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: LOW-COST INTELLIGENT SURVEILLANCE SYSTEM BASED ON FAST CNN
Review round: 1
Reviewer: 3

Output:
Basic reporting: The paper is written in a good quality English and reads well. Minor revisions are necessary to make sure that the paper is self contained. Please remember that PeerJ is not a NN community journal. You should explain things like alpha coefficients  (line 189) and CNN, RPN, ROI pooling (line 326). Make sure that all the terms and concepts used in the paper are explained.

The major problem of the paper has not described any related works and the text has no references in it. Before the paper is accepted the following two improvements must be done:
1) The existing related works must be analysed. Before doing your research, you must have defined the problem and checked the existing solutions. Maybe the problem is already solved and there is no need for additional research? I truly believe that it is not the case, but you have to prove it in the paper by showing that you know what the related works are and what are their shortcomings.
2) Appropriate referencing must be done in the text. Whenever some thoughts are coming from the literature, the reference must follow. All the papers that are in your reference list must be referenced from the text.

Minor things:
1) I believe Figure one contains copyrighted image that is identified by a watermark. Such image can not be used in your paper.
2) Quality of Figure 8 is low. Can it be improved somehow?
3) The used terminology must be improved to increase the accuracy of the way how the ideas are expressed. For example, line 32: is it about range of activations or range of activation functions? Line 275: is the system designed in Python or developed in Python?
4) Lines 331-341. Are these really steps? What is done in step 2? No description available, just some numbers. Also step 3. A single class includes fewer data. Similarly, Step 10.

The code is shared, but it is not well enough described. There is just a source with some comments in it. It is not clear, where can the reader find the most important things. This must be explained better.

There is also something wrong with figure numbering. Since the figures do not correspond to the captions.
Experimental design: The research fully complies with the scope of the journal. Still, the knowledge gap is not well defined since there is no description of related works and no references in the text.

The experiments seem sound and the results are of quite high accuracy, still the experimental setup is not well enough described. You should describe under what conditions the system was tested. Was it tested only on the images from validation data set or some kind of system's performance in at least laboratory environment was done?

Line 395: You are stating that the system has "high accuracy between 16% and 99%". Is 16% high accuracy? If so, then you should explain it in details. What is the accuracy of other systems?

The used data set is not provided (if it is in the supplemented files), it was not possible to check it without any further instructions available.
Validity of the findings: Only the final results are provided. it is unclear how exactly the experiment was done. Was there any real deployment in laboratory environment or only the object recognition part was done in the experiments? Figure 8 that should show the setup is missing.
Additional comments: The paper is interesting and shows a nice application of CNNs to partial automation of video surveillance systems. Technically the implementation seems good. Still, a journal paper still must be written by providing all the details about the experiments and positioning your research among all other efforts done within this area, where lately many works exist.


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: LOW-COST INTELLIGENT SURVEILLANCE SYSTEM BASED ON FAST CNN
Review round: 1
Reviewer: 4

Output:
Basic reporting: The method exposed is well explained and easily followed with a minimal understanding of the Computer Vision field. The text is well written and understandable, even for a non-native English speaker. The article has a good structure that explains all the important and interesting points of the research. Figures and tables complement the text giving relevant information about the methods, process and the obtained results.

On the other hand, the article has a mayor problem that is the lack of references inside the text itself. This makes it difficult to to read the references at some point. For example:
- In page 3 line 105 "Several recognition approaches involving the support vector machine (SVM), artificial neural networks (NN), decision trees, and other rule-based classification systems have been proposed." in which some methods are described and it would be useful to check the linked articles/books/other.

- In page 3 line 116 "For instance, it is considered that the perpetrators often visit various places at different times before committing crimes, and they can thus be detected and located at different sites" its hard to find the reference in the Bibliography. The exact reference should be inserted here.

Other points to consider are:

- Figure 1 has a watermark. There could be problems with this figure, it is better to change it.

- Page 3 line 122 "fast R-CNN" should be corrected to "Faster R-CNN", it is a different method than the used here. This also occurs in line 419.

- The first paragraph of the section "Hardware Design" may be changed or complemented with a table with the costs of all of the components. Also, the price of the ultrasonic sensor is not indicated.
Experimental design: This article introduces a novel system to save costs without reducing the accuracy and increasing much the computational time of the method used (a Faster R-CNN). With this, it is intended to help in the task of indoor security, monitoring certain areas with surveillance cameras and doing the processing with various Raspberry Pi 3, having a central system in a low cost PC. The Neural Network architecture is well explained and the election of the layers are justified. However, the negative point of using pooling layers (loss of information) is not explained and should be mentioned in the section "System concepts".

Aditionally, transfer learning is used to take advantage of other already trained systems, saving the long training times for the selected architecture. Nevertheless, some references should be added to explain this term, such as the book "Deep Learning" by I. Goodfellow Et. Al. that has a specific chapter that explains it.

Also, other metrics could be used to improve the results section, such as precision and recall (and F1-score), specially for the dangerous objects. Moreover, the recall metric explains how many dangerous objects are correctly extracted and should be the metric to maximize.

As a minor comment, it would be useful to mention the Faster R-CNN architecture in the software section.
Validity of the findings: The authors don't present a novel model of identifying people and/or threats using security cameras nor a method for improving its speed. Instead, they present a complete system to perform this detection which is simple and cheap. With this, security systems could be complemented with the proposed one and also serves as a support for the security staff. Moreover, with the simplicity of the implementation and the reduced costs it could be widely adopted by different companies. Results present a sufficient way of performing the detection, being fast and accurate. Additionally, they present a way of reducing the processing costs, performing this operation only when a ultrasonic sensor detects movement.

As a comment, it is interesting to introduce a "Future work" section or paragraph, showing how this work can be extended, such as:

- Using other architectures for the CNN (YOLO, SSD, Mask R-CNN, etc.)

- Extend the system to detect other type of objects or even behaviours (like theft or violence).

- Complement the system with another PC with more resources capable of performing online learning to re-train the system with new images.
Additional comments: The research carried out by this article is interesting not only for the computer science community but for the general public, since it takes advantage of previous studies in the field of computer vision and uses it in the field of security, which is of general interest. On the other hand, mistakes in references should be corrected, which will improve the general reading, and change the figure with the watermark, which will avoid problems for the authors.


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

Input:
Title: LOW-COST INTELLIGENT SURVEILLANCE SYSTEM BASED ON FAST CNN
Review round: 2
Reviewer: 1

Output:
Basic reporting: First of all, goob job on taking into account all the relevant feedback from the previous review. The paper has improved substantially. However, I must say that in this document the English is sometimes lacking, specially when using verb tenses (i.e. the sentence on lines 73-74; "utilizes" on line 75). These makes the paper hard to read, even to the point that some sentences cannot be understood properly (i.e. lines 151-153). I strongly advice you to revise the paper carefully.
On the other hand, I commend your effort on properly displaying the references throughout the article.
Experimental design: All the experiment are now better explained and easier to follow and reproduce.  Some terms still lack definition, like "context-based propagation" on line 384, or "N order reachable matrix" on line 382. Defining these may be out of the scope of the paper, but adding a reference to an article explaining them would be welcomed.
Validity of the findings: I commend your effort on generating your own image dataset. If every picture was took by the same group - as the ones shown in the examples - I deeply encourage you to test your model against images from other sources - specially with other people appearing holding weapons and such. This would be very insightful and show whether the model is robust.
On line 471, when you say "In particular, the system tracked multiple humans in only 0.9 s.", is that a particular instance, or the mean tracking time? The first one would be very informative, while the latter isn't.
Additional comments: I am obligued to remark that your code looks very similar to the one that Adrian Rosebrock writes in his blog Pyimagesearch. Specially this post have identical lines: https://www.pyimagesearch.com/2019/04/15/live-video-streaming-over-network-with-opencv-and-imagezmq/. If you have used his work for reference, I strongly encourage you to include Pyimagesearch between your references.


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

Input:
Title: LOW-COST INTELLIGENT SURVEILLANCE SYSTEM BASED ON FAST CNN
Review round: 2
Reviewer: 2

Output:
Basic reporting: The paper is written in a good quality English and reads well. Still it should be reread for minor typos. Some examples that must be corrected:

Line 126: resurches
Line 222:  where each of it is pixel's shows
Line 422:  second is to defined analysis
Line 426:  is would be an anchor
Line 540:  to achieved
Line 568: use of brackets.

The bibliography and use of references are sufficient. The state of the art is described adequately.

Terms mostly are explained for a reader that is familiar with the machine learning and neural networks. One minor remark: on line 476 explain the essence of the metrics used instead of providing a class name.
Experimental design: The research matches the scope of the journal and the  explanation of the knowledge gap has been added.

