Exposing Emerging Trends in Smart Sustainable City Research Using Deep Autoencoders-Based Fuzzy C-Means

Round 1

Reviewer 1 Report

In order to improve the quality of this work, some comments have been provided as below.

(1)The motivation of proposing the new method should be clarified.

(2)A concluding remark should be provided to support authors’ viewpoints in this study, especially in why they propose a new method. In addition, we cannot find the development of related works, for example the pro and cons of the current methods.

(3)In section 4, it should provide more detailed implemental procedure, not introduction. For examples, in 1st paragraph of subsection 4.2, it should describe how to build corpus and lexicons.

(4)The introduction of used method should be described step by step followed the Figure 1.

(5)In step 3, why did authors use DFCM+, LDA, NMF and EFCM? Why not else?

(6)In section 5, authors should provide the results of analysis. In this section, why did authors cite related work?

(7)What’s the unique finding in this work?

(8)An additional discussion section is needed.

(9)Please highlight the major contribution of this study.

(10)In line 353, what's Figure A1?

Author Response

Please see the attachment

Author Response File: Author Response.pdf

Reviewer 2 Report

This paper work talks about a deep auto- encoders-based fuzzy C-means, is proposed for analyzing the research topic trend. The topics generated by this proposed algorithm have relative higher coherence values than those generated by previously used topic detection methods, namely non-negative matrix factorization (NMF), latent Dirichlet allocation, and Eigen space-based fuzzy C-means. I read the manuscript with great interest and believe its topic is important and relevant. Although the manuscript is overall well-written and structured, it might benefit from additional spell/language checking.

Comments

The introduction is not clear and less literature is used. Follow these instruction: The introduction should briefly place the study in a broad context and highlight why it is important. It should define the purpose of the work and its significance, including specific hypotheses being tested. The current state of the research field should be reviewed carefully and key publications cited. Please highlight controversial and diverging hypotheses when necessary. Finally, briefly mention the main aim of the work and highlight the main conclusions. Keep the introduction comprehensible to scientists working outside the topic of the paper.

What was the key motivation behind using deep auto-encoders-based fuzzy C-means?

What is meant by Loss function ℒ(?,?) in Deep Autoencoders? Explain in detail.

What are the limitations of the present work?

Describe the impact of Covid-19 on Smart Sustainable City Research.

Below papers has some interesting implications that you could discuss in your introduction and how it relates to your work.

• Ijaz, Muhammad Fazal, Muhammad Attique, and Youngdoo Son. "Data-Driven Cervical Cancer Prediction Model with Outlier Detection and Over-Sampling Methods." Sensors10 (2020): 2809.
• Ali, Farman, et al. "A smart healthcare monitoring system for heart disease prediction based on ensemble deep learning and feature fusion." Information Fusion63 (2020): 208-222.
• Li, Fang-Qi, Shi-Lin Wang, and Gong-Shen Liu. "A Bayesian Possibilistic C-Means clustering approach for cervical cancer screening." Information Sciences501 (2019): 495-510.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

1.The motivation of proposing the new method should be clarified. I mean why authors combined deep learning and Fuzzy C-mean.

2.Authors should provide academic evidences to support their viewpoints. For example, authors claimed “The most widely used text-mining algorithms for detecting research topic trends are latent Dirichlet allocation (LDA), clustering, and non- negative matrix factorization (NMF). ”What’s the academic support?

3.In related work, authors didn’t provide a concluding remark.

4. why did authors use DFCM+, LDA, NMF and EFCM? Why not else?  Authors should provide academic evidences to support their viewpoints.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

All my comments are addressed, hence manuscript is accepted. 

Author Response

Dear Reviewer 2,

Thank you very much for reviewing our manuscript. We also greatly appreciate you for your complimentary comments and suggestions. The manuscript has certainly benefited from these insightful revision suggestions.

Thank you again for consideration of our revised manuscript.

Best regards,

Anne Parlina

Department of Electrical and Computer Engineering
Faculty of Engineering - Universitas Indonesia
Depok 16424 – Indonesia
Email: anne.parlina@ui.ac.id

Round 3

Reviewer 1 Report

All of my comments have been fully considered in hit revised version.

This manuscript is a resubmission of an earlier submission. The following is a list of the peer review reports and author responses from that submission.