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Pavlo Maruschak, Ihor Konovalenko, Yaroslav Osadtsa, Volodymyr Medvid, Oleksandr Shovkun, Denys Baran, Halyna Kozbur and Roman Mykhailyshyn
Modern neural networks have made great strides in recognising objects in images and are widely used in defect detection. However, the output of a neural network strongly depends on both the training dataset and the conditions under which the image was ac...
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David Hanny and Bernd Resch
With the vast amount of social media posts available online, topic modeling and sentiment analysis have become central methods to better understand and analyze online behavior and opinion. However, semantic and sentiment analysis have rarely been combine...
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Wendimu Fanta Gemechu, Wojciech Sitek and Gilmar Ferreira Batalha
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Alexander Isaev, Tatiana Dobroserdova, Alexander Danilov and Sergey Simakov
This study introduces an innovative approach leveraging physics-informed neural networks (PINNs) for the efficient computation of blood flows at the boundaries of a four-vessel junction formed by a Fontan procedure. The methodology incorporates a 3D mesh...
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Aravind Kolli, Qi Wei and Stephen A. Ramsey
In this work, we explored computational methods for analyzing a color digital image of a wound and predicting (from the analyzed image) the number of days it will take for the wound to fully heal. We used a hybrid computational approach combining deep ne...
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