findings: It has been moderately improved.
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: A COMPARATIVE ANALYSIS ON QUESTION CLASSIFICATION TASK BASED ON DEEP LEARNING APPROACHES
Review round: 2
Reviewer: 3

Output:
Basic reporting: Although there was an overall improvement there are still some language issues with the document. examples L97 covert, L141 to understand to apply, L212 - we evaluated to explain L284 - repeated text.

The inflected/agglutinative issue was also not clarified, there is no explanation of these terms that are used in the text (although inflected now appears only in the abstract). A simple definition would be enough.

The structure was slightly improved although there is still are still explanations of established techniques (peephole LSTM are not new and should be cited) in the proposed methodology section. This could mislead the reader to think the LSTM and GRU variations in this section are new. This article should be cited when refering to peephole LSTMs: "Gers, Felix A., Nicol N. Schraudolph, and Jürgen Schmidhuber. "Learning precise timing with LSTM recurrent networks." Journal of machine learning research 3.Aug (2002): 115-143."
Experimental design: The Turkish dataset was still not properly explained, namely how it was adapted from an English dataset. The authors provide the repository with the code used, although I also could not find the questions corpus there, only a link to the Turkish wikipedia dump.
Validity of the findings: The lines that the authors provide in their response to not match the PDF I have, at least lines 639-648 on the track changes file are something else. Anyway it would be nice if there was a better explanation of the difficulty in processing the Turkish language, such as lemmatization, for those that are unfamiliar with this language. It was interesting to know that it does not have grammatical gender or noun classes but then the techniques used do not seem to differ from the ones used for English.
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: A COMPARATIVE ANALYSIS ON QUESTION CLASSIFICATION TASK BASED ON DEEP LEARNING APPROACHES
Review round: 3
Reviewer: 1

Output:
Basic reporting: The form of the manuscript looks good at present.
Experimental design: The experimental design of this presented study is credible.
Validity of the findings: The scientific contribution of this paper has been well demonstrated.
Additional comments: Congratulations!


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 COMPARATIVE ANALYSIS ON QUESTION CLASSIFICATION TASK BASED ON DEEP LEARNING APPROACHES
Review round: 3
Reviewer: 2

Output:
Basic reporting: The manuscript has been moderately revised according to the previous reviews. I have no other comments.
Experimental design: I have no other comments.
Validity of the findings: I have no other comments.
Additional comments: I have no other 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: MODELING A DEEP TRANSFER LEARNING FRAMEWORK FOR THE CLASSIFICATION OF COVID-19 RADIOLOGY DATASET
Review round: 1
Reviewer: 1

Output:
Basic reporting: The Primary contribution of the paper looks interesting and presentation also looks fine. Literature review aspect is also very nice. And results and overall article structure is also fine. All the terminologies are used promptly.
The Novelty of the paper is also good and even the paper can be used for real-time analysis.
Experimental design: The Methodology is also good and nice work contribution is observed in the paper.
Research questions are well defined. But Research Gaps needs more work to be done in proper form.
Validity of the findings: The results are Ok. And conclusions and analysis is written and described in proper manner.
Data is also good. Conclusions are well stated and even the problem is well laid and defined in the paper.
Additional comments: The Paper needs the following Technical Revisions and is Requested for re-review:

1. Add Objectives of the paper at the end of Introduction. Add Organization of the paper.
2. No need for any sub-heading "RELATED WORKS". Just take one heading "Literature review" and merge everything there. At the end of Literature review, highlight in 9-15 lines what overall technical gaps are observed in existing works, that led to the design of the proposed methodology.
3. Add the Methodology aspect to this paper- Your proposed model, Algorithm or Flowchart of the Data Analysis.
4. Add more Analysis to this aspect.
5. Add future scope to this paper.
6. Add the following references to this paper:

1. Zivkovic, M., Bacanin, N., Venkatachalam, K., Nayyar, A., Djordjevic, A., Strumberger, I., & Al-Turjman, F. (2021). COVID-19 cases prediction by using hybrid machine learning and beetle antennae search approach. Sustainable Cities and Society, 66, 102669.
2. Kumar, A., Sharma, K., Singh, H., Srikanth, P., Krishnamurthi, R., & Nayyar, A. Drone-Based Social Distancing, Sanitization, Inspection, Monitoring, and Control Room for COVID-19. Artificial Intelligence and Machine Learning for COVID-19, 153.
3. Devi, A., & Nayyar, A. Perspectives on the Definition of Data Visualization: A Mapping Study and Discussion on Coronavirus (COVID-19) Dataset. Emerging Technologies for Battling Covid-19: Applications and Innovations, 223.
4. Sharma, K., Singh, H., Sharma, D. K., Kumar, A., Nayyar, A., & Krishnamurthi, R. Dynamic Models and Control Techniques for Drone Delivery of Medications and Other Healthcare Items in COVID-19. Emerging Technologies for Battling Covid-19: Applications and Innovations, 1.
5. Alzubi, J., Nayyar, A., & Kumar, A. (2018, November). Machine learning from theory to algorithms: an overview. In Journal of physics: conference series (Vol. 1142, No. 1, p. 012012). IOP Publishing.


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: MODELING A DEEP TRANSFER LEARNING FRAMEWORK FOR THE CLASSIFICATION OF COVID-19 RADIOLOGY DATASET
Review round: 1
Reviewer: 2

Output:
Basic reporting: -
Experimental design: -
Validity of the findings: -
Additional comments: -please add colorful picture of measurements (optionally);;; + arrows what is what;;;;
-please add block diagram of the proposed research;;;;
-please add photo/photos of application of the proposed research ;;;; what is result of the analysis? ;;;;;
-please add sentences about future analysis;;;
-Figures should have better quality;;;;
-please add arrows to photos what is what;;;
-formulas and fonts should be formatted;;;;
-fonts in figures should be bigger;;;;
-please add labels to figures;;;
-references should be 2018-2021 Web of Science about 50% or more ;; 30-40 at least.;;;;
-Please compare with other methods, justify. Advantages or Disadvantages of neural network;;;
-is there possibility to use the proposed methods for other problems?  ;;;;
for example;;;;

1)
Irfan, M.; Iftikhar, M.A.; Yasin, S.; Draz, U.; Ali, T.; Hussain, S.;
Bukhari, S.; Alwadie, A.S.; Rahman, S.; Glowacz, A.; et al.
Role of Hybrid Deep Neural Networks (HDNNs), Computed Tomography,
and Chest X-Rays for the Detection of COVID-19.
Int. J. Environ. Res. Public Health
2021, 18, 3056.
https://doi.org/10.3390/ijerph18063056

-Conclusion: point out what are you done;;;;


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: MODELING A DEEP TRANSFER LEARNING FRAMEWORK FOR THE CLASSIFICATION OF COVID-19 RADIOLOGY DATASET
Review round: 1
Reviewer: 3

Output:
Basic reporting: The title, abstract, introduction, methods, results and discussion are appropriate for the content of the text. Furthermore, the article is well constructed, the experiments are well conducted, and analysis is well performed. The figures are relevant, but the quality of the figures are low. The figures are not labelled and described well.
Experimental design: The angle of the research is original and the research is within the scope of the journal. Research question is well defined, relevant and meaningful. The overview and their proposal for a more suitable technology is highly technical, ethical and logistical.
Validity of the findings: The introduction is comprehensive. The findings are meaningful. The conclusions are well stated and relevant to the research questions.
Additional comments: This study trained a Deep Transfer-Learning Model (DTL) to classify the COVID-19 radiology dataset of chest X-ray images in both binary (COVID-19 or Normal) and three-class (COVID-19, Viral-Pneumonia or Normal) classification scenarios. The authors fine-tuned the model further for optimal performance. In their experiments, they achieved a test accuracy of 98%.

Editorial Criteria
BASIC REPORTING
The title, abstract, introduction, methods, results and discussion are appropriate for the content of the text. Furthermore, the article is well constructed, the experiments are well conducted, and analysis is well performed. The figures are relevant, but the quality of the figures are low. The figures are not labelled and described well.
EXPERIMENTAL DESIGN
The angle of the research is original and the research is within the scope of the journal. Research question is well defined, relevant and meaningful. The overview and their proposal for a more suitable technology is highly technical, ethical and logistical.
VALIDITY OF THE FINDINGS
The introduction is comprehensive. The findings are meaningful. The conclusions are well stated and relevant to the research questions.

