AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Air Pollution

Showing 21 to 30 of 231 articles

Clear Filters

Enhancing PM2.5 prediction by mitigating annual data drift using wrapped loss and neural networks.

PloS one
In many deep learning tasks, it is assumed that the data used in the training process is sampled from the same distribution. However, this may not be accurate for data collected from different contexts or during different periods. For instance, the t...

Predicting On-Road Air Pollution Coupling Street View Images and Machine Learning: A Quantitative Analysis of the Optimal Strategy.

Environmental science & technology
Integrating mobile monitoring data with street view images (SVIs) holds promise for predicting local air pollution. However, algorithms, sampling strategies, and image quality introduce extra errors due to a lack of reliable references that quantify ...

XIS-PM: A daily spatiotemporal machine-learning model for PM in the contiguous United States.

Environmental research
Air-pollution monitoring is sparse across most of the United States, so geostatistical models are important for reconstructing concentrations of fine particulate air pollution (PM) for use in health studies. We present XGBoost-IDW Synthesis (XIS), a ...

A machine learning framework for short-term prediction of chronic obstructive pulmonary disease exacerbations using personal air quality monitors and lifestyle data.

Scientific reports
Chronic Obstructive Pulmonary Disease (COPD) is a heterogeneous disease with a variety of symptoms including, persistent coughing and mucus production, shortness of breath, wheezing, and chest tightness. As the disease advances, exacerbations, i.e. a...

Enhancing air quality predictions in Chile: Integrating ARIMA and Artificial Neural Network models for Quintero and Coyhaique cities.

PloS one
In this comprehensive analysis of Chile's air quality dynamics spanning 2016 to 2021, the utilization of data from the National Air Quality Information System (SINCA) and its network of monitoring stations was undertaken. Quintero, Puchuncaví, and Co...

A Systematic Study of Popular Software Packages and AI/ML Models for Calibrating In Situ Air Quality Data: An Example with Purple Air Sensors.

Sensors (Basel, Switzerland)
Accurate air pollution monitoring is critical to understand and mitigate the impacts of air pollution on human health and ecosystems. Due to the limited number and geographical coverage of advanced, highly accurate sensors monitoring air pollutants, ...

Assessing the effectiveness of long short-term memory and artificial neural network in predicting daily ozone concentrations in Liaocheng City.

Scientific reports
Ozone pollution affects food production, human health, and the lives of individuals. Due to rapid industrialization and urbanization, Liaocheng has experienced increasing of ozone concentration over several years. Therefore, ozone has become a major ...

High-resolution spatio-temporal estimation of street-level air pollution using mobile monitoring and machine learning.

Journal of environmental management
High spatio-temporal resolution street-level air pollution (SLAP) estimation is essential for urban air quality management, yet traditional methods face significant challenges in capturing the detailed spatial and temporal variability of pollution. M...

A study on the impact of meteorological and emission factors on PM concentrations based on machine learning.

Journal of environmental management
PM pollution, a major environmental and health concern, is influenced by a complex interplay of emission sources and meteorological conditions. Accurately identifying these factors and their contributions is essential for effective pollution manageme...

Combining deep learning and machine learning techniques to track air pollution in relation to vegetation cover utilizing remotely sensed data.

Journal of environmental management
The rapid urban expansion in Dhaka, the capital of Bangladesh, has escalated air pollution levels and led to a significant decrease in green spaces. This study employed machine learning (ML) and deep learning (DL) techniques to examine the relationsh...