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: SPAMMER GROUP DETECTION AND DIVERSIFICATION OF CUSTOMERS’ REVIEWS
Review round: 1
Reviewer: 2

Output:
Basic reporting: This paper presents a study on spam review identification and review diversification. Solving these two tasks will help improve customer experience for making better informed purchasing choices. The paper presented a Spammer group detection (SGD) method, which involves three stages: 1) construct a co-reviewer graph; 2) cluster reviewers using Structural Clustering Algorithm for Networks (SCAN); and 3) identify spammer groups using eight spammer behavioral features. Review diversification was designed to display reviews representing different sentiments and product/service features. Experiments were conducted on two datasets in different languages, English and Urdu.

In the introduction section, the paper gave a nice overview of the SGD method. It would be better if the authors could enrich the description about the review diversification part (line 93-95). Similarly, in literature review, the paper presented existing clustering methods and their comparison with a proposal for review spammer detection. It would be better if a comparison between the proposed review diversification method and existing ones (line 185-189).

Section 4.2 and Figure 4 presented that the review diversification method used features. However, it is unclear what the features were. Were these features word-based features (from looking at the excel sheet provided in the supplemental material)? It is good to see the authors provided an excel sheet showing the features for the Daraz dataset. It would be better if the authors can describe in details how they used the features in the paper.
Experimental design: The paper provided detailed parameter analysis of tuning different deep learning models. However, it is unclear to me why the authors introduced deep learning models for evaluation and how the deep learning models were used to evaluate the SGD method. Were spammer scores described in Section 4.1.3 used as the ground truth labels which were further used for training and testing the deep learning models?

In Section 4.1, SGD needs several parameters, e.g., threshold of review time and threshold of review rating (Equation (2)), minimum number of neighbors (Equation (3)), threshold of review time (Equation (5)), and threshold of spammer group identification (line 358-359). The paper mentioned those parameters were determined through experimental evaluations. However, it is unclear how those parameters were tuned.

For the review diversification evaluation, the results could be more convincing if they are compared with existing methods described in the literature review section similar to what the authors did for spammer group detection comparison.

Please provide the reference of the standard evaluation measure for diversification “divScore” (line 551-552).
Validity of the findings: Thanks for providing the dataset. However, it is unclear how the datasets provided in the supplemental material were used for experiments. There are few places in the paper mentioning the dataset statistics, e.g., line 202-203, line 507-508, line 518-520, and line 566-572. However I found it is difficult to match those statistics against those in the excel sheets provided in the supplemental material. The Yelp dataset for review diversification is missing.

Were the reviews in Figure 1 obtained before and after running your diversification algorithm? If not, could you provide examples before and after you run your review diversification algorithm? How does your algorithm help to present a comprehensive overview of a product or service features?
Additional comments: This paper presents a study on spam review identification and review diversification. The paper presented a Spammer group detection (SGD) method, which involves three stages: 1) construct a co-reviewer graph; 2) cluster reviewers using Structural Clustering Algorithm for Networks (SCAN); and 3) identify spammer groups using eight spammer behavioral features. Review diversification was designed to display reviews representing different sentiments and product/service features. Experiments were conducted on two datasets in different languages, English and Urdu.

Solving these two tasks will help improve customer experience for making better informed purchasing choices. The paper is easy to follow. The paper could be stronger if more descriptions and experiments on the review diversification part, descriptions on parameter setting for SGD, and more descriptions on how much data are used for each experimental settings are provided,.


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: SPAMMER GROUP DETECTION AND DIVERSIFICATION OF CUSTOMERS’ REVIEWS
Review round: 2
Reviewer: 1

Output:
Basic reporting: Thanks for the authors for updating the paper and uploading the datasets. The revised version and rebuttal letter read well to me and clarify most of my questions . I only have two comments. Please see them below.
Experimental design: The deep learning models are used to evaluate the accuracy of review spam detection model. Why do you use deep learning models for evaluation? In my opinion, you should use the ground truth --- the spammer scores described in Section 4.1.3 for evaluation. Is it because you have limited ground truth data? Then you will need to rely on an automatic metric to approximate the ground truth. How much ground truth data do you have? You use the ground truth to train your deep learning models right? What are the performance of the deep learning models? If the deep learning model performs not well, it is not a good approximation of ground truth. If the deep learning model works well, why don't you just use them for spammer detection, as you mentioned they have advantages of using non-handcrafted features.
Validity of the findings: The size for the Daraz dataset should be 3,923. Why did you select only one single product for the review diversification task in Section in Section 5.2? Your proposed method would be better justified and generalized by using reviews from more products.
Additional comments: The authors have provided arguments about using SGD for spammer detection and deep learning models for evaluation answering #4 question in my previous review. However, it is unclear how both methods perform against the ground truth data. Please see my comments at the "experimental design" section. For the review diversification experiments in Section 5.2, it would be good if the authors could provide results on more products but not just one project. I hope the authors could address these two questions in the next version of 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: CYBER-ATTACK METHOD AND PERPETRATOR PREDICTION USING MACHINE LEARNING ALGORITHMS
Review round: 1
Reviewer: 1

Output:
Basic reporting: The paper utilises different Machine Learning techniques to predict the attack method and attacker. A dataset from of attacks (2015 - 2019) is used.
The paper in its current form suffers from these shortcomings that the authors should consider.

The paper is well written and easy to follow.
The English language needs some minor revisions and edits.
The authors provide their code and data.

1- The related work is missing context and insights. This section is a group of relevant papers, however, there is no link or discussion between them.
The authors report multiple studies that do similar work, however, no insights are reported. The authors are advised to give the reader some context to understand their approach afterwards.

2- In the abstract, the authors should stress on the problem and what they are trying to solve, how they solve it, etc. The dataset should also be mentioned. In line 25, SVM with a linear kernel is said to be the best, however, the authors do not provide any discussion about this. What exactly is being predicted.

3- Table 2 should be moved to the related work section.

4- It is advised that the authors be more precise when reporting other people research. What dataset do they use? What are they predicting/detecting? etc.

5- Some references are old. For example in line 36, a recent supporting reference about UK and US figures is needed.
Lines 70-73 require a supporting reference. The paper claims that social engineering attacks are the most difficult. Is this based on their findings? Is it in one place or is this a global finding?

6- In Figure 1, are these insights based on the dataset? If so, the authors are advised to make this clear. Also, why is there a peak in 2017? Were measures put in place to reduce the damage in 2018 and 2019?
Experimental design: 1- The aim and objective of this paper are vague. I recommend that the authors clarify the problem they are tackling. Also, who will this model benefit and how? Placing this paper in the wider research context is required.

2- The dataset is comparably small (~900 records). Is there any way the validate the model on a larger dataset? Also, I was expecting validating the model on a benchmark dataset.

3- The authors are advised to add a description of how the dataset is generated.

4- In the dataset, did the authors consider overlapping attacks? what about cases where multiple methods are used? How is this handled?

5- The authors provide the background of each of the algorithms they use, however, no discussion/investigation of the results is provided. Not enough details of the experiments are mentioned in the paper.
Validity of the findings: 1- The paper barely discusses the results. The authors are advised to provide more results and comment on them. Also, the limitations of the work are not mentioned in the paper. I recommend adding a section for limitations and discussion.

2- The authors claim that they predict the perpetrator, however, the dataset comprises 'Known/Unknown' categories. how is this prediction evaluated?

3- Label encoder is good when encoding ordinal data, however, since some of the columns comprise nominal data, One-Hot encoding is better to use in this case.
The year should be dropped from Table 1, the authors do not use it.

4- In lines 269-282, how are the results comparable? The paper says that its performance is better than seven studies. However, the datasets are not the same and the parameters are different, how is the comparison conducted The authors are advised to recheck this and validate their findings.
Additional comments: 1- In lines 59 - 62, the authors should refer to the sections and not chapters.

2- The authors do not consider any discusses IoT attacks in section I.

3- In line 92, it should be 78.9\%.

4- In Table 2, it would be beneficial to add the year and the country. Also, adding features relations would add to this table.


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: CYBER-ATTACK METHOD AND PERPETRATOR PREDICTION USING MACHINE LEARNING ALGORITHMS
Review round: 1
Reviewer: 2

Output:
Basic reporting: In this paper, the analyze cyber-crime in two different models with machine-learning methods and predict what effect the determined features have on the detection of cyber-attack method and the perpetrator. In their approach, it is seen that the Support Vector Machine Linear is the most successful in the cyber-attack method with 95% of accuracy. The Logistic Regression is the leading method in detecting the attacker with a rate of 65.4% of accuracy. The results reveal that the higher the level of victim’s education and income are, the less the probability of cyber-attack is.
Experimental design: The Support Vector Machine Linear is the most successful in the cyber-attack method with 95% of accuracy. The Logistic Regression is the leading method in detecting the attacker with a rate of 65.4% of accuracy. The results reveal that the higher the level of victim’s education and income are, the less the probability of cyber-attack is.
Validity of the findings: Good
Additional comments: 1. The main objectives and contributions are to be highlighted in the paper.
2. The related works section should be summarized by the gaps identified in the existing literature and how the authors are going to address them in this paper (atleast one of them).
3. Some of the recent and relevant works such as the following can be discussed in the paper:
a) An effective feature engineering for DNN using hybrid PCA-GWO for intrusion detection in IoMT architecture
b) A Novel PCA-Firefly based XGBoost classification model for Intrusion Detection in Networks using GPU
c) A Review on Cyber Crimes on the Internet of Things
4) The authors should justify why they chose the machine learning algorithms they have used in the paper.