Overall, I think this study is novel and will be of interest to others in the community of Artificial Intelligence Applications for COVID-19. This study proposed a solution by using Deep Learning Convolutional Neural Network Model to classify the COVID-19 X-ray images into three classes: COVID-19, Viral-Pneumonia or Normal categories. This proposed model also outperformed existing methods in terms of accuracy. Some of the weaknesses are the not always easy readability of the text which establishes unclear logical links between concepts. In general, the work is convincing except some major and minor comments below:

Major Comments:

The authors included multiple datasets (Qatar University, University of Dhaka and  Mendeley dataset). I wonder if the batch size and the number of training epochs vary from one dataset to another. Are they application dependent?

I noticed that the X-ray images were resized to 224×224 pixels? How was the size decided?  I wonder if the accuracies on different image sizes were computed?

Minor Comments:
Figure 4, 5, 6, 7: recommend improving the resolution; recommend removing the background grey color and grid lines.

Figure 8, 9, 10: the red/green.blue texts are really hard to read. Please make them clear. Please remove the “Output” on top of each subplot.

Line 670-671, “The fine-tuned models gave better results when also compared to other models that used ordinary pre-trained VGG-16 and VGG-19”: this sentence needs to remove “when also”.

A reference is needed for OpenCV in line 427.

It will be more clear if the authors can add the equations for the dimension and number of parameters calculation.

It will be more clear to have a section of Abbreviations.


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: MODELING A DEEP TRANSFER LEARNING FRAMEWORK FOR THE CLASSIFICATION OF COVID-19 RADIOLOGY DATASET
Review round: 2
Reviewer: 1

Output:
Basic reporting: The Revised paper looks Ok and can be recommended for Publication. Now the literature, technical flow and figures looks Ok.
Experimental design: The experimental design looks broad and elaborate. And can be accepted.
Validity of the findings: The Results and ANALYSIS section is Ok and well elaborated.
Additional comments: The Paper stands Accepted with no further revisions.


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: CULTURAL DIFFERENCES IN MUSIC FEATURES ACROSS TAIWANESE, JAPANESE AND AMERICAN MARKETS
Review round: 1
Reviewer: 1

Output:
Basic reporting: The title is too ambiguous and grammatically wrong.
The authors have to check term usages on the overall manuscript, e.g., boom in music streaming.

Motivation is not reasonable: "Preferences for music can be represented through music features."
User preferences are combinations of item features and users' tastes.
Please, give some better explanations without logical leaps.

I cannot find the originality of this study.
The authors merely classified musics into market regions by using the conventional ML-based classifiers.
Although the authors said that contributions of musical features to the classification accuracy can show cultural differences between the market regions, they did not provide adequate discussions for this point (just a feature has higher contribution to this market region than the other regions).

There have been numerous studies for classifying musics according to their physical features.
Therefore, the following sentence cannot be the contribution of this study.
"We demonstrate that machine learning can reveal both the magnitude of differences
38 in music preference across Taiwanese, Japanese, and American markets, and where these preferences are different."
Average readers have already known that we can do these things with the conventional ML models.
Experimental design: The experimental subjects and procedures are adequate.
Also, they have been described well.
Validity of the findings: The findings of this paper should be cultural differences between music markets.
However, the authors have concentrated on the effectiveness of the machine learning on classifying musics.
According to this point, the abstract and introduction should be modified.

Also, to reveal the cultural differences, contributions of musical features to the classification accuracy are not enough.
Please, add more in-depth analysis to ensure the originality of this 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: CULTURAL DIFFERENCES IN MUSIC FEATURES ACROSS TAIWANESE, JAPANESE AND AMERICAN MARKETS
Review round: 1
Reviewer: 2

Output:
Basic reporting: no comment
Experimental design: no comment
Validity of the findings: no comment
Additional comments: The paper is, generally, well-described and clear.
However, Figure 2 and 3 need to be redrawn since they are not clear.


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: CULTURAL DIFFERENCES IN MUSIC FEATURES ACROSS TAIWANESE, JAPANESE AND AMERICAN MARKETS
Review round: 1
Reviewer: 3

Output:
Basic reporting: Overall, this research paper is well structured. Although it is a minor thing, authors are suggested to prove read the article to the professional reader to improve the quality of the word choice and the paper's story flow.
Experimental design: The authors define the experiment steps in a good way. However, several things still need to be clarified:
1.	The sufficiency of the sample size.
2.	The justification of the features that the authors used for classification.
3.	The training-testing split ratio whether it is optimum.
4.	The tuned cross-validation parameter if it is enough.
Validity of the findings: 1.	The authors are strongly encouraged to use a statistical measure to compare the model's quality rather than just comparing the accuracy and AUC score. For instance, using Delong's method to compare the model performance.
2.	It will be better for the authors to explicitly restate this study's novelty, on how this study differs from other studies and its contribution, in the conclusion part. It could link the finding and the research goal stated initially and make the importance of this study more sound.
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: CULTURAL DIFFERENCES IN MUSIC FEATURES ACROSS TAIWANESE, JAPANESE AND AMERICAN MARKETS
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 significantly revised their manuscript according to review comments. The authors have changed their main contribution from classifying popular music according to cultural backgrounds to analyzing popular music.

However, I still do not agree that the analysis methods used in this study have novelty.
Also, the authors have not shown applications or potential applications of the analysis methods.

Therefore, the current version of the manuscript is not suitable for journals in the computer science area.
I want to recommend the authors search for journals dealing with data analysis results, not analysis methodologies.


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: CULTURAL DIFFERENCES IN MUSIC FEATURES ACROSS TAIWANESE, JAPANESE AND AMERICAN MARKETS
Review round: 2
Reviewer: 2

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: DIGITAL SECURITY VULNERABILITIES AND THREATS IMPLICATIONS FOR FINANCIAL INSTITUTIONS DEPLOYING DIGITAL TECHNOLOGY PLATFORMS AND APPLICATION: FMEA AND FTOPSIS ANALYSIS
Review round: 1
Reviewer: 1

Output:
Basic reporting: According to the authors, this study explores and provides a step-by-step analysis of the 19 potential vulnerabilities and threats failures affecting the integration of IoT. The work is somehow good and the problem researched by the author in the IoT domain is acceptable. Something I want to mention to improve the quality of the manuscript.

The problem should be better introduced in the abstract of the manuscript.

The research objectives and contributions are not clear enough as it mention in the introduction section line 107.
Experimental design: The methodological approach needs to be synthesized.

The presentation of the methodology section should be more strong.

No experimental design is mentioned.

The author mentioned in line no 367
"Based on the expert’s opinions derived through the questionnaire, the corresponding fuzzy numbers were calculated using the aggregated group matrix for Occ, Sev and Det for each vulnerability and threats identified. " which the method author used for the questionnaire.

The flow chart figure should be redrawn with high quality.
Validity of the findings: The finding is not clear in the manuscript. The author should add finding with bullets
Additional comments: Author work is better but some strong revision and manuscript should be revised thoroughly. some point I mentioned above for cosidration.


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: DIGITAL SECURITY VULNERABILITIES AND THREATS IMPLICATIONS FOR FINANCIAL INSTITUTIONS DEPLOYING DIGITAL TECHNOLOGY PLATFORMS AND APPLICATION: FMEA AND FTOPSIS ANALYSIS
Review round: 1
Reviewer: 2

Output:
Basic reporting: The authors used the failure mode effect analysis (FMEA) and fuzzy technique for order of preference by similarity for ideal solution (FTOPSIS) techniques to classify vulnerabilities and threats affecting IoT, big data, and cloud computing.

The structure of the paper includes the following sections: 1) introduction, 2) related work, 3) FMEA and fuzzy theory application,   4) methodology, 5) analysis and results, 6) discussion, and 7) conclusion.

The authors should define risk analysis in section 2.

The authors could include the following references:
N. Javadian, M. Kazemi, F. Khaksar-Haghani, M. Amiri-Aref and R. Kia, "A general fuzzy TOPSIS based on new fuzzy positive and negative ideal solution," 2009 IEEE International Conference on Industrial Engineering and Engineering Management, Hong Kong, 2009, pp. 2271-2274, doi: 10.1109/IEEM.2009.5373055.
S. Sun, Q. Wu and G. Liu, "Multi-level decision-making model for product design based on Fuzzy set theory," 2006 First International Symposium on Pervasive Computing and Applications, Urumqi, 2006, pp. 841-846, doi: 10.1109/SPCA.2006.297543.
Experimental design: The authors explain their methodology and provide a flow chart in figure 1. Their study requires a mixed methodology (qualitative and quantitative). The authors used the data of 234 questionnaires.

