Popular GitHub Repositories for Spam Detection

Code for KDD 2020 paper Robust Spammer Detection by Nash Reinforcement Learning

LLM for Email Spam Detection

This is a Text Analysis App which can be used to find a detailed analysis of a particular text. This includes 5 main types of Analysis - Spam/Ham Detection, Sentiment Analysis, Stress Detection, Hate & Offensive Content Detection, Sarcasm Detection

Email Spam-detection is an ANN app with TensorFlow. The idea is simple - given an email you’ve never seen before, determine whether or not that email is Spam

这个项目使用双向长短期记忆网络（Bi-directional LSTM）模型对邮件进行垃圾邮件和正常邮件的分类，使用了自然语言处理（NLP）技术，例如分词、填充和去除停用词。它旨在创建一个有效的邮件分类器，同时通过诸如提前停止等策略来解决过拟合问题。

Open-MalSec 是一个开源数据集，专为网络安全研究和应用而创建。

An NLP based APP includes features for spam detection, sentiment analysis, stress detection, hate and offensive content detection, and sarcasm detection. It leverages Natural Language Processing (NLP) techniques and machine learning models to analyze and classify text inputs. Table of Contents

In this Repository, I upload my Research and Development Projects which I have done in Bachelor’s Degree (2015-19). The Projects are An Approach for Spam Detection in YouTube Comments Based on Supervised Learning and Conversational AI Chatbot Based on Encoder-Decoder Architectures with Attention Mechanism.

Collection of NLP Examples

Developed a machine learning program to identify when an email might be a spam.

WebApp qui met à disposition un réseau de neurones pour détecter les SPAM dans un SMS.

LLM based spam detection software

微调BERT模型用于垃圾邮件检测。

Machine Learning Final Project (COMP-432)

finetunig GPT2 for detecting spams

Facial Expression Analysis project using Py-Feat: Recognising Legit, Phish, and SPAM emails and differentiating between DeepFake and Real Videos

The aim of this project is to create a machine learning model based on Active Learning to identify and filter out spam in YouTube comments.

Spam email classifier using Natural Language Processing (NLP) techniques. Train models like Naive Bayes on spam datasets. Libraries: nltk, scikit-learn, pandas Skills: Text classification, feature extraction (TF-IDF, Bag of Words)

End-to-end project for classifying spam emails using NLP, Machine Learning, and Deep Learning techniques