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

Many works have been done to handle the uncertainties in the data using type1 fuzzy regression. Few type 2 fuzzy regression works used interval type 2 forindeterminate modeling using type 1 fuzzy membership. The current surveyproposes a triangular type-2 fuzzy regression (TT2FR) model to ameliorate theefficiency of the model by handling the uncertainty in the data. The triangularsecondary membership function is used instead of widely used interval typemodels. In the proposed model, vagueness in primary and secondary fuzzy sets isminimized and also, a specified x-plane of observed value is included in thesame {\alpha}- plane of the predicted value. Complex calculations of the type-2fuzzy (T2F) model are simplified by reducing three dimensional type-2 fuzzy set(3DT2FS) into two dimensional interval type-2 fuzzy (2DIT2F) models. Thecurrent survey presents a new regression model of T2F by considering the moregeneral form of T2F membership functions and thus avoids high complexity. Theperformance of the developed model is evaluated using the TAIEX and COVID-19forecasting datasets. Our developed model reached the highest performance ascompared to the other state-of-art techniques. Our developed method is ready tobe tested with more uncertain data and has the potential to use to predict theweather and stock prediction.

Accurate Prediction Using Triangular Type-2 Fuzzy Linear Regression