spam classification has special attention. In this paper, we applied various machine learning and deep learning techniques for SMS spam detection. we used a dataset to train the machine …

This project is about building a spam detection system for SMS messages using deep learning techniques in TensorFlow2. Three different architectures, namely Dense Network, LSTM, and …

... main idea of this detection system is to process the collected SMSs and apply a machine learning method to classify them and identify those that are considered to be spam or phishing...

In this thesis, we propose a modi ed Transformer model for SMS spam messages detection.

Here is a fundamental process for creating a machine learning model for spam email detection: 1) Data Gathering: Gather both valid and spam emails as part of a sizable and varied dataset. 2) …

This project focuses on creating a spam detection system for SMS messages using deep learning techniques in TensorFlow2. Three different architectures—Dense Network, LSTM, and Bi …

Fig -6: Architecture Diagram of Email Spam Detection The project architecture diagram provides a comprehensive overview of how the different components of the email spam detection system …

Survey presents at taxonomy of the Twitter spam detection approaches and attempts to offer a detailed description of recent developments in the domain. The aim of this paper is to identify

These messages, i.e., spam, are annoying and potentially malicious by exposing SMS users to credential theft and data loss. To mitigate this persistent threat, we propose a new...