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Multi-source AI news clustered, deduplicated, and scored 0–100 across authority, cluster strength, headline signal, and time decay.

  1. TOOL · arXiv cs.AI English(EN) ·

    Crop Recommendation and Agricultural Query Answering System Using Spatio-Temporal Graph Neural Networks and Hybrid Retrieval Augmentation

    Researchers have developed a system for precision agriculture that uses Spatio-Temporal Graph Neural Networks (STGCN) and a Transformer-based model to forecast weather for the next 30 days across 1,359 locations in Nepal. The STGCN model demonstrated superior accuracy in predicting weather patterns. This system combines weather forecasts with soil data to provide localized crop recommendations and includes a Retrieval-Augmented Generation chatbot to answer farmers' questions in natural language, all accessible via a mobile application. AI

    IMPACT Enhances agricultural decision-making with AI-driven weather forecasts and crop recommendations, potentially improving yields and resilience.

  2. RESEARCH · arXiv stat.ML English(EN) · · [2 sources]

    Vessel Traffic Flow Prediction on Sparse Data via Spatio-Temporal Graph Neural Networks with a Learnable Tweedie Head

    Researchers have developed a new plug-and-play output module, the learnable Tweedie head, designed to enhance spatio-temporal graph neural networks (ST-GNNs) for predicting vessel traffic flow. This module specifically addresses the challenge of sparse and intermittent maritime data, which often causes conventional ST-GNNs to produce overly conservative predictions. By optimizing the Tweedie unit deviance and learning node-level variance, the new head improves forecasting accuracy, particularly for non-zero events, as demonstrated in experiments using real-world AIS data from the Ports of Los Angeles and Long Beach. AI

    IMPACT Enhances forecasting accuracy for sparse maritime data, potentially improving smart port operations and navigational safety.