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

Input:
Title: CYBER-ATTACK METHOD AND PERPETRATOR PREDICTION USING MACHINE LEARNING ALGORITHMS
Review round: 2
Reviewer: 1

Output:
Basic reporting: - I thank the authors for their efforts in modifying the paper and replying to the concerns.
- The authors improved the language of the paper, however, I recommend using shorter sentences and having another round of language revision.
Experimental design: - I thank the authors for reporting other matrices which better demonstrate the performance of their proposed model.
- In section III.B, the authors mention the use of cross-validation with k = 3 and a split of 80% for training and 20% for testing. This is inconsistent, please revise.
- Reporting the training performance doesn't add much to the discussion. The authors should focus on validation and testing performance.
Validity of the findings: - It is unclear which fold is reported in Figure 7 and Figure 9.
- How are the models used simultaneously, the paper doesn't provide a hybrid model or an aggregation method for the eight models. The authors should review this claim.
- The authors should not use label encoder for nominal data
Additional comments: - There is an extra sentence in line 312 "formulas are used."
- I recommend improving the resolution of Figure 1. Also, is there ant relevance to the colours/grouping? This should be added to the text.
- The authors should add the labels (percentages) to Figure 6 and Figure 8 to improve readability.


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: CYBER-ATTACK METHOD AND PERPETRATOR PREDICTION USING MACHINE LEARNING ALGORITHMS
Review round: 2
Reviewer: 2

Output:
Basic reporting: The authors have addressed all my comments. The paper can be accepted for publication in its current form now.
Experimental design: Satisfactory
Validity of the findings: Satisfactory
Additional comments: The authors have addressed all my comments. The paper can be accepted for publication in its current form now.


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: CYBER-ATTACK METHOD AND PERPETRATOR PREDICTION USING MACHINE LEARNING ALGORITHMS
Review round: 3
Reviewer: 1

Output:
Basic reporting: The authors replied to my concerns.
Experimental design: The authors replied to my concerns.
Validity of the findings: The authors replied to my concerns.
Additional comments: I thank the authors for their efforts in modifying the paper and replying to the 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: SMART APICULTURE MANAGEMENT SERVICES FOR DEVELOPING COUNTRIES—THE CASE OF SAMS PROJECT IN ETHIOPIA AND INDONESIA
Review round: 1
Reviewer: 1

Output:
Basic reporting: I really enjoyed this article.  It is well done and clearly presented.  The last paragraph of text in the conclusion section seemed to have some formatting issues, but otherwise did not see any issues.
Experimental design: This was a case study, so there was not much in the way of experimental design. The project was presented and explained well and clearly, including acknowledging and address potential weaknesses.
Validity of the findings: Very nice as a case study.  Hard to question the validity of their expierence.
Additional comments: I very much enjoyed reading this 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: SMART APICULTURE MANAGEMENT SERVICES FOR DEVELOPING COUNTRIES—THE CASE OF SAMS PROJECT IN ETHIOPIA AND INDONESIA
Review round: 1
Reviewer: 2

Output:
Basic reporting: The paper presents a useful tool for developing beekeeping solutions.
The process of design/development was well described and defined.
Moreover, the authors also proposed a standard beehive, hopefully, useful for the two target countries. Other strengths of the work:
- Clear and unambiguous, professional English used throughout.
- Literature references, sufficient field background/context provided.
- Professional article structure, figures, tables.
Work weakness:
- Lack of field experimentation.
Experimental design: Strengths of the work related to experimental design:
- Original primary research within the aims and scope of the journal.
- Methods described with sufficient detail.
Work weakness
The authors must define a research question.
Validity of the findings: Conclusions are well stated, but not linked to the research question.
Additional comments: All text - Put the names of species os bee highlighted. Preferably use italics.
Line 58 - Please verify the Eva Crane format citation.
Line 111 - Witch other?
line 113 - API management.
Line 137 to 142 - please avoid the use of quotations.
figure 1 - the 'design solution' shouldn't be before 'producing solution'?
line 300 - Is it 200 € for each monitoring unit?
line 308 - Should be stored or was stored.
In figure 6 - what means the blue and red cubes in this figure? Please insert a subtitle for each element.
line 355 - Around 95% of ...
lines 478 to 484 - a problem in the draft formation.


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: SMART APICULTURE MANAGEMENT SERVICES FOR DEVELOPING COUNTRIES—THE CASE OF SAMS PROJECT IN ETHIOPIA AND INDONESIA
Review round: 2
Reviewer: 1

Output:
Basic reporting: The authors did a good work taking into account all comments and suggestions by the reviewers. I have no further comments for the Authors.
Experimental design: The authors did a good work taking into account all comments and suggestions by the reviewers. I have no further comments for the Authors.
Validity of the findings: The authors did a good work taking into account all comments and suggestions by the reviewers. I have no further comments for the Authors.
Additional comments: The authors did a good work taking into account all comments and suggestions by the reviewers.
I have no further comments for the Authors.


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: IOT-CCAC: A BLOCKCHAIN-BASED CONSORTIUM CAPABILITY ACCESS CONTROL APPROACH FOR IOT
Review round: 1
Reviewer: 1

Output:
Basic reporting: In this paper authors proposed IoT-CCAC based blockchain framework and results showed that IoT-CCAC approach gives promising results and a good fit for city and business network applications.

The paper is well written and easy to follow, however the novelty of the paper is limited.
Experimental design: Authors presented detailed description of  IoT-CCAC module but blockchain details are missing. Not sure which platform of blockchain is used by authors and why they chose only that. They have mentioned two blockchains (Ethereum private blockchain, Hyperledger Fabric).
Validity of the findings: The python code of the experiment is not attached and therefore hard to verify results. I would suggest either to publicly upload the code and instructions on github or attach here as supplementary file.
Additional comments: Overall the paper looks fine and well written. But it is important to verify the results presented by authors and it is only worth to accept the paper if experiments can be verified. Therefore I would suggest a major revision with above 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: IOT-CCAC: A BLOCKCHAIN-BASED CONSORTIUM CAPABILITY ACCESS CONTROL APPROACH FOR IOT
Review round: 1
Reviewer: 2

Output:
Basic reporting: The paper discusses the utilization of the blockchain technology for IoT access control.
The general presentation of the paper is good which makes it very to read since the main contributions are clearly highlighted.

I suggest adding some references to Table 1 and 3.
Experimental design: no comment
Validity of the findings: no comment
Additional comments: The authors presented in Table 3 a list of the classical DB's disadvantages. For instance the mutability, the point of failure,. ... etc. However what would be the general thoughts in the case of the use of  a nosql DB (or any other big-data implementation) where the database management system ensures a high degree of redundancy. and recovery.


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: IOT-CCAC: A BLOCKCHAIN-BASED CONSORTIUM CAPABILITY ACCESS CONTROL APPROACH FOR IOT
Review round: 1
Reviewer: 3

Output:
Basic reporting: While the article includes tables and graphs representing experiment results, authors do not provide raw data.
Experiment methodology should be described in greater detail, as further explained in the second part of this review.
Authors use clear and technically correct English, provide relevant references and article is structured in an acceptable format.
Experimental design: It is not clear if the measurement results presented were obtained from only one measurement or if they are an average of multiple runs. The label of Table 4 states numbers presented are an average of 100 transactions and lines 372-274 that four groups of 10, 50, 100 and 200 concurrent transactions were used. However, it is unclear if measurements for these four groups were repeated or run only once. The initial state of both local VM and online node used for experiment can have an impact on the accuracy of the results.
Security analysis is superficial, and although preconditions presented improve IOT-CACC model security, attack prevention mechanisms are not exhaustive. The statement on lines 359-360 does not specify what checks are made or even references the IOT-CACC authorization process provided in system architecture.
Validity of the findings: Findings presented are based on preliminary results and make it difficult to call them robust. Due to deficiencies mentioned in the second part of this review, it is hard to compare results with related work conclusively. As such, the statement on lines 389-391 requires more experiments for direct comparison.
Additional comments: IOT-CACC model presented is promising, but further investigation to support presented conclusions is required.


