Artificial Intelligent Collaborative Synchronous in Realtime using High Speed Verify-Identify Tracking Recognition for E-learning

Abstract

Collaborative Synchronous e-Learning can provide high levels of interaction for distance learning initiatives. With the rapid evolution of technology, face recognition login and tracking, continuous product evaluation is necessary to ensure optimal methods and resources for connecting students, instructors, and educational content in rich, online learning communities. This article presents the analysis of online, synchronous learning solutions. We are focusing on their abilities to meet technical and pedagogical needs in higher education. To make a solid comparison, the systems were examined in online classrooms with instructors, guest speakers, and students. Relative to usability, instructional needs, technical aspects, and compatibility are outlined for systems. We propose Verify-Identify Tracking Recognition Model for five algorithms. The result of the experiment, (2D)2PCA algorithm can recognize learner’s accuracy for Verify- Identify learner 99.46 percentage for 50 learners.