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 ...

Supervised learning algorithms like Random Forests, XGBoost, and LSTMs dominate crypto trading by predicting price directions ...

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, ...

A signal-processing–based framework converts DNA sequences into numerical signals to identify protein-coding regions. By integrating spectral ...

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 ...

Quiq reports on the role of automation in customer service, highlighting tools like AI for questions, ticket classification, ...

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 ...

As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models designed to process graph-structured data. Currently, GNNs are effective ...

SCAN project aims to build European GNSS-based and AI-driven technologies to detect and assess roadway pavement problems.

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

The tax agency accounts for nearly half of Treasury’s AI use cases, with a heavy focus on IT and some fraud-fighting tech, ...