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Summary of findings and potential for real-world deployment in emergency response networks.
Justify the use of Deep Learning (CNN/RNN) for better classification accuracy over traditional algorithms. 5. Results & Discussion GF011222-SMS-EA.rar
Discuss the impact of synthetic data generation if the dataset was imbalanced. 6. Conclusion Summary of findings and potential for real-world deployment
A brief summary (250 words) covering the increasing reliance on SMS for rapid emergency broadcasting, the dataset used (GF011222), the machine learning models applied (e.g., Random Forest or CNN), and the final accuracy results. 2. Introduction the dataset used (GF011222)
Discuss existing SMS spam detection using TF-IDF vectorization .
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