Type 2 diabetes mellitus (T2D) prevalence in the United States varies substantially across spatial and temporal scales, attributable to variations of socioeconomic and lifestyle risk factors. Understanding these variations in risk factors contributio...
Rural land valuation plays an important role in the development of land use policies for agricultural purposes. The advance of computational software and machine learning methods has enhanced mass appraisal methodologies for modeling and predicting e...
Deep learning in k-space has demonstrated great potential for image reconstruction from undersampled k-space data in fast magnetic resonance imaging (MRI). However, existing deep learning-based image reconstruction methods typically apply weight-shar...
Studies in health technology and informatics
34042884
The main goal of this project was to define and evaluate a new unsupervised deep learning approach that can differentiate between normal and anomalous intervals of signals like the electrical activity of the heart (ECG). Denoising autoencoders based ...
OBJECTIVE: This study pursues three main objectives: 1) exploring the spatial distribution patterns of human brucellosis (HB); 2) identifying parameters affecting the disease spread; and 3) modeling and predicting the spatial distribution of HB cases...
A better understanding of the tumor immune microenvironment (TIME) could lead to accurate diagnosis, prognosis, and treatment stratification. Although molecular analyses at the tissue and/or single cell level could reveal the cellular status of the t...
BACKGROUND: The rise of spatial transcriptomics technologies is leading to new insights about how gene regulation happens in a spatial context. Determining which genes are expressed in similar spatial patterns can reveal gene regulatory relationships...
Neural networks : the official journal of the International Neural Network Society
34773899
The prediction of crowd flows is an important urban computing issue whose purpose is to predict the future number of incoming and outgoing people in regions. Measuring the complicated spatial-temporal dependencies with external factors, such as weath...
Spatial models for occupancy data are used to estimate and map the true presence of a species, which may depend on biotic and abiotic factors as well as spatial autocorrelation. Traditionally researchers have accounted for spatial autocorrelation in ...
Multivariate time series forecasting has long been a research hotspot because of its wide range of application scenarios. However, the dynamics and multiple patterns of spatiotemporal dependencies make this problem challenging. Most existing methods ...