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

Accumulation of corporate data in the cloud has attracted more enterpriseapplications to the cloud creating data gravity. As a consequence, networktraffic has become more cloud centric. This increase in cloud centric trafficposes new challenges in designing learning systems for streaming data due toclass imbalance. The number of classes plays a vital role in the accuracy ofthe classifiers built from the data streams. In this paper, we present a vectorquantization-based sampling method, which substantially reduces the classimbalance in data streams. We demonstrate its effectiveness by conductingexperiments on network traffic and anomaly dataset with commonly used ML modelbuilding methods; Multilayered Perceptron on TensorFlow backend, Support VectorMachines, K-Nearest Neighbour, and Random Forests. We built models usingparallel processing, batch processing, and randomly selecting samples. We showthat the accuracy of classification models improves when the data streams arepre-processed with our method. We used out of the box hyper-parameters of theseclassifiers and auto sklearn for hyperparameter optimization.

Sampling Streaming Data with Parallel Vector Quantization -- PVQ