Artificial Intelligence Based CoughMonitoring System For Covid-19

Infection with the SARS-CoV-2 virus results in Covid 19, an infectious illness. Most persons who get Coronavirus will only experience mild to moderate symptoms and will get better without any special care. Some people get very sick and need medical attention. The rising mortality toll from COVID-19 underscores the importance of developing methods for early detection of the disease, which might aid in containing the epidemic and facilitating the creation of tailored mitigation strategies. Current research in chaotic dynamics indicates that coughs and other vocal sounds include lung health data that can be used for symptomatic reasons. Mel frequencies Cepstral Coefficients (MFCC) are applied to cough samples, and then the audio data from coughs is fed into a GridsearchCV model using a KNN-based classification method. Our model was developed using 217 samples from training data and 55 from testing data. Cough tests conducted on both males and females are included in the dataset. An evaluation found that the model had an accuracy of 83.3%. © 2022 IEEE.

The effect of the Caputo fractional difference operator on a new discrete COVID-19 model.

This study aims to generalize the discrete integer-order SEIR model to obtain the novel discrete fractional-order SEIR model of COVID-19 and study its dynamic characteristics. Here, we determine the equilibrium points of the model and discuss the stability analysis of these points in detail. Then, the non-linear dynamic behaviors of the suggested discrete fractional model for commensurate and incommensurate fractional orders are investigated through several numerical techniques, including maximum Lyapunov exponents, phase attractors, bifurcation diagrams and C 0 algorithm. Finally, we fitted the model with actual data to verify the accuracy of our mathematical study of the stability of the fractional discrete COVID-19 model.