Author:
Yasir Babiker Hamdan ,Sathish
Abstract
An identifying the news are real or fake instantly with high accuracy is a challenging work. The deep learning algorithm is implementing here to acquire very accurate separation of real and fake news rather than other methods. This research work constructs naïve bayes and CNN classifiers with Q-learning decision making. The two different approaches detect fake news in online and it gives to decision making section which is designed at tail in our research. The deep decision making section compares the input and make the decision wisely and it provides the more accurate output rather than single classifiers in deep learning. This research work comprises compare between our proposed works with single classifiers.
Publisher
Inventive Research Organization
Reference27 articles.
1. [1] Roy, A., Basak, K., Ekbal, A., & Bhattacharyya, P. (2018). “A Deep Ensemble Framework for Fake News Detection and Classification” ArXiv, abs/1811.04670.
2. [2] N. X. Nyow and H. N. Chua, "Detecting Fake News with Tweets’ Properties," 2019 IEEE Conference on Application, Information and Network Security (AINS), Pulau Pinang, Malaysia, 2019, pp. 24-29, doi: 10.1109/AINS47559.2019.8968706.
3. [3] Pathak, Ajeet & Mahajan, Aditee & Singh, Keshav & Patil, Aishwarya & Nair, Anusha. (2020). “Analysis of Techniques for Rumor Detection in Social Media” Procedia Computer Science. 167. 2286-2296. 10.1016/j.procs.2020.03.281.
4. [4] Tijare, Poonam. (2019). “A Study on Fake News Detection Using Naïve Bayes, SVM, Neural Networks and LSTM”
5. [5] A. Jain and A. Kasbe, "Fake News Detection," 2018 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS), Bhopal, 2018, pp. 1-5, doi: 10.1109/SCEECS.2018.8546944.
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39 articles.
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