The segmentation of interesting objects within the image is an important process to provide information for other procedures. The major problem of image segmentation is the number of objects suitable for classifying objects. This research aims to present on Image Segmentation using Fuzzy C- Mean Via Image Filter. The objects will be split up using their object intensities. The proposed method can classify several objects at the same process. Therefore it saves time to process less than other existing methods. The proposed method is consisted of three steps: noise reduction histogram smoothing and object classification. The results of the proposed method found that objects are automatically classified by Fuzzy C-Mean. Moreover the algorithm that successfully calculates the proper number of segmentations

This report aims to present the random noise elimination of image based on edge preservation. This solution is a pixel processing using 29 templates. Each template consists of the considered pixel and its neighbor pixels. To eliminate noises the smoothing process replaces the new pixel intensity into the same considered processing pixel by the mean intensity of neighbor pixels. The condition of the template selection is based on the minimum variance of templates. As the experimental results this solution brings the peak signal-to-noise ratio (PSNR) higher than Gaussian filter Tomita’s marks Fukuda’s marks and Nagao’s marks and also gains contrast difference.

The forecasting method is important because it can predict phenomena in the future. The accuracy of the forecast depends on the model and the initial conditions. In addition the predictability measurement method is important can be check the accurate of forecasts. In this research is winter monsoon forecasts in Thailand by the shallow water model. The data from The Bjerknes Centre for Climate Research (BCCR) University of Bergen Norway. The global climate model is Bergen Climate Model (BCM) Version 2.0 (BCCR-BCM2.0) of the Intergovernmental Panel on Climate Change (IPCC) is used. The Lyapunov exponent (LE) is the predictability measurement method for verify the efficiency of model and establish the new predictability measurement method by the time series analysis. The result to show that the new predictability measurement method by the time series analysis can be measure the efficiency of the winter monsoon forecasts in Thailand by the shallow water model for December 2015 to December 2056 are suitable.

The study on the analysis of long-term relationship among stock markets from ASEAN trading linkage: After Integration was aimed to investigate its long-term relationships as well as the short-term adjustability to establish balance among the stock markets in Thailand Malaysia and Singapore after the ASEAN unification. The research data were compiled daily from the stock markets in the three countries from 16 October 2012 until 30 December 2014. The analysis was to obtain long-term cointegration using Johansen and short-term adjustable ability to gain balance with Vector Error Correction Model (VECM) The result revealed that ASEAN trading link showed long-term relationships in good balance as well as short-term adjustable ability to gain balance of the models among the three countries. It was found that factors influencing Thailand and Malaysia stock markets were accordingly correlated opposed from Singapore which means that the three countries are well-balanced in the long and short run. It may be concluded that the stock markets in the three countries will demonstrate good integration after the ASEAN Trading Link

This research aim to study disclosure of accounting comply with TFRS were collected from Factors affecting disclosure of accounting comply with TFRS : Case Study the listed Companies in the Energy and Utilities Section in stock exchange of Thailand. The data 24 companies by using annual Financial Statement and Notes to Financial Statements. From 2009-2013 fiscal year. Statistical tools use for analyzing is multiple regression analysis at 0.001 and 0.05 significant level. The results Found that company size capital structure and year of changing to Financial Reporting Standards. Correlated with the level of disclosure at the 0.01 significant level. Furthermore The size of the audit firm correlated with the level of disclosure at the 0.05 significant level. The results suggested that the Changes from accounting standards to financial reporting required to disclose more information.