Activity Classification of Student in Intelligent Classroom using MDNFM Model

  • Rahul Kumar Pandey, Dr. Gyanesh Shrivastava, Dr. Umesh Kumar Pandey

Abstract

Smart learning developed a greater influence in learning and stimulates the society towards betterment in all fields and to grow up in the competitive world. Smart learning is tuned with the advancement in technology and makes progressive development in the life of the students. The instructors or the faculty when indulged in taking lectures cannot monitor the activities of the students appropriately. Therefore, traditional methods employ the use of several face detection algorithms to monitor the activities of the students we propose a Multi-tasking Deep Neuro-Fuzzy Model (MDNFM) for the activity monitoring of the students in the classroom. Initially, the images of the students in the classroom are captured through web cameras and other accessories placed in the smart class the performance of the MDNFM model is compared with other existing approaches and more accurate results are obtained using the proposed MDNFM model.

Published
2021-11-27
How to Cite
Rahul Kumar Pandey, Dr. Gyanesh Shrivastava, Dr. Umesh Kumar Pandey. (2021). Activity Classification of Student in Intelligent Classroom using MDNFM Model. Design Engineering, 87 - 105. Retrieved from http://thedesignengineering.com/index.php/DE/article/view/6899
Section
Articles