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The purposes of this research were to predict and assign a scale value of multilayer PCB. After the PCB board underwent a process of lamination press, it made sheets be sized according to customers' needs. The collected data came from an electronics components manufacturing company in which there are the configuration data of the scale of the multilayer PCB board which went back from January 2018 to June 2019. The dependent variables were the percentage scale values of the multilayer PCB board and the independent variables were factors affecting the determination of the percentage scale values of the multilayer PCB board. Those factors were layer count, distance, core thickness, thickness Cu side (thickness of copper on laminate core board), thickness Cu Foil, Tg (glass transition temperature of material), cut direction, Axis, streak (Warp x Fill) and material brand. The models we used for analyzing were a multiple linear regression method (MLR), a support vector regression method (SVR), and a decision tree regression method. Those methods were the supervised learning models in machine learning and they were processed by using the RStudio program, mean square error (MSE) and mean absolute percent error (MAPE) for comparison of the efficiency of models for scale predictions. The result revealed that the MSE and the MAPE value of the support vector regression model are minimal, which means it is the most suitable model for the data of scale predictions of multilayer PCB board due to helping increase the accuracy in assigning scale values and helping save the time of the production process as a consequence of errors in assigning scale values. [ABSTRACT FROM AUTHOR]

This research aims to study the performance of data mining techniques in medical datasets. The data in this research contaita information of patients with breast cancer, diabetics and patients with hyperthyroidism. All datasets were collected from UCI databasee Mmachine learning, in particular Decision Tree C4.5, Naïve Bayes, Neural Networks, Random Forest and Deep Learning techniques were used to create the models of disease Breast cancer, diabetes and hypothyroidism prediction models. In order to measure the performance of prediction models, 10-fold cross validation was utilized to divide the data into training and testing sets. Accuracy, sensitivity and specificity of the prediction models were used to compare the prediction performance of each model. The experimental results showed that the Decision Tree C4.5 technique was the best technique in modeling the prognosis of hypothyroidism. It provided 99.86 % accuracy, 99.85 % sensitivity and 100 % specificity. [ABSTRACT FROM AUTHOR]

This research aimed (1) to create cardiovascular risk diagnosis prediction models using algorithms including Neural Network, Random Forest, Naïve Bayes, K-Nearest Neighbors and Decision Tree (2) five algorithms were used with attribute selection and (3) comparing the model performance using 10f Fold cross validation method. Tools usee were MySQL and RapidMiner Studio programs. The data see comprised people who had been screened as patients with cardiovascular disease that were collected from the Saraburi Provincial Public Health Office during 2018-2019 from 12 Saraburi hospitals and 126 health promoting hospitalsreIt was found that the model with the best prediction performance was the neural network model with attribute selection having 99.29% accuracy, and the lowest was the decision tree model with 70.39% accuracy. This research concluded that the neural network model with attribute selection of the best qualificationt should be further developed for early diagnosis of cardiovascular risk web applications. [ABSTRACT FROM AUTHOR]

Thai University Central Admission System, abbreviated to "TCAS," is an educational reform policy under the Ministry of Higher Education, Science, Research and Innovation (MHESI), together with the Council of University Presidents of Thailand (CUPT) as the hub of operations. All universities have joined TCAS following five admissions application rounds. This policy has been implemented since the academic year of 2018. Mahasarakham University has participated in TCAS in every academic year and all admissions application rounds. Every year numerous candidates failed admissions into programs chosen for clearing house entering the TCAS system. There are problems that candidates overlooked scores on the General Aptitude Test (GAT) and Professional and Academic Aptitude Test (PAT) as a key component in a consideration of the program chosen. The candidate's selection behavior toward a program is typically based on his/her feelings, preferences, and peers, and parent's determination. In this study, the Data Mining technique was adopted, and Decision Tree was used to analyze the data. A result of the analysis was further used to develop a decision support system (DSS) for the program to be chosen by the applicants to test the program selection and to analyze the opportunity to achieve before the actual selection of the respective program. The results found that the Decision Tree at C4.5 represented an accuracy of 91.64 percent, and a total of 358 decision rules can be developed across 84 programs, and decision rules can be developed as a decision support system for program selection to the TCAS of Mahasarakham University. [ABSTRACT FROM AUTHOR]