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Paper#Medical Imaging, Graph Neural Networks, Time Series Analysis, Tobacco Use Prediction🔬 ResearchAnalyzed: Jan 3, 2026 16:13

GNN-TF for Forecasting Tobacco Use from Brain Imaging and Tabular Data

Published:Dec 29, 2025 01:58
1 min read
ArXiv

Analysis

This paper introduces a novel Graph Neural Network model with Transformer Fusion (GNN-TF) to predict future tobacco use by integrating brain connectivity data (non-Euclidean) and clinical/demographic data (Euclidean). The key contribution is the time-aware fusion of these data modalities, leveraging temporal dynamics for improved predictive accuracy compared to existing methods. This is significant because it addresses a challenging problem in medical imaging analysis, particularly in longitudinal studies.
Reference

“The GNN-TF model outperforms state-of-the-art methods, delivering superior predictive accuracy for predicting future tobacco usage.”

PermalinkArXiv
Paper#LLM🔬 ResearchAnalyzed: Jan 3, 2026 16:37

LLM for Tobacco Pest Control with Graph Integration

Published:Dec 26, 2025 02:48
1 min read
ArXiv

Analysis

This paper addresses a practical problem (tobacco pest and disease control) by leveraging the power of Large Language Models (LLMs) and integrating them with graph-structured knowledge. The use of GraphRAG and GNNs to enhance knowledge retrieval and reasoning is a key contribution. The focus on a specific domain and the demonstration of improved performance over baselines suggests a valuable application of LLMs in specialized fields.
Reference

“The proposed approach consistently outperforms baseline methods across multiple evaluation metrics, significantly improving both the accuracy and depth of reasoning, particularly in complex multi-hop and comparative reasoning scenarios.”

PermalinkArXiv