Reviewer has chosen not to be AnonymousOverall Impression:
AcceptTechnical Quality of the paper:
Clear noveltyData availability:
All used and produced data (if any) are FAIR and openly available in established data repositoriesLength of the manuscript:
The authors need to elaborate more on certain aspects and the manuscript should therefore be extended (if the general length limit is already reached, I urge the editor to allow for an exception)
Summary of paper in a few sentences:
The manuscript is related to the use of machine learning methods for COVID19 detection.
Reasons to accept:
The idea seems to be interesting. Different machine learning techniques have been used to explore distinct performance metric.
Reasons to reject:
The manuscript is related to the use of machine learning methods for COVID19 detection. The idea seems to be interesting. Different machine learning techniques have been used to explore distinct performance metric. However, to maintain the general interest of the reader, the manuscript need to be revised as per the following suggestions:
- First of all, more background work need to be addressed. The novelty and worth of this work need to be reflected in the introduction section.
- There should be a separate related works section.
- As the whole story revolved around the use of machine learning applications. Therefore, a brief paragraph regarding the basics and foundation of the machine learning techniques need to be added in the methodology section. The general applciations of the machine learning techniques need to be briedly highlighted. For this authors may read and refer the following manuscripts.
o Kim, Gi Bae, et al. "Machine learning applications in systems metabolic engineering." Current opinion in biotechnology 64 (2020): 1-9.
o Kaur, Prableen, and Manik Sharma. "Analysis of data mining and soft computing techniques in prospecting diabetes disorder in human beings: a review." Int. J. Pharm. Sci. Res 9 (2018): 2700-2719.
o Kaur, Prableen, and Manik Sharma. "A Smart and Promising Neurological Disorder Diagnostic System: An Amalgamation of Big Data, IoT, and Emerging Computing Techniques." Intelligent Data Analysis: From Data Gathering to Data Comprehension (2020): 241-264.
o Arora, Sankalap, Manik Sharma, and Priyanka Anand. "A novel chaotic interior search algorithm for global optimization and feature selection." Applied Artificial Intelligence 34.4 (2020): 292-328.
o Gan, Lirong, Huamao Wang, and Zhaojun Yang. "Machine learning solutions to challenges in finance: An application to the pricing of financial products." Technological Forecasting and Social Change 153 (2020): 119928.
o Saadatmand, Mohammadsaleh, and Tuğrul U. Daim. "Technology Intelligence Map: Finance Machine Learning." Roadmapping Future: Technologies, Products and Services (2021): 337-3
- The results presented in the figure 2 need to be discussed in mreo detailed manner.
- The strength and limitation of this work need to be clearly addressed.