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Month: June 2019

News

Using machine learning to examine the relationship between asthma and absenteeism

In this study, we found that machine learning was able to effectively estimate […]

News

Using machine learning to understand the temporal morphology of the PM2.5 annual cycle in East Asia

PM2.5 air pollution is a significant issue for human health all over the world, […]

News

Time-series analysis of satellite-derived fine particulate matter pollution and asthma morbidity in Jackson, MS

In order to examine associations between asthma morbidity and local ambient air pollution […]

News

Combining domain filling with a self-organizing map to analyze multi-species hydrocarbon signatures on a regional scale

For the period of the Barnett Coordinated Campaign, October 16–31, 2013, hourly concentrations […]

News

Estimating the daily pollen concentration in the atmosphere using machine learning and NEXRAD weather radar data

Millions of people have an allergic reaction to pollen. The impact of pollen […]

News

Applying Deep Neural Networks and Ensemble Machine Learning Methods to Forecast Airborne Ambrosia Pollen

Allergies to airborne pollen are a significant issue affecting millions of Americans. Consequently, […]

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