Abstract
This paper describes a method for drug discrimination with near infrared spectroscopy based on SSAE-ELM. ELM instead of the BP was introduced to fine-tuning SSAE, which can reduce the training time of SSAE and improve the practical application of the deep learning network. The work in the paper used near infrared diffuse reflectance spectroscopy to identify Aluminum-plastic packaging of cefixime tablets drugs from different manufacturers as examples to verify the proposed method. Specifically, we adopted SSAE-ELM to binary and multi-class classification discriminations with different sizes of drug dataset. Extensive experiments were conducted to compare the performances of the proposed method with ELM, BP, SVM and SWELM. The results indicate that the proposed method not only can obtain high discrimination accuracy with superior stability but also reduce the training time of SSAE in binary and multi-class classification. Therefore, the SSAE-ELM classifier can achieve an optimal and generalized solution for spectroscopy identification.
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Acknowledgements
We are grateful for the financial support of the National Natural Science Foundation of China (No. 21365008, 61562013).
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Zhang, W. et al. (2018). Near Infrared Spectroscopy Drug Discrimination Method Based on Stacked Sparse Auto-Encoders Extreme Learning Machine. In: Lu, H., Xu, X. (eds) Artificial Intelligence and Robotics. Studies in Computational Intelligence, vol 752. Springer, Cham. https://doi.org/10.1007/978-3-319-69877-9_22
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DOI: https://doi.org/10.1007/978-3-319-69877-9_22
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