Enhancing Email Spam Detection through Ensemble Learning : A Combined Approach of LSTM and Neural Network

A powerful method for spotting spam messages, using neural networks with a focus on Long Short Term Memory(LSTM) technology is introduced. With the persistent problem of unwanted messages in digital communication, a dependable spam detection system is crucial. Traditional methods often struggle to keep up with evolving spamming tactics, calling for a flexible and adaptive solution. The suggested model employs a layered neural network design, incorporating LSTM cells to grasp time-based patterns in text data. This enables the model to recognize subtle clues in message content, leading to better accuracy in identifying spam. Extensive testing was carried out on a varied dataset containing both regular and spam messages, showing improved performance compared to standard methods. Moreover, the model shows strong adaptability, performing well across different message lengths and styles. By using embeddings and attention mechanisms, the model becomes even better at picking up on language cues that indicate spam. Empirical results demonstrate a noticeable increase in precision, recall, and F1score, indicating the model’s effectiveness in distinguishing spam from legitimate messages. This approach represents a significant step forward in the field of spam detection, offering a reliable solution to tackle the ever-changing world of unwanted digital communication.