This project implements a Recurrent Neural Network (RNN) model to detect spam emails. The model is trained on text data and leverages the sequential nature of RNNs to effectively …

Aug 2, 2021 · To improve the email spam detection using the hybrid deep learning algorithms consisting of CNN and RNN by optimizing the hidden neurons for improved cyber security. To …

Sep 30, 2023 · 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 …

It meticulously examines the frameworks for machine learning-ba sed spam detection, highlighting the transition from Support Vector Machines (SVM), Random Forests, and K-Nearest …

Jan 1, 2021 · Several models and techniques to automatically detect spam emails have been introduced and developed yet non showed 100% predicative accuracy. Among all proposed …

Our results demonstrate promising accuracy rates, low false positive rates, and excellent generalization performance, positioning Phishing CNN as a valuable tool in the fight against …

In order to achieve that we have worked with both labeled and unlabeled data and proposed deep learning methods for spam review detection which includes Multi-Layer Perceptron (MLP), …

In order to achieve that we have worked with both labeled and unlabeled data and proposed deep learning methods for spam review detection which includes Multi-Layer Perceptron (MLP), …

Now, malignant entities can utilize deep learning techniques to create spam that is harder than ever to be identified. Spam detection is mostly conducted manually by platforms but today, …

o address these limitations, we developed a spam filter using deep neural networks. In this work, various deep neural networks such as RNN, LSTM, GRU, Bidirecti.