A collaborative approach to training AI models can yield better results, but it requires finding partners with data that ...

Structural economic models, while parsimonious and interpretable, often exhibit poor data fit and limited forecasting performance. Machine learning models, by contrast, offer substantial flexibility ...

AI and machine learning are revolutionizing drug discovery, development, and lifecycle management, addressing industry ...

An AI-driven digital-predistortion (DPD) framework can help overcome the challenges of signal distortion and energy inefficiency in power amplifiers for next-generation wireless communication.

Machine learning enables real-time PCB defect detection using a FOMO model on a Raspberry Pi. Learn how with this ...

Antimicrobial resistance (AMR) is an increasingly dangerous problem affecting global health. In 2019 alone, methicillin-resistant Staphylococcus aureus (MRSA) accounted for more than 100,000 global ...

A team of researchers from the University of Chicago's Pritzker School of Molecular Engineering (UChicago PME) has used ...

Estimating the location of contact is a primary function of artificial tactile sensing apparatuses that perceive the environment through touch. Existing contact localization methods use flat geometry ...

A recent study, “Picking Winners in Factorland: A Machine Learning Approach to Predicting Factor Returns,” set out to answer a critical question: Can machine learning techniques improve the prediction ...

To create their contactless approach, the researchers created a machine learning algorithm capable of analyzing aspects of ...