English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Weighted Outlier Detection is a method for identifying unusual or anomalousdata points in a dataset, which can be caused by various factors like humanerror, fraud, or equipment malfunctions. Detecting outliers can reveal vitalinformation about system faults, fraudulent activities, and patterns in thedata, assisting experts in addressing the root causes of these anomalies.However,creating a model of normal data patterns to identify outliers can bechallenging due to the nature of input data, labeled data availability, andspecific requirements of the problem. This article proposed theWePaMaDM-Outlier Detection with distinct mass data mining domain, demonstratingthat such techniques are domain-dependent and usually developed for specificproblem formulations. Nevertheless, similar domains can adapt solutions withmodifications. This work also investigates the significance of data modeling inoutlier detection techniques in surveillance, fault detection, and trendanalysis, also referred to as novelty detection, a semisupervised task wherethe algorithm learns to recognize abnormality while being taught the normalclass.

WePaMaDM-Outlier Detection: Weighted Outlier Detection using Pattern Approaches for Mass Data Mining