Title :- Leveraging Deep Learning Architectures for Identifying Fake News
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Scenario -1 :
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Implementation plan:
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Step 1: Initially we collect and load the IFND Dataset.

Step 2: Next we implement data pre preprocessing using the CoreNLP toolkit

Step 3: Next we implement feature extraction process using RMS-BERT-CapsNet method

Step 4: Next we encode and store the textual and visual data using VAE-based Deep-Shallow multimodal fusion method

Step 5: Next we implement EWC-GEM Continuous learning for training process

Step 6: Next we implement a Vision Transformer with a Bidirectional Long Short-Term Memory algorithm to improve the classification accuracy.

Step 7: Finally, Generate the graph for,

7.1: Number of Epochs vs. Accuracy (%)

7.2: Number of Epochs vs. Loss (%)

7.3: Number of Epochs vs. F1-Score (%)

7.4: Number of Epochs vs. Training Loss

7.5: Number of Epochs vs. Validation Loss

Scenario -2 :
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Implementation plan:
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Step 1: Initially we collect and load the albanian-fake-news-corpus Dataset.

Step 2: Next we implement data pre preprocessing using the CoreNLP toolkit

Step 3: Next we implement feature extraction process using RMS-BERT-CapsNet method

Step 4: Next we encode and store the textual and visual data using VAE-based Deep-Shallow multimodal fusion method

Step 5: Next we implement EWC-GEM Continuous learning for training process

Step 6: Next we implement a Vision Transformer with a Bidirectional Long Short-Term Memory algorithm to improve the classification accuracy.

Step 7: Finally, Generate the graph for,

7.1: Number of Epochs vs. Accuracy (%)

7.2: Number of Epochs vs. Loss (%)

7.3: Number of Epochs vs. F1-Score (%)

7.4: Number of Epochs vs. Training Loss

7.5: Number of Epochs vs. Validation Loss

Software Requirement:
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1. Development Tool: Python – 3.11.4

2. Operating System: Windows 11 (64-bit)

Dataset link:
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Scenario -1 :
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https://www.kaggle.com/datasets/sonal...

Scenario -2 :
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https://www.kaggle.com/datasets/gentr...

Note:
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1) If the above plan does not satisfy your requirement, please provide the processing details, like the above step-by-step.

2) Please note that this implementation plan does not include any further steps after it is put into implementation.

3) This project is only based on simulations. Not a real time project.

4) If the above plan satisfies your requirement please confirm with us.

EXISTING:
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We make an existing process based on Reference 1 - Title: EFND: ASemantic, Visual, and Socially Augmented Deep Framework for Extreme Fake News Detection
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1. #FakeNewsDetection 2. #MachineLearning 3. #PythonProgramming 4. #DataScience 5. #AIForGood 6. #NewsVerification 7. #TechForChange 8. #DigitalLiteracy 9. #FactChecking 10. #MLInAction
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