Results 91 to 100 of about 5,438,478 (196)
Aspect Level Sentiment Analysis Based on Sentiment-Enhanced and Dual Graph Convolutional Network [PDF]
Jisuanji gongchengAspect Level Sentiment Analysis (ALSA) is designed to detect the sentiment polarity aspect of a given sentence. Most existing studies have reported the construction of Graph Convolutional Networks (GCN) on syntactic dependency trees to obtain syntactic ...
DAI Wei, WANG Fengyu, JI Changpeng
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Scalable Graph Convolutional Networks With Fast Localized Spectral Filter for Directed Graphs
IEEE Access, 2020 Graph convolutional neural netwoks (GCNNs) have been emerged to handle graph-structured data in recent years. Most existing GCNNs are either spatial approaches working on neighborhood of each node, or spectral approaches based on graph Laplacian ...
Chensheng Li +4 more
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DeepCPP: A Deep Learning‐Based Framework for Enhanced Prediction of Cell Penetrating Peptides
Advanced Intelligent Systems, EarlyView.DeepCPP is a multimodal deep learning framework designed to improve the identification of cell‐penetrating peptides. By integrating sequence‐derived evolutionary information with structural graph representations through a dual‐encoder architecture and fusion strategy, the model captures complementary molecular features.
Soualihou Ngnamsie Njimbouom +2 more
wiley +1 more source
Variational Graph Convolutional Networks for Dynamic Graph Representation Learning
IEEE AccessThe ubiquitous and ever-evolving nature of cyber threats demands innovative approaches that can adapt to the dynamic relationships and structures within network data.
Aabid A. Mir +4 more
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A deep graph convolutional neural network architecture for graph classification.
PLoS ONE, 2023 Graph Convolutional Networks (GCNs) are powerful deep learning methods for non-Euclidean structure data and achieve impressive performance in many fields. But most of the state-of-the-art GCN models are shallow structures with depths of no more than 3 to
Yuchen Zhou +3 more
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Chemie Ingenieur Technik, EarlyView.This work explores generative AI for automated revision of Piping and Instrumentation Diagrams (P&IDs). We frame P&ID correction as a translation problem, converting attributed P&ID graphs into sequences and learning revisions with a transformer‐based model.
Lukas Schulze Balhorn +5 more
wiley +1 more source
Electron, EarlyView.Physics‐driven advances in optical nanobiosensors for rapid, miniaturized, and point‐of‐care diagnostics for next‐generation decentralized and personalized healthcare based on sensor intelligence. ABSTRACT Public health emergencies and the escalating burden of chronic diseases necessitate a paradigm shift from centralized laboratory testing to rapid ...
Vishal Chaudhary +5 more
wiley +1 more source
iMetaMed, EarlyView.By shifting from highly variable taxonomic abundance to conserved ecological network topologies, our Dual‐Stream Graph Attention Network (DS‐GAT‐CP) resolves the “abundance paradox,” enabling highly accurate and cross‐regionally robust non‐invasive screening for Type 2 Diabetes. ABSTRACT Gut microbiome‐based machine learning models for Type 2 Diabetes (
Changjian Wu +4 more
wiley +1 more source
Gong-kuang zidonghuaSkeleton-sequence-based behavior recognition models are characterized by fast processing speeds, low computational requirements, and simple structures. Graph convolutional networks (GCNs) have advantages in processing skeleton sequence data.
WANG Jianfang +3 more
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Interdisciplinary Medicine, EarlyView.Individualized brain–gut association networks suggest potential neuropathological mechanisms in MDD and MDDSI. Multilevel brain–gut association changes involving HSV‐1 and inflammation may be relevant to MDD, whereas alterations related to insulin metabolism may play a potential role in the neuropathological mechanisms of MDDSI.
Wei Wang +10 more
wiley +1 more source