ABSTRACT
In this global pandemic of COVID-19, there is a critical need for self-protective devices, and the most important of them is a face mask. Our project’s main aim is for identifying the presence of a face mask on person's face. A strategy should be formulated to make the people accept this essential safety measure. To check this, a face mask detector system should be used. To check the presence of a face mask on a human face, the primary step is the detection of human face. This can be divided into two parts: verifying faces on the images and detection of masks on their faces. Face masks are used to prevent cross-contamination as part of an infection control strategy. Using TensorFlow, Kera's library, and OpenCV, we created a very rudimentary convolutional neural network (CNN) model. Our experiment demonstrates that it operates effectively on test data, having a precision of 100% and a recall of 99%, respectively. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
A large number of people have been affected by the Coronavirus pandemic. In addition, it is a tremendous influence on mental health for millions of people around the world. Nowadays, most people are expressing their opinions on several incidents or situations through social media platforms. There were many tweets, posts;comments, etc., are being posted across social media platforms. Therefore, content related to COVID-19 over social media can be considered to analyze and understand people’s opinions and situations. The model aims to collect the tweets with the hashtag COVID-19 from Twitter and analyze tweets’ sentiments. This model has utilized various libraries in Python and natural language processing (NLP). Therefore, with the help of this model, we can classify tweets into one of the following categories: positive, negative, extremely positive, extremely negative, and neutral. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.