## 1 Introduction
### Motivation and Contribution  
The motivation for this research work is to contribute and serve the community in this crucial time of emergency. While other countries have to learn from those countries who had controlled the COVID-19 situation in well mannered. The objective of our proposed SSDWG to disinfect a large number of people to save them from COVID-19. Our proposed SSDWG can reduce the chances of the spread of COVID-19 carriers and it helps to break the chain and local transmission of COVID-19 if we install SSDWG on every populated places entrance. There is still a need to adopt precautions because the vaccine of COVID-19 is still under trial, and those vaccines that have been developed and matured are not available for the developing countries in the first stage. Accordingly, there is only one way to control and minimize the spread by adopting SOP's that are given by the World Health Organization (WHO), such as social distancing and wearing a mask. The United States Center for Disease Control (CDC) advised the public to wear a face mask or any cloth to cover their nose and mouth, they said that people can spread COVID-19, even if they do not feel sick. It is scientifically proven that a mask could reduce the chances of spread of COVID-19 by 90% [8]. The contributions and research outcomes of our proposed model are as follows. * Our proposed SSDWG includes multi-features to disinfect many people from the COVID-19 without using a manual disinfection system. * Our proposed system can help to track vulnerable individuals' records for further trace and control. * The proposed model also includes the module to check an individual's temperature while using a contact-free temperature sensor without any human intervention and store the temperature in the database. * According to the WHO, wearing a face mask in public places can protect an individual from COVID-19, as evidenced by experiments. The second module of SSDWG is to detect individuals wearing a mask or not and classify individuals in three classes, which are face with proper mask (FWPM), face with improper mask (FWIPM), and face without a mask (FWOM). In this module, we trained our machine learning models using three datasets, and we attained significant accuracy. * The sub-module of our face mask identification system is to identify the types of face masks, either N-95 or surgical masks. Our Proposed Smart Screening and Disinfection Walkthrough Gate (SSDWG) have two main modules, the architecture of our model is shown in Materials and Methods section. The first module deals with disinfecting and measure the temperature of individuals in a contact-free manner using the MLX90614 sensor. This module also includes motion and ultrasonic sensors to detect the presence of an individual, activating the disinfection spray system, the whole architecture of the first module is shown in Materials and Methods section. Moreover, the second module is designed to detect and classify the masked faces  
Figure 5: Global vs. Local spread of COVID-19 in Pakistan. into different classes using deep learning algorithms. In the mask detection module, we used different datasets for training and testing purposes obtained from kaggle, Github, and Bing, the detailed statistics of these datasets are shown in Dataset section. The dataset consist images included, masked face, proper mask faces and improper mask faces. We did classification to classify the type of the masks into surgical and N-95. We have done pre-processing to clean datasets from noise and then fed them into the models for training and testing purposes. We used the transfer learning technique by embedding layers of five different pre-trained models to achieve our proposed objectives, the process of transfer learning can be found in Materials and Methods section. We attain significant accuracy in the face mask detection module. We achieved the highest accuracy rate of 99.8%, and 99.6% by using the VGG-16 and MobileNetV2 respectively, the detailed accuracy results can be found in Experimental Results section.