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Machine Learning for Predatory Journals by kozasurche is a document available to read on EtoBox.

What is Machine Learning for Predatory Journals about?

This conference paper discusses the use of machine learning to classify predatory journals, which pose a threat to academic integrity and research quality. The authors developed a classification system that utilizes text content and keywords from journal websites to distinguish between legitimate and predatory journals, achieving a recall rate exceeding 90%. The research aims to provide scholars with a reliable method to identify predatory journals and avoid submitting their work to such publishers.

Author

kozasurche

Language

EN