BiLSTM, an ICD-11 automatic coding model using MC-BERT and label attention. Experiments on clinical records show 83.86% ...

What gaming need in 2026 is a clearer distinction between independence and scale, a better mid-size development label than ...

When I first started working with multi-agent collaboration (MAC) systems, they felt like something out of science fiction. It’s a group of autonomous digital entities that negotiate, share context, ...

Abstract: Multi-view multi-label classification is a crucial machine learning paradigm aimed at building robust multi-label predictors by integrating heterogeneous features from various sources while ...

In an era of rapidly growing multimedia data, the need for robust and efficient classification systems has become critical, specifically the identification of class names and poses or styles. This ...

On June 11, 2025, Vanguard filed an application with the U.S. Securities and Exchange Commission for a multi-class active ETF structure. The filing requests approval of a structure where Vanguard can ...

As organizations evolve, traditional data classification—typically designed for regulatory, finance or customer data—is being stretched to accommodate employee data. While classification processes and ...

Introduction: Alzheimer's disease (AD) and Parkinson's disease (PD) are two of the most prevalent neurodegenerative disorders, necessitating accurate diagnostic approaches for early detection and ...

As the scope of the Democratic Party's 2024 election loss sunk in and the inevitable recriminations began, the so-called "consultant class" emerged as the most unifying target of blame for a party ...

Abstract: Multi-label classification (MLC) involves assigning multiple labels to each instance from a predefined set of labels. With the increasing prevalence of multi-label datasets in real-world ...