IEEE

Analysis of spam detection techniques using machine learning

Abstract: In recent years, the receipt of spam by billions of users around the world has increased. In general, for spam emails are admitted all emails that is sent without the express consent of the ...
IEEE

Enhancing Machine Learning Algorithms for Imbalanced Data—A Case Study: Spam Detection

Abstract: Nowadays, emails are among the most common and cost-effective ways to share and exchange information. However, because of their simplicity, they are highly vulnerable to various threats. In ...
GitHub

SMS Spam Detection: Comparing Deep Learning Approaches

5,572 SMS messages. 747 spam. Can a custom neural network compete with BERT? This project compares three deep learning approaches to SMS spam detection: a custom LSTM architecture built from scratch, ...
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 ...
ahajournals.org

Abstract Fri013: AI-Driven Gait Classification for Peripheral Artery Disease (PAD) Detection Using Machine Learning and Nonlinear Gait Dynamics

Introduction: Peripheral Artery Disease (PAD) is a progressive vascular disorder impairing mobility, raising fall risk, and reducing quality of life. Early detection is key to preventing amputations ...
C&EN

Using Response Surface Methodology To Enhance Limited Data Sets for Machine Learning: A Polyvinyl Butyral Synthesis-Based Chemistry Undergraduate Teaching Case

School of Chemical Engineering and Light Industry, Guangdong University of Technology, Guangzhou, 510006, China ...