An Online Exam Proctoring System Using The GMP-DCNN Approach for the Education Sector
Keywords:
Online examination, Online Exam Proctoring (OEP), Schaeffer Weighted Kookaburra Optimization (SWKO), Geometric Mean Pooling based Deep Convolutional Neural Network (GMP-DCNN), Kendall Rank Correlated Diamond Search (KRCDS), Davies Bouldin Score based K-Means (DBS-KM), Weiner Filter (WF), and Viola Jones (VJ).Abstract
For the education sector, online examination is an effective tool. However, it has many security issues. Thus, various techniques were developed in prevailing research works. But the performance is still lacking. For solving this issue, a Geometric Mean Pooling-based Deep Convolutional Neural Network (GMP-DCNN)-based Online Exam Proctoring (OEP) system is proposed in this paper. Primarily, video, audio, screen recorder, and app setting screenshots are considered as the input. Next, frame conversion, Kendall Rank Correlated Diamond Search (KRCDS), and Weiner Filter (WF) techniques pre-process the video data. Then, by using the Davies Bouldin Score-based K-Means (DBS-KM) algorithm, the objects are segmented. The face points are identified from the detected objects by using Viola Jones (VJ). Subsequently, the features are extracted from the objects and face points. On the other side, by utilizing WF, the noise is removed from the audio signal. Next, from the noise-removed signal, features are extracted. Next, pre-processing and feature extraction phases are also carried out from the screen recorder. The app setting screenshot was also extracted; from the app setting screenshot, the features were also extracted. By utilizing Schaeffer Weighted Kookaburra Optimization (SWKO), significant features are selected from the extracted features. Next, selected features and all the pre-processed data are inputted to the GMP-DCNN. An alert message is sent to the invigilator if any misbehavior is present. Experimental analysis shows that GMP-DCNN achieves 98.8% accuracy.
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