In Table 1, the authors define the failure modes and dimensions of IoT, BDA, and cloud computing. However, the failure modes appear more for a traditional infrastructure than for emerging technologies. Which is the explanation for this?
Validity of the findings: According to the results described by the authors, lack of back-up electric generator under the infrastructure risk dimension is the most dangerous threat. In your interpretation of results, why this threat is the most dangerous?

In the conclusions, the authors wrote "Fundamental limitation from this study is the reliance on security experts from financial institutions and their views may not entirely reflect views from other security experts from different industries with IoTs, BDA and CC deployment.", and I agree with this. From this point, I provide the following suggestions to the authors:
a) modify the title of the paper to "Digital Security Vulnerabilities and Threats implications on Financial Institutions using FMEA and FTOPSIS" or something similar.
b) change the idea of IoT, BDA, CC to security in the financial sector, providing evidence about the data breaches and cyber-attacks in the last year.
c) as future work, you can return to this paper including the opinion of security experts from IoT, BDA, and CC industries. In this way, the failure modes and dimensions will be more in line with emerging technologies.
Additional comments: The authors proposed an interesting technique to evaluate and classify threats and vulnerabilities; however, the dimensions and failure modes are not aligned to IoT deployment, Data analysis, and cloud computing technologies because each one has its vulnerabilities and threats. Moreover, security experts work in the financial area where the security requirements are very specific based on international standards (ISO 27000, PCI DSS, and others).


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: DIGITAL SECURITY VULNERABILITIES AND THREATS IMPLICATIONS FOR FINANCIAL INSTITUTIONS DEPLOYING DIGITAL TECHNOLOGY PLATFORMS AND APPLICATION: FMEA AND FTOPSIS ANALYSIS
Review round: 2
Reviewer: 1

Output:
Basic reporting: In this study, the author explores and provides a step-by-step analysis of the potential vulnerabilities and threats failures affecting the integration of IoT, Big Data Analytics, and Cloud Computing towards data management. The authors have done an extension towards the discussions of pervasive vulnerabilities and threats originating from integrating digital applications and platforms for data management. A detailed review and classification of these threats and vulnerabilities are 39 vital for sustaining the nature of digital integration for firms. The study highlights some major issues in the IoT Big data domain but there are some issues show be solved to improve the quality of work.
Experimental design: The methodology of the study is too much concise there should be some more details for clear understanding.

The methodology diagram should be redrawn and make some understandable especially the flow of data,
Validity of the findings: The results of this study are significant and also present in a well way.
Additional comments: As the author explores and provides a step-by-step analysis of the potential vulnerabilities and threats failures affecting the integration of IoT, Big Data Analytics, and Cloud Computing towards data management. The work is significant t but the grammar and typos errors are the weakness of the paper such as have seemingly --> has seemingly,  from information ---> form an information system..


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: DIGITAL SECURITY VULNERABILITIES AND THREATS IMPLICATIONS FOR FINANCIAL INSTITUTIONS DEPLOYING DIGITAL TECHNOLOGY PLATFORMS AND APPLICATION: FMEA AND FTOPSIS ANALYSIS
Review round: 2
Reviewer: 2

Output:
Basic reporting: The authors improved the version of the paper, including the comments and suggestions by reviewers. The authors included the references suggested and changed the title of the article.

I suggest authors review the document to avoid finger mistakes. For example, on page 6, the authors wrote "resource u sage".

I suggest authors include a paragraph in the introduction section to provide a vision of financial institutions and the adoption of IoT, Big Data Analytics (BDA), and Cloud Computing.
Experimental design: The authors improved the version of the paper, including the comments and suggestions by reviewers. They provide a better explanation of their results.
Validity of the findings: The findings provide the first step to understand and resolve the problem of adopting emerging technologies by financial institutions.
Additional comments: I suggest authors include a paragraph in the introduction section to provide a vision of financial institutions and the adoption of IoT, Big Data Analytics (BDA), and Cloud Computing.


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: DIGITAL SECURITY VULNERABILITIES AND THREATS IMPLICATIONS FOR FINANCIAL INSTITUTIONS DEPLOYING DIGITAL TECHNOLOGY PLATFORMS AND APPLICATION: FMEA AND FTOPSIS ANALYSIS
Review round: 3
Reviewer: 1

Output:
Basic reporting: In this study authors explore and provides a step-by-step analysis of the potential vulnerabilities and threats failures affecting the integration of IoTs, Big Data Analytics and Cloud Computing towards data management. A multi-dimensional analysis, Failure Mode Effect Analysis and Fuzzy Technique for Order of Preference by Similarity for Ideal Solution were combined to evaluate and rank the potential vulnerabilities and threats affecting emerging technology integration. A sample of 234 security experts from the banking industry with adequate knowledge in IoTs, Big Data Analytics and Cloud Computing were used for the survey. The results revealed a lack of back-up electric generators, a failure of firewall protection and a lack of information security audit as the high-ranking vulnerabilities and threats failures based on the closeness of coefficient scores to significantly affect Internet of Things, Big Data, and Cloud Computing integration. Further this study an extension of the discussions of pervasive vulnerabilities and threats originating from integrating digital applications and platforms for data management.
Experimental design: The experimental flow still needs improvement as I describe in the last review.
Explanation of methodology is not present in a well way.
Validity of the findings: Results and discussion are described in appropriate ways but I will suggest some graphical representation of results.
Additional comments: Typos and grammar still a problem in the article.


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: DIGITAL SECURITY VULNERABILITIES AND THREATS IMPLICATIONS FOR FINANCIAL INSTITUTIONS DEPLOYING DIGITAL TECHNOLOGY PLATFORMS AND APPLICATION: FMEA AND FTOPSIS ANALYSIS
Review round: 4
Reviewer: 1

Output:
Basic reporting: This study explores and provides a step-by-step analysis of the potential vulnerabilities and threats failures affecting the integration of IoTs, Big Data Analytics and Cloud Computing towards data management. A multi-dimensional analysis, Failure Mode Effect Analysis and Fuzzy Technique for Order of Preference by Similarity for Ideal Solution were combined to evaluate and rank the potential vulnerabilities and threats affecting emerging technology integration. A sample of 234 security experts from the banking industry with adequate knowledge in IoTs, Big Data Analytics and Cloud Computing were used for the survey. The results revealed lack of backup electric generators, a failure of firewall protection and a lack of information security audit as the high-ranking vulnerabilities and threats failures based on the closeness of coefficient scores to significantly affect Internet of Things, Big Data, and Cloud Computing integration.
Experimental design: No comments
Validity of the findings: No comments
Additional comments: The details are present in a well way as compared to the previous version of the manuscript. All concern point are discussed in detail.  The author should still need to focus on grammar such as lots of mistakes of 's', 'es' are present in the 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: DIGITAL SECURITY VULNERABILITIES AND THREATS IMPLICATIONS FOR FINANCIAL INSTITUTIONS DEPLOYING DIGITAL TECHNOLOGY PLATFORMS AND APPLICATION: FMEA AND FTOPSIS ANALYSIS
Review round: 5
Reviewer: 1

Output:
Basic reporting: The author addressed the challenges from an information systems perspective and has noted that more research is needed to identify potential vulnerabilities and threats affecting the integration of IoTs, BDA and CC for data management. They conducted a step-by-step analysis of the potential vulnerabilities and threats affecting the integration of IoTs, Big Data Analytics, and Cloud Computing for data management. They combined multi-dimensional analysis, Failure Mode Effect Analysis and Fuzzy Technique for Order of Preference by Similarity for Ideal Solution to evaluate and rank the potential vulnerabilities and threats.
The paper is now in good shape according to peerj acceptance rules.
Experimental design: No Comment.
Already improved by the author in the first revision.
Validity of the findings: Results are strong and present in well way
Additional comments: Please improve the grammar and typos before publishing.


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: AN EXPERIENCE SELECTING QUALITY FEATURES OF APPS FOR PEOPLE WITH DISABILITIES USING ABDUCTIVE APPROACH TO EXPLANATORY THEORY GENERATION
Review round: 1
Reviewer: 1

Output:
Basic reporting: The authors have explained very well first 3 sections. There is need of addition in section 2 that how the proposed work is different with existing one.
Experimental design: All the results are mentioned in an effective manner.
Validity of the findings: NIL
Additional comments: Acceptable


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: AN EXPERIENCE SELECTING QUALITY FEATURES OF APPS FOR PEOPLE WITH DISABILITIES USING ABDUCTIVE APPROACH TO EXPLANATORY THEORY GENERATION
Review round: 1
Reviewer: 2

Output:
Basic reporting: This paper "An experience selecting quality features of apps for
people with disabilities using abductive approach to
explanatory theory generation"  is well organized and reasonable in structure. The comments are as follows:

Does the paper contribute to the body of knowledge?: Yes.

