Chaiwuttisak, Pornpimol
Loading...
18 results
Now showing 1 - 10 of 18
- Some of the metrics are blocked by yourconsent settings
Item type:Publication, Analysis of accidental deaths during songkran festival using data mining(2019-01-01)The objective of this research is to analyse the deaths of people during Songkran holidays and to develop a model for the classification of deaths caused by road accidents. Data used in this research studies including Injuries and the loss of life in the accident between 2008 and 2014, a total of 2,875 people from the database of the Digital Government Development Office. The statistics used in the hypothesis testing are the Chi-square test statistic, Independent variables are behavior factors: drinking, not wearing a helmet, physical environmental factors such as the time when the road accident occurs, the type of road that caused the accident and the dependent variable was the death and injured person. The hypothesis testing at the significance level of 0.05 showed that all variables are associated with death during Songkran holidays. In addition, data mining techniques are applied to this research. Decision Tree, Bayesian Learning, Logistic Regression and Neural Network are applied to identify deaths described by a set of attributes and compare the accuracy of data classification with various data mining techniques. As the result, it was found that logistic regression can be correctly classified higher than other classification techniques with a precision of 72.20%. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Machine Learning Based Decision Support System for High-School Study(2021-01-01)The objectives of this study are to investigate the correlations between personal factors, learning factors family, and economic factors affecting high-school study program selection and also to create and compare models of high-school study program selection with data mining techniques and to develop a decision support system for high-school study program selection with a data mining technique. Data were analyzed by five data mining techniques, and models of high-school study program selection were constructed. These models were then used to construct a decision support system from data mining software called RapidMiner Studio 9. The research findings were as follows personal factors, learning factors family, and economic factors affecting high-school study program selection, and from the result of high-school study program selection, the Decision Tree method, C4.5 algorithm provided the highest accuracy. Therefore, the researcher selected the forecasting model with the Decision Tree method, C4.5 algorithm together with the selection of features with the backward elimination method to create a decision support system. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Applying Convolutional Neural Network with Candlestick Images to Predict Corn Price Movement(2025-01-01)The objective of this research is to study the effect of hyperparameters on corn price movement prediction models, namely batch size and learning rate, and create a model to predict the corn price movement in the Chicago Board of Trade (CBOT) based on candlestick images at 5-day and 20-day timeframes. The data are split into three sets, namely, training set, validation set, and test set, with a ratio of 70:10:20. The models presented in this research are CNN, VGG-16, and Efficientnet-B0, which must be fine-tuned. The study’s findings on hyperparameter values within a 5-day timeframe revealed that the optimal batch size and learning rates for all three models were a batch size of 16 with a learning rate of 0.001 and a timeframe of 20 days with a dataset size of 16. However, the suitable learning rate for the CNN model was 0.001, while for the VGG-16 and EfficientNet-B0 models, it was 0.0001. Subsequently, the hyperparameter values were fine-tuned for each model and tested the model with the test set. The study findings revealed that at the 5-day timeframe, the customized CNN model outperformed other models in predicting corn price movement, with an accuracy of 55.39%, while at a 20- day timeframe, the model with the highest accuracy was EfficientNet-B0, with an accuracy of 55.03%. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Factors affecting efficiency of police stations in metropolitan police division 3(2019-07-08)The objective of this research is to evaluate the relative performance efficiency and determine the factors affecting the efficiency of 11 police stations in the Metropolitan Police Division 3. The first stage is to analyze the efficiency of the police station by Data Envelopment Analysis (DEA) that measures the variable return to scale (VRS) and considering output-orientation. Input variable is the number of police officers. Output variables are the percentage of arrests with remand in custody, from the total amount of arrests, the percentage of arrests with remand in traffic offences, from the total amount of traffic offences, an average score of people’s satisfaction on facilities of the police station, operational processes and the service of the staffs. Secondary data are collected from 11 police stations during January and December 2017, for a total of one year. Primary data, which are the satisfaction score, are obtained from the sample survey. For the second stage, the factors affecting the performance efficiency of the police station are analyzed using multiple regression analysis. The efficient score calculated in the first stage is defined as the dependent variable. The results show that 4 police stations or 36% pure are technically efficient and there is only one police station (or 9%) with scale efficiency. In addition, the population density within the area responsible for the police station has affected the pure technical efficiency of the police station. The number of community resources within the area responsible for the police station has an effect on the scale efficiency of the police station. