Publications

A machine learning approach for forecasting the efficacy of pyridazine corrosion inhibitors

Theoretical Chemistry Account, 144 (8), 2024 – Springer 

Robust Machine Learning for Predicting Thermal Stability of Metal-Organic Framework

 Chemistry Africa, 2024 – Springer

Investigation of Corrosion Inhibition Capability of Pyridazine Compounds via Ensemble Learning

Journal of Materials Engineering and Performances, 2024 – Springer

iLDA: A new dimensional reduction method for non-Gaussian and small sample size datasets

Egyptian Informatics Journal, 27, 2024, 100533 – Elsevier

A feature restoration for machine learning on anti-corrosion materials

Case Studies in Chemical and Environmental Engineering, 10, (2024), 100902 – Elsevier

Harnessing the XGBoost Ensemble for Intelligent Prediction and Identification of Factors with a High Impact on Air Quality: A Case Study of Urban Areas in Jakarta Province, Indonesia

Data Science and Emerging Technologies, 2024, 319–334 – Springer

Maximizing complex features to minimize the detectability of content-adaptive steganography

Multimedia Tools and Applications, 2024 – Springer

A comprehensive approach utilizing quantum machine learning in the study of corrosion inhibition on quinoxaline compounds

Artificial Intelligence Chemistry, 2 (2), (2024), 100073 – Elsevier

Machine learning for pyrimidine corrosion inhibitor small dataset

Theoretical Chemistry Account,  143, (65), 2024 – Springer

Implementation of quantum machine learning in predicting corrosion inhibition efficiency of expired drugs

Variational quantum circuit-based quantum machine learning approach for predicting corrosion inhibition efficiency of pyridine-quinoline compounds

Materials Today Quantum, 2, 100007 (2024) – Elsevier

Development of quantum machine learning to evaluate the corrosion inhibition capability of pyrimidine compounds

Materials Today Communications, 39, 108758 (2024) – Elsevier

SMILES-based machine learning enables the prediction of corrosion inhibition capacity

MRS Communications, 14, 379–387,  (2024) – Springer

Prediction of Corrosion Inhibition Efficiency Based on Machine Learning for Pyrimidine Compounds: A Comparative Study of Linear and Non-linear Algorithms

KnE Engineering, 68-77, (2024)

Implementation of Polynomial Functions to Improve the Accuracy of Machine Learning Models in Predicting the Corrosion Inhibition Efficiency of Pyridine-Quinoline Compounds as Corrosion Inhibitors

KnE Engineering, 78-87, (2024)

Prediction of anti-corrosion performance of new triazole derivatives via machine learning

Computational and Theoretical Chemistry, 1236, 114599 (2024) – Elsevier

A machine learning approach to predict the efficiency of corrosion inhibition by natural product-based organic inhibitors

Physica Scripta, 99(3), 036006 (2023) – IoP Science

Investigation of Best QSPR-Based Machine Learning Model to Predict Corrosion Inhibition Performance of Pyridine-Quinoline Compounds

Journal of Physics: Conf. Ser., 2673(1), 012014 (2023)

https://doi.org/10.1088/1742-6596/2673/1/012014

Machine learning investigation to predict corrosion inhibition capacity of new amino acid compounds as corrosion inhibitors

Results in Chemistry, 6, 101126 (2023)

https://doi.org/10.1016/j.rechem.2023.101126

Data-driven investigation to model the corrosion inhibition efficiency of Pyrimidine-Pyrazole hybrid corrosion inhibitors

Computational and Theoretical Chemistry, 1229, 114307 (2023)

https://doi.org/10.1016/j.comptc.2023.114307

A combination of machine learning model and density functional theory method to predict corrosion inhibition performance of new diazine derivative compounds

Materials Today Communications, 35, 106402 (2023)

https://doi.org/10.1016/j.mtcomm.2023.106402

A first principle study of adsorption and dissociation of H2 molecule on MoS2 and Ni-promoted MoS2 surfaces

AIP Conf. Proc., 2748, 020050 (2023)

https://doi.org/10.1063/5.0138531

A machine learning approach for corrosion small datasets

npj Materials Degradation, 7(18), (2023) https://doi.org/10.1038/s41529-023-00336-7