AIMC Topic: Housing

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Changes in the Neighborhood Built Environment and Chronic Health Conditions in Washington, DC, in 2014-2019: Longitudinal Analysis.

JMIR formative research
BACKGROUND: Google Street View (GSV) images offer a unique and scalable alternative to in-person audits for examining neighborhood built environment characteristics. Additionally, most prior neighborhood studies have relied on cross-sectional designs...

Optimization of house price evaluation model based on multi-source geographic big data and deep neural network.

PloS one
The real estate market requires effective and precise house price prediction, as conventional models often face difficulties in generalization, computational efficiency, and interpretability. The research problem is addressed by introducing the House...

Testing regular expression searches and machine learning models to determine housing instability and low income status from primary care electronic medical record data in Toronto, Ontario.

BMC public health
BACKGROUND: Housing and income are important social determinants of health (SDoH). Primary care providers often do not have information about these determinants, which could be used to support equitable health system planning and care delivery. The a...

An improved artificial gorilla troops optimizer for BP neural network-based housing price prediction.

PloS one
In the context of global economic austerity in the post epidemic era, housing, as one of the basic human needs, has become particularly important for accurate prediction of house prices. BP neural network is widely used in prediction tasks, but their...

Forecasting second-hand house prices in China using the GA-PSO-BP neural network model.

PloS one
While the traditional genetic algorithms are capable of forecasting house prices, they often suffer from premature convergence, which adversely affects the reliability of the forecasts. To address this issue, the research employs a genetic-particle s...

Modeling the latent impacts of extreme floods on indoor mold spores in residential buildings: Application of machine learning algorithms.

Environment international
Floods can severely impact the economy, environment and society. These impacts can be direct and indirect. Past research has focused more on the former impacts. Of the indirect impacts, those on mold growth in indoor environments that affect human re...

Extracting Housing and Food Insecurity Information From Clinical Notes Using cTAKES.

Health services research
OBJECTIVE: To assess the utility and challenges of using natural language processing (NLP) in electronic health records (EHRs) to ascertain health-related social needs (HRSNs) among older adults.

Identifying predictors of spatiotemporal variations in residential radon concentrations across North Carolina using machine learning analytics.

Environmental pollution (Barking, Essex : 1987)
Radon is a naturally occurring radioactive gas derived from the decay of uranium in the Earth's crust. Radon exposure is the leading cause of lung cancer among non-smokers in the US. Radon infiltrates homes through soil and building foundations. This...

Advancing ensemble learning techniques for residential building electricity consumption forecasting: Insight from explainable artificial intelligence.

PloS one
Accurate electricity consumption forecasting in residential buildings has a direct impact on energy efficiency and cost management, making it a critical component of sustainable energy practices. Decision tree-based ensemble learning techniques are p...

Development of a High-Resolution Indoor Radon Map Using a New Machine Learning-Based Probabilistic Model and German Radon Survey Data.

Environmental health perspectives
BACKGROUND: Radon is a carcinogenic, radioactive gas that can accumulate indoors and is undetected by human senses. Therefore, accurate knowledge of indoor radon concentration is crucial for assessing radon-related health effects or identifying radon...