Machine Learning and Cloud Computing Based Adaptable Structure for Intelligent Covid Monitoring in the Work Environment
Keywords:
COVID-19, Internet of Things (IoT), covid monitoring system, big data, butterfly-optimized multitemporal support vector machine (BO-MTSVM)Abstract
As a result of the new coronavirus outbreak's global expansion and the respiratory diseases it causes in people, COVID-19 has become a major global pandemic. The only way to stop this spread, according to the World Health Organization, is to increase testing and isolate those who are sick. In the meantime, the clinical testing that is now being used is time-consuming. Systems for remote diagnosis may be useful in this situation. The healthcare industry generates a large quantity of data, which we process using certain machine learning algorithms to identify the presence of illness. Several IoT-enabled sensors are accessible to detect the patient's entire information about a specific person's behavior, human anatomy, and physiology. The information gathered by the sensors is sent to the internet and linked to a cloud server. Physicians may access patient records stored on the web server and preserve them there, giving them access to the information from anywhere on the globe. Any unexpected change in a patient's data, while they are using the healthcare system, will unavoidably result in the patient's data being immediately uploaded to the appropriate doctor. In rural and distant places, this kind of healthcare system would be most beneficial. We proposed a novel butterfly-optimized multitemporal support vector machine (BO-MTSVM) approach to overcome the aforementioned problems. The suggested technique performs better than other current methods in COVID monitoring, according to simulation data.
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