 The paper proposes the first attempt
to improve the MARS tool, aiming to provide specialists relevant data, reducing noise
effects, accomplishing better predictive results to enhance their investigations.

 this methodology allows the developers to use to analyze and develop software for the health informatics model and create a space in which software engineering and machine learning experts can work together on the machine learning model lifecycle.

Is the paper technically sound?: Yes. The paper is technically sound and is of very high quality. The various claims in the paper are quite well supported by the experiments and evaluate on a set of real data sets. the manuscript presents many FIGURE and many tables, this supports the researches idea and methodology.  The results on real-world data sets are promising and motivate further investigations into the use of approximate inference in this context.

Is the subject matter presented in a comprehensive manner?: no. The presentation isn't comprehensive it needs to better organize and simplify the main objectives of the idea of the manuscript topic

Are the references provided applicable and sufficient?: Yes. Among all references, only poor references are in the past three years. In order to highlight the innovation of this work, it is better to cite other six up-to-date references to be applicable and sufficient enough to provide relevant materials about this novel approach software.
Experimental design: This paper "An experience selecting quality features of apps for
people with disabilities using abductive approach to
explanatory theory generation"  is well organized and reasonable in structure. The comments are as follows:

Does the paper contribute to the body of knowledge?: Yes.

 The paper proposes the first attempt
to improve the MARS tool, aiming to provide specialists relevant data, reducing noise
effects, accomplishing better predictive results to enhance their investigations.

 this methodology allows the developers to use to analyze and develop software for the health informatics model and create a space in which software engineering and machine learning experts can work together on the machine learning model lifecycle.

Is the paper technically sound?: Yes. The paper is technically sound and is of very high quality. The various claims in the paper are quite well supported by the experiments and evaluate on a set of real data sets. the manuscript presents many FIGURE and many tables, this supports the researches idea and methodology.  The results on real-world data sets are promising and motivate further investigations into the use of approximate inference in this context.

Is the subject matter presented in a comprehensive manner?: no. The presentation isn't comprehensive it needs to better organize and simplify the main objectives of the idea of the manuscript topic

Are the references provided applicable and sufficient?: Yes. Among all references, only poor references are in the past three years. In order to highlight the innovation of this work, it is better to cite other six up-to-date references to be applicable and sufficient enough to provide relevant materials about this novel approach software.
Validity of the findings: This paper "An experience selecting quality features of apps for
people with disabilities using abductive approach to
explanatory theory generation"  is well organized and reasonable in structure. The comments are as follows:

Does the paper contribute to the body of knowledge?: Yes.

 The paper proposes the first attempt
to improve the MARS tool, aiming to provide specialists relevant data, reducing noise
effects, accomplishing better predictive results to enhance their investigations.

 this methodology allows the developers to use to analyze and develop software for the health informatics model and create a space in which software engineering and machine learning experts can work together on the machine learning model lifecycle.

Is the paper technically sound?: Yes. The paper is technically sound and is of very high quality. The various claims in the paper are quite well supported by the experiments and evaluate on a set of real data sets. the manuscript presents many FIGURE and many tables, this supports the researches idea and methodology.  The results on real-world data sets are promising and motivate further investigations into the use of approximate inference in this context.

Is the subject matter presented in a comprehensive manner?: no. The presentation isn't comprehensive it needs to better organize and simplify the main objectives of the idea of the manuscript topic

Are the references provided applicable and sufficient?: Yes. Among all references, only poor references are in the past three years. In order to highlight the innovation of this work, it is better to cite other six up-to-date references to be applicable and sufficient enough to provide relevant materials about this novel approach software.
Additional comments: This paper "An experience selecting quality features of apps for
people with disabilities using abductive approach to
explanatory theory generation"  is well organized and reasonable in structure. The comments are as follows:

Does the paper contribute to the body of knowledge?: Yes.

 The paper proposes the first attempt
to improve the MARS tool, aiming to provide specialists relevant data, reducing noise
effects, accomplishing better predictive results to enhance their investigations.

 this methodology allows the developers to use to analyze and develop software for the health informatics model and create a space in which software engineering and machine learning experts can work together on the machine learning model lifecycle.

Is the paper technically sound?: Yes. The paper is technically sound and is of very high quality. The various claims in the paper are quite well supported by the experiments and evaluate on a set of real data sets. the manuscript presents many FIGURE and many tables, this supports the researches idea and methodology.  The results on real-world data sets are promising and motivate further investigations into the use of approximate inference in this context.

Is the subject matter presented in a comprehensive manner?: no. The presentation isn't comprehensive it needs to better organize and simplify the main objectives of the idea of the manuscript topic

Are the references provided applicable and sufficient?: Yes. Among all references, only poor references are in the past three years. In order to highlight the innovation of this work, it is better to cite other six up-to-date references to be applicable and sufficient enough to provide relevant materials about this novel approach software.


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: AN EXPERIENCE SELECTING QUALITY FEATURES OF APPS FOR PEOPLE WITH DISABILITIES USING ABDUCTIVE APPROACH TO EXPLANATORY THEORY GENERATION
Review round: 1
Reviewer: 3

Output:
Basic reporting: In this paper the authors present an study that determines the most relevant quality factors of apps for people with disabilities utilizing the abductive approach to the generation of an explanatory theory. The paper reads well and it is well organized. Very few recent references are included.
Experimental design: The contributions of the paper and the research questions should be clarified.
Validity of the findings: The results are well presented and support the findings presented in the discussion and conclusions sections.
Additional comments: Some aspects to improve:
- Introduction: clarify the contributions and research questions.
- Related Work: Analyze the references more deeply and include more references of the latest 5 years.


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: AN EXPERIENCE SELECTING QUALITY FEATURES OF APPS FOR PEOPLE WITH DISABILITIES USING ABDUCTIVE APPROACH TO EXPLANATORY THEORY GENERATION
Review round: 2
Reviewer: 1

Output:
Basic reporting: The manuscript is the best one that can contribute to scientific research.  I recommend acceptance to publish in the peerj journal, according to the instructions and conditions of publication of peerj journal.
Experimental design: All results are displayed intact and clearly
Validity of the findings: Perfect to an extent
Additional comments: Acceptable


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: AN EXPERIENCE SELECTING QUALITY FEATURES OF APPS FOR PEOPLE WITH DISABILITIES USING ABDUCTIVE APPROACH TO EXPLANATORY THEORY GENERATION
Review round: 2
Reviewer: 2

Output:
Basic reporting: The authors have addressed all my concerns.
Experimental design: The authors have addressed all my concerns.
Validity of the findings: The authors have addressed all my concerns.
Additional comments: The authors have addressed all 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: SELF-LEARNING NETWORK-BASED SEGMENTATION FOR REAL-TIME BRAIN M.R. IMAGES THROUGH HARIS
Review round: 1
Reviewer: 1

Output:
Basic reporting: 1
The authors introduce a lot of existing studies. To convince the future readers, the paper needs a comparison table, which uses columns for the existing studies and the manuscript, and rows for the attributes.

2
Figures 4 and 5 need complete graph legends.
Figures 4 and 5 are not cited. Please explain these figures.

3
a majority of those approaches lack inaccuracy
-> a majority of those approaches lack accuracy

4
"is presented in figure 4 below" in Line 360 should be "is presented in figure 6 below".

5
Please add the time unit (e.g., seconds or milliseconds) to Figure 6.
Experimental design: 1
The authors claim that "The article's main objective
is ... computationally and technically efficient than many
other approaches like CNN and HARIS algorithm." in Abstract.
However, the computational cost of the proposed algorithm is higher than those of CNN or HARIS, according to Figure 6.
The objective of the manuscript should be clarified precisely.

2
Please explain the HARIS algorithm in detail. Moreover, please add the reference of the paper which proposes the HARIS.
Validity of the findings: 1.
Your code does not work.

For example,
"from tensorflow.keras.preprocessing.image import ImageDataGnerator" in Line 9 of pcnn-Copy1.py should be
"from tensorflow.keras.preprocessing.image import ImageDataGenerator".

