Digital Library

1. 1. Anomaly detection and attack type classification mechanism using Extra Tree and ANN
   Min-Gyu Kim, Myung-Mook Han, Vol. 23, No. 5, pp. 79-85, Oct. 2022
   10.7472/jksii.2022.23.5.79
   Keywords: Extreme Random Forest, Artificial neural network, Anomaly Detection, Anomaly Detection and Attack type Classification, Network intrusion detection
2. 2. Exploring the Predictive Variables of Government Statistical Indicators on Retail sales Using Machine Learning: Focusing on Pharmacy
   Gwang-Su Lee, Vol. 23, No. 3, pp. 125-135, Jun. 2022
   10.7472/jksii.2022.23.3.125
   Keywords: Machine Learning, Random Forest, XGBoost, LightGBM, CatBoost, Government Statistical Indicators
3. 3. A research on the emotion classification and precision improvement of EEG(Electroencephalogram) data using machine learning algorithm
   Hyunju Lee, Dongil Shin, Dongkyoo Shin, Vol. 20, No. 5, pp. 27-36, Oct. 2019
   10.7472/jksii.2019.20.5.27
   Keywords: DEAP dataset, ICA, Arousal-Valence plane, Random Forest, Attribute Selected Classifier
4. 4. The prediction of appearance of jellyfish through Deep Neural Network
   CHEOLHUN HWANG, Myung-Mook Han, Vol. 20, No. 5, pp. 1-8, Oct. 2019
   10.7472/jksii.2019.20.5.1
   Keywords: deep neural network, Bootstrapping, Data Expansion, Transfer Learning, Random Forest, Sequence Forward Selection
5. 5. Research on improving correctness of cardiac disorder data classifier by applying Best-First decision tree method
   Hyun-Ju Lee, Dong-Kyoo Shin, Hee-Won Park, Soo-Han Kim, Dong-Il Shin, Vol. 12, No. 6, pp. 63-72, Dec. 2011
   Keywords: ECG, classifier, R-R interval, SVM, MLP, Random Forest, B-F Tree, Accuracy