The experiments seem sound and the results are of quite high accuracy, the description of the experimental setup has been detailed in the resubmitted version. You also described the accuracy of 16% in the rebuttal letter. I still believe that this can be better explained in the article. Consider this as reviewer's personal opinion.
Validity of the findings: The figure with experimental setup was added. The same as the data collection methodology and experimental setup description.
Additional comments: No further comments. I believe, the paper is ready to publish after minor technical improvements.


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: LOW-COST INTELLIGENT SURVEILLANCE SYSTEM BASED ON FAST CNN
Review round: 3
Reviewer: 1

Output:
Basic reporting: My main comments have been considered and mistakes have been corrected. A few new mistakes regarding number agreement have been introduced, for example on line 551 "a multiple tasks". (You can not use "a" with plural), "every pixels of the image  have"  on line 222. Please carefully reread the whole paper to check English.
Experimental design: The only Major question is regarding the sentence: "However, I utilized works done by (Rosebrock A., 2019) as a reference for design CNN model." If you have used some other person's work as a basis of your own solution, then you have to clearly show what is your contribution - what has been improved from the original code? What were the problems in the original solution? Why did it need any improvements? Does your solution solve the identified problems? I mean, you have to clearly position your work within the current state of the art and clearly show your contribution.
Validity of the findings: The clarification regarding the abovementioned use of other person's works must be done to make sure that the novelty of the paper is significant enough.
Additional comments: No further 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: METEOMEX: OPEN INFRASTRUCTURE FOR NETWORKED ENVIRONMENTAL MONITORING AND AGRICULTURE 4.0
Review round: 1
Reviewer: 1

Output:
Basic reporting: The article should include a section named Background to demonstrate how the work fits into the broader field of knowledge.
Relevant prior literature must be referenced, for example:
Mulla, D. J. (2013). Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps. Biosystems engineering, 114(4), 358-371.
Saura, J. R., Reyes-Menendez, A., & Palos-Sanchez, P. (2019). Mapping multispectral Digital Images using a Cloud Computing software: applications from UAV images. Heliyon, 5(2), e01277.
Seelan, S. K., Laguette, S., Casady, G. M., & Seielstad, G. A. (2003). Remote sensing applications for precision agriculture: A learning community approach. Remote sensing of environment, 88(1-2), 157-169.
Experimental design: The research work is clearly defined. It is relevant and meaningful.
Validity of the findings: The investigation was conducted rigorously but the author should improve the conclusion.
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: METEOMEX: OPEN INFRASTRUCTURE FOR NETWORKED ENVIRONMENTAL MONITORING AND AGRICULTURE 4.0
Review round: 1
Reviewer: 2

Output:
Basic reporting: 1) The paper has several strong points, as it has a good literature review and is very didactic about the proposed system implementation. However, there is one major problem as there is no scientific contribution.

The article describes a technical solution for a common problem in smart farms, although it uses and describes commercial hardware modules and free software. Thus, there is no new technique, hypothesis or study.

This is notable in the text, as there is no problem stated, no highlights of the paper scientific contribution and its main objective.

2) The introduction does provide sufficient background and include several relevant references. It is one of the strongest points of the paper. Most of references are from the past 5 years and there is an excellent review of them. However, I would recommend avoiding some parts as L49-53 (perishable food) and L60-64 (factories examples), they are a bit off the paper main topic. An explanation of smart farms and its context would be great before L65. In addition, the introduction seems to be missing an ending paragraph to conclude the idea and relate to the paper’s main topic.

3) English language and style are fine, with just a few minor spell checks required. However, some paragraphs are fragmented, thus the text needs a small revision (For example, L46-47).

4) Most of figures are good with enough information. One exception is the text in figures 6, 7, 8 and 11, which is not legible in the pdf.
Experimental design: It is an interesting topic and matches the paper scope. Although it isn’t an easy implementation and requires a lot of study and tests, there is no relevant and meaningful scientific contribution. It is a technical solution for a problem.

A solution would be to make a comparison study with other solutions and actually test its performance or propose a new structure in communication. There are several solutions for this application with better technologies, using Zigbee and Lora networks (for example). However, it would still be hard to find a scientific gap for the study.

My suggestion for the author is to change the paper scope/type to a literature review. The paper review is excellent and updated. There aren’t many reviews about these systems lately and would be great with your example of implementation with MeteoMex.

For this, only a few things have to change. For example, an expansion of the literature review as a “first part”, an example of sensor module construction with MeteoMex for a second part and some “study cases” as a third part.
Validity of the findings: There is no finding in the paper, although it is an excellent description of a technical solution with free software and accessible hardware.
Additional comments: I believe the paper is not suitable for publication as it is now. However, I suggest to turn it to a literature review paper as I described in 2.


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

Input:
Title: METEOMEX: OPEN INFRASTRUCTURE FOR NETWORKED ENVIRONMENTAL MONITORING AND AGRICULTURE 4.0
Review round: 1
Reviewer: 3

Output:
Basic reporting: No comment
Experimental design: No comment
Validity of the findings: No comment
Additional comments: The MeteoMex project appears to be a fairly complete set of design files and software examples necessary for making a functional sensor reporting device. I have raised some issues in the specific comments below that I feel would make the manuscript more readable and useful to the interested reader. While the files are made available on Github (a commercial site with an uncertain long-term existence), I would recommend that you also create a permanent archived copy of the existing repository on a long-term archiving site such as zenodo.org, which provides free archiving. It’s certainly appropriate to provide updated files on Github on an ongoing basis.
The introduction could be more concise, as the listing of the various challenges of food production, storage, wastewater treatment, etc. seem to go on for a long time before the crux of the project is reached, which is a system to help monitor various aspects of these various industries and environments.
Line 19 Abstract: “analog, digital and I2C sensors” should be reworded, since in this context I2C is a digital protocol and a sensor using the I2C protocol is not different from the generic term “digital sensor”.
Line 46-47: This is a sentence fragment
Line 57-58: This sentence doesn’t seem relevant to the thrust of the paper. It could be removed.
Line 73: agriculture 4.0 is used here without a useful definition. The only definition I find appears at the end of the paper at line 380.
Line 86: AA battery does not need periods after each A.
Line 88-90: It’s not immediately clear what the purpose or service provided by Blynk, ThingSpeak, MQTT is, for a reader not already familiar with those services. This sentence should be split into two, and the role of these services should be more clearly explained. The MQTT acronym should be defined.
Line 90-91: This sentence conveys no useful information and could be cut to make the introduction more concise.
Line 93-94: This sentence gives a brief description of the attributes of the MeteoMex project, but no description of the specific capabilities (i.e. what can/could it measure, how frequently, what kind of power requirements). Those capabilities are described in more detail in section 2, but a brief rundown should be provided in the introduction.
Figure 1: Which part the term “shield” is referring to in the caption isn’t self-evident until the reader has read the text and figured out that the PCB referred to in the caption is the shield. I would suggest changing the initial sentence of the caption to read “A) circuit and B) PCB “shield”.”
Line 99: The units of the board dimensions are given as mm squared, but the number provided are the linear dimensions (mm), so the mm2 label should be changed to mm.
Line 120-121: The DeepSleep mode is mentioned a few times in the manuscript, but it is never properly explained what this mode does. The sentence on Line 121 confuses me, since the way it is written makes it sound like the jumper needs to be closed manually in order to wake the device, and that this would need to be closed by the user each time they wanted to wake the device. But normally I would assume that a deep sleep mode would have some facility to automatically wake itself, either on a regular time interval or because of some external sensor interrupt being triggered, rather than the user needed to physically close a jumper. If the deep sleep mode works normally, this sentence should be reworded to say something like “For enabling the DeepSleep mode, the jumper J1 must be closed.”
Line 124: The manufacturer (Maxim) for the DS18B20 should be listed.
Line 127: From what I read, the maximum input voltage for the analog input on the Wemos is 3.2V, so it might be worth mentioning that analog sensors that use a 5V supply must still limit their output range from 0 to 3.2V to avoid damaging the analog channel on the microcontroller.
Line 131: The manufacturer and part number for the turbidity sensor should be provided.
Line 132: The manufacturer for the JSN-SR04T sensor should be provided.
Line 158: change “why” to “so”
Line 164-165: Awkward phrasing for the sentence beginning “Programming a DeepSleep/WakeUp…”
Line 166: What is an accumulator in this context? A battery?
Line 239: Using a neural network to estimate solar irradiance from temperature humidity and pressure seems overly complicated and not relevant to this project. A lux or PAR sensor (though PAR sensors are expensive) could give similar information on irradiance and also interface to the MeteoMex.
Lines 247-253: The process of calibrating a soil moisture sensor is glossed over here. Please provide a reference to give a more detailed description of a proper calibration routine.
Line 254-256: After giving the soil temperature in Celsius, the units change to Kelvin for the subsequent temperature values. Why not continue using Celsius, especially if they are interchangeable with respect to numeric value?
Line 260: I assume the temperature peak was at 12 pm, not am.
Line 261: The phrasing of this sentence is awkward. It would read better as “… point is only exposed to direct sunlight for a short period of the day.”
Line 270: “economical” instead of “economic”
Lines 272-281: I’m not sure that the example of monitoring air temperature a server room merits a separate section and figure here. Monitoring air temperature was adequately covered in section 3.3. I think most of section 3.4 could be cut to help make the paper more concise, and a mention of the server room air temperature results could be made in a sentence in the paragraph on lines 265-269 where another air temperature monitoring usage is described.
Line 329: change “importing” to “important”


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: METEOMEX: OPEN INFRASTRUCTURE FOR NETWORKED ENVIRONMENTAL MONITORING AND AGRICULTURE 4.0
Review round: 2
Reviewer: 1

Output:
Basic reporting: ok
Experimental design: ok
Validity of the findings: ok
Additional comments: ok


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: METEOMEX: OPEN INFRASTRUCTURE FOR NETWORKED ENVIRONMENTAL MONITORING AND AGRICULTURE 4.0
Review round: 2
Reviewer: 2

Output:
Basic reporting: As previously written, the article describes a technical solution for a common problem in smart farms, although it uses and describes commercial hardware modules and free software. Thus, there is no new technique, hypothesis or study.

