Researchers from Michigan Tech and the University of California, Los Angeles (UCLA) are collaborating on a machine learning model that aims to break the cubic scaling barrier of quantum mechanics.

Configurable high-bandwidth RISC-V cores with vector units can be made to directly address challenging applications like machine learning, AI, and other cutting-edge spaces.

Using routinely collected data in electronic health records, predictive modeling stratified risks for developing food allergy among infants, according to a study published in Allergy.This ...

The life and health insurance industry landscape across the globe is now confronting mounting challenges such as increasingly ...

PURPOSEReal-world data (RWD) derived from electronic health records (EHRs) are often used to understand population-level relationships between patient characteristics and cancer outcomes. Machine ...

Recent advancements in machine learning have significantly impacted the domain of high-speed electronic systems. By leveraging state‐of‐the‐art algorithms and novel network architectures ...

Delivering smarter smarts to edge and free-standing devices, tinyML can be part of a broader AI/ML deployment. How tinyML differs from mainstream machine learning. How tinyML is being applied ...

Gynecological cancers, including breast, ovarian, and cervical malignancies, account for a significant global health burden among women. The review outlines how a spectrum of machine learning (ML) ...

The rapid advancement of artificial intelligence (AI) and machine learning systems has increased the demand for new hardware components that could speed up data analysis while consuming less power. As ...

Research conducted at the University of Vaasa paves the way for smart packaging that indicates product condition through ...

When genetic testing reveals a rare DNA mutation, doctors and patients are frequently left in the dark about what it actually means. Now, researchers at the Icahn School of Medicine at Mount Sinai ...