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: DELAY MINIMIZATION BASED UPLINK RESOURCE ALLOCATION FOR DEVICE-TO-DEVICE COMMUNICATIONS CONSIDERING MMWAVE PROPAGATION
Review round: 1
Reviewer: 1

Output:
Basic reporting: The research status in introduction and related works sections is insufficient. I suggest that more references should be supplemented and author should classify and summarize these works systematically.
Experimental design: no comment
Validity of the findings: no comment
Additional comments: This paper study the resource allocation problem in OFDM uplink communication. Comments as follows:
1. The reason why the proposed methods outperform other algorithms should be analyzed in the paper.
2. Inadequacies of the proposed methods should be described as well.
3. More suggestions and details should be added in future 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: DELAY MINIMIZATION BASED UPLINK RESOURCE ALLOCATION FOR DEVICE-TO-DEVICE COMMUNICATIONS CONSIDERING MMWAVE PROPAGATION
Review round: 1
Reviewer: 2

Output:
Basic reporting: See the comments to the authors.
Experimental design: See the comments to the authors.
Validity of the findings: See the comments to the authors.
Additional comments: The paper proposed a delay minimisation resource allocation method for D2D and CUE users in 5G based network uplink by considering delay bound value. The topic of this paper is interesting, however, the paper requires some revision in term of organisation and presentation. Please see my comments given below:

	1. Authors should rewrite the abstract by highlighting the background, research problem, methodology and important outcome of the proposed work. Many statements in the are unclear, I think proofread the manuscript from a professional proofreader will be helpful. For example " …….. it is proposed an approach considering multifractal traffic envelope …….".
	2. In abstract authors mentioned that they considered multifractal traffic
	envelope process and service curve, but I couldn't find much explanation about these two terms. If you are introducing some jargon, you need to explain them properly beforehand.
	3. The citation style looks inappropriate, for example, "Mishra et al. (2016)" should be "(Mishra et al., 2016)". Please check the correctness and revise accordingly.
	4. The paper requires a separate section for related works. The related work didn't cover the literature and state-of-the-art of the presented work appropriately. You need to add more very relevant and recent work.
	5. I suggest to add a notation table for the symbols used in the paper.
	6. Some text in Figure 2 is unclear, please revise. Adapted figures from some other paper might raise copyright issue, I hope you have already asked for permission from the respective bodies to reuse the materials.
	7. Why you have chosen MATLAB for conducting the simulation, justification is required.
	8. Figures are taking too much space, you can try to reorganise the figures so that you can use space efficiently.
	9. In the results and discussion section, you need to justify why the proposed method is performing better compared to the existing methods.


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: DELAY MINIMIZATION BASED UPLINK RESOURCE ALLOCATION FOR DEVICE-TO-DEVICE COMMUNICATIONS CONSIDERING MMWAVE PROPAGATION
Review round: 2
Reviewer: 1

Output:
Basic reporting: no comment
Experimental design: no comment
Validity of the findings: no comment
Additional comments: The authors failed to respond to one of reviewer's comments.  Inadequacies of the proposed methods are still not mentioned and possible solutions should be given as well.


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: DELAY MINIMIZATION BASED UPLINK RESOURCE ALLOCATION FOR DEVICE-TO-DEVICE COMMUNICATIONS CONSIDERING MMWAVE PROPAGATION
Review round: 2
Reviewer: 2

Output:
Basic reporting: no comment
Experimental design: no comment
Validity of the findings: no comment
Additional comments: Thank you for addressing all the suggested comments, I am happy with the revision.


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: DELAY MINIMIZATION BASED UPLINK RESOURCE ALLOCATION FOR DEVICE-TO-DEVICE COMMUNICATIONS CONSIDERING MMWAVE PROPAGATION
Review round: 3
Reviewer: 1

Output:
Basic reporting: no comment
Experimental design: no comment
Validity of the findings: no comment
Additional comments: The revised version can be accepted.


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 ENSEMBLE APPROACH BASED ON TRANSFORMATION FUNCTIONS FOR NATURAL GAS PRICE FORECASTING CONSIDERING OPTIMAL TIME DELAYS
Review round: 1
Reviewer: 1

Output:
Basic reporting: This paper presents a new ensemble approach based on transformation functions for natural gas price forecasting. Then, this approach was applied to the natural gas price dataset. With respect to the obtained results, the new technique is able to provide acceptable results in terms of accuracies. In this reviewer’s opinion, the idea of using a new ensemble approach based on transformation functions for natural gas price forecasting looks promising, but several concerns should be clarified for possible publication. Some comments are listed below.
Experimental design: The experimental results are not convincing. The author should explain how to choose algorithms to evaluate the proposed method.
Validity of the findings: The proposed method consists of several parts. In this reviewer’s opinion, an ablation study is necessary to verify the effectiveness of each part and to analyze the role of the main hyperparameters.
Additional comments: This paper presents a new ensemble approach based on transformation functions for natural gas price forecasting. Then, this approach was applied to natural gas price dataset. With respect to the obtained results, the new technique is able to provide acceptable results in terms of accuracies. In this reviewer’s opinion, the idea of using a new ensemble approach based on transformation functions for natural gas price forecasting looks promising, but several concerns should be clarified for possible publication. Some comments are listed below.
1.How to choose hyper-parameters such as learning rate? How about using the method for dynamic determination of learning rate (e.g using AdaGrad or Adam)?
2.How about the time complexity and space complexity of the proposed algorithm when compared with the previous methods?
3.It is recommended to add some details about how to prevent overfitting.
4.Generally speaking, deep learning methods are time-consuming. Please add some considerations about the computational load of the proposed method. Is GPU based method or Heterogeneous computing been considered while computing gradient?
5.The experimental results are not convincing. The author should explain how to choose algorithms to evaluate the proposed method.
6.What is the state-of-the-art method in this area? What is the performance of the proposed method when compared with the previous state-of-the-art method?
7. The proposed method consists of several parts. In this reviewer’s opinion, an ablation study is necessary to verify the effectiveness of each part and to analyze the role of the main hyperparameters.


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 ENSEMBLE APPROACH BASED ON TRANSFORMATION FUNCTIONS FOR NATURAL GAS PRICE FORECASTING CONSIDERING OPTIMAL TIME DELAYS
Review round: 1
Reviewer: 2

Output:
Basic reporting: - The article was written in English, good grammar, clear and unambiguous
- the article includes the introduction and background which demonstrate the way of work of the research with the relevant literature references
- the structure of the article is in accordance with the format.
-please make the resolution for these figures clearly: Figure1, Figure 2, Figure 3, Figure 4, Figure 5, Figure 6, Figure 7, Figure 8, Figure 9, Figure 11, and Figure 15.
- Please make the line of the graphic for the targets and outputs for Figure 12, Figure 14, Figure 16, Figure 17, Figure 18, Figure 19, Figure 20 are detailed and clear. My suggestion: make the shape of lines are extremely different such as:
______ outputs
-o-o-o-o Targets
so that, we can know the results of outputs and targets very clearly.
- the result of this study are good with the clear definitions and related to the theorems
Experimental design: this research is original and has the aims and scope of this journal
Validity of the findings: This is an interesting study of forecasting and useful.
Additional comments: this study forecast the natural gas price and I recommended accepting this article with minor revisions according to the three areas' 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: AN ENSEMBLE APPROACH BASED ON TRANSFORMATION FUNCTIONS FOR NATURAL GAS PRICE FORECASTING CONSIDERING OPTIMAL TIME DELAYS
Review round: 1
Reviewer: 3

Output:
Basic reporting: no comment
Experimental design: no comment
Validity of the findings: Fundamental information was not observed, for example
1. The process of selection for the mother wavelet function.
2. Details about data, origin, sampling, nature.
3. The forecast horizon, short-term, long-term.
4. The time lag selection.
On the other hand I have observed some errors, confused or incomplete information, among them:
The autor no define all variables in some equations, for example in 1 and 2, 12, 13
In equation 10 are not included the inputs of the MLP, however in text are described.

Finally, the manuscript contains many techniques, in the preprocessing stage and in forecasting stage, Wavelet+Fuzzy and MLP+RBF+GMDH+ANFIS+SVR, respectively, which accuracy results are similar.
I found a confussion in: figures 1, 2 and 10, section 3.8, because were created various models, however, line 374 and equation 18 do not  define properly the work that was done. I find that for me is difficult to identify the contribution of this manuscript.
Additional comments: Fundamental information was not observed, for example
1. The process of selection for the mother wavelet function.
2. Details about data, origin, sampling, nature.
3. The forecast horizon, short-term, long-term.
4. The time lag selection.
On the other hand I have observed some errors, confused or incomplete information, among them:
The autor no define all variables in some equations, for example in 1 and 2, 12, 13
In equation 10 are not included the inputs of the MLP, however in text are described.

