Environmental Health

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

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 vermistabilization approach using pineapple pomace (PP) to enhance the bioremediation of paper mill sludge (PMS) facilitated by Eudrilus eugeniae. The...

PM concentration prediction using machine learning algorithms: an approach to virtual monitoring stations.

One of the most important pollutants is PM, which is particularly important to monitor pollutant levels to keep the pollutant concentration under control. In this research, an attempt has been made to predict the concentrations of PM using four Machine Learning (ML) models. The ML methods include Light Gradient Boosting Machine (LGBM), Extreme Gradient Boosting Regressor (XGBR), Random Forest (RF)...

Comprehensive Raman spectroscopy analysis for differentiating toxic cyanobacteria through multichannel 1D-CNNs and SHAP-based explainability.

Cyanobacterial blooms pose significant environmental and public health risks due to the production of toxins that contaminate water sources and disrup...

Uncovering key sources of regional ozone simulation biases using machine learning and SHAP analysis.

Atmospheric chemical transport models (CTMs) are widely used in air quality management, but still have large biases in simulations. Accurately and eff...

Explainable deep learning models for predicting water pipe failures.

Failures within water distribution networks (WDNs) lead to significant environmental and economic impacts. While existing research has established var...

Prediction of surface water pollution using wavelet transform and 1D-CNN.

Permanganate index (COD), total nitrogen, and ammonia nitrogen are important indicators that represent the degree of pollution of surface water. This ...

Prediction of river dissolved oxygen (DO) based on multi-source data and various machine learning coupling models.

Too low a concentration of dissolved oxygen (DO) in a river can disrupt the ecological balance, while too high a concentration may lead to eutrophicat...

Reliability and validity of a novel single-lead portable electrocardiogram device for pregnant women: a comparative study.

BACKGROUND: WenXinWuYang, a novel portable Artificial Intelligence Electrocardiogram (AI-ECG) device, can detect many kinds of abnormal heart disease ...

Measuring the Level of Aflatoxin Infection in Pistachio Nuts by Applying Machine Learning Techniques to Hyperspectral Images.

This paper investigates the use of machine learning techniques on hyperspectral images of pistachios to detect and classify different levels of aflato...

PM concentration prediction using a whale optimization algorithm based hybrid deep learning model in Beijing, China.

PM is a significant global atmospheric pollutant impacting visibility, climate, and public health. Accurate prediction of PM concentrations is critica...

Air pollution and prostate cancer: Unraveling the connection through network toxicology and machine learning.

BACKGROUND: In recent years, air pollution has been demonstrated to be associated with the occurrence of various diseases. This study aims to explore ...

Structural Similarity, Activity, and Toxicity of Mycotoxins: Combining Insights from Unsupervised and Supervised Machine Learning Algorithms.

A large number of mycotoxins and related fungal metabolites have not been assessed in terms of their toxicological impacts. Current methodologies ofte...

Predicting Toxicity toward Nitrifiers by Attention-Enhanced Graph Neural Networks and Transfer Learning from Baseline Toxicity.

Assessing chemical environmental impacts is critical but challenging due to the time-consuming nature of experimental testing. Graph neural networks (...

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

Ozone pollution affects food production, human health, and the lives of individuals. Due to rapid industrialization and urbanization, Liaocheng has ex...

Prediction of school PM by an attention-based deep learning approach informed with data from nearby air quality monitoring stations.

Predicting indoor air pollutants concentrations in schools is essential for ensuring a healthy learning environment. Traditional measurements methods ...

Enhanced water quality prediction model using advanced hybridized resampling alternating tree-based and deep learning algorithms.

Water quality modeling in riverine systems is crucial for effective water resource management and pollution mitigation planning. However, the intricat...

Deep structured learning with vision intelligence for oral carcinoma lesion segmentation and classification using medical imaging.

Oral carcinoma (OC) is a toxic illness among the most general malignant cancers globally, and it has developed a gradually significant public health c...

Machine learning-based analysis of microplastic-induced changes in anaerobic digestion parameters influencing methane yield.

Microplastics (MPs) present significant challenges for anaerobic digestion (AD) processes used in energy recovery from contaminated organic waste. Giv...

Development of a deep neural network model based on high throughput screening data for predicting synergistic estrogenic activity of binary mixtures for consumer products.

A paradigm of chemical risk assessment is continuously extending from focusing on 'single substances' to more comprehensive approaches that examines t...

AI-MET: A deep learning-based clinical decision support system for distinguishing multisystem inflammatory syndrome in children from endemic typhus.

The COVID-19 pandemic brought several diagnostic challenges, including the post-infectious sequelae multisystem inflammatory syndrome in children (MIS...