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Measuring Efficiency of Thailand’s Football Premier Leagues Using Data Envelopment Analysis(2019-01-01)Football is considered one of the most popular sports in Thailand and has high influence on the nation economy. Thai Premier League is football competition at the top of Thai football league system. The objective of the paper is to evaluate both sportive and financial efficiency of football clubs in Thai Premier League during 2014–2015 football seasons with Data Envelopment Analysis (DEA) model: CCR DEA and super-efficiency DEA. We consider four input factors: the stadium capacity, the capital Investment, the administrative expenses. Nine output variables investigated on the efficiency of football clubs are the number of supportive attendances, the total score in 2015, the total revenues, net assets, the number of trophies, the qualification for AFC for the last and the next season, the qualification for Thai Premier League for the last and the next season. The results show that two football clubs are not efficient. Later, it is found that both football clubs mentioned are relegated to lower league in the next competition 2016/2017. This evidence illustrates that super-efficiency DEA models can be successively to distinguish the efficient clubs and rank the football clubs in Thai Premier League. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Predicting the Workload in Debt Collections to Improve the Efficiency of Manpower Planning(2023-01-01)This research aimed to predict volume workloads in debt collections and compare the accuracy of forecasting methods. Three forecasting methods were considered in the study: the SARIMA model, the random forest model, and long short-term memory. The data used in this study were a time series of a daily debt collection volume workload due 1, 5, 10, 15, 20, and 25 in the used car loan corporation. They were divided into 2 datasets. The first dataset containing the past data from June 2020 to October 2020 was used for selecting the most suitable model, and the second dataset containing the past data from November 2020 to December 2020 was used for comparing the prediction accuracy of each forecasting model in terms of mean absolute percentage error (MAPE). Consequently, the lowest MAPE achieved by LSTM for different due dates was 6.79%, 6.24%, 7.08%, 10.88%, 12.45%, 10.17%, respectively, while the MAPE achieved by random forest was 11.72%, 11.27%, 8.94%, 11.92%, 13.91%, 16.67%, respectively, and the MAPE achieved by SARIMA was 23.35%, 32.87%, 32.58%, 30.07%, 33.93%, 27.31%, respectively. It indicates that the LSTM model was the most accurate to forecast the daily debt collection volume workload of the field debt collector in advance of the future. The predicted workload can be used as a piece of supporting information for adequate workforce planning of the corporation’s used car loan. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Forecasting export value in the automobile industry(2018-06-20)The study aims to investigate the appropriate time series techniques for forecasting export values of cars and auto parts (Million Baht). The monthly data are gathered from the website of Ministry of Commerce from January 2010 to December 2017. The data are divided to two datasets. One dataset during January 2010 and December 2016 is consists of 84 observations which is used for build the forecasting model by using Winters' Additive Exponential Smoothing and Box- Jenkins. Another dataset between January 2017 and December 2017 are used for comparing forecast accuracy based on the lowest value of Mean Absolute Deviation (MAD) and Mean Absolute Percent Error (MAPE) and selecting the most appropriate forecasting model. The result shows that Box- Jenkins is the best method for this time series data. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Applying an Improved Ant Colony Optimization to solve the Homogeneous Fixed Fleet Close Open Mixed Vehicle Routing Problem(2021-04-01) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A Comparative Study of Machine Learning and Deep Learning Models for Technology Stock Price Prediction Using News Sentiment and Economic Indicators(2026-01-01)This study developed predictive models for the closing prices of five leading technology stocks: GOOGL, MSFT, AAPL, NVDA, and META by employing five advanced machine learning and deep learning techniques: Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM). The modeling framework integrated sentiment scores derived from financial news articles specific to each stock using the VADER Sentiment Analysis tool, in conjunction with a range of macroeconomic indicators. Model performance was evaluated separately for each stock using Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) as primary metrics. To determine whether statistically significant differences existed among the predictive performance of the models across all stocks, the Friedman test was employed, followed by the Wilcoxon signed-rank test for post-hoc pairwise comparisons. The empirical results indicated that XGBoost achieved superior predictive accuracy for MSFT and AAPL, GRU outperformed other models for NVDA and META, while RNN yielded the most accurate forecasts for GOOGL. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Latent topic analysis of the post property for sales to predict a selling price of second-hand condominiums(2021-10-22)This research objective is to study the latent topics analysis in selling post of real estate of second-hand condominium by using Latent Dirichlet Allocation (LDA) and build a price prediction model of second-hand condominium using multiple linear regression and artificial neural networks by measuring and comparing the performance of the second hand condominium price prediction model with root mean square error (RMSE). This experiment included four variables are room size, number of bathroom, number of bedroom and latent topics from LDA. The result of LDA indicated that selling post of real estate can be separated into 4 topics, in which finding the factors that affect the price use the regression analysis method to get five variables are room size, number of bathroom, floors, topic 2 and topic 4. The RMSE based on the multiple linear regression analysis was 1.349, while the RMSE based on artificial neural network was 1.156. Thus, it can be concluded that the predictive model using the artificial neural networks is superior to multiple linear regression.