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Deep learning for pollen allergy surveillance from twitter in Australia.

BMC medical informatics and decision making
BACKGROUND: The paper introduces a deep learning-based approach for real-time detection and insights generation about one of the most prevalent chronic conditions in Australia - Pollen allergy. The popular social media platform is used for data colle...

Applying machine learning to forecast daily Ambrosia pollen using environmental and NEXRAD parameters.

Environmental monitoring and assessment
Approximately 50 million Americans have allergic diseases. Airborne plant pollen is a significant trigger for several of these allergic diseases. Ambrosia (ragweed) is known for its abundant production of pollen and its potent allergic effect in Nort...

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

Environmental monitoring and assessment
Millions of people have an allergic reaction to pollen. The impact of pollen allergies is on the rise due to increased pollen levels caused by global warming and the spread of highly invasive weeds. The production, release, and dispersal of pollen de...

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

International journal of environmental research and public health
Allergies to airborne pollen are a significant issue affecting millions of Americans. Consequently, accurately predicting the daily concentration of airborne pollen is of significant public benefit in providing timely alerts. This study presents a me...

Real-time accident detection: Coping with imbalanced data.

Accident; analysis and prevention
Detecting accidents is of great importance since they often impose significant delay and inconvenience to road users. This study compares the performance of two popular machine learning models, Support Vector Machine (SVM) and Probabilistic Neural Ne...

Unveiling tropospheric ozone by the traditional atmospheric model and machine learning, and their comparison:A case study in hangzhou, China.

Environmental pollution (Barking, Essex : 1987)
Tropospheric ozone in the surface air has become the primary atmospheric pollutant in Hangzhou, China, in recent years. Previous analysis is not enough to decode it for better regulation. Therefore, we use the traditional atmospheric model, Weather R...

Research on soil moisture prediction model based on deep learning.

PloS one
Soil moisture is one of the main factors in agricultural production and hydrological cycles, and its precise prediction is important for the rational use and management of water resources. However, soil moisture involves complex structural characteri...

Deep learning for identifying environmental risk factors of acute respiratory diseases in Beijing, China: implications for population with different age and gender.

International journal of environmental health research
This study focuses on identifying environmental health risk factors related to acute respiratory diseases using deep learning method. Based on respiratory disease data, air pollution data and meteorological environmental data, cross-domain risk facto...

Automated detection of koalas using low-level aerial surveillance and machine learning.

Scientific reports
Effective wildlife management relies on the accurate and precise detection of individual animals. These can be challenging data to collect for many cryptic species, particularly those that live in complex structural environments. This study introduce...

Deep learning and process understanding for data-driven Earth system science.

Nature
Machine learning approaches are increasingly used to extract patterns and insights from the ever-increasing stream of geospatial data, but current approaches may not be optimal when system behaviour is dominated by spatial or temporal context. Here, ...