An Efficient Deep Learning Classifiers Algorithm for Examining Public Perception of Covid SOPs
Keywords:
Deep Learning, datasets, machine learning, COVID, virus, WHO, public perceptionAbstract
In December 2019, Wuhan, China, reported the first coronavirus case, confirming the current pandemic. Following that, it swept across the entire world. The leading cause of this disease is still unknown, though. Governments are emphasizing physical distance and wearing masks in public places. The people mostly ignore the safety SOPs, which results in a surge in infected people rates and a healthcare burden on the national economy. Through the identification of face masks, this study used machine learning and deep learning models to analyse sentiment. Then, it compared the effectiveness of machine learning and deep learning techniques RMFD, SMFD, and LFW using three modern datasets. We used two deep learning algorithms, Convolutional Neural Network and Visual Geometry Group, and one machine learning algorithm Histogram of Oriented Gradient-Support vector machine, for accurate facemask detection. The reason for using a machine learning algorithm is that it has achieved promising results regarding face mask detection accuracy when trained with a large amount of data. The findings of a comparative analysis with accuracy results have been discussed at the end, and the conclusion is that the proposed framework produced more excellent accuracy results.
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