Market growth is driven by industrial automation, predictive maintenance demand, AI/ML analytics adoption, IoT integration, and the need to reduce downtime and operational costs.Austin, Jan. 27, 2026 ...

One of the most difficult challenges in payment card fraud detection is extreme class imbalance. Fraudulent transactions ...

Who is a data scientist? What does he do? What steps are involved in executing an end-to-end data science project? What roles are available in the industry? Will I need to be a good ...

Review re-maps multi-view learning into four supervised scenarios and three granular sub-tiers, delivering the first unified blueprint for researchers to navigate classification, clustering, ...

There is often no straightforward explanation for the various types of violence that occur around the world. In fact, even ...

MIT researchers have identified significant examples of machine-learning model failure when those models are applied to data other than what they were trained on, raising questions about the need to ...

An Ensemble Learning Tool for Land Use Land Cover Classification Using Google Alpha Earth Foundations Satellite Embeddings ...

Tabular foundation models are the next major unlock for AI adoption, especially in industries sitting on massive databases of ...

Machine learning is revolutionizing fundamental science by tackling long-standing mathematical challenges. A key example is ...

Satyam Kumar's remarkable academic journey began in rural Bihar, leading him to crack the IIT-JEE at 13 and later pursue advanced AI and brain-computer interface research in the US. His work at UT ...

Smartphone cameras are leaning hard on AI, but is it helping or hurting image quality? I look at why hardware still matters ...