Sensorimotor associations are typically thought to require days of training to consolidate in sensory cortex, yet adaptive behavior can emerge within minutes. Here, we developed a barrel ...

Logistic regression is a statistical method used to model binary outcome variables, such as whether a patient recovers or not, using a set of predictors. There are many competing methods for ...

Systemic inflammatory response plays a key role in the pathogenesis of endometriosis. This study aimed to investigate the association between the aggregate index of systemic inflammation (AISI), ...

Abstract: Fuzzy classification models are important for handling uncertainty and heterogeneity in high-dimensional data. Although recent fuzzy logistic regression approaches have demonstrated ...

The rapid uptake of supervised machine learning (ML) in clinical prediction modelling, particularly for binary outcomes based on tabular data, has sparked debate about its comparative advantage over ...

ABSTRACT: Since transformer-based language models were introduced in 2017, they have been shown to be extraordinarily effective across a variety of NLP tasks including but not limited to language ...

This cross-sectional study investigates the interplay of lifestyle, behavioral, and psychosocial factors in predicting depressive symptoms among Chinese college students (N=508) using binary logistic ...

This set of notebooks enables the analysis of comorbidities associated with male infertility using structured EHR data. First, we identified nonoverlapping patients with male infertility and patients ...

ABSTRACT: This study addresses the research question: What is the effect of higher education liberalization on the regional distribution of universities and educational attainment in Zambia? The ...

Your browser does not support the audio element. This guide demonstrates how to use the TensorFlow Core low-level APIs to perform binary classification with logistic ...