Hand gesture recognition can be used in many applications such as interactive data analysis or American Sign Language detection. Current systems are either expensive, unable to run in real time, or require the user wear devices such as custom gloves. We propose an inexpensive solution for predicting hand gestures in real time that uses Microsoft’s Kinect camera. Our system involves training a r...

The relationships between geological features and rockmass behaviors under complex geological environments were investigated based on multiple intelligence classifiers. Random forest, support vector machine, bayes’ classifier, fisher’s classifier, logistic regression, and neural networks were used to establish models for evaluating the rockmass stability of slope. Samples of both circular failu...

Machine learning-based classification techniques provide support for the decision making process in the field of healthcare, especially in disease diagnosis, prognosis and screening. Healthcare datasets are voluminous in nature and their high dimensionality problem comprises in terms of slower learning rate and higher computational cost. Feature selection is expected to deal with the high dimen...

In this work, we show that by using a recursive random forest together with an alpha beta filter classifier, it is possible to classify radar tracks from the tracks’ kinematic data. The kinematic data is from a 2D scanning radar without Doppler or height information. We use random forest as this classifier implicitly handles the uncertainty in the position measurements. As stationary targets ca...

Most people still have difficulty accessing finance because of a lack or even no credit history. This study aims to develop data model that predicts customer's ability pay from various aspects other than uses the CRSIP-DM (Cross Industry Standard Process Model for Data mining) method. The used in this is Home Credit Default Risk dataset collected by documentation techniques. were then analyzed ...