Random Forest (RF) is known as one of the best classifiers in many fields. They are parallelizable, fast to train and to predict, robust to outlier, handle unbalanced data, have low bias, and moderate variance. Apart from these advantages, there are still opportunities to increase RF efficiency. The absence of recommendations regarding the number of [...]

Continue reading about Depth Limitation and Splitting Criteria Optimization on Random Forest for Efficient Human Activity Classification

by Endang Anggiratih and Agfianto Eko Putra

Abstract

Ship identification on satellite imagery can be used for fisheries management, monitoring of smuggling activities, ship traffic services, and naval warfare. However, high-resolution satellite imagery also makes the segmentation of the ship difficult in the background, so that to handle it requires reliable features so that it can be [...]

Continue reading about Ship Identification on Satellite Image Using Convolutional Neural Network and Random Forest