Combination of Neural Networks and Fuzzy Clustering Algorithm to Evalution Training Simulation-Based Training

Abstract

Abstract-With the advancement of computer technology, computer simulation in the field of education are more realistic and more effective. The definition of simulation is to create a virtual environment that accurately and real experiences to improve the individual. So Simulation Based Training is the ability to improve, replace, create or manage a real experience and training in a virtual mode. Simulation Based Training also provides large amounts of information to learn, so use data mining techniques to process information in the case of education can be very useful. So here we used data mining to examine the impact of simulation-based training. The database created in cooperation with relevant institutions, including 17 features. To study the effect of selected features, LDA method and Pearson's correlation coefficient was used along with genetic algorithm. Then we use fuzzy clustering to produce fuzzy system and improved it using Neural Networks. The results showed that the proposed method with reduced dimensions have 3% better than other methods.

Predicting the Depression of the South Korean Elderly using SMOTE and an Imbalanced Binary Dataset

“…Especially, it has been known that gradient boosting machine (GBM), which generates many classifiers and combines the predictions to derive more accurate results, and ensemble learning models such as random forest have much higher sensitivity and accuracy than a single decision tree [18,19]. Nonetheless, since the predictive performance of the ensemble learning model has been mainly tested using simulation data [20], it is necessary to conduct additional validation and verification for confirming the predictive performance of the ensemble learning model for using it for disease data, which are mostly imbalanced data [21].…”