Finally, the manuscript contains many techniques, in the preprocessing stage and in forecasting stage, Wavelet+Fuzzy and MLP+RBF+GMDH+ANFIS+SVR, respectively, which accuracy results are similar.
I found a confussion in: figures 1, 2 and 10, section 3.8, because were created various models, however, line 374 and equation 18 do not  define properly the work that was done. I find that for me is difficult to identify the contribution of this 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: AN ENSEMBLE APPROACH BASED ON TRANSFORMATION FUNCTIONS FOR NATURAL GAS PRICE FORECASTING CONSIDERING OPTIMAL TIME DELAYS
Review round: 2
Reviewer: 1

Output:
Basic reporting: The article is now in an acceptable state
Experimental design: The experimental design is reasonable
Validity of the findings: The conclusion is reasonable and sufficient
Additional comments: I have no more opinions


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 ENSEMBLE APPROACH BASED ON TRANSFORMATION FUNCTIONS FOR NATURAL GAS PRICE FORECASTING CONSIDERING OPTIMAL TIME DELAYS
Review round: 2
Reviewer: 2

Output:
Basic reporting: The article presents structure and clarity.
Experimental design: The experimental design has been detailed properly.
Validity of the findings: The findings may be of interest for forecasting researchers
Additional comments: The atention for arbitration observations have improved the article. I consider that there is a significant effort of authors because have used and compared different techniques to reach more accuracy in short term forecasting.


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: ENTROPY BASED C4.5-SHO ALGORITHM WITH INFORMATION GAIN OPTIMIZATION IN DATA MINING
Review round: 1
Reviewer: 1

Output:
Basic reporting: no comment
Experimental design: no comment
Validity of the findings: no comment
Additional comments: This paper describes a new classification algorithm to improve information management. The use of the SHO algorithm has a significant role to produce optimal values. This paper also explains that the proposed method has a better level of accuracy than the previous methods.
This paper is well written. Gap research is well presented. The proposed method can be described systematically. Unfortunately, there are only a few small errors but it is quite annoying, i.e., in Figure 1. The SHO algorithm is not well illustrated in the end. If this can be corrected, it will make this paper of higher quality.


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: ENTROPY BASED C4.5-SHO ALGORITHM WITH INFORMATION GAIN OPTIMIZATION IN DATA MINING
Review round: 1
Reviewer: 2

Output:
Basic reporting: The manuscript is overall well written and documented. However, I have the following comments:
Lines 53-56. I suggest to re-write these sentences as a reader may understand that sensitivity is the only important metric. Or that the models can easily predict the minority classes.

Lines 63-64. I suggest to modify the sentence as parameters are still needed for decision trees, e.g., purity, minimum number of samples in a node, etc.

Line 78-79. I suggest to move the sentence introducing decision tree to previous paragraph, lines 57-59.

Line 166. Define the acronyms before using them. What is RLBOR?

Paragraph 174. Sentence: “C4.5 is one of the best…”. Add references and justification to prove this affirmation.

Paragraph 179. Sentence: “The decision tree trained process is enormous and deep.” Please explain why, and compared to what?

Lines 184-187. Please clarify that these disadvantages (e.g., large data) may only happen on some applications. Decision trees are among the fastest models to train compared to other models (ANN, SVM, etc.).

Algorithm 1. First loop, “for i=1: number of data”, number of data samples? If so, should this not loop through the features? That is, calculate the information gain for each feature.

Algorithm 1. Second for loop is also confusing, should this not be a recursive process? That is, function calling itself twice with the left and right partition of the data.

Paragraph 188. I am not sure I understand the sentence “The current attribute node is one and…”.

Paragraph 188. Sentence: “It is necessary to perform supervised data mining on the targeted dataset”. Not sure I understand why is it necessary? For which applications? For deriving decision trees?

Line 194. Please indicate why is SHO better and why, compared to what? Add references.

Line 199. PSO abbreviation not defined. Also, add references to prove the remark.

Lines 243, 257, and 266. Fix the number of subsections. They all have 1.

Line 267. Can you please explain why Ray’s quadratic entropy is better? Compared to
what and why? Maybe this sentence can be stated after explaining the results from Table 6.

Figures 2 and 3. Please increase the resolution. Text is not readable.

The following are a few grammar suggestions.
1.	Use of comma before respectively, e.g., “x, y, and z, respectively.”
2.	Capitalize each letter after “.” punctuation mark. Example, line 133.
3.	Avoid starting sentences with the “It” pronoun. Although the contexts for most sentences are clear, others may be confusing for a reader. Lines 115, 124, 131, 140, 144, etc. For example, the sentence in paragraph 179: “It measures the information related to classification obtained on the basis of the same prediction”. Does this sentence refer to gain ratio or information gain? Also, in that same sentence, change “form” to “from”.
4.	Add a comma before “such as”. Lines 21, 32, etc.
5.	Use a comma before which when it introduces a nonrestrictive phrase. Don't use a comma before which when it's part of a prepositional phrase, such as “in which.”
6.	Please use ID3 abbreviation consistently. The manuscript has Id3 in different paragraphs.
7.	Data is plural, please replace “data is” by “data are” in the manuscript.
Finally, please ensure references follow the PeerJ reference style.
Experimental design: The experimental design is well performed. However, I have the next suggestions:

Equation 9. Why rand(0.7,0.9)?
Equation 11. Define all variables used, e.g., hl and hm.
Equation 12. Define hb.
Equation 16. Define hr.
Equation 17. Define Rr, i.e., the radius is calculated for each pray or pack of prays?
Equation 18. Define K and H.

I would suggest adding an additional paragraph and possibly a figure explaining how the SHO is implemented for the optimization of the gain. Moreover, fitness defined in equation 8 considers only the gain at a specific node. How is SHO used to optimize all gain for splitting non-terminal nodes? Is it required to run one SHO per non-terminal node?

Line 235. Although the manuscript mentions that cross-validation was used, there are no details about the tuning of the parameters for the different models. Please define if default parameters were used or if these are tuned. If tuned, which technique did you consider to tune them (grid search, random search, etc.) and which data used (did you use validation set?). Finally, mention that 10-fold cross-validation was used.

Can you please describe the parameters used for SHO, ACO, lower/upper bounds, etc.? That is, how many iterations, trees, etc. were used, and why? Where these set randomly or optimized?
Validity of the findings: Results are well described. I would only suggest adding an average row in Tables 2-6 to better observe the overall performance improvement. Additionally, highlighting in bold the best performance obtained for each dataset would increase readability.

Fig. 2 and Table 7. Are convergence and computational time shown for a single dataset or an average?

Can you please explain why RUN.m uses k=100 for the number of trees? Are you reporting the best results obtained from these 100 SHO runs?

While the code is very well documented, it only includes the SHO implementation. For reproducibility purposes, I would suggest providing the other optimization algorithms (ACO and PSO) if implemented. Otherwise, refer to the code/source used.
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: ENTROPY BASED C4.5-SHO ALGORITHM WITH INFORMATION GAIN OPTIMIZATION IN DATA MINING
Review round: 2
Reviewer: 1

Output:
Basic reporting: Caption Table 4. Replace “ALO” by “ACO”
Few grammar suggestions:

Line 228. Replace “So, chose 0.7 and 0.9 values as the random values.” By “Therefore, 0.7 and 0.9 were the selected random values.”

Line 236. Use comma before respectively.

Line 274, and 268. Replace “The accuracy of our proposed work is almost stable than the other.” By “The accuracy of our proposed work is more stable compared to the accuracy achieved by the other considered algorithms.”

Line 269. Can you please explain what do you mean by: “The accuracy of classification is depended on the training dataset.”. This phrase may be interpreted as the reported results are those obtained from the training data.
Experimental design: Refer to the validity of findings section.
Validity of the findings: Below I repeat my original comment, Authors reply, and my new comment:

Original reviewer’s comment: Can you please explain why RUN.m uses k=100 for the number of trees? Are you reporting the best results obtained from these 100 SHO runs?

Author’s reply: k=100 is for the number of times the tree is generated. RUN.m file is run 100 times. After 100 SHO runs, we have attain the best result that result is presented in the article.

Second revision – reviewer’s comment: Ideally, data would need to be split into training, validation, and test sets. A validation set is used to tune the parameters. In this case, if you ran 100 executions, the best performing model (based on validation set) is then used to test the performance of the final model by using the test set. What I am trying to say here, the execution of those 100 different trees is considered to be part of the training phase.
Some studies reserve the validation from the training set, while other use nested cross-validation (see https://stats.stackexchange.com/questions/103828/use-of-nested-cross-validation).
My concern is that other algorithms (ACO, PSO, and CS) were not given the same opportunities to achieve better results if these algorithms were not run 100 times too. Arguably, you would be able to continue to improve results on test data just by expanding the number of executions (that is why these iterations should be considered as the training phase). These models would overfit the test data.

