Omicron Virus Data Analytics Using Extended RNN Techniquee

Abstract

Asadi Srinivasulu, Anand Kumar Gupta, Kamal Kant Hiran, TarkeswarBarua, Goddindla Sreenivasulu, Sivaram Rajeyyagari and Madhusudhana Subramanyam

The OMICRON case that tainted human beings become first observed in China towards the end of 2021. From that point, OMICRON has spread practically all nations on the planet. To conquer this issue, it requires a fast work to recognize people tainted with OMICRON all the more rapidly. This research article proposes that RNN techniques to be utilized for rapid detection and predicting of OMICRON infections. RNN is finished utilizing the Elman agency and implemented to the OMICRON dataset gathered from Kaggle. The dataset accommodates of 75% preparing information and 25% analyzing information. The learning boundaries utilized were the most extreme age, secret hubs, and late learning. Results are for this exploration results show the level of precision is 88.28. Oddity is one of the elective conclusions for potential OMICRON illness is Recurrent Neural Network (RNN).

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