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

Maritime domain awareness is critical for protecting sea lanes, ports,harbors, offshore structures and critical infrastructures against commonthreats and illegal activities. Limited surveillance resources constrainmaritime domain awareness and compromise full security coverage at all times.This situation calls for innovative intelligent systems for interactivesituation analysis to assist marine authorities and security personal in theirroutine surveillance operations. In this article, we propose a novel situationanalysis framework to analyze marine traffic data and differentiate variousscenarios of vessel engagement for the purpose of detecting anomalies ofinterest for marine vessels that operate over some period of time in relativeproximity to each other. The proposed framework views vessel behavior asprobabilistic processes and uses machine learning to model common vesselinteraction patterns. We represent patterns of interest as left-to-right HiddenMarkov Models and classify such patterns using Support Vector Machines.

An Analytic Framework for Maritime Situation Analysis