PONE-D-24-20413Towards sustainable solutions: effective waste classification framework via enhanced deep convolutional neural networksPLOS ONE

Dear Dr. Uddin,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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ACADEMIC EDITOR: Major Revision

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Please submit your revised manuscript by Sep 23 2024 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

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If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Jyotir Moy Chatterjee

Academic Editor

PLOS ONE

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 This information should be included in your cover letter; we will change the online submission form on your behalf.

Additional Editor Comments:

The following limitations needs to be addressed:

1. Despite using four publicly available datasets, the study may still lack sufficient diversity to fully represent global waste variations, especially in regions with unique waste profiles.

2. The quality of images in the datasets might vary, which could impact the model’s performance, especially in real-world scenarios where image quality may be inconsistent.

3. While the model was successfully deployed in a web-based system, its scalability to larger, more complex systems or across different geographies remains untested.

4. The model’s performance in diverse environmental conditions (e.g., different lighting or weather conditions) was not explicitly tested, which could affect real-world accuracy.

5. The evaluation was conducted on publicly available datasets rather than real-time data from waste management facilities, potentially limiting the model's real-world applicability.

6. The DenseNet201 architecture and the parallel CNN branches might require significant computational resources, which could be a barrier for deployment in resource-constrained environments.

7. The integration of DenseNet201 with SE mechanisms and parallel CNNs adds complexity, which might make the model more difficult to interpret and deploy.

8. The study does not address potential latency issues in real-time waste classification, which could be critical for practical applications.

9. The study does not explore the potential for deploying the model on edge devices, which could be important for on-site waste management in remote or under-resourced areas.

10. The model might overfit to the specific datasets used for training, reducing its generalization capability to unseen data.

11. If the datasets used are biased towards certain types of waste, the model may not perform well on underrepresented waste types.

12. The study does not thoroughly investigate the model’s robustness to adversarial attacks or noisy data, which are common in real-world scenarios.

13. The energy consumption of the model, especially in large-scale deployments, is not addressed, which could be a significant factor in sustainable waste management.

14. While the study introduces a novel approach, it may not include comprehensive comparisons with a wide range of existing waste classification methods.

15. The long-term stability and performance of the model over time, particularly as waste compositions change, have not been explored.

16. The cost-effectiveness of implementing this advanced model in various waste management contexts has not been analyzed.

17. The complexity of the model may require specialized training for users, which could be a barrier to widespread adoption.

18. The model may not be easily adaptable to specific local waste management needs, which could limit its effectiveness in diverse settings.

19. The deployment of AI in waste management could raise ethical and privacy concerns, especially if data from waste is linked to individuals or households.

20. The web-based deployment of the model assumes reliable internet access, which may not be available in all areas, particularly in remote or developing regions.

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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

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

Reviewer #1: No

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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

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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

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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 abstract needs modification. The references are to be cited properly. The research gap and limitations are to be included. A tree diagram may be added. The figure title and legends are missing. MCC, Kappa and GDR may be included. Time complexity may be analyzed. A comparison with ML classifier will be included.

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6. 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: Harikumar Rajaguru

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[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

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PONE-D-24-20413R1Towards sustainable solutions: effective waste classification framework via enhanced deep convolutional neural networksPLOS ONE

Dear Dr. Uddin,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

==============================

ACADEMIC EDITOR: The editor comments on the paper are not properly addressed by the authors.

==============================

Please submit your revised manuscript by Nov 11 2024 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

• A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
• A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
• An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Jyotir Moy Chatterjee

Academic Editor

PLOS ONE

Additional Editor Comments:

The editor comments on the paper are not properly addressed by the authors.

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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: 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

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

Reviewer #1: N/A

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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

**********

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

**********

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: All the corrections are included in the paper. The paper is well modified with all corrective measures and concerns raised by the reviewers. The paper is improved a lot in this revised form.