Even though the author affirms it is a development of a scientific tool, there are several other similar tools on the market, with same low cost and different technologies. The paper do not show how significant are these changes regards the other products in the market.

The author did cite PeerJ Criteria, but I would also like to remember "PeerJ Computer Science judges articles on scientific validity and suitability to join the scholarly literature".

The author did some minor improvements but did not address the main problem in the paper as it is a scientific paper.
Experimental design: As stated before, there is no relevant and meaningful scientific contribution. It is a technical solution for a problem. The author did not made the suggested modifications.
Validity of the findings: There is no finding in the paper, although it is an excellent description of a technical
solution with free software and accessible hardware.

There are several ways to deal with a technical solution with a scientific approach.

After a quick search, there are several examples below of open solutions. It also shows that this solution could have been compared with several others and it hasn't. In addition, most of these solutions have more than 5 years, are common and thus, there is no scientific validity.

"Open source hardware to monitor environmental parameters in precision agriculture" - https://doi.org/10.1016/j.biosystemseng.2015.07.005

"Irrig‐OH: An Open‐Hardware Device for Soil Water Potential Monitoring and Irrigation Management" - https://doi.org/10.1002/ird.1989

"Open Hardware: A Role to Play in Wireless Sensor Networks?"  - https://doi.org/10.3390/s150306818

"SEnviro: A Sensorized Platform Proposal Using Open Hardware and Open Standards" - https://doi.org/10.3390/s150305555
Additional comments: If there is no interest in adapting to a review paper, the contribution could be handled in several other ways. But the author did not address this situation accordingly.


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: METEOMEX: OPEN INFRASTRUCTURE FOR NETWORKED ENVIRONMENTAL MONITORING AND AGRICULTURE 4.0
Review round: 2
Reviewer: 3

Output:
Basic reporting: No comment
Experimental design: No comment
Validity of the findings: No comment
Additional comments: I have read through the author’s responses to the reviewers and the revised version of the manuscript. I feel that my original comments have been addressed appropriately, and that the author’s responses to the other reviewers’ comments were appropriate. Overall the manuscript has been improved, and should be a useful contribution to the literature.


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: ANALYSIS OF HISTORICAL ROAD ACCIDENT DATA SUPPORTING AUTONOMOUS VEHICLE CONTROL STRATEGIES
Review round: 1
Reviewer: 1

Output:
Basic reporting: The paper is by and large clear, but would still benefit from language editing.
Experimental design: The paper applies an appropriate method, although it does not achieve control for random variation like the Empirical Bayes method.
Validity of the findings: The findings are valid.
Additional comments: There is limited overlap between the hazardous road locations identified in the two periods. Could tjis lead to false alarms?


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: ANALYSIS OF HISTORICAL ROAD ACCIDENT DATA SUPPORTING AUTONOMOUS VEHICLE CONTROL STRATEGIES
Review round: 1
Reviewer: 2

Output:
Basic reporting: This manuscript falls within the scope of the journal. Overall, the article is well-designed and quite readable.
Experimental design: This manuscript falls within the scope of the journal. Overall, the article is well-designed and quite readable.
Validity of the findings: This manuscript falls within the scope of the journal. Overall, the article is well-designed and quite readable.
Additional comments: This manuscript falls within the scope of the journal. Overall, the article is well-designed and quite readable.


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: ANALYSIS OF HISTORICAL ROAD ACCIDENT DATA SUPPORTING AUTONOMOUS VEHICLE CONTROL STRATEGIES
Review round: 1
Reviewer: 3

Output:
Basic reporting: Number of sentences that will need to be rephrased in an academic style
Experimental design: no cooment
Validity of the findings: In the past, car accidents have been investigated for a variety of reasons. Nevertheless, we need to study the analysis that has been carried out on the same dataset. In the end, I would like to see a comparison table for the same dataset between the current research and previous ones.
Additional comments: The subject is hot; however, the chosen dataset has a number of limitations. I wish an AI approach has been used to extend the dataset before using 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: ANALYSIS OF HISTORICAL ROAD ACCIDENT DATA SUPPORTING AUTONOMOUS VEHICLE CONTROL STRATEGIES
Review round: 2
Reviewer: 1

Output:
Basic reporting: I believe the paper has been adequately revised and is now suitable for publication.
Experimental design: Is adequate
Validity of the findings: Is adequate
Additional comments: I believe the paper has been adequately revised and is now acceptable for publicatiion.


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: OTITIS MEDIA DETECTION USING TYMPANIC MEMBRANE IMAGES WITH A NOVEL MULTI-CLASS MACHINE LEARNING ALGORITHM
Review round: 1
Reviewer: 1

Output:
Basic reporting: In this paper a novel convolutional neural network is proposed for determination of abnormalities or infections in human tympanic membrane using image processing methods. The paper technically seems useful regarding experimental applications. The proposed study aims to classify middle ear otoscope images based on the deep convolution neural networks (CNNs). To this aim, a novel deep CNN model including a combination of the channel and spatial model (CBAM), residual blocks, and hypercolumn technique have been embedded into the proposed model. The achieved results have been evaluated as promising.
Experimental design: To improve the quality of study, I have presented my comment as follows:
1.	According to my knowledge, earwax is not an OM type, please clarity this issue on the manuscript.
2.	There is a need for a more detailed explanation of some of the details of the Eardrum database in particular.
3.	Authors must explain clearly the advantage of proposed algorithm.
4.	In related work, paper with reference number [19] author mentioned the number of image sample as 10.544. How is it possible?
Validity of the findings: 5.	What is the initial learning rate vale of proposed model for training the dataset?
6.	How many Positive and negative images taken for performing the tests of proposed model?
7.	Author mentioned that training period shortened for the proposed work when compared with pre-trained CNNs, as we know transfer-learning takes less amount of time when compared to learning from basic on the particular dataset.
Additional comments: After minor revision, I think the manuscript can be proposed for publishing.
With regards.


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: OTITIS MEDIA DETECTION USING TYMPANIC MEMBRANE IMAGES WITH A NOVEL MULTI-CLASS MACHINE LEARNING ALGORITHM
Review round: 1
Reviewer: 2

Output:
Basic reporting: The authors use methods that seem to be familiar to those in computer science, which I am not.   Similarly, I am not the intended audience and could not understand the terminology.

As an example:  the "down" function is defined in terms of the "down" function and it was not any clearer after reading the material.   So I assume most readers are already familiar with this.

The emphasis on computational efficiency is important but does not appear useful in a data set with only few hundred observations.  Otherwise this is OK.
Experimental design: They compare several clustering methods.  This is appropriate.
Validity of the findings: The findings appear valid.

What is the "gold standard" for evaluating sensitivity and specificity?  The article begins by saying the visual observation is "subjective and error-prone" so there is no way of telling whether their methods are correctly classifying OM cases.
Additional comments: Nothing additional


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: PERFORMANCE ANALYSIS OF LIGHTWEIGHT CNN MODELS TO SEGMENT INFECTIOUS LUNG TISSUES OF COVID-19 CASES FROM TOMOGRAPHIC IMAGES
Review round: 1
Reviewer: 1

Output:
Basic reporting: This article presents an in-depth analysis of the performence of selected, most-known Deep Convolutional Neural Networks, such as SegNet or VGG UNet, on prepared dataset containing lungs' CT slices of patients with COVID-19.
Authors have prepared a well written and understandable descriptions of used CNN models giving the recipient complete knowledge from some basic fundamentals to more specific characteristics.
The experimental setup is clearly described.
There are however some areas to improve.
Experimental design: Authors of this article claims that the lightweight CNN models perform better than other NN models. However according to the Table 2 we can clearly see that the best results are achived by the HR Net which, in comparison to other 3 NN's from this article, is the most complex in terms of the number of hidden layers and it is the second highest model in terms of Model Size.
If however all this models are classified by the authors as light-weight, there should be a comparison between a deeper model and the selected once to justify the claim of this article.
Validity of the findings: Findings are well described and does not contain any major errors.
Additional comments: The article is written with mostly correct English, with some minor drawbacks (For example in section "Dataset Considered" there is written "The dataset used consists of..." where there should be "The used dataset consists of...").
Also some places should be corrected visually (to be more consistant), for example in Introduction there is "scan are:- ground glass opacities,consolidation,...". The colon implies that it is a list of elements, but there is also a single dash only before the first element. It makes the text look inconsistant and less professional.