Please upload the correct source files and also add a readme file, which contains information about the sources and the execution procedure.

2.
In the evaluations, the authors compared the proposed algorithm with Twin Centric GA with SGO, HARIS, and CNN.
Please describe why the authors selected these algorithms to be compared.
Further, please describe why the authors did not compare many other algorithms introduced in Introduction Section.
Moreover, please describe the details of Twin Centric GA with SGO.
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: SELF-LEARNING NETWORK-BASED SEGMENTATION FOR REAL-TIME BRAIN M.R. IMAGES THROUGH HARIS
Review round: 1
Reviewer: 2

Output:
Basic reporting: The reporting aspect of the work is up to the mark.
Experimental design: The experimental aspect is well highlighted.
Validity of the findings: The findings have been compared well with previously reported approaches.
Additional comments: The following aspects need to be addressed:
1. A few training time learning error convergence plots are required.
2. Some details regarding the approach adopted for fixing the network architecture should be incorporated.
3. At what stage of the network, does the learning saturate?
4. A confusion matrix should be used to report the best and worst time performance of the network.
5. What shall be the difference in performance with and without the fuzzy portion adopted in the work?
6. Some results relating the  fuzzy entropy-based thresholding technique to the overall performance of the network should be included.
7. What cost functional is used to optimize the performance of the network?


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: SELF-LEARNING NETWORK-BASED SEGMENTATION FOR REAL-TIME BRAIN M.R. IMAGES THROUGH HARIS
Review round: 2
Reviewer: 1

Output:
Basic reporting: 1.
There are several description errors.
For example;
-	There is a missing citation in Line 70.
-	Section numbers are missing in Lines 85-87, and 146.
-	"\sum_{i=0}^n" in Equations 1, 3, and 6 should be "\sum_{i=1}^n" or "\sum_{i=1}^{n-1}" because the length is n.

2.
It is impossible to calculate Equation 5. This is because we need the value of V_i to calculate V_i.

3.
The description of Page 8 is almost identical to the description of https://classic.d2l.ai/chapter_recurrent-neural-networks/bi-rnn.html
Be careful of plagiarism. Moreover, it is not necessary the explanation, because it is the basics of RNNs.

4.
Please cite and discuss several important research papers on deep neural networks using codon encoding and discuss them.
For example;
Zhang, Kaiming, et al. "CRIP: predicting circRNA–RBP-binding sites using a codon-based encoding and hybrid deep neural networks." Rna 25.12 (2019): 1604-1615.
Experimental design: 1.
The experiment using data augmentation suffers from data leakage. According to the explanation of the manuscript, the dataset size was increased using data augmentation, and then the authors split the increased data into the training data and the validation data. The performance of the proposed machine learning model was evaluated using the validation data.
Therefore, the validation data and the training data can have several samples created by the same sample.

2.
Please explain why the authors select Sum, Max, Norm, EWA, and PA as the features for the input of the machine learning model.

3.
The materials submitted to PeerJ contain "cs-58149-test.json," and Table 1 in the manuscript shows the sample size of the test dataset. However, according to the manuscript, it seems that the authors did not use the test dataset.
If so, please explain why the authors did not use the test dataset.

4.
The source code "cs-58149-advanced-gru-lstm.ipynb" contains error logs.
---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
/opt/conda/lib/python3.7/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
   2888             try:
-> 2889                 return self._engine.get_loc(casted_key)
   2890             except KeyError as err:
.....
.....
.....
---------------------------------------------------------------------------
Please upload the correct source files.
Validity of the findings: 1.
The authors constructed machine learning models that predict mRNA sequences responsible for the degradation of the COVID-19 mRNA vaccine. The structure of the machine learning models is simple and straightforward. Moreover, codon encoding is not novel in the research field on deep neural networks for RNA. The feature selection might be novel and practical, but the reason why the authors selected the features is not described well.

Therefore, the authors should clarify their technical contributions. Moreover, please describe why the authors selected the features.
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: SELF-LEARNING NETWORK-BASED SEGMENTATION FOR REAL-TIME BRAIN M.R. IMAGES THROUGH HARIS
Review round: 2
Reviewer: 2

Output:
Basic reporting: Its is OK.
Experimental design: It has been adequately designed.
Validity of the findings: After review and modifications, the finding now stand validated.
Additional comments: The manuscript is now ready for publication.


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: SELF-LEARNING NETWORK-BASED SEGMENTATION FOR REAL-TIME BRAIN M.R. IMAGES THROUGH HARIS
Review round: 3
Reviewer: 1

Output:
Basic reporting: The authors have satisfactorily responded to all my questions and made the necessary changes to the manuscript.
Experimental design: The authors have satisfactorily responded to all my questions and made the necessary changes to the manuscript.
Validity of the findings: The authors have satisfactorily responded to all my questions and made the necessary changes to the manuscript.
Additional comments: The authors have satisfactorily responded to all my questions and made the necessary changes to the 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: SELF-LEARNING NETWORK-BASED SEGMENTATION FOR REAL-TIME BRAIN M.R. IMAGES THROUGH HARIS
Review round: 3
Reviewer: 2

Output:
Basic reporting: The paper submitted investigated the use of self-learning network-based segmentation for brain MRI through HARIS and the reported methodology achieved an accuracy of 77% towards segmenting real-time images into multiple regions. The analysis workflow is interesting in general. However, please consider the following comments.

Comment 1:
A major improvement and a carefully proofread spell check are required since the English in the present manuscript is not of publication quality. Indicative:

1.	Lines 21-23
2.	Lines 53-58
3.	Line 83
4.	Lines 89-91
5.	Lines 94-97
6.	Lines 104-106
7.	Line 133, the word “discrete” is used as a verb.
8.	Lines 156-157
9.	Lines 192-194
10.	Lines 203-208
11.	Lines 233-241
12.	Lines 326-328
13.	Lines 332-333
14.	Lines 377-378

Comment 2:
Lines 94-97: Could you please elaborate on the following: “the issue of the massive number of segmented regions”.

Comment 3:
Lines 110-111: It is hard to understand what the authors want to state here.

Comment 4:
Lines 148-149: Please rephrase. What does computationally compatible mean? Is it computationally feasible?

Comment 5:
Line 205: Did you quantify an accepted level of noise for your data?

Comment 6:
Lines 213-216: Please rephrase the following: “for additional images upon segmenting the image.”.

Comment 7:
Lines 218-220: Please elaborate on the rationale.

Comment 8:
Lines 235-237: “… each of the convolutional layers is being guided through various techniques …”. This is quite confusing.

Comment 9:
Some figures seem to be of low resolution. Please provide images of higher quality.

Comment 10:
Figure 3 presents the loss across the epochs (as the y-axis label indicates) and not the learning rate. The authors should check if that is intended or it is misplaced.

Comment 11:
Table 1: Please include the percentage symbol somewhere in the table. I assume that numbers are percentages.
Experimental design: Comment 1:
Line 366: Did the authors use a changing/decaying learning rate? If so please elaborate and specify the strategy used. If not, the learning rate should be constant.

Comment 2:
Lines 406-409: “executing the code repeatedly for 35 images”. what do you mean? Also, what specifically is scaled up for evaluating the confusion matrix? What does “scaled up” mean?

Comment 3:
Please consider describing more carefully the research question, the rationale as well as the analysis pipeline.
Validity of the findings: The impact of the method is not clearly stated. Also, the conclusion statement should be linked to the initial research question.
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: SELF-LEARNING NETWORK-BASED SEGMENTATION FOR REAL-TIME BRAIN M.R. IMAGES THROUGH HARIS
Review round: 4
Reviewer: 1

Output:
Basic reporting: No comment
Experimental design: No comment
Validity of the findings: No comment
Additional comments: Thank you for taking into consideration my comments. The manuscript presents a novel segmentation architecture, however, please check again grammar and spelling mistakes.

* "to address noises in the original MRI images" -> "to address noise..."

* "renewable avenue"  -> What do you mean?

* "trains and validating with ground facts" -> What do you mean?

* “layer is driven  by”: Do the authors mean consists of or comprise?

* "which are slides across the original input image" -> slides of the image?

* "Over numerous rounds, the technique is repeated over multiple iterations to identify all the key features in the input image" -> What do the authors mean? Does this refer to the training epochs?

* "The adaptive structural similarity index(ASSI) approach" -> Is this a metric and not an approach?

* "Overfitting occurs ...., resulting in improper categorization of the abnormal area due to an abundance of data". -> Why this is happening? Please elaborate on that, otherwise please rephrase.

