TMCnet

Machine Learning Advances in Predictive Cyber Threat Detection

Cyber threats are increasing in speed and complexity, driving the need for advanced detection techniques. Machine learning is ...
EurekAlert!

Smarter machine-learning models improve phishing website detection

Febraury 10, 2026—Phishing websites remain a persistent cybersecurity threat, exploiting users by imitating trusted online services. New machine-learning tools could help organisations flag more ...
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

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 ...
C&EN

Developing a Community-Specific Daily Weather Health Risk Index Across Australia Using Explainable Machine Learning

Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia ...
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 ...
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 ...