cs.AIMar 20, 2026

Leveraging Natural Language Processing and Machine Learning for Evidence-Based Food Security Policy Decision-Making in Data-Scarce Making

Authors: Karan Kumar Singh, Nikita Gajbhiye

Organizations: Sharda University

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Abstract

Food security policy formulation in data-scarce regions remains a critical challenge due to limited structured datasets, fragmented textual reports, and demographic 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. Experimental evaluation on a 1200-sample hybrid dataset across 25 districts demonstrates superior predictive performance, achieving 91 percent classification accuracy, 0.89 precision, 0.85 recall, and an F1 score of 0.86 under imbalanced conditions. Comparative analysis shows a 13 percent performance improvement over classical SVM and 17 percent over Logistic Regression models. Precision Recall evaluation confirms robust minority class detection (average precision around 0.88). Fairness aware optimization reduces demographic parity difference to 3 percent, ensuring equitable rural urban policy inference. The results validate that transformer based contextual learning significantly enhances policy intelligence in low resource governance environments, enabling scalable and bias aware hunger prediction systems.

Figures & tables

AuthorsTechniqueOutcome
Carneiro et al. (2025)NLP + MLFood insecurity drivers identified
Reddy et al. (2025)Bayesian NN98% accuracy
Arora et al. (2025)Gradient BoostingF1 = 0.94
Eltahir et al. (2025)Bayesian ML94.07% accuracy
Aydın et al. (2025)ML + NLP91% accuracy
Maguluri et al. (2024)CNN-Transformer98.88% accuracy
Table 1: Summary of Existing Approaches
Figure 1: Proposed Methodological Layout
Figure 2: Backend Operation Layout
Figure 3: Time vs Epoch
Figure 4: Convolution Layout
Figure 5: Technique vs Accuracy
Figure 6: NLP Model methodological layout
Figure 7: model performance evaluation
Figure 8: Precision-Recall Curve
Figure 9: Fairness Analysis
Figure 10: Matrix Layout
Figure 11: Performance Matrix Layout
ModelAccuracyF1AUCMinority RecallFairness GapData EfficiencyInterpretability
Logistic Regression790.750.82LowHighHighHigh
SVM830.810.88MediumMediumMediumMedium
DistilBERT (Proposed)940.910.95HighLowMediumLow
Table 2: Model Comparison Based on Performance, Fairness, and Interpretability
Figure 12: ROC Curve Analysis

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