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

This paper introduces T-Incident, a robust, low-cost architecture for detecting and enriching road events based on heterogeneous data fusion. A spatiotemporal model for merging incident, non-incident and social media data has been developed. In addition, this latter data source was filtered using natural language processing methods for pattern detection capable of describing the event and its vicinity. A learning-based model was also developed to identify these patterns and detect the types of events. The methodology results showed the best parameters for the T-Incident approach, providing an accurate incident detection and description service above 90% for the F1 score, Recall and Precision metrics.

Serviço de Detecção e Enriquecimento de Eventos Rodoviários Baseado em Fusão de Dados Heterogêneos para VANETs