The ensemble machine learning methods incorporating random subspace and random forest employing genetic fuzzy rule-based systems as base learning algorithms were developed in Matlab environment. The methods were applied to the real-world regression problem of predicting the prices of residential premises based on historical data of sales/purchase transactions. The accuracy of ensembles generate...

Verifying pollen germination using microscopic images is a difficult task. It usually time-consuming and may entail reduced accuracy reproducibility. Therefore, in this study, we used random forest (RF) convolutional neural network (CNN) models to perform image classification on raw data corresponding pollens with different rates; the were obtained via flow cytometry. A heat map, which was base...

Species distribution models (SDMs) have become an essential tool in ecology, biogeography, evolution and, more recently, in conservation biology. How to generalize species distributions in large undersampled areas, especially with few samples, is a fundamental issue of SDMs. In order to explore this issue, we used the best available presence records for the Hooded Crane (Grus monacha, n = 33), ...

ÐGibbsian fields or Markov random fields are widely used in Bayesian image analysis, but learning Gibbs models is computationally expensive. The computational complexity is pronounced by the recent minimax entropy (FRAME) models which use large neighborhoods and hundreds of parameters [22]. In this paper, we present a common framework for learning Gibbs models. We identify two key factors that ...

A stroke is a fatal disease that usually occurs to the people over age of 65. The treatment progress medical field growing rapidly, especially with technological advance, emergence various record data sets can be used in records identify trends these using mining. purpose this study was propose model classify survivors mining, by utilizing from kaggle sharing dataset. models proposed were AdaBo...