Vessel Destination Prediction Using a Graph-Based Machine Learning Model

Gouareb, Racha; Can, Francois; Ferdowsi, Sohrab; Teodoro, Douglas

doi:10.1007/978-3-030-97240-0_7

Cited by 3 publications

(2 citation statements)

References 36 publications

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“…As a general conclusion, the use of the super-graph topological features seems to consistently increase the predictive power of the models. This is consistent with works in other domains that show the benefit of topological features in the predictive power of machine learning models based on graph abstractions [14].…”

Section: Resultssupporting

confidence: 91%

On Graph Construction for Classification of Clinical Trials Protocols Using Graph Neural Networks

Ferdowsi

Copara

Gouareb

et al. 2022

Artificial Intelligence in Medicine

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A recent trend in health-related machine learning proposes the use of Graph Neural Networks (GNN's) to model biomedical data. This is justified due to the complexity of healthcare data and the modelling power of graph abstractions. Thus, GNN's emerge as the natural choice to learn from increasing amounts of healthcare data. While formulating the problem, however, there are usually multiple design choices and decisions that can affect the final performance. In this work, we focus on Clinical Trial (CT) protocols consisting of hierarchical documents, containing free text as well as medical codes and terms, and design a classifier to predict each CT protocol termination risk as "low" or "high". We show that while using GNN's to solve this classification task is very successful, the way the graph is constructed is also of importance and one can benefit from making a priori useful information more explicit. While a natural choice is to consider each CT protocol as an independent graph and pose the problem as a graph classification, consistent performance improvements can be achieved by considering them as super-nodes in one unified graph and connecting them according to some metadata, like similar medical condition or intervention, and finally approaching the problem as a node classification task rather than graph classification. We validate this hypothesis experimentally on a large-scale manually labeled CT database. This provides useful insights on the flexibility of graphbased modeling for machine learning in the healthcare domain.

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Section: Resultssupporting

confidence: 91%

On Graph Construction for Classification of Clinical Trials Protocols Using Graph Neural Networks

Ferdowsi

Copara

Gouareb

et al. 2022

Artificial Intelligence in Medicine

Self Cite

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“…By performing convolutions on graphs with arbitrary structures, graph neural networks can learn rich spatial features. In recent years, they have been successfully applied in various domains, such as trajectory prediction [14,15] and traffic flow prediction [16].…”

Section: Introductionmentioning

confidence: 99%

Research on Multi-Port Ship Traffic Prediction Method Based on Spatiotemporal Graph Neural Networks

Mei

et al. 2023

JMSE

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The intelligent maritime transportation system has emerged as a pivotal component in port management, owing to the rapid advancements in artificial intelligence and big data technology. Its essence lies in the application of digital modeling techniques, which leverage extensive ship data to facilitate efficient operations. In this regard, effective modeling and accurate prediction of the fluctuation patterns of ship traffic in multiple port regions will provide data support for trade analysis, port construction planning, and traffic safety management. In order to better express the potential interdependencies between ports, inspired by graph neural networks, this paper proposes a data-driven approach to construct a multi-port network and designs a spatiotemporal graph neural network model. The model incorporates graph attention networks and a dilated causal convolutional architecture to capture the temporal and spatial dimensions of traffic variation patterns. It also employs a gated-mechanism-based spatiotemporal bi-dimensional feature fusion strategy to handle the potential unequal relationships between the two dimensions of features. Compared to existing methods for port traffic prediction, this model fully considers the network characteristics of the overall port and fills the research gap in multi-port scenarios. In the experiments, real port ship traffic datasets were constructed using data from the Automatic Identification System (AIS) and port geographical information data for model validation. The results demonstrate that the model exhibits outstanding robustness and performs well in predicting traffic in multiple sub-regional port clusters.

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