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 COMBINED MODEL FOR SHORT-TERM WIND SPEED FORECASTING BASED ON EMPIRICAL MODE DECOMPOSITION, FEATURE SELECTION, SUPPORT VECTOR REGRESSION AND CROSS-VALIDATED LASSO
Review round: 1
Reviewer: 1

Output:
Basic reporting: Figures presented in the manuscript should be improved:

1) From Fig.1, the reader might think that from IMF(1) (high-frequency) to IMF(n-1), the proposed hybrid approach utilizes FS+SVR combination for predicting the IMF component because of the 3 dots placed at the right of the pipeline processing IMF1. However, from the codes submitted, if the reviewer correctly understands, it seems that FS+SVR is applied only to the very first IMF, while LassoCV is used for all the remaining lower frequency components, including the residue. Is it possible to clarify this issue? If I correctly understood the proposed method, it is better to modify Fig. 1 to avoid this confusion.

2) Fig. 2-6. It is better to add the labels and scales to the X-axis. From .csv files, it is understandable that those axis represent time expressed in hours, but it can be good to put the labels and scales for X-axis.

The representation of some tables can be improved:

3) In the opinion of this reviewer, it can be better to present tables 4-5 (the improvement comparison) in form of tables 8 and 9.
Experimental design: The proposed methodology explanation and description should be elaborated more by the authors of the manuscript. Specifically:

1) The challenges of predicting high-frequency IMF and low-frequency IMF with residue are not well stated in the text of the manuscript. Why do the authors consider different models for predicting those components and thus, construct a hybrid approach? Why not use the same FS+SVR or LassoCV for all the extracted intrinsic modes? These questions should be better explained in the text of manuscript.

2) Feature Selection subsection. From this subsection, the definition of raw features is not very clear. Are these features comprise the statistical feature parameters of lags created from IMF components? Or the lags (i.e., time-series) are used as features? Please, clarify this in the text of the manuscript.

3) It is recommended to add more details in the text (formulations and explanations) regarding the step 3 from Fig.1, where the predictions of each IMF are combined in the ensemble and final prediction is made.

4) From experimental results the predictive performance of the proposed model looks good. But are there any limitations of the proposed model? Please, state them in the discussion section if they are any.
Validity of the findings: No comment.
Additional comments: The general comments can be split into the major and minor comments which are represented as below.

Major comments:
1) How exactly the author combined EMD with the referenced models for the second comparison in the Discussion section? Do those techniques utilize EMD in the same manner as the proposed method? In the opinion of this reviewer, this explanation should be elaborated more.

2) Fig.1. At the bottom of this figure, Step 3 shows the process of merging the prediction results obtained for IMF components into the ensemble. Is it possible to create an additional figure which shows how the proposed method predicts individual IMF components, for example, for a single 3-step prediction, and how these predictions then form the ensemble? In the opinion of this reviewer, this additional figure can be useful for the potential readers of the manuscript since it provides a better understanding of the intermediate results.

Minor comments:
1) In the Introduction section and along with the text of the manuscript, the author mentions short-, medium-, and long-term wind speed forecasting scenarios. In the Results section, the experimental results for the proposed and referenced models are obtained while testing 1-step, 2-step, and 3-step prediction strategies. As a reader, I am curious, whether these strategies used in the experiment cover those 3 scenarios (short-, medium-, long-term predictions) mentioned in Introduction or, for example, they all considered short-term or medium-term prediction scenarios? Is it possible to briefly clarify this moment?

2) In the Introduction section of the manuscript there are a lot of acronyms that are not defined in the text of the manuscript. Some of them are well-known, such as ANN, SVM, and ARIMA, but some of them are not. It is a good practice to define the acronyms before their use in the text.

3) Does the proposed method support online learning, i.e., the update of prediction models when the newly unseen instances of data arrive?

4) There are some minor typos in the manuscript, such as at lines 282-283. It is recommended to double-check the manuscript and fix them.


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 COMBINED MODEL FOR SHORT-TERM WIND SPEED FORECASTING BASED ON EMPIRICAL MODE DECOMPOSITION, FEATURE SELECTION, SUPPORT VECTOR REGRESSION AND CROSS-VALIDATED LASSO
Review round: 1
Reviewer: 2

Output:
Basic reporting: Dear Author, from my own observations,

If possible, could you please update more background and principles of Empirical Mode Decomposition (EMD), Feature Selection (FS), and Support Vector Regression (SVR) in the specific areas of wind speed forecasting based on your research topic.

The details of training data and testing data should be listed and respectively demonstrated the percentage of training data and testing data of the original dataset in terms of case studies or/and experimentations.

Which are the most advantages of your proposed method in comparison with other existing algorithms of EMD and SVR in the areas of Short-Term Forecasting (STF) based on wind speed data? If possible, could you please have in comparison between with and without noisy signals/environments for STF system and contrast the simulation results between the two scenarios mentioned above. Alternatively, could you please provide either some Tables or update simulation results to make an explanation the advantages of your proposed methodology of EMD and SVR in the fields of wind speed STF and update some statistical criterion based on your research topic.
Experimental design: The proposed algorithm should be demonstrated as the form of Algorithm Environment, which can be efficaciously followed by readers. Additionally, the flowchart of proposed method mentioned in this paper should also be updated and included the dataset collections, pre-processing of dataset, post-processing of dataset, feature extractions, feature selections, and the performances of forecasting, respectively.
Validity of the findings: Alternatively, for one thing, different types of experimental samples and the number of feature selection might have either positive or negative impacts for the performances of wind speed forecasting for more complicated industrial systems to some extent. Based on this reason, could you please provide an evidence to make explanations the availability and feasibility of the proposed methodology based on wind speed data for STF system?

For another thing, the characteristics of both healthy and faulty simulated wind speed data based on time-domain should be modified, and correspondingly the energy distribution of IMFs should also be analysed and discussed.
Additional comments: Recently, a new survey paper of wind turbine systems concentrates on fault diagnosis, prognosis and resilient control, which included model-based, signal-based, and knowledge-based (data-driven) techniques to demonstrate the characteristics of fault detection, classification, and isolation in various faulty scenarios. Additionally, the novel EMD or/and SVR techniques for STF have also been proposed in some papers.

If possible, could you please update these References for having in comparison with other algorithms in the specific field of your research topic in terms of wind speed data for STF system, as well as making any comments? The References are shown in the following below.

Reference 1:  A. T. Eseye, et. al., “Short-Term Forecasting of Heat Demand of Buildings for Efficient and Optimal Energy Management Based on Integrated Machine Learning Models,” in IEEE Transactions on Industrial Informatics, vol. 16, no. 12, pp. 7743–7755, Dec. 2020.

Reference 2:  “An Overview on Fault Diagnosis, Prognosis and Resilient Control for Wind Turbine Systems,” Processes, vol. 9, no. 2, p. 300, Feb. 2021.

Reference 3:  M. Sajjad, et. al., “A Novel CNN-GRU-Based Hybrid Approach for Short-Term Residential Load Forecasting,” in IEEE Access, vol. 8, pp. 143759–143768, 2020.

Reference 4:  Y. Fu, et. al., “Actuator and Sensor Fault Classification for Wind Turbine Systems Based on Fast Fourier Transform and Uncorrelated Multi-Linear Principal Component Analysis Techniques,” Processes, vol. 8, no. 9, p. 1066, Sep. 2020.


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 COMBINED MODEL FOR SHORT-TERM WIND SPEED FORECASTING BASED ON EMPIRICAL MODE DECOMPOSITION, FEATURE SELECTION, SUPPORT VECTOR REGRESSION AND CROSS-VALIDATED LASSO
Review round: 2
Reviewer: 1

Output:
Basic reporting: No comment.
Experimental design: No comment.
Validity of the findings: No comment.
Additional comments: If possible, could the authors please update more basic concepts and fundamentals of Short-Term Wind Speed Forecasting (STWSF), Empirical Mode Decomposition (EMD), Feature Selection (FS), and Support Vector Regression (SVR) based on the research topic in this paper?

In this paper, the descriptions of starting layer, convolutional layer, hidden layer, and pooling layer should be modified. How many convolutional layers and hidden layers have been determined in this paper, and what is the functionality of these layers in the experimentations and proposed methodologies? Meanwhile, if possible, could the authors please make an explanation for choosing the number of these layers mentioned above and update some descriptions and statements on the functionality of these aforementioned layers?

Specifically, it is worthy to point out, the various numbers of convolutional layers, hidden layers, and pooling layers might have either positive or negative impacts on the performances and characteristics of Short-Term Wind Speed Forecasting (STWSF) -- Empirical Mode Decomposition (EMD) -- Feature Selection (FS) -- Support Vector Regression (SVR), namely STWSF-EMD-FS-SVR, for more complicated industrial systems to some extent. Based on this reason, could the authors please provide an evidence to make some explanations of the availability, feasibility, and capability of the proposed method STWSF-EMD-FS-SVR based on the research topic in this paper?

The detailed information of training data and testing data should also be modified and respectively demonstrated the percentage of training data and testing data of the original or/and experimental dataset regarding case studies or/and experimentations based on the research topic in this paper for STWSF-EMD-FS-SVR.

