Anomaly detection

Concept and definition

Anomaly detection

What is Anomaly detection?

Anomaly detection is a machine learning technique used to identify unusual or anomalous patterns in data. The goal of anomaly detection is to find observations that deviate significantly from normal or expected behaviour.

In other words, anomaly detection is a technique that allows artificial intelligence systems to identify data that does not conform to expected patterns, which can be very useful in detecting fraud, security intrusions, system failures, and other unexpected events that may have a negative impact on the performance or security of a system.

Anomaly detection is a widely used technique in industry and can be applied to a variety of fields, such as critical infrastructure monitoring, disease detection in the medical field, error detection in industrial production, and financial data analysis.

« Back to glossary

Do you want to get in touch?

CDRs contain data that a telecommunications company collects about phone calls, such as time and length of call. This data can be used in analytical applications.
Fill the form
Share:
What is Surety Insurance?

You are probably wondering, what is surety insurance and how does it help your company? In today's economic environment, [...]

Read More »
Basic concepts for building commercial software with artificial intelligence

The first thing you need to know is the limits of AI and after mastering the basic concepts you will be able to build a large commercial software with intelligent [...]

Read More »
Abbreviated History of Artificial Intelligence

The content of this article synthesizes part of the chapter "Concept and brief history of Artificial Intelligence" of the thesis Generation of Artificial [...]

Read More »
Cloud solutions for SMEs

Cloud computing services or solutions, whether in Spain or anywhere else in the world, are infrastructures, platforms or systems that are used in the cloud.

Read More »
See more entries
© Gamco 2021, All Rights Reserved - Legal notice - Privacy - Cookies