Using machine learning to examine the relationship between asthma and absenteeism
In this study, we found that machine learning was able to effectively estimate […]
Month: June 2019
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, […]
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 […]
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 […]
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 […]
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, […]