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- W4386014783 abstract "It has been well understood that fake news recognition is a persistent challenge to cognitive computing and autonomous systems in general, and to Artificial Intelligence (AI), machine knowledge learning, and computational linguistics in particular. This work develops an Autonomous Fake News Recognition (AFNR) system by cognitive computing theories underpinned by Intelligent Mathematics (IM) such as concept algebra and semantic algebra. A training-free methodology and a formal algorithm for Differential Sematic Analysis (DSA) are designed in Real-Time Process Algebra (RTPA) and implemented in MATLAB. The AFNR system is implemented in the Anaconda environment with Python, the natural language toolkit (NLTK), and an English parser – Spacy. Compared to the classical data-driven neural network methodologies, AFNR and DSA have demonstrated a significant improvement against the level of accuracy over the randomly selected and large-scale benchmark of a fake news database. The DSA methodology for fake news recognition has enabled autonomous machine knowledge learning and semantic comprehension towards differential and robust semantic analyses for fake news in natural languages. The AFNR system has reached an accuracy level of 70.1%, which over performs the top ranked teams in DataCup’19 with the highest reported accuracy of 55.0%." @default.
- W4386014783 created "2023-08-21" @default.
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- W4386014783 date "2023-01-01" @default.
- W4386014783 modified "2023-09-24" @default.
- W4386014783 title "An Autonomous Fake News Recognition System by Semantic Learning and Cognitive Computing" @default.
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- W4386014783 doi "https://doi.org/10.1007/978-3-662-67868-8_6" @default.
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