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• W3216996917 startingPage "104134" @default.
• W3216996917 abstract "In this study, the adsorption of methylene blue (MB) dye using an aquatic plant, Azolla pinnata (AP) was modelled using several various supervised machine learning (ML) algorithms, aiming to accurately predict the adsorption capacity under various experimental conditions. The ML algorithms used in this study are the artificial neural network (ANN), random forests (RF), support vector regression (SVR), and instance-based learner (IbK). The SVR algorithm was trained using three kernels: radial basis function (RBF), Pearson VII universal kernel (PUK), and polynomial kernel (PolyK). The experimental data (adsorbent dosage, pH, ionic strength, initial dye concentration, and contact time) served as input for training the algorithms and with the adsorption capacity as the output. The performance of the algorithms was optimised based on the values of correlation coefficient (R) and fine-tuned using several error functions (e.g. mean absolute error, root mean square error, and non-linear chi-squared). The best performing ML algorithm in this study is SVR-RBF which achieves the highest value in R (0.994) and has the lowest error." @default.
• W3216996917 created "2021-12-06" @default.
• W3216996917 creator A5011618364 @default.
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• W3216996917 creator A5091459453 @default.
• W3216996917 date "2022-03-01" @default.
• W3216996917 modified "2023-10-14" @default.
• W3216996917 title "Machine learning approaches to predict adsorption capacity of Azolla pinnata in the removal of methylene blue" @default.
• W3216996917 cites W1033762434 @default.
• W3216996917 cites W1969223596 @default.
• W3216996917 cites W1973919371 @default.
• W3216996917 cites W1977042562 @default.
• W3216996917 cites W1977876351 @default.
• W3216996917 cites W1978805977 @default.
• W3216996917 cites W1988195734 @default.
• W3216996917 cites W1992262649 @default.
• W3216996917 cites W1994300384 @default.
• W3216996917 cites W1995644766 @default.
• W3216996917 cites W2001496965 @default.
• W3216996917 cites W2006580294 @default.
• W3216996917 cites W2015802587 @default.
• W3216996917 cites W2020188045 @default.
• W3216996917 cites W2022915141 @default.
• W3216996917 cites W2023400970 @default.
• W3216996917 cites W2023475165 @default.
• W3216996917 cites W2024632154 @default.
• W3216996917 cites W2027122161 @default.
• W3216996917 cites W2027996893 @default.
• W3216996917 cites W2028070629 @default.
• W3216996917 cites W2028499970 @default.
• W3216996917 cites W2029024482 @default.
• W3216996917 cites W2055522016 @default.
• W3216996917 cites W2060914137 @default.
• W3216996917 cites W2061245347 @default.
• W3216996917 cites W2080886726 @default.
• W3216996917 cites W2083780116 @default.
• W3216996917 cites W2092126505 @default.
• W3216996917 cites W2112017750 @default.
• W3216996917 cites W2122496402 @default.
• W3216996917 cites W2126819771 @default.
• W3216996917 cites W2133990480 @default.
• W3216996917 cites W2134241543 @default.
• W3216996917 cites W2147703441 @default.
• W3216996917 cites W2150869884 @default.
• W3216996917 cites W2242269017 @default.
• W3216996917 cites W2300474821 @default.
• W3216996917 cites W2314104404 @default.
• W3216996917 cites W2315637777 @default.
• W3216996917 cites W2616656881 @default.
• W3216996917 cites W2726797587 @default.
• W3216996917 cites W2752700139 @default.
• W3216996917 cites W2789751646 @default.
• W3216996917 cites W2791218182 @default.
• W3216996917 cites W2793344462 @default.
• W3216996917 cites W2800120410 @default.
• W3216996917 cites W2807645461 @default.
• W3216996917 cites W2808970502 @default.
• W3216996917 cites W2809259644 @default.
• W3216996917 cites W2892252688 @default.
• W3216996917 cites W2904289597 @default.
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• W3216996917 cites W2948030792 @default.
• W3216996917 cites W2963389298 @default.
• W3216996917 cites W2981063960 @default.
• W3216996917 cites W2981840206 @default.
• W3216996917 cites W2986355473 @default.
• W3216996917 cites W3019451705 @default.
• W3216996917 cites W3027705944 @default.
• W3216996917 cites W3093674893 @default.
• W3216996917 cites W3112713772 @default.
• W3216996917 cites W3119086294 @default.
• W3216996917 cites W3130942636 @default.
• W3216996917 cites W3133818266 @default.
• W3216996917 cites W3137547425 @default.
• W3216996917 cites W3155593272 @default.
• W3216996917 cites W3160524862 @default.
• W3216996917 cites W3164184246 @default.
• W3216996917 cites W3165899271 @default.
• W3216996917 cites W3167417310 @default.
• W3216996917 cites W3173522932 @default.
• W3216996917 cites W3175049696 @default.
• W3216996917 cites W3181013390 @default.
• W3216996917 cites W3181155454 @default.
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• W3216996917 cites W3195110676 @default.
• W3216996917 cites W3195422782 @default.
• W3216996917 cites W3200840429 @default.
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• W3216996917 doi "https://doi.org/10.1016/j.jtice.2021.11.001" @default.
• W3216996917 hasPublicationYear "2022" @default.
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