With respect to the other Author’s response: “The parameters of SHO, ACO, PSO, and CS values are tabulated in Table 2. The parameter table is updated in the revised manuscript. We have taken random values for our proposed optimization work like iterations and number of populations”

These number of iterations and populations should be part of the training phase which is optimized by using the validation set. Final results would be those obtained by testing the final model on the test data that has not been seen during any training phase.
If validation set was not used to compare C4.5-SHO against C4.5, ID3, and CART. Then authors need to carefully indicate the parameters used for each of these algorithms to demonstrate that all were given the same opportunities and indicate so in the manuscript. The authors should indicate that a validation set was not used.
Similarly, I suggest authors describe the search space for the other C4.5 implementations (C4.5 ACO, C4.5 PSO, and C4.5 CS), i.e., where they also ran 100 times? Was a random search also performed?
Additional comments: I thank the authors for the modifications.
While the Authors answered most of my comments. I still have a concern related to the optimization of parameters (optimization algorithms and decision tree algorithms).


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 REAL-TIME APPROACH OF DIAGNOSING RICE LEAF DISEASE USING DEEP LEARNING-BASED FASTER R-CNN FRAMEWORK
Review round: 1
Reviewer: 1

Output:
Basic reporting: No comment
Experimental design: 1. Your description of Sub-section "Data collection" is not explanatory enough. Since the experiment was done using two datasets, the first dataset was collected from online database (Kaggle database) and the second database is a primary dataset. Give a detailed summary of the total number of images collected from each database (you can use a Table).

2. For a better view and understanding of the different steps involved in your methodology.  Kindly insert all the different stages steps in a textbox with appropriate caption such as:
•	The steps of Faster R-CNN:
•	RPNs for candidate regions
•	Training processes: Faster R-CNN training model:
This would help the reader understand the steps better.

3. Improve discussion of your findings in figures 6 and 7 of Section: Detection and visualization and failure analysis.

4.	Figure 9 shows the confusion matrix, it is expected to more detailed as the rate of misclassification of each class is not specified on the figure. Use a more labelled confusion matrix showing the wrong predictions on each class.
Validity of the findings: 1. To identify the validity of your findings, it is expected that the results achieved should be compared with experimental results from related studies. Since an online/ publicly available dataset was used (Kaggle database).

2. Highlights what are the limitations or challenges of your model and how it affects the overall performance.
Additional comments: The paper is very interesting to read with the aim of addressing the challenges of real-time rice leaves disease spot identification using Deep Learning-based Faster R-CNN Framework.


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 REAL-TIME APPROACH OF DIAGNOSING RICE LEAF DISEASE USING DEEP LEARNING-BASED FASTER R-CNN FRAMEWORK
Review round: 1
Reviewer: 2

Output:
Basic reporting: The English language should be improved to ensure that an international audience can clearly understand your text. Some examples where the language could be improved include lines 164-165, 262 the current phrasing makes comprehension difficult.
The summary of the existing technology in the introduction is not comprehensive. I suggest you add a more detailed description in lines 112-116.
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: A REAL-TIME APPROACH OF DIAGNOSING RICE LEAF DISEASE USING DEEP LEARNING-BASED FASTER R-CNN FRAMEWORK
Review round: 2
Reviewer: 1

Output:
Basic reporting: The authors have improved the clarity and the discussions raised showing sufficient knowledge in the field background.
Experimental design: Authors have further shown satisfactory and detailed discussion of all the methodology steps in the proposed Faster R-CNN.
Validity of the findings: Authors have shown from the comparison table of the experimental results, that the performance of Faster R-CNN framework was effective in the real-time detection of Rice Leaf Diseases when compared with some of the state-of-the-art methods.
Additional comments: Authors have carefully addressed all the issues raised in previous 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 REAL-TIME APPROACH OF DIAGNOSING RICE LEAF DISEASE USING DEEP LEARNING-BASED FASTER R-CNN FRAMEWORK
Review round: 2
Reviewer: 2

Output:
Basic reporting: This is a well-written paper containing interesting results which merit publication. For the benefit of the reader, however, certain statements require further justification. There are given below.
1.	In the Model architecture with Faster R-CNN part, more mathematical inference can help readers understand the network.
2.	More detail can be shown about the architecture of CNN and ROI in Figure 4.
Experimental design: no comment
Validity of the findings: no comment
Additional comments: no comment


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

Input:
Title: DETERMINISTIC CLUSTERING BASED COMPRESSIVE SENSING SCHEME FOR FOG-SUPPORTED HETEROGENEOUS WIRELESS SENSOR NETWORKS
Review round: 1
Reviewer: 1

Output:
Basic reporting: no comment
Experimental design: no comment
Validity of the findings: no comment
Additional comments: Lines 22-23, “An open question in this approach is how to integrate … for specific tasks”. What is the approach you mention ? this is not clear for readers. The interest for merging CS and rouing protocols must be more shown.
Line 59, A “.” is missing after [5].
Lines 77-78, the definition of parameters is not well done (e.g. belonging symbol)
Line 81, please check the expression “Then ||L||1 –norm minimization”.
Lines 82-83, the text alignment becomes center, fix this issue.
Line 83, Equation (2) is not well defined.
Line 84, what is considered as the most important communication? Remember the main idea.
Line 108, the “and” can be replaced by “;” or a “,”
Line 229, “The working of DCCS” can be replaced by “The functioning of DCCS”
Line 233, “The basic aim” can be replaced by “The main objective”
Line 234, “X” is a variable that gives the amount of data? please explain it
Line 237, “… depends only on the RE of the nodes …” can be replaced by “… depends only on the residual energy (RE) of nodes”
Line 241, I suggest to remember how the reference [50] find the number of CH (NCH)

•	The paper is well organized and the english language is acceptable. However. There are few incomprehensible expressions;
•	We suggest to put the paragraph (Lines 182 - 201) rather  in the Introduction before the paragraph of the paper organization;
•	According the CH selection description in lines 237-246, the CHs elected are the NCH nodes with the highest residual energy ? If so did you assume each CH can communicate with all the FN ?
•	The way the Fog leader (You should explain this agent) is choosen is confused and not explain ? This is because early you said that the positions of FNs are predefined (i guess also their location) and details about the selection of fog leader is missing, or is it predefined ?
•	In Algorithm 1 (Lines 266) I think “Ci” must be replaced by “ch in Ci” because Ci is the set of CH and you want each non-CH nodes check its closest CH
•	Another simple way to improve Algorithm 1 is to consider CHset as the set of CHs and Ci is the i-th CH of the set.
•	When designing the DCCS algorithm, you assumed the location of Fog Nodes (FN) are predetermined, during your experimentations; the position of the other nodes (normal and advanced nodes) is randomly achieved? If yes, how many tests did you performed and what were the standard deviation of the tests.
•	What is the nature of the DCCS algorithm you proposed? is it a centralized or a distributed clustering approach ?
o	Authors have to clarify this issue!
o	The selection of the CH is it performed through a deistibuted or centralized manner ?
•	I am confused during the evaluation of the RSMP, indeed, the authors use MATLAB for simulation, however, is MATLAB appropriate for simulation in cloud environment? Can you please that choice?


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: DETERMINISTIC CLUSTERING BASED COMPRESSIVE SENSING SCHEME FOR FOG-SUPPORTED HETEROGENEOUS WIRELESS SENSOR NETWORKS
Review round: 1
Reviewer: 2

Output:
Basic reporting: The professional writing used throughout this paper need minor revision; as the document contains some grammatical and typing errors.
The structure of the document should be revised, as it makes it difficult for readers to follow.
Authors should add related works on data reconstruction methods.
More details are given in the attached files.
Experimental design: To tackle the data acquisition problem in large scale WSNs, the authors proposed a routing protocol based on deterministic clustering combined with the compressive sensing. Nevertheless, the performance evaluation is not consistent, hence needs major revisions.
More details are given in the attached files.
Validity of the findings: In this paper, the authors proposed a routing protocol based on clustering, a compressive sensing scheme and a data reconstruction algorithm to take energy consumption and data acquisition problems in large-scale wireless sensors networks. The document is poorly structured that it did not highlight the originality of this work. Therefore, it requires major revisions. Detailed comments provided will help the authors highlight the importance of novelty of this work.
Additional comments: To tackle the data acquisition problem in large scale WSNs, the authors proposed a routing protocol based on deterministic clustering combined with the compressive sensing.
The authors are highly advised to review the quality of the paper. Since it contains several grammatical and typing errors. The use of software such as Grammarly may help to improve the document quality.


