Machine learning methods continue to show promise in the analysis of data from genetic association studies because of the high number of variables relative to the number of observations. However, few best practices exist for the application of these methods. We extend a recently proposed supervised machine learning approach for predicting disease risk by genotypes to be able to incorporate gene...

Hyperspectral sensors enable the study of the chemical properties of scene materials remotely for the purpose of identification, detection, and chemical composition analysis of objects in the environment. Hence, hyperspectral images captured from earth observing satellites and aircraft have been increasingly important in agriculture, environmental monitoring, urban planning, mining, and defense...

When performing finite element analysis of mechanical devices, it is difficult to calculate including the linear motion guide because cost huge. In this paper, two models considering rigidity are proposed. One an iterative calculation method that repeats load distribution theory and FEM. The other a direct using non-linear spring follows obtained by analysis. As result verification experiments ...

Abstract Dimensionality reduction is the essence of many data processing problems, including filtering, compression, reduced-order modeling and pattern analysis. While traditionally tackled using linear tools in fluid dynamics community, nonlinear from machine learning are becoming increasingly popular. This article, halfway between a review tutorial, introduces general framework for dimensiona...