Make corrections or additions to the rules and patterns captured as an anomaly indication at an advanced level.

Using machine learning to study patterns of changes that occur andn allow the machine to evalute, perform dynamic weight calculations and analyze risk factors will greatly help determine the targets that need to be explored

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Save a lot of resources and produce accurate intelligence products to prevent crime and accidents at sea.

Glassocean provides a simple and ready-to-use display to be used by analysts in addition, the user can make corrections or additions to the rules and patterns captured as an anomaly indication at an advanced level

Anomaly Variables

Many factors affect ship risk assessment, including:

Compliance

Age of ship, Flag, RO, Company performance, Number of deficiencies & Number of detention

Bahaviors Analysis

Turned off AIS, False Identity, Teleporting, Rendezvous, Draft change, And more

Weather

Significant wave, current, wind

And more

Key Features

Data Collection :

Provides the ability to conduct data acquisition to carry out risk assessments, including AIS / Weather / Zone / Historical Causalities data, etc.

Risk Scoring :

Analytical modeling system for conducting score assessments. The assessment system is dynamic based on the risk rules that feed into the analytic process.
Robust learning & training mechanism for testing machine-learning output and testing models.
The risk value coefficient is dynamic and can change at any time (not static variables).
Provides a minimum of 40 predefined anomaly features and alerts used to detect abnormal/illegal activities based on behavior analysis.
The customer can add alerts for behavior analysis.
Display the risk value in a simple quantitative form.
It has the function of filtering vessels based on the value of the risk score of each Vessel.
It can display a history of vessel risk assessment.