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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

This article introduces a technique for using recurrent neural networks to forecast Ae. aegyptimosquito (Dengue transmission vector) counts at neighborhood-level, using Earth Observation data inputs as proxies to environmental variables. The model is validated using in situdata in two Brazilian cities, and compared with state-of-the-art multioutput random forest and k-nearest neighbor models. The approach exploits a clustering step performed before the model definition, which simplifies the task by aggregating mosquito count sequences with similar temporal patterns.

Dengue Vector Population Forecasting Using Multisource Earth Observation Products and Recurrent Neural Networks