AI Medical Compendium Journal:
The Science of the total environment

Showing 11 to 20 of 226 articles

Unlocking the potential of Eudrilus eugeniae in mitigating the pollution risk of pesticides and heavy metals: Fostering machine learning tactics to optimize environmental health.

The Science of the total environment
Agro-industrial waste management remains a critical challenge in sustainable development, particularly due to contamination with heterogeneous micropollutants such as heavy metals (HMs), pesticides, and polyphenols. This study explores an innovative ...

Forecasting deforestation and carbon loss across New Guinea using machine learning and cellular automata.

The Science of the total environment
The island of New Guinea harbors some of the world's most biologically diverse and highly endemic tropical ecosystems. Nevertheless, progressing land-use change in the region threatens their integrity, which will adversely affect their biodiversity a...

Machine learning unveils large-scale impact of Posidonia oceanica on Mediterranean Sea water.

The Science of the total environment
Posidonia oceanica is a protected endemic seagrass of the Mediterranean Sea that fosters biodiversity, stores carbon, releases oxygen, and provides habitat to numerous sea organisms. Leveraging augmented research, we collected a comprehensive dataset...

Machine learning unveils the role of biochar application in enhancing tea yield by mitigating soil acidification in tea plantations.

The Science of the total environment
Biochar, a widely utilized soil amendment in environmental applications, has been employed to enhance tea cultivation. This study utilized three machine learning models to investigate the effects of biochar on tea growth and yield, with the random fo...

Using machine learning to predict selenium content in crops: Implications for soil health and agricultural land utilization in longevity regions.

The Science of the total environment
Selenium (Se) is an indispensable trace element to human health, yet its biological tolerance threshold is relatively narrow. The potential application of machine learning methods to indirectly predict the Se content in crops across regional areas, t...

Cyanobacteria hot spot detection integrating remote sensing data with convolutional and Kolmogorov-Arnold networks.

The Science of the total environment
Prompt and accurate monitoring of cyanobacterial blooms is essential for public health management and understanding aquatic ecosystem dynamics. Remote sensing, in particular satellite observations, presents a good alternative for continuous monitorin...

Machine learning-based identification of animal feeding operations in the United States on a parcel-scale.

The Science of the total environment
The increasing global demand for meat and dairy products, fueled by rapid industrialization, has led to the expansion of Animal Feeding Operations (AFOs) in the United States (US). These operations, often found in clusters, generate large amounts of ...

The abiologically and biologically driving effects on organic matter in marginal seas revealed by deep learning-assisted model analysis.

The Science of the total environment
The biogeochemical processes of organic matter exhibit notable variability and unpredictability in marginal seas. In this study, the abiologically and biologically driving effects on particulate organic matter (POM) and dissolved organic matter (DOM)...

Spatiotemporal modeling of long-term PM concentrations and population exposure in Greece, using machine learning and statistical methods.

The Science of the total environment
The lack of high-resolution, long-term PM observations in Greece and the Eastern Mediterranean hampers the development of spatial models that are crucial for providing representative exposure estimates to health studies. This work presents a spatial ...

Machine learning-based prediction and model interpretability analysis for algal growth affected by microplastics.

The Science of the total environment
Microplastics (MPs), the plastic debris smaller than 5 mm, are ubiquitous in waterbodies and have been shown to be toxic to aquatic organisms, especially to microalgae. The aim of this study is to use machine learning models to predict the effects of...