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Signal #159140NEUTRAL

Enhanced Artificial Neural Networks Using QHAdamW in Air Quality Forecasting

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arXiv:2608.21463v1 Announce Type: cross Abstract: The study employed an Artificial Neural Network in combination with the optimized Adaptive Moment Estimation (Adam) algorithm, currently the only AQI forecasting model available in the Philippines. The modified QHAdamW - Quasi-Hyperbolic Momentum (QHAdam) and Adam with decoupled weight decay (AdamW) were both extensions of the Adam optimizer, and both offer unique advantages for training ANN. The proposed QHAdamW optimizer addresses the issues on convergence, generalization, and forecasting performance of Adam. Hyperparameter tuning results revealed that 0.01 and 0.001 were the most effective optimal values for the generalization performance of QHAdamW. The comparative analysis results using seven evaluation metrics revealed that the error value range is lower, and the regression coefficient, having a value approximately equal to 1, improved the model accuracy performance. Likewise, the model converges to a satisfactory level of perform...

arXiv Neural/NEabout 3 hours ago
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Enhanced Artificial Neural Networks Using QHAdamW in Air Quality Forecasting | Steek AI Signal | Steek