Rare events, especially those that could potentially negatively impact society, often require
humans' decision-making responses. Detecting rare events can be viewed as a prediction …

Rare event prediction involves identifying and forecasting events with a low probability using
machine learning (ML) and data analysis. Due to the imbalanced data distributions, where …

Objective Nowadays, COVID-19 is spreading rapidly worldwide, and seriously threatening
lives. From the perspective of security and economy, the effective control of COVID-19 has a …

Although cerebral stroke is a important public worldwide health problem with more than 43
million global cases reported recently, more than 90% of metabolic risk factors are …

Abstract According to the World Health Organization, most of the world population is affected
by chronic diseases, obesity, cardiovascular diseases and diabetes while another dominant …

Purpose To investigate an identification method for precancerous gastric cancer based on
the fusion of superficial features and deep features of gastroscopic images. The purpose of …

Abstract Background and Objective Diabetes is a disease that requires early detection and
early treatment, and complications are likely to occur in late stages of the disease …

Imbalanced data always has a serious impact on a predictive model, and most under-
sampling techniques consume more time and suffer from loss of samples containing critical …

Data preprocessing remains an important step in machine learning studies. This is because
proper preprocessing of imbalanced data can enable researchers to reduce defects as …

Abstract Recently, Convolutional Neural Networks (CNN) methods achieved impressive
success in semantic segmentation tasks. However, challenges like class imbalance around …