I would also suggest adding references to articles below because of similar topics and interesting applications of deep learning in COVID-19 treatment:
- "Neural Network powered COVID-19 spread forecasting model"
- "Artificial intelligence and COVID-19: deep learning approaches for diagnosis and treatment"


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: PERFORMANCE ANALYSIS OF LIGHTWEIGHT CNN MODELS TO SEGMENT INFECTIOUS LUNG TISSUES OF COVID-19 CASES FROM TOMOGRAPHIC IMAGES
Review round: 1
Reviewer: 2

Output:
Basic reporting: Improve the quality/resolution of image in equation (3).

Improve the quality of equation (4) (6) which are unreadable, make quality and resolution higher.

Improve the quality of some pictures (fig 2, fig 5, fig 6, fig. 10).

Check if the article meets the PeerJ style requirements.
Experimental design: More explain the pre-processing  operation. It would be interesting if you use other pre-processing  method and compared them.

Explain obtained results presented on the fig 9.

On the Figure 10 are presented glyph plots for used methods. In my opinion it's not necessary but in other way you must increase the quality of the graph. I think moreover you should analyze and discuss the data from table 1.
Validity of the findings: The article is interesting but need some extensions and modification. Can you show how your proposition work correctly and incorrectly in case of some input images?
Additional comments: Do you have any other example where your method can be used? Do you think that your proposed method can be improved and how? Authors can extend the bibliography to include articles: Neural network powered COVID-19 spread forecasting model, Small Lung Nodules Detection based on Fuzzy-Logic and Probabilistic Neural Network with Bio-inspired Reinforcement Learning, A neuro-heuristic approach for recognition of lung diseases from X-ray images


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: PERFORMANCE ANALYSIS OF LIGHTWEIGHT CNN MODELS TO SEGMENT INFECTIOUS LUNG TISSUES OF COVID-19 CASES FROM TOMOGRAPHIC IMAGES
Review round: 2
Reviewer: 1

Output:
Basic reporting: Article presents well documented analysis of the selected models on prepared dataset containing lungs' CT slices of patients with COVID-19
Experimental design: Authors have responded clearly on given concerns. No further actions are required.
Validity of the findings: Findings are well described and there are no visible mistakes.
Additional comments: Authors have responded clearly to all given previously concerns. The article is clear and well written.


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: PERFORMANCE ANALYSIS OF LIGHTWEIGHT CNN MODELS TO SEGMENT INFECTIOUS LUNG TISSUES OF COVID-19 CASES FROM TOMOGRAPHIC IMAGES
Review round: 2
Reviewer: 2

Output:
Basic reporting: I have no critical comments.
Experimental design: I have no critical comments.
Validity of the findings: I have no objections.
Additional comments: The authors followed my suggestions and answered my questions clearly, so I have no further objection.


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: ADAPTIVE NEURAL PD CONTROLLERS FOR MOBILE MANIPULATOR TRAJECTORY TRACKING
Review round: 1
Reviewer: 1

Output:
Basic reporting: no comment
Experimental design: no comment
Validity of the findings: no comment
Additional comments: My main comments are summarized as follows:

1-	The PID control is well studied in the literature, the main contribution of your work and the main differences between the existing work should be declared in the introduction.
2-	The literature review is not enough.
3-	In the industry, all control systems work in noisy environment in which there exist faults, disturbances/noise and delays, what about these uncertainties in your work. Please discuss
•	Fault-tolerant control for a class of quantised networked control of nonlinear systems with unknown time-varying sensor faults, International Journal of Control 93 (3), 619-628.
•	A new online delay estimation-based robust adaptive stabilizer for multi-input neutral systems with unknown actuator nonlinearities, ISA transactions 2017, 139-148.
•	Less-conservative robust adaptive control of neutral systems with mixed time-delays, International Journal of Systems Science 48 (4), 675-685.
•	Anti-windup adaptive PID control design for a class of uncertain chaotic systems with input saturation, ISA transactions 66, 176-184.
•	Adaptive stabilization of neutral systems with nonlinear perturbations and mixed time‐varying delays, International Journal of Adaptive Control and Signal Processing 29, 1328-1340.
•	A new unmatched-disturbances compensation and fault-tolerant control for partially known nonlinear singular systems, ISA Transactions 104, September 2020, 310-320
•	….
4-	In Fig. 2, the author used derivative operator d/dt although they deal with difference equations. Please correct it.
5-	You should compare your experimental results with the existing results.
6-	Some remarks should be added to declare the effects of the design parameters in the simulation 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: ADAPTIVE NEURAL PD CONTROLLERS FOR MOBILE MANIPULATOR TRAJECTORY TRACKING
Review round: 1
Reviewer: 2

Output:
Basic reporting: This paper presents adaptive neural PD controllers applied to mobile manipulator trajectory tracking. The paper uses clear, unambiguous, technically correct text. and its structure is consistent and easy to follow. Raw data is shared and supports the results presented.
Experimental design: The paper is into the scope of the journal. It is suggested that the methods should be described with sufficient detail to be reproducible by another investigator.
Validity of the findings: The proposed controllers are interesting, however an important aspect is related to stability. This study must be included within the paper. Furthermore, the proposed control only takes into account the kinematics of the robot. It would be important that the dynamics of the robot be taken into account. Finally, it is important to analyze the robustness of the controllers in the presence of disturbances, non-modeled dynamics and measurement noise.
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: ADAPTIVE NEURAL PD CONTROLLERS FOR MOBILE MANIPULATOR TRAJECTORY TRACKING
Review round: 2
Reviewer: 1

Output:
Basic reporting: accept
Experimental design: accept
Validity of the findings: accept
Additional comments: My recommendation is "acceptance"


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: ADAPTIVE NEURAL PD CONTROLLERS FOR MOBILE MANIPULATOR TRAJECTORY TRACKING
Review round: 2
Reviewer: 2

Output:
Basic reporting: .
Experimental design: .
Validity of the findings: .
Additional comments: The reviewer thanks the authors for the response given to each of the concerns raised in the review process. The reviewer considers that this manuscript can be considered for possible 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: COMPARING GENERAL AND SPECIALIZED WORD EMBEDDINGS FOR BIOMEDICAL NAMED ENTITY RECOGNITION
Review round: 1
Reviewer: 1

Output:
Basic reporting: This paper presents a performance evaluation of different word embeddings for the task of named-entity recognition (NER) in the biomedical domain.

In brief, the paper compares two word embeddings (GloVe word embeddings trained on a Common Crawl corpus and word2vec word embeddings trained on PubMed abstracts and PubMed Central articles) over three experiments.

The paper is clear, well organised, and well presented throughout.

However, it is affected by a major shortcoming which is the very limited scope of its evaluation. As it stands, I do not believe this paper can be recommended for acceptance, but the evaluation could be expanded to become appropriate.
Experimental design: The experimental design and reporting are appropriate. The experiments are organised to evaluate the impact of 1) the type of word embeddings and 2) the training set size on NER accuracy, and the impact 3) of the type of word embeddings on the correlation between the embeddings’ pairwise similarity and human-annotated pairwise similarity. Three NER models are considered.

The only remark is that when the authors mention differences between scores, they should use "percentage points" (pp) as unit, not "%" (e.g., "1.68% and 1.35%" should be: "1.68 pp and 1.35 pp". The differences are not percentages; the absolute values are.

Another minor remark is about the name used for the Common Crawl word embeddings: there exist other popular Common Crawl word embeddings, obtained with different algorithms; for instance, fastText's. The authors should say upfront that these are GloVe Common Crawl word embeddings. The Pyysalo description is ok as is.
Validity of the findings: The current findings seem valid. However, as I have already mentioned in the opening field, the scope of the evaluation is too limited to make it contemporary and comprehensive. The manuscript is organised in the vein of its main reference, Unanue at al. [2017], but three years have passed since, and many other word embeddings and models have been proposed. To gain an idea, one can easily browse sites such as NLP-progress (http://nlpprogress.com/english/named_entity_recognition.html) and Papers with Code (https://paperswithcode.com/task/named-entity-recognition-ner).