* "The hyperparameters like the learning rate that are presented in the current study are at an acceptable level after 43 epochs, and other parameters like the training and validation studies state that the model fine-tuned” -> What do the authors mean by learning rate is at an acceptable level?

From the technical perspective: what happens if unlabeled data further used as new training samples are misclassified? Also, what is the minimum amount of acceptable labelled data to initially train the model?

minor: I do suggest the use of "MR images" instead of MRI images since "I" corresponds to imaging.


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: SELF-LEARNING NETWORK-BASED SEGMENTATION FOR REAL-TIME BRAIN M.R. IMAGES THROUGH HARIS
Review round: 5
Reviewer: 1

Output:
Basic reporting: no comment
Experimental design: no comment
Validity of the findings: no comment
Additional comments: All comments have been properly addressed. I would appreciate if authors will elaborate a little bit on the following (included in Peerj_Rebuttal.docx) since all of my comments highlighted specific parts of the document with text that was copied from the original manuscript.

"...The recommendation of the reviewer seems to be misplaced with other manuscript of peerj publications. Hence majority of the comments seems to be irrelevant to the current 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: SECURE BIOMETRIC AUTHENTICATION WITH DE-DUPLICATION ON DISTRIBUTED CLOUD STORAGE
Review round: 1
Reviewer: 1

Output:
Basic reporting: Grammar and typo mistakes are there.
Experimental design: no comment
Validity of the findings: no comment
Additional comments: Introduction should be revised: (1) Introduce the problem (2)discuss about some of the existing solutions (3)identify the gap or scope of improvement (4) discuss in order to address the identified gaps what is the methodology used  (5) list out the contributions.
Literature review should have a table to summarize research gap in each of the existing solutions.
Literature review/Introduction should include recent references such as:
An enhanced approach for three factor remote user authentication in multi - server environment
A Review of Machine Learning Algorithms for Cloud Computing Security
Enabling Secure Authentication in Industrial IoT with Transfer Learning empowered Blockchain

Most figures are of poor quality. What's the use of Fig. 1?
Can Fig. 3 and Fig. 4 be integrated to depict the proposed work? Also, explain the same step-by-step.
All graphs should be uniform and analyzed in detail.
Conclusion should not reflect Abstract and should conclude the work.


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: SECURE BIOMETRIC AUTHENTICATION WITH DE-DUPLICATION ON DISTRIBUTED CLOUD STORAGE
Review round: 1
Reviewer: 2

Output:
Basic reporting: A biometric authentication scheme is proposed for the requested users to give access permission in a cloud-distributed environment and, at the same time, alleviate data redundancy. To achieve this, a cryptographic technique is used by service providers to generate the bio-key for authentication, which will be accessible only to authenticated users. A Gabor ﬁlter with distributed security and encryption using XOR operations is used to generate the proposed bio-key (biometric generated key) and avoid data deduplication in the cloud, ensuring avoidance of data redundancy and security.
Experimental design: Satisfactory.
Validity of the findings: Satisfactory.
Additional comments: 1. Check the paper thoroughly for grammatical mistakes. There are some grammatical errors which have to be rectified.
2. What are the main contributions of the current work?
3. The literature survey section can be summarized as a table.
4. Some of the recent and relevant works such as the following can be discussed in the paper: "Load balancing of energy cloud using wind driven and firefly algorithms in internet of everything, A systematic review on clone node detection in static wireless sensor networks, IMCFN: Image-based malware classification using fine-tuned convolutional neural network architecture".
5. A detailed analysis on the results achieved has to be discussed in the results section.
6. Discuss the limitations of the current work in conclusion.


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: SECURE BIOMETRIC AUTHENTICATION WITH DE-DUPLICATION ON DISTRIBUTED CLOUD STORAGE
Review round: 1
Reviewer: 3

Output:
Basic reporting: - Please highlight the contribution clearly in the introduction
-The author should highlight the contribution clearly in the introduction and provide a comparison note with existing studies.
- Some Paragraphs in the paper can be merged and some long paragraphs can be split into two.
- The quality of the figures can be improved more. Figures should be eye-catching. It will enhance the interest of the reader. e.g., "Figure 1. Security development stages", "Figure 3. Overview of the proposed architecture"
- Authors should add the most recent reference:
1) https://ieeexplore.ieee.org/abstract/document/9316223
2) https://ieeexplore.ieee.org/abstract/document/9359538
3) A. Naeem, A. R. Javed, M. Rizwan, S. Abbas, J. C. -W. Lin and T. R. Gadekallu, "DARE-SEP: A Hybrid Approach of Distance Aware Residual Energy-Efficient SEP for WSN," in IEEE Transactions on Green Communications and Networking, doi: 10.1109/TGCN.2021.3067885.

- please give a proofread check to the paper.
Experimental design: No comments
Validity of the findings: - What are the computational resources reported in the state of the art for the same purpose?
- What are the evaluations used for the verification of results?
- Clearly highlight the terms used in the algorithm and explain them in the text.
Additional comments: - Please cite each equation and clearly explain its terms.


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: SECURE BIOMETRIC AUTHENTICATION WITH DE-DUPLICATION ON DISTRIBUTED CLOUD STORAGE
Review round: 2
Reviewer: 1

Output:
Basic reporting: In this article, a biometric authentication scheme is proposed for the requested users to give access permission in a cloud-distributed environment and, at the same time, alleviate data redundancy. To achieve this, a cryptographic technique is used by service providers to generate the bio-key for authentication, which will be accessible only to authenticated users. A Gabor filter with distributed security and encryption using XOR operations is used to generate the proposed bio-key (biometric generated key) and avoid data deduplication in the cloud, ensuring avoidance of data redundancy and security.
Experimental design: Satisfactory.
Validity of the findings: Satisfactory.
Additional comments: The authors have addressed all the comments. I accept the paper in its present form.


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: SECURE BIOMETRIC AUTHENTICATION WITH DE-DUPLICATION ON DISTRIBUTED CLOUD STORAGE
Review round: 2
Reviewer: 2

Output:
Basic reporting: The authors have addressed my suggestions. I would like to accept this paper.
Experimental design: The authors have addressed my suggestions. I would like to accept this paper.
Validity of the findings: The authors have addressed my suggestions. I would like to accept this paper.
Additional comments: The authors have addressed my suggestions. I would like to accept this 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: ACTUATOR BEHAVIOUR MODELLING IN IOT-FOG-CLOUD SIMULATION
Review round: 1
Reviewer: 1

Output:
Basic reporting: The paper well written and presented. The related works are considered. The paper structure and figures are adequate.

However, the paper do not clearly explain why the proposed simulator is better than previously implemented Edge/Fog simulators.
Experimental design: 1. The validation methodology is not presented at all. This is essential for the simulation paper.

2. Why authors use Java to implement their simulator?!?! Java is a very bad choice for suimulators since it has a poor performance compared to C/C++.
Validity of the findings: Unfortunately, authors do not validate the accuracy of their simulator. It is impossible to check if the simulator works or not.
Additional comments: In this paper, authors present a new simulator, DISSECT-CF-Fog, to model operating costs and latencies for Cloud/Edge/Fog infrastructures. Even though the paper is well written and presented, I think the provided results and conclusions cannot be verified since authors do not provide any validation methodology or validation results for their simulator.

I understand that there are a lot of papers that present similar simulators without providing any justification for their accuracy. However, I think that such papers cannot be considered as scientific since the presented experimental results and conclusions are not verifiable (see works from Karl Popper).

Overall, I have the following major concerns:

1. There is no any validation/verification chapter which clearly explains how authors verified their simulator.

2. Authors do not clearly explain how DISSECT-CF-Fog differs from previous simulators. Why is it better?

3. I do not understand why authors used Java for a simulator!!! This is obviously a wrong choice given the fact that Java was not designed for high performance computing. Why authors did not use C/C++?


