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Socioeconomic Factors

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Passivity and stability analysis of neural networks with time-varying delays via extended free-weighting matrices integral inequality.

Neural networks : the official journal of the International Neural Network Society
This paper is concerned with the problem of passivity for uncertain neural networks with time-varying delays. First, the recently developed integral inequality called generalized free-matrix-based integral inequality is extended to estimate further t...

Using Bayesian dynamical systems, model averaging and neural networks to determine interactions between socio-economic indicators.

PloS one
Social and economic systems produce complex and nonlinear relationships in the indicator variables that describe them. We present a Bayesian methodology to analyze the dynamical relationships between indicator variables by identifying the nonlinear f...

An intelligent algorithm for identification of optimum mix of demographic features for trust in medical centers in Iran.

Artificial intelligence in medicine
Healthcare quality is affected by various factors including trust. Patients' trust to healthcare providers is one of the most important factors for treatment outcomes. The presented study identifies optimum mixture of patient demographic features wit...

Prevalence and Disparities in Tobacco Product Use Among American Indians/Alaska Natives - United States, 2010-2015.

MMWR. Morbidity and mortality weekly report
An overarching goal of Healthy People 2020 is to achieve health equity, eliminate disparities, and improve health among all groups.* Although significant progress has been made in reducing overall commercial tobacco product use, disparities persist, ...

Estimating causal effects for survival (time-to-event) outcomes by combining classification tree analysis and propensity score weighting.

Journal of evaluation in clinical practice
RATIONALE, AIMS AND OBJECTIVES: A common approach to assessing treatment effects in nonrandomized studies with time-to-event outcomes is to estimate propensity scores and compute weights using logistic regression, test for covariate balance, and then...

Using deep learning and Google Street View to estimate the demographic makeup of neighborhoods across the United States.

Proceedings of the National Academy of Sciences of the United States of America
The United States spends more than $250 million each year on the American Community Survey (ACS), a labor-intensive door-to-door study that measures statistics relating to race, gender, education, occupation, unemployment, and other demographic facto...

Remote sensing-based measurement of Living Environment Deprivation: Improving classical approaches with machine learning.

PloS one
This paper provides evidence on the usefulness of very high spatial resolution (VHR) imagery in gathering socioeconomic information in urban settlements. We use land cover, spectral, structure and texture features extracted from a Google Earth image ...

Use of a machine learning framework to predict substance use disorder treatment success.

PloS one
There are several methods for building prediction models. The wealth of currently available modeling techniques usually forces the researcher to judge, a priori, what will likely be the best method. Super learning (SL) is a methodology that facilitat...

Scaling Laws in City Growth: Setting Limitations with Self-Organizing Maps.

PloS one
Do scaling relations always provide the means to anticipate the relationships between the size of cities, costs of maintenance, and the socio-economic benefits resulting from their growth? Scaling laws are considered a universal principle that descri...