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The manuscript can be accepted in its current form.
[# PeerJ Staff Note - this decision was reviewed and approved by Claudio Ardagna, a PeerJ Section Editor covering this Section #]
[# PeerJ Staff Note - Once again, we apologize for the tone of the comments in the first round of review. #]
Unfortunately, none of the things requested by me in the amendments have been done, and this article is rejected in my opinion.
Unfortunately, none of the things requested by me in the amendments have been done, and this article is rejected in my opinion.
Unfortunately, none of the things requested by me in the amendments have been done, and this article is rejected in my opinion.
Unfortunately, none of the things requested by me in the amendments have been done, and this article is rejected in my opinion.
Ok
Ok
Ok
The required are successfully incorporated.
Based on reviewers' comments the manuscript needs some further revisions.
[# PeerJ Staff Note - Please accept our apologies for the tone of the comments in the previous round of review. #]
Based on my review of the revised version, I recommend it be accepted for publication. The authors have thoroughly addressed the concerns raised in the previous comments and have made proper improvements to the manuscript.
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Unfortunately, there are still some remaining issues.
1- Your research is not clearly summarised in the abstract. You should improve the clarity in the abstract.
2- It is still not clear how your proposed model works and how it differs from other existing methods? Please improve your discussion in this regard.
Please correct these items.
Thanks.
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1. The abstract should be generalized and still needs more improvement, the abstract clearly mentions the methodology, results, and how these results will be more valuable than previous models.
2. The author should clearly mention the novelty of the proposed model.
3. An independent is needed to evaluate the generalization power and overfitting issue of the proposed model.
4. In the paper the author used ensemble learning via the majority voting concept, however, I did not find any section related to ensemble learning with some mathematical formulas as provided in recently used predictors i.e., iACP-GAEnsC, iAtbP-Hyb-EnC, iAFPs-EnC-GA, and Target-ensC_NP. the authors are advised to cover this part by citing these.
5. The comparison analysis of the proposed model with existing models is highly recommended.
Based on reviewers comments the manuscript needs Major revision.
The paper focuses on the detection of protein allergens using machine learning techniques.
1a. In the abstract, the first sentence is a bit unclear. Authors might want to revise it to make it more concise and easier to understand.
1b. It would be helpful to provide more context on why the detection of protein allergens is important.
1c. Also, I encourage the authors to mention dataset details and how the proposed mechanism outperformed the other techniques in the abstract.
2. The paper's contribution needs to be strengthened.
3. Please double-check the referencing style (inline and end reference); And make sure to provide all bibliography information.
4. Please cite more refernces fom 2022 and 2023.
5. Please provide further explanation regarding Figure 13.
6. It is also necessary to mention limitations or potential future directions for this work. Are there any specific types of allergens that the proposed mechanism may not be able to detect?
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Unfortunately, the abstract part is very dumb and vague and is mostly storytelling.
Dataset features are not described in the Dataset section
In the proposed model section:
Preprocessing !!!
Before !!!!
Next!!! Again before!!!
I do not understand your method at all.
Deep learning and DBN are not explained at all, while bold is your work!!!!
Apparently MLP has done better!!!!
What is your main evaluation criterion?
What is the success of your work?
The authors have discussed in detail the related entities in the field of allergy control and have presented a protein allergen classification approach based on deep learning and ensemble techniques. The hyper-parameters used in different AI techniques are also clearly described. The evaluation of the proposed mechanism is done by using various performance parameters and is compared with the other machine learning and deep learning techniques and the related work. However, I would like to recomend the authors to make the following minor changes before the manuscript is accepted for publication:
1. The introduction should be broken down into 2 more paragraphs.
2. There are some grammatical errors. The authors should read the paper carefully to omit them.
3. Future work must be presented to give direction to researchers who want to work on the same line.
4. Precise title should be given to table 1.
5. Consise sub titles should be used in section 2
6. The main contribution of the paper should be added as a bullet points in the last paragraph of introduction section.
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