IN ORDER TO ACHIEVE OPTIMAL MODEL CONVERGENCE WE EMPLOYED A QUANTUM GRADIENT DESCENT ALGORITHM COMBINED WITH META-HEURISTIC FEATURE FUSION. THROUGH ITERATIVE DEEP LEARNING ENSEMBLES WITH A STOCHASTIC DYNAMIC PROGRAMMING APPROACH, WE FINE-TUNED THE HYPERPARAMETERS USING A GENETIC ALGORITHM WITH SIMULATED ANNEALING. LINEAR REGRESSION; LINEAR REGRESSION
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