Chaiwuttisak, Pornpimol
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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, Multi-Criteria Decision-Making for Investment Portfolio Selection in Thailand's Stock Market(2023-07-21)This research aimed to determine the relative importance of four criteria for decision-making by using each one with a popular decision-making method and evaluating the outcomes and using these criteria to provide ranked alternatives (according to each criterion's relative importance) for making an investment decision. The four criteria were obtained from a comprehensive literature review related to securities investment. The investment data analyzed were past investment data on trading securities under the Energy and Utilities category of the SET50 index in the Stock Exchange of Thailand. The analysis was done through an Analytic Hierarchy Process (AHP) and a Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS). Opinions of three experts with experience in giving securities investment advice were collected and arranged into pair-wise comparison matrices that were used in AHP. AHP and TOPSIS calculations were done in Microsoft Excel. The results of the study show that the most important criterion was financial fundamentals with a weight of 44.59%; the second rank criterion was technical factors with a weight of 20.15%; the third-rank criterion was risk factors with a weight of 19.64%; and the last rank criterion was fundamentals of structure and sustainable development with a weight of 15.62%. In addition, the outcome of security ranking by TOPSIS and the past security ranking data were significantly similar as analyzed by a hypothesis statistical test with two dependent samples. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Text Mining Analysis of Comments in Thai Language for Depression from Online Social Networks(2021-01-01)The objectives of this research were to analyze the relationship of the phrases or words commonly found in the comments from depression hashtag on Twitter using the association rules. The data used in this study were collected from comments in Thai language via depression hashtag on Twitter during 1 January 2019 to 31 January 2019, in total of 1,500 comments. According to the comments in Thai language on social media collected by using Rapidminer Studio 9 software to get the word about depression and used to analyze relationships of words from a text comment to get the format data Association. The frequency of words and phrases in a form of presentation is used to describe the various opinions about the depression that has a presentation on social media. According to the model performance in each of the above methods, it was found that Euclidean Distance provided the best result due to the smallest average distance at all points in each cluster which was equal to 152.504. The association analysis, a total of 30 association rules were obtained, the support of 0.5% and the 80% confidence. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Biclustering similarity measures for heterogeneous data(2018-09-26); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Analysis of Driver's Attention through the Internet of Things (IOTs) for Preventing Road Accident of Natural Gas Vehicles(2021-04-01) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A Study of the Queuing System of the Local Government Registration Office in Ladkrabang District Conducted Using a Computer Simulation Model(2022-07-01)The objective of this research was to determine, by computer simulation, the queuing times for a queuing system used by the local government registration office in Ladkrabang District, Thailand, and propose a way to improve the queuing system to reduce the average waiting time for people coming to receive a variety of services. The services included the following: issuing a Thai citizen ID Card, civil registration, and general citizen registration. The simulation models were created using Arena software. The queuing system simulation (with open hours 8:00 to 16:00 each day) was based on real data collected between December 16 and December 28, 2019. The performance indicators included the average waiting time and the average utilization of the staff members. It was found that the average waiting time was 50 minutes. In addition, the average waiting time for people receiving civil registration service was greater than for the other services. The average staff utilizations during productive hours were not balanced at the different service points. Thus, four ways were formulated to improve the queue system and through the simulations seeking to find the best queue system. The best system was found to include combining the citizen ID card approval service point with the ID card delivery service point, moving two staff members previously stationed at the citizen ID card approval point and two staff members stationed at the general registration service point to the civil registration service point, and finally providing more staff members to the civil registration service point. As a result, the average waiting time would be reduced by 45.91% and the average staff utilization at the civil registration services would be reduced by 13.63 %. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Artificial Intelligence Application in Automated Odometer Mileage Recognition of Freight Vehicles(2021-01-01)Transportation cost management is necessary for entrepreneurs in industry and business. One way to do this is to report the daily mileage numbers read by the employees of a company, but still encountering errors in human mileage reading, resulting in incorrect information received and difficulties to plan effective revenue management. As well as, increasing workload and creating complications for employees in checking mileage information. Therefore, the objective of this research is to create a machine learning model for detecting and reading the mileage numbers 2 types of freight vehicles: Analog and Digital. It can be divided into 2 parts: 1) detect mileage is used to identify the position of the mileage in the image and cut only the mileage by removing the unrelated background from the image 2) detect numbers and reads miles. Both use object detection with the Faster-RCNN. The results show that to detect the position of the miles and cut only the number of miles to read the numbers correctly, 187 images from 220 test images, which is correct for the model, representing 85%. The results of the study achieve satisfactory performance that meet the requirements needed for real-life applications in the transportation and logistics industry.1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Measuring efficiency of Thailand's football premier leagues using data envelopment analysis(2018-06-20)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 three 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.1 - 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.1 - 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)1