Researchers at Bethel University are studying how to teach computers to define "normal" data and then detect anomalies.
The team used mathematical models and real-world data to determine ways to detect needle-in-the-haystack anomalies and report them in real time, using far less computational power than conventional systems.
Their algorithm is based on recognizing a sudden increase of distance between vectors in a high-dimensional vector space.
The researchers tested the algorithm by installing a webcam in an office window to pick up a feed of outdoor foot traffic. Each quadrant in the field has its own anomaly detector attached to it, and if something enters into that box previously unseen by the system, an alert is sent, says Bethel's Brian Turnquist.
Turnquist thinks streamlining data will enable Internet of Things technology to advance, which could be used to increase life expectancy and crime-fighting capabilities.
From Bethel University
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