AIMC Topic: Spatial Analysis
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Identification and transfer of spatial transcriptomics signatures for cancer diagnosis.
Breast cancer research : BCR
Jan 13, 2020
BACKGROUND: Distinguishing ductal carcinoma in situ (DCIS) from invasive ductal carcinoma (IDC) regions in clinical biopsies constitutes a diagnostic challenge. Spatial transcriptomics (ST) is an in situ capturing method, which allows quantification ...
Toward safer highways, application of XGBoost and SHAP for real-time accident detection and feature analysis.
Accident; analysis and prevention
Dec 20, 2019
Detecting traffic accidents as rapidly as possible is essential for traffic safety. In this study, we use eXtreme Gradient Boosting (XGBoost)-a Machine Learning (ML) technique-to detect the occurrence of accidents using a set of real time data compri...
Spatial Lifecourse Epidemiology Reporting Standards (ISLE-ReSt) statement.
Health & place
Dec 4, 2019
Spatial lifecourse epidemiology is an interdisciplinary field that utilizes advanced spatial, location-based, and artificial intelligence technologies to investigate the long-term effects of environmental, behavioural, psychosocial, and biological fa...
An artificial neural network ensemble approach to generate air pollution maps.
Environmental monitoring and assessment
Nov 7, 2019
The objective of this research is to propose an artificial neural network (ANN) ensemble in order to estimate the hourly NO concentration at unsampled locations. Spatial interpolation methods and linear regression models with regularization have been...
Machine Learning and Natural Language Processing for Geolocation-Centric Monitoring and Characterization of Opioid-Related Social Media Chatter.
JAMA network open
Nov 1, 2019
IMPORTANCE: Automatic curation of consumer-generated, opioid-related social media big data may enable real-time monitoring of the opioid epidemic in the United States.
Using GIS, geostatistics and Fuzzy logic to study spatial structure of sedimentary total PAHs and potential eco-risks; An Eastern Persian Gulf case study.
Marine pollution bulletin
Aug 16, 2019
GIS, geo-statistics and autocorrelation analysis were employed to reveal spatial structure of sedimentary ∑16PAHs. Global Moran's I index outlined significant ∑PAHs clusters for the entire region (Moran's I index =0.62, Z-score = 25.6). Anselin Moran...
Comparison of neuron-based, kernel-based, tree-based and curve-based machine learning models for predicting daily reference evapotranspiration.
PloS one
May 31, 2019
Accurately predicting reference evapotranspiration (ET0) with limited climatic data is crucial for irrigation scheduling design and agricultural water management. This study evaluated eight machine learning models in four categories, i.e. neuron-base...
Predictive risk mapping of human leptospirosis using support vector machine classification and multilayer perceptron neural network.
Geospatial health
May 14, 2019
Leptospirosis is a zoonotic disease found wherever human is in direct or indirect contact with contaminated water and environment. Considering the increasing number of cases of this disease in the northern part of Iran, identifying areas characterize...
Deep neural-kernel blocks.
Neural networks : the official journal of the International Neural Network Society
Mar 28, 2019
This paper introduces novel deep architectures using the hybrid neural-kernel core model as the first building block. The proposed models follow a combination of a neural networks based architecture and a kernel based model enriched with pooling laye...
SALMANTICOR study. Rationale and design of a population-based study to identify structural heart disease abnormalities: a spatial and machine learning analysis.
BMJ open
Feb 13, 2019
INTRODUCTION: This study aims to obtain data on the prevalence and incidence of structural heart disease in a population setting and, to analyse and present those data on the application of spatial and machine learning methods that, although known to...
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