Which are the most advantages of the proposed method in comparison with other existing algorithms of Empirical Mode Decomposition (EMD) in the areas of Short-Term Wind Speed Forecasting (STWSF), Feature Selection (FS), and Support Vector Regression (SVR) based on data-driven approach? A data-driven method of fault diagnosis has been demonstrated in a recent survey paper (https://doi.org/10.3390/pr9020300). Could the authors please explain which are the differences among Empirical Mode Decomposition (EMD), Variational Mode Decomposition (VMD), and Ensemble Empirical Mode Decomposition (EEMD) techniques? Meanwhile, evidence of using the proposed EMD framework rather than VMD or EEMD methods should also be supported. Alternatively, could the authors please provide either some TABLES or update Simulation or/and Experimental Results to make an explanation of the advantages of the proposed methodology EMD, as well as update some statistical criteria based on the research topic in this paper?

The proposed algorithm STWSF-EMD-FS-SVR should be demonstrated as the form of Algorithm Environment, which can be efficaciously followed by readers. Additionally, the flowchart of the proposed method mentioned in this paper should also be updated and included the determined dataset collections, pre-processing of the dataset, post-processing of the dataset, feature extractions, feature selections, and the performances and characteristics of the proposed algorithm STWSF-EMD-FS-SVR. A data-driven and supervised machine learning-based fault detection and fault classification has been addressed in a journal paper (https://doi.org/10.3390/pr8091066), which includes respectively the determined dataset collections, pre-processing of the dataset, post-processing of the dataset, feature extractions, and feature selections.

Alternatively, both different topologies of experimental samples and the various numbers of selected significant features might also have either positive or negative impacts on the performances and characteristics of STWSF-EMD-FS-SVR for more complicated industrial systems to some extent. Based on this reason, could the authors please support an evidence to make some explanations of the availability, feasibility, and capability of the proposed methodology STWSF-EMD-FS-SVR based on the research topic in this paper?

If possible, could the authors please have in comparison between with and without Additive White Gaussian Noise (AWGN) signals/environments (including the different values of signal-to-noise ratio [SNR]) for the system mentioned in this paper? As well as, the authors should contrast the simulation results between these scenarios mentioned above. Alternatively, could the authors please provide either some Tables or updated simulation results to make an explanation of the advantages of the proposed methodology of STWSF-EMD-FS-SVR in the fields of data-driven short-term wind speed forecasting and update some statistical criteria based on the research topic?


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 SURVEY OF SYBIL ATTACK COUNTERMEASURES IN IOT-BASED WIRELESS SENSOR NETWORKS
Review round: 1
Reviewer: 1

Output:
Basic reporting: -
Experimental design: -
Validity of the findings: -
Additional comments: This is a survey article (or review article) addressing countermeasures in two types of attack, Sybil and Black Hole in IoT and WSN. While I appreciate the topic and scope, there are drawbacks in the rest of paper the authors need to improve.

1. The paper does not have a single focus. I could not know what the paper tries to focus either attack or countermeasure/ Sybil+Black hole or WSN/IoT. In additional, I do not know why the authors select to survey on these two attacks. Why two together, not only one? This can weaken the paper.
2. This is a survey paper, but the authors take too much effort on methodology and research questions while very few in the main knowledge (compared to the methodology).  Nevertheless, the authors do not need to focus only articles published in five years. This could be indicated as a critical review article if the survey is more comprehensive with your own discussion. If the authors intend to propose a survey article, the authors need much more works on content gathering and statistics (quantitative analysis). Study from this survey paper https://ieeexplore.ieee.org/document/8792139

3. Overall, the body of knowledge (from the Security Attack section) is too limited where the reader almost learn nothing besides list of related literature with summary. For example, Sybil attack in Blockchain and electronic vote is not found. I suggest that the authors convert to a Review Article by focusing on one attack, studying on more literature (not only the last five years) especially on more recent articles, writing a critical discussion coming up with something the authors can use such as a framework, and removing the methodology section.


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 SURVEY OF SYBIL ATTACK COUNTERMEASURES IN IOT-BASED WIRELESS SENSOR NETWORKS
Review round: 1
Reviewer: 2

Output:
Basic reporting: No comment.
Experimental design: No comment.
Validity of the findings: No comment.
Additional comments: The paper provides an intensive survey on Sybil and Blackhole attacks and their countermeasures, in the context of IoT, with focus on relatively recent research work. It appears that they covered most of the major recent work in this area that I know of. The survey is comprehensive and did a good job in classifying the different approaches. The comparing between the major approaches is clear and well laid out.


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 SURVEY OF SYBIL ATTACK COUNTERMEASURES IN IOT-BASED WIRELESS SENSOR NETWORKS
Review round: 2
Reviewer: 1

Output:
Basic reporting: -
Experimental design: -
Validity of the findings: -
Additional comments: The authors have made major improvement especially in terms of existing studies in a number of contexts, clear contribution, and performance comparison. This review paper is now much more comprehensive. Thus, it can be accepted.

* Some typos are found e.g. maskss in line 77. Language editing is needed.
* In the cover letter, the authors mentioned "Table 4", while this Table does not exist in the paper, and there is no need for Table 4.


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 SURVEY OF SYBIL ATTACK COUNTERMEASURES IN IOT-BASED WIRELESS SENSOR NETWORKS
Review round: 2
Reviewer: 2

Output:
Basic reporting: No comment.
Experimental design: No comment.
Validity of the findings: No comment.
Additional comments: The paper provides an intensive survey on Sybil and Blackhole attacks and their countermeasures, in the context of IoT, with focus on relatively recent research work. It appears that the authors covered most of the major recent work in this area that I know of. The survey is comprehensive and did a good job in classifying the different approaches. The comparison between the major approaches is clear and well laid out.

The revised version of the paper has improved the findings and 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: KL-MOB: AUTOMATED COVID-19 RECOGNITION USING A NOVEL APPROACH BASED ON IMAGE ENHANCEMENT AND A MODIFIED MOBILENET CNN
Review round: 1
Reviewer: 1

Output:
Basic reporting: Grammatical mistakes need to be rectified.
There are some writing mistakes such as Covid1-9 in line number 145. The manuscript should be thoroughly checked.
Equation-2 should be rewritten to differentiate - sign.
Figure 6 and 7 should be enlarged enough to read values.
Experimental design: Experimental design is OK.
Explanation of proposed architecture needs to be re-write and it should meet academic writing criteria.
How KL-Divergence used and by using it, how your work has been enhanced? Justify.
Authors have added KL-Divergence loss, work should also be compared with other loss methods to show work efficiency.
Validity of the findings: Table 1 shows that classes are imbalanced and large variance. How it is handled during training?
The loss and accuracy graphs need to be presented. Epoch and training explanation needs to present.
Authors can compare his work with baseline Deep CNN classifier models.
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: KL-MOB: AUTOMATED COVID-19 RECOGNITION USING A NOVEL APPROACH BASED ON IMAGE ENHANCEMENT AND A MODIFIED MOBILENET CNN
Review round: 1
Reviewer: 2

Output:
Basic reporting: The proposed research described the automated COVID-19 recognition using a
novel approach based on image enhancement and a modified MobileNet CNN. The COVID-19 recognition using image processing-related techniques has not remained a novel research area. Secondly, the presented research work does not advance science. So I do not recommend the publication of the manuscript.
Experimental design: The manuscript has numerous organization and research design issues. The authors did not discuss how they collected data, data pre-processing techniques used in the conducted experiments, technical specifications of used equipment, etc. Similarly, the data processing and applications layers lacked sufficient detail for a critical assessment, but my opinion based on the information provided is that these aspects also do not advance the science.
Validity of the findings: The authors have not carried out a rigorous literature review of the existing research works, a detailed comparison analysis, and identification of appropriate research gaps. I recommend that authors review peer-reviewed articles of the last three years of reputed journals that are significantly lacking in the proposed research work. Furthermore, the discussion of the results concerning the proposed research work is not organized correctly. The conclusion section also needs a revision.
Additional comments: The proposed research described the automated COVID-19 recognition using a
novel approach based on image enhancement and a modified MobileNet CNN. The COVID-19 recognition using image processing-related techniques has not remained a novel research area. The presented research work does not advance science even after combining image enhancement and the CNN methodologies. So I do not recommend the publication of the manuscript.

Firstly, the manuscript has numerous organization and research design issues. The authors did not discuss how they collected data, data pre-processing techniques used in the conducted experiments, technical specifications of used equipment, etc. Similarly, the data processing and applications layers lacked sufficient detail for a critical assessment, but my opinion based on the information provided is that these aspects also do not advance the science.

Secondly, the abstract and introduction lack the discussion of the need for the proposed system. I recommend authors carry out a detailed literature survey again, identify open issues, and add an additional section representing the novelty of the proposed approach. I also want to recommend authors carry out some more real-time experiments. Furthermore, the discussion of the results concerning the proposed research work is not organized correctly. The conclusion section also needs a revision.

     For all the above reasons, I would not recommend publishing this manuscript. It requires lots of revisions. If authors want to publish this manuscript, then it is essential that they should re-conduct the presented experiments and re-write the manuscript after conducting a rigorous literature survey. After making the above changes, the manuscript may be submitted here or elsewhere.


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: KL-MOB: AUTOMATED COVID-19 RECOGNITION USING A NOVEL APPROACH BASED ON IMAGE ENHANCEMENT AND A MODIFIED MOBILENET CNN
Review round: 1
Reviewer: 3

Output:
Basic reporting: Clear, unambiguous, technical English language has been used in the paper. Still the manuscript can be improved by avoiding a few grammatical errors.

Sufficient field background/context provided, however I feel Literature review part(preferably in a tabular format) can be enhanced further by adding related papers from the current year i.e. 2021.
A short paragraph on MobileNet can form a baseline for the proposed research so authors are advised to add it in the literature.

Professional article structure, figures, tables. Raw data shared.

