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Investigation of an amino acid compound as a corrosion inhibitor via ensemble learning

Lingga Dewi, Adhe and Akrom, Muhamad (2024) Investigation of an amino acid compound as a corrosion inhibitor via ensemble learning. Journal of Multiscale Materials Informatics, 1 (2). pp. 14-18. ISSN 3047-5724

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Abstract

In this study, we evaluate the performance of various machine learning models, including Random Forest (RF), Bagging (BAG), AdaBoost (ADA), Artificial Neural Network (ANN), and Support Vector Machine (SVM), using metrics such as R², Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). The results indicate that AdaBoost (ADA) achieves the highest performance with an R² of 0.999, RMSE of 2.32, and MAE of 2.24, making it the most accurate model with the smallest prediction errors. Bagging (BAG) also performs exceptionally well, with an R2 of 0.996, RMSE of 3.09, and MAE of 2.92. The Artificial Neural Network (ANN) exhibits a high R2 of 0.999, though RMSE and MAE values are not provided. Random Forest (RF) and Support Vector Machine (SVM) show good performance with R² values of 0.982 and 0.970, respectively, but are outperformed by the ensemble methods. The findings underscore the superiority of ensemble techniques, particularly AdaBoost, in achieving high predictive accuracy and minimal errors in this context.

Item Type: Article
Subjects: Q Science > Q Science (General)
T Technology > T Technology (General)
Depositing User: dladmin fts
Date Deposited: 29 Nov 2024 05:29
Last Modified: 29 Nov 2024 05:29
URI: https://dl.futuretechsci.org/id/eprint/65

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