Effect of Virtual Sample Generation in Predicting Corrosion Inhibition Efficiency on Pyridazine
DOI:
https://doi.org/10.30871/jaic.v9i2.9131Keywords:
Corrosion, Inhibitor, Linear Interpolation, Gaussian Noise Augmentation, Virtual Sample GenerationAbstract
The purpose of this research is to study how the application of virtual sample generation using the linear interpolation and gaussian noise augmentation method impacts the improvement of prediction model performance in the case of corrosion inhibition efficiency using pyridazine. Random Forest Regressor, Gradient Boosting Regressor, and Bagging Regressor are the models used. The coefficient of determination (R2) values for each model are -0.06, 0.05, and 0.12 on the initial data; the RMSE values are 34.80, 32.90, and 31.65, respectively. After the use of virtual sample development, the R2 values significantly increased to 0.99, 0.96, and 0.99, while the RMSE values significantly decreased to 1.59, 2.88, and 1.25. The research results show that the linear interpolation method can enrich the dataset without altering the data distribution pattern, this method significantly improves the model's accuracy. This performance improvement demonstrates the ability of virtual sample generation to overcome the limitations of the original data; ultimately, this results in a more accurate and reliable predictive model. In the field of material efficiency prediction especially for material technology applications and corrosion control this research helps develop data augmentation methods for similar cases.
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[1] E. H. El Assiri et al., “Development and validation of QSPR models for corrosion inhibition of carbon steel by some pyridazine derivatives in acidic medium,” Heliyon, vol. 6, no. 10, Oct. 2020, doi: 10.1016/j.heliyon.2020.e05067.
[2] K. Rasheeda, V. D. P. Alva, P. A. Krishnaprasad, and S. Samshuddin, “Pyrimidine derivatives as potential corrosion inhibitors for steel in acid medium – An overview,” International Journal of Corrosion and Scale Inhibition, vol. 7, no. 1, pp. 48–61, 2018, doi: 10.17675/2305-6894-2018-7-1-5.
[3] O. S. I. Fayomi, I. G. Akande, and S. Odigie, “Economic Impact of Corrosion in Oil Sectors and Prevention: An Overview,” in Journal of Physics: Conference Series, Institute of Physics Publishing, Dec. 2019. doi: 10.1088/1742-6596/1378/2/022037.
[4] R. S. Iyer, N. S. Iyer, R. A. P, and A. Joseph, “Harnessing machine learning and virtual sample generation for corrosion studies of 2-alkyl benzimidazole scaffold small dataset with an experimental validation,” J Mol Struct, vol. 1306, p. 137767, 2024, doi: https://doi.org/10.1016/j.molstruc.2024.137767.
[5] M. Akrom, “Investigation Of Natural Extracts As Green Corrosion Inhibitors In Steel Using Density Functional Theory,” 2022.
[6] T. W. Quadri et al., “Development of QSAR-based (MLR/ANN) predictive models for effective design of pyridazine corrosion inhibitors,” Mater Today Commun, vol. 30, p. 103163, 2022, doi: https://doi.org/10.1016/j.mtcomm.2022.103163.
[7] M. Akrom, “Green Corrosion Inhibitors for Iron Alloys: A Comprehensive Review of Integrating Data-Driven Forecasting, Density Functional Theory Simulations, and Experimental Investigation,” Journal of Multiscale Materials Informatics, vol. 1, no. 1, pp. 22–37, Apr. 2024, doi: 10.62411/jimat.v1i1.10495.
[8] M. Akrom, S. Rustad, and H. K. Dipojono, “Variational quantum circuit-based quantum machine learning approach for predicting corrosion inhibition efficiency of pyridine-quinoline compounds,” Materials Today Quantum, vol. 2, p. 100007, Jun. 2024, doi: 10.1016/j.mtquan.2024.100007.
[9] T. W. Quadri et al., “Predicting protection capacities of pyrimidine-based corrosion inhibitors for mild steel/HCl interface using linear and nonlinear QSPR models,” J Mol Model, vol. 28, no. 9, p. 254, 2022, doi: 10.1007/s00894-022-05245-1.
[10] D.-C. Li and I.-H. Wen, “A genetic algorithm-based virtual sample generation technique to improve small data set learning,” Neurocomputing, vol. 143, pp. 222–230, 2014, doi: https://doi.org/10.1016/j.neucom.2014.06.004.
[11] S. Wu, B. Wang, J. Zhao, M. Zhao, K. Zhong, and Y. Guo, “Virtual sample generation and ensemble learning based image source identification with small training samples,” International Journal of Digital Crime and Forensics, vol. 13, no. 3, pp. 34–46, May 2021, doi: 10.4018/IJDCF.20210501.oa3.
