Genomic surveillance—the process of monitoring and sequencing pathogens—is one of the most important tools for detecting ...
Abstract: A number of machine learning (ML) algorithm based small signal modeling of Gallium Nitride (GaN) High Electron Mobility Transistors (HEMTs) have been reported in literature. However, these ...
Background: Machine learning (ML) and deep learning (DL) show promise for fall risk prediction, but prior reviews focused mainly on real-time fall detection, in-hospital falls, or conventional ...
Order the book at https://www.wiley.com/en-us/Machine+Learning+and+Big+Data+with+KDB%2B+Q-p-9781119404750. We have added .quantQ.math namespace with various ...
Abstract: Artificial intelligence-based machine learning models have been widely used to explore and address various mental health-related problems in recent years, including depression. In this study ...
ABSTRACT: In real-world applications, datasets frequently contain outliers, which can hinder the generalization ability of machine learning models. Bayesian classifiers, a popular supervised learning ...
There are no tools to identify patients who have a worse prognosis than others. Objective: This study aimed to process a sample of electronic health records of patients with COVID-19 in order to ...
Clinical prediction models aim to predict outcomes in individuals, to inform diagnosis or prognosis in healthcare. Hundreds of prediction models are published in the medical literature each year, yet ...
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