AI Medical Compendium Journal:
Chemosphere

Showing 1 to 10 of 147 articles

Comparing neural network architectures for simulating pollutant loads and first flush events in urban watersheds: Balancing specialization and generalization.

Chemosphere
This study investigates the effectiveness of artificial neural networks (ANNs) models in predicting urban water quality, specifically focusing on first flush (FF) event classification and pollutant event mean load (EML) predictions for total suspende...

Integrating surface chemistry properties and machine learning to map the toxicity landscape of superparamagnetic iron oxide nanoparticles.

Chemosphere
The relationship between Superparamagnetic Iron Oxide Nanoparticles (SPIONs) surface chemistry and their toxicological outcomes is crucial for biomedical applications, including drug delivery and imaging diagnostics. SPIONs' surface properties-such a...

Enhancing process monitoring and control in novel carbon capture and utilization biotechnology through artificial intelligence modeling: An advanced approach toward sustainable and carbon-neutral wastewater treatment.

Chemosphere
Integrating carbon capture and utilization (CCU) technologies into wastewater treatment plants (WWTPs) is essential for mitigating greenhouse gas (GHG) emissions and enhancing environmental sustainability, but further advancements in process monitori...

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

Chemosphere
Predicting indoor air pollutants concentrations in schools is essential for ensuring a healthy learning environment. Traditional measurements methods pose challenges in cost, maintenance, and time. This study proposes a new approach using a deep lear...

Physics-informed neural networks for enhanced reference evapotranspiration estimation in Morocco: Balancing semi-physical models and deep learning.

Chemosphere
Reference evapotranspiration (ETo) is essential for agricultural water management, crop productivity, and irrigation systems. The Penman-Monteith (PM) equation is the standard method for estimating ETo, but its data-intensive nature makes it impracti...

ANN-assisted comprehensive screening of silica gel-alunite composite sorbent system for efficient adsorption of toxic nickel ions: Batch and continuous mode water treatment applications.

Chemosphere
Through batch and fixed-bed column operations, nickel ions were extracted from a contaminated aqueous media by adsorption onto silica gel-immobilized alunite (Sg@Aln). A three-layer backward-propagating network with an ideal pattern of 5-10-1 and 4-1...

Assessing the environmental determinants of micropollutant contamination in streams using explainable machine learning and network analysis.

Chemosphere
Even at trace concentrations, micropollutants, including pesticides and pharmaceuticals, pose considerable ecological risks, and the increasing presence of synthetic chemical substances in aquatic systems has emerged as a growing concern. Moreover, l...

Alternative assessment of machine learning to polynomial regression in response surface methodology for predicting decolorization efficiency in textile wastewater treatment.

Chemosphere
This study investigated the potential of machine learning (ML) as a substitute for polynomial regression in conventional response surface methodology (RSM) for decolorizing textile wastewater via a UV/HO process. While polynomial regression offers li...

Machine learning models for quantitatively prediction of toxicity in macrophages induced by metal oxide nanoparticles.

Chemosphere
As nanotechnology advances, metal oxide nanoparticles (MeONPs) increasingly come into contact with humans. The inhaled MeONPs cannot be effectively cleared by cilia or lung mucus. In the last decade, potential immune toxicity arising from exposure to...

Machine learning based workflow for (micro)plastic spectral reconstruction and classification.

Chemosphere
With the advancement of artificial intelligence, it is foreseeable that computer-assisted identification of microplastics (MPs) will become increasingly widespread. Therefore, exploring a machine learning-based workflow to facilitate the identificati...