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.

Predicting Students' Drop-Out Rate Using Machine Learning Models: A Comparative Study

One of the major challenges facing Private Higher Educational Institutes in India is to reduce drop-out rate of first year students. The problem has got exacerbated post-covid specially for Engineering and management discipline. This research is an empirical study on efficiency and accuracy of various Machine Learning (ML) Prediction algorithms to predict the drop-out rate of students based on dataset available on predictors such as Family size, Study time, Time spent on extra- curricular activities, Time spent on Internet, Health, Absenteeism etc. A comparison of the performance of the ML models based on 'Accuracy' and 'F1 score' (to cater for variations in costs of false positives and false negatives) has been made to identify the best algorithm for given problem. This would help HEIs to identify potential drop-out students beforehand and take course correction measures thus improving retention. The study is conducted for B.Tech First year students with a sample size of 395 students using Logistic regression and K-NN algorithms. This preliminary work could be extended using other ML models such as;Support Vector Machine (SVM), Naïve Bayes, Decision Tree etc. or a combination of in an ensemble fashion in future. © 2022 IEEE.

Prediction of COVID-19 Disease by ARIMA Model and Tuning Hyperparameter Through GridSearchCV

The COVID-19 pandemic has significantly impacted the mental, physiological, and financial well-being of people around the globe. It has threatened lives and livelihoods and triggered supply chain disruptions and economic crises. In every country, there are risks and long-term implications. Planners and decision-makers could benefit from a forecasting model that anticipates the spread of this virus, thereby providing insight for a more targeted approach, advanced preparation, and drive better proactive collaboration. The signs and symptoms of a disease like COVID-19 are hard to define and predict, particularly during times of pandemic. Several epidemiological studies have been successful in identifying predictors, using artificial intelligence (AI). This paper explores various methodologies for tuning the hyperparameters of the auto-regressive integrated moving average (ARIMA) model, using GridSearchCV, to predict and analyze the occurrence of COVID-19 in populations. In time series analysis, hyperparameters are crucial and the GridSearchCV methodology results in greater predictive accuracy. The parameters proposed for the analysis of daily confirmed cases, recovered cases, and deceased cases in India were ARIMA (4, 1, 5), ARIMA (5, 1, 1), and ARIMA (5, 1, 1), respectively. The performance of the model with different configurations was evaluated using three measurements: root mean square error (RMSE), R2 score, and mean absolute error (MAE). These results were compared with a state-of-the-art method to assess model selection, fitting, and forecasting accuracy. The results indicated accuracy and continuous growth in the number of confirmed and deceased cases, while a decreasing trend was graphed for recovered cases. In addition, the proposed ARIMA using a GridSearchCV model predicted more accurately than existing approaches. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Covid Cough Classification using KNN Classification Algorithm

Covid 19 is an infectious disease that is caused by infection due to SARS-CoV-2 virus. The vast majority of people infected with Corona virus will encounter mild to moderate symptoms and recover without any special treatment. In some case, some people become seriously ill and require clinical consideration. Because of the increase in number of death due to COVID-19, an techniques for the early discovery of the illness is very much needed that might assist with restricting its spread just as help in the development of targeted surrounding solutions. Coughs and other vocal sounds contain pulmonary health data that can be utilized for symptomatic purposes, and ongoing examinations in chaotic dynamics have shows a nonlinear phenomenon exists in vocal signs. Cough samples are transformed with Mel frequency Cepstral Coefficients (MFCC) and the cough audio data is fitted into a GridsearchCV model with KNN based classification algorithm. The number of training data for used for training our model is 217 and remaining 55 data were used for testing the model. The dataset contains the cough tests from both male and female. When evaluated the model could get a precision of 83.3%. © 2022 IEEE.