Table 11.

The methods, properties, and features of hybrid -COVID-19 mechanisms.

Authors	Main idea	Advantages	Research challenges	Security mechanism?	Dataset	Using TL?	Method	Usage?
Abdel-Basset, Chang [60]	Proposing a semi-supervised few-shot segmentation model.	-The FSS-2019-nCov's generalization efficiency improves as a result of semi-supervised learning.	-Lack of volumetric data representation	No	The Italian Society of medical and interventional radiology made two annotated CT datasets available for model evaluation.	No	CNN	Detection in chest CT
			-Owing to a lack of supervision, it was impossible to achieve a very accurate segmentation.
Pereira, Guerin [82]	Predicting the COVID-19 pandemic dynamics using a data-driven approach.	-Moderate accuracy	-High energy consumption	No	JHU dataset	No	LSTM-SAE + Autoencoder	Estimating statistics, such as peaks and the number of reported cases
		-High robustness	-High delay
			-High complexity
Chaves-Maza and Martel [83]	Using SOM + MLP to investigate factors that significantly impact the survival rate	-High prediction ability	-Dataset is insufficient	No	The dataset included 2221 Spanish entrepreneurs and 769 variables collected between 2008 and 2012 during the financial crisis.	No	SOM + MLP	Examining the key factors that have an impact on the survival rate of entrepreneurs during the COVID-19
		-High scalability	-High complexity
			-High energy consumption
Leichtweis, de Faria Silva [84]	Exploring the impact of many factors on the spread rate of COVID-19 using the GAN model.	-Showing that the development of COVID-19 has a negative association with local temperature, according to the findings.	-High complexity	Yes	The dataset was collected from reported cases of COVID-19 and their respective GHS notes and climate data for 52 countries.	No	SOM + GAM	To investigate how temperature, relative humidity, solar radiation index, affect COVID-19 spread rate
			-Low security
Al-Waisy, Mohammed [85]	Providing a hybrid multi-model DL System for COVID-19 detection.	-Accuracy rate of 99.93%	-A broad and difficult dataset containing numerous COVID-19 cases is not taken into account.	No	Cohen's GitHub Repository + Radiopaedia dataset, Italian society of medical and interventional radiology.	No	DBNs + Convolutional DBN	Detection in chest X-ray
		sensitivity of 99.90%
		specificity of 100
		the precision of 100%
		F1-score of 99.93%
		MSE of 0.021%
Rosa, De Silva [86]	Using the DBN for subject recognition and tree–CNN–based affective analysis for emotion identification.	-Accuracy higher than 0:90	-High complexity	No	A total of 18,597,314 messages were extracted from online social networks to create the dataset.	No	DBN+	In the case of COVID-19, event detection
		-Can detect an event almost three days before other approaches.	-High delay				Tree CNN
Hooshmand, Ghobadi [87]	Finding drugs on COVID-19 using a multimodal RBM technique	-High clustering ability	-Clinical trials, such as in vitro or in vivo experiments, must be conducted.	No	Harmonizome and LINCS dataset	No	mm-RBM	Finding similar drugs to treat COVID-19
		-Low energy consumption
Ibrahim, Kamaruddin [46]	Considering 9 different factors for performance evolution of MLP and RBF methods.	-High accuracy	-All possible scenarios aren't taken into account.	No	In April 2020, a dataset of COVID-19 cases was collected in 41 Asian countries.	No	MLP + RBF	Look into the spread of COVID-19 and the factors that contribute to death
		-Low complexity
Shoaib, Raja [88]	Using a hybrid model based on nonlinear autoregressive with radial base function.	-High accuracy	The data set is sparse and inadequate.	No	The use of a network obtained the data set.	No	Nonlinear autoregressive + RBF	COVID-19 progression forecasting for various countries
		-Low convergence time
		-High stability
Dhamodharavadhani, Rathipriya [89]	Proposing SNN models and their hybrid versions with the NAR-NN for COVID-19 mortality prediction.	-Appropriate accuracy	-High complexity	No	From January 20, 2020, to May 30, 2020, the dataset includes India's confirmed cases and death cases.	No	PNN + RBFNN + GRNN	Estimate the number of COVID-19 death cases in India in the future
			-High delay
			-High energy consumption