Artificial intelligence technologies for Maritime Surveillance applications

Fontana, V.; Blasco, José Manuel Delgado; Cavallini, Alberto; Lorusso, Nicola; Scremin, Alessandro; Romeo, Antonio

doi:10.1109/mdm48529.2020.00067

Cited by 3 publications

(1 citation statement)

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“…In the aforementioned experiments, the prediction effect of the deep learning DNN risk prediction model was typically inferior to that of the majority of machine learning prediction models. This may be due to the fact that the DNN model is a sort of deep learning model, and training deep learning models requires a large amount of data [50][51][52].…”

Section: Discussionmentioning

confidence: 99%

Assessing China’s Investment Risk of the Maritime Silk Road: A Model Based on Multiple Machine Learning Methods

Zhang

Wang

et al. 2022

Energies

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The maritime silk road policy of China brings opportunities to companies relating to overseas investment. Despite the investment potentials, the risks cannot be ignored and have still not been well assessed. Considering the fact that ICRG comprehensive risk has certain subjectivity, it is not completely applicable to China’s overseas investment. Therefore, based on the data of the China Statistical Yearbook and International Statistical Yearbook, a new indictor is adopted to better capture the Chinese investment risk and to make our prediction more objective. In order to acquire the ability to predict the investment risk in the future which is essential to stakeholders, machine learning techniques are applied by training the ICRG data of the previous year and Outward Foreign Direct Investment (OFDI) data of the next year together. Finally, a relative reliable link has been built between the OFDI indicator in the next year and the left ICRG indicators in the last year with both the best precision score of 86% and recall score of 86% (KNN method). Additionally, the KNN method has a better performance than the other algorithms even for high-level risk, which is more concerning for stakeholders. The selected model cannot only be used to predict an objective and reasonable investment risk level, but can also be used to provide investment risk predictions and suggestions for stakeholders.

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Section: Discussionmentioning

confidence: 99%