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BackgroundNon-suicidal self-injury (NSSI) is common among adolescents and young adults and remains difficult to detect early using conventional approaches. machine learning (ML) has increasingly been applied to develop p...

Abstract Objective Machine learning-based approaches were applied to explore prognostic risk factors for patients with tuberculous meningitis (TBM) and develop a risk prediction model for their 1-year post-treatment poor...

Abstract Humans are exposed to thousands of chemicals throughout their life. Many of these chemicals are detected in blood and have been catalogued in the Blood Exposome Database. Comprehensive hazard assessment of a che...

Introduction Lung cancer (LC) is the leading cause of cancer-related mortality worldwide, primarily due to diagnosis at advanced stages. Although low-dose computed tomography (LDCT) screening reduces lung cancer mortalit...

Abstract Accurate prediction of invasive mechanical ventilation (IMV) requirement in patients with community-acquired pneumonia (CAP) is a key clinical challenge. Conventional static risk stratification scores have limit...

Abstract Biochar yield prediction plays a critical role in optimizing pyrolysis processes and advancing sustainable biomass utilization. This study introduces a hybrid machine learning framework that integrates Decision ...

Abstract High-electron-mobility transistors (HEMTs) based on wide bandgap (WBG) materials like gallium nitride (GaN) are vital for next-generation power electronics and high-frequency applications, offering high breakdow...

Artificial intelligence (AI), including machine learning, deep learning, reinforcement learning, and generative AI, has the potential to advance critical care nephrology (CCN) by enhancing prediction accuracy, improving ...

ABSTRACT The objective of this study was to assess behavior patterns in Brazilian farm milk prices. We employed a structural time series techniques model, the Unobserved Component Model (UCM), which is part of the family...

Abstract Neural circuits construct internal ‘world-models’ to guide behavior. The predictive processing framework posits that neural activity signaling sensory predictions and concurrently computing prediction-errors...

Abstract Classifying exam questions using Bloom’s Taxonomy is a critical task in education. However, performing this classification manually is often inefficient, prone to subjective errors and demands significant time...

IntroductionEarly accurate assessment of the clinical status of severely injured patients is crucial for guiding the surgical treatment strategy. Several scales are available to differentiate between risk categories. The...

Abstract Accurate prediction of terrestrial water cycle variables like soil moisture (SM) and evapotranspiration (ET) is crucial, yet existing Deep Learning (DL) models face limitations. Many adopt Single-Task Learning (...

IntroductionEarly detection of Alzheimer’s disease (AD) is critical for timely intervention, particularly during the mild cognitive impairment (MCI) stage. This study aimed to develop and evaluate a multidomain speech ...

ABSTRACT Sustainable production of adequate quantities of food to support a growing human population is a worldwide goal. Under current feeding conditions in the United States, dairy cattle convert dietary nitrogen to mi...

The aqueous solubility of drugs/drug candidates (Sw) is one of the crucial physicochemical parameters in drug discovery studies and any computational method to predict the solubility is highly in demand in the pharmaceut...

In this letter we investigate the pulsational properties of ZZ Ceti stars on the basis of new white dwarf evolutionary models calculated in a self-consistent way with the predictions of time dependent element diffusion a...

Multivariable regression models are powerful tools that are used frequently in studies of clinical outcomes. These models can use a mixture of categorical and continuous variables and can handle partially observed (censo...

Prediction of species’ distributions is central to diverse applications in ecology, evolution and conservation science. There is increasing electronic access to vast sets of occurrence records in museums and herbaria, ...

El control del ruido en ambientes industriales se ha convertido en una actividad profesional de creciente importancia. En muchos casos, las soluciones técnicas involucradas poseen un alto costo, motivo por el cual se ...

"La predicción del contenido de carbono orgánico total (COT) es un indicador crucial para evaluar las características geoquímicas de las rocas madre. Aunque los enfoques basados en regresión múltiple y la clasifica...

The fifth phase of the Coupled Model Intercomparison Project (CMIP5) will produce a state-of-the- art multimodel dataset designed to advance our knowledge of climate variability and climate change. Researchers worldwide ...

Abstract In a 1935 paper and in his book Theory of Probability, Jeffreys developed a methodology for quantifying the evidence in favor of a scientific theory. The centerpiece was a number, now called the Bayes factor, wh...

We propose a technique for producing `visual explanations' for decisions from a large class of Convolutional Neural Network (CNN)-based models, making them more transparent. Our approach - Gradient-weighted Class Activat...