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: ACTUATOR BEHAVIOUR MODELLING IN IOT-FOG-CLOUD SIMULATION
Review round: 1
Reviewer: 2

Output:
Basic reporting: See General comments for the author
Experimental design: See General comments for the author
Validity of the findings: See General comments for the author
Additional comments: This paper proposes an extended version of DISSECT-CF-Fog to support actuators and mobility and analyze their performances in terms of cost and execution time. It introduced a comparison to the existing simulators in terms of communication direction, actuator events, mobility, and position. Overall, the subject considered in the paper is topical and important.  The paper is properly organized, and relatively easy to follow. The general idea of the proposed solution is clear, but the authors should justify the motivation for the proposed solution. On the downside, there are loose points in the paper in which authors should address them in a revised paper. My concerns are listed below:

- Please improve the quality of the paper presentation (e.g., in the introduction, try to further emphasize the relevance of this work).
- Please consider performing multiple repetitions of the experiments (e.g., to perform statistical hypothesis testing) and to use a more complex scenario.
- What about the scalability problem? Does the proposed model work for large cloud data center and under any offered workload? In other words, if the number of IoT devices and servers increases, what is the impact on the performance of the proposed model?
- In the second use case, what will happen if we exceed 10,000 patients? Does the system performance not deteriorate?
- According to existing research work, to have reliable results, it is necessary to perform each experiment ten times and we take the average values instead of the three times that you have mentioned in the evaluation Section.
- Please present the limitations of your work.

Minor comments:

- There are grammatical errors and typos throughout the entire manuscript. For instance :
    o Add the figure number in the following sentence: "These kinds of actions are noted by gray dashed arrows in the figure".
   o Correct the word “actuator” in Table 2.
- The English used in the paper is not bad. However, it will be important to revise the English well to further improve 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: ACTUATOR BEHAVIOUR MODELLING IN IOT-FOG-CLOUD SIMULATION
Review round: 2
Reviewer: 1

Output:
Basic reporting: no comment
Experimental design: no comment
Validity of the findings: no comment
Additional comments: This version has well addressed all 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: ACTUATOR BEHAVIOUR MODELLING IN IOT-FOG-CLOUD SIMULATION
Review round: 2
Reviewer: 2

Output:
Basic reporting: The paper provides an overview on an extension of prior work, the implementation of an "actuator" (subscriber) model for the standalone IoT/Fog simulator DISSECT-CF-Fog.

The storyline of the paper requires some improvements, to make the paper more objective (in the abstract, in introduction).

The abstract needs to be simplified and present concrete contributions and achieved results.

The terms used, sensor/actuator is misleading and should be revised. The authors address IoT environments, where there are, from an abstract perspective, publishers and subscribers. An actuator is a device that is used to manipulate the physical environment, e.g., temperature control.
While a consumer can be an actuator but also any other type of cyber-physical system. Or a software-based agent. If the authors truly want to model actuators, which type are they addressing? Linear, motors, relays, solenoids?

In related work, FogFlow is missing. Did the authors consider a comparison?
Also, the "mobility" feature should benefit from a more clear description. Which type of mobility is being covered? consumer? publisher? any node?

- A background section introducing DISSECT-CF-Fog should be added, as it would help to understand whether or not the contributions of this paper agains prior work are relevant.

- The implementation section currently provides a high-level specification of the actuator module. This should be changed to make the flow of the implementation clearer.

The decision on relying on random mobility (which has been shown not to be the best model in terms of simulations) needs to be justified.
Experimental design: - The experimental aspects described in section 4 needs to be made more objective, avoiding claims such as relying on two of the most frequently applied use-cases - do the authors have a proof on this?
- Several papers debate scenarios for Fog relying on different simulators. One of the problems with the experimental design are the design choices, such as selecting latency to be between 10 ms and 20ms  due to fog node locality/co-placement. The authors should justify the choice of parameters, or propose parameters that can provide a better understanding of the variability impact, creating scenarios with values for "low", "medium", "high" delay, etc. Similar remarks go to other parameters, such as node cost.
- a patient, in the simulations, stands exactly for what? 1 data flow, VBR or CBR? What is the size of the packets sent? The overall load is given, but what is the impact in terms of patients served? Similarly for all parameters described in Table 5.
- for the large-scale experiments, where are the results?
Validity of the findings: - the value-add of the proposed extension is not clear, as there was no comparison against a specific benchmark.
- the experimental design impacts the overall validity, as the selection of parameters seems to have been done in an ad-hoc way.
- Not clear what is the value-add for mobile scenarios, and the introduction states that the extension has been developed for an Internet of Mobile things...
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: ACTUATOR BEHAVIOUR MODELLING IN IOT-FOG-CLOUD SIMULATION
Review round: 3
Reviewer: 1

Output:
Basic reporting: English should be polished a little more...E.g.:
- data is a plural noun
- of DISSECT-CF-Fog strongly build -> of DISSECT-CF-Fog is strongly built
- .By -> . By

Other issues seem to be solved
Experimental design: the raised issues seem to be solved
Validity of the findings: the raised issues seem to be solved
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: BOOLEAN LOGIC ALGEBRA DRIVEN SIMILARITY MEASURE FOR TEXT BASED APPLICATIONS
Review round: 1
Reviewer: 1

Output:
Basic reporting: 1. There are so many errors and ambiguous expressions. The paper needs to further improve the language.
2. The structure should be further improved.
3. The motivation and contribution are not clear and enough. The necessity and effectiveness of your proposed approach should be highlighted. The literature references should be added.
4. The related work is not comprehensive. This part should review the latest research progress and the related information. The details of the different measurement methods are not related work, and they should be in the basic methods.
Experimental design: 1. The experimental design flow chart can be supplemented to improve the readability of this paper.
2. The experimental results should be added quantitative comparative analysis, not only which method is better.
20. The detailed discussion should be further added.
Validity of the findings: 1. The novel similarity measurement method is one of the hot topics in the current text mining. The research is meaningful, but the motivation and contribution need to be further highlighted in this paper.
2. The experimental results' description needs to be supplemented to verify the validity of the findings.
Additional comments: A new similarity measurement method based on Boolean logic algebra (BLAB-SM) is proposed to calculate the distance between texts in this paper. Comprehensive experiments have been conducted in two datasets to show the new method's superiority in seven measures. However, there are still some problems that need to be improved.
1. The motivation and contribution are not clear and enough. The necessity and effectiveness of your proposed approach should be highlighted.
2. The related work is not comprehensive. This part should review the latest research progress and the related information. The details of the different measurement methods are not related work, and they should be in the basic methods.
3. Why do you use TF/IDF for feature selection instead of N-gram, word2vec, and other methods?
4. Why do you choose the KNN classifier and K-means clustering method? Please make a supplementary statement.
5. There are some expression and description errors in this paper, e.g., line 20, lines 22-25, lines 38, lines 163-177.
6. The abbreviation should follow the full name, followed by a detailed description of the method improvement section, e.g., line 101, STB-SM, line 105 SMTP, line 111 PDSM.
7. The details of the two vectors doc1 and doc2 should be added in line 121 to improve the readability of the following methods.
8. The variable descriptions of Pdoc1 and Pdoc2 should be added in line 155.
9. The formulas in 163-177 are poorly aligned.
10. The 'Where' in lines 179, 245, 422, 430 should be 'where'.
11. In line 180 'the variable var', where is this variable?
12. In line 240, the same expressions can be removed.
13. In line 244, the formula should be rewritten to be consistent, e.g., the bracket (), ti, tj and tj. Please modify the similar expressions in the full manuscript, e.g., lines 254-257.
14. There are so many formulas and variables description should be revised.
15. Please check lines 261-264, and lines 284-289, e.g., 2x in line 263, 2x in line 289. What's the meaning of x, is variable or multiplication?
16. In 323 f2 X f4, X should be the multiplication, not X.
17. The formula number should be added.
18. There are many TF/IDF, TF-IDF, and TFIDF, and they should be revised to the same expression.
19. The experimental results should be added quantitative comparative analysis, not only which method is better from line 470 to line 591.
20. The discussion should be further added in line 625.

The figures and tables are very important and should be shown directly in the manuscript as well. I think a complete manuscript is more conducive to review.


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: BOOLEAN LOGIC ALGEBRA DRIVEN SIMILARITY MEASURE FOR TEXT BASED APPLICATIONS
Review round: 1
Reviewer: 2

Output:
Basic reporting: The article should include sufficient introduction and background to demonstrate how the work fits into the broader field of knowledge. Relevant prior literature should be appropriately referenced.
Experimental design: The submission should clearly define the research question, which must be relevant and meaningful. The knowledge gap being investigated should be identified, and statements should be made as to how the study contributes to filling that gap.
Validity of the findings: The findings of the paper is not validated effectively.
Additional comments: The problem being studied in the paper is basically important however the contribution of the paper is not enough.


