Development of Sabotage Behavior Detection by Hand Image Analysis and Machine Learning
出町 和之 陳 実 堀 智之 （東京大）
Convolutional Neural NetworkDeep Neural NetworkHand Behavior IdentificationInsider SabotageLong Short Term MemoryNuclear SecurityTime-Series Data Analysis
In this research, a new method was developed to identify the “hand behavior” of malicious sabotage behaviors. The Convolutional Neural Network (CNN) and the Long Short Term Memory (LSTM) were applied for analysis of the time-series data of hand behavior images and identification of hand behavior.