## 5 Discussion  
Body temperature measuring is adequate for preventing an outbreak of COVID-19. Fever, dry cough, sore throat, headache, muscle or body aches, congestion or runny nose, nausea or vomiting, and diarrhea are the most significant common symptoms of COVID-19. To measure individuals' temperature manually consumes a considerable amount of human resources, time, and administrative resources. ref. [45] introduced a prototype system that consists of a contact-free temperature sensor. Their proposed system has the features of contact-free temperature measurement and attendance, which are taken at the entrances of school campuses in Hong Kong. Their experiments showed that the system could measure body temperature with adequate accuracy for screening purposes.  
A pandemic-like flu requires rapid temperature measuring. ref. [46] authors designed a low-cost, scalable device used for measuring temperature to control the spread of the flu pandemic. Their proposed temperature measuring module is developed by a medical-grade version of the Melexis MLX90614 series of smart infrared temperature sensors. When the temperature is high, an alarm will alert the authorized person at the entrance to find the suspected student. We also used the Melexis MLX90614 contact-free temperature measure sensor in our proposed model. We also compared the comparative features of our  
Figure 24: Graph generated by Internet of Things (IoT) web portal.  
Figure 23: Database view of stored data.  
model with previous work that was done by different authors. Table 12 shows our model's comparative characteristics with previous work.  
We have also compared our results of the mask detection and classification module with previous work that was done by different researchers. The authors [30] used ResNet-50 for feature extraction from images and YOLO v3 for face mask detection and classification. Two datasets were used; one is the Medical Masks Dataset (MMD), published by [PERSON], which consists of 682 images. The second public masked face dataset is a Face Mask Dataset (FMD), which consists of 853 images. The authors combined these two datasets, and the merged dataset contains 1415 images by removing bad quality images and redundancy, and they achieved 81% accuracy for mask detection. We also used a pre-trained ResNet-50 model for face masked detection, and we achieved 99.2% accuracy. We also used the Inception V3 model and achieved 99.46% accuracy. Similarly, the authors of [27] used fine-tuning for transfer learning the Inception v3 model to classify the images into a mask or no mask and achieved 99.2% accuracy. The authors used the Simulated Masked Face dataset, which consists of 1570 images, in which 785 images are masked facial images and 785 unmasked facial images. Simulated face masks are the images of individuals on which face masks are added on individual's faces by photo editing techniques. Further, we used the fine-tuned VGG-16 model in which we achieved 99.81% accuracy. A similar model was used by the authors of [50] for face mask detection and classification. Their dataset contains 25,000 images and 224 \(\times\) 224 pixel resolution. 80% of the data is used for training and 20% for the testing, and they achieved an accuracy rate of 96%.