ABSTRACT
"Health is Wealth” such a wealth of people is being affected and they are put to death on deathbeds in millions by a newly discovered virus called n-CoV (Corona virus). Covid is so-called mysterious because people affected by this disease are asymptotic. This infectious disease is mostly transmitted through droplets when an infected person coughs or sneezes. One of the main precautions that everyone must follow is wearing a mask. Some people wear masks improperly whenever they visit crowded places where there are high chances of this disease being spread. We have designed a real-time face mask detector that aims at detecting masks worn by people to reduce the transmission of this virus and make people wear masks in crowded areas. We can install this system at the entrance of places where there is more number of crowds. The detector follows Convolutional Neural Network (CNN), a part of Deep-Learning used to analyse visual imagery. It takes an input image, assigns importance (learnable weights and biases) to various aspects/objects of the image, and differentiates one from the other. Cascade Classifier detects the frontal face. Results have shown the detector classifies people with and without masks at an accuracy of 96.97%. © 2022 WMSCI.All rights reserved.
ABSTRACT
Internet of Medical Things (IoMT) is a smart interwoven technology enabled by the advancements made in multi-disciplined fields of medical devices, networking technologies, healthcare applications and artificial intelligence. The current spread of the coronavirus disease (COVID-19) globally has thrown innumerable challenges against human survival. To overcome this pandemic situation, an innovative healthcare solution is vital for saving human lives and mitigating the viral spread. We propose an E-Health+ system that can provide remote patient assistance anytime, anywhere. E-Health+ makes use of artificial intelligence in edge nodes for data processing coupled with Federated learning for swift prognostic medical advice for connected patients during their critical times in IoMT. The medical advice or assistance provided is based on the requests arising in a real-time basis with minimal response times, thereby reducing latency and also the much-needed privacy preservation towards the sensitive patient data.