The Big Data Analytics, Artificial Intelligence and Machine Learning research cluster tackles important problems and develops real-life applications, harnessing technologies to extract insights and ...

The database should also be compatible with the tools and frameworks used for Machine Learning and AI. For example, some databases may have built-in support for specific Machine Learning libraries ...

New tool combines biological knowledge with machine learning to help researchers extract meaningful insights from complex ...

Ansys SimAI is a physics-agnostic and cloud-enabled computer-aided engineering tool that predicts performance of complex ...

Logistic Regression is a widely used model in Machine Learning. It is used in binary classification, where output variable can only take binary values. Some real world examples where Logistic ...

Traditionally, drug discovery relied heavily on trial and error, with long timelines and high costs. The introduction of ...

So it’s no surprise that 83 percent of organizations have increased their AI/ML budgets year-on-year, according to Alorithmia’s “2021 Enterprise Trends in Machine Learning.” For example, major ...

Although they mostly go unnoticed by humans, small earthquakes occur much more frequently than large earthquakes, and knowing ...

Light-based data made clearer with new machine learning method. ScienceDaily . Retrieved July 12, 2025 from www.sciencedaily.com / releases / 2025 / 04 / 250428220611.htm ...

Upon completion of this course, participants should be able to: ¿ Understand how big data and machine learning can complement traditional data and analytical techniques in macroeconomic analysis and ...