Sarah Alnegheimish's research interests reside at the intersection of machine learning and systems engineering. Her objective: to make machine ...

User and entity behavior analytics can become a foundational tool for keeping pace with cybersecurity threats in the age of ...

Key Takeaways AI-powered systems can detect anomalies, forecast component failures, and provide real-time insights to enhance pilot decision-making.The global A ...

Industries from retail to finance are using clustering to personalize services, detect fraud, monitor equipment and improve ...

Machine learning models can employ pattern recognition to automatically identify and prevent potentially fraudulent ...

Contributor Content In 2025, integrating artificial intelligence (AI) and machine learning (ML) into cybersecurity is no longer a futuristic ideal but a functional reality. As cyberattacks grow more ...

Supercharging your data analysis strategy with machine learning, data science, and custom-trained LLMs can unlock a higher ...

Logistic Regression is a widely used model in Machine Learning ... Some real world examples where Logistic Regression is used are Email Spam classifier, Fraud/Not Fraud detection, Tumor Malignant ...

The primary vulnerabilities identified include data breaches, ransomware attacks, IoT device exploitation, adversarial ...

Big Data engineering, when approached strategically, becomes a core enabler of robust data privacy and security.