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 method of Native Bayesian classification data mining in weather forecast has some defects, such as there is not independent of each other between predictors, but a certain relevance which results in the decrease of prediction accuracy. This paper explores an improved algorithm which is based on the theory of discrete Bayesian Networks, and combines with Hadoop distributed file system and parallel processing programming models to predict rainfall. The experiments show that the improved algorithm not only makes the classification prediction more reliable but also improves the efficiency greatly. In addition, it provides a solution of huge amounts of data mining in the other fields.

The Research of Meteorological Data Mining Using Discrete Bayesian Networks Classifier Based on Hadoop