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Synopsis
Document from the year 2024 in the subject Computer Sciences - Computational linguistics, grade: 10, VIT University (VIT), course: Computer Science, language: English, abstract: The fake news on social media and various other media is wide spreading and is a mat- ter of serious concern due to its ability to cause a lot of social and national damage with destructive impacts. A lot of research is already focused on detecting it. Here we take three data sets namely " fake news and real news", "ISOT" and "LIAR". We try to implement six machine learning models on these data sets and trying to find their accu- racy and precision. The models we uses are Decision Tree, Random Forest, Support vector machine, Naive Bayes, KNN and LSTM. WE use tools like python scikit learn and NLP. Python scikit library can be used for feature extraction and textual analysis. We tries to find out which model works best on which data keeping the complexity of the data in mind. We would like to find a perfect model for any of the regional language. But the constrain is the availability of good dataset . So we try to propose a new dataset.
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About the Author
Balamurugan Rengeswaran received the B.E. degree in Computer Science and Engineering in 2010 from the Government College of Engineering, Salem and the M.E. degree in Computer Science and Engineering in 2012 from the Bannari Amman Institute of Technology, Sathyamangalam. He is completed his Ph.D in Information and Communication Engineering in 2016 from Anna University, Chennai. Currently he is working as an Associate Professor in Department of Computer Science and Engineering in Vellore Institute of Technology, Vellore. He has published more than 20 papers in various international journals and conferences. His areas of interest include data mining and meta-heuristic optimization techniques.
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A Study of Tackling Fake News with Machine Learning Approaches
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Document from the year 2024 in the subject Computer Sciences - Computational linguistics, grade: 10, VIT University (VIT), course: Computer Science, language: English, abstract: The fake news on social media and various other media is wide spreading and is a mat- ter of serious concern due to its ability to cause a lot of social and national damage with destructive impacts. A lot of research is already focused on detecting it. Here we take three data sets namely ' fake news and real news', 'ISOT' and 'LIAR'. We try to implement six machine learning models on these data sets and trying to find their accu- racy and precision. The models we uses are Decision Tree, Random Forest, Support vector machine, Naive Bayes, KNN and LSTM. WE use tools like python scikit learn and NLP. Python scikit library can be used for feature extraction and textual analysis. We tries to find out which model works best on which data keeping the complexity of the data in mind. We would like to find a perfect model for any of the regional language. But the constrain is the availability of good dataset . So we try to propose a new dataset. 64 pp. Englisch. Seller Inventory # 9783389024041
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A Study of Tackling Fake News with Machine Learning Approaches
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Document from the year 2024 in the subject Computer Sciences - Computational linguistics, grade: 10, VIT University (VIT), course: Computer Science, language: English, abstract: The fake news on social media and various other media is wide spreading and is a mat- ter of serious concern due to its ability to cause a lot of social and national damage with destructive impacts. A lot of research is already focused on detecting it. Here we take three data sets namely ' fake news and real news', 'ISOT' and 'LIAR'. We try to implement six machine learning models on these data sets and trying to find their accu- racy and precision. The models we uses are Decision Tree, Random Forest, Support vector machine, Naive Bayes, KNN and LSTM. WE use tools like python scikit learn and NLP. Python scikit library can be used for feature extraction and textual analysis. We tries to find out which model works best on which data keeping the complexity of the data in mind. We would like to find a perfect model for any of the regional language. But the constrain is the availability of good dataset . So we try to propose a new dataset.GRIN Publishing GmbH, Waltherstraße 23, 80337 München 64 pp. Englisch. Seller Inventory # 9783389024041
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A Study of Tackling Fake News with Machine Learning Approaches
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Document from the year 2024 in the subject Computer Sciences - Computational linguistics, grade: 10, VIT University (VIT), course: Computer Science, language: English, abstract: The fake news on social media and various other media is wide spreading and is a mat- ter of serious concern due to its ability to cause a lot of social and national damage with destructive impacts. A lot of research is already focused on detecting it. Here we take three data sets namely ' fake news and real news', 'ISOT' and 'LIAR'. We try to implement six machine learning models on these data sets and trying to find their accu- racy and precision. The models we uses are Decision Tree, Random Forest, Support vector machine, Naive Bayes, KNN and LSTM. WE use tools like python scikit learn and NLP. Python scikit library can be used for feature extraction and textual analysis. We tries to find out which model works best on which data keeping the complexity of the data in mind. We would like to find a perfect model for any of the regional language. But the constrain is the availability of good dataset . So we try to propose a new dataset. Seller Inventory # 9783389024041
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A Study of Tackling Fake News with Machine Learning Approaches
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Taschenbuch. Condition: Neu. A Study of Tackling Fake News with Machine Learning Approaches | Balamurugan Rengeswaran (u. a.) | Taschenbuch | Englisch | 2024 | GRIN Verlag | EAN 9783389024041 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand. Seller Inventory # 129323657