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
{"title":"处理科学研究中的溯源识别计算机算法","authors":"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","doi":"10.7769/gesec.v15i7.3887","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":145860,"journal":{"name":"Revista de Gestão e Secretariado","volume":"13 2","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Processing of computer algorithms for traceability identification in scientific research\",\"authors\":\"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\",\"doi\":\"10.7769/gesec.v15i7.3887\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":145860,\"journal\":{\"name\":\"Revista de Gestão e Secretariado\",\"volume\":\"13 2\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-07-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Revista de Gestão e Secretariado\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.7769/gesec.v15i7.3887\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Revista de Gestão e Secretariado","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.7769/gesec.v15i7.3887","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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.