Outlier detection is an important topic in machine learning and has been used in a wide range of applications. Outliers are objects that are few in number and deviate from the majority of objects. As ...

A monthly overview of things you need to know as an architect or aspiring architect. Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with ...

In my last few articles, I've looked at a number of ways machine learning can help make predictions. The basic idea is that you create a model using existing data and then ask that model to predict an ...

Nearly all leading-edge designs today are multi-die assemblies, which means that one defective die can render the entire device unusable. But not all dies that are deemed defective should be scrapped, ...

This book is a practical guide to spotting the parts of a dataset that deviate from the norm, even when they're hidden or intertwined among the expected data points. Brett Kennedy explains how outlier ...

Real-time precise point positioning (RT-PPP) depends on accurate satellite clock products, but phase jumps, frequency jumps and complex clock anomalies can destabilize network clock estimation.

Researchers have developed CAMV, a framework that automatically extracts contextual information from datasets and generates ...

The method inputs Doppler observations, satellite positions (from ephemeris), elevation angles, azimuth angles, and C/N₀ values. It groups potential multipath/NLOS faults using elevation, azimuth ...

Development and Validation of a Machine Learning Approach Leveraging Real-World Clinical Narratives as a Predictor of Survival in Advanced Cancer Artificial intelligence (AI) models for medical image ...