Line no. 85 states "The motivation behind choosing Mobile CNN is that it not only helps to reduce overfitting but also runs faster than regular CNN with many fewer parameters (Howard et al., 2017; Yu et al., 2020)".Please specify.

KL divergence loss function must be explained with proper citations and references.

The section "Classification Neural Network Model"  line no.127 should be presented in a tabular format to make it readable and understandable.
Experimental design: Original primary research within Aims and Scope of the journal.

Research question well defined, relevant & meaningful.

However, the explicit summary can be included which should state the important aspects of the proposed research to fill an identified knowledge gap.

Methods described with sufficient detail & information to replicate. However there are many Contrast enhancement techniques such as Filtering with morphological operator or Histogram equalization or Median filtering. The notion of choosing CLAHE should be justified with more details.

Training testing percentage not mentioned clearly in Table 1. The number of images for each class.

Line no. 164 needs a full stop.
Line no 173 "Adaptation of such an approach introduced…" reframe this.
Same nomenclature should be used throughout the paper such as table 3 contains "our method" whereas table 5 uses "proposed method"
Validity of the findings: Conclusions are well stated, linked to original research question & limited to supporting results.

% improvement in the evaluation can be added in the results table as the last row to state the impact of the proposed model.

Author should include some small portion of future work mentioning following:
https://www.sciencedirect.com/science/article/pii/S2405844021013141
https://doi.org/10.1155/2021/8828404

What about using Grad CAM for visual explanations of the detection as given in https://peerj.com/articles/cs-348/ and https://ieeexplore.ieee.org/abstract/document/9391727

https://doi.org/10.7717/peerj-cs.340 for Lime tool in XAI

If data is not balanced authors must state some data augmentation techniques and particularly the advanced using GANs as in https://link.springer.com/article/10.1007%2Fs00500-019-04602-2

or in the upcoming domains such as transfer learning/domain adaptation/adversarial learning etc.
Additional comments: Overall a well written paper with impressive 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: KL-MOB: AUTOMATED COVID-19 RECOGNITION USING A NOVEL APPROACH BASED ON IMAGE ENHANCEMENT AND A MODIFIED MOBILENET CNN
Review round: 2
Reviewer: 1

Output:
Basic reporting: The emergence of the novel coronavirus pneumonia (Covid-19) pandemic at the end of 2019 led to worldwide chaos. However, the world breathed a sigh of relief when a few countries announced the development of a vaccine and gradually began to distribute it. Nevertheless, the emergence of another wave of this pandemic returned us to the starting point. At present, early detection of infected people is the paramount concern of both specialists and health researchers.
Experimental design: This paper proposes a method to detect infected patients through chest x-ray images by using the large dataset available online for Covid-19 (COVIDx), which consists of 2128 X-ray images of Covid-19 cases, 8066 normal cases, and 5575 cases of pneumonia. A hybrid algorithm is applied to improve image quality before undertaking neural network training. This algorithm combines two different noise-reduction filters in the image, followed by a contrast enhancement algorithm. To detect Covid-19, we propose a novel convolution neural network (CNN) architecture called KL-MOB (Covid-19 detection network based on the MobileNet structure).
Validity of the findings: The performance of KL-MOB is boosted by adding the Kullback–Leibler (KL) divergence loss function when trained from scratch. The KL divergence loss function is adopted for content-based image retrieval and fine-grained classification to improve the quality of image representation. The results are impressive: the overall benchmark accuracy, sensitivity, specificity, and precision are 98.7%, 98.32%, 98.82%, and 98.37%, respectively.
Additional comments: (i)	Pls give the reasons why “patients can have a good chance of survival if they are diagnosed sufficiently early.”?
(ii)	Why do we need noise reduction here in “One way around these issues is to use proper image preprocessing techniques for noise reduction and contrast enhancement.”?
(iii)	Some COVID-19 papers could be discussed, see “MIDCAN: A multiple input deep convolutional attention network for Covid-19 diagnosis based on chest CT and chest X-ray” and “PSSPNN: PatchShuffle stochastic pooling neural network for an explainable diagnosis of COVID-19 with multiple-way data augmentation”
(iv)	Eq.2 what does Z mean?
(v)	How do you design the model?
(vi)	What is the effect of KL divergence?
(vii)	What can you find from “In contrast, the output vector 256 in the original data achieved the best value with an accuracy of 93.24 %.”?


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: DETERMINING THE NUMBER OF HIDDEN LAYER AND HIDDEN NEURON OF NEURAL NETWORK FOR WIND SPEED PREDICTION
Review round: 1
Reviewer: 1

Output:
Basic reporting: In this paper, the topology of the neural network is determined using principal component analysis (PCA) and K-Means clustering. Some important comments
1. Major contributions of the paper must be represented point-wise.
2. The last paragraph of the Introduction must be the Structure of the paper.
3. Why topology is important?
4. Why PCA is used. Mention properly.
5. How the dataset is chosen? Whether it is genuine? justify.
6. How variance is calculated?
Experimental design: 1. Why RMSE is chosen for evaluating topology?
2. How the number of neurons is calculated?
3. Why training is conducted using 70% of the data from the dataset, while testing is done using 30% of the data from the dataset?
4. DIscuss the experimental environment.
Validity of the findings: 1. Technical discussion on results must be mentioned.
2. The authors must use the proper software to draw graphs.
Additional comments: 1. The English language must be improved.
2. All the key terms of the equations must be defined.
3. Draw a flowchart for a better understanding of the proposed scheme.
4. Add section number.
5. Try to give the figures and tables in the appropriate places.
6. Include the following references to improve the reference section:
Feature recognition of abstract art painting multilevel based convolutional ancient recognition neural network method”, Journal of Interconnection Networks, 2021. DOI: https://doi.org/10.1142/S0219265921410036
“Efficient algorithm for big data clustering on single machine,” CAAI Transactions on Intelligence Technology, vol. 5, no. 1, pp. 9-14, 2020.
“Nonlinear neural network based forecasting model for predicting COVID-19 cases”, Neural Processing Letters, 2021. DOI: 10.1007/s11063-021-10495-w
“Ensemble algorithm using transfer learning for sheep breed classification”, Proc. of the 2021 IEEE 15th International Symposium on Applied Computational Intelligence and Informatics (SACI), IEEE, Timisoara, Romania, pp. 199-204, 2021. DOI: 10.1109/SACI51354.2021.9465609
“Advances on QoS-aware web service selection and composition with nature-inspired computing,” CAAI Transactions on Intelligence Technology, vol. 4, no. 3, pp. 159-174, 2019
“Survey on cloud model based similarity measure of uncertain concepts,” CAAI Transactions on Intelligence Technology, vol. 4, no. 4, pp. 223-230, 2019.
“Fast and secure data accessing by using DNA computing for the cloud environment”, IEEE Transactions on Services Computing, 2020. DOI: 10.1109/TSC.2020.3046471
“Securing multimedia by using DNA based encryption in the cloud computing environment”, ACM Transactions on Multimedia Computing, Communications, and Applications, vol. 16, no. 3s, 2020. DOI: https://doi.org/10.1145/3392665


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: DETERMINING THE NUMBER OF HIDDEN LAYER AND HIDDEN NEURON OF NEURAL NETWORK FOR WIND SPEED PREDICTION
Review round: 1
Reviewer: 2

Output:
Basic reporting: Organization of the content good used professional language. Used state of art for solving problem.
Experimental design: Data collection and algorithms used for processing are good to find hidden patterns in data for wind speed prediction using ANN.
Validity of the findings: Impressive results with high accuracy RMSE value depicting how model learns from data.
Additional comments: Novel work with different hybrid 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: DETERMINING THE NUMBER OF HIDDEN LAYER AND HIDDEN NEURON OF NEURAL NETWORK FOR WIND SPEED PREDICTION
Review round: 1
Reviewer: 3

Output:
Basic reporting: The main aim of the paper is the determination of topology of neural network whose objective function is regression.

The authors made a systematic contribution to the research literature in this area, however the relevance to objective function is uncertain.
Experimental design: The manuscript is well written.  The introduction part is relevant and sufficient information about  the previous study findings is presented.

The main concern here is the provision of rationale  for the use of PCA and K-means clustering to determine the topology should be provided.
Validity of the findings: Overall,  the results are clear and compelling with  two possible exceptions.
1.	 The use of performance comparison with the mentioned author methods.  Are the methods benchmark methods to compare??
2.	Also,  are the methods used for comparison using the same objective function proposed in the research.
Additional comments: The authors can also better explain whether the performance measure RMSE sufficient to prove the efficiency of PCA and K-means in the proposed statement.


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: NAVIGATING THE AMINO ACID SEQUENCE SPACE BETWEEN FUNCTIONAL PROTEINS USING A DEEP LEARNING FRAMEWORK
Review round: 1
Reviewer: 1

Output:
Basic reporting: A proofread for language clarity and minor errors is advisable (especially in the abstract)
Intro and background cover the majority of necessary topics
The structure is clear and figures are close to ready with minor tweaks
Raw data and code all seem to be openly available. I'd like the naming system for the data files to be made clearer in a metadata file or in the supplementary PDF though.
Experimental design: I very much like the application of AAEs to this topic. The exploratory and descriptive aspects of the work are pretty well defined. However the conditions for success in the design aspects are less clearly articulated (see below). The inclusion of the necessary code  greatly aids replicability.
Validity of the findings: The author will need to be more cautious in their ever-interpretation of several results. Specifically, the protein designs were not experimentally validates, so there is no guarantee that they have the predicted function. Even seemingly minor differences to native sequences can have large effects on activities. In the absence of a collaboration to biochemically characterise the designs (likely beyond the scope of this work), care should be taken to be clear that the method has generated potentially designs (suggested to be promising by several in silico test). It is noted in L485-488, but needs to be more consistently framed throughout e.g. clarify "successfully transfer" versus "predicted to successfully transfer" or "likely to transfer".
Additional comments: Abstract & intro

L14 - "Protein sequence / function space" be cautious to be clear whether you're referring to sequence space, function space, or the space of mappings from sequence-to-function (which can be significantly different in both concept and structure).

