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Fig. Experimental Results
From above Fig. Experimental Results, it could be established that the accuracy will increase with the growth in the quantity of
epochs, and there may be a decrease in the lack of the testing set.
VII. CONCLUSION enrich the dataset. This would allow the model to generalize
The proposed convolutional neural network (CNN) better across larger and more varied datasets. Implementing
architecture for automated fake news detection represents a unique feature selection algorithms could strengthen the
significant advancement in the fight against misinformation. model's resilience against datasets with missing or
By successfully classifying news articles into different incomplete information, thereby improving overall accuracy.
categories with an impressive accuracy, this model highlights
the potential of machine learning to address the pervasive Even though the improvisation arised from the proposed
issue of fake news dissemination. model, there are quiet many things that may be worked on.
There's always a scope for improvising preceding work via
 The model was trained on a substantial dataset introducing new filters and studying features in CNN that can
comprising many news articles, enhancing its be very useful to the software and its subject.
robustness and reliability in real-world applications.
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