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First-order PDES for Graph Neural Networks: Advection And Burgers Equation Models

Published in , 2024

This paper presents new Graph Neural Network models that incorporate two first-order Partial Differential Equations (PDEs). These models do not increase complexity but effectively mitigate the over-smoothing problem.

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Enhanced Underwater Image Dehazing Using an Improved Dark Channel Prior Method

Published in , 2024

We propose an improved dehazing technique based on the Dark Channel Prior (DCP) method, called the Improved Dark Channel Prior (IDCP). The IDCP combines a novel airlight estimation strategy, termed Maximum Ratio Estimation (MRE), and an enhanced transmission map estimation method, termed Red Channel Retaining (RCR). IDCP incorporates the red channel, often overlooked in underwater imaging due to rapid attenuation, to improve color restoration and image clarity.

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Learnable Weight Graph Neural Network for River Ice Classification

Published in Journal 1, 2025

This paper proposed a model incorporating a graph neural network (GNN) to distinguish between ice and water from SAR images on the winters of 2017–2021 with emphasis on the Beauharnois Canal and Lake St Lawrence regions of the Saint Lawrence River.

Recommended citation: Qu, Y.; Soleymani, A.; Sudom, D.; Scott, K.A. Learnable Weight Graph Neural Network for River Ice Classification. Proceedings 2024, 110, 30. https://doi.org/10.3390/proceedings2024110030
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Talk 1 on Relevant Topic in Your Field

Published: March 01, 2012

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Tutorial 1 on Relevant Topic in Your Field

Published: March 01, 2013

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Talk 2 on Relevant Topic in Your Field

Published: February 01, 2014

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Conference Proceeding talk 3 on Relevant Topic in Your Field

Published: March 01, 2014

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Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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