Design and Selection of Machine Learning Methods Using Radiomics and Dosiomics for Normal Tissue Complication Probability Modeling of Xerostomia

This paper presents a comparison of various machine learning methods applied to the problem of recognizing grasp types involved in object manipulations performed with a data glove. Conventional wisdom holds that data gloves need calibration in order to obtain accurate results. However, calibration is a time-consuming process, inherently user-specific, and its results are often not perfect. In contrast, the present study aims at evaluating recognition methods that do not require prior calibration of the data glove. Instead, raw sensor readings are used as input features that are directly mapped to different categories of hand shapes. An experiment was carried out in which test persons wearing a data glove had to grasp physical objects of different shapes corresponding to the various grasp types of the Schlesinger taxonomy. The collected data was comprehensively analyzed using numerous classification techniques provided in an open-source machine learning toolbox. Evaluated machine learning methods are composed of (a) 38 classifiers including different types of function learners, decision trees, rule-based learners, Bayes nets, and lazy learners; (b) data preprocessing using principal component analysis (PCA) with varying degrees of dimensionality reduction; and (c) five meta-learning algorithms under various configurations where selection of suitable base classifier combinations was informed by the results of the foregoing classifier evaluation. Classification performance was analyzed in six different settings, representing various application scenarios with differing generalization demands. The results of this work are twofold: (1) We show that a reasonably good to highly reliable recognition of grasp types can be achieved—depending on whether or not the glove user is among those training the classifier—even with uncalibrated data gloves. (2) We identify the best performing classification methods for the recognition of various grasp types. To conclude, cumbersome calibration processes before productive usage of data gloves can be spared in many situations.

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Selection of wells for hydraulic fracturing based on mathematical modeling using machine learning methods

Neftyanoe khozyaystvo - Oil Industry ◽

10.24887/0028-2448-2019-11-38-42 ◽

2019 ◽

Vol 11 ◽

pp. 38-42

Author(s):

A.F. Azbukhanov ◽

◽

I.V. Kostrigin ◽

K.A. Bondarenko ◽

M.N. Semenova ◽

...

Keyword(s):

Mathematical Modeling ◽

Machine Learning ◽

Hydraulic Fracturing ◽

Learning Methods ◽

Machine Learning Methods ◽

Selection Of

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MACHINE LEARNING AS A CORPORATION'S TOOL FOR SELECTION OF SUPPLIERS

Vestnik Universiteta ◽

10.26425/1816-4277-2019-9-48-53 ◽

2019 ◽

pp. 48-53

Author(s):

V. V. Baklushinskii ◽

E. V. Pustynnikova

Keyword(s):

Machine Learning ◽

Consumer Behavior ◽

Learning Models ◽

Learning Methods ◽

Machine Learning Methods ◽

Securities Trading ◽

Machine Learning Models ◽

Selection Of

In the economics and finance, machine learning methods have spread when solving the problems of consumer behavior research and in currency and securities trading. However, they are poorly developed in dealing with issues related to interaction between enterprises. The article presents the results of the compilation and testing of machine learning models, created to assess the reliability of enterprises as suppliers. According to the analysis, carried out in the article, machine learning methods are applicable when conducting supplier evaluations. This article has been written on the theme of expanding the scope of machine learning in the field of analysis of the behavior of commercial enterprises.

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Analysis and object markup of hyperspectral images for machine learning methods

Information Technology and Nanotechnology ◽

10.18287/1613-0073-2019-2391-309-317 ◽

2019 ◽

pp. 309-317

Author(s):

V P Gromov ◽

L I Lebedev ◽

V E Turlapov

Keyword(s):

Machine Learning ◽

Pearson Correlation ◽

Principal Component ◽

Hyperspectral Images ◽

Spectral Features ◽

Two Dimensional ◽

Learning Methods ◽

Machine Learning Methods ◽

Reference Signature ◽

Selection Of

The development of the nominal sequence of steps for analyzing the HSI proposed by Landgrebe, which is necessary in the context of the appearance of reference signature libraries for environmental monitoring, is discussed. The approach is based on considering the HSI pixel as a signature that stores all spectral features of an object and its states, and the HSI as a whole - as a two-dimensional signature field. As a first step of the analysis, a procedure is proposed for detecting a linear dependence of signatures by the magnitude of the Pearson correlation coefficient. The main apparatus of analysis, as in Landgrebe sequence, is the method of principal component analysis, but it is no longer used to build classes and is applied to investigate the presence in the class of subclasses essential for the applied area. The experimental material includes such objects as water, swamps, soil, vegetation, concrete, pollution. Selection of object samples on the image is made by the user. From the studied images of HSI objects, a base of reference signatures for classes (subclasses) of objects is formed, which in turn can be used to automate HSI markup with the aim of applying machine learning methods to recognize HSI objects and their states.

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