Search

Attendance is an important aspect to record the presence of students. The presence of someone is a way to address that the student is carrying out their obligations to come. Usually, attendance is done manually using pen-paper. This digital era has brought forth new technologies that can help deal with this absence of students in organisations. Automatic Face Recognition (AFR) technologies have made many improvements as well as advancements in the field of biometrics. It uses RealTime Face Recognition as a solution to keep a record of the attendance. The Face recognition approach has proven to be the best of all Biometrical sources of identification. We propose a face recognition desktop application that will automatically update a student's attendance into the database on the web server, providing an ease to the faculty. Even students can know their attendance just by a login. A report based on student’s attendance can help in evaluating the attendance eligibility criteria. In this face recognition desktop application, we use Machine Learning and Deep Learning algorithms to capture multiple images of students to reduce human errors and proxy attendance. This desktop application is much more efficient and safer than the traditional pen paper method. Keywords: Attendance system, Automated attendance, Image Processing, Face detection, Feature matching, Face recognition.

COVID-19 originally known as Corona virus has been declared as pandemic by the World Health Organization on 11th March 2020. This infectious disease discovered from Wuhan, China in December 2019 and has affected millions of people around the world. Every country around the world is undergoing global economic crises and therefore, it’ s the need of an hour to predict the prevalence and incidence of this disease throughout the world. This will help the medical practitioners and government agencies in India to make key decisions and appropriate measures to demystify the disease and prevent the country from global economic recession. This paper aims to analyze the number of cases in India by utilizing the machine learning techniques and exploratory data analysis to observe the growth patterns and map the increase in the frequency of those infected. The source of data was authentic COVID-19website which was showing confirmed diseased cases of Delhi, Uttar Pradesh and India as a whole. The count of confirmed cases taken from 14th March 2020 to 3rd September 2020 put together will help to know how effective the current efforts have been and also help to realize the need of working further to combat this virus. This research focuses on predicting the possible number of confirmed cases using techniques of data mining, data analysis with particularly regression, clustering and predictive analysis. The primary focus is to predict the number of cases in the coming month and finding out that whether there is relation between temperature with number of confirmed cases or not.