Quantum-inspired deep learning optimisation for real-time student engagement analysis in virtual classrooms

Quantum computing (QC) employs quantum-mechanical principles such as superposition and entanglement to solve specific problems far more efficiently than classical computers. E-learning is the only option for students and teachers during the pandemic. Conversely, it is challenging for an instructor to observe every student’s engagement while educating online. Students are distracted during such activities. The teachers want to identify their students' states, whether they are concentrated or concerned with teaching. Hence, from a teacher’s viewpoint, it is significant to assess students' levels of engagement to understand their actual reactions and take the necessary steps to involve students and help them achieve goals, machine learning (ML) and deep learning (DL) is deployed for predictive analytics of a student’s performance and engagement depending upon interactions, contribution in class, etc. In this paper, the Deep Learning-Driven Quantum Inspired Moth Flame Optimizer for Real-Time Student Engagement Analysis (DLQIMFO-RSEA) method is proposed. The DLQIMFO-RSEA method aims to categorise student engagement in online classes. To accomplish this, the DLQIMFO-RSEA method uses the YOLOv5 object detection model with backbones SPPF, CBS, and CSPI-X. Next, the image pre-processing stage employs the Wiener filter (WF) to remove the noise. For feature extraction, the InceptionResNetV2 technique is used. Furthermore, a stacked autoencoder (SAE) is applied for detection. At last, the parameter tuning process is performed by the quantum-inspired moth flame optimiser (QIMFO) model to improve the classification performance of the SAE model. The comparison analysis of the DLQIMFO-RSEA approach showed superior accuracy of 94.34 % compared to other models on the student engagement dataset.