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 ...

QA teams now use machine learning to analyze past test data and code changes to predict which tests will fail before they run. The technology examines patterns from previous test runs, code commits, ...

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 ...

The small and complicated features of TSVs give rise to different defect types. Defects can form during any of the TSV ...

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

This story is part of an AI series looking at how WSU is driving innovation in research and teaching through artificial intelligence. View the entire ...

From Deep Blue to modern AI, how chess exposed the shift from brute-force machines to learning systems, and why it matters AI ...

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

Modern conservation technology has one simple goal, even though achieving it is anything but simple: to spot a threat or ...

Updates to the VersaONE Universal SASE Platform include AI-enhanced data protection, AI-guided troubleshooting, and expanded ...

As India’s digital footprint expands, more and more people are grappling with non-substance addictions like excessive internet use, gaming, smartphone and social media dependence.

A new topology-based method predicts atomic charges in metal-organic frameworks from bond connectivity alone, making large-scale computational screening practical.