L16 - The paper doesn't really end up differentiating between "relationships between protein positions and functions" versus "sequence patterns associated with functions". Might be better to combine these into a single concept for the abstract.

L21 - State the three model families here!

L23-25 - "The study also reports and analyzes for the first time two sampling strategies based on latent space interpolation and latent space arithmetic to generate intermediate protein sequences sharing sequential and functional properties of original sequences issued from different families and functions." Be very careful with this statement. Yes, the strategies generate intermediate protein sequences. No, they don't necessarily share functional properties (would need to be experimentally verified via recombinantly expressed protein)! Handled better at L351's "correspond to plausible sequences".

L30 - Again "successfully transfer functional properties between sub-families". Not experimentally confirmed. These protein designs could turn out to be non-functional (e.g. see success rates for Hilvert's or Baker's designs e.g "Robust design and optimization of retroaldol enzymes" doi.org/10.1002/pro.2059)

Methods

L148 - For the initial deep learning model, were amino acids each treated as different characters, or biochemical similarities taken into account? Note: there are multiple ways to encode protein sequence information for  (e.g. "Evolution of Sequence-Diverse Disordered Regions in a Protein Family: Order within the Chaos" doi.org/10.1093/molbev/msaa096, "A method to predict functional residues in proteins" doi.org/10.1038/nsb0295-171, "Principal components analysis of protein sequence clusters" doi.org/10.1007/s10969-014-9173-2).

L176 - What was the dimensionality before dimensionality reduction?

Results

L252 - It would be worth noting why gappy columns were removed in this instance. I.e. In what way do they otherwise skew results?

L252 - How were remaining gaps handled? Were they simply treated as another character as with other amino acids, or encoded differently?

L272 - some of the colours of Fig 1 are extremely similar, making the in-image legend hard to follow. I recommend in at least panel A, overlaying the labels 1 to 11 over / next to their respective clusters

L272 - It's worth noting Fig S3 equivalent for HUP and TPP in the legend of Fig 1A.

L338 - some text on Fig 2 is unreadably small. I recommend enlarging the text on the right of panel B, or possibly making the panels vertically arranged so that each is full page width

L386 - "(Panels A and D)", I assume of Fig S6?

Discussion & conclusion

L482 - The comparison to image generation (or other AAN applications) might be interesting to note in more detail. How do these tasks compare in practice (e.g. accuracy, separation of clusters)?

L469-487 - How do the observations here compare to VAEs or similar methods? E.g. is the smooth transition during interpolation unique to this method or a common feature?

L508 - "Promising avenues" rather than "solutions" probably more accurate (assuming solutions in the sense of solution to a problem, rather than solution to an equation)

Minor language notes

A proofread for clarity and minor errors is advisable (especially in the abstract)
L16 - Missing comma between "functions, capture"
L17 - Probably best to include an oxford comma between "functions, or" to be safe
L17 - probably plural areas
Some sentences also get quite long. I recommend a quick read of the Structure of Prose and Stress Position sections of www.americanscientist.org/blog/the-long-view/the-science-of-scientific-writing.


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: NAVIGATING THE AMINO ACID SEQUENCE SPACE BETWEEN FUNCTIONAL PROTEINS USING A DEEP LEARNING FRAMEWORK
Review round: 1
Reviewer: 2

Output:
Basic reporting: 1.  The  most  important  issue  is,  that  the  difference  between  source,  queryand target families is not clear.  In the methods section, source and query sub-families are nicely explained (l 213 - 214).  However target sub-families are first mentioned in the results section (l 323).  It seems like the source - query  pair  is  used  for  latent  space  arithmetic  and  query - target  pair for interpolation, but this is never clarified.  For the reader to understand it better either use the same word pair throughout or clearly explain the different word pairs and why they should be distinguished.

2.  Another  important  point  is  the  improvement of language.   For example, a  few  sentences  that  should  be  inspected  are  in  the  following lines:
l 16-20, l 28-31, l 56-58, l 465-467, l 477-479
Other small grammatical errors like missing words or conjugation of verbs should be corrected in the following lines:
l 41, l 50, l 56, l 110, l 188, l 195, l 381, l 438, l 461-462, l 492, l 493, l 495

3.  Another  important  issue  that  needs  clarification  concerns  figure  3  including corresponding caption and flow text (l 389 - 409). The caption of the figure is clear on its own, but more difficult to understand when looking at the flow text. There seem to be two paragraphs describing the same figure in different words. Lines 401 - 409 correspond to the figure captions and the sub-families given in the caption are very helpful. However, the  bullet points (l 392 - 396) are difficult to relate to the description of the symbols in figure 3.  As mentioned under issue 1, there is a confusion between the source - query and query - target pairs.

4.  One less important point is a missing reference for latent space arithmetic (l 209 - 210, l 482 - 483). One refernence is mentioned in the results section (l 358), but to make this clearer you should also refer to it in the methods and discussion.

5.  Another point concerns the Shannon entropy. It nicely visualises the variability in biological protein space compared to the generated sequences. But the Shannon entropy is first mentioned in the results (l 329). It is not entirely clear how and why it is used. To clarify this, you could describe it to the methods and add a reference (see experimental design).
Experimental design: 1.  The methods are generally well referenced and described, except for the Shannon entropy calculation, as mentioned above. Please add an explanation and reference (e.g.  Jost, 2006, Oikos, 113) to the methods.

2.  The  names  of  the  scripts  are  very  descriptive  and  show  their  purpose. However, you could make the scripts more useful for other researchers by including a README to the scripts folder.
Validity of the findings: 1.  Statistical measures would make the findings more valid, i.e.R2 for fitted curves or lines in figure 2. You could also add if  there is a statistically significant difference between the groups in figure 3.
Additional comments: 1.  A glossary could be of help, especially for the issue addressed above (1.Basic reporting). This  would  avoid confusion, for example between the source, query and target sub-families.

2.  I had a look at the older version of  the paper (”Exploring  protein  se-quence and functional spaces using adversarial autoencoder”, 2020) containing more figures in the main text. I would support the  decision to move some figures from the supplementary in the current version back to the main text.  Especially supplementary figure 2 could be helpful in the mainmanuscript.

3.  You could elaborate more on how much of the sequence space is known. Useful references are for example Taverna  and  Goldstein  (2002,  Proteins,  46),  Goldstein  and  Pollock (2016,  Protein  Science,  25),  Marchi  et  al.   (2019,  PLOS  Computational Biology, 15) or Alva et al.  (2010, Protein Science, 19).


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: DECODERS CONFIGURATIONS BASED ON UNET FAMILY AND FEATURE PYRAMID NETWORK FOR COVID-19 SEGMENTATION ON CT IMAGES
Review round: 1
Reviewer: 1

Output:
Basic reporting: English needs to be moderately revised to be Grammarly correct.
What is the novelty of the proposed research, what is your main contribution?
Could you please explain the difference the Unet family?

“Ronneberger et al. (2015a) and ? demonstrated that the improvement of segmentation tasks recently relied 138 more on the encoder-decoder than other architecture.” What does the question mark mean?
Do you mean that all the features from Unet family are used? Or any specific Unet is finally used?
What the meaning of Cn?
Check equation 2 and equation 7? Why do you have two definitions for Jaccard?
Could you compare your proposed method with the recently published method, such as “Covid-19 Classification by FGCNet with Deep Feature Fusion from Graph Convolutional Network and Convolutional Neural Network”
“Diagnosis of COVID-19 by Wavelet Renyi Entropy and Three-Segment Biogeography-based Optimization”.
Experimental design: See the basic reporting
Validity of the findings: See the basic reporting
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: DECODERS CONFIGURATIONS BASED ON UNET FAMILY AND FEATURE PYRAMID NETWORK FOR COVID-19 SEGMENTATION ON CT IMAGES
Review round: 1
Reviewer: 2

Output:
Basic reporting: For the most part, this report is clear and well organized. I very much appreciate the clear structure of the paper and the concise language. However, there are still some minor issues throughout the paper.
Here are several issues that I found.
1. In the abstract, line 24 to 26 has grammar issues.
2. There are almost no citations that are included in the introduction part.
3. Line 137 to 140 has some grammar issues.
4. Quality of Figure 6 can be improved
5. Table 5 might be more clear if it's rotated by 90 degrees.
6. Equation 2 is weirdly placed.
7. The discussion about recall precision and F1 score is a bit redundant since it is a fairly commonly used metric.
Experimental design: The experiment of this paper is well designed. However, It might be beneficial to include the result of the segmentation on the unaugmented image also. Given that in the benchmark papers, the authors didn't use the same augmentation step, it is a weaker claim to say that the improvement in results presented in this work mainly comes from the superior structure without showing those results.
Validity of the findings: The finding of the paper is valid.
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: DECODERS CONFIGURATIONS BASED ON UNET FAMILY AND FEATURE PYRAMID NETWORK FOR COVID-19 SEGMENTATION ON CT IMAGES
Review round: 1
Reviewer: 3

Output:
Basic reporting: English writing is not clear, many sentences are awkward.
In many places upper-case letters are used instead of lower case where is not necessary.
The text in most figures is not clear. Try saving your drawings as tiff images and not pngs. Make them bigger from the beginning.
The introduction is totally unrelated to the paper’s topic. The paper is about developing, identifying, and evaluating deep learning methods for image segmentation and not about information technology in medicine.
The related works section is very badly written. It is just an enumeration of citations with no descriptions in between. It doesn’t tell a story nor it presents a clear logical
Lines 115-119 in the Methods section, should be moved at the end of the Introduction.

