Uncertainty quantification in multi-class image classification using chest X-ray images of COVID-19 and pneumonia.

This paper investigates uncertainty quantification (UQ) techniques in multi-class classification of chest X-ray images (COVID-19, Pneumonia, and Normal). We evaluate Bayesian Neural Networks (BNN) and the Deep Neural Network with UQ (DNN with UQ) techniques, including Monte Carlo dropout, Ensemble Bayesian Neural Network (EBNN), Ensemble Monte Carlo (EMC) dropout, across different evaluation metrics. Our analysis reveals that DNN with UQ, especially EBNN and EMC dropout, consistently outperform BNNs.

Statistical approaches to identifying significant differences in predictive performance between machine learning and classical statistical models for survival data.

Research that seeks to compare two predictive models requires a thorough statistical approach to draw valid inferences about comparisons between the performance of the two models. Researchers present estimates of model performance with little evidence on whether they reflect true differences in model performance. In this study, we apply two statistical tests, that is, the 5 × 2-fold cv paired t-test, and the combined 5 × 2-fold cv F-test to provide statistical evidence on differences in predictive performance between the Fine-Gray (FG) and random survival forest (RSF) models for competing risks.

The screening of emerging micropollutants in wastewater in Sol Plaatje Municipality, Northern Cape, South Africa.

Although pollutants pose environmental and human health risks, the majority are not routinely monitored and regulated. Organic pollutants emanate from a variety of sources, and can be classified depending on their chemistry and environmental fate. Classification of pollutants is important because it informs fate processes and apposite removal technologies.

Deep Learning for SARS COV-2 Genome Sequences.

The SARS-CoV-2 virus which originated in Wuhan, China has since spread throughout the world and is affecting millions of people. When there is a novel virus outbreak, it is crucial to quickly determine if the epidemic is a result of the novel virus or a well-known virus. We propose a deep learning algorithm that uses a convolutional neural network (CNN) as well as a bi-directional long short-term memory (Bi-LSTM) neural network, for the classification of the severe acute respiratory syndrome coronavirus 2 (SARS CoV-2) amongst Coronaviruses.