Dual Optimized Event Prediction Using Meta-Heuristic Algorithms with a Distributed Deep Model for Multi-Event Forecasting

In today's technology-driven decision-making era, there is a growing demand for methodical solutions that leverage hybrid nature-inspired protocols with deep learning models (DLM).An evolution in data management through structured systems is essential.We propose a revolutionary method that combines two algorithms with distributed learning to address vast and high-velocity data streams, tackling challenges associated with noisy and imbalanced raw data sources.This study introduces an innovative integration of two nature-inspired protocols for feature selection, specifically targeting multi-event and unbalanced medical data sources in time-series-based event prediction models for source optimization.Our work utilizes the Dragonfly and Tuna Swarm algorithms within a hybrid optimization framework for feature selection.Additionally, we designed a Distributed Deep Model (DDM) to achieve high classification and prediction accuracy for multi-event data sources.Our proposed Dual Optimized Event Prediction with Distributed Deep Model (Dual-OEP-DDM) excels across key performance metrics, including accuracy, precision, recall, and F1-score.Comprehensive evaluation in dynamic event environments demonstrates that our model achieves an accuracy of 99.94%, sensitivity of 99.86%, F1-score of 99.87% (in single-event scenarios), and specificity of 99.89%, showcasing its superiority over existing models.