In recent years, the exponential growth of digital documents has been met by rapid progress
in text classification techniques. Newly proposed machine learning algorithms leverage the …

Defect prediction is one of the key challenges in software development and programming
language research for improving software quality and reliability. The problem in this area is …

Background Medical texts present significant domain-specific challenges, and manually
curating these texts is a time-consuming and labor-intensive process. To address this …

Legal scholars have been trying to predict the outcomes of trials for a long time. In recent
years, researchers have been harnessing advancements in machine learning to predict the …

The question of molecular similarity is core in cheminformatics and is usually assessed via a
pairwise comparison based on vectors of properties or molecular fingerprints. We recently …

Importance Predicting short-and long-term survival of patients with cancer may improve their
care. Prior predictive models either use data with limited availability or predict the outcome …

Background: Named entity recognition (NER) models are essential for extracting structured
information from unstructured medical texts by identifying entities such as diseases …

Currently, the most widespread neural network architecture for training language models is
the so-called BERT, which led to improvements in various Natural Language Processing …

Human Leukocyte Antigen (HLA) is like a device that monitors the internal environment of
the body. T lymphocytes immediately recognize the HLA molecules that are expressed on …

This paper investigates the design of active and passive beamforming in a reconfigurable
intelligent surface (RIS)-aided multi-user multiple-input single-output (MU-MISO) system with …