All reviews of published articles are made public. This includes manuscript files, peer review comments, author rebuttals and revised materials. Note: This was optional for articles submitted before 13 February 2023.
Peer reviewers are encouraged (but not required) to provide their names to the authors when submitting their peer review. If they agree to provide their name, then their personal profile page will reflect a public acknowledgment that they performed a review (even if the article is rejected). If the article is accepted, then reviewers who provided their name will be associated with the article itself.
Dear Author,
Good work , you have completed all of the requirements of the reviewers.
[# PeerJ Staff Note - this decision was reviewed and approved by Rong Qu, a PeerJ Computer Science Section Editor covering this Section #]
Dear Authors,
Your manuscript looks good, and you have made improvements. However, there are a few suggestions remaining. Please complete and re-submit for a final decision.
All comments addressed by the authors
All comments addressed by the authors
All comments addressed by the authors
to strengthen the proposed work, the authors can include the following works
1. Semantic Information Extraction from Multi-Corpora Using Deep Learning
2. Solving User Priority in Cloud Computing Using Enhanced Optimization Algorithm in Workflow Scheduling
3. Statistical Performance Evaluation of Various Metaheuristic Scheduling Techniques for Cloud Environment
Clear and unambiguous, professional English used throughout.
Original primary research within Aims and Scope of the journal.
Impact and novelty not assessed. Meaningful replication encouraged where rationale & benefit to literature is clearly stated.
1. Is it cost-efficient?
2. Revise the references with the latest references that were published in 2020–2023, and update the literature accordingly.
Good Work Carried out . But we have some suggestions to make the article more technical strength.
Image Augmentation can be done online during training or offline before training starts. Authors preferred the offline approach, why?
What is the version of Tensorflow? Give the necessary information for the reproducibility of the proposed study.
The introduction part is not sufficient, literature is not presented sufficiently. Thus, the number of referenced studies is insufficient.
Based on reviewers' suggestions update and resubmit another version.
[# PeerJ Staff Note: It is PeerJ policy that additional references suggested during the peer-review process should only be included if the authors are in agreement that they are relevant and useful. You are NOT required to cite articles published in PeerJ Computer Science #]
[# PeerJ Staff Note: Please ensure that all review and editorial comments are addressed in a response letter and any edits or clarifications mentioned in the letter are also inserted into the revised manuscript where appropriate. #]
- Instead of using 'man' use 'mankind' or a similar proper word
- Line 49, instead of using 'involuntarily' prefer data-driven and refine the sentence accordingly
- Line 64, 'classifying diseased animals from healthy animals' refine this sentence since this part is not meaningful, i.e. remove 'healthy' word
- Introduction is not well organized
- A visual result for Fuzzy Inference System (FIS) should be given.
- all symbols should in in math style, i.e., 'pz' (noise probability) in the text
- Figure qualites (1, 2, 3, 7)are low. Use EMF for word and SVG/pdf for latex to create figures in vector format. Such figures provides very high quality for reading and printout. Use of these vector format also lead to reduced pdf size for the paper
- Figure 2 is not informative at all
- In table 5, highlight the best performance as bold. Also, consider presenting Table 5 as figure since tabular information is difficult to grasp by the reader
- Semi-supervised Generative Adversarial Network are already proposed in the literature. Also using data augmentation is not a novelty as well.
- Authors says that 'full-resolution photos are not practical for consumer-grade hardware' and 'sensors will have energy constraints' which is not a limitation of the method but limitation of the hardware they have or maybe they aim for developing an embedded system. If so, this should be clearly stated.
- Image Augmentation can be done online during training or offline before training starts. Authors preferred offline approach, why?
- What is the version of Tensorflow? Give necessary information for reproducibility of the proposed study.
- Did labeling of 1500 photos of cats and dogs are done by by experts?
- What kind of ilnesses have these cats and dogs? give statistics (frequency) of these ilnesses, i.e. 150 dogs have xxx ilness
- Did you use train-test set approach or train-validation-test approach? This is important to judge the real indication of the performance (91% of accuracy) given in the paper. If rain-validation-test approach is used then 91% of accuracy seems reasonable but if train-test set approach is used then 91% of accuracy is not difficult to obtain
- Authors suggest that SVM is not sufficient, and they list possible reasons. But one of the main reason is incapability of SVM to extract features automatically, unlike CNN.
- In figure 5, backgrounds are removed for some unhealty animals. Why? Is this common in the whole dataset?
- Figure 7, ROC curve, should be generated with more operating points. In its current form it is only generated for FPR=0, FPR=0.25, FPR=1, which causes curve and AUC to be inaccurate.
- Introduction part is not sufficient, literature is not presented sufficiently. Thus, number of referenced studies are insufficient.
- Authors should cite some studies from PeerJ Computer Science as they thought their study is contributing the studies in PeerJ. If they could not able to cite some studies from PeerJ then that means their study is not a proper one for this specific journal.
comments are mentioned in a separate file.
comments are mentioned in a separate file.
comments are mentioned in a separate file.
All text and materials provided via this peer-review history page are made available under a Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.