Table 7.

Techniques, attributes, and characteristics of monitoring and tracking-COVID-19 applications

Authors	The basic objective	Pros	Limitations in study	Security method?	Simulation environments	Dataset and Size of Dataset	Using TL?	Mechanism	Application?
Dong, Qiao [103]	Intending to use the XGBoost and a non-contact monitoring system to assess prospective COVID-19 patients automatically	Precision = 92.5 percent, recall rate = 96.8%, and AUC = 98.0 percent were achieved.-Stable and robust -Low complexity	-High energy consumption	No	Not mentioned	Local dataset (Small dataset)	No	XGBoost + LR algorithm	Non-contact screening system
Jaber, Alameri [104]	Using a CNN model to maximize the illness classification process with the fewest possible deviations	-98.76 percent accuracy -Low variation rate	–Low robustness	No	MATLAB	Open research dataset (Small dataset)	No	CNN	Monitoring COVID-19 patient health
Zhang, Zhu [105]	Proposing body temperature monitoring for COVID-19 prevention regularly based on ML	-High accuracy	-Low flexibility	No	Python	Total of 31,713 entries dataset ( Large size dataset)	No	RF	Body temperature monitoring
Zhang, Liu [106]	Offering an emotion-aware system that includes discriminative emotion identification utilizing CNN	-High accuracy -High precision	-Low robustness	No	Python	eNTERFACE’ 05 datasets, SEED dataset, and DEAP database (Large dataset)	Yes	CNN	Emotion-aware and monitoring
Castiglione, Umer [107]	Presenting a technique that gathers data from health centers and saves it to a data warehouse for analysis using ML	-0.754 precision -0.794 recall -0.810 recall -F-score of 0.802	-Low security -Low flexibility	No	Python using Scikit-learn	Real-time dataset (Small dataset)	No	Random forest	Monitoring COVID-19