A powerful artificial intelligence (AI) tool could give clinicians a head start in identifying life-threatening complications ...

Korea University researchers have developed a machine-learning framework that predicts solar cell efficiency from wafer quality, enabling early wafer screening and optimized production paths. Using ...

Machine learning for health data science, fuelled by proliferation of data and reduced computational costs, has garnered ...

Microelectromechanical systems (MEMS) electrothermal actuators are widely used in applications ranging from micro-optics and microfluidics to nanomaterial testing, thanks to their compact size and ...

In data analysis, time series forecasting relies on various machine learning algorithms, each with its own strengths. However, we will talk about two of the most used ones. Long Short-Term Memory ...

Data Normalization vs. Standardization is one of the most foundational yet often misunderstood topics in machine learning and data preprocessing. If you''ve ever built a predictive model, worked on a ...

Insulin resistance - when the body doesn't properly respond to insulin, a hormone that helps control blood glucose levels - ...

Predicting hospitalization risk among patients on hemodialysis is vital, given their high rate of unplanned hospitalizations related to fluid overload and infections.

Phishing websites remain a persistent cybersecurity threat, exploiting users by imitating trusted online services. New ...

The CMS Collaboration has shown, for the first time, that machine learning can be used to fully reconstruct particle ...