AN Identification and Prediction Model Based on PSO

Hui Wang, Tie Cai, Dongsheng Cheng, Kangshun Li and Ying Zhou
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Hui Wang: Shenzhen Institute of Information Technology, China
Tie Cai: Shenzhen Institute of Information Technology, China
Dongsheng Cheng: Shenzhen Institute of Information Technology, China
Kangshun Li: Dongguan City University, China
Ying Zhou: Shenzhen Institute of Information Technology, China

International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 2024, vol. 18, issue 1, 1-15

Abstract: According to the spectral characteristics of different Chinese medicinal materials, the types of Chinese medicinal materials and the origin of Chinese medicinal materials are identified. Construct a fragmented clustering model. Firstly, the mid-infrared sample data is preprocessed, the Laida criterion model is established, and the abnormal data is eliminated; then the slicing model is used to divide the spectral wave into different regions according to the spectral characteristics. The data of each slice is clustered through the k-means clustering model. The origin of Chinese medicinal materials is identified by the support vector machine model. The data of Chinese medicinal materials with a known origin of a certain type of Chinese medicinal materials is used as the training sample set, and the data of Chinese medicinal materials with unknown origin is used as the test set.

Date: 2024
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