Boongasame, Laor

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

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    Thai Morning Glory Price Forecasting Using Deep Learning
    (2025-01-01)
    Waeodi, Kanokwan
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    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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    A Comparative Study of Sentiment Analysis Methods for Detecting Fake Reviews in E-Commerce
    (2023-06-01)
    Puttarattanamanee, Maneerat
    ;
    Boongasame, Laor
    ;
    Thammarak, Karanrat
    The popularity of the e-commerce system has increased, especially under the COVID scenario. Consumer product reviews from the past have had a significant impact on influencing consumers' purchasing decisions. Fake reviews—those written by humans and computers that engage in dishonest behavior—are consequently generated to increase product sales. The fake reviews hurt consumers and are dishonest. The goal of this research is to examine and evaluate the performance of various methods for identifying fake reviews. The well-known and widely-used Amazon Review Data (2018) dataset was used for this research. The first 10 product categories on Amazon.com with favorable feedback will be provided in the data section. After that, perform fundamental data preparation procedures such as special character trimming, bag of words, TF-IDF, etc. The models are trained to create a dataset for detecting fake reviews. This research compares the performance of four different models: GPT-2, NBSVM, BiLSTM, and RoBERTa. The hyperparameters of the models are also tuned to find the optimal values. The research concludes that the RoBERTa model performs the best overall, with an accuracy of 97%. GPT-2 has an overall accuracy of 82%, NBSVM has an overall accuracy of 95%, and BiLSTM has an overall accuracy of 92%. The research also calculates the Area Under the Curve (AUC) for each model and finds that RoBERTa has an AUC of 0.9976, NBSVM has an AUC of 0.9888, BiLSTM has an AUC of 0.9753, and GPT-2 has an AUC of 0.9226. It can be observed that the RoBERTa model has the highest AUC value, which is close to 1. Therefore, it can be concluded that this model provides the most accurate prediction for detecting fake reviews, which is the main focus of this research.
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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.
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    Design and Implement Deepfake Video Detection Using VGG-16 and Long Short-Term Memory
    (2024-01-01)
    Boongasame, Laor
    ;
    Boonpluk, Jindaphon
    ;
    Soponmanee, Sunisa
    ;
    Muangprathub, Jirapond
    ;
    Thammarak, Karanrat
    This study aims to design and implement deepfake video detection using VGG-16 in combination with long short-term memory (LSTM). In contrast to other studies, this study compares VGG-16, VGG-19, and the newest model, ResNet-101, including LSTM. All the models were tested using Celeb-DF video dataset. The result showed that the VGG-16 model with 15 epochs and 32 batch sizes had the highest performance. The results showed that the VGG-16 model with 15 epochs and 32 batch sizes exhibited the highest performance, with 96.25% accuracy, 93.04% recall, 99.20% specificity, and 99.07% precision. In conclusion, this model can be implemented practically.