Abstract: Most machine learning methods assume fixed probability distributions, limiting their applicability in nonstationary real-world scenarios. While continual learning methods address this issue, ...

Net, a hybrid model that improves energy consumption prediction in low-energy buildings, enhancing accuracy and ...

Last month, the Sedona Conference Working Group 13 Annual Meeting and the ASU Arkfeld Conference on eDiscovery, Law, and ...

Ligand-based drug design combines AI and QSAR modeling to prioritize drug candidates, minimizing preclinical failures and ...

Personalized algorithms may quietly sabotage how people learn, nudging them into narrow tunnels of information even when they start with zero prior knowledge. In the study, participants using ...

If you've been scrolling through Coursera's 500+ AI courses feeling overwhelmed, you're not alone—but the platform's 2026 updates have made the choice clearer than ever. With new professional ...

Researchers use statistical physics and "toy models" to explain how neural networks avoid overfitting and stabilize learning in high-dimensional spaces.

Why it matters: Linear algebra underpins machine learning, enabling efficient data representation, transformation, and optimization for algorithms like regression, PCA, and neural networks. Python ...

A novel AI-assisted biosensor for choline quantification in milk combines chemiluminescence and smartphone technology, ...

Abstract: Feature selection is a pivotal step in machine learning, aimed at reducing feature dimensionality and improving model performance. Conventional feature selection methods, typically framed as ...

In automation, precision and reliability are no longer optional; they are requirements. For a wide variety of machine types and processes, linear guides provide that accuracy and high-capacity travel.