The growing presence of diverse pollutants, including heavy metals, organic compounds,
pharmaceuticals, and emerging contaminants, poses significant environmental and health …

The development of machine learning-based ensemble models for the prediction of complex
processes with non-linear nature (such as adsorption) has been remarkably advanced over …

In this research, an upgraded and environmentally friendly process involving WO3/Co-ZIF
nanocomposite was used for the removal of Cefixime from the aqueous solutions. Intelligent …

End product quality prediction is one of the key issues in smart manufacturing. Reliable
evaluation and parameter optimization is needed to ensure their high-quality production …

With growing environmental consciousness, biomaterials (BMs) have garnered attention as
sustainable materials for the adsorption of hazardous water contaminants. These BMs are …

Water pollution poses a significant environmental threat due to the discharge of organic
dyes from industrial processes. In this study, we investigated a novel adsorptive composite …

The increasing presence of antibiotics in aquatic environments necessitates the
development of effective remediation strategies. This review comprehensively explores the …

Machine learning (ML), as an advanced data analysis tool, simulates the learning process of
the human brain, enabling the extraction of features, discovery of patterns, and making …

In order to improve the traditional “trial and error” material design method, machine learning-
yield strength and machine learning-fracture strain models are incorporated into one system …

Feature selection is a multi-objective problem, which can eliminate irrelevant and redundant
features and improve the accuracy of classification at the same time. Feature selection is a …