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: DETERMINISTIC CLUSTERING BASED COMPRESSIVE SENSING SCHEME FOR FOG-SUPPORTED HETEROGENEOUS WIRELESS SENSOR NETWORKS
Review round: 2
Reviewer: 1

Output:
Basic reporting: The remarks and suggestions have been addressed.
Experimental design: Enough for publication in PeerJ Computer Science.
Validity of the findings: no comment
Additional comments: no comment


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

Input:
Title: DETERMINISTIC CLUSTERING BASED COMPRESSIVE SENSING SCHEME FOR FOG-SUPPORTED HETEROGENEOUS WIRELESS SENSOR NETWORKS
Review round: 2
Reviewer: 2

Output:
Basic reporting: The authors answered most of the comments I raised. The paper has been restructured. Nevertheless, authors are strongly recommended to proofread this document; the writings may not encourage more readers.
Experimental design: no comment
Validity of the findings: no comment
Additional comments: See the attachment


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: ENHANCING SEMANTIC BELIEF FUNCTION TO HANDLE DECISION CONFLICTS IN SOS USING K-MEANS CLUSTERING
Review round: 1
Reviewer: 1

Output:
Basic reporting: Use professional English in the text. For example, revision is required under the proposed system heading: The second phase presents the reversal of Glest's Java code generating into OWL Ontologies of all systems in each cluster. Plus merging the OWL ontologies for conflict detection and get a decision for each S-SoS.
Experimental design: Research question well defined, relevant & meaningful.

Rigorous investigation performed to a high technical & ethical standard.

Methods described with sufficient detail & information to replicate.
Validity of the findings: All underlying data have been provided; they are robust, statistically sound, & controlled.

Conclusions are well stated, linked to original research question & limited to supporting results.
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: ENHANCING SEMANTIC BELIEF FUNCTION TO HANDLE DECISION CONFLICTS IN SOS USING K-MEANS CLUSTERING
Review round: 1
Reviewer: 2

Output:
Basic reporting: No comment
Experimental design: Add more experiments
Validity of the findings: Variety of datasets should be used for validity of your proposed method
Additional comments: This manuscript propose the Smart Sematic Belief Function Clustered System of Systems (SSBFCSoS). SSBFCSoS consider an enhancement for method Ontology Belief Function System of Systems (OBFSoS). However, the paper, has more serious methodological flaws enumerated below.

*The implementation details should be clearly specified in terms of hardware and software used in the experiments.
*   Each notation of all equations should be clearly described
* Compare your proposed method with other relative clustering algorithms. you just compare it with one algorithm .
* More datasets should be used for authentication of your proposed method.
However, despite many errors the text is simple and readable. Summarizing, although the overall concept is fine, the paper is very unprofessionally written and thus the reported results are very unconvincing. Therefore, I advise the Authors to add more experiments in your manuscript and resubmit it once again.


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: ENHANCING SEMANTIC BELIEF FUNCTION TO HANDLE DECISION CONFLICTS IN SOS USING K-MEANS CLUSTERING
Review round: 1
Reviewer: 3

Output:
Basic reporting: 1. The English in the present manuscript is not of publication quality and require major improvements. Please carefully proof-read to eliminate grammatical errors.
2. Sufficient introduction and background are given.
3. Please add some more recent references.
4. The figures and tables must be explained in detail in the text. Explain what is going on in the figure and table. Figure 1 needs improvement. It is not easy to understand Figure 1.
5. Try to explain each step of the case study according to the proposed method. Apply and show the case study values in clustering techniques. Show the steps and results produced according to the algorithms used. e.g. where and how k-means is applied in the case study and what are the results produced.
Experimental design: 1. If the quality of this article is improved, only then it will meet the aim and scope of the journal.
2. The knowledge gap investigated is not clearly justified. Add some more references to strengthen your problem.
3. The method needs improvement. The phases mentioned in the method do not give a clear picture and it is not easy for a reader to reproduce the result.
4. Mention the enhanced proposed form in the equation or algorithm. What is the final enhanced form? Defining Euclidean, Squared Euclidean, Manhattan, and Cosine distance measure is not enough. You need to explain at what stage you have applied what method.
Validity of the findings: Using clustering techniques require more sound statistical explanation. What results are produced with what clustering technique, statistically?
Additional comments: The article needs much improvement. The quality of the article is not very good but still can be improved. There are lots of grammatical mistakes.  Try to work on it. It can be improved. Good luck.


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: ENHANCING SEMANTIC BELIEF FUNCTION TO HANDLE DECISION CONFLICTS IN SOS USING K-MEANS CLUSTERING
Review round: 2
Reviewer: 1

Output:
Basic reporting: Clear and unambiguous, professional English used throughout.
Literature references, sufficient field background/context provided.
Professional article structure, figures, tables. Raw data shared.
Experimental design: Original primary research within Aims and Scope of the journal.
Validity of the findings: All underlying data have been provided; they are robust, statistically sound, & controlled.
Additional comments: All minor revisions are submitted.


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: ENHANCING SEMANTIC BELIEF FUNCTION TO HANDLE DECISION CONFLICTS IN SOS USING K-MEANS CLUSTERING
Review round: 2
Reviewer: 2

Output:
Basic reporting: The author has modified paper according to suggestions, and now the organization of the paper is improved and further required explanation has been provided.
Experimental design: The authors performed extensive experiments to test the performance of their algorithm . Experiment results are well elaborated. but I found some minor issues in the manuscript that need to be rectified like more clustering algorithms should be compare with the proposed method .
Validity of the findings: no comment
Additional comments: The author has modified paper according to suggestions, and now the organization of the paper is improved and further required explanation has been provided.

Although the language structure has been improved and authors were able to overcome grammatical mistake but it still needs to optimized.I suggest that authors should review the paper once more and optimize and repitative elements should be consolidated.


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: ENHANCING SEMANTIC BELIEF FUNCTION TO HANDLE DECISION CONFLICTS IN SOS USING K-MEANS CLUSTERING
Review round: 2
Reviewer: 3

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: ENHANCING SEMANTIC BELIEF FUNCTION TO HANDLE DECISION CONFLICTS IN SOS USING K-MEANS CLUSTERING
Review round: 3
Reviewer: 1

Output:
Basic reporting: Now this paper have incorporated the solution of all necessary requirements and they have worked on modifying the paper in the light of the recommendations. Format along with style of the paper is fine now.
Experimental design: The authors performed extensive experiments to test the  their method . Experiment results are well elaborated.
Validity of the findings: The author has modified paper according to suggestions, and now the organization of the paper is improved and further required explanation has been provided.
Additional comments: it is a well written paper and suggested changes were made by authors. The article may be proof read from native English speaker to facilitate readers. it may accepted in current 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: A VISUAL ANALYTIC APPROACH FOR THE IDENTIFICATION OF ICU PATIENT SUBPOPULATIONS USING ICD DIAGNOSTIC CODES
Review round: 1
Reviewer: 1

Output:
Basic reporting: Firstly, the paper was well written. Clear, unambiguous, professional English language used throughout. Secondly, the introduction gave a clear description of the problem and relevant studies. Figures are high quality and well described.
Experimental design: The research question is well defined and meaningful. The experimental was well designed. They also provide an online demo of the subpopulation identification in MIMIC dataset.
Validity of the findings: The real contribution of this paper is the STAD-R. The proposed metric to measure the similarity between diagnosis profiles is not bad for scenario which want to identify subsets in a target population with specific diagnosis. The order or rank of target diagnosis was well utilized to distinguish the subsets. My concern is such a rank-based similarity is not fully taking advantage of the semantic relationship among different diagnosis.  In this paper, the author mapped the original ICD code to clinically meaningful sections and it will help to reduce the impact of semantic relationship.  The author also discussion about the similarity of procedure based on similar metric. I do not think both of these similarity methods are the perfect way to measure patient similarity. There are some studies consider both the order and the semantic relationship among diagnosis profile. Maybe the author can discuss this.

In this paper, the network generated based on STAD-R is very clear and impressive to distinguish subpopulation with specific diagnosis.  I am curious about the results that using a more complicated similarity metric in all MIMIC patients and visualize them using the STAD-R . Hopefully, it will provide a whole map of all patients and can also help to identify subpopulation based on network directly without providing a diagnosis code.
Additional comments: It's a very impressive work. Congratulation to the authors.


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 VISUAL ANALYTIC APPROACH FOR THE IDENTIFICATION OF ICU PATIENT SUBPOPULATIONS USING ICD DIAGNOSTIC CODES
Review round: 1
Reviewer: 2

Output:
Basic reporting: In Figure 1 and Figure 3, titles and labels are difficult to read since the font size is small.
In Figure 2, the formulas below networks are difficult to read since the font size is small.
The Louvain method is used to identify patient communities, but it is not explained.
Experimental design: No comment.
Validity of the findings: At row 288, there is a comment about the points near to the first patient community that are not part of the same community as defined by the Louvain algorithm. Is it possible to compare STAD to other dimensionality reduction methods even in terms of number and impact of this kind of disagreement?
Additional comments: At row 254, it is said that “total bar lengths decrease as the position in the list increases”, but in Figure 4 the total bar lengths decrease for lower positions, so towards the bottom.
At row 285, it is not clear if t-SNE does take the global structure into account, or if t-SNE does not take the global structure into account.


