Quantitative structure–activity relationship model for amino acids as corrosion inhibitors based on the support vector machine and molecular design

By Hongxia Zhao, Xiuhui Zhang, Lin Ji, Haixiang Hu, Qianshu Li. 2014

162 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Materials Chemistry, Metals and Alloys

View Paper: https://www.semanticscholar.org/paper/d9ee614c38836fa9d593fc2a36ec059ce6d654f0

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Civil and Structural Engineering

1. Zakariya Yahya Algamal
2. A. Jmiai
3. Moncef L. Nehdi
4. L. Bazzi
5. S. Issami
6. B. Ibrahimi

In Materials Chemistry

1. Zakariya Yahya Algamal
2. A. Jmiai
3. Moncef L. Nehdi
4. L. Bazzi
5. S. Issami
6. B. Ibrahimi

In Metals and Alloys

1. Zakariya Yahya Algamal
2. A. Jmiai
3. L. Bazzi
4. S. Issami
5. B. Ibrahimi

Citations in the corpus

• Amino acids and their derivatives as corrosion inhibitors for metals and alloys (2017, methodology): “However, there is a trend in recent works on amino acids to use some derived mathematical methods from artificial intelligence, called ‘‘machine learning methods”, like: support vector machine (Zhao et al., 2014), genetic function approximation (Aouniti et al.”
• Machine Learning Algorithms in Civil Structural Health Monitoring: A Systematic Review (2020, background): “Causes of degradation of bridge structures include materials aging [49], corrosion of metals [137] and structural supports [140], mechanical overloading and other damage mechanisms [24].”
• Quantitative structure–activity relationship model for prediction study of corrosion inhibition efficiency using two‐stage sparse multiple linear regression (2016, methodology): “[21] developed a non-linear QSAR model for the corrosion IE of 19 amino acids based on quantum descriptors using the support vector machine.”