ensemble-temporal-weighting-causal-inference-hierarchical-attribution

Ensemble of Temporal Weighting, Causal Inference, and Hierarchical Attribution towards SHAP Optimization

During the past few years, the need for transparency and interpretability has been intensified owing to significant advancements in data-driven models, ...

multimodal-knowledge-graphs-based-on-rule-based-linguistic-analysis

Multimodal Knowledge Graphs Based on Rule-Based Linguistic Analysis and Computer Vision

Visual-Semantic Hindi-Aligned Multimodal Knowledge Graph (VISHAM-KG) is a framework designed to construct consistent multimodal knowledge graphs (KGs) ...

Indira Gandhi Delhi Technical University for Women

The agricultural economy heavily relies on poultry production, making accurate forecasting of poultry data crucial for optimizing revenue, streamlining resource utilization, and maximizing productivity. This research introduces a novel application of the N-BEATS architecture for multi-dimensional poultry data forecasting with enhanced interpretability through an integrated Explainable AI (XAI) framework. Leveraging its advanced capabilities in time series modeling, N-BEATS is applied to predict multiple facets of poultry disease diagnostics using a multivariate dataset comprising key environmental parameters. The methodology empowers decision-making in poultry farm management by providing transparent and interpretable forecasts. Experimental results demonstrate that N-BEATS outperforms conventional deep learning models, including LSTM, GRU, RNN, and CNN, across various error metrics, achieving MAE of 0.172, RMSE of 0.313, MSLE of 0.042, R-squared of 0.034, and RMSLE of 0.204. The positive R-squared value indicates the model's robustness against underfitting and overfitting, surpassing the performance of other models with negative R-squared values. This study establishes N-BEATS as a superior and interpretable solution for complex, multi-dimensional forecasting challenges in poultry production, with significant implications for enhancing predictive analytics in agriculture.

ArticleTotal : 2	Year
Publication title	2025
Publication title	2026

Nonita Sharma

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