Line 42 – “We” needs to be changes to lowercase “we”.
Line 58 -  “Imaging” needs to be changes to lowercase “imaging”.
Line 137 – There is a missing reference.
Figure 6 and 9 – Text is not clear. Try to improve the quality of your image.
Experimental design: This paper proposes a new improved method that combines pretrained ResNet architectures with Unet++ for image segmentation. The application described here is lung segmentation in CT images.
The research question addressed in the paper is unclear.
The methods section is more like a second related works section. Evaluation metrics are described in more details compared to the actual methods.
Validity of the findings: It looks like the proposed approach improves the results compared to other methods, but at what cost. No training or inference time are reported in the paper. What is the main advantage of the presented method?
The paper specifies a github repository, however the code uploaded there is not enough to replicate the experiments in the paper.
It is unclear to me what makes some of the 20 configurations new.
Additional comments: The proposed method looks more like a brute force approach than a new improved method.
The paragraph about a graphical interface is irrelevant to 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: DECODERS CONFIGURATIONS BASED ON UNET FAMILY AND FEATURE PYRAMID NETWORK FOR COVID-19 SEGMENTATION ON CT IMAGES
Review round: 2
Reviewer: 1

Output:
Basic reporting: NONE
Experimental design: NONE
Validity of the findings: NONE
Additional comments: None


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: DECODERS CONFIGURATIONS BASED ON UNET FAMILY AND FEATURE PYRAMID NETWORK FOR COVID-19 SEGMENTATION ON CT IMAGES
Review round: 2
Reviewer: 2

Output:
Basic reporting: The paper meets all criteria in basic reporting.
Experimental design: The experimental design is solid.
Validity of the findings: The findings in the paper are valid
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: AN ENSEMBLE MACHINE LEARNING MODEL BASED ON MULTIPLE FILTERING AND SUPERVISED ATTRIBUTE CLUSTERING ALGORITHM FOR CLASSIFYING CANCER SAMPLES
Review round: 1
Reviewer: 1

Output:
Basic reporting: The draft is very preliminary. It is very verbose as some similar sentences/concepts were repeated several times, and a better organization is required to make it simple and clear.

The code is available through github.
Experimental design: The overall design is OK, but more details were required.
Validity of the findings: For the validation purpose, the authors should provide an independent dataset of the same cancer, using the genes selected by their filters on their current dataset, and then evaluate the classification performance on the independent dataset. In this case, their models were more general.
Additional comments: Review of  Manuscript 54940, "An Ensemble Machine Learning Model based on Multiple Filtering and Supervised Attribute Clustering Algorithm for Classifying Cancer Samples "

This paper applied machine learning methods to classify cancer samples based on the gene expression profiles. Their method was an ensemble classification model based on 7 sets of genes from 7 different filters, namely: Fisher-score, Chi-square, Relief, t-test, Mutual information, SNR and Pearson correlation. Briefly, for a microarray gene expression dataset of U samples, and V genes, a filterScore was calculated for each gene for each filter. Then, for each filter, a greedy algorithm was used to choose the best subsets of genes that could best classify the samples. After that, data of a subset of genes were selected for one of 4 classical ML methods: K-Nearest Neighbor, Naive Bayes, Support vector machine, and Decision tree. Finally, a vote from all 7 filters concludes the class of a sample.

The draft is very preliminary. It is very verbose as some similar sentences/concepts were repeated several times, and a better organization is required to make it simple and clear.

Main comments:
	1.Please add a summary table of the 7 filters in this paper. That table should include the definitions of each filter, and how the FT scores were calculated. In the current setting, the reviewer was confused about the differences between t-test and Chi-square, as most time, these 2 methods contain the same information when there are 2 classes.
	2.The figure legends were missing.
	3. Please use “gene”, or “variate”, or “feature” consistently throughout the draft.
	4. From line 309 to 323, for each variable, please note whether they were matrixes, vectors, or just genes.
	5. Why not just tell the readers Y=7, to reduce symbols in the draft.
	6. Line 341-343:
“Compute two augmented representatives 〖AR〗_xk^+ and 〖AR〗_xk^- by adding G_j and its complement with 〖AR〗_xk respectively
Compute class relevance value of 〖AR〗_xk^+ and 〖AR〗_xk^- using 〖FT〗_x score function”
Please write clearly how those values were calculated.
	7. Line 250: 〖GS〗_x=〖GS〗_x⁄G_m , Is this updating GSx values? From Line 315,”〖GS〗_x is a subset of top-ranked genes of G selected using 〖FT〗_x score function and SD_x is corresponding sub dataset.”, GSx is a set of genes. Really confusing.
	8. A venn diagram of genes selected for each filters could help understand the distinct contributions of each filter for each dataset.


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 MACHINE LEARNING MODEL BASED ON MULTIPLE FILTERING AND SUPERVISED ATTRIBUTE CLUSTERING ALGORITHM FOR CLASSIFYING CANCER SAMPLES
Review round: 1
Reviewer: 2

Output:
Basic reporting: There are some nonstandard quotations of references. Such as lines 59,61,67,75,96, 99, 102 and so on. The references 5,10,63 and so on.
Experimental design: The authors proposed a supervised algorithm for classifying cancer samples. It will be reasonable if they compare it with some supervised algorithms instead of unsupervised algorithms.
Validity of the findings: no comment
Additional comments: This manuscript proposed a machine learning based on a new ensemble classification model named MFSAC-EC for classifying cancer samples and gene selection. They used some classifiers and data sets for comparison, and obtained better results. Though novel, such minor improvement may not lead to novel biomarker discovery. There are still some questions. The language needs significant corrections as there are many grammatical errors and typos.
Below are specific questions, suggestions, and comments.
（1）	The authors proposed a supervised algorithm for classifying cancer samples. It will be reasonable if they compare it with some supervised algorithms instead of unsupervised algorithms.
（2）	How is the data imbalance addressed? Using accuracy as an evaluation metric may be misleading given that the datasets are commonly imbalanced in cancer research.
（3）	There are some nonstandard quotations of references. Such as lines 59,61,67,75,96, 99, 102 and so on. The references 5,10,63 and so on.
（4）	More information about feature selection should be detailed in the Introduction.
（5）	There is some redundant information. In the Results, such as lines 430-432; 434-437; 451-454 and so on. Please check the whole manuscript.
（6）	Figure 4 is very hard to read, please consider enlarging the figure and improve the resolution.


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 MACHINE LEARNING MODEL BASED ON MULTIPLE FILTERING AND SUPERVISED ATTRIBUTE CLUSTERING ALGORITHM FOR CLASSIFYING CANCER SAMPLES
Review round: 1
Reviewer: 3

Output:
Basic reporting: - A very complex ensemble learning based method is proposed for cancer classification on microarray data and identification of important genes for classification
- Achieved very good classification accuracy (90~100%) on 10 public datasets with limited number of samples (maximum is 181 samples)
Experimental design: - The proposed MFSAC-EC method consists of two stages [See Figure 3]
Stage 1: create sub-datasets and select associated genes to be used for classifier creation in stage 2

  > Step 1: use 7 filter score functions ((1) Fisher-score (2) Chi-square (3) Relief (4) t-test (5) Mutual information (6) SNR and (7) Pearson Correlation) to select sub-datasets and associated genes
  > Step 2: use supervised attribute/gene clustering algorithm (SAC) to refine select sub-datasets and associated genes
  >Step 3: identify most important genes for classification by counting occurrence frequency of genes in step 2

Stage 2: ensemble multiple classifiers using sub-datasets from stage 1
  > (training) Use sub-datasets and associated genes form step 2 of stage 1 to train 4 types of classifiers ((1) K-Nearest Neighbor, (2) Naive Bayes, (3) Support vector machine, (4) and Decision tree)
  > (apply) Use majority voting to ensemble result from 4 classifiers
Validity of the findings: - Achieved very good classification accuracy (90~100%) on 10 public datasets with limited number of samples (maximum is 181 samples)
    > Performed better than other well-known ensemble learning classifiers [table 7, 8, Figure 8]
    > Performed better than other well-known gene selection methods [Figure 7]
Additional comments: - Method is very complex (does it need to be so complex in order to perform well?) and there are too many options and parameters to adjust for performance optimization. For example, there are many choices of score functions and ensembled classifiers. And, how to adjust associated parameters of those filter score functions and classifiers.
   > No processing time comparison data. Ensemble method could take much longer to implement and execute.
   > Superior performance is claimed on small data samples.

- Authors should consider unsupervised methods and potentially deep learning as it has been shown numerous times that it can be much more efficient at generalizing over large data pools. At least the authors should discuss why they did not use unsupervised methods or deep learning.

- [Line 265-272] Description of merging process and its connection with Figure 2 is not easy to understand.
   > “Here in the case of gene1, the augmented1+ gives the better score while for gene2 augmented2- gives a better relevance score.”
         - The better score for gene1 and better relevance score of gene2 are not provided.

