Modeling of Semiconductors Refractive Indices Using Hybrid Chemometric Model

Modeling of Semiconductors Refractive Indices Using Hybrid Chemometric Model

Luqman E. Oloore Taoreed O. Owolabi  Sola Fayose  Muideen Adegoke  Kabiru O. Akande  Sunday O. Olatunji 

Department of Physics and Engineering Physics, Obafemi Awolowo University, Ile-Ife A234, Nigeria

Physics and Electronics Department, Adekunle Ajasin University, Akungba Akoko 342111, Nigeria

Physics Department, King Fahd University of Petroleum and Minerals, Dhahran 34464, Saudi Arabia

Department of System Engineering, King Fahd University of Petroleum and Minerals, Dhahran 34464, Saudi Arabia

Institute for Digital Communications, School of Engineering, University of Edinburgh, Edinburgh, Postal code EH8 9AB, United Kingdom

Computer Science Department, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam 31433, Saudi Arabia

Corresponding Author Email:
12 August 2018
| |
20 September 2018
| | Citation



A support vector regression (SVR)-based model and its hybrid (HSVR), both optimized with gravitational search algorithm (GSA), for accurate estimation of refractive indices of semiconductors using their energy gaps as descriptors are presented. The proposed GSA-HSVR model demonstrates a better predictive and generalization ability than ordinary GSA-SVR model. The performances of the proposed models are compared with the existing Moss and Ravindra’s models and a better agreement with the experimental values were observed coupled with lowest mean absolute error of GSA-HSVR model. Considerable high coefficient of correlation and very small root mean square error also characterize GSA-HSVR model. The proposed GSA-HSVR model proves its identity and effectiveness compared to existing predictive models, in terms of accuracy, using simply accessible descriptor. It also reduces the estimation challenges accompanying determination of refractive indices of semiconductors.


support vector regression, gravitational search algorithm, energy gaps, refractive indices and hybrid intelligent

1. Introduction
2. Theoretical Background
3. Development of GSA-SVR and GSA-HSVR Models
4. Results and Discussion
5. Conclusions

The contribution of Akande O Kabiru is acknowledged.


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