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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

The ensemble of classifiers for identification of agricultural suitability of soils on the basis of physiographic information was created in accordance with the stacking algorithm. It is comprised of five neural networks of various structures. The deciding element was a neural classifier optimised on the basis of input vectors composed of the indications of five classifiers making up the lower level. Among the architectures studied, the best result was achieved using the Radial Basis Function network as the decisive classifier, composed with the use of the constructivist Feature Space Mapping algorithm. In this configuration, the group correctly identified more than 99% of the elements of the validation set. The models may be used as tools for predicting expected soil condition, which is helpful in assessment of the range of substantial transformations.

isrn soil science

An Ensemble of Neural Classifiers and Constructivist Algorithms in the Identification of Agricultural Suitability Complexes of Soils on the Basis of Physiographic Information