of cassava price based on the 11-year data (from 2005 to 2015) obtained from the Thai Tapioca Starch Association and Office of Agricultural Economics. Various techniques were applied for the forecast such as Artificial Neural Network; Support Vector Machine... that Hybrid Technique demonstrated the lowest value of error followed by Artificial Neural Network; k-Nearest Neighbor and Support Vector Machine; respectively. Therefore; it could be concluded that using the Hybrid Technique to forecast the price of cassava

The main purpose of this study is to develop the intrinsic plagiarism detection in Thai academic writing system using Support Vector Machine model (SVM.) as well as comparing performance of two different kinds of input and feature and then analyzes... whether the length of input has an effect on accuracy. This study uses 300 pieces of master theses of undergraduate students from Chulalongkorn University consists of 5,155,589 words in total. Support Vector Machine model applied in the research is libsvm

classifier approaches are conducted in this experiment. The study shows that the most effective classifiers ranked from the highest accuracy are Support Vector Machine; Naïve Bayes; Decision Tree; and Multi Layer Perceptron. Support Vector Machine produces

. The prediction model has been designed; trained; and tested with some empirical machine learning algorithms namely Decision Tree; Random Forest; K-Nearest Neighbor; and Support Vector Machine. Finally; the experimentation shows that Decision Tree and Random... the diagnosis complexity and treatment cost. In this study; a data mining and machine learning model is proposed to predict three types of heart blocks; such as 1st-degree A-V block; Left Bundle Branch Block (LBBB); and Right Bundle Branch Block (RBBB

This paper presents to compare prediction models for road deaths on road network by data mining techniques. In this work; the classifier is selected from four prediction algorithms: Random Forest (RF); Support Vector Machine (SVM); K-Nearest

lead time for reservoir inflow forecasting. The two well-knows ML: Support Vector Regression (SVR) and Random Forests (RF); were used to predict water inflow volume into the reservoir. Both methods will improve the efficiency of the reservoir inflow...The problem of reservoir inflow forecasting plays a critical role in reservoir management. However; reservoir inflow forecasting must be necessarily accurate and timely. This paper presents practical machine learning (ML) technique and the optimal

-learning based classification is exploited to develop a model in discriminate positive and negative risk of being MDD according to Thai screening questionnaire(2Q). From evaluation; the best machine-learning technique for the task was support vector machine... major depressive disorder (MDD) from Thai posts in social media. Unlike using questionnaire which requires a person to personally perform; the proposed method can help in an MDD screening task to cover in mass-screening on social media. The machine

The purposes of this study are to find out linguistic features to be used in Thai clause segmentation using support vector machine (SVM) model as well as to compare efficiency of those features on clause segmentation system. The corpus used

vector machine model and text similarity measurement. The third is to evaluate the effectiveness of the prototype system developed in 2 aspects: the suitability of the input characteristics to be used in the system and the suitability of the features used...This research is based on 4 objectives: first, to analyze the linguistic features used to classify plagiarized text and non-plagiarized text. The next is to develop a prototype system for extrinsic academic plagiarism detecting using a support

of all original features; a feature optimisation process is performed. The optimal feature set is searched by a machine learning approach; like naïve Bayes and support vector machine classifier. Hand-drawn ground-truth images from expert ophthalmologists