Pollution risk assessment by designing predictive binary classification models of substituted benzenes centered on data mining and machine learning techniques

There is a growing need for industry and global regulatory agencies to develop rapid chemical safety assessment through more reliable theoretical models. Thus , quantitative structure–toxicity relationship (QSTR) models are preferred by regulators to bring chemicals to market rather than long and ex...

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Veröffentlicht in:	Environmental science and pollution research international Jg. 32; H. 35; S. 21092 - 21116
Hauptverfasser:	N’guessan, Aubin, Dali, Brice, Esmel, Elvice Akori, Moussé, Logbo Mathias, Ziao, Nahossé, N’guessan, Raymond Kré, Megnassan, Eugene
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.07.2025 Springer Nature B.V
Schlagworte:	Algorithms Aquatic Pollution Atmospheric Protection/Air Quality Control/Air Pollution Benzene Benzene - toxicity Chemicals Classification Data Mining Datasets Decision Trees Earth and Environmental Science Ecotoxicology Environment Environmental Chemistry Environmental Health Environmental risk Evaluation Feature selection Hydrocarbons Laboratory animals Learning algorithms Machine Learning Neural networks Organic chemistry Quantitative Structure-Activity Relationship Regression analysis Research Article Risk Assessment Statistical analysis Statistical models Support vector machines Tetrahymena pyriformis - drug effects Toxicants Toxicity Variables Waste Water Technology Water Management Water Pollution Control SMOTE Data mining QSTR Risk assessment Substituted benzenes Machine learning Tetrahymena pyriformis ClustOfVar
ISSN:	1614-7499, 0944-1344, 1614-7499
Online-Zugang:	Volltext
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