SPAM DETECTION MODEL USING TENSORFLOW AND DEEP LEARNING ALGORITHM

Abstract

As technology is becoming an integral aspect of every person's life since it makes living simpler and more efficient. As technology advances, spam attacks are becoming increasingly widespread. In this paper, we propose a solution to tackle this issue by comparing Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM) deep learning techniques. Our objective is to develop a spam detection model that can effectively identify and filter out spam text. To evaluate the performance of our proposed model, we conducted experiments on a meticulously curated dataset consisting of spam and ham instances. The RNN model demonstrated exceptional accuracy, achieving an accuracy rate of 98.36%. Comparative analysis revealed that the CNN model achieved an accuracy rate of 97.10%, while the LSTM model attained an accuracy rate of 92.85%. These findings highlight the superior performance of the RNN model in accurately detecting spam using the TensorFlow platform. This research contributes to the advancement of spam detection methodologies, providing valuable insights for the development of effective spam filtering systems in the digital realm.