Boongasame, Laor

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Boongasame, Laor
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Boongasame, Labor
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laor.bo@kmitl.ac.th

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    Thai Morning Glory Price Forecasting Using Deep Learning
    (2025-01-01)
    Waeodi, Kanokwan
    ;
    Boongasame, Laor
    ;
    Thammarak, Karanrat
    This study established advanced machine-learning-driven forecasting models to enhance the accuracy of price predictions for Thai morning glory, a widely consumed leafy green vegetable. The models were trained using historical price, weather, and rainfall data using time-series forecasting methods, specifically LSTM and CNN. The findings indicate that stepwise feature selection minimizes prediction errors and improves MSE, RMSE, MAPE, and MAE. Preliminary experiments revealed that the LSTM model with feature selection outperformed the other models, particularly in feature selection. Employing standard hyperparameters of 100 epochs, 32 batches, and five windows, the model demonstrated superior performance with a lower MSE (0.0010), RMSE (0.0274), MAPE (3.7803), and MAE (0.0158) than the CNN model. Statistical hypothesis testing revealed significant variations between the LSTM and CNN models, with feature selection p-values below 0.05. These results indicate that LSTM with feature selection models optimized through refined hyperparameters leads to more accurate Thai morning glory price forecasting, providing valuable insights for stakeholders in their decision-making processes. Additionally, this study can forecast prices for 5, 7, 14, and 21 days in advance based on different Window_len values, addressing various planning needs. The 5- and 7-day forecasts support short-term decision-making, such as scheduling harvest cycles and weekly market planning, whereas the 14-day forecast assists farmers in optimizing planting schedules and logistics. Furthermore, the 21-day forecast is beneficial for medium-term market planning, including negotiating forward contracts and adjusting distribution strategies to maximize profitability.
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    Laor Initialization: A New Weight Initialization Method for the Backpropagation of Deep Learning
    (2025-07-01)
    Boongasame, Laor
    ;
    Muangprathub, Jirapond
    ;
    Thammarak, Karanrat
    This paper presents Laor Initialization, an innovative weight initialization technique for deep neural networks that utilizes forward-pass error feedback in conjunction with k-means clustering to optimize the initial weights. In contrast to traditional methods, Laor adopts a data-driven approach that enhances convergence’s stability and efficiency. The method was assessed using various datasets, including a gold price time series, MNIST, and CIFAR-10 across the CNN and LSTM architectures. The results indicate that the Laor Initialization achieved the lowest K-fold cross-validation RMSE (0.00686), surpassing Xavier, He, and Random. Laor demonstrated a high convergence success (final RMSE = 0.00822) and the narrowest interquartile range (IQR), indicating superior stability. Gradient analysis confirmed Laor’s robustness, achieving the lowest coefficients of variation (CV = 0.2230 for MNIST, 0.3448 for CIFAR-10, and 0.5997 for gold price) with zero vanishing layers in the CNNs. Laor achieved a 24% reduction in CPU training time for the Gold price data and the fastest runtime on MNIST (340.69 s), while maintaining efficiency on CIFAR-10 (317.30 s). It performed optimally with a batch size of 32 and a learning rate between 0.001 and 0.01. These findings establish Laor as a robust alternative to conventional methods, suitable for moderately deep architectures. Future research should focus on dynamic variance scaling and adaptive clustering.