An evaluation of appropriate scope is, for instance, the manuscript’s own reference Batbaatar and Ryu (2019) which has compared 14 word embeddings.

To make the evaluation sizable and interesting, yet not overwhelming for the authors, I recommend the following extensions:

1) LUKE. The official code can be found at the link on NLP-progress. The authors should use the embeddings produced by LUKE with their models, and possibly also with the provided NER classifier. The model is pretrained over Wikipedia pages. Of course, fine tuning over Common Crawl and PubMed would further value to the comparison.

2) Flair https://github.com/flairNLP/flair. Its Python library is straightforward to use; please see the example.

My recommendation is not prescriptive in terms of specific methods. I simply recommend expanding the evaluation to embrace more modern, contextualised word embeddings to make it contemporary.
Additional comments: I do not have any 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: COMPARING GENERAL AND SPECIALIZED WORD EMBEDDINGS FOR BIOMEDICAL NAMED ENTITY RECOGNITION
Review round: 1
Reviewer: 2

Output:
Basic reporting: The paper presents an evaluation of how pre-trained vectors of general corpora can be helpful in BioNER. Despite that, many previous works already proved that domain-specific trained embeddings are better than general ones. The paper is well written, and the structure is connected and presented in the right way. The authors were able to present a good list of related works. Figures and Tables were all clear.
Experimental design: no comment
Validity of the findings: no comment
Additional comments: - Grammar errors:
There was only one grammar error in line 277, where the word "Table" was repeated two times. Just remove the repeated word.
- Results and paper contribution:
Results were confused a little bit. The authors reported that some previous works already made this research contribution, such as Segura-Bedmar et al. (2015) “who found that the performance achieved with the DrugBank corpus and the CRF model was 1% higher when using general instead of specific word embedding.” So, in terms of general vs domain-specific embeddings, Segura already proved that general is sometimes a good source for NER systems. The only contribution in this paper is that the BiLSTM-CRF was better than the CRF. This contribution can fit to a short paper or a poster in a conference more than a 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: COMPARING GENERAL AND SPECIALIZED WORD EMBEDDINGS FOR BIOMEDICAL NAMED ENTITY RECOGNITION
Review round: 1
Reviewer: 3

Output:
Basic reporting: The article is written in English that is easy to understand. The overarching goal of the project is clear. However, since not everyone is familiar with the term "word embedding" or "biomedical named entity recognition", concise explanations of the terms would be helpful. Also, it's critical to make it clear to a general reader how the training sets and validation sets are obtained, and how the class labels are defined and assigned for a sample or data point.
Experimental design: As stated in the "basic reporting" section above, the actual experimental design is probably OK.  However, explanations need to be given to the two basic terms, the obtainment of training sets and the validation sets, and the class labels.

While it's OK to use precision/recall for performance measurement, it's not clear why it is preferred over sensitivity/specificity/AUC. Is it because true negatives (TNs) are not easy or impossible to label? If so, it would help to explain it briefly.

A minor issue is about the description about precision, recall, and F1-score between line 223 and 224. These terms are well known. I am not sure it's worth putting them in the manuscript. Instead, references to these terms can be given in the article.
Validity of the findings: If the training sets and validation sets are constructed correctly, and the class labels are assigned in the right way, the F1-score,  the performance plot, and the correlation between the machine results and the human results are convincing enough.
Additional comments: This is a reasonable research project with potentially publishable results. More explanations to the terms, training sets, validation sets, and class labels are critical for readers to assess the scientific basis of the research.


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: OPEN-SOURCE DATA MANAGEMENT SYSTEM FOR PARKINSON’S DISEASE FOLLOW-UP
Review round: 1
Reviewer: 1

Output:
Basic reporting: The level of language used is very adequate. Figures and references are accurate. However, the Rapid Application Development (RAD) concept is not trivial and could be better described (line 137). The article correctly describes the context of the study and helps to understand the issues related to the management of Parkinson's disease data. The examples given by the authors are relevant. The objectives of the study are correctly identified. The structure of the article is clear. The code of the application presented is open-source. The screenshots would perhaps benefit from more care.
Experimental design: The use of SUS questionnaires is an excellent introduction to assessing the usability of a tool, especially since the number of participants is very large. The technologies used are basic but well described.
The description of the development environment is not necessarily relevant, as the purpose of the paper is not a performance test.
The description of the VCM model seems superfluous in view of the paper issue. On the other hand, the choice of the MVC model compared to a MVVM model allowing more interactivity should be explained.
The evaluation method is very well described and well documented.
Validity of the findings: The results of the study are clearly stated. The results of the HMI are illustrated by screenshots. However, the Data Query module could be more detailed. The results obtained for the usability study are well described. The statistical method used is appropriate and puts the results into perspective. The subject of the study and the approach used are timely and relevant. It would be relevant for the author to situate the added value of SINOBI compared to a classical clinical data warehouse.
It would perhaps be interesting to specify if it is possible to extend the project to other pathologies or to explain the methodology envisaged for the integration of other data sources.
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: OPEN-SOURCE DATA MANAGEMENT SYSTEM FOR PARKINSON’S DISEASE FOLLOW-UP
Review round: 1
Reviewer: 2

Output:
Basic reporting: 1. "Specific systems developed to manage people with PD and other neurological disorders has been previously reported. An effort to integrate interinstitucional databases can be found in [14], as well as applications using cell phone in [15], web-based systems in [15], [16], [17], and wearable sensors [16] to monitor PD patients, producing a large amount of data."

The studies cited in this paragraph could be better described, since they seem to be strong related to this one. Also, the four paragraphs before the mentioned one could be summarized, since the direct correlation with this study is not strong.
Experimental design: 2. The authors opted to use SUS to assess usability and the support for that  in the context of medical applications is weak. Also, just one cited study is related with medical application. It exist standards focused on medical applications, for instance, IEC 62366.

3. "Usability should aid the design of an interface that allows the user to perform tasks easily. In this way, the usability of a system should focus on developing interfaces that are easy to handle and quick to learn."

This definition is too much simple, and I agree with the importance of this part of the text to situate the reader and pace the reading. However, the definition of usability is not simple as that and should not be neglected in this kind of research paper.

4. "The usability test (SUS) of the system was performed by 36 examiners."
I advise to describe the examiners sample.

5. I did not noticed if a training was or was not offered to the examiners.
Validity of the findings: 6. Who can be the user actor in Figure 3? What users in this case were considered by the authors?

7. It would be interested contrast the achieved results in terms of Usability with a profile more related with PD health professionals.

8. Why Kendall coefficient was chosen to evaluate the agreement among the evaluators? Make this clear in the text.
Additional comments: The paper presents a data management system for Parkinson's disease follow-up. The authors made the ideia itself very clear, and also there is no doubt about the importance of this kind of development to the field. Also, the manuscript is well written.

The usability was evaluated. What else could be evaluated in the proposed system considering its purpose?


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: OPEN-SOURCE DATA MANAGEMENT SYSTEM FOR PARKINSON’S DISEASE FOLLOW-UP
Review round: 2
Reviewer: 1

Output:
Basic reporting: No comment.
Experimental design: No comment.
Validity of the findings: No comment.
Additional comments: All review points were addressed, explained and resolved.


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: OPEN-SOURCE DATA MANAGEMENT SYSTEM FOR PARKINSON’S DISEASE FOLLOW-UP
Review round: 2
Reviewer: 2

Output:
Basic reporting: English is adequate and the literary references too. The figures clarify important details of the text and the results are presented in a consistent manner. All terms were clear and objective.
Experimental design: The research subject is original and highly relevant. The necessary ethical aspects were used and the software will bring great gains for professionals and researchers linked to the disease in question. The state of the art was well done, even though there are not many works directly related to this software. This fact demonstrates how innovative this research is. The primary impacts were measured through the usability of the software and the rest will appear through its use in the academic community over time.
Validity of the findings: The tests, as well as the conclusion were done properly. The results are conclusive and the challenge is to present future works that can expand the areas of operation of this software.
Additional comments: a) Put the meaning of the acronym also in the native language: SIDABI.
b) Put information that involves financial aspects, related to the disease, in the item “backgound”. Example: how much does the Brazilian National Health System spend on Parkinson's annually?
c) Are there old databases for other diseases? What gains have they brought to the medical community? Could your software bring gains close to these? Note: Use more elements to value the work, especially numbers that involve financial aspects.
d) Why was MVC used? Do companies that develop complex software use it? What do they point to as strengths and weaknesses? Are there any success stories in the medical field documented? If so, they could be in the bibliographic references. The answers to these questions could be covered in a paragraph.
e) Why was an Apache server chosen? Does it meet all the needs of the project (security, availability, etc.)? Justify in the text.
f) Does the software respect the new rules of the “general data protection law” (Brazil)? Give details in the text.
g) The text should explain in detail the main "use cases". This would show the differential of the work for the Parkinson's treatment area and the complexity of the project. The contribution must be evidenced in all stages of the article.


