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Full-Text Articles in Statistics and Probability
Machine Learning Model Comparison And Arma Simulation Of Exhaled Breath Signals Classifying Covid-19 Patients, Aaron Christopher Segura
Machine Learning Model Comparison And Arma Simulation Of Exhaled Breath Signals Classifying Covid-19 Patients, Aaron Christopher Segura
Mathematics & Statistics ETDs
This study compared the performance of machine learning models in classifying COVID-19 patients using exhaled breath signals and simulated datasets. Ground truth classification was determined by the gold standard Polymerase Chain Reaction (PCR) test results. A residual bootstrapped method generated the simulated datasets by fitting signal data to Autoregressive Moving Average (ARMA) models. Classification models included neural networks, k-nearest neighbors, naïve Bayes, random forest, and support vector machines. A Recursive Feature Elimination (RFE) study was performed to determine if reducing signal features would improve the classification models performance using Gini Importance scoring for the two classes. The top 25% of …
Bidirectional Testing For Repairable Systems Reliability: Power Asymmetries, Panel Of Control Charts, And Reliability Graphics, Sung Keun Koo
Bidirectional Testing For Repairable Systems Reliability: Power Asymmetries, Panel Of Control Charts, And Reliability Graphics, Sung Keun Koo
UNLV Theses, Dissertations, Professional Papers, and Capstones
To a practitioner who chooses to be on the safe side, we offer an option between a cocktail of tests and a solo one-size-fits-all test as a needed antidote to power asymmetries. Two bidirectional tests are first established to empower a basic pair of tests as asymmetrical performances. Unsurprisingly, we’ve seen either bidirectional device championing in one alternative setting, but also being turned against the very setting altered with just one of the composed elements. Progressing by filtering out the bad and enhancing the good, we assemble a hybrid from the empowered pair to restore power symmetries that are thought …