【IEEE ACCESS】论文发表记录 1_ieee access 中国文章

官网：IEEE Access：多学科开放获取期刊

期刊水平：

IEEE Access - 中国科学院文献情报中心期刊分区表升级版 (fenqubiao.com)

 时间轴：

第一次提交：2024 年 1 月 17 日

第一次结果：2024 年 2 月 27 日  拒绝但建议重投（3个审稿人：一个建议接收，两个拒绝且不建议重投）

第二次提交：2024 年 3 月 8 日

第二次结果：2024 年 3 月 23 日  接收（2个审稿人：均建议接收）

提交终稿： 2024 年 3 月 25 日

 Early Access：2024 年 3 月 29 日

正式发表：-

审稿人意见：

第一次提交

Reviewer: 1

Recommendation: Accept (minor edits)

Comments:
In this paper, the authors propose an edge computing-based multitask framework for XXX. The research is innovative, and the experiments are well-conducted. This research integrates A and B, reflecting sufficient application value. I recommend the acceptance of this paper.

Reviewer: 2

Recommendation: Reject (do not encourage resubmit)

Comments:
I have carefully reviewed your work and would like to provide you with some feedback and suggestions for improvement:

Method part:
1) The author should introduce the structure of the feature extraction backbone network in more detail, the model chosen and why XXXNet was chosen, and what is its performance?
2) Please provide a more thorough explanation of the architecture and the reasoning behind the design choices. Additionally, consider including any hyperparameter settings or optimization techniques utilized.
3) Could the authors provide more details on the specific loss functions used and how they contribute to improving the generalization of the main task? Any comparisons or experiments are needed to demonstrate the effectiveness of using these auxiliary tasks.
4) It is recommended that the author describe the specific implementation methods, network structures and training strategies of A branch and B branch in more detail. Furthermore, the authors should explain the relationship between these two branches and how do they work together to achieve XXX image segmentation and classification?

Other changes:
1) I propose to provide a more detailed explanation of the challenges faced in XXX. Elaborate on the specific limitations of existing methods.
2) Are there any specific scenarios where your method may not perform as well? How can these limitations be addressed or mitigated in future work?
3) How does your method fare in terms of memory usage?
4) How does your method perform on other public data sets? It is recommended to add other data sets and demonstrate the superiority of the method through its performance on other different data sets.
5）Suggest further conducting the latest literature research work.
6）Suggest adding more experimental results.

Reviewer: 3

Recommendation: Reject (do not encourage resubmit)

Comments:
The topic in the paper is not novel and has some reports (Reference 6-8). Even the authors have a new method for the question. Compared with formal works, there are only a few innovations and no advanced results. The presentation also needs improvements.

第一次修改：

主要是添加了一些内容，以阐述性内容居多，没有补充实验之类的。

第二次提交

Reviewer: 1

Recommendation: Accept (minor edits)

Comments:
In the revised manuscript, the authors have addressed all reviewers' concerns. I recommend the acceptance of this paper.

Reviewer: 2

Recommendation: Accept (minor edits)

Comments:
The paper looks better now