Environmental Health

Predictive modeling of copper (II) adsorption from aqueous solutions by sawdust: a comparative analysis of adaptive neuro-fuzzy interference system (ANFIS) and artificial neural network (ANN) approaches.

Heavy metal ions are considered to be the most prevalent and toxic water contaminants. The objective of thois work was to investigate the effectiveness of employing the adsorption technique in a laboratory-size reactor to remove copper (II) ions from an aqueous medium. An adaptive neuro-fuzzy inference system (ANFIS) and a feed-forward artificial neural network (ANN) were used in this study. Four ...

Illuminating patterns of firefly abundance using citizen science data and machine learning models.

As insect populations decline in many regions, conservation biologists are increasingly tasked with identifying factors that threaten insect species and developing effective strategies for their conservation. One insect group of global conservation concern are fireflies (Coleoptera: Lampyridae). Although quantitative data on firefly populations are lacking for most species, anecdotal reports sugge...

Gelato: a new hybrid deep learning-based Informer model for multivariate air pollution prediction.

The increase in air pollutants and its adverse effects on human health and the environment has raised significant concerns. This implies the necessity...

Amino-functionalized novel biosorbent for effective removal of fluoride from water: process optimization using artificial neural network and mechanistic insights.

Aqueous fluoride ( ) pollution is a global threat to potable water security. The present research envisions the development of novel adsorbents from ...

Integrated assessment of potentially toxic elements in soil of the Kangdian metallogenic province: A two-point machine learning approach.

The accumulation of potentially toxic elements in soil poses significant risks to ecosystems and human well-being due to their inherent toxicity, wide...

The association between PM components and blood pressure changes in late pregnancy: A combined analysis of traditional and machine learning models.

BACKGROUND: PM is a harmful mixture of various chemical components that pose a challenge in determining their individual and combined health effects d...

3D multi-robot olfaction in naturally ventilated indoor environments: Locating a time-varying source at unknown heights.

Source localization is significant for mitigating indoor air pollution and safeguarding the well-being and safety of occupants. While most study focus...

Discovery of Covalent Lead Compounds Targeting 3CL Protease with a Lateral Interactions Spiking Neural Network.

Covalent drugs exhibit advantages in that noncovalent drugs cannot match, and covalent docking is an important method for screening covalent lead comp...

Ecotoxicological impacts of landfill sites: Towards risk assessment, mitigation policies and the role of artificial intelligence.

Waste disposal in landfills remains a global concern. Despite technological developments, landfill leachate poses a hazard to ecosystems and human hea...

Toxicity prediction and classification of Gunqile-7 with small sample based on transfer learning method.

Drug-induced diseases are the most important component of iatrogenic disease. It is the duty of doctors to provide a reasonable and safe dose of medic...

Application of machine learning in prediction of Pb adsorption of biochar prepared by tube furnace and fluidized bed.

Data mining by machine learning (ML) has recently come into application in heavy metals purification from wastewater, especially in exploring lead rem...

A fuzzy interval dynamic optimization model for surface and groundwater resources allocation under water shortage conditions, the case of West Azerbaijan Province, Iran.

The allocation of water in areas which face shortage of water especially during hot dry seasons is of utmost importance. This is normally affected by ...

Convolutional Neural Networks Facilitate Process Understanding of Megacity Ozone Temporal Variability.

Ozone pollution is profoundly modulated by meteorological features such as temperature, air pressure, wind, and humidity. While many studies have deve...

Application of Convolutional Neural Network for Decoding of 12-Lead Electrocardiogram from a Frequency-Modulated Audio Stream (Sonified ECG).

Research of novel biosignal modalities with application to remote patient monitoring is a subject of state-of-the-art developments. This study is focu...

A hybrid prediction model of dissolved oxygen concentration based on secondary decomposition and bidirectional gate recurrent unit.

Dissolved oxygen is one of the important comprehensive indicators of river water quality, which reflects the degree of pollution in the water body. Mo...

Do machine learning methods lead to similar individualized treatment rules? A comparison study on real data.

Identifying patients who benefit from a treatment is a key aspect of personalized medicine, which allows the development of individualized treatment r...

Feasibility and validity of using deep learning to reconstruct 12-lead ECG from three‑lead signals.

BACKGROUND: In the field of mobile health, portable dynamic electrocardiogram (ECG) monitoring devices often have a limited number of lead electrodes ...

BREATH-Net: a novel deep learning framework for NO prediction using bi-directional encoder with transformer.

Air pollution poses a significant challenge in numerous urban regions, negatively affecting human well-being. Nitrogen dioxide (NO) is a prevalent atm...

High-spatial resolution ground-level ozone in Yunnan, China: A spatiotemporal estimation based on comparative analyses of machine learning models.

Monitoring ground-level ozone concentrations is a critical aspect of atmospheric environmental studies. Given the existing limitations of satellite da...

An intelligent interval forecasting system based on fuzzy time series and error distribution characteristics for air quality index.

Due to the emergency environment pollution problems, it is imperative to understand the air quality and take effective measures for environmental gove...