This research aims to reactivate object-oriented databases using intelligent tools to improve performance and accuracy in ...

Federated learning offers a new foundation for AI — one where privacy, transparency and innovation can move together.

In Recentive, the Federal Circuit acknowledged the growing significance of AI and machine learning and emphasized that its holding is limited to generic machine learning applications.

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 ...

A new campaign exploiting machine learning (ML) models via the Python Package Index (PyPI) has been observed by cybersecurity researchers. ReversingLabs said threat actors are using the Pickle file ...

(b) Quantum machine learning in augmenting quantum cryptography. The rest of the article explores the fundamental aspects of quantum cryptography, the role of QML in Quantum cryptography, and three ...

Conclusion Machine learning is driving a transformational shift in endpoint security by enabling predictive threat detection. Leveraging data-driven insights, monitoring endpoint behaviors, and ...

A researcher with expertise in cybersecurity, Vishwadeep Saxena explores the transformative impact of machine learning (ML) on modern application security. His recent study highlights how ML is ...

As we move further into the digital age, the partnership between ML and cryptography will be crucial in protecting our data and communications.

Berklee announces core principles that will guide our work and partnerships in the areas of machine learning (generative AI).

The number of machine identities is booming thanks to the growth of cloud and AI -- and it’s posing real security problems by giving hackers way more ...