SPAM DETECTION SYSTEM USING DEEP MACHINE LEARNING
SPAM DETECTION SYSTEM USING DEEP MACHINE LEARNING
Authors:
MOOTALA SRAVANI DIGVIJA 1, MALEPATI SAI MALLIK2, DIKONDA RUKESH3 , AKULA PAVAN DURGA4, DR.M.V.PAVAN KUMAR 5
1Student of Information Technology , Sri Mittapalli College of Engineering
2 Student of Information Technology , Sri Mittapalli College of Engineering
3 Student of Information Technology , Sri Mittapalli College of Engineering
4 Student of Information Technology , Sri Mittapalli College of Engineering
5 Head of The Department of CSE , Sri Mittapalli College of Engineering
Abstract - Spam messages are a persistent issue in digital communication, affecting email services, messaging platforms, and social networks. Traditional machine learning methods often struggle to capture contextual and sequential dependencies in text data. This paper proposes a spam detection system using Long Short-Term Memory (LSTM), a type of Recurrent Neural Network (RNN), to effectively model sequential text patterns. The system leverages text preprocessing, tokenization, and word embedding techniques to convert raw messages into meaningful representations. Experimental results demonstrate that the LSTM-based model achieves higher accuracy and better generalization compared to traditional classifiers such as Naïve Bayes and Support Vector Machines.
Key Words: Spam Detection, LSTM, Deep Learning, NLP, Text Classification, RNN