Boongasame, Laor

Preferred name

Boongasame, Laor

Alternative Name

Boongasame, Labor

Main Affiliation

School of Science

laor.bo@kmitl.ac.th

Now showing 1 - 10 of 17

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COVID-19 Fake News Detection with Deep Learning
(2023-01-01)
Kowirat, Rutchaneewan
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Boongasame, Laor
Social media has become one of the most popular channels to keep updated with daily news because it can quickly and easily access information. This advantage is used by malicious people to spread fake news widely. Since the COVID-19 pandemic, fake news has become a huge social problem, causing people to panic and misunderstand how to cure or protect themselves from the virus. So, the goal of this research is to use deep learning as the Recurrent Neural Network (RNN) model to find fake news about COVID-19 in the Thai language on social media and help filter information by classifying real and fake news.
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Thai Morning Glory Price Forecasting Using Deep Learning
(2025-01-01)
Waeodi, Kanokwan
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Boongasame, Laor
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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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Fake News Detection on Social Media: Case Study of 2019 Novel Coronavirus
(2021-12-17)
Kowirat, Rutchaneewan
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Boongasame, Laor
Fake news is news that is created with the intent to deceive and mislead readers. It is a problem that occurs in every era because it creates misunderstandings for people through a variety of media channels such as newspapers, radio, or television. Nowadays, fake news has become a big problem. When social media has become another channel to increase the spread of fake news and came to play a big role during the epidemic like COVID-19. Fake news creates panic and creates false knowledge of how to protect yourself from COVID-19. Therefore, the objective of this research is to create a method that can detect fake news on social media. It focuses only on news related to COVID-19. In addition, the information was extracted directly from social media such as Twitter. Moreover, this research applying machine learning processes to classify real and fake news. From the experimental results, the accuracy was measured at 99.92% with the Decision Tree model.
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A Comparative Study of Sentiment Analysis Methods for Detecting Fake Reviews in E-Commerce
(2023-06-01)
Puttarattanamanee, Maneerat
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Boongasame, Laor
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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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A Genetic Algorithm Approach for Intermodal Cooperation with High-Speed Rail: The Case of Thai Transportation System
(2020-12-01)
Boongasame, Laor
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Temdee, Punnarumol
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Kumnungkit, Kanchana
The Thai government has a plan to start the first operation of the Thai High-Speed Rail (THSR) in 2021. However, ensuring the profit of THSR while limiting the project impacts on existing transport options is challenging. In this study, an approach for identifying the optimal travel frequency for impacted transportation services after the THSR operation is implemented. The genetic algorithm (GA) is introduced with specific value functions of various transport options, including rail, bus, and van, to reschedule each travelling option under the intermodal cooperation model. The constraint of GA is that the profit of the individual transport option in the next generation will have to be higher than the total profit of the previous generation. From the case study between Nakhon Ratchasima and Bangkok, the simulation results show that THSR and other transport options have overall gain higher profits after the start of THSR operation. Regarding social welfare theory, the simulation results show that the profit of each transport option is proven to be stable concerning travel schedule frequency after implementation of the THSR system.
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Blockchain-based Trusty Buyer Coalition Scheme Using A Group Signature
(2022-01-01)
Boongasame, Laor
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Chaising, Supansa
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Temdee, Punnarumol
Without trust, buyers may not join a coalition. Despite the tremendous need for trustworthy relationships in buyer coalitions, no current buyer coalition scheme explicitly tackles confidence issues with blockchain technology. This study proposes an algorithmic design, the blockchain-based trusty buyer coalition scheme, to satisfy the trust requirement among different actors while forming the coalition. All activities forming a coalition through a decentralized public ledger can be explicitly examined. Consequently, the proposed algorithm can ensure anonymity within a community, resulting in trusting relationships. Furthermore, the proposed algorithm can ensure correctness and accountability by recognizing misbehavior and enforcing alternative forms of punishment. Additionally, the discovered algorithm can be applied to mobile commerce applications.
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Gold-Price Forecasting Method Using Long Short-Term Memory and the Association Rule
(2023-01-01)
Boongasame, Laor
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Viriyaphol, Piboonlit
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Tassanavipas, Kriangkrai
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Temdee, Punnarumol
