Real-Time Deep Intelligence Analysis and Visualization of COVID-۱۹ Using FCNN Mechanism

The Analytic visualization suggests representing knowledge during a visual type that may be charts, graphs, lists, or maps. The COVID ۱۹ detection and analysis of spreading is very important for countries. Database management with respect to virus deep analysis is a critical task to the researcher through conventional algorithms. The RNA, DNA, and biological data are helping to the bio-inspired algorithm but its implementation can be complex by software tools. Therefore, an effective technique is required to cross over the above limitations. So that covid ۱۹ pandemic data analysis is performed through FCNN (Fully conventional Neural Network) pre-training network. The dataset is collected from social media, Kaggle, and GitHub databases. At ۱st stage, the auto stack encoding process is applied later same data is processed with FCNN deep learning classifier. In this research work, covid-pandemic affects parameters like infected persons, deaths, active cases, and recovering cases. The FCNN is take care of feature extraction, training, testing, and classification. Finally using a confusion matrix accuracy of ۹۸.۳۴%, sensitivity ۹۷.۶۳%, Recall ۹۸.۲۶%, and F measure ۹۸.۸۳% had been estimated.

Keywords:

DNA RNA sequence , Covid-۱۹ , SARS-CoV-۲ , coronavirus , Pandemic