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

This paper addresses the challenges of detecting anomalies in cellularnetworks in an interpretable way and proposes a new approach using variationalautoencoders (VAEs) that learn interpretable representations of the latentspace for each Key Performance Indicator (KPI) in the dataset. This enables thedetection of anomalies based on reconstruction loss and Z-scores. We ensure theinterpretability of the anomalies via additional information centroids (c)using the K-means algorithm to enhance representation learning. We evaluate theperformance of the model by analyzing patterns in the latent dimension forspecific KPIs and thereby demonstrate the interpretability and anomalies. Theproposed framework offers a faster and autonomous solution for detectinganomalies in cellular networks and showcases the potential of deeplearning-based algorithms in handling big data.

Interpretable Anomaly Detection in Cellular Networks by Learning Concepts in Variational Autoencoders