Long Short Term Memory based Covid-19 Mutation Detection for RNA Sequence dataset

ABSTRACT

Mutation detection for the various strains of Covid 19 evolves the constraints of time, accuracy and precision. RNA sequencing with deep learning enables the detection of the mutation variant from the sequence dataset and helps for the development of tests that are used for the diagnosis and future predictions. Analyzing and researching on Covid- 19 structure and the epidemiological study aid to the accurate methodology selection and process implementation. Efficient data preprocessing of the RNA sequence adds to the accuracy of the model which was built using LSTM. This paper proposes a Long Short Term Memory (LSTM) based deep learning modeling helps the RNA sequence dataset model to predict the RNA mutant variant. The model acquired an accuracy of 91.7 % and a loss function of 3.08%. © 2022 IEEE.