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

... 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...

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) …

In this thesis, we address the SMS spam detection problem by proposing a modi ed model based on the Transformer [77], a relatively new attention-based model. Addition-

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 …

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

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 …

Detection and Classification of Legitimate and Spam Emails using K-Nearest Neighbor Augmented with Quadratic Sieve Algorithm