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How do navy escorts influence piracy risk in East Africa? A Bayesian network approach
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How do navy escorts influence piracy risk in East Africa? 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References

  • 2019An advanced fuzzy Bayesian-based FMEA approach for assessing maritime supply chain risks229 cited
  • 2016A novel flexible model for piracy and robbery assessment of merchant ship operations128 cited
  • 2003Customer satisfaction analysis: Identification of key drivers106 cited
  • 2020The analysis of maritime piracy occurred in Southeast Asia by using Bayesian network89 cited
  • 2014A Bayesian network to manage risks of maritime piracy against offshore oil fields83 cited
  • 2014THE WELFARE COST OF LAWLESSNESS: EVIDENCE FROM SOMALI PIRACY71 cited
  • 1995ROUGH SEAS?66 cited
  • 2017The long-term impact of maritime piracy on seafarers’ behavioral health and work decisions56 cited
  • 2014Economic shocks in the fisheries sector and maritime piracy54 cited
  • 2013Quantitative risk analysis – Ship security analysis for effective risk control options47 cited
  • 2023Towards safe navigation environment: The imminent role of spatio-temporal pattern mining in maritime piracy incidents analysis40 cited
  • 2015The declining impact of piracy on maritime transport in the Indian Ocean: Statistical analysis of 5-year vessel tracking data40 cited
  • 2023PiracyAnalyzer: Spatial temporal patterns analysis of global piracy incidents38 cited
  • 2019Marine piracy prediction and prevention: Policy implications

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38 cited
  • 2023Leverage Bayesian Network and Fault Tree Method on Risk Assessment of LNG Maritime Transport Shipping Routes: Application to the China–Australia Route36 cited
  • 2023Quantitative analysis of maritime piracy at global and regional scales to improve maritime security31 cited
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    • 2024Hotspot analysis of global piracy and armed robbery incidents at sea: A decadal review of regional vulnerabilities and security strategies9 cited
    • 2025Maritime security threats: Classifying and associating patterns in piracy and armed robbery incidents5 cited