Fully trained BiLSTM outperforms zero-shot and LoRA-adapted TSFMs on California wildfire PM2.5 under leave-one-incident-out evaluation, especially at hazardous AQI thresholds.
Ensemble-based deep learning for estimating PM2.5 over California with multisource big data including wildfire smoke.Environment International, 155:106721, 2021
2 Pith papers cite this work, alongside 290 external citations. Polarity classification is still indexing.
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A three-layer trust framework integrating human, AI, and interaction perspectives is proposed to align multi-stakeholder trust criteria for AI-driven mental health support.
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Evaluating the Generalizability of Foundation Models for Extreme Environmental Events: Case Study of California Wildfire PM2.5
Fully trained BiLSTM outperforms zero-shot and LoRA-adapted TSFMs on California wildfire PM2.5 under leave-one-incident-out evaluation, especially at hazardous AQI thresholds.
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Aligning Human-AI-Interaction Trust for Mental Health Support: Survey and Position for Multi-Stakeholders
A three-layer trust framework integrating human, AI, and interaction perspectives is proposed to align multi-stakeholder trust criteria for AI-driven mental health support.