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Airborne diseases cause detriment in the human life. Many were found in history such as TB, SARS, MERS and recently COVID 19. These diseases hit the dead rate and crushes health and wealth of the world population. Mostly, airborne diseases will spread rapidly in crowdy places. Especially, in case of COVID-19, Wearing mask and monitoring body temperature by individual is good solution to prevent rapid spread of the disease. So, keeping safety measures in monitoring face mask and body temperature is crucial in crowdy places such as Airports, railway stations, Bus Stations, malls, temples, etc. is obligatory. With a focus on emphasizing the health of the people we proposed integrated system that monitors body temperature and face mask of each individual and open/close pathway gate to allow after knee verification. Proposed Prototype uses Raspberry pi to monitor Face mask using CNN and Arduino to monitor body temperature and enable motor drivers to open or close the pathway Gate. Efficiency and loss of Proposed CNN model was trained and tested with multiple epoches. [ABSTRACT FROM AUTHOR]

Aim:This study article's major goal is to classify facial expressions more accurately by utilizing Convolutional Neural Networks (CNNs) in contrast to AlexNet. Materials and Methods: The data of the facial expressions is taken from the FER2013 available on kaggle. The Convolutional neural network (CNN) is the algorithm most common method of visual analysis with best accuracy and Alexney is also an algorithm used here to compare the accuracy of novel image classification. Results: The Convolution neural network (CNN) produces 82% accuracy in predicting facial expressions on the dataset, whereas AlexNet produces 76% accuracy. Convolutional neural network (CNN) is better than AlexNet. The study groups differ from one another in a statistically significant way (p <0.05). Conclusion: Convolutional Neural Network provides better outcomes in accuracy rate when compared to AlexNet for predicting facial expressions. [ABSTRACT FROM AUTHOR]

Aim: The aim of this research article is classification of facial expression recognition Convolutional Neural Network (CNN) compared with ResNet yielded better accuracy. Materials and Methods: The data of the facial expressions is taken from the FER2013 available on kaggle. The convolutional neural network (CNN) is the most widely used algorithm for image analysis with best accuracy and ResNet is also an algorithm used here to compare the accuracy of Novel image classification. Results: The Convolution neural network (CNN) produces 82.14% accuracy in predicting facial expressions on the dataset, whereas ResNet produces 78.01% accuracy. Convolutional neural network (CNN) is better than ResNet. With (p<0.05), there is a statistically significant difference between the research groups. Conclusion: Convolutional Neural Network provides better outcomes in accuracy rate when compared to ResNet for predicting facial expressions. [ABSTRACT FROM AUTHOR]