AIMC Topic: Spatial Analysis

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Exploiting dynamic spatio-temporal graph convolutional neural networks for citywide traffic flows prediction.

Neural networks : the official journal of the International Neural Network Society Oct 28, 2021
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...
Neural Networks, Computer Weather Spatial Analysis Humans
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CoSTA: unsupervised convolutional neural network learning for spatial transcriptomics analysis.

BMC bioinformatics Aug 9, 2021
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, Computer Transcriptome Cluster Analysis Spatial Analysis
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Machine Learning and Artificial Intelligence-driven Spatial Analysis of the Tumor Immune Microenvironment in Pathology Slides.

European urology focus Aug 3, 2021
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...
Humans Neoplasms Spatial Analysis Machine Learning Artificial Intelligence Tumor Microenvironment
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Adaptive convolutional neural networks for accelerating magnetic resonance imaging via k-space data interpolation.

Medical image analysis May 16, 2021
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...
Magnetic Resonance Imaging Spatial Analysis Humans Algorithms Image Processing, Computer-Assisted Neural Networks, Computer
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Spatial prediction of human brucellosis (HB) using a GIS-based adaptive neuro-fuzzy inference system (ANFIS).

Acta tropica May 9, 2021
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...
Humans Neural Networks, Computer Incidence Brucellosis Fuzzy Logic Animals Geographic Information Systems Climate Spatial Analysis Linear Models Cluster Analysis Models, Statistical
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A spatially based quantile regression forest model for mapping rural land values.

Journal of environmental management Apr 6, 2021
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...
Linear Models Environmental Monitoring Spatial Analysis Machine Learning Algorithms
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Geographically weighted machine learning model for untangling spatial heterogeneity of type 2 diabetes mellitus (T2D) prevalence in the USA.

Scientific reports Mar 26, 2021
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...
Machine Learning Risk Factors Quality of Life Exercise United States Obesity Diet Socioeconomic Factors Prevalence Spatial Analysis Humans Geography Life Style Diabetes Mellitus, Type 2 Sedentary Behavior
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Bilateral attention decoder: A lightweight decoder for real-time semantic segmentation.

Neural networks : the official journal of the International Neural Network Society Jan 30, 2021
The encoder-decoder structure has been introduced into semantic segmentation to improve the spatial accuracy of the network by fusing high- and low-level feature maps. However, recent state-of-the-art encoder-decoder-based methods can hardly attain t...
Semantics Pattern Recognition, Automated Machine Learning Spatial Analysis
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Applying machine learning, text mining, and spatial analysis techniques to develop a highway-railroad grade crossing consolidation model.

Accident; analysis and prevention Jan 22, 2021
The consolidation of Highway-Railroad Grade Crossing (HRGC) is one of the effective approaches to decrease the number of crashes between trains and vehicles. From 2015-2019, there were 57 HRGC crashes at crossings in East Baton Rouge Parish (EBRP), r...
Railroads Data Mining Spatial Analysis Male Models, Theoretical Accidents, Traffic Humans Machine Learning
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Adopting Machine Learning and Spatial Analysis Techniques for Driver Risk Assessment: Insights from a Case Study.

International journal of environmental research and public health Jul 18, 2020
Traffic violations usually caused by aggressive driving behavior are often seen as a primary contributor to traffic crashes. Violations are either caused by an unintentional or deliberate act of drivers that jeopardize the lives of fellow drivers, pe...
Risk Assessment Humans Geographic Information Systems Machine Learning China Accidents, Traffic Pedestrians Automobile Driving Spatial Analysis
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