A Hybrid technolgy using Machine learning and blockchain technology to prevent Covid

Since the year 2020, there has been an outbreak of the respiratory infection that caused a high peak mortality rate, which has led to an increase in the prevalence of Covid. The unanticipated development of the COVID-19 sickness as well as its unchecked global spread show the limitations of the currently available healthcare systems in their ability to respond to emergencies that harm the general population's health. As a result of cutting-edge technology like AI and biological computing (BC) these issues treated promisingly for the covid pandemic. In particular, BC assist in early detection to aid in the fight against pandemics. With the protocols that have been put in place to avoid infections, including the use of masks, social isolation within a radius of 6 meters, routine testing, and two doses of vaccinations. This system comprises the detection of masks, people, and temperatures, as well as the monitoring of information, tracking of in-person contact, and the present state of a person's medical record. Diseases are now able to be traced, and their transmission can be stopped, thanks to advances in technology and the growing prevalence of smartphone use. Because of the reopening of more economic sectors and the continuous widespread distribution of Covid, it is even more important to ensure that you adhere to the provided instructions in order to avoid contracting an infection. © 2023 IEEE.

Deep Learning Mask Face Recognition with Annealing Mechanism

Face recognition (FR) has matured with deep learning, but due to the COVID-19 epidemic, people need to wear masks outside to reduce the risk of infection, making FR a challenge. This study uses the FaceNet approach combined with transfer learning using three different sizes of validated CNN architectures: InceptionResNetV2, InceptionV3, and MobileNetV2. With the addition of the cosine annealing (CA) mechanism, the optimizer can automatically adjust the learning rate (LR) during the model training process to improve the efficiency of the model in finding the best solution in the global domain. The mask face recognition (MFR) method is accomplished without increasing the computational complexity using existing methods. Experimentally, the three models of different sizes using the CA mechanism have a better performance than the fixed LR, step and exponential methods. The accuracy of the three models of different sizes using the CA mechanism can reach a practical level at about 93%.