ABSTRACT
Managing attendance is a vital task for every institution. Considering the COVID pandemic where many organizations have resorted to online mode of working, it has become imperative to maintain social distancing and digitize various processes. Thus, for maintaining attendance of the students of schools/colleges or employees of a company, a touchless attendance system is required that records the attendance by capturing faces and does not waste time. This one-of-a-kind application uses a client–server model and captures the faces of students/employees through video feeds from mobile phone cameras, and the images are sent to a server, where image processing is used to process the faces. Further, with the help of dlib and the face recognition library, it identifies the faces and records the attendance in the software itself. The processed image is again sent back to the client android application, and the user gets notified about their attendance. Additional functionalities for data analysis and updating data have also been added to the system. Thus, the whole attendance system is an effort to make the attendance activity easy and efficient. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
Managing attendance is a vital task for every institution. Considering the COVID pandemic where many organizations have resorted to online mode of working, it has become imperative to maintain social distancing and digitize various processes. Thus, for maintaining attendance of the students of schools/colleges or employees of a company, a touchless attendance system is required that records the attendance by capturing faces and does not waste time. This one-of-a-kind application uses a client–server model and captures the faces of students/employees through video feeds from mobile phone cameras, and the images are sent to a server, where image processing is used to process the faces. Further, with the help of dlib and the face recognition library, it identifies the faces and records the attendance in the software itself. The processed image is again sent back to the client android application, and the user gets notified about their attendance. Additional functionalities for data analysis and updating data have also been added to the system. Thus, the whole attendance system is an effort to make the attendance activity easy and efficient. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
Background: We prospectively studied 160 patients of asymptomatic covid 19 infection for their clinical course and outcome. Methods: Demographic and clinical characteristics from the 160 asymptomatic SARS-CoV-2 positive patients were obtained over a period of 3 months. Results: Overall, mean age of the patients was 35.66 years. HYPERTENSION was the commonest comorbidity. However, 142 patients (88.75%) were found to be without any co-morbidities.17 patients (10.6%) developed symptoms on 3rd day whereas 7.5%, 6.8%, 3.7% and 1.2% became symptomatic on 2nd, 4th, 1st and 5th day respectively. Conclusion: Cough was the most common symptom seen followed by fever, sore throat, generalized weakness and dyspnea. Asymptomatic patients were mostly without any comorbidities and hence remained asymptomatic and overall outcome was better.