PONE-D-23-00889BAOS-CNN: A novel deep neuroevolution algorithm for multispecies seagrass detectionPLOS ONE

Dear Dr. Noman,

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PLOS ONE

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Additional Editor Comments:

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

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3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The paper presents Deep Neuroevolutionary (DNE) model

that can automate the architectural engineering

Since the authors are proposing novel deep neuroevolution algorithm, I am not able to find related work in the paper that can justify this novel contribution.

Author must add related work and compare their novel deep neuroevolution algorithm with other similar or near-to-similar proposed algorithms.

How the evaluation of the fitness value of the candidates are computed. Kindly explain flow chart with the help of example

There is no appropriate explanation of algorithm 1 in the paper.

Is ECU-MSS-2 confidential dataset?

Present the comparison between qualitative and quantitative evaluation in tabular format

Reviewer #2: The paper presents a significant advancement in the field of metaheuristic algorithms, by combining the Lévy flight technique with the recently proposed Atomic Orbital Search (BAOS) algorithm. The innovative approach and promising results of the BAOS-CNN framework, particularly in automating architectural engineering and hyperparameter tuning of CNNs, is commendable.

The highlight of the paper is undoubtedly the application of BAOS-CNN to optimize a CNN model on a multi-species seagrass dataset. The achieved accuracy of 97.48%, which outperforms seven other evolutionary algorithms, is impressive. The use of BAOS to create a random population and evolve a new generation for searching the optimal architecture and hyperparameters combination of the CNN models showcases the power and versatility of this novel algorithm.

The authors' success in achieving high accuracy on different versions of the publicly available 'DeepSeagrass' dataset further underscores the effectiveness of the proposed BAOS-CNN model. The fact that it outperforms previously reported state-of-the-art accuracies on this dataset is a substantial accomplishment.

The paper also provides a valuable contribution by minimizing the requirement of manual CNN model architectural engineering and hyperparameter tuning. This feature enhances the practicality and usability of the algorithm in real-world applications, making it a potentially powerful tool in image classification or object detection in marine or other submerged environments.

In terms of future work, the authors' plan to apply ensemble learning techniques to enhance the performance by combining multiple evolutionary algorithm-based CNNs’ performances is promising. This approach could further improve the accuracy of the model and broaden the range of its applications.

Overall, the authors should be commended for their innovative work in developing and applying the BAOS-CNN framework. The paper is well-structured and the findings are clearly presented. The proposed algorithm has great potential and sets the stage for exciting future developments in the field of deep neuroevolutionary algorithms. I look forward to seeing these future developments and the potential real-world applications that this algorithm could enable.

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Reviewer #1: No

Reviewer #2: Yes: Michał Jasiński

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BAOS-CNN: A novel deep neuroevolution algorithm for multispecies seagrass detection

PONE-D-23-00889R1

Dear Dr. Noman,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Jin Liu

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

After carring out the reviewers suggestions, this manuscript can be accepted for publication now.

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

Reviewer #2: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

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4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: I am not able to find related work in the paper that can

justify this novel contribution

Author must add realted work section with clear contribution

Reviewer #2: I accept revision. no more changes are reqired. So I hope this paper can be accepted now in the PLOS ONE journal.

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7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Asadullah Shaikh

Reviewer #2: Yes: Michał Jasiński

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