AI Present: Random Interval Query and Face Recognition Attendance System for Classroom using Machine Learning

COVID-19 has triggered a global crisis never been seen before. In numerous sectors of society, the epidemic has produced a huge demand for new problem-solving solutions. Important difficulties have arisen in the field of education and related learning technologies because of the shift from eye to eye to online virtual learning. To battle the spread of COVID-19, several countries have temporarily closed educational institutions. Teachers all across the world have been forced to use internet conferencing as a result of educational institution closures. Online forums' virtual classes are widely considered as the only option to meet face to face in physical classes. In this aspect, teachers face a significant issue in managing student attendance in virtual classes. Student participation is a proportion of their involvement in the study, and it is linked to their active learning. Keeping track of student presence during virtual learning, on the other hand, is a major difficulty. Naming students who appear to be present in classrooms is a simple but time-consuming operation. Due to COVID-19 outbreak and widespread usage of virtual aids, disaster risk management must build a proper tracking system for monitoring student involvement and participation during virtual learning as soon as possible. This research work introduces a novel strategy to solve the significant demand created by the epidemic. Further, the proposed research study introduces the random interval query and face recognition based attendance management system to build a highly efficient and robust learning participation management system (from now on, AI Present). As far as known, no mechanized framework has been developed for following the students' participation and guarantee their involvement during the learning process.