Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
The evolution of the information and communication technologies has dramatically increased the number of people with access of Internet, which lead to increase fake news. This amplified the old problem of fake news, which became a major concern nowadays due to the negative impact it brings to the communities. In order to tackle the rise and spreading of fake news. We created a machine learning model using a verified dataset from Kaggle which classifies the data using term frequency and inverse document frequency to predict whether the given news is fake or not. For optimizing the output, now we are building a new deep learning model and will compare the accuracy between the previous Multinomial Naive Bayes and current Neural Network. For testing this model we will create an integration of the project into the web to test and produce the output.
"Optimization of Neural Network for News Classification", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.8, Issue 7, page no.358 - 361, July-2023, Available :http://www.ijrti.org/papers/IJRTI2307054.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator