Objectives: Radiomics has a novel value in accurately and noninvasively characterizing non-
small cell lung cancer (NSCLC), but the role of peritumoral features has not been discussed …

The prediction of stock market movements is a critical task for investors, financial analysts,
and researchers. In recent years, significant advancements have been made in the field of …

Purpose Cognitive diagnostic tests (CDTs) assess cognitive skills at a more granular level,
providing detailed insights into the mastery profile of test-takers. Traditional algorithms for …

This paper presents the development of an intelligent, machine learning-based Markov
chain model to investigate loan risk and strategies for controlling credit risk. The model …

The compound classification strategies addressed in this study encounter challenges
related to either low efficiency or accuracy. Precise classification of chemical compounds …

Sepsis, a severe systemic response to infection, represents a pressing global public health
challenge. Time series research, including the analysis of medical data, encounters …

This paper proposes a Metapath-Infused Exponential Decay graph neural network (MIED)
approach for node embedding in heterogeneous graphs. It is designed to address …

Using trajectory data to query and analyze contact events is an emerging method for
disease prevention and control. Existing contact event query processings only focus on …

INTRODUCTION: A lightweght lung image segmentation model was explored. It was with
fast speed and low resouces consumed while the accuracy was comparable to those SOAT …

Most application scenarios of cognitive diagnostic tests (CDTs) are confronted with multiple
constraints. Some existing well-developed algorithms for constructing CDTs transforms the …