Comparison of Eye and Face Features on Drowsiness Analysis.

Drowsiness is one of the leading causes of traffic accidents. For those who operate large machinery or motor vehicles, incidents due to lack of sleep can cause property damage and sometimes lead to grave consequences of injuries and fatality. This study aims to design learning models to recognize drowsiness through human facial features.

Fault Diagnosis and Fault Frequency Determination of Permanent Magnet Synchronous Motor Based on Deep Learning.

The early diagnosis of a motor is important. Many researchers have used deep learning to diagnose motor applications. This paper proposes a one-dimensional convolutional neural network for the diagnosis of permanent magnet synchronous motors.

Early prediction of coronavirus disease epidemic severity in the contiguous United States based on deep learning.

In November 2019, the coronavirus disease outbreak began, caused by the novel severe acute respiratory syndrome coronavirus 2. In just over two months, the unprecedented rapid spread resulted in more than 10,000 confirmed cases worldwide. This study predicted the infectious spread of coronavirus disease in the contiguous United States using a convolutional autoencoder with long short-term memory and compared its predictive performance with that of the convolutional autoencoder without long short-term memory.

Mortality Prediction of Septic Patients in the Emergency Department Based on Machine Learning.

In emergency departments, the most common cause of death associated with suspected infected patients is sepsis. In this study, deep learning algorithms were used to predict the mortality of suspected infected patients in a hospital emergency department. During January 2007 and December 2013, 42,220 patients considered in this study were admitted to the emergency department due to suspected infection.