Research on Optimization of Process Parameters of Traditional Chinese Medicine Based on Data Mining Technology

Data mining technology and methods are used to effectively optimize manufacturing process parameters due to the complexity and uniqueness of the process parameters. The data-mining-based optimization method for traditional Chinese medicine (TCM) process parameters is presented, along with a list of...

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Veröffentlicht in:Computational intelligence and neuroscience Jg. 2022; S. 1 - 9
Hauptverfasser: Li, Xue, Yue, Hao, Yin, Jinlong, Song, Yan, Yin, Jinling, Zhu, Xinlei, Huang, Bingchang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Hindawi 02.03.2022
John Wiley & Sons, Inc
Schlagworte:
Algorithms
Analysis
Asthma
Classification
Control theory
Cosmetics
Data Mining
Decision trees
Discriminant analysis
Drugs
Interdisciplinary subjects
Laboratories
Manufacturing
Medical diagnosis
Medical records
Medical research
Medicine, Chinese
Medicine, Chinese Traditional
Modernization
Optimization
Optimization models
Pharmaceutical industry
Process parameters
Product quality
Solvents
Support vector machines
Technology
Technology application
Traditional Chinese medicine
Weight analysis
China
ISSN:1687-5265, 1687-5273, 1687-5273
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Zusammenfassung:Data mining technology and methods are used to effectively optimize manufacturing process parameters due to the complexity and uniqueness of the process parameters. The data-mining-based optimization method for traditional Chinese medicine (TCM) process parameters is presented, along with a list of process parameters that have shown to be effective in actual production. The influencing factors of process parameters are analyzed and modeled using an attribute weight analysis and classification analysis algorithm. The optimization scheme of process parameters that meet the requirements is selected, and an example is given for verification, by selecting data records that fall within a certain error range and incorporating the rules of association knowledge discovery. The support vector classification algorithm has a higher accuracy, despite the algorithm's results being understandable. The support vector regression algorithm developed a reliable process optimization model.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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Academic Editor: Xin Ning
ISSN:1687-5265
1687-5273
1687-5273
DOI:10.1155/2022/2278416