Development of an Enhanced Convolutional Neural Network for Self-proctoring in Online Examination Systems

Abstract

Self- proctoring systems leverage on artificial intelligence algorithms to detect abnormal behaviour during examination, and the Convolutional Neural Network (CNN) has been proven to be one of the most preferred deep learning algorithms to serve this purpose. Convolutional neural networks (CNN) are good for human activity recognition, detection and classification purposes, however, they require a lot of training data which can slow down execution and increase computational time during classification. Therefore, this research addressed the aforementioned problem by developing an enhanced convolutional neural network for online examination self-proctoring systems. The technique was achieved by selecting optimal weight values in the Convolutional network with a Gravitational Search Algorithm optimizer which ultimately improved the model’s computation time. The result of the CNN-GSA technique achieved an F1-Score of 91%, Accuracy of 83.33%, Precision of 83% and a recall of 99.98% at 0.657 seconds.