A cognitive effective framework for analysis, monitoring and identifying variant of coronavirus disease

Airborne diseases have contributed in an incredible way to increase worldwide morbidity and disability during the last few years. The omicron is an airborne disease that spreads rapidly all over the world, with many infected persons. As a result, detecting and identifying the positive cases of omicron for treatment on time, and preventing the disease from spreading is a constant challenge and difficult process. Several medical tests have been used and analyzed just to identify omicron but all are ineffective and not properly accurate at an early stage. The current study examines the enormous potential of the latest progress of IoT after incorporating a framework using fog-cloud computing for omicron viruses. This paper presented a novel framework based on IoT technology for diagnosis such disease on time. The proposed model uses the weighted naive bayes algorithm that has ability to analyze the health condition and predict probabilistic health state vulnerability of an individual. In addition, the porposed framework indulges the utilization of Long Short-Term-Memory and Enhanced temporal data-based Re-current neural network (LSTM-ERNN) for the predictivity of the spread of such virus. The experimental outcomes indicate that the proposed system achieves an accuracy of 98.7%, a Recall of 97.8%, a precision of 92.8%, and the f-measure consists of 97.4%. The experimental results depicted that the proposed approach has performed better outcomes as compared to other state-of-the-art approaches.