"Booking-Classification"

"This study presents a novel \u2018Optimized Parallelized Ensemble Learning\u2019 (OPEL) theory, that enhances multi-ensemble learning through a unique combination of a Parallel multi-Model Execution (PME), Consensus-Based Model Selection (CMS) and an Optimised Parallel Voting Mechanism (OPVM). Thus, optimizing computational efficiency, speed-ups of up to 1.3ms for some samples and model accuracy accomplished varying participating voting models dynamically for any given sample through model selection, weighting, and parallel execution strategies and utilizing performance metrics like the Matthews correlation coefficient to select top-performing models. This thus outperforms ensemble models like AdaBoost and others. Unlike existing methods such as A-Stacking or Distributed XGBoost, OPEL supports real-time dynamic model selection and multi-model parallel execution, significantly reducing runtime while improving accuracy. Experimental simulations on real-world datasets demonstrated significant reductions in computation time with 1.3x speedup and improvements in model accuracy, about 5.6% improvement on weather-based sales prediction datasets compared to conventional ensemble methods. A paired t-test confirmed the statistical significance of these improvements, highlighting OPEL\u2019s potential in distributed and resource-constrained environments. OPEL's novel contribution lies in its run-time optimized voting and parallel selection mechanism, making it suitable for edge-AI and resource-constrained environments, assuming that the performance weights and MCC scores don\u2019t remain stable during execution in dynamic environments with non-stationary data. The study also demonstrates significant gains in computational speed and accuracy through parallelization and advanced voting techniques, with a time complexity reduction as defined by Amdahl's Law. The framework was validated as both computationally efficient and robust in diverse, large-scale AI applications."