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 VISUAL ANALYTIC APPROACH FOR THE IDENTIFICATION OF ICU PATIENT SUBPOPULATIONS USING ICD DIAGNOSTIC CODES
Review round: 3
Reviewer: 1

Output:
Basic reporting: No comment.
Experimental design: No comment.
Validity of the findings: No comment.
Additional comments: After this revision, authors have adequately addressed the reviewers’ comments, therefore I think that the paper could be published.


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: FEATURE-BASED DETECTION OF AUTOMATED LANGUAGE MODELS: TACKLING GPT-2, GPT-3 AND GROVER
Review round: 1
Reviewer: 1

Output:
Basic reporting: The manuscript is very well written and clear. Even without a background in NLP, it is possible to understand the methods, simulations, and results. The classifier itself is based on plausible assumptions, allows an intuitive interpretation of (most) features, and performs comparably well as more computationally-intensive competitors. Overall, I think the present manuscript does not have any major flaws. There are still several points that should be improved in a revision:

Detection problem (p.2):
A forth goal that is desirable is the applicability of the classifier to text generated by people who may not be native speakers or members of minorities. This is question of the ethics of using AI methods within a social context. It is sufficient to shortly mention this issue somewhere in the main text (maybe in the discussion or after l.112)

Abbreviations:
The authors should check whether all abbreviations are defined when first mentioned (e.g., SVM, GPT, BERT, tf-idf, POS-tag, ...).

Appendix:
The headings of the feature sets could mention to which subsection in the main text they belong (e.g., coherence, repetitiveness, ...)
Tables: The Labels for rows and columns should clearly distinguish between "Test Data" and "Training Data" as opposed to "Classifier" - since the same type of classifier (neural network) is used for all cases (e.g., Table 3). Moreover, columns and rows should not be switched between tables (e.g., Table 9 & 10).

Discussion:
It could be highlighted that the present work has another benefit: it shows directions for future improvements for machine-generated language.

### Minor Issues:

l. 34: Instead of "bad actors", it would be more appropriate to refer to "actors with questionable/immoral/unethical intentions" (or similar)

l. 56: methods of educating the public may also be difficult to implement effectively from a psychological perspective, e.g.: Lewandowsky, S., Ecker, U. K. H., Seifert, C. M., Schwarz, N., & Cook, J. (2012). Misinformation and its correction: Continued influence and successful debiasing. *Psychological Science in the Public Interest*, *13*(3), 106–131. https://doi.org/10.1177/1529100612451018

l.143: "not to overfit" -> is this a typo? I think this should be "not to underfit"

Table 2: Are the human data sets merged for all training and test sets or matched to the corresponding data set of the language-generation method?

l.383-386: it should be clarified that for each point of the grid, the neural network is trained with an optimization algorithm (backwards propagation?). Currently, this reads as if the models were trained with a grid search - but this appears to be only the case for some of the tuning parameters.

Table 6: It was difficult to detect the pattern highlighted in italics. Underscored numbers are better to detect. Moreover, the column order could be adjusted to have the truncated (s,xl) and full-distribution (s-k,xl-k) datasets next to each other (this holds for all tables). This would in general facilitate recognizing the qualitative patterns discussed by the authors.

l.466: how was overfitting detected? if the model performs well, this cannot be too bad?
Experimental design: Feature definition:
As the authors state correctly, a major benefit of the classifier is that the features have a direct intuitive interpretation and can be communicated to lay audiences and practitioners using these methods. To further highlight this fact, the authors could add a few concrete examples of sentences in which some of the less common features of the classifier are illustrated (either in the main text or on the tables in the appendix). For instance, the new "conjunction overlap" (l.252) seems to refer to matches of the form:  ""[x y z] and [x y z]". Similarly, specific examples would facilitate the discussion of named entities and coreference chains.  I think this would further strengthen the argument that the feature-based classifier has an intuitive interpretation in contrast to competitors. However, this is merely an optional suggestion and not a mandatory  requirement for a revision.

Accuracy measures:
I think it is a good idea to use AUC instead of accuracy. The authors could discuss this in l.79-88. Moreover, it might help to discuss the concepts of sensitivity   (probability of detecting machine-generated language) and specificity (counter-probability of the false-positive rate) of binary classifications. The AUC is a measure that takes both criteria into account within a single number.

Ensemble methods:
Why are the classifiers combined with the "tf-idf-baseline models"? According to the argument in l.409-413, it seems more appropriate to combine two feature-based classifiers - one trained with a truncated training set (s-k, xl-k) and another one with the full distribution (s, xl). This makes sense as both classifiers outperform the tf-idf models (l.453). Maybe this is what the authors did, but it is currently not clear.
Validity of the findings: Expectations/hypotheses:
The authors state several hypotheses about the expected direction of differences between machine- and human-generated text. For instance, "We expect a more diverse, human-written text to have a higher share of unique words" (l.248) or "We expect human text to contain more sentiment-related keywords" (l.310). The authors could pick up these hypotheses later in the results section and discuss whether their hypotheses hold in the trained classifiers (this would not require additional tables).

Transferability:
The authors discuss transferability across training sets of different language-generation methods. At some place (e.g., Discussion or l.89-101), it is important to also discuss the issue whether the training sets are representative of "real" text. Put differently, it is not clear whether the classifiers trained with these data would work on data from twitter, facebook, etc. Maybe the authors can specify boundary conditions that need to hold for their classifier to be applicable.

Data:
The GPT-2 samples are currently not available (https://storage.googleapis.com/gpt-2/output-dataset/v1/). The authors state correctly that: "These addresses would need to be updated in the code should they ever change."
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: FEATURE-BASED DETECTION OF AUTOMATED LANGUAGE MODELS: TACKLING GPT-2, GPT-3 AND GROVER
Review round: 1
Reviewer: 2

Output:
Basic reporting: I consider the text to be a very good entry into the issue of detecting automatically generated text. The authors reflect on related topics (detection of fake news, authorship attribution, etc.), which gives readers a broader context - the methods are common or similar.
Experimental design: As far as I can judge, I do not see any inconsistencies in the text, the analytical part is given clearly and is based on verifiable methodology and data.
Validity of the findings: The results are absolutely credible.
Additional comments: In further research, from my perspective, the article asks questions to observe the repetition (word forms, grammatical forms) in a small and, on the contrary, a large range of text, probably the cohesion of the text in these different scopes is controlled by opposite tendencies. Even if this were not the case, the text turns out to be inspiring questions based on clearly analyzed data.


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: SYNTACTIC- AND MORPHOLOGY-BASED TEXT AUGMENTATION FRAMEWORK FOR ARABIC SENTIMENT ANALYSIS
Review round: 1
Reviewer: 1

Output:
Basic reporting: Line 58: I do not agree with the word choice “infeasible”.
Annotation is feasible but it requires time and is expensive. It has already been done to other languages.

Line 109 WordNet is a hierarchical dictionary.
Some typing and grammatical errors:
-	line 39 word should world.
-	Line 85 describe should be described
-	Line 109 the word resembles should be replaced by grouped
-	Line 203 replace ‘, briefly, lists’ with summarize.
-	Line 214 215 review the verb tenses
-	Line 485 ‘is’ should bet ‘it’
-	Line 506 ‘like’ should be ‘likes’
-	Line 533, usage of ‘whereas’ is not correct.
-	Line 574, “thee” should be “three”
Line 206: I am not sure this is correct and what it even means. You need to clearly clarify this point.
Line 227 to 233: the Latin form of the Arabic words is incorrect, and it is not consistent. The ‘a’ in Modarsh should be “A”. Also, the sh is not a good way to represent the “t marboutap”. I suggest you use the same form as seen in the rules (line 279 and on).

The quality of the figures seems to be low.
Is the reader expected to fill the gaps in Table 6?

Since your objective is data augmentation, there were several efforts for augmenting the Arabic WordNet and some of them were used to develop Arabic sentiment and emotion lexicons. These should be included in your related work:
- Musa Alkhalifa and Horacio Rodríguez. 2009. Automatically extending NE coverage of Arabic WordNet using Wikipedia. In Proceedings of the 3rd International Conference on Arabic Language Processing (CITALA’09)
- Musa Alkhalifa and Horacio Rodríguez. 2010. Automatically extending named entities coverage of Arabic WordNet using Wikipedia. Int. J. Inf. Commun. Technol. 3, 3 (2010), 20—36
- Benoît Sagot and Darja Fišer. 2011. Extending WordNets by learning from multiple resources. In Proceedings of the 5th Language and Technology Conference (LTC’11).
- Badaro, Gilbert, Hazem Hajj, and Nizar Habash. "A Link Prediction Approach for Accurately Mapping a Large-scale Arabic Lexical Resource to English WordNet." ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 19, no. 6 (2020): 1-38.
Experimental design: Rule 4 does not seem to work. The adjective is now describing ‘AHmd’ instead of his voice.
Same for Rule 5, rule 7, rule 18, 19, 21, The class label is remaining the same because the content did not change. Therefore, any manipulation of the constituents would result in the same sentiment class label.

