Boongasame, Laor
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Preferred name
Boongasame, Laor
Alternative Name
Boongasame, Labor
Main Affiliation
Email
laor.bo@kmitl.ac.th
26 results
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Item type:Publication, COVID-19 Fake News Detection with Deep Learning(2023-01-01) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Thai Morning Glory Price Forecasting Using Deep Learning(2025-01-01); Thammarak, KaranratThis 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. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Fake News Detection on Social Media: Case Study of 2019 Novel Coronavirus(2021-12-17) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A Comparative Study of Sentiment Analysis Methods for Detecting Fake Reviews in E-Commerce(2023-06-01); Thammarak, KaranratThe 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. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, The theory of maximal social welfare feasible coalition(2007-01-01); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, An ELECTRE III Based CBR Approach to Combinatorial Portfolio Selection(2019-06-01); Boonjing, VeeraInvestors generally learn from historical data and use it to improve future investment decisions. However, existing portfolio selection research rarely considers such a concept. This paper proposes a novel approach to the combinatorial portfolio selection problem. Our solution employs the Case-Based Reasoning (CBR) approach with ELECTRE III, based on the Industry Classification Benchmark standards. The target stocks are ranked based on their similarities to a reference case, allowing specified selection criteria. Similarities are calculated based on selected ELECTRE III metrics. Experimental results with the Stock Exchange of Thailand data show that our CBR approach with ELECTRE III outperforms the traditional method. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A Genetic Algorithm Approach for Intermodal Cooperation with High-Speed Rail: The Case of Thai Transportation System(2020-12-01); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Combinatorial portfolio selection with the ELECTRE III method: Case study of the stock exchange of Thailand(2017-01-01) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Blockchain-based Trusty Buyer Coalition Scheme Using A Group Signature(2022-01-01); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Gold-Price Forecasting Method Using Long Short-Term Memory and the Association Rule(2023-01-01);