- Figure 3. No differentiation between data block and processing block in the block diagram. They are both shown as rectangular shape blocks. To help the reader, they could be shown in different shape of blocks.
   > Figure 2, Figure 4 (invisible), Figure 9
       - Charts without axis titles or invisible axis titles


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 MACHINE LEARNING MODEL BASED ON MULTIPLE FILTERING AND SUPERVISED ATTRIBUTE CLUSTERING ALGORITHM FOR CLASSIFYING CANCER SAMPLES
Review round: 2
Reviewer: 1

Output:
Basic reporting: In the revised manuscript, the authors addressed all my concerns.
Experimental design: no comment
Validity of the findings: no comment
Additional comments: In the revised manuscript, the authors addressed all my concerns.


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

Input:
Title: A NOVEL AIR QUALITY MONITORING AND IMPROVEMENT SYSTEM BASED ON WIRELESS SENSOR AND ACTUATOR NETWORKS USING LORA COMMUNICATION
Review round: 1
Reviewer: 1

Output:
Basic reporting: n/a
Experimental design: n/a
Validity of the findings: n/a
Additional comments: The Research Paper needs the following Minor Revisions and is subject for the following revisions and after revisions, the paper is recommended for re-review:
1 Make sure you use the same style of notation and make sure all be consistent.
2 Thoroughly revise the manuscript to correct all typo/grammatical/spelling errors.
The authors consider editing some sentences to be more appropriate, for example:
- Line 63: Besides hardware, wireless communication techniques in WSAN networks are also a significant issue
- Line 273: The second Task, the second, transmitters every time it is received from the nodes to gateway. Moreover, the last Task is optional and ensures the visual mechanisms on OLED for debugging.
- Line 284: Anyone can build apps without permission from big companies or the government
3 Authors should clearly define the terms LoRa and LoRaWAN used in manuscripts. Some places there is confusion between these two terms, for example line 94, line 95, line 156, line 160.
4 The author clarifies the impact of FreeRTOS on system performance. What is the size of this operating system? Is FreeRTOS really effective when applied to a small microcontroller like Atmega328P? Get some line updates for an explanation FreeRTOS applied in this system.
5 The author updates more information about the ESP-NOW protocol used in the system. State the reason for using this protocol in the proposed system, when LoRaWAN has a very optimal coverage distance.
6 Why do the authors suggest using a hybrid protocol between LoRa and LoRaWAN? Can the recommendation system fully utilize LoRaWAN? The author presents some discussion on this issue.
7 In the proposed figure, do outdoor nodes use an air quality improvement mechanism? Get some line updates for this issue.
8 The full-duplex relay node algorithm is ambiguous. Tasks have not been clearly shown their priority. The graphical representation of the sequential algorithm has not yet exploded the advantage of the parallel work of FreeRTOS.
9 Where is the Kalman filter used in this proposed system? Is it really necessary to use digital filters in the proposed model?


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 NOVEL AIR QUALITY MONITORING AND IMPROVEMENT SYSTEM BASED ON WIRELESS SENSOR AND ACTUATOR NETWORKS USING LORA COMMUNICATION
Review round: 1
Reviewer: 2

Output:
Basic reporting: no comment and please see below
Experimental design: no comment and please see below
Validity of the findings: no comment and please see below
Additional comments: After reviewing this manuscript, the reviewer suggests the following corrections. After that,  the final decision for this manuscript will be made:
* The authors check the abbreviations to ensure that they are consistent throughout in the manuscript. The authors correct some unclear sentences in the manuscripts, for example:
- Line 215 and 216: First, as mentioned, two LoRa modules’ simultaneous operation enables the system to expand without increasing communication delay.
- Line 219: … in half-duplex mode, the system performance will be improved
- Line 223: The downside of this 223 way of working is to increase communication delay. However, it can increase redundancy for the system
* What is the purpose of using the Blynk dashboard, while TagoIO already provides dashboards that can be displayed on Smartphones very well? The authors provide some reference to the Blynk dashboard.
* What communication class is the LoRa modules in the LoRaWAN system using? The authors present some information about this communication class.
* Add a few sentences in the conclusion to highlight the contributions of the work.
* Add some sentences describing the scalability of the clusters in the system. How many sensors is the system capable of supporting? How many nodes can the system scale up?
* In general, the modules in the whole manuscript are presented clearly, however, the author should add a complete model of the system to highlight the contribution of this paper.
* The tests in the EXPERIMENTATION AND RESULTS section are not clear. Discuss how these tests are performed and the time it takes to collect the data.
* Some system parameters have not been fully added to table 1, such as SF. Please check again and complete 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: A NOVEL AIR QUALITY MONITORING AND IMPROVEMENT SYSTEM BASED ON WIRELESS SENSOR AND ACTUATOR NETWORKS USING LORA COMMUNICATION
Review round: 2
Reviewer: 1

Output:
Basic reporting: The revision is now better than the previous version and good for reading.
Experimental design: The questions and objectives of this paper is now clear and well-defined. The experimental results can also validate what the authors find.
Validity of the findings: The contributions now are clear.
Additional comments: This paper is revised accordingly and can be accepted for publication. The reviewer appreciates the work.


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

Input:
Title: A NOVEL AIR QUALITY MONITORING AND IMPROVEMENT SYSTEM BASED ON WIRELESS SENSOR AND ACTUATOR NETWORKS USING LORA COMMUNICATION
Review round: 2
Reviewer: 2

Output:
Basic reporting: The revised version of the paper is well written and organized and the authors perfectly reply to all the reviewer comments.
Experimental design: All good
Validity of the findings: No more work reqiured.
Additional comments: No 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 INTEGRATED THREE-TIER TRUST MANAGEMENT FRAMEWORK IN MOBILE EDGE COMPUTING USING FUZZY LOGIC
Review round: 1
Reviewer: 1

Output:
Basic reporting: This paper presents an integrated three-tier trust management framework that evaluates cloud service providers in three main domains; tier I-evaluates service provider compliance to the agreed service level agreement, tier II computes the processing performance of a service provider based on its number of successful processes, and tier III-measures the violations committed by a service provider during its processing in the MEC network. The three-tier evaluation is performed in phase I computation. In phase II, a service provider trust value and status are gained through the integration of the three tiers using the developed overall trust fuzzy inference system (FIS).
Experimental design: Satisfacatory
Validity of the findings: Satisfactory
Additional comments: 1. Even though the motivations of the current work are clearly discussed, the contributions of the paper are not discussed. What are the main contributions of the current work?
2. The literature review section can be summarized as a table.
3. Some of the recent works on fuzzy systems, cloud computing and security such as the following can  be discussed "Load balancing of energy cloud using wind driven and firefly algorithms in internet of everything, Big data for cybersecurity: Vulnerability disclosure trends and dependencies, Senti‐eSystem: A sentiment‐based eSystem‐using hybridized fuzzy and deep neural network for measuring customer satisfaction".
4.  The main contribution of any paper is the proposed work. This section is very small in this paper. It has to be elaborated with discussion on he novelty and more detailed discussion on the proposed work.
5. The limitations and the future scope of the current work can be discussed in conclusion.
6. Also, the application of big data analytics in dealing with big data in the cloud can be added as a future direction as discussed in "A survey on blockchain for big data: Approaches, opportunities, and future directions".


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 INTEGRATED THREE-TIER TRUST MANAGEMENT FRAMEWORK IN MOBILE EDGE COMPUTING USING FUZZY LOGIC
Review round: 1
Reviewer: 2

Output:
Basic reporting: Many grammatical errors and typos are found (all the way from Abstract to Conclusion).
Experimental design: Comparison of the proposed work with existing work can be useful.
Validity of the findings: no comment
Additional comments: 1. The flow of the introduction should be.. introduce the problem at high level, discuss about some of the existing solutions, identify the gap or scope of improvement, and then discuss in order to address the identified gaps what is the methodology we are using after that we have to list out the contributions
2. Literature review needs to be revised majorly and extended by including some latest articles. Comparison of different works can be summarized in a table.
3. Section 3 contains very less contents. Can it be merged with Section 4 along with Section 5?
4. Figures quality are poor.
5. Comparison of the proposed work with existing work can be useful.
6. Conclusion should be revised to write the concluded facts and should not reflect abstract.
7. The authors can discuss about the following relevant papers in their work:
Consumer Electronic Devices: Evolution and Edge Security Solutions
A Sequence Number Prediction Based Bait Detection Scheme to Mitigate Sequence Number Attacks in MANETs
A Trusted Social Network using Hypothetical Mathematical Model and Decision-based Scheme


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 INTEGRATED THREE-TIER TRUST MANAGEMENT FRAMEWORK IN MOBILE EDGE COMPUTING USING FUZZY LOGIC
Review round: 1
Reviewer: 3

Output:
Basic reporting: In this paper, the authors presented an integrated three-tier trust management framework
that evaluates cloud service providers in three main domains. Below are some comments to improve the paper.

- Please highlight the contribution clearly in the introduction
- this paper lacks in Novelty of the proposed approach. The author should highlight the contribution clearly in the introduction and provide a comparison note with existing studies.
- Some Paragraphs in the paper can be merged and some long paragraphs can be split into two.
- The quality of the figures can be improved more. Figures should be eye-catching. It will enhance the interest of the reader.
- Figure 11, only the graph area should be added to the paper. remove grey borders. and the same for others.
- The background of figure "Figure 12 Service Providers Overall Trust results" should be white with font color black.