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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: No

**********

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

PONE-D-24-20413R2Towards sustainable solutions: effective waste classification framework via enhanced deep convolutional neural networksPLOS ONE

Dear Dr. Uddin,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Apr 23 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

• A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
• A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
• An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Jyotir Moy Chatterjee

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Additional Editor Comments:

The work to be revised as follows:

1. The model primarily depends on publicly sourced data, which may introduce biases and inconsistencies in data quality. This limits its applicability to diverse global waste management scenarios, as the datasets may not fully capture variations in waste profiles across different regions​

2. The integration of DenseNet201 with squeeze-and-excitation (SE) mechanisms and parallel CNN branches significantly increases computational complexity. This makes it difficult to deploy in resource-constrained environments, such as mobile or embedded systems​

3. The study does not thoroughly examine the model’s robustness against adversarial attacks or noisy data. This could impact reliability in real-world scenarios where images may have varying quality due to environmental factors​

4. The system has only been deployed on a local server, and its scalability to larger, more complex systems or across different geographies remains untested. Additionally, real-time classification latency and energy consumption in large-scale deployments have not been evaluated​

5. The work does not incorporate visualization techniques for explainability, making it difficult to interpret the model’s decision-making process. The lack of explainability tools could hinder trust and adoption by stakeholders who require transparency in AI-based waste classification​

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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 #2: (No Response)

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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 #2: Yes

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

Reviewer #2: Yes

**********

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 #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 #2: Yes

**********

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 #2: This paper presents a novel approach to waste classification using deep learning techniques, specifically a customized DenseNet201 architecture with parallel CNN branches and squeeze-and-excitation (SE) attention mechanisms. The work is timely and relevant given the growing global challenges of waste management and the need for automated, accurate classification systems.

Strengths:

Comprehensive evaluation: The authors have evaluated their model across four distinct datasets, which is more thorough than many existing studies that focus on a single dataset. This approach demonstrates the model's robustness and adaptability.

Detailed architectural explanation: The paper provides a clear and detailed explanation of the proposed architecture, including justifications for specific design choices like parallel CNN branches and the SE attention mechanism.

Extensive performance metrics: The authors have used a wide range of metrics beyond just accuracy, providing a more complete picture of the model's performance.

Real-world application: The development of a web-based system for deploying the model demonstrates its practical applicability beyond theoretical exploration.

Thorough comparative analysis: The authors have compared their approach against numerous existing models and provided a comprehensive literature review.

Areas for improvement:

Dataset representation: While four datasets are used, there could be deeper discussion on how well these represent global waste variations, especially in regions with unique waste profiles. Consider addressing whether certain geographic-specific waste types might be underrepresented.

Image quality considerations: The performance on varying image qualities is not extensively discussed. Since real-world applications would encounter images of varying qualities, this deserves more attention.

Computational efficiency: While the empirical time complexity is briefly addressed, a more detailed analysis of computational requirements would strengthen the paper, especially considering deployment scenarios with limited computational resources.

Model interpretability: The proposed architecture is complex, which might make interpretation of its decisions challenging. Some discussion on explainability methods that could complement this system would be valuable.

Long-term performance stability: The paper doesn't address how the model might perform over time as waste compositions change. A brief discussion on potential strategies for model updating would enhance the practical relevance.

Technical suggestions:

The confusion matrices in Figures 5, 9, 13, and 17 provide valuable insights, but further analysis of the specific misclassifications (especially between similar materials like paper and cardboard) would strengthen the discussion.

Consider including a brief ablation study to quantify the specific contributions of the parallel CNN branches and the SE attention mechanism to the overall performance improvement.

The empirical time complexity analysis in Table 10 is useful, but could be expanded to include comparisons with other architectures to provide context for deployment considerations.

The web implementation described in Section 6.1 could benefit from more details on potential latency issues in real-time classification scenarios.

Section 6.3 on threats to validity provides good insights, but could be expanded to address potential issues with model generalization to unseen waste types or novel categories.

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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 #2: No

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[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

Towards sustainable solutions: effective waste classification framework via enhanced deep convolutional neural networks

PONE-D-24-20413R3

Dear Dr. Uddin,

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.

An invoice will be generated when your article is formally accepted. Please note, if your institution has a publishing partnership with PLOS and your article meets the relevant criteria, all or part of your publication costs will be covered. Please make sure your user information is up-to-date by logging into Editorial Manager at Editorial Manager® and clicking the ‘Update My Information' link at the top of the page. If you have any questions relating to publication charges, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Jyotir Moy Chatterjee

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments: