Machine learning for health data science, fuelled by proliferation of data and reduced computational costs, has garnered considerable interest among researchers. The debate around the use of machine ...

WiMi Releases Hybrid Quantum-Classical Neural Network (H-QNN) Technology for Efficient MNIST Binary Image Classification ...

Researchers have demonstrated, for the first time, that transfer learning can significantly enhance material Z-class identification in muon tomography, even in scenarios with limited or completely ...

The idea that quantum computing could transform medical artificial intelligence (AI) has gained momentum in recent years, driven by advances in cloud-accessible quantum platforms and hybrid computing ...

Over the last several decades, urban planners and municipalities have sought to identify and better manage the socioeconomic dynamics associated with rapid development in established neighborhoods.

Medical researchers at Mass General Brigham say the self-supervised foundational model can identify inherent features from ...

For customers who must run high-performance AI workloads cost-effectively at scale, neoclouds provide a truly purpose-built ...

As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models ...

AI isn't a single capability, and "using AI" isn't a strategy. The strategy is to know what we're building, why it matters ...

What: New analyses using early observations from the European Space Agency’s Euclid mission examine how galaxy mergers trigger active galactic nuclei (AGN), luminou ...

Breakthrough AI foundation model called BrainIAC is able to predict brain age, dementia, time-to-stroke, and brain cancer ...

A recent narrative review concluded that artificial intelligence (AI) has a significant impact on gastroenterology, with ...