In the process of analysing data, outlier detection (i.e., anomaly detection or novelty identification) is often misinterpreted
to an identification of rare observations, occurrence or an item, which deviates highly from enormous data and never conforms to well-
defined ideologies of a normal behaviour. The samples could stimulate more suspicion of being produced from various techniques, or
appear unpredictable with the remaining portion of the specific dataset. Anomaly detection draws application in different domains such
as neuroscience, statistics, machine vision, medicine, financial fraud, law enforcement and cyber security. The data that has been
collected from real-life applications are rapidly increasing in dimension and size. As the aspect of dimensionality keeps increasing, data
items become significantly sparse, amounting to an identification of variances becoming problematic. In addition, more conventional
approaches for anomaly detection cannot function in a proper manner. In this paper, we have evaluated the applications and methods of
anomaly detection.
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Cite this article
Sidny Chalhoub, “A Critical Review of the Applications and AI Techniques for Anomaly Detection", vol.2, no.3, pp. 098-109, July 2022. doi: 10.53759/181X/JCNS202202013.