首页|Howard University Researcher Furthers Understanding of Machine Learning (Ensembl e Techniques for Robust Fake News Detection: Integrating Transformers, Natural L anguage Processing, and Machine Learning)
Howard University Researcher Furthers Understanding of Machine Learning (Ensembl e Techniques for Robust Fake News Detection: Integrating Transformers, Natural L anguage Processing, and Machine Learning)
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Fresh data on artificial intelligence are presented in a new report. According to news originating from Washington, Di strict of Columbia, by NewsRx correspondents, research stated, "The proliferatio n of fake news across multiple modalities has emerged as a critical challenge in the modern information landscape, necessitating advanced detection methods." Financial supporters for this research include Nsf. The news correspondents obtained a quote from the research from Howard Universit y: "This study proposes a comprehensive framework for fake news detection integr ating text, images, and videos using machine learning and deep learning techniqu es. The research employs a dual-phased methodology, first analyzing textual data using various classifiers, then developing a multimodal approach combining BERT for text analysis and a modified CNN for visual data. Experiments on the ISOT f ake news dataset and MediaEval 2016 image verification corpus demonstrate the ef fectiveness of the proposed models. For textual data, the Random Forest classifi er achieved 99% accuracy, outperforming other algorithms."
Howard UniversityWashingtonDistrict of ColumbiaUnited StatesNorth and Central AmericaCyborgsEmerging Technol ogiesMachine LearningNatural Language Processing
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