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Anomaly detection in the context of data science is detecting a data sample that is out of the ordinary and does not fit into the general data pattern (or an outlier). This deviation can result from a ...

Anomaly detection is the process of identifying events or patterns that differ from expected behavior. Anomaly detection can range from simple outlier detection to complex machine learning algorithms ...

In a bid to improve quality control and reduce production errors, manufacturers are increasingly adopting automated anomaly detection systems. These systems automatically identify defects in ...

Machines fail. By creating a time-series prediction model from historical sensor data, you can know when that failure is coming Anomaly detection covers a large number of data analytics use cases.

Video anomaly detection is a research hotspot in the field of computer vision, attracting many researchers.Video anomaly detection differs from traditional video analysis. Usually, abnormal events ...

Unlike pattern-matching, which is about spotting connections and relationships, when we detect anomalies we are seeing disconnections—things that do not fit together. Anomalies get much less attention ...