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

A newly introduced method called Taylor-based Optimized Recursive ExtendedExponential Smoothed Neural Networks Forecasting method is applied and extendedin this study to forecast numerical values. Unlike traditional forecastingtechniques which forecast only future values, our proposed method provides anew extension to correct the predicted values which is done by forecasting theestimated error. Experimental results demonstrated that the proposed method hasa high accuracy both in training and testing data and outperform thestate-of-the-art RNN models on Mackey-Glass, NARMA, Lorenz and Henon mapdatasets.

Taylor-based Optimized Recursive Extended Exponential Smoothed Neural Networks Forecasting Method