Prediction of Omicron cases in India using LSTM: An advanced approach of artificial intelligence

IF 1.1 Q1 MATHEMATICS
N. Yadav, D. K. Saini, Akanksha Uniyal, Nidhi Yadav, Maya S. Bembde, Dharmesh Dhabliya
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Abstract

Several mutants of SARS COV-2 have been reported so far, each one having different severity and infectivity rate. Omicron has enhanced resistance to host immunity and displays higher transmission. 15 of the 37 known mutations in the spike protein’s receptor-binding domain (RBD), the primary target of neutralising antibodies, are found there. An enhanced RNN, or sequential network, called a long short-term memory network, permits informatics data to endure. It is capable of resolving the RNN’s vanishing gradient issue. RNN, also referred to as a recurrent neural network, is utilized for persistent memory. “In medication, the right analysis and the perfect opportunity are the keys for effective treatment. For the current review, the gathered Past information was utilized to train the model, and afterward this trained model was utilized to test new information and afterward for utilized for the advancement of the prediction model”. The trained ML model’s presentation or approval was assessed utilizing some piece of accessible past datasets and this method known as validation process. According to findings of the current study, Indian citizens can aid their nation in the fight against Omicron by adhering to the directions and instructions provided by the Indian government. We ought to cooperate to overcome the new lethal variation of Coronavirus.
使用LSTM预测印度的Omicron病例:一种先进的人工智能方法
到目前为止,已经报道了几种SARS COV-2突变体,每种突变体具有不同的严重程度和传染性。欧米克隆增强了对宿主免疫的抵抗力,并显示出更高的传播能力。刺突蛋白受体结合域(RBD)是中和抗体的主要目标,在那里发现了37种已知突变中的15种。增强型RNN或顺序网络,称为长短期记忆网络,允许信息学数据持久存在。它能够解决RNN的梯度消失问题。RNN,也被称为递归神经网络,用于持久记忆。“在药物治疗中,正确的分析和完美的机会是有效治疗的关键。对于当前的回顾,收集到的“过去”信息被用来训练模型,然后这个训练好的模型被用来测试新的信息,然后被用来推进预测模型。经过训练的ML模型的演示或批准是利用一些可访问的过去数据集进行评估的,这种方法称为验证过程。根据目前的研究结果,印度公民可以通过遵守印度政府提供的指示和指示来帮助他们的国家对抗欧米克隆。我们应该合作战胜新型冠状病毒的致命变异。
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来源期刊
CiteScore
2.70
自引率
23.50%
发文量
141
期刊介绍: The Journal of Interdisciplinary Mathematics (JIM) is a world leading journal publishing high quality, rigorously peer-reviewed original research in mathematical applications to different disciplines, and to the methodological and theoretical role of mathematics in underpinning all scientific disciplines. The scope is intentionally broad, but papers must make a novel contribution to the fields covered in order to be considered for publication. Topics include, but are not limited, to the following: • Interface of Mathematics with other Disciplines • Theoretical Role of Mathematics • Methodological Role of Mathematics • Interface of Statistics with other Disciplines • Cognitive Sciences • Applications of Mathematics • Industrial Mathematics • Dynamical Systems • Mathematical Biology • Fuzzy Mathematics The journal considers original research articles, survey articles, and book reviews for publication. Responses to articles and correspondence will also be considered at the Editor-in-Chief’s discretion. Special issue proposals in cutting-edge and timely areas of research in interdisciplinary mathematical research are encouraged – please contact the Editor-in-Chief in the first instance.
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