- Authors should add the most recent reference:
1) https://onlinelibrary.wiley.com/doi/abs/10.1002/spe.2846
2) https://ieeexplore.ieee.org/abstract/document/9068217
3) https://link.springer.com/article/10.1007/s11063-020-10414-5
- please give a proofread check to the paper.
Experimental design: - What are the computational resources reported in the state of the art for the same purpose?
Validity of the findings: - What are the evaluations used for the verification of results?
- Clearly highlight the terms used in the algorithm and explain them in the text.
Additional comments: 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: DESCRIPTIVE ANALYSIS OF DENTAL X-RAY IMAGES USING VARIOUS PRACTICAL METHODS: A REVIEW
Review round: 1
Reviewer: 1

Output:
Basic reporting: The authors presented a comprehensive survey on dental x-ray imaging.
Experimental design: Can be improved.
Validity of the findings: Not applicable.
Additional comments: 1. Is it the first of it kind survey on dental x-ray imaging? If not compare the current survey with existing surveys and highlight how it is different or enhances the existing surveys.
2. Some of the recent works on application of DN/CNN on several domains such as the following can be discussed when discussing about ML algorithms. "Deep learning and medical image processing for coronavirus (COVID-19) pandemic: A survey, A novel PCA–whale optimization-based deep neural network model for classification of tomato plant diseases using GPU, Hand gesture classification using a novel CNN-crow search algorithm".
3. A good survey should present a detailed section on challenges faced by the existing methodologies and also give directions to the researchers who want to carry out the research in that domain. I suggest that the authors can add a section, "challenges and future directions".
4. A good survey paper should have eye catching images. Check this paper for reference "Deep learning and medical image processing for coronavirus (COVID-19) pandemic: A survey"


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: DESCRIPTIVE ANALYSIS OF DENTAL X-RAY IMAGES USING VARIOUS PRACTICAL METHODS: A REVIEW
Review round: 1
Reviewer: 2

Output:
Basic reporting: Paper is well organized.
Experimental design: Rigorous investigation performed to a high technical & ethical standard
Validity of the findings: Conclusions are well stated, linked to original research question & limited to supporting results.
Additional comments: Please check grammar to submit final 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: DESCRIPTIVE ANALYSIS OF DENTAL X-RAY IMAGES USING VARIOUS PRACTICAL METHODS: A REVIEW
Review round: 1
Reviewer: 3

Output:
Basic reporting: no comment
Experimental design: A review method was chosen as research design
Validity of the findings: Appropriate
Additional comments: The authors have chosen a very important topic. The paper Is very interesting and timely. I suggest the authors incorporate these changes to further improve the quality of the paper.

1.	There are many Typos and grammatical errors throughout the manuscript.
2.	Figures clarity should be thoroughly enhanced.
3.	The introduction lacks many important references in the field.
4.	I wish to reorganize your paper with better and more clarity by Rectifying the above comments and submit the revised version.
5.	Introduction and related work is not synchronized.
6.	The conclusion and future work part can be extended to have a better understanding of the approach and issues related to that which  can be taken into consideration for future work.

Please cite the following relevant literature but not limited to:
1) Rehman, Z. U., Zia, M. S., Bojja, G. R., Yaqub, M., Jinchao, F., & Arshid, K. (2020). Texture based localization of a brain tumor from MR-images by using a machine learning approach. Medical hypotheses, 141, 109705.
2) Bhattacharya, S., Maddikunta, P. K. R., Pham, Q. V., Gadekallu, T. R., Chowdhary, C. L., Alazab, M., & Piran, M. J. (2021). Deep learning and medical image processing for coronavirus (COVID-19) pandemic: A survey. Sustainable cities and society, 65, 102589.
3) Sai Ambati, L., Narukonda, K., Bojja, G. R., & Bishop, D. (2020). Factors Influencing the Adoption of Artificial Intelligence in Organizations-From an Employee's Perspective.
4) Shabaan, M., Arshad, K., Yaqub, M., Jinchao, F., Zia, M. S., Boja, G. R., ... & Munir, R. (2020). Survey: smartphone-based assessment of cardiovascular diseases using ECG and PPG analysis. BMC medical informatics and decision making, 20(1), 1-16.


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: DESCRIPTIVE ANALYSIS OF DENTAL X-RAY IMAGES USING VARIOUS PRACTICAL METHODS: A REVIEW
Review round: 2
Reviewer: 1

Output:
Basic reporting: In this article, the authors have provided a comprehensive survey of dental image segmentation and analysis by investigating more than 130 research works conducted through various dental imaging modalities, such as various modes of X-ray, CT(Computed Tomography), CBCT (Cone Beam Computed Tomography), etc.
Experimental design: Satisfactory
Validity of the findings: Satisfactory
Additional comments: The authors have addressed all the comments. The manuscript can be accepted for publication.


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

Input:
Title: DESCRIPTIVE ANALYSIS OF DENTAL X-RAY IMAGES USING VARIOUS PRACTICAL METHODS: A REVIEW
Review round: 2
Reviewer: 2

Output:
Basic reporting: The authors have addressed my suggestions. I would like to accept this paper.
Experimental design: The authors have addressed my suggestions. I would like to accept this paper.
Validity of the findings: The authors have addressed my suggestions. I would like to accept this paper.
Additional comments: 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: DESCRIPTIVE ANALYSIS OF DENTAL X-RAY IMAGES USING VARIOUS PRACTICAL METHODS: A REVIEW
Review round: 2
Reviewer: 3

Output:
Basic reporting: Everything looks good. Authors did a good job to modify the manuscript based on reviewer comments.
Experimental design: NA
Validity of the findings: NA
Additional comments: Good job.


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: SEMI-SUPERVISED ASSOCIATIVE CLASSIFICATION USING ANT COLONY OPTIMIZATION ALGORITHM
Review round: 1
Reviewer: 1

Output:
Basic reporting: no comment
Experimental design: no comment
Validity of the findings: no comment
Additional comments: The paper topic is timely, structure wise is nice, and writing style is understandable and readable. Performance evaluation is perfect and sufficient to prove the system behaviour.  So, no concerns with the paper from these perspectives. However, the paper suffers some major issues as below:
1.  I don’t seem to be able to figure out where the list of novelty is. Please make sure you have clearly identified them in the first section.
2. Please pay more attention to the motivation of the work.
3. The authors should evaluate and include computational complexity of the proposed approach using asymptotic measurements.
4. The authors should use statistical analysis to determine the significant difference among the competing methods. There are some statistical methods for analyzing competing classifiers over multiple data sets.
5. Train and test validation is utilized to compute the accuracy in the comparison.  It has been proved that cross-validation is most adequate for comparing supervised classification algorithms than holdout validation  (train and test)  Cross-validation is used to overcome the limitations of the holdout validation , that is why it makes no sense to compute the accuracy using holdout validation. The correct method to compute the accuracy (or other measures) is cross-validation or repeated cross-validation.
6. The description is unclear on how to train the model. For example, the description of the training data set is lacking. Without this, it is hard to tell if the method is implemented properly.
7. The evaluation is not sufficient.  The authors may provide an adequate evaluation by using a larger dataset and comparing it to more state-of-the-art methods. With which, the authors may improve the impact of the work as well.
8. Theoretical explanations are very less in proposed approach.
9. Critical analysis with comparison with state of the art would have raised the quality of the paper.
10. Proofread the paper and double check the information of the references for completeness.


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: SEMI-SUPERVISED ASSOCIATIVE CLASSIFICATION USING ANT COLONY OPTIMIZATION ALGORITHM
Review round: 1
Reviewer: 2

Output:
Basic reporting: If possible, could you please describe how would the Rule-Based Semi-Supervised Associative Classification Approach and Ant Colony Optimization (ACO) work. Could you update more details of the principles and fundamentals of Unsupervised Learning, Semi-Supervised Learning, and Supervised Learning algorithms?

Figure 1, Page 8:

Could you respectively update more descriptions in Figure 1 on Page 8 from T1 to T6, and the “Infinite Component” at the bottom? What are the representations about Solid Line and Dotted Line? If possible, could you please mark these information mentioned above into the Figure 1?
Experimental design: The details of training data and testing data should be listed and respectively demonstrated the percentage of training data and testing data of the original dataset in terms of case studies or/and experimentations.

Line 195, Page 9:

Authors mentioned “Algorithm 1 illustrates the proposed ST-AC-ACO algorithm.” in Line 195 on Page 9. However “Algorithm 1 SSAC-ACO” is shown on Page 11, which cannot be efficaciously followed by readers.

The proposed algorithm ST-AC-ACO should be demonstrated as the form of Algorithm Environment, which can be efficaciously followed by readers. Additionally, the flowchart of proposed method ST-AC-ACO mentioned in this paper should also be updated and included the dataset collections, pre-processing of dataset, post-processing of dataset, feature extractions, feature selections, and the performances of fault classification.

Alternatively, different topologies of experimental samples and the number of significant feature selections might have either positive or negative impacts for the performances of fault classification for more complicated industrial systems to some extent. Based on this reason, could you please provide an evidence to make explanations the availability and feasibility of the proposed methodology based on ST-AC-ACO in the field of Semi-Supervised Associative Classification?
Validity of the findings: Which are the most advantages of your proposed method Self-Training Associative Classification using Ant Colony Optimization (ST-AC-ACO) in comparison with other existing techniques of fault classification? Could you please explain which are the differences between Semi-Supervised Learning and Supervised Learning algorithms and support an evidence of using Semi-Supervised Learning rather than Supervised Learning. Alternatively, could you please provide either some Tables or update simulation results to make an explanation the advantages of your proposed methodology ST-AC-ACO of fault classification and update some statistical criterion based on your research topic.
Additional comments: Recently, a new survey paper of wind turbine systems concentrates on fault diagnosis, prognosis and resilient control, which includes model-based, signal-based, and knowledge-based (data-driven) techniques to demonstrate the characteristics of fault detection, classification, and isolation in various faulty scenarios. In addition, some proposed methodologies on Semi-Supervised Learning and Ant Colony Optimisation have been addressed in the following journal papers.

