Analysis of student sentiment during video class with multi-layer deep learning approach

Abstract

<p>The modern education system is an essential part of the rise of technology. The E-learning education system is not just an experimental system; it is a vital learning system for the whole world over the last few months. In our research, we have developed our learning method in a more effective and modern way for students and teachers. For significant implementation, we are implementing convolutions neural networks and advanced data classifiers. The expression and mood analysis of a student during the onlineclass is the main focus of our study. For output measure, we divide the final output result as attentive, inattentive, understand, and neutral. Showing the output in real-time online class and for sensory analysis, we have used support vector machine (SVM) and OpenCV. The level of 5*4 neural network is created for this work. An advanced learning medium is proposed through our study. Teachers can monitor the live class and different feelings of a student during the class period through this system.</p>

Sentimental analysis of audio based customer reviews without textual conversion

“…In [5], authors have worked on a popular word embedding approaches in identifying sentiments. Salehin et al [6] discusses the use of support vector machine (SVM) and OpenCV in analysing student sentiments in an online class. Further, Moung et al [7] have used an ensemble of methods in identifying sentiments in images.…”

Driver drowsiness monitoring system based on facial Landmark detection with convolutional neural network for prediction

“…The CNN needs less preprocessing for input images than other classification algorithms for the feature extraction stage; it's made up of multiple layers, containing convolutional layers, rectified linear unit (ReLU) activation function pooling layers, and normalization layers. There are fully connected layers for the classification stage and one classification layer [19], [20].…”