Environmental Health

Beyond accuracy: Reproducibility must lead AI advances in radiology.

Time-series domain adaptation via sparse associative structure alignment: Learning invariance and variance.

Domain adaptation on time-series data, which is often encountered in the field of industry, like ano...

Optimized ensemble deep learning for predictive analysis of student achievement.

Education is essential for individuals to lead fulfilling lives and attain greatness by enhancing th...

Machine learning for environmental justice: Dissecting an algorithmic approach to predict drinking water quality in California.

The potential for machine learning to answer questions of environmental science, monitoring, and reg...

Precise management and control around the landfill integrating artificial intelligence and groundwater pollution risks.

The Landfill plays an important role in urban development and waste disposal. However, landfill leac...

Tracking the impact of heavy metals on human health and ecological environments in complex coastal aquifers using improved machine learning optimization.

The rising heavy metal (HM) pollution in coastal aquifers in rapidly urbanizing areas such as Dammam...

Utilizing machine learning to evaluate heavy metal pollution in the world's largest mangrove forest.

The world's largest mangrove forest (Sundarbans) is facing an imminent threat from heavy metal pollu...

Assessing spirlin Alburnoides bipunctatus (Bloch, 1782) as an early indicator of climate change and anthropogenic stressors using ecological modeling and machine learning.

Combining single-species ecological modeling with advanced machine learning to investigate the long-...

Artificial intelligence in microplastic detection and pollution control.

The rising prevalence of microplastics (MPs) in various ecosystems has increased the demand for adva...

Accurate PM urban air pollution forecasting using multivariate ensemble learning Accounting for evolving target distributions.

Over the past decades, air pollution has caused severe environmental and public health problems. Acc...

Use of machine learning algorithms to determine the relationship between air pollution and cognitive impairment in Taiwan.

Air pollution has become a major global threat to human health. Urbanization and industrialization o...

Machine learning based-model to predict catalytic performance on removal of hazardous nitrophenols and azo dyes pollutants from wastewater.

To maintain human health and purity of drinking water, it is crucial to eliminate harmful chemicals ...

Spatial source, simulating improvement, and short-term health effect of high PM exposure during mutation event in the key urban agglomeration regions in China.

Air quality in China has significantly improved owing to the effective implementation of pollution c...

Elevating hourly PM forecasting in Istanbul, Türkiye: Leveraging ERA5 reanalysis and genetic algorithms in a comparative machine learning model analysis.

Rapid urbanization and industrialization have intensified air pollution, posing severe health risks ...

Examining optimized machine learning models for accurate multi-month drought forecasting: A representative case study in the USA.

The Colorado River has experienced a significant streamflow reduction in recent decades due to clima...

Age prediction from 12-lead electrocardiograms using deep learning: a comparison of four models on a contemporary, freely available dataset.

The 12-lead electrocardiogram (ECG) is routine in clinical use and deep learning approaches have bee...

Deep transfer learning-based bird species classification using mel spectrogram images.

The classification of bird species is of significant importance in the field of ornithology, as it p...

Long-term trend forecast of chlorophyll-a concentration over eutrophic lakes based on time series decomposition and deep learning algorithm.

Long-term trend forecast of chlorophyll-a concentration (Chla) holds significant implications for eu...

Identifying heavy metal sources and health risks in soil-vegetable systems of fragmented vegetable fields based on machine learning, positive matrix factorization model and Monte Carlo simulation.

Urban fragmented vegetable fields offer fresh produce but pose a potential risk of heavy metal (HM) ...

Do machine learning methods improve prediction of ambient air pollutants with high spatial contrast? A systematic review.

BACKGROUND & OBJECTIVE: The use of machine learning for air pollution modelling is rapidly increasin...

MPCNN: A Novel Matrix Profile Approach for CNN-based Single Lead Sleep Apnea in Classification Problem.

Sleep apnea (SA) is a significant respiratory condition that poses a major global health challenge. ...