ArticleScientific reports2026
Developing machine learning regression model optimized using Tabu search harmony search algorithm for prediction of mass transfer in membranes.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
We used several advanced machine learning models for analysis of a separation unit in water purification and treatment via pressure-driven membrane. After removing outliers using the Isolation Forest method and normalizing features with a Min-Max scaler, three regression models-Gaussian Process Regression (GPR), Deep Gaussian Process Regression (DGP), and Heteroscedastic Gaussian Process Regression (HGP)-were trained and optimized for correlation of data. Hyperparameters were tuned using a hybrid Tabu Search-Harmony Search (TS-HS) algorithm. Among the models, HGP showed the strongest performance, with an R
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