Training a large artificial intelligence model is expensive, not just in dollars, but in time, energy, and computational ...

Major release delivers seamless Ignition SCADA, enterprise-grade security, advanced ML algorithms, and private cloud ...

Abstract: Hyperspectral image (HSI) classification faces persistent challenges arising from high spectral dimensionality, complex spatial-spectral dependencies, and limited labeled samples. Although ...

ABSTRACT: Automatic detection of cognitive distortions from short written text could support large-scale mental-health screening and digital cognitive-behavioural therapy (CBT). Many recent approaches ...

The South Dakota High School Activities Association (SDHSAA) discussed a new model for classifying teams at its Board of Directors meeting on Jan. 21, but Executive Director Dan Swartos said the new ...

Analyst Insight: As 2025 comes to an end, one reality has become clear: The traditional linear product lifecycle management model has reached its limits. For years, PLM served retailers well by ...

Generative modeling, representation learning, and classification are three core problems in machine learning (ML), yet their state-of-the-art (SoTA) solutions remain largely disjoint. In this paper, ...

Enterprises, eager to ensure any AI models they use adhere to safety and safe-use policies, fine-tune LLMs so they do not respond to unwanted queries. However, much of the safeguarding and red teaming ...

ABSTRACT: Support vector machines are recognized as a powerful tool for supervised analysis and classification in different fields, particularly geophysics. In summary, SVMs are binary classifiers.

In microbiome studies, addressing the unique characteristics of sequence data—such as compositionality, zero inflation, overdispersion, high dimensionality, and non-normality—is crucial for accurate ...