在人工智能时代检测伪造的文件
Mehdi Dadkhah1,2, Marilyn H Oermann3, Mihály Hegedüs4
1Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Amritapuri, Kerala, India.
Diagnosis (Berlin, Germany)
|August 17, 2023
概括
这项研究引入了一种机器学习方法,使用决策树来检测假科学论文,这是人工智能加剧的日益严重的问题. 该方法有效地识别出欺诈性手稿,帮助研究人员和出版商确保研究完整性.
科学领域:
- 科学完整性 科学完整性
- 研究伦理研究伦理学
- 图书统计学 图书统计学
背景情况:
- 造纸厂制造科学论文并销售作者权,在医疗保健和其他领域构成了重大挑战.
- 越来越多的人工智能 (AI) 在纸厂手稿生成中的使用加剧了虚假出版物的问题.
- 目前用于检测欺诈性研究的现有方法不足以解决人工智能驱动的造纸厂运营问题.
研究的目的:
- 开发和介绍一种用于检测伪造科学论文的新方法.
- 利用机器学习,特别是决策树,来识别伪造的研究.
- 为作者,编辑,出版商和临床医生提供一个工具来验证研究真实性.
主要方法:
- 采用了利用决策树的机器学习方法.
- 模型培训和验证的数据来自Web of Science和各种科学期刊.
- 决策树模型被训练来区分真实和伪造的文件.
主要成果:
- 开发的方法有效地识别了假的科学论文.
- 一个案例研究表明了基于决策树的检测方法的实际实用性和准确性.
- 结果证实了拟议方法在现实场景中的可行性.
结论:
- 提出的方法提供了一种可靠的方式来检测各种科学学科的假论文.
- 作者,编辑和出版商可以利用这个工具来调查个人手稿或分析研究收藏.
- 临床医生和其他利益相关者可以使用这种方法来确保发表的文章代表真实,经过同行评审的研究,保护基于证据的实践.
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