How did you deal with the discrepancy in the transliteration between the Stanford tool and Arabic WordNet?

All experiments setups suffer from very ambiguous descriptions. For example, experiment 1, what were the features used as input to the different classifiers? What were the percentages of training/testing data?

I am not sure how the negation rules resulted in fully correct sentences especially that in the simple example provided for the boy likes the food, there are incorrect sentences with the usage of ‘laysa’. Moreover, how as the verb conjugation handled?
Validity of the findings: The analysis of the results is very shallow. While the first part of the paper has good content about the Arabic complexity, the second part suffers from lack of details.
Additional comments: Unfortunately, the current shape of the paper suffers from multiple deficiencies as mentioned in the sections above.
In summary, justifications for the selected rules need to be justified. Would a simple shuffling of the words result in the same accuracy improvement in experiment 1?
All experiments require further details and clarity. Testing the efficiency of the approach should be performed on a larger dataset. More detailed analysis of the results is required.


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: SYNTACTIC- AND MORPHOLOGY-BASED TEXT AUGMENTATION FRAMEWORK FOR ARABIC SENTIMENT ANALYSIS
Review round: 1
Reviewer: 2

Output:
Basic reporting: see my comments below
Experimental design: see my comments below
Validity of the findings: see my comments below
Additional comments: The manuscript is centered on an interesting topic. Organization of the paper is good and the proposed method is quite novel.

The manuscript, however, does not link well with recent literature on sentiment analysis appeared in relevant top-tier journals, e.g., the IEEE Intelligent Systems department on "Affective Computing and Sentiment Analysis". Also, latest trends in multilingual sentiment analysis are missing, e.g., see Lo et al.’s recent survey on multilingual sentiment analysis (from formal to informal and scarce resource languages) and Oueslati et al.’s review of sentiment analysis research in Arabic language. Finally, check recent resources for multilingual sentiment analysis, e.g., BabelSenticNet.

The manuscript presents some bad English constructions, grammar mistakes, and misuse of articles: a professional language editing service is strongly recommended (e.g., the ones offered by IEEE, Elsevier, and Springer) to sufficiently improve the paper's presentation quality for meeting PeerJ CS’ high standards.

Finally, double-check both definition and usage of acronyms: every acronym should be defined only once (at the first occurrence) and always used afterwards (except for abstract and section titles). Also, it is not recommendable to generate acronyms for multiword expressions that are shorter than 3 words (unless they are universally recognized, e.g., AI).


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: SYNTACTIC- AND MORPHOLOGY-BASED TEXT AUGMENTATION FRAMEWORK FOR ARABIC SENTIMENT ANALYSIS
Review round: 2
Reviewer: 1

Output:
Basic reporting: Comments were addressed.
Experimental design: Comments were addressed.
Validity of the findings: Comments were addressed.
Additional comments: The previous comments were addressed and the quality of the article has been significantly improved.


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

Input:
Title: SYNTACTIC- AND MORPHOLOGY-BASED TEXT AUGMENTATION FRAMEWORK FOR ARABIC SENTIMENT ANALYSIS
Review round: 2
Reviewer: 2

Output:
Basic reporting: see below
Experimental design: see below
Validity of the findings: see below
Additional comments: The authors have addressed most of the concerns raised by the reviewers and their revisions have substantially improved the manuscript. However, there are still some minor issues to be addressed, namely:
1) presentation is better but there is still room for improvement
2) some references have incomplete compilation, e.g., missing page numbering


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 DEEP LEARNING ALGORITHM TO DETECT CORONAVIRUS (COVID-19) DISEASE USING CT IMAGES
Review round: 1
Reviewer: 1

Output:
Basic reporting: Authors uses a deep learning approach to classify coronavirus CT images with a being careful of accuracy and computational speed. Language is clear and easy understandable. Literature is well referenced. Figures are clear and describe well the problem, data, methodology and results. Deep learning method is very simple, but it seems to be effective.
Experimental design: It is in the scope of PeerJ. It uses a very simple deep learning model composed of three layers, without preprocess data. Cross validation is used in the right way to support the results.
Validity of the findings: It seems that the model can learn to detect healthy patients and those suffering from COVID-19 with a good accuracy.

I have just a doubt on how it works on data, because they are highly unbalanced, but it seems to not affect the results. I suggest to add a reflection on the validity of the output supported by the good results shown in the cross-validation.
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 DEEP LEARNING ALGORITHM TO DETECT CORONAVIRUS (COVID-19) DISEASE USING CT IMAGES
Review round: 1
Reviewer: 2

Output:
Basic reporting: The authors proposed a deep-learning based computer-aided detection (CADe) scheme using a convolutional neural network (CNN) to detect coronavirus disease (COVID-19) using a publicly available CT database.
The topic is very important however I disappointed the construction of study, detailed discussion, and in writing and presentation of results.
Experimental design: # Materials & Methods
## Convolutional Neural Network (CNN)
This paragraph contains only the general of CNN. This is redulant.

# Proposed Method
Figure 3 and Figure 4 are opposite.

# Results
This paragraph is not the result. Generally, computer-aided diagnosis schemes for medical imaging were evaluated using receiver operating characteristic (ROC) analysis. I think the authors need to provide more detailed results.

“Fig.4 is showing a sample result.” – What is “a sample result”?

# Discussions
“As it shown the overall accuracy of the proposed model is more than 84%”- Where is it shown?
Validity of the findings: There are some recent researches to detect COVID-19 using a deep learning algorithm. The authors used a publicly available COVID-19 dataset, so you should compare the proposed model and the state-of-the-art detection algorithm in terms of the detection accuracy. The current presentation remains unconvincing.
Additional comments: While I appreciate the effort of your work presented and the significance of detecting COVID-19, I think the authors needs to improve the focus of the paper and provide more information on the methods used and the 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: A DEEP LEARNING ALGORITHM TO DETECT CORONAVIRUS (COVID-19) DISEASE USING CT IMAGES
Review round: 1
Reviewer: 3

Output:
Basic reporting: In this manuscript authors present a simple CNN model to classify CT images for coronavirus. Language is clear, even though there are some minor typos.
The Convolutional Neural Network used in this paper is really simple and almost shallow but it does the job pretty well.
Experimental design: The approach is well described and there's rigorous investigation proven by the rich bibliography.
Cross-validation is used in the right way and the model performs well even though no pre-processing is used.
Validity of the findings: This paper presents a model that can detect COVID-19 affected patients starting from CT images with a reasonably good amount of accuracy.
A deeper model would have required more data, so the proposed approach is a valid one.
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: CLASSIFICATION OF THE DRIFTING DATA STREAMS USING HETEROGENEOUS DIVERSIFIED DYNAMIC CLASS-WEIGHTED ENSEMBLE
Review round: 1
Reviewer: 1

Output:
Basic reporting: Authors propose a new ensemble method for learning from drifting data streams, which is always a commendable and interesting research direction. The paper certainly is well-written and the idea of using a heterogeneous ensemble is always interesting, as most works in data stream mining focus on homogeneous ensembles. The idea of the proposed ensemble is clearly presented and easy to follow. However, there is a couple of things that must be improved regarding the presentation, positioning the proposed ensemble among state-of-the-art approaches, as well as the experimental design. Regarding the manuscript properties falling under "basic reporting" part, my main comments are to be found below:

1. Please update your figures, make them more readable, scale them properly, as in many figures there is a lot of white spaces and plots are squeezed to a very narrow range
2. There is no need to describe used data sets, they are well-known.
3. Please update your literature on data stream mining, ensemble learning for data streams, role of ensemble diversity in data stream mining, and learning under limited supervision. Various recent papers published in 2018-2020 period are missing.
Experimental design: Experimental setup needs a major revision and improvement before this paper can be considered further. My main comments are below:

1. Used real data streams are old and very simple. There are more recent real data stream benchmarks published recently in the literature. Please extend your study with at least several of them.
2. Your experimental setup misses comparison with two state-of-the-art ensemble methods that are currently considered as best ensemble learners for drifting data streams - Adaptive Random Forest and Kappa Updated Ensemble. Please include comparison with those algorithms to make your experimental study relevant. At this point you compare yourself only with ensemble algorithms that are known to underperform when compared to ARF and KUE.
3. Do you calculate your performance metrics in a prequential fashion?
4. How does your method work under limited access to class labels? Assumption that you work with a fully labeled data stream is unrealistic. It would be interesting to add an experimental section, where your evaluate the impact of limited supervision on your method.
Validity of the findings: I will need to re-evaluate the conclusions and findings after Authors improve the experimental section. At this point, I find them well-written and interesting.
Additional comments: Well-written and interesting research paper, but requires a major update of the experimental section in order to make it comparable to the state-of-the-art.