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The study addresses the growing issue of online recruitment fraud by utilizing Long Short-Term Memory (LSTM) networks to detect fraudulent job advertisements. A novel dataset combining job ads from multiple sources was created to improve model accuracy, achieving a notable 97% success rate in identifying scams. The research highlights the importance of advanced machine learning techniques and data balancing methods, such as SMOTE, to enhance fraud detection in the evolving landscape of online hiring.
- Author
- akankshagavhane03
- Language
- EN