Journal of Clinical and Medical Sciences

Extended RNN Technique for Omicron Virus Data Analytics

John Ming^* and Robert Sammy Department of Medical Sciences, St. Joseph Medical Center, Stockton, California, USA
^*Correspondence: John Ming, Department of Medical Sciences, St. Joseph Medical Center, Stockton, California, USA, Email:

Received: 04-Apr-2022, Manuscript No. JCMS-22-16985; Editor assigned: 07-Apr-2022, Pre QC No. JCMS-22-16985 (PQ); Reviewed: 25-Apr-2022, QC No. JCMS-22-16985; Revised: 02-May-2022, Manuscript No. JCMS-22-16985 (R); Published: 09-May-2022, DOI: 10.35248/2593-9947.22.6.188

Description

The Omicron case that tainted human beings become first observed in China towards the end of 2021. Since then, Omicron has expanded to almost every country on the earth. To overcome this problem, it will be necessary to work quickly to identify persons who have been contaminated with Omicron. Recurrent Neural Network (RNN) algorithms are proposed to be used for quick detection and prediction of Omicron infections. RNN is completed with the help of the Elman agency and applied to the Omicron dataset obtained from Kaggle. The dataset contains 75% information preparation and 25% information analysis. The most extreme age, secret hubs, and late learning were used as learning boundaries. The level of precision for this exploration is 88.28 according to the results.

Recurrent Neural Network is one of the elective findings for probable Omicron disease. The World Health Organization classified COVID-19 a global pandemic in March 2020. The Omicron virus is one of the COVID-19 disease's mutant viruses. The Omicron is a respiratory infection that can infect people through the air and by physical (real) contact. The most wellknown adverse effects are hacking, winding, loss of taste and/or smell, mild and/or high fever, brain ache, and muscle throb [1,2]. The illness has a significant impact on various aspects of life [3]. The conditions show that the omicron is becoming a critical issue that must be resolved quickly.

Omicron's confirmed case involves 13,84,16,498 people, 29,75,875 death cases, and 192 countries. This figure continues to rise on a daily basis (today). One of the initiatives that can be undertaken on a continuous basis is to slow down the rate of contamination improvement. Contamination that spreads quickly necessitates quick survival. Finding, testing, isolation, detachment, and medicine are the most commonly used tactics in the fight against illness spread [4]. In the case of Omicron confirmation, for example, Reverse Transcription PCR (RTPCR) is still ineffective in addressing and controlling infection transmission [5]. Computerized reasoning, such as the usage of artificial intelligence, becomes a forewarning approach that should be used to improve the detection strategy. The faster material polluted through Omicron is identified, the faster a treatment may be applied. It is highly likely that a high-quality attempt will be made to slow down the rate of development. As a result, an early elective framework for those polluted by Omicron is essential.

The Recurrent Neural Network, which is part of the Artificial Neural Network paradigm, is one of the options. Artificial Neural Network is a synthetic brainpower innovation that targets the cerebrum of persons who are trying to replicate their scholastic experience within their heads. Recurrent NN (RNN) is a type of fictitious brain with a main structure of repeating small cells. To investigate original data, intermittent micro cells are framed and arranged with historical data. The technique described is to predict Omicron with 12 symptoms by adjusting the erudition boundary of Recurrent NN (RNN) to find an optimal boundary.

REFERENCES

1. Tayarani N. Applications of artificial intelligence in battling against Covid-19: A literature review. Sol Fractals. 2021;142:110338.

   [Crossref] [Google Scholar] [PubMed]

2. Maghded HS, Ghafoor KZ, Sadiq AS, Curran K, Rawat DB, Rabie K. A novel AI-enabled framework to diagnose COVID-19 using smartphone embedded sensors: Design study. Data Sci. 2020;180:187.

   [Crossref] [Google Scholar] [PubMed]

3. Chamola V, Hassija V, Gupta V, Guizani M. A comprehensive review of the COVID-19 pandemic and the role of IoT, drones, ai, blockchain, and 5G in managing its impact. 2020. Inf Sci. 2021;5:15.

   [Crossref] [Google Scholar]

4. Varela-Santos S. Melin P. A new approach for classifying COVID-19 based on its manifestation on chest X-rays using texture features and neural networks. Inf Sci. 2021;545:403414.

   [Crossref] [Google Scholar] [PubMed]

5. Vaishya R, Javaid M, Khan IH, Haleem A. Artificial Intelligence (AI) applications for COVID-19 pandemic. Metab Syndr Clin Res. 2020;14:337-339.

   [Crossref] [Google Scholar] [PubMed]