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

Input:
Title: AN ADAPTIVE WEIGHTING MECHANISM FOR REYNOLDS RULES-BASED FLOCKING CONTROL SCHEME
Review round: 1
Reviewer: 1

Output:
Basic reporting: no comment
Experimental design: no comment
Validity of the findings: no comment
Additional comments: This paper proposes a novel control scheme for a swarm of UAVs that
employs the original Reynolds rules but adopts an adaptive weight allocation
mechanism based on the current context than being fixed at the beginning.

This paper contains some interesting results. Please find the following comments that may help improve the paper.

(1) It is suggested to highlight the challenges of the investigated problem.

(2) The significance of using an adaptive weight allocation mechanism needs to be explained.

(3) What is the meaning of step 3 in algorithm 1?

(4) Please explain the steps 6-9 in algorithm 2.

(5) The simulation is done on Pygame. What is the advantage and disadvantage compared to other software?

(6) This paper is about flocking control. The following three references should be included in the introduction section to present readers a complete literature review. B. Shucker, T. D. Murphey, and J. K. Bennett, “Convergence-preserving switching for topology-dependent decentralized systems,” IEEE Trans. Robot., vol. 24, no. 6, pp. 1405-1415, Dec. 2008. B. Ning, Q.-L. Han, Z. Zuo, J. Jin, and J. Zheng, “Collective behaviors of mobile robots beyond the nearest neighbor rules with switching topology,” IEEE Trans. Cybern., vol. 48, no. 5, pp. 1577-1590, May 2018. R. Olfati-Saber, “Flocking for multi-agent dynamic systems: Algorithms and theory,” IEEE Trans. Autom. Control, vol. 51, no. 3, pp. 401-420, Mar. 2006.

(7) In figure 2, the decrease of average distance from 4.4 to 4.8 looks strange, please explain the reason.

(8) The language of this paper needs to be improved.


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

Input:
Title: AN ADAPTIVE WEIGHTING MECHANISM FOR REYNOLDS RULES-BASED FLOCKING CONTROL SCHEME
Review round: 1
Reviewer: 2

Output:
Basic reporting: The paper has been written in reasonably good English. Introduction and background may be enough for the specialist. In general, it would be nice to have a bit more details. There are over 20 relatively recent references. Also, major work from the past is pointed out. Figures are generally well together with the text. Perhaps a more rigorous usage of the units on axis, whenever applicable, would be relevant. For example, distance should have units, even if it is just “unit” (explained in the text).
Experimental design: The experiment is purely software-based, which may be enough for the current paper, but some real-life validation would enhance the value. So far the impact of factors (external forces) making it closer to the real-life is proposed only in upcoming works.
Validity of the findings: Better comparison with state of the art would be in order. No doubt that the results are interesting and useful, should the findings be verified in real life and against competing solutions.
Additional comments: It may be that the notation used in line68 is acceptable in the specialist circles (if time is taken as unity for example), but generally adding space coordinates in m, “unit” or similar (position vector) to the speed (velocity vector)  in m/s, “unit”/s or similar should be avoided. There seems to be some problem with indexes - ws (line 168) seems to be alone.


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: CORRECT AND STABLE SORTING FOR OVERFLOW STREAMING DATA WITH A LIMITED STORAGE SIZE AND A UNIPROCESSOR
Review round: 1
Reviewer: 1

Output:
Basic reporting: The article is written carefully, but I think there are some corrections that can be made. Figure 2 is very good, but the blocks (A,B,C,D,E) could be marked with soft rectangles.
In the caption of Fig 2 you write "(e)" but I thing should be "(E)"
Adapt the size of figures and tables to the size of the pages.
The equations (6),(7), (9), (10) and (11) are the same. Similarly, the equation (13), (14), (16-19) are also the same.  In both cases define the formula only once and note that the other probabilities are equal.
Experimental design: The authors of the article provide an interesting proposal for a sorting algorithm for streaming data on a uniprocessor constrained by a limited storage size. In my opinion, the article can be accepted after editorial corrections.
Validity of the findings: The article is clearly written and transparent so I have no critical comments.
Additional comments: Do you see any possibilities to improve this algorithm?
List possible practical applications of this algorithm when it is better than others.


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

Input:
Title: CORRECT AND STABLE SORTING FOR OVERFLOW STREAMING DATA WITH A LIMITED STORAGE SIZE AND A UNIPROCESSOR
Review round: 1
Reviewer: 2

Output:
Basic reporting: The article prepared by the authors is professionally written and presented in a good way. I recommend only small changes to improve the transparency of the article and to show the advantages of the created solution.
Experimental design: The design of the experiment does not contain any major errors.
Validity of the findings: This article does not compare enough with other similar solutions to this problem invented by other researchers. Such a comparison should be made, the preferred form is a table, which clearly shows the differences between the responses to this issue.
Additional comments: -Equation (15) needs better formatting.
- All fractions in the text should be changed to the correct way of notation (not containing slash). An example of this is in line 371.
-Figure no. 2 needs to be in higher quality.
- Equations no. (2), (3), (4) should be centered.
-The legends of the graphs in figure 3 must be enlarged or deleted otherwise they are very illegible.


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: CORRECT AND STABLE SORTING FOR OVERFLOW STREAMING DATA WITH A LIMITED STORAGE SIZE AND A UNIPROCESSOR
Review round: 2
Reviewer: 1

Output:
Basic reporting: The authors took into account my suggestions in the article so I have no objections.
Experimental design: Everything is fine.
Validity of the findings: I have no objections.
Additional comments: In my opinion, the article should be accepted.


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

Input:
Title: CORRECT AND STABLE SORTING FOR OVERFLOW STREAMING DATA WITH A LIMITED STORAGE SIZE AND A UNIPROCESSOR
Review round: 2
Reviewer: 2

Output:
Basic reporting: Thank you for taking my comments into account, the current version of the paper is, in my opinion, ready to be accepted.
Experimental design: The experimental design has no significant flaws.
Validity of the findings: The validity of the findings has no significant flaws.
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: DEEP LEARNING BASED RESPIRATORY SOUND ANALYSIS FOR DETECTION OF CHRONIC OBSTRUCTIVE PULMONARY DISEASE
Review round: 1
Reviewer: 1

Output:
Basic reporting: This article shows an interesting solution that fits into the current needs of the healthcare system around the World. Authors have created a well performing CNN model recognizing, in a very short time, Chronic Obstructive Pulmonary Disease (COPD). Because of that it could help the doctors, especially in overpopulated areas, to diagnose, with higher accuracy, more patients in the same time, which could potentially lead to faster treatment and less health complications.
The article is well and clearly written and provides all used resources, however some minor changes are required.
Experimental design: After analyzing your code I can see that in your work you have used Adam optimization algorithm, however it is not mentioned in your article. As we know there are many different training algorithms and because of that I have a question:
 - Why have you chosen such algorithm? Were there any experiments carried out with other algorithms like NAdam, RMSprop, etc.? And how does they compare with each other (training times, accuracy, ...)

You have mentioned that other systems takes a very long time to train and predict. How does it perform in your case? Is it faster than other methods with similar results? What is the device requirenment to run the network with reasonable predicting times and to train the model?
Validity of the findings: All findings are well documented, clear to read and does not contain any major errors.
Additional comments: This article is well made and contains valuable knowledge. It's easy to read.


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: DEEP LEARNING BASED RESPIRATORY SOUND ANALYSIS FOR DETECTION OF CHRONIC OBSTRUCTIVE PULMONARY DISEASE
Review round: 1
Reviewer: 2

Output:
Basic reporting: Table VII should be corrected: the headers are large, axis descriptions illegible.

Table VIII explain word abbreviations Se Sp

Fig 3-6 no axes description.

Fig 3-fig 11 are large and not clear
Experimental design: More precisely explain and describe the feature extraction methods between input analogic signal, spectrogram and input to your neural network.

More precisely explain how the architecture of convolutional neural networks looks like (convolutional, pooling). Can you show some examples of each operation?
Validity of the findings: Authors shows interesting applied of CNN for detection of chronic obstructive pulmonary disease, but numer of examples form experiments is small, so give more examples for other types of images
Additional comments: Explain where the presented method can be used in practice.

How this method can be improved and developed in the future?

Authors can extend the bibliography to include articles: Bio-inspired voice evaluation mechanism, DecomVQANet: Decomposing visual question answering deep network via tensor decomposition and regression


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: DEEP LEARNING BASED RESPIRATORY SOUND ANALYSIS FOR DETECTION OF CHRONIC OBSTRUCTIVE PULMONARY DISEASE
Review round: 2
Reviewer: 1

Output:
Basic reporting: Article is overall well written and contains interesting and revealing content. Authors have used professional English and included accurate literature references. Proofs are clear and does not contain any visible errors.
Experimental design: Authors have replied to all given critique and have given clear responses.
Validity of the findings: All findings are well documented, clear to read and does not contain any visible errors.
Additional comments: This article is well made and contains valuable knowledge. It's easy to read.


