GitHub

Machine Learning spam classifier achieving 98% accuracy using SVM, NLP, and TF-IDF vectorization

An intelligent spam detection system that classifies SMS/email messages with 98.48% accuracy using machine learning. This project compares multiple algorithms and provides comprehensive performance ...
Scientific Research Publishing

Explainable Machine Learning in Risk Management: Balancing Accuracy and Interpretability ()

1 School of Taxation and Public Administration, Shanghai Lixin University of Accounting and Finance, Shanghai, China. 2 School of Business, Computing and Social Sciences, University of Gloucestershire ...
GitHub

MusXIII/SMS_SPAM_DETECTION

This project implements a machine learning model to classify SMS messages as "spam" or "ham" (not spam) using Decision Trees and TF-IDF vectorization. CS_Project_II/ ├── dataset/ │ └── spam.csv # SMS ...
IEEE

LoRA and ReFT Optimized Explainable Machine Learning and Deep Learning Framework for SMS Spam Detection

Abstract: Short Message Service (SMS) spam detection aims to identify and filter unsolicited and potentially harmful messages, a growing problem affecting communication security and user trust. This ...
IEEE

Spam SMS Detection from 5G Mobile Core Network Using Machine Learning-based Federated Learning

Abstract: The widespread use of SMS as a communication method emphasizes its crucial role in contemporary society. SMS offers a convenient and reliable means of transmitting information across various ...