Face mask recognition has been growing rapidly after corona insistent last years for its multiple uses in the areas of Law Enforcement Security purposes and other commercial uses Face appears spreading others to corona a novel approach to perform face new line detection and face mask recognition is proposed. The proposed system to classify face mask detection using COVID-19 precaution both in images and videos using convolution neural network. Extensive experimentation on the datasets and the performance evaluation of the proposed methods are exhibited. Further, we made a successful attempt to preserve inter and intra class variations of face mask detection using symbolic approach. We studied the different classifiers like Support Vector Machine and a Symbolic Classifier. The project is developed as a prototype to monitor temperature measurement and to detect mask for the people. The first method is performed using temperature sensor used to detect the present temperature of the body and automatically spray the sanitizer. In the second method, the work is designed to provide a safety system for the people in order to avoid COVID-19. We proposed continuous monitoring of the people conditions and store the people’s data in the server using the Deep learning concept. In order to investigate the performance the proposed method an extensive experimentation is conducted on 50 various Image dataset. We conducted experimentation under varying of training and testing percentage for 10 random trails. From the results we could observe that, the results obtained for symbolic approach is better than the conventional approach.
The main goal was to demonstrate that EEG and its derivatives may be utilised to recreate brain function using EEG data. This is vital to determine the application's source activities in order to assess various strategies that address the reverse issue, hence accessibility to a standardized EEG dataset is essential. Physiological and psychological tests could be used to determine alertness or activity levels in particular. Furthermore, changes of psychological measurements can be influenced by a variety of cognitive notions. Heartbeat, skin temperature, and brainwaves activities, in example, were susceptible to several psychological categories such as sleepiness, tension, and so on. EEG, on the other hand, delivers a robust resolving power and continuous recording the cerebral activity. An EEG records either periodic and irregular brainwaves. ML approaches are used to classify the physical movement of the heart brain per its condition. The main purpose is using categorization to improve the effectiveness of testing condition segmentation. Multimodal modeling, which is built upon localised machine learning, is a rather appealing option to bipolar neuroimaging, particularly in terms of increased sensibility to alterations in experiment settings.
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