Open AccessAttendance and Performance Monitoring Using ML
Author(s) -
Krishna Shahu,
Patel Karan N,
R. Srinath Reddy,
P. C. Kumar,
Professor J. Mary Stella
Publication year - 2022
Publication title -
international journal for research in applied science and engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2321-9653
DOI - 10.22214/ijraset.2022.40767
Subject(s) - attendance , lagging , computer science , task (project management) , artificial intelligence , multimedia , mathematics education , psychology , engineering , political science , statistics , systems engineering , mathematics , law
Attendance Management is very important for every organizations. Marking and maintaining attendance is a very time consuming task. It takes lots of time to mark attendance manually. It is difficult to analyse attendance of students how frequently one is skipping classes. There will be some problems regarding proxy attendance of some students. The possible solution to this problem is to use an automatic attendance system which uses face recognition techniques. This system will mark attendance electronically and recorded attendance will be stored in a database. The preparation of a question paper for the Internal Assessment Exam can be automatically generated with the help of teachers according to RBT levels. The Internal Assessment is very important for students. This system will give individual student reports based on the RBT levels of students' performance in the internal assessment exam and can be analysed, exactly in which type of RBT level the student is lagging behind. Keywords: Attenndance Monitoring; deep learning; convolutional neural networks (CNN), Machine Learning, paper setter, Internal Assesment Analysis, RBT(Revised Blooms Taxonomy) can be used;