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: BOOLEAN LOGIC ALGEBRA DRIVEN SIMILARITY MEASURE FOR TEXT BASED APPLICATIONS
Review round: 1
Reviewer: 3

Output:
Basic reporting: I read the paper with interest although I must admit that at times it was difficult for me to follow due to frequent grammar and spelling issues. Below, for purposes of illustration, I list the spelling and grammar issues I spotted in the abstract alone.
Replace “information retrieval (IR)” with “the information retrieval (IR)”
Replace “data mining (DM) and” with “data mining (DM), and” (i.e. introduce a serial comma).
Replace “the similarity measures” with “similarity measures”
Replace “core stone” with “cornerstone”
Replace “algorithms upon which their performance is completely dependent” with “algorithms. Indeed their performance is completely dependent on these measures.”
Replace “is still in the full swings” with “is still uncompleted”. Perhaps rather than state that the quest is still ongoing, it may be stronger to point to what is wrong with existing similarity measures (i.e. arguments pertaining to their inefficiency and lack of effectiveness).
“Fastest runt time”. Fastest relative to what?
Replace “and K-means” with “and the K-means”
The sentence starting with “Using TF-IDF” is long and difficult to follow. I would suggest breaking this up into two.
Replace “Obtained results illustrate” with “Our results indicate”
Delete “, as a promising measure,”
Replace “has told that” with “establishes that”
Replace “: (1) BLAB-SM, as the most efficient and significantly-effective similarity measure, could replace state-of-art measures for text-based applications” with “BLAB-SM is not only more efficient but also significantly more effective than state-of-the-art similarity measures.
Based on the above (and my reading of the manuscript itself), my first recommendation is to have your paper thoroughly proofread and edited for readability by a native speaker. As a further general style recommendation, I would recommend the authors please try to refrain from extreme statements “perfect techniques” (line 43), “indisputably” (line 647) or self-congratulatory remarks “results are drawn elegantly” (line 87), as in my experience such wording may lead the critical reader to want to find ways to disagree with that which is being claimed. Try to aim for a more neutral tone. Please also write out all acronyms in full the first time they are mentioned (e.g. SMTP on line 105).
Before initiating the review of similarity and distance measures in the related work section (line 91), it would be useful to the reader to introduce a brief section detailing the various criteria on which the quality of similarity measures can be gauged and compared, before systematically reviewing the strengths and weaknesses of each along these criteria. All the claims about effectiveness and efficiency in this section are currently difficult to interpret because either it is unclear how effectiveness was operationalized, what other similarity measure(s) the focal measure is being compared to, or both (e.g., line 105: “shown highly effective when run against some similarity measures” is quite ambiguous to me).
Furthermore, I would suggest integrating the material in lines 93-122 in the paragraphs that follow, so that each of these paragraphs introduces the focal measures, elaborates how each is computed and discusses the evidence for the effectiveness and efficiency of each. It may also help to introduce a table in which each of these measures can easily be compared to one another.

Line 259 and line 266: Please replace “worst case” with something like “perfect dissimilarity”. Dissimilarity in my mind does not have to be a bad thing. Likewise, on p . 283 replace “the best case” with “Perfect similarity”
Experimental design: Other than stating on line 50 that little is known about the run time efficiency of existing measures, the introduction and related work section does not make a particularly compelling case for why we need yet another similarity measure. That being the case I felt the manuscript in its current form does not really succeed in establishing a knowledge gap.

Indeed, the fact that run time has not been studied cannot be used as input to the conclusion that such run times are bad. What are the gains to be expected from a similarity measure that is more effective and efficient? What exactly is the huge problem that the introduction of a new measure is going to resolve?

I feel that demonstrating how existing measures are suboptimal, along the lines I outline above (and beyond) would really strengthen the case for introducing BLAB-SM.

When it comes to the properties described as of line 296, please clarify at the outset that these properties complement one another and that one property may serve to decrease similarity while the other is simultaneously increasing it (for instance on the face of it property 1 and 2 oppose one another). To further clarify this, perhaps it may help to start the description of each property with “All else remaining the same, …”. Please also reference each property to the extant literature. Assuming I am understanding correctly, I think it would be clearer to frame property 3 in terms of the number of features that are present in both vectors. I did not understand property 6. Finally, I was missing a property pertaining to the scaling of the similarity measure. Clearly, a similarity measure must be bounded for it to be useful and interpretable across studies. Furthermore, I would expect a similarity measure to be independent of the number of features and documents (N), so that I can compare similarities obtained from different corpora.
Validity of the findings: Tables 1-3  (see p. 59-63) appear to be incomplete, in that I would expect to see all four possible combinations of A and B. Also please replace “table truth” with “truth table” in the title.

In the section on Machine Description, I found it curious the authors rely on software (Windows 7) that has been discontinued for some time now.
To generate the training and testing data (see line 281), I would recommend using k-fold cross-validation. Currently, it is unclear to me what the authors mean with “was divided individually and then combined as training and testing data.” Despite the data being well known, I would recommend including references to both datasets. Furthermore, and at minimum, I would like to see more detail on the datasets, in terms of size, origin, and descriptive data.
I would have liked to read more about why the authors decided to use KNN and K-means to validate BLAB-SM. Why these methods and not others? Furthermore, in the section entitled Experimental setup, I was missing a description of the KNN algorithm. Here at minimum, I would have liked to read a little more about what the classes were for each dataset, and how they were balanced.

In the section on the merits of BLAB-SM (line 898), I thought it may be worthwhile to reflect more on the merits defined in terms of reductions in energy consumption if BLAB-SM were to be adopted on a large scale.
Additional comments: Thank you for giving me the opportunity to review your paper, entitled “Boolean logic algebra-driven similarity measure for text-based applications”. The paper presents a novel similarity measure, that may augment or even replace existing measures because of its competitive efficiency and effectiveness metrics. I also appreciated the fact that the authors chose to focus on a problem as fundamental as defining a new similarity measure. I hope the authors will find my review useful in further strengthening their work.


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: BOOLEAN LOGIC ALGEBRA DRIVEN SIMILARITY MEASURE FOR TEXT BASED APPLICATIONS
Review round: 2
Reviewer: 1

Output:
Basic reporting: 1. There are also some minor errors in the paper, for example, Line 38 information retrieval (IR), data mining (DM), and machine learning (ML). They should be the same as the content in Abstract.
2. Line 238-250, 349-352, 375-379, 454-455, the format of these formulas are so bad. So please carefully revise the whole paper.
3. The whole structure still has a problem and not clear enough, especially the experimental results.
Experimental design: 1. The structure of the experimental result is still needed to improve and make it more clear.
Validity of the findings: That's enough.
Additional comments: Please revise the whole paper and further improve the structure of the experimental results.


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: 1

Output:
Basic reporting: No comment
Experimental design: No comments
Validity of the findings: No comment
Additional comments: Authors need to add number of cases and death tolls
Authors need to add a paragraph on the increase in cases despite massive vaccination globally
A summary table of related studies showing number of dataset use, where it's obtain (I.e. online or hospital), model use (Resnet, AlexNet, GoogleNet etc) and result interms of sensitivity, AUC, accuracy etc and reference is required.
A flow chart can be added to the methodology section in terms of data collection, processing, data split, performance evaluation etc.
Figure 5, 7, 9, 11 can be move to supplementary file since they are listed in the tables.
Authors need to provide sound and scientific reason why they choose 60-40 for training and validation or testing. Some of the studies vary data partition (60-40, 70-30, 80-20, 90-10)


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: 2

Output:
Basic reporting: The English of this manuscript is okay but should be polished.
Please add some methodological details to the abstract section. Which parts are novel?
COVID studies are being studied intensely. The advantages over the current situation should be explained in support of the literature.
Contributions must be rewritten.
Related works section should be supported with a table.  Also add please: Coronavirus (covid-19) classification using ct images by machine learning methods, Coronavirus (covid-19) classification using deep features fusion and ranking technique, Classification of Coronavirus (COVID‐19) from X‐ray and CT images using shrunken features, Classification of COVID-19 in Chest CT Images using Convolutional Support Vector Machines.
Also manuscript structure shuld be improved.
Experimental design: The novelty of the methods should be presented more clearly in the methodology section.
Two novel CNN architectures are proposed. However, this novelty is not mentioned in the method section. In this case, the method section is almost nonexistent. Please include a method section that highlights and proves the novelty.
Validity of the findings: More COVID datasets should be used to validate the proposed model.
The proposed method should be compared with the state-of-the-art.
Additional comments: Two novel CNN architectures are proposed. However, this novelty is not mentioned in the method section. In this case, the method section is almost nonexistent. Please include a method section that highlights and proves the novelty.