Environmental Health

Spectral fusion-based machine learning classifiers for discriminating membrane breakage in multiple scenarios.

Membrane breakage can lead to filtration failure, which allows harmful substances to enter the effluent, posing potential hazards to human health and the environment. This study is an innovative combination of fluorescence and ultraviolet-visible (UV-Vis) spectroscopy to identify membrane breakage. It aims to unravel more comprehensive information, improve detection sensitivity and selectivity, an...

A deep learning based multi-model approach for predicting drug-like chemical compound's toxicity.

Ensuring the safety and efficacy of chemical compounds is crucial in small-molecule drug development. In the later stages of drug development, toxic compounds pose a significant challenge, losing valuable resources and time. Early and accurate prediction of compound toxicity using deep learning models offers a promising solution to mitigate these risks during drug discovery. In this study, we pres...

Predicting carbon dioxide emissions in the United States of America using machine learning algorithms.

Carbon dioxide (CO) emissions result from human activities like burning fossil fuels. CO is a greenhouse gas, contributing to global warming and clima...

Graph neural networks-enhanced relation prediction for ecotoxicology (GRAPE).

Exposure to toxic chemicals threatens species and ecosystems. This study introduces a novel approach using Graph Neural Networks (GNNs) to integrate a...

DeepChestGNN: A Comprehensive Framework for Enhanced Lung Disease Identification through Advanced Graphical Deep Features.

Lung diseases are the third-leading cause of mortality in the world. Due to compromised lung function, respiratory difficulties, and physiological com...

Daily scale air quality index forecasting using bidirectional recurrent neural networks: Case study of Delhi, India.

This research was established to accurately forecast daily scale air quality index (AQI) which is an essential environmental index for decision-making...

Discriminating bloom-forming cyanobacteria using lab-based hyperspectral imagery and machine learning: Validation with toxic species under environmental ranges.

Cyanobacteria are major contributors to algal blooms in inland waters, threatening ecosystem function and water uses, especially when toxin-producing ...

Behavioral toxicological tracking analysis of Drosophila larvae exposed to polystyrene microplastics based on machine learning.

Microplastics, as a pivotal concern within plastic pollution, have sparked widespread apprehension due to their ubiquitous presence. Recent research i...

Predicting dust pollution from dry bulk ports in coastal cities: A hybrid approach based on data decomposition and deep learning.

Dust pollution from storage and handling of materials in dry bulk ports seriously affects air quality and public health in coastal cities. Accurate pr...

Artificial Intelligence-Assisted Optimization of Antipigmentation Tyrosinase Inhibitors: Molecular Generation Based on a Low Activity Lead Compound.

Artificial intelligence (AI) molecular generation is a highly promising strategy in the drug discovery, with deep reinforcement learning (RL) models ...

Transportation infrastructure upgrading and green development efficiency: Empirical analysis with double machine learning method.

In order to deal with the environmental problems such as pollution emissions and climate change, sustainable development in the field of transportatio...

Impact of long-term mining activity on groundwater dynamics in a mining district in Xinjiang coal Mine Base, Northwest China: insight from geochemical fingerprint and machine learning.

Long-term coal mining could lead to a serious of geo-environmental problems. However, less comprehensive identification of factors controlling the gro...

Potential risk assessment and occurrence characteristic of heavy metals based on artificial neural network model along the Yangtze River Estuary, China.

Pollution from heavy metals in estuaries poses potential risks to the aquatic environment and public health. The complexity of the estuarine water env...

Prediction of potentially toxic elements in water resources using MLP-NN, RBF-NN, and ANFIS: a comprehensive review.

Water resources are constantly threatened by pollution of potentially toxic elements (PTEs). In efforts to monitor and mitigate PTEs pollution in wate...

Application of machine learning and multivariate approaches for assessing microplastic pollution and its associated risks in the urban outdoor environment of Bangladesh.

Microplastics (MPs) are an emerging global concern due to severe toxicological risks for ecosystems and public health. Therefore, this is the first st...

An artificial intelligence-based model for predicting reproductive toxicity of bisphenol analogues mixtures to the rotifer Brachionus calyciflorus.

The joint toxicity effects of mixtures, particularly reproductive toxicity, one of the main causes of aquatic ecosystem degradation, are often overloo...

Application of machine learning in the study of cobalt-based oxide catalysts for antibiotic degradation: An innovative reverse synthesis strategy.

This study addresses antibiotic pollution in global water bodies by integrating machine learning and optimization algorithms to develop a novel revers...

Mapping Blood Lead Levels in China during 1980-2040 with Machine Learning.

Lead poisoning is globally concerning, yet limited testing hinders effective interventions in most countries. We aimed to create annual maps of county...

Optimization of α-L-arabinofuranosidase CcABF on clarification and beneficial active substances in fermented ginkgo kernel juice by artificial neural network and genetic algorithm.

This study aimed at using α-L-arabinofuranosidase CcABF to improve the clarity and active substances in fermented ginkgo kernel juice by artificial ne...

Identification of agricultural surface source pollution in plain river network areas based on 3D-EEMs and convolutional neural networks.

Agricultural non-point sources, as major sources of organic pollution, continue to flow into the river network area of the Jiangnan Plain, posing a se...