Environmental Health

Deep learning artificial neural network framework to optimize the adsorption capacity of 3-nitrophenol using carbonaceous material obtained from biomass waste.

The presence of toxic chemicals in water, including heavy metals like mercury and lead, organic pollutants such as pesticides, and industrial chemicals from runoff and discharges, poses critical public health and environmental risks leading to severe health issues and ecosystem damage; education plays a crucial role in mitigating these effects by enhancing awareness, promoting sustainable practice...

Daily PM2.5 concentration prediction based on variational modal decomposition and deep learning for multi-site temporal and spatial fusion of meteorological factors.

Air pollution, particularly PM2.5, has long been a critical concern for the atmospheric environment. Accurately predicting daily PM2.5 concentrations is crucial for both environmental protection and public health. This study introduces a new hybrid model within the "Decomposition-Prediction-Integration" (DPI) framework, which combines variational modal decomposition (VMD), causal convolutional neu...

Prediction of Cytochrome P450 Substrates Using the Explainable Multitask Deep Learning Models.

Cytochromes P450 (P450s or CYPs) are the most important phase I metabolic enzymes in the human body and are responsible for metabolizing ∼75% of the c...

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 anomaly detection and sensor data forecasting, but re...

Optimized ensemble deep learning for predictive analysis of student achievement.

Education is essential for individuals to lead fulfilling lives and attain greatness by enhancing their value. It improves self-assurance and enables ...

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 regulatory enforcement is evident, but there is cause...

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 leachate may also bring more serious pollution and hea...

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 leads to significant risks to public health and e...

Utilizing convolutional neural networks for discriminating cancer and stromal cells in three-dimensional cell culture images with nuclei counterstain.

SIGNIFICANCE: Accurate cell segmentation and classification in three-dimensional (3D) images are vital for studying live cell behavior and drug respon...

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 pollution, posing grave ecological and human health ris...

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-term population dynamics of the rheophilic fish sp...

MMFSyn: A Multimodal Deep Learning Model for Predicting Anticancer Synergistic Drug Combination Effect.

Combination therapy aims to synergistically enhance efficacy or reduce toxic side effects and has widely been used in clinical practice. However, with...

Artificial intelligence in microplastic detection and pollution control.

The rising prevalence of microplastics (MPs) in various ecosystems has increased the demand for advanced detection and mitigation strategies. This rev...

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. According to the World Health Organization (WHO), fin...

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 over the past few decades have increased the air po...

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 such as nitrophenols and azo dyes, considering the...

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 control measures. However, mutation events caused b...

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 and necessitating accurate PM predictions for effe...

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 climate change, resulting in pronounced hydrological dr...

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 been shown to have the identify features not immediat...