The storage capabilities and advanced in data collection has led to an information load and the size of databases increases in dimensions, not only in rows but also in columns. Data reduction (DR) plays a vital role as a data prepossessing techniques in the area of knowledge discovery from the huge collection of data. Feature selection (FS) is one of the well known data reduction techniques, which deals with the reduction of attributes from the original data without affecting the main information content. Based on the training data used for different applications of knowledge discovery, FS technique falls into supervised, unsupervised. In this paper an extensive survey on supervised FS technique describing the different searching approach, methods and application areas with an outline of a comparative study is covered.
The COVID-19 pandemic is causing a global health crisis. Public spaces need to be safeguarded from the adverse effects of this pandemic. Wearing a facemask has become an adequate protection solution many governments adopt. Manual real-time monitoring of face mask wearing for many people is becoming a difficult task. This paper applies three heterogeneous deep transfer learning models, viz., ResNet50, Inception-v3, and VGG-16, to prepare an ensemble classification model for detecting whether a person is wearing a mask. The ensemble classification model is underlined by the concept of the weighted average technique. The proposed framework is based on two phases. An off-line phase that aims to prepare a classification model by following training-testing steps to detect and locate facemasks. Then in the second online phase, it is deployed to detect real-time faces from live videos, which are captured by a web-camera. The prepared model is compared with several state-of-the-art models. The proposed model has achieved the highest classification accuracy of 99.97%, precision of 0.997, recall of 0.997, F1-score of 0.997 and kappa coefficient 0.994. The superiority of the model over state-of-the-art compared methods is well evident from the experimental results.
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