AI RESEARCH
Leveraging Natural Language Processing and Machine Learning for Evidence-Based Food Security Policy Decision-Making in Data-Scarce Making
arXiv CS.AI
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ArXi:2603.20425v1 Announce Type: new Food security policy formulation in data-scarce regions remains a critical challenge due to limited structured datasets, fragmented textual reports, and graphic bias in decision-making systems. This study proposes ZeroHungerAI, an integrated Natural Language Processing (NLP) and Machine Learning (ML) framework designed for evidence-based food security policy modeling under extreme data scarcity. The system combines structured socio-economic indicators with contextual policy text embeddings using a transfer learning based DistilBERT architecture.