This study presents a statistical approach to accurately predict the effective temperatures of pre-main sequence stars, which are necessary for determining stellar ages using the isochrone methodology and cutting-age starspots-dependent models. By tr...
The classification of the current density vector map (CDVM) reconstructed from magnetocardiogram (MCG) is an important indicator for assessing cardiac function and state in clinical diagnosis. Given the limited widespread application of MCG, research...
In epidemiology, traditional statistical methods such as logistic regression, linear regression, and other parametric models are commonly employed to investigate associations between predictors and health outcomes. However, non-parametric machine lea...
Student well-being prediction is of great significance for promoting personalized education and preventing mental health problems, but existing methods suffer from limitations including lack of psychological theory guidance, neglect of student relati...
Alzheimer's Disease (AD) is a very common neurodegenerative disorders and early detection using electroencephalography (EEG) can enable timely intervention, however, existing computational models often lack robustness, interpretability, and clinical ...
Blood cancer diagnosis through microscopic image analysis is challenging due to subtle morphological differences between cell stages and subtypes. This study aims to develop a Hybrid Involutional-Convolutional Neural Network (HICNN) for automated leu...
Hepatitis B is a global health burden and can persist for years, with nearly two billion infections worldwide, where its spread is influenced by environmental heterogeneity, host-pathogen interactions, and vaccination-induced immune variability. Prop...
For surface-based breathing motion prediction, which is essential to overcome inherent system latencies of active motion management strategies in radiotherapy, long short-term memory (LSTM) networks and related networks-bidirectional LSTMs (BiLSTMs),...
Problemy sotsial'noi gigieny, zdravookhraneniia i istorii meditsiny
Dec 15, 2025
The article considers issues of training models of convolutional neuronic network (CNN) for automated identification of point functions of visualization to discern mammography pictures belonging to negative, false benign and malignant cases, targetin...
BACKGROUND: In an ophthalmology emergency department, determining treatment urgency is crucial for patient safety and the efficient use of resources. The aim of this study was to use artificial intelligence to develop a neural network and evaluate it...
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