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: DEEP LEARNING BASED RESPIRATORY SOUND ANALYSIS FOR DETECTION OF CHRONIC OBSTRUCTIVE PULMONARY DISEASE
Review round: 2
Reviewer: 2

Output:
Basic reporting: Check fig. 3,4,6  axis descriptions are truncated.
I have no more critical comments.
Experimental design: In my opinion it is ok.
Validity of the findings: OK
Additional comments: The authors followed my suggestions so I have no further objections.


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: NUMERICAL BEHAVIOR OF NVIDIA TENSOR CORES
Review round: 1
Reviewer: 1

Output:
Basic reporting: Spotted typos:

(line 174) in a several ways
Experimental design: The paper presents the methods to obtain essential parameters of the floating
point arithmetic of bespoke floating-point hardware present in NVIDIA compute
cards in Tesla product group. The tested cards were V100 and T4 and the code
for tests was made publicly available. This allows the authors to circumvent
the lack of low-level details of hardware.
Validity of the findings: Briefly stated, what is available under the provided link and what requirements
must be met by the user system? Is a Github account required? CUDA versions
that are supported? And which CUDA versions support tensor32 format?
Additional comments: NVIDIA Pascal P100 supports fp16 but no tensor cores' arithmetic computational
model. Is that correct? Could this be clarified somewhere for Table 1? Also,
are the Geforce GTX cards such as 2060 and share the same hardware?

Table 2 has room for adding values mandated by IEEE for their formats. Would it
be possible to include some of them?

How is the implementation of tensor32 on the tested V100 differ from the same on
T4 and A100? Does the type represent the new standard put forth by NVIDIA or
each microarchitecture features a bespoke variant rendering each a unique type
with subtle differences?

How is NVIDIA profiler involved in confirming the use and probing the behavior
of tensor cores?


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: NUMERICAL BEHAVIOR OF NVIDIA TENSOR CORES
Review round: 1
Reviewer: 2

Output:
Basic reporting: This paper does an interesting work of performing experiments which are not reported in the Tensor Cores documentation. The authors should have explained the definition of terms like Subnormal numbers, normalization in the Introduction Section as new readers may find it difficult to follow.
Experimental design: The design and choice of experiments were interesting. As Tensor Cores are designed to accelerate Matrix Multiplications, it will be interesting to perform an application like Convolutional Neural networks (Eg. Vgg16) or any other scientific computation instead of just (4*4*4) matrix multiplication with arbitrary values.
Validity of the findings: A table with a summary of Experiments A,B,C,D would be more appealing to the reader. As the experiments were performed on V100 Architecture, does it apply to other architectures like Turing and TPUs ?
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: NUMERICAL BEHAVIOR OF NVIDIA TENSOR CORES
Review round: 1
Reviewer: 3

Output:
Basic reporting: 1. + References, results, etc. sufficiently present and included in a coherent manner in the paper.
2. - The authors use "held exactly" in several places (e.g., 3.1.2) when they mean held as if it were a IEEE 754 single precision floating point computation (not an exact format).
Experimental design: 1. - The authors do not specifically identify the knowledge gap relative to Hickmann and Bradford's work. For example, subnormal value handling and creation, addition schedule in (2) (see align and truncate discussion in Hickmann and Bradford), and monotonicity (follows from align and truncate) are present in prior works. The current work seems to only fill the gap of more explanation, open code, and application to a new GPU.
2. + Provide code appears to be sufficient to easily replicate author's work. Even if not, the descriptions within the paper are sufficient to replicate the work.
3. - Why did the authors not determine if there was a third carry bit for the 5-operand accumulation? Or was it confirmed that it didn't exist in the V100? (this may simply be a reporting issue)
Validity of the findings: 1. - The assertions/conclusions of the findings should be phrased in such a way as to indicate these are "likely" numerical features since there were not exhaustive tests and there is no claim that a standard is adhered to or verified against.
2. - Certain assertions with regard to computation ordering/hardware are overly stated. For example, lines 230-234 "Furthmore, this exmperiment demonstrates that none of hte additions is performed in parallel with the first addition to the largest magnitude value..." Simply means that the alignment step of the additions all happens first, not that the actual adds are sequential (see Hickmann and Bradford's estimated design).
Additional comments: While the paper reads well in terms of explanation of logic and I appreciate the provided code, it is unclear what knowledge gap is being filled. When comparing point by point with Hickmann and Bradford I cannot find such a gap.


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: NUMERICAL BEHAVIOR OF NVIDIA TENSOR CORES
Review round: 2
Reviewer: 1

Output:
Basic reporting: Thank you for addressing the issues from my review.

No further comments in this part.
Experimental design: Thank you for addressing the issues that expanded and clarified (for me) the experimental sections of the manuscript.

I have no additional comments in this part.
Validity of the findings: Thank you for responding to the suggestions wrt validity of findings.

No comments to add in this revision.
Additional comments: Thank you for the thorough review.

I reviewed the manuscript and have no further comments to report.


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: NUMERICAL BEHAVIOR OF NVIDIA TENSOR CORES
Review round: 2
Reviewer: 2

Output:
Basic reporting: Thanks for adding the paragraph which define all the terms
Experimental design: no comment
Validity of the findings: The summery table seems to be fine.
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: NUMERICAL BEHAVIOR OF NVIDIA TENSOR CORES
Review round: 2
Reviewer: 3

Output:
Basic reporting: No change.
Experimental design: From response letter:
13 a. The paper's results confirm Hickman and Bradford -- an integer adder tree (compressor or otherwise) will maintain all carries since there is no rounding/truncation. This is what would be referred to as a 24-bit datapath. If this were not the case it would not meet Nvidia's claim of "accumulating with at least single precision" and there would be large errors observed or internal rounding points. I still see no knowledge gap here. I would suggest using the phrasing you added "the internal significant of the tensor cores is most likely 27 bits wide in Volta cards and 28 bits wide in Turing cards" in your conclusion, etc. rather than "carry-out" bits.

b. While I do appreciate the logic of testing extreme values and the likelihood that hardware behaves in consistent manner, a single observation hardly refutes someone else's observation.
Validity of the findings: Rewording has addressed comments.
Additional comments: Overall, based on the responses and published tests, I support accepting this work with a suggestion to change in 13a.


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 PERCEPTUAL TWO LAYER IMAGE FUSION USING DEEP LEARNING FOR IMBALANCED COVID-19 DATASET
Review round: 1
Reviewer: 1

Output:
Basic reporting: The article proposes propose a new method for augmenting a dataset of chest X-ray (CXR) images using a deep learning network combined with perceptual two-layer image fusion. The proposed method is validated on the dataset of images obtained from COVID-19 cases.
Discussion of related work should more focus on the methods specifically applied for COVID-19 CXR image analysis, fusion and classification. Many recent successful methods are not mentioned. Also discuss hybrid methods in which deep learning methods are combined with nature inspired optimization algorithms such as presented by Sahlol et al. (Scientific Reports, 10(1)). How is your approach different from other similar works using NSCT such as described by Xinqiang et al. (2017 2nd International Conference on Image, Vision and Computing (ICIVC), pp. 396–400). You should discuss these and other works in order to provide motivation for your own method. Discuss the limitations of these works and suggest how your approach overcomes these limitations. What is the knowledge gap being filled by this article?
The Methods section should have some formal definition and discussion of the problem solved by this article. What do you mean by image decomposition? Define formally to avoid confusion and misinterpretation.
The dataset preprocessing must be described in detail instead of stating that it “includes many steps”. Describe every step, including any methods used and their parameter values in a way that would allow the replicability of your study.
Experimental design: Why you have selected the VGG16 architecture? Discuss alternatives and support you design choice by proper arguments.
For experimental evaluation of fused images you used image quality metrics, but ultimate evaluation of their usability would be the increased accuracy of disease (COVID-19, in your case) diagnostics.
Validity of the findings: Although your results are compelling, the article should be improved in the following ways:
The presentation of the results must be supported by the statistical analysis section.
Your dataset is very small (just 87 images from 25 cases). The method should be validated on a larger dataset of images, if possible.
Some findings such as “images that are more useful” (L. 560) and  “The image was more apparent” (L. 564) are not supported? Who evaluated this “usefulness” and “apparentness” and how?
Additional comments: Visualization of results should be improved.
Figure 1: correct spelling errors (“Hisogram”), some text is garbled (unreadable).
Figure 3: explain all abbreviations in the caption of the figure.
Figure 4 lacks of necessary details: add the size of inputs and outputs of each network layer.
Figures 8, 9: what is the meaning of labels P1 … P7? Explain all labels/abbreviations in the figure caption. What is represented on the y-axis? I assume the value cannot exceed 1, so adjust the range accordingly.
Figure 10: use bars instead of line plot for showing the STD values of fused image.
Figure 11: grouping of metric values by method/algorithm does not allow clear comparison between metric values. I suggest to group by metrics, while color is used to discern between methods.
Figure 12 is not clear. That are you trying to convey? Maybe a different type of diagram would be more useful?