Since gold prices influence international economic and monetary systems, numerous studies have been conducted to forecast gold prices. Nonetheless, studies employing the linear relationship method usually fail to explain the change in the pattern of the gold price. This study introduces a new paradigm that incorporates association rules and long short-term memory (LSTM) as a nonlinear-based method. For simulation, the proposed method was analyzed with data from Yahoo Finance from January 2010 to December 2020. The association rule was used to choose features relevant to the gold spot (GS) in the US Dollar Index (DXY). The LSTM forecast the gold price with a range of hyperparameter settings. The simulation results showed that the proposed method—the LSTM with GS and DXY, or LSTM-GS-DXY—resulted in low mean absolute percentage error (MAPE) metrics. In addition, the proposed LSTM-GS-DXY system outperformed the simple moving average (SMA), weight moving average (WMA), exponential moving average (EMA), and auto-regressive integrated moving average (ARIMA).
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An Extension of Laor Weight Initialization for Deep Time-Series Forecasting: Evidence from Thai Equity Risk Prediction
(2025-09-01)
Petchpol, Katsamapol
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Boongasame, Laor
This study presents a gradient-informed proxy initialization framework designed to improve training efficiency and predictive performance in deep learning models for time-series forecasting. The method extends the Laor Initialization approach by introducing backward gradient norm clustering as a selection criterion for input-layer weights, evaluated through a lightweight, architecture-agnostic proxy model. Only the numerical input layer adopts the selected initialization, while internal components retain standard schemes such as Xavier, Kaiming, or Orthogonal, maintaining compatibility and reducing overhead. The framework is evaluated on a real-world financial forecasting task: identifying high-risk equities from the Thai Market Surveillance Measure List, a domain characterized by label imbalance, non-stationarity, and limited data volume. Experiments across five architectures, including Transformer, ConvTran, and MMAGRU-FCN, show that the proposed strategy improves convergence speed and classification accuracy, particularly in deeper and hybrid models. Results in recurrent-based models are competitive but less pronounced. These findings support the method’s practical utility and generalizability for forecasting tasks under real-world constraints.
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Enhanced Feature Selection via Hierarchical Concept Modeling
(2024-12-01)
Saelee, Jarunee
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Wetchapram, Patsita
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Wanichsombat, Apirat
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Intarasit, Arthit
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Muangprathub, Jirapond
The objectives of feature selection include simplifying modeling and making the results more understandable, improving data mining efficiency, and providing clean and understandable data preparation. With big data, it also allows us to reduce computational time, improve prediction performance, and better understand the data in machine learning or pattern recognition applications. In this study, we present a new feature selection approach based on hierarchical concept models using formal concept analysis (FCA) and a decision tree (DT) for selecting a subset of attributes. The presented methods are evaluated based on all learned attributes with 10 datasets from the UCI Machine Learning Repository by using three classification algorithms, namely decision trees, support vector machines (SVM), and artificial neural networks (ANN). The hierarchical concept model is built from a dataset, and it is selected by top-down considering features (attributes) node for each level of structure. Moreover, this study is considered to provide a mathematical feature selection approach with optimization based on a paired-samples t-test. To compare the identified models in order to evaluate feature selection effects, the indicators used were information gain (IG) and chi-squared (CS), while both forward selection (FS) and backward elimination (BS) were tested with the datasets to assess whether the presented model was effective in reducing the number of features used. The results show clearly that the proposed models when using DT or using FCA, needed fewer features than the other methods for similar classification performance.
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Enhancing Predictive Capabilities for Identifying At-Risk Stocks Using Multivariate Time-Series Classification: A Case Study of the Thai Stock Market
(2025-01-01)
Petchpol, Katsamapol
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Boongasame, Laor
This study proposes a multivariate time-series classification approach using deep learning to predict stocks likely to be flagged by the Market Surveillance Measure List in the Thai stock market. Formulated as a binary classification problem, the model distinguishes At-Risk and Normal stocks based on two primary datasets: End-of-Day stock prices and Market Surveillance Measure List records, incorporating trading volumes and technical indicators. To address data imbalance, concept drift, and long-term dependencies, the framework integrates feature engineering, cost-sensitive learning, and rolling window training. Experimental results show deep learning models significantly outperform traditional baseline methods in capturing financial risk patterns. The study identifies models that effectively balance predictive accuracy with computational efficiency, with performance varying based on forecasting horizons. Despite improvements from specialized techniques, the study identifies challenges in long-term financial risk prediction. These findings support market surveillance, algorithmic trading, and portfolio risk management, with future work exploring explainable AI, adaptive learning, and alternative data sources to enhance interpretability and long-term forecasting.

Boongasame, Laor

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