## 4 Experimental Results
We used two pre-trained models (VGG-16 and MobileNetV2), which achieved the highest accuracy in the face mask detection and classification module, as described before. For this module, we split our dataset 22,973 images for training and 5743 images for testing and feed to the two fine-tuned models, which are VGG-16 and MobileNetV2. The process of fine-tuning both models for transfer learning is the same as described before. We computed and reported the accuracy and kappa values of both models for the types of face masks identification and classification. Table 9 reported the comparative accuracy and kappa values of both of the models, and we conclude that the VGG-16 achieved higher accuracy than MobileNetV2. We also reported the confusion matrix of both models. Figure (a) shows the confusion matrix of VGG-16 on test data, and a similarly on (b) shows the confusion matrix of MobileNetV2 on test data for the identification and classification of types of face masks, as shown in Figure 19.  
We computed and compared the results regarding the class-wise accuracy, precision, and other parameters for the evaluation of our models individually. We reported the class-wise accuracy, precision, recall, and f  
Further, we also plotted the accuracy, precision, recall, and f1-score of five models with respect to each face mask classes (Surgical, and N-95) which shown in Figure 20.  
We have also designed and developed a dashboard to handle the data that are stored in the database from SSDWG. We presented it in a significant way, in which a data analyst can perform any needed operation. Here is some visual demonstration of our IoT-based web portal. While using our web portal, an analyst can perform different types of analysis after login into our COVID-19 spread control portal. The login page is shown in Figure 21.  
All of the data, including time, temperature, status, photo of the suspected, and wearing a mask or not, are stored in our database, as shown in Figures 22 and 23. An analyst can analyze all of the information from the portal in different charts or graphs, as shown in Figure 24, and, further, he will confirm all of the detail proceeding from a particular department regarding the suspected case of COVID-19. An analyst can generate different types of reports on a daily or weekly basis from our web portal and send all of the information \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Mask Type** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline N-95 & 97.37\% & 0.97 & 0.98 & 0.97 \\ Surgical & 97.37\% & 0.98 & 0.97 & 0.97 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Statistics using MobileNetV2 for types of face masks detection and classification. Figure 20: Statistics of two models in mask type detection and classification. The figure (**a**) shows the performance metrics using VGG-16. While the figure (**b**) represents the performance metrics for types of face masks identification and classification (N-95 or Surgical) using MobileNetV2. shown in Figure 24 to the higher authorities of the concerned department. Many walks through gates for sanitization have been developed and installed in various places and cities. Still, those are not automated, and people have to interact with the shower button for sanitization. Because buttons have a surface that itself becomes the reason for COVID-19 spread or as a carrier, and these manual sanitization gates are working for disinfection purposes only. Hence, our proposed model is an autonomous system. It works without any intervention of human resources. It also detects the the temperature of every passing person who has at least one main symptom of COVID-19. It also detects individuals' face masks in three classes: FWPM, FWIPM, and FWOM. It also performed the identification and classification for types of face masks in two classes (N-95 and surgical mask), which are explained in the above section.