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 PERCEPTUAL TWO LAYER IMAGE FUSION USING DEEP LEARNING FOR IMBALANCED COVID-19 DATASET
Review round: 1
Reviewer: 2

Output:
Basic reporting: Language style is acceptable, however I suggest to consult structure of sentences and vocabulary. Formatting of algorithms and tables is not unified, make them all equal and unified for better look. The tile of section on page 9 goes together with algorithm, revise it.
Experimental design: The choice of applied CCN-VGG16 is not justified. Why this structure was selected for experimental research? Hay you tried other models? What kind of filters have been used in your neural network model? How do they work? Can you show some results? How is the histogram used in your research? What is the role of histogram application to evaluate the image?
Validity of the findings: Comparisons to other models are necessary to show result over other approaches, compare them and draw conclusions. Why such nn was selected if also other existing models are available? Please show comparative results over images. Authors may relate the research to: Neural network powered COVID-19 spread forecasting model, A neuro-heuristic approach for recognition of lung diseases from X-ray images, Bio-inspired methods modeled for respiratory disease detection from medical images.
Additional comments: What are conclusions from applied methods? Do you have any special observations regarding covid features? Are the features repeated in al images? How is your model related to hard and lighter cases? Is the efficiency the same for both cases?


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 PERCEPTUAL TWO LAYER IMAGE FUSION USING DEEP LEARNING FOR IMBALANCED COVID-19 DATASET
Review round: 1
Reviewer: 3

Output:
Basic reporting: Paper is well organized and but need considerable improvement for acceptance in “PeerJ”, in current format.
Experimental design: NA
Validity of the findings: NA
Additional comments: This paper proposes a system about novel perceptual two-layer image fusion using to predict COVID-19. The authors discussed, they used CNN for features extraction and fusion method. The author claims that the proposed method have good results on COVID-19 patients X-ray images.
There are some observations, corrections and suggestions regarding this paper.
•	In NSCT decomposition, describe properly what is image X and Y.
•	Proposed model is not presented properly, should be improved and polished to make it more easily understandable.
•	What is temporal consistency in fusion? Describe properly.
•	What is the training and testing ratio?
•	 What is the learning rate of CNN in this work?
•	What is the size of VVG feature size?
•	What is the size of fusion features size? Length of feature vector?
•	How to calculate weight?
•	Results are limited. Performance measures should be more. The proposed work should be evaluated with a number of performance measures to prove its validity.
•	Figure 1 is not in good quality redraw it.
•	In Figure 1
Paper is well organized and but need considerable improvement for acceptance in “PeerJ”, in current format.


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 PERCEPTUAL TWO LAYER IMAGE FUSION USING DEEP LEARNING FOR IMBALANCED COVID-19 DATASET
Review round: 2
Reviewer: 1

Output:
Basic reporting: no comment
Experimental design: no comment
Validity of the findings: no comment
Additional comments: In my opinion, the present version of the paper 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: A NOVEL PERCEPTUAL TWO LAYER IMAGE FUSION USING DEEP LEARNING FOR IMBALANCED COVID-19 DATASET
Review round: 2
Reviewer: 2

Output:
Basic reporting: as stated before
Experimental design: as stated before
Validity of the findings: as stated before
Additional comments: The Authors worked on the paper however my concerns are left the same without any proper answer. There are no justifications for chosen models. We can not see comparative results so it is impossible to evaluate the model in relation to other methods. Authors did not selected nn architecture and gave no descriptive examples. Also features of symptoms are neither presented nor discussed.


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: COMPARISON OF MACHINE LEARNING AND DEEP LEARNING TECHNIQUES IN PROMOTER PREDICTION ACROSS DIVERSE SPECIES
Review round: 1
Reviewer: 1

Output:
Basic reporting: No Comment
Experimental design: No Comment
Validity of the findings: No Comment
Additional comments: The paper is well written and addresses an important research problem. One of the key research problem identified is, as quoted in the paper, " the highly imbalanced positive and negative sample dataset is one of the major problems in promoter recognition as it leads to model overfitting and makes the model less generic." Author should explain more clearly about how this challenge is address in the preparation of the dataset and in conducting the experiments. Apart from that the experiments are well conducted and documented. Performance evaluation parameters are well know, could have been explain briefly with citations.


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: COMPARISON OF MACHINE LEARNING AND DEEP LEARNING TECHNIQUES IN PROMOTER PREDICTION ACROSS DIVERSE SPECIES
Review round: 1
Reviewer: 2

Output:
Basic reporting: Bhandari et al., proposed and compared the results obtained from one-hot vector encoding and frequency based tokenization on CNN/RF/LSTM models for the promoter classification in three organisms. The authors claim the prediction exceeds previous methods. The writing of the manuscript and flow of ideas could be improved.
1. First paragraph in the Results section may be redundant as some of these sentences repeat what has already been described in Methods.
2. The following are some questions related to some of the manuscript sentences.

Line 84 - Mention why these methods are less effective.
Line 90 - Some may argue that ML are statistical methods (e.g., Bayes). Give an example of which statistical methods you are referring to.
Line 116 - Use scientific names for the organisms not previously defined.
Line 117 - Not clear. Is the number of promoter and non-promoter sequences is equal to 100 or is it 100 for each?
Line 131 - Use consistent names and terms. Yeast was previously defined as Fungus, A. thaliana as Plant, etc.
Experimental design: The following are my remarks/concerns about this work.

1. Can you please explain how simple features are produced when using samples from the same genome as background/negative sequences? (line 125)
2. Considering the amount of samples (35,000 and 6,000), I would recommend to use cross-validation to assess the performance of the models. Are the positive/negative samples ratio 1:1, if so, what would be the results of testing the entire genome for TSS where the number of negative samples is much larger than positive samples. With this test, the functionality of the method would be demonstrated.
3. It is not clear how many of these samples are extracted from each organism as well as the number of positive/negative. In Methods section, it is not clearly defined if one model is derived from each organism or are these sequences pooled?
4. Finally, if a data split of 90/10 for training and testing was used, how do you use the 1,000 bp long human sequences? how many of these sequences are you using? Again, in results (line 225) it is not clear how these sequences are used, i.e., to test the performance of which model?
5. Can authors please explain if and how the hyperparameters of each ML/DL model were tuned? Did authors tune these parameters using only the training data? did authors use validation set?
6. In line 288, authors mentioned that using a synthetic background dataset resulted in model with high sensitivity and specificity. Could this be because of the methodology for shuffling these promoters regions? did shuffling cause a random distribution of nucleotides? For this, an evaluation of the models with real genome sequences would be needed.
Validity of the findings: 1. The validity of the finding can be assessed only after understanding the details about the parameter tuning of the models, data split (training, validation, test), number of samples for each organism, and ratio of samples.
2. Table 5 shows the comparison of the proposed method against other related works. I would like to see the author following exactly what the comparing methods did (same training/validation/test data) to establish the advantage of the proposed method. I find it very difficult to compare models trained and tested on different datasets.
3. Data should be made available online for reproducibility purposes.
Additional comments: Writing and style.
The following are few recommendations to improve the writing:
Line 48 - Replace "We employed the deep..." by "We employed different deep..."
Line 48 - Replace "... mainly CNN and recurrent..." by "... mainly CNN, recurrent..."
Line 54 - Replace "for pre-processing." by "for data pre-preprocessing."
Line 64 - Do not capitalize words in the sentence. Replace "...Genetic..." by "...genetic..."
Line 64 - Avoid the use of dangling modifiers. Replace ..."engineering. It ..." by "...engineering as it ..."
Line 94 - Make sure abbreviations are defined before using them (e.g., TSS).
Line 102 - Rewrite sentence. Example: "..., 669 promoters of 80 nucleotides long..."
Line 110 - use abbreviations already defined. Replace "deep learning" by "DL".
Line 122 - add "and" before feature encoding.
Line 138 - replace "automatically" to "seamlessly"
Line 143 - bp not defined.
Line 183 - LSTM already defined. RNN not defined.
Line 198-199 - avoid dangling modifiers.

Punctuation marks
Review the following remarks in the entire manuscript.
Line 66 - Use "," before "which".
Line 73 - Use comma before and after e.g.
Line 74 and 77 - Consider replacing etc by ", among others."
Line 76 - Use comma before "such as"
Line 105 - Avoid capitalization of words in the middle of the sentences.
Line 147 - Add comma before respectively.
Use comma before the use of viz.