The COVID-19 pandemic has spread across the globe, hitting almost every country. To stop the spread of the COVID-19 pandemic, this article introduces face mask detection on a gate to assure the safety of Instructors and students in both class and public places. This work aims to distinguish between faces with masks and without masks. A deep learning algorithm You Only Look Once (YOLO) V5 is used for face mask detection and classification. This algorithm detects the faces with and without masks using the video frames from the surveillance camera. The model trained on over 800 video frames. The sequence of a video frame for face mask detection is fed to the model for feature acquisition. Then the model classifies the frames as faces with a mask and without a mask. We used loss functions like Generalize Intersection of Union for abjectness and classification accuracy. The datasets used to train the model are divided as 80% and 20% for training and testing, respectively. The model has provided a promising result. The result found shows accuracy and precision of 95% and 96%, respectively. Results show that the model performance is a good classifier. The successful findings indicate the suggested work's soundness.  

The COVID-19 pandemic has spread across the globe, hitting almost every country. To stop the spread of the COVID-19 pandemic, this article introduces face mask detection on a gate to assure the safety of Instructors and students in both class and public places. This work aims to distinguish between faces with masks and without masks. A deep learning algorithm You Only Look Once (YOLO) V5 is used for face mask detection and classification. This algorithm detects the faces with and without masks using the video frames from the surveillance camera. The model trained on over 800 video frames. The sequence of a video frame for face mask detection is fed to the model for feature acquisition. Then the model classifies the frames as faces with a mask and without a mask. We used loss functions like Generalize Intersection of Union for abjectness and classification accuracy. The datasets used to train the model are divided as 80% and 20% for training and testing, respectively. The model has provided a promising result. The result found shows accuracy and precision of 95% and 96%, respectively. Results show that the model performance is a good classifier. The successful findings indicate the suggested work's soundness.

Hepatitis is the most serious disease in developing countries. Therefore, early diagnosis is very important to obstacle the effect that can happen as a consequence of this disease. In this case, deep learning can solve the issue at an early stage.  An innovative deep learning-based technique to identify hepatitis is presented in this study. In this study 45 layers, convolutional neural network (CNN) architecture connected with three fully connected layers is used in the proposed architecture. The two classes of collected hepatitis datasets are then used to train the suggested CNN model. The model achieved 0.934 classification accuracy. The proposed model was compared to the state of the art at the time. The outcome presented implies that the model's performance is remarkable.