If possible, could you please update any References for having in comparison with other algorithms in the specific field of your research topic for fault classification, as well as making any comments? These References are shown in the following below.

Reference 1:  X. Wang, et. al., “EnAET: A Self-Trained Framework for Semi-Supervised and Supervised Learning With Ensemble Transformations,” in IEEE Transactions on Image Processing, vol. 30, pp. 1639–1647, 2021.

Reference 2:  S. Mittal, et. al., “Semi-Supervised Semantic Segmentation With High- and Low-Level Consistency,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 43, no. 4, pp. 1369–1379, April 2021.

Reference 3:  “An Overview on Fault Diagnosis, Prognosis and Resilient Control for Wind Turbine Systems,” Processes, vol. 9, no. 2, p. 300, Feb. 2021.

Reference 4:  W. -N. Chen, et. al., “Ant Colony Optimization for the Control of Pollutant Spreading on Social Networks,” in IEEE Transactions on Cybernetics, vol. 50, no. 9, pp. 4053–4065, Sep. 2020.

Reference 5:  C. Juang, et. al., “Multiobjective Rule-Based Cooperative Continuous Ant Colony Optimized Fuzzy Systems With a Robot Control Application,” in IEEE Transactions on Cybernetics, vol. 50, no. 2, pp. 650–663, Feb. 2020.

Reference 6:  “Actuator and Sensor Fault Classification for Wind Turbine Systems Based on Fast Fourier Transform and Uncorrelated Multi-Linear Principal Component Analysis Techniques,” Processes, vol. 8, no. 9, p. 1066, Sep. 2020.


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: SEMI-SUPERVISED ASSOCIATIVE CLASSIFICATION USING ANT COLONY OPTIMIZATION ALGORITHM
Review round: 2
Reviewer: 1

Output:
Basic reporting: Paper looks good
Experimental design: I feel that this paper has the ability to deal with the real time on-field experimentation and the provided results are completely convincing.
Validity of the findings: The proposed scheme describes the author have a deep theoretical knowledge and very good orientation in the problem solving approach to delivered in the paper.
Additional comments: In the final version of revision, authors carefully addressed technical comments in the main text. I recommend acceptance on this round.


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: SEMI-SUPERVISED ASSOCIATIVE CLASSIFICATION USING ANT COLONY OPTIMIZATION ALGORITHM
Review round: 2
Reviewer: 2

Output:
Basic reporting: no comment
Experimental design: no comment
Validity of the findings: no comment
Additional comments: Which are the most advantages of your proposed method Semi-Supervised Associative Classification using Ant Colony Optimization Algorithm (SS-AC-ACOA) in comparison with other existing techniques of fault classification? Whether or not the following topic (https://doi.org/10.3390/pr9020300) can be discussed? Could you please explain which are the differences among Unsupervised Machine Learning (USML), Semi-Supervised Machine Learning (SSML), and Supervised Learning (SML) algorithms and support an evidence of using SSML rather than USML or/and SML. Alternatively, could you please provide either some TABLES or update Simulation or/and Experimental Results to make an explanation the advantages of your proposed methodology SS-AC-ACOA of fault classification and update some statistical criterion based on the research topic of this paper?

Additionally, the flowchart of proposed method SS-AC-ACOA mentioned in this paper should also be updated and included the dataset collections, pre-processing of dataset, post-processing of dataset, feature extractions, feature selections, and the performances of fault classification. Whether or not the following topic (https://doi.org/10.3390/pr8091066) can be discussed?

Specifically, it is worthy to point out, different topologies of experimental samples and the number of significant feature selections might have either positive or negative impacts for the performances of fault classification for more complicated industrial systems to some extent. Based on this reason, could you please provide an evidence to make explanations the availability, feasibility, and capability of the proposed methodology based on SS-AC-ACOA in the field of Semi-Supervised Associative Classification?


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: ON THE CLASSIFICATION OF SIMPLE AND COMPLEX BIOLOGICAL IMAGES USING KRAWTCHOUK MOMENTS AND GENERALIZED PSEUDO-ZERNIKE MOMENTS: A CASE STUDY WITH FLY WING IMAGES AND BREAST CANCER MAMMOGRAMS
Review round: 1
Reviewer: 1

Output:
Basic reporting: The authors use  Krawtchouk moments and Generalized
Pseudo-Zernike moments for the classification of fly wing
images and breast cancer mammograms. Experimental results reveal that the proposed feature extraction method produces prominent accuracy on both domains.
Experimental design: NA
Validity of the findings: The authors have produced some interesting results. However, I found the following lacking in the paper.
1. The performance has not compared with existing methods.
2. More evaluation methods may be used for the comparison
3. Additional papers related to breast cancer paper can enhance the quality of the paper:
Sitaula, C., Aryal, S. Fusion of whole and part features for the classification of histopathological image of breast tissue. Health Inf Sci Syst 8, 38 (2020). https://doi.org/10.1007/s13755-020-00131-7
Additional comments: The reviewer found lacking novelty in the paper. Specifically, the paper just utilizes well-established features without any novelty.  Also, the performance comparison is insufficient.  Rather than focussing on multiple datasets, I suggest working on only one with a deeper research understanding.  I believe that the paper is unable to meet the expectation of the PeerJ Computer Science journal.


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: ON THE CLASSIFICATION OF SIMPLE AND COMPLEX BIOLOGICAL IMAGES USING KRAWTCHOUK MOMENTS AND GENERALIZED PSEUDO-ZERNIKE MOMENTS: A CASE STUDY WITH FLY WING IMAGES AND BREAST CANCER MAMMOGRAMS
Review round: 1
Reviewer: 2

Output:
Basic reporting: - English should be improved. There still have some unclear or ambiguous parts.
- Literature review should be re-organized to group some similar papers into one paragraph.
- Quality of the figures should be improved.
Experimental design: - The authors indeed had two case studies on fly wing images and breast cancer mammograms. Why did they use the title as 'a case study'? Also, the same in the whole text.
- The two case studies are also big questions. Why did they use these two datasets since they are not relevant to each other?
- DCOM format is at 2D or 3D?
- How did the authors deal with hyperparameter optimization of the models?
- Measurement metrics (i.e., accuracy, sensitivity, specificity, ...) have been used in previous biomedical studies such as PMID: 33816830, PMID: 33735760, and PMID: 33260643. Therefore, the authors are suggested to refer to more works in this description.
- Source codes should be provided for replicating the methods.
Validity of the findings: - In Figure 4, the text is not displayed clearly.
- Besides training, the authors should have some validation data.
- ROC curves and AUC values should be reported in binary classification.
- The authors should compare the predictive performance to previous studies on the same problem/data.
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: ON THE CLASSIFICATION OF SIMPLE AND COMPLEX BIOLOGICAL IMAGES USING KRAWTCHOUK MOMENTS AND GENERALIZED PSEUDO-ZERNIKE MOMENTS: A CASE STUDY WITH FLY WING IMAGES AND BREAST CANCER MAMMOGRAMS
Review round: 1
Reviewer: 3

Output:
Basic reporting: The manuscript is clear and professional language is used.

Literature references and field background is sufficient.

Raw data is shared, results are relevant to the hypotheses.
Experimental design: The study is within the aims and scope of the journal.

Research question is well defined, relevant and meaningful. The research aims to fill the gap of predicting between fly species by their wing patterns and between benign or malignant masses in in mammograms. The study provides a model with a broad application area. The model is described with sufficient detail to replicate.
Validity of the findings: The limitations are clearly stated in the discussion.  All data were provided, statistically sound and controlled. Benefit to the literature is stated, conslusions are linked to the scientific question at hand and limited to 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: ON THE CLASSIFICATION OF SIMPLE AND COMPLEX BIOLOGICAL IMAGES USING KRAWTCHOUK MOMENTS AND GENERALIZED PSEUDO-ZERNIKE MOMENTS: A CASE STUDY WITH FLY WING IMAGES AND BREAST CANCER MAMMOGRAMS
Review round: 2
Reviewer: 1

Output:
Basic reporting: NA
Experimental design: NA
Validity of the findings: Thank you for the rebuttal letter. However, I noticed my comments have not been addressed carefully.

1. If there is no novelty in the paper, it is worthless to publish the paper.
2. To publish the paper and disseminate the scientific claims, we need to evaluate thoroughly, for example, using more evaluation measures, etc.
3. We also need to compare our model with contending methods available in the literature although the dataset is new.

Given such questions unanswered sufficiently by the authors, the reviewer is inclined to clear rejection.
Additional comments: NA


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: ON THE CLASSIFICATION OF SIMPLE AND COMPLEX BIOLOGICAL IMAGES USING KRAWTCHOUK MOMENTS AND GENERALIZED PSEUDO-ZERNIKE MOMENTS: A CASE STUDY WITH FLY WING IMAGES AND BREAST CANCER MAMMOGRAMS
Review round: 2
Reviewer: 2

Output:
Basic reporting: No comment.
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: ON THE CLASSIFICATION OF SIMPLE AND COMPLEX BIOLOGICAL IMAGES USING KRAWTCHOUK MOMENTS AND GENERALIZED PSEUDO-ZERNIKE MOMENTS: A CASE STUDY WITH FLY WING IMAGES AND BREAST CANCER MAMMOGRAMS
Review round: 2
Reviewer: 3

Output:
Basic reporting: No comment other than my previous comments.
Experimental design: No comment other than my previous comments.
Validity of the findings: No comment other than my previous comments.
Additional comments: No additional comments