전자파 인체영향 연구논문에 대한 연구형태 자동분류 연구.

This study presents a research category classification method for scientific literature on the human health risk of electromagnetic fields. The proposed method uses the document embedding model, Doc2Vec, to convert the research article text data into a vector, through which it trains three binary classifiers to determine whether the article belongs to animal experiments, cell experiments, or epidemiological studies. Finally, by using the trained binary classifiers, the research articles were automatically classified into one of three research categories. The proposed method was implemented for performance evaluation with 120 research articles on the human health risk of electromagnetic fields. Among the 120 research articles, 90 were used as a training dataset, while 30 were used as a test dataset. The implementation results showed that the proposed method can classify the research articles on the human health risk of electromagnetic fields with 88% accuracy on average. [ABSTRACT FROM AUTHOR]