ABSTRACT
Human potential is being expanded with the development of AI. Increases in both the quantity and quality of medically-verified reports of successful treatments, as well as the rate at which our understanding of these treatments is expanding, are providing a fresh perspective on human administration. This research was driven by the need to bring attention to the need of using AI to combat the COVID-19 Crisis, and it focuses on the current state of AI applications in clinical administration while treating COVID-19. Deciphering this pathogen also requires the use of Big Data, which we stress. We also give an overview of several intelligence methodologies and procedures that can be employed for various medical information-based pandemics. We categorise the various forms of artificial intelligence now employed in health care research, such as neural networks, conventional support vector machines, and cutting-edge knowledge acquisition. Areas that use Intelligence cloud computing to combat other viruses like COVID-19 have also received attention. This study was conducted to assist medical practitioners and scientists in overcoming challenges they have encountered in processing COVID-19 large data sets. Some of the researched methodologies have proposed important advances in medical research methodology, however their accuracy is only around 90%. We wrap off with a deep dive into how AI could give security researchers a leg up in the struggle against this type of malware. © 2022 IEEE.
ABSTRACT
The new coronavirus, initially detected in Wuhan, China, has spread worldwide, wreaking devastation. The speed with which it spreads and the severity of the epidemic have made this a global emergency. COVID-19 is a very concerning disease from a public health perspective, making it critical to take precautions against its transmission, such as limiting close personal contact and using protective gear. The primary objective of this research is to create a system that can recognise human face masks and determine whether individuals are attempting to maintain social distance. Alterations to everyone's daily routines. In such stages, everyone must always keep their identity concealed. Because the massive number of populations has changed since the Outbreak of the Coronavirus pandemic, finding people who are not wearing veils is a challenge. The global spread of COVID-19 has altered society. Many of us are staying in our homes, avoiding contact with city dwellers, and adjusting our routines-such as when and where we go to work or school-in ways we never would have imagined. We need updated timetables as we transition away from outmoded procedures. What stands out the most is the widespread practise of hiding one's face behind a veil or other kind of covering whenever we enter a public building. Wearing a veil or covering one's face may provide some comfort while also preventing the spread of the COVID-19 virus. By preventing anybody, even the unwitting carriers, from spreading the virus, widespread usage of protective clothing has the potential to significantly reduce the rate of disease spread in a given area. Thus, the importance of the veil and its identification are made very plain. There has been a rise in the importance of face recognition frameworks, which are especially useful in hospitals and medical clinics where privacy of patients is a concern. They're also vital in places like airports, sports stadiums, warehouses, and other such crowded areas where heavy foot traffic necessitates strict security measures to ensure everyone's safety. The framework of face veil recognition can ensure our safety and the safety of those around us. This assignment may serve as a digitally administered test anywhere from a classroom to a hospital to a bank to an airport terminal. Through the use of photo processing and extensive learning, we are able to recognise human faces and separate them into two groups, those with and without head coverings. The assignment will let a person who is responsible for screening people to do so even if they are located at a remote location, while still being able to effectively screen and provide guidance. Open CV, Tensor Flow, and Keras are some of the Python libraries used. With Deep Learning, As part of their model preparation, these activities make use of Convolution Neural Networks, a subset of Deep Neural Networks. © 2022 IEEE.