Incorporating Drone and AI to Empower Smart Journalism via Optimizing a Propagation Model
Round 1
Reviewer 1 Report
The paper integratee a drone with AI to empower the aerial journalism via training a neural network using NN-RBFN approach. The proposed work can enhance aerial media. The paper is well organized, but there is some flaw or typo. For example, in line 321, '(Gt) transmitter altitude (A),' the '(Gt)' is redundant.
Author Response
Dear Reviewer,
Thank you for accepting our manuscript titled “Incorporating Drone and AI to Empower Drones for Smart Journalism via Optimizing a Propagation Model” with an opportunity to address the reviewers’ comments, which seem fair and reasonable. As indicated below, we have checked all comments provided point-by-point response to the comments, then updating the manuscript accordingly. Attached in two files: “Point-by-point response to the comments” and ‘Revised manuscript version’.
Kind regards,
Author Response File: 
Author Response.pdf
Reviewer 2 Report
The current paper deals with a timely and interesting topic that we interest a lot of MDPI readers. I believe that sustainability aspects need to be emphasized, in relation to the scientific novelty and contribution of the conducted work, making the Sustainability journal suitable for publishing this research. Another week point of the paper is its loose structure. This can be improved by modifying both the Introduction and the Conclusions sections. The introduction, along with the motivation for the research should describe the paper structure and introduce the reader to what is being presented in the paper. A common practice is to provide a briefing of the paper sections in the last paragraph of the introduction. Moreover, the Conclusions (which should be Section 5 and not 6) should provide a summarization of the conducted experiments. Most readers will only read the abstract, introduction and conclusions, and this should be enough to get the picture of the presented work.
The Introduction and the Related work section suggest that there is a rising interest in aerial journalism. This could be supported by referencing relevant review papers that cover the bigger picture on the significance of using drones for news-gathering and journalistic practice, like Holton et al. (2015). Unmanned Aerial Vehicles: Opportunities, barriers, and the future of “drone journalism”. Journalism practice, 9(5), 634-650). Concerning the terms Smart Media and Smart Journalism, related definitions of the implied use/concept and overall the incorporated perspectives need to be added, before using these terms. Regarding this aspect, latest (MDPI) publications on the subject, address data-driven solutions for drone operation planning, being quite relevant to the data-driven approach followed by the authors, so that it should be included (and commented) in the provided state-of-research review: Niarchos et al. (2022). A Semantic Preprocessing Framework for Breaking News Detection to Support Future Drone Journalism Services. Future Internet, 14(1), 26.
Section 3 also needs improvement. The objectives of the work are not presented clearly. In 3.1, a conceptual architecture is given without being presented in text, listing possible applications of aerial journalism. Right after, the authors claim that the application comprises three parts, and describe a communication channel for drone operation. It seems a little irrelevant to describe possible applications with the operation channels in the sensors section. Maybe there should be some explanatory transitional paragraphs between them, or maybe the conceptual architecture should be presented before 3.1.
342: Neural 342 Networks (NN) is the most candidate that could be considered for the optimization due to its simplicity and well-known high performance.
This sentence needs to be rephrased due to grammatical errors. Also, there should be a better justification for why a specific architecture was chosen. There are multiple machine learning and neural network architectures. Why is the proposed architecture preferable? Have the authors tried other models and this one outperforms their performance? It is not in any case self-evident that the specific NN is the best candidate for any decision-making problem. This applies to most of the technical solutions proposed by the paper. All the choices that have been made concerning the technical aspects of the system architecture must be supported either by experimental results that are provided in the paper, or with a reference to existing literature on the subject.
In machine learning problems, the dataset plays a crucial role in the robustness of the trained models. I suggest that the authors should describe more thoroughly the dataset that has been used. What is its size? How was it split for validation and testing? There are some related comments in the Results and Discussion section, when commenting on Figure 7, however, the adopted configuration needs to be presented more clearly and in more details, even while designing and providing motivation of the proposed approach. Also, you should consider providing a link of the used/accommodated dataset, making it publicly available for future research purposes.
Minor Comments:
31: It will speed up the review and typesetting process à this looks like it shouldn’t be here
There are several issues concerning grammatical and syntactical errors. I suggest that the paper should be carefully proofed by a native English speaker. E.g.:
225: This section aims to present the integra-225 tion between drone and NN-RBFN to support new approach of aerial journalism
228: Follows by introducing the proposed NN-RBFN framework to optimize 228 a channel model when it presents proof of concept at Souq Okaz.
342: Neural 342 Networks (NN) is the most candidate that could be considered for the optimization due to its simplicity and well-known high performance.
607: Where these technologies can contribute to offer various 607 media and journalism services in timely and cost-effective manners
…etc.
Author Response
Dear Reviewer,
Thank you for accepting our manuscript titled “Incorporating Drone and AI to Empower Drones for Smart Journalism via Optimizing a Propagation Model” with an opportunity to address the reviewers’ comments, which seem fair and reasonable. As indicated below, we have checked all comments provided point-by-point response to the comments, then updating the manuscript accordingly. Attached in two files: “Point-by-point response to the comments” and ‘Revised manuscript version’.
Kind regards,
Author Response File: Author Response.pdf
Reviewer 3 Report
Efforts and contribution by researchers are appreciable. I want to bring your attention towards few possible changes:
(1) Figure 14 is not cited in my knowledge. Check it.
(2) It will be a great help to readers if authors can write the organization of their sections in the end of Section 1 (i.e. Introduction Section).
(3) How you justify application of NN-RBFN approaches for your work? May we apply back propagation algorithm here?
(4) I suggest authors to include this relevant paper in references and cite it:
https://www.mdpi.com/1999-5903/14/1/26
Author Response
Dear Reviewer,
Thank you for accepting our manuscript titled “Incorporating Drone and AI to Empower Drones for Smart Journalism via Optimizing a Propagation Model” with an opportunity to address the reviewers’ comments, which seem fair and reasonable. As indicated below, we have checked all comments provided point-by-point response to the comments, then updating the manuscript accordingly. Attached in two files: “Point-by-point response to the comments” and ‘Revised manuscript version’.
Kind regards,
Author Response File: Author Response.pdf
Round 2
Reviewer 2 Report
The authors responded to my review remarks in a satisfactory manner. The only minor issue that has to be resolved is that Ref [7] also appears as Ref [17] (these two references have to be merged into a single one). I think the error was present in the first submission but I am afraid that I forgot to mention it (sorry for the inconvenience). Another minor issue would be to enhance the caption of the Figure 15, so that it will become more self-sustained. Congratulations on your research effort and results.
Author Response
Dear Reviewer,
Thank you for accepting our manuscript titled “Incorporating Drone and AI to Empower Drones for Smart Journalism via Optimizing a Propagation Model” with an opportunity to address the reviewers’ comments, which seem fair and reasonable.
As indicated below, we have checked all comments provided point-by-point response to the comments, then updating the manuscript accordingly.
Attached in two files: “Point-by-point response to the comments” and ‘Revised manuscript version’.
Kind regards,
Author Response File: Author Response.pdf
