Finding high-performing catalysts, which are used to accelerate processes from chemical manufacturing to energy production, ...

Retail LLMs promise raw computing power in edge settings. But what are the considerations that face decision-makers in the ...

Abstract: The article investigates the specific features of binary classification aggregated methods application to solving the problems of technical diagnostics. The study found that the use of ...

ABSTRACT: Automatic detection of cognitive distortions from short written text could support large-scale mental-health screening and digital cognitive-behavioural therapy (CBT). Many recent approaches ...

This study aims to establish an interpretable disease classification model via machine learning and identify key features related to the disease to assist clinical disease diagnosis based on a ...

Acute sleep deprivation significantly impacts cognitive function, contributes to accidents, and increases the risk of chronic illnesses, underscoring the need for reliable and objective diagnosis. Our ...

The goal of a machine learning binary classification problem is to predict a variable that has exactly two possible values. For example, you might want to predict the sex of a company employee (male = ...

Bipolar disorder is a complex psychiatric condition characterized by alternating mood episodes, ranging from depression to mania. Accurate and timely detection of a patient’s current mood state is ...

Abstract: Accurate and timely diagnosis of disease is a considerable challenge particularly in low-resource environments with limited computational resources. Existing image processing frameworks such ...