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According to the comments of reviewers, and after comprehensive consideration, it is decided to accept it.
[# PeerJ Staff Note - this decision was reviewed and approved by Claudio Ardagna, a PeerJ Section Editor covering this Section #]
author made changes based on review comments
experiment is good.
valid results
author made changes based on review comments
The review comments are incorporated in the revised manuscript
NIL
NIL
NIL
Please revise the paper according to the reviewer's comments.
**PeerJ Staff Note:** Please ensure that all review and editorial comments are addressed in a response letter and that any edits or clarifications mentioned in the letter are also inserted into the revised manuscript where appropriate.
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Dataset details need to be clearly mentioned or more details required about dataset.
Research topic is good and has lot of applications.
Results are fine, however the evaluation metrics considered need to be specified.
1. This paper discuss about Multi-class waste segregation using YOLO single shot detector and using robotic arm sysyem
2. List of keywords missing
3. In abstract, add proposed model name
4. What was the training-testing ratio of classifier
5. How the model evlauated? What were the evaluation metrics for the proposed model?
6. Why PIL format?
7. Results section, yolo model performance metrics were missing
8. Dataset section can be added, specifying the number of images, image size etc can be specified
It seems like there are several key issues with the study that need to be addressed to improve its quality and increase the likelihood of publication. Let's elaborate on each concern:
Abstract lacks exact results:
The abstract should provide a concise summary of the study's objectives, methods, results, and conclusions.
Include specific numerical findings or key outcomes to give readers a clear understanding of the study's significance.
Missing dataset description in the Methods section:
Clearly outline the characteristics of the dataset used in the study, including its source, size, variables, and any preprocessing steps applied.
A comprehensive dataset description is crucial for transparency and reproducibility.
Lack of data on proposed model validation:
Present details on the validation process for the proposed models, including the metrics used and the criteria for model selection.
Validation results contribute to the credibility of the study and help readers assess the reliability of the proposed models.
Limited discussion and lack of comparison to existing studies:
Expand the discussion section to delve into the implications of the results and their significance in the broader context of existing literature.
Compare the findings to relevant studies in the field to highlight the study's contributions and limitations.
Conclusion missing exact results:
The conclusion should reiterate the key findings and their implications.
Include specific numerical results to reinforce the study's main contributions and provide a clear takeaway for readers.
Limited explanation of utilized algorithms:
Expand the section on the algorithms used in the study, providing a detailed explanation of their underlying principles.
Include references to relevant literature or documentation for readers seeking a deeper understanding of the employed algorithms.
Figures captions lack exact details:
Ensure that figure captions provide sufficient information for readers to interpret the visual elements.
Include details such as axis labels, units, and any pertinent information needed to understand the figures without referring to the main text.
Addressing these concerns will not only enhance the clarity and completeness of the study but also increase its chances of being accepted for publication in its present form. It's essential to prioritize transparency, thoroughness, and engagement with existing research in the field.
The basic informations are given very clearly.
1. In Figure1, how the dataset creation and annotation are carried out?
2. Input from camera is live video processing. how the video is convered into images and processed further.
3. What are the different types of frames are taken for pre-processing of frames? Kindly brief it.
The model can be compared with existing model and then validate the proposed result is good.
NIL
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