[12] M. R. Rosyid, L. Mawaddah, A. P. Santosa, M. Akrom, S. Rustad, and H. K. Dipojono, “Implementation of quantum machine learning in predicting corrosion inhibition efficiency of expired drugs,” Mater Today Commun, vol. 40, p. 109830, 2024, doi: https://doi.org/10.1016/j.mtcomm.2024.109830.
[13] T. Sutojo, S. Rustad, M. Akrom, A. Syukur, G. F. Shidik, and H. K. Dipojono, “A machine learning approach for corrosion small datasets,” Npj Mater Degrad, vol. 7, no. 1, p. 18, 2023, doi: 10.1038/s41529-023-00336-7.
[14] Q.-X. Zhu, Z.-H. Wang, Y.-L. He, and Y. Xu, “A Monte Carlo and Kernel Density Estimation based virtual sample generation method for small data modeling problem,” in 2020 Chinese Automation Congress (CAC), 2020, pp. 1123–1128. doi: 10.1109/CAC51589.2020.9326486.
[15] W. Herowati et al., “Prediction of Corrosion Inhibition Efficiency Based on Machine Learning for Pyrimidine Compounds: A Comparative Study of Linear and Non-linear Algorithms,” KnE Engineering, Mar. 2024, doi: 10.18502/keg.v6i1.15350.
[16] S. Budi, M. Akrom, G. A. Trisnapradika, T. Sutojo, and W. A. E. Prabowo, “Optimization of Polynomial Functions on the NuSVR Algorithm Based on Machine Learning: Case Studies on Regression Datasets,” Scientific Journal of Informatics, vol. 10, no. 2, pp. 151–158, May 2023, doi: 10.15294/sji.v10i2.43929.
[17] M. Akrom, S. Rustad, and H. K. Dipojono, “SMILES-based machine learning enables the prediction of corrosion inhibition capacity,” MRS Commun, vol. 14, no. 3, pp. 379–387, 2024, doi: 10.1557/s43579-024-00551-6.
[18] M. Akrom, S. Rustad, and H. Kresno Dipojono, “Machine learning investigation to predict corrosion inhibition capacity of new amino acid compounds as corrosion inhibitors,” Results Chem, vol. 6, Dec. 2023, doi: 10.1016/j.rechem.2023.101126.
[19] C. Cui, J. Tang, H. Xia, J. Qiao, and W. Yu, “Virtual sample generation method based on generative adversarial fuzzy neural network,” Neural Comput Appl, vol. 35, no. 9, pp. 6979–7001, 2023, doi: 10.1007/s00521-022-08104-5.
[20] M. Akrom et al., “DFT and microkinetic investigation of oxygen reduction reaction on corrosion inhibition mechanism of iron surface by Syzygium Aromaticum extract,” Appl Surf Sci, vol. 615, p. 156319, 2023, doi: https://doi.org/10.1016/j.apsusc.2022.156319.
[21] M. Akrom, S. Rustad, H. K. Dipojono, and R. Maezono, “A comprehensive approach utilizing quantum machine learning in the study of corrosion inhibition on quinoxaline compounds,” Artificial Intelligence Chemistry, vol. 2, no. 2, p. 100073, 2024, doi: https://doi.org/10.1016/j.aichem.2024.100073.
[22] M. Akrom, S. Rustad, and H. K. Dipojono, “Development of quantum machine learning to evaluate the corrosion inhibition capability of pyrimidine compounds,” Mater Today Commun, vol. 39, p. 108758, 2024, doi: https://doi.org/10.1016/j.mtcomm.2024.108758.
[23] M. Akrom, S. Rustad, and H. K. Dipojono, “Investigation of Corrosion Inhibition Capability of Pyridazine Compounds via Ensemble Learning,” J Mater Eng Perform, 2024, doi: 10.1007/s11665-024-10129-x.
[24] J. Yang, X. Yu, Z.-Q. Xie, and J.-P. Zhang, “A novel virtual sample generation method based on Gaussian distribution,” Knowl Based Syst, vol. 24, no. 6, pp. 740–748, 2011, doi: https://doi.org/10.1016/j.knosys.2010.12.010.
[25] M. Akrom, S. Rustad, A. G. Saputro, and H. K. Dipojono, “Data-driven investigation to model the corrosion inhibition efficiency of Pyrimidine-Pyrazole hybrid corrosion inhibitors,” Comput Theor Chem, vol. 1229, p. 114307, 2023, doi: https://doi.org/10.1016/j.comptc.2023.114307.
[26] T. W. Quadri et al., “Computational insights into quinoxaline-based corrosion inhibitors of steel in HCl: Quantum chemical analysis and QSPR-ANN studies,” Arabian Journal of Chemistry, vol. 15, no. 7, p. 103870, 2022, doi: https://doi.org/10.1016/j.arabjc.2022.103870.
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