处理科学研究中的溯源识别计算机算法

Hilda Carolina de Jesus Rios Fraga, Vagner de Oliveira Machado, Julian Reina, André Lucas Coelho dos Santos, Bruno Santos Oliveira, Antônio Carlos dos Santos Souza
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引用次数: 0

摘要

科学研究是开发新知识的重要组成部分,也是一项多学科、耗时长、易出错的工作,因此必须在可验证的条件下进行,才能有助于安全决策。我们的目标是从科学文章中自动提取高质量信息,提供可靠、可追溯和安全的知识。为此,本研究从识别可追溯性和可重复性模式的角度出发,使用算法自然语言处理方法,展示了对科学文章中所含信息的识别,无论其研究领域如何。因此,在这项工作中,使用了 Naive Bayes (NB)、余弦相似度 (Cosine Similarity)、词袋 (BOW) 和神经网络 (RN) 等语言。结果,可以在分析的文章中识别出九种可追溯性模式,并利用准确率不低于 70% 的算法提出了一个人工智能模型,证明了所分析科学文章的可追溯性和可重现性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Processing of computer algorithms for traceability identification in scientific research
Considering that scientific research is an essential part of the development of new knowledge and a multidisciplinary, time-consuming and error-prone task, it must be conducted under verifiable conditions in order to contribute to safe decision-making. The aim is to extract quality information from scientific articles automatically, presenting reliable, traceable and safe knowledge. To this end, this study investigates the perspective of identifying traceability and reproducibility patterns, using algorithmic Natural Language Processing methods, to demonstrate the identification of information contained in scientific articles, regardless of the research area. Therefore, in this work, the languages ​​Naive Bayes (NB), Cosine Similarity, Bag of Words (BOW) and Neural Networks (RN) were used for this purpose. As a result, it was possible to identify nine traceability patterns in the articles analyzed and propose an Artificial Intelligence model using algorithms with a minimum accuracy of 70%, demonstrating the traceability and reproducibility of the scientific articles analyzed.
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