At the forefront of discovery, where cutting-edge scientific questions are tackled, we often don't have much data. Conversely ...

One of the current hot research topics is the combination of two of the most recent technological breakthroughs: machine learning and quantum computing.

These steps often stall machine learning projects between experimentation and production because of a lack of engineering resources or the complexity of debugging pipelines.

Previously, S&P only had data on about 2 million SMEs, but its AI-powered RiskGauge platform expanded that to 10 million.

Typical Azure Machine Learning Project Lifecycle (source: Microsoft). At the upcoming Visual Studio Live! @ Microsoft HQ 2025 conference in Redmond, Eric D. Boyd, founder and CEO of responsiveX, will ...

Machine learning-powered security systems should be used as a tool, not as a replacement for security teams for web apps.

QSR brands struggle to retain app users after initial downloads, as traditional marketing focuses on installs rather than long-term engagement. Machine learning offers solutions by analyzing user ...

Machine learning is a multibillion-dollar business with seemingly endless potential, but it poses some risks. Here's how to avoid the most common machine learning mistakes.

The approach proposed in this work uses a selected set of features present in the Unified Modeling Language (UML) Sequence diagrams and customized ‘Machine-Learning Regression-analysis (ML-RA)’ ...

The Earth BioGenome Project seeks to genetically profile over a million plants, animals, and fungi as they build an atlas of complex life on Earth.

Businesses need better planning to make their supply chains more agile and resilient. After explaining the shortcomings of traditional planning systems, the authors describe their new approach ...