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Deep learning has shown its greatest impact in major applications like image processing, speech recognition, and video classification. Datasets train deep learning models in Euclidean space with fixed dimensions and sequences. In order to come up with difficulties in feature engineering, graph neural networks (GNNs) come into existence. Graph neural network is also a deep learning framework that helps in modeling data structures and graphs. Recent developments are focusing much on GNN-based applications. This study encompasses the cutting-edge technologies used in various applications. This study analyzes various areas of GNN-based applications. This study dwells deep with GNN for biomedical and traffic flow analysis. This helps in getting more insights on GNN over various applications with profound knowledge.

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Graph Neural Networks Applications: A Dwell into Biomedical Applications and Traffic Flow Analysis

  • 章节

  • 发表日期：

    2025年

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  • M Jansi Rani,
  • S. Usha,
  • M Karuppasamy,
  • K Poorani,
  • M Prabha

摘要

Deep learning has shown its greatest impact in major applications like image processing, speech recognition, and video classification. Datasets train deep learning models in Euclidean space with fixed dimensions and sequences. In order to come up with difficulties in feature engineering, graph neural networks (GNNs) come into existence. Graph neural network is also a deep learning framework that helps in modeling data structures and graphs. Recent developments are focusing much on GNN-based applications. This study encompasses the cutting-edge technologies used in various applications. This study analyzes various areas of GNN-based applications. This study dwells deep with GNN for biomedical and traffic flow analysis. This helps in getting more insights on GNN over various applications with profound knowledge.