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

An activation function has a significant impact on the efficiency androbustness of the neural networks. As an alternative, we evolved a cutting-edgenon-monotonic activation function, Negative Stimulated Hybrid ActivationFunction (Nish). It acts as a Rectified Linear Unit (ReLU) function for thepositive region and a sinus-sigmoidal function for the negative region. Inother words, it incorporates a sigmoid and a sine function and gaining newdynamics over classical ReLU. We analyzed the consistency of the Nish fordifferent combinations of essential networks and most common activationfunctions using on several most popular benchmarks. From the experimentalresults, we reported that the accuracy rates achieved by the Nish is slightlybetter than compared to the Mish in classification.

Nish: A Novel Negative Stimulated Hybrid Activation Function