Abstract

Digital media supply chain systems have experienced rapid expansion due to increasing demand for real-time content distribution and adaptive forecasting mechanisms. However, the variability in cross-modal data streams has created significant uncertainty in predicting demand, resource allocation, and delivery efficiency. Traditional forecasting models have struggled to capture nonlinear dependencies across heterogeneous media sources, leading to inconsistent performance in dynamic environments. This study proposed a Neuro-Fuzzy Evolutionary Cross-Modal Forecasting (NFECF) framework to address these limitations. The framework integrated neural network learning capabilities with fuzzy inference reasoning and evolutionary optimization strategies to enhance predictive accuracy. The neuro component modeled nonlinear relationships across multimodal datasets, while the fuzzy layer handled uncertainty in data interpretation. Evolutionary optimization refined model parameters through iterative selection and adaptation. Experimental results demonstrate that the proposed NFECF framework achieved 0.41 MAE, 0.60 RMSE, 4.5% MAPE, 0.97 R², and 96% forecasting accuracy, outperforming LSTM, NFIS, and GA Regression significantly in cross-modal digital media supply chain forecasting tasks.

Authors

Parul Dhull, Suresh Kumar Sharma
Sri Karan Narendra Agriculture University, India

Keywords

Neuro-Fuzzy Systems, Evolutionary Optimization, Cross-Modal Forecasting, Digital Media Supply Chain, Predictive Analytics