scholarly journals Clinical Data Analysis: An Opportunity to Compare Machine Learning Methods

2016 ◽  
Vol 100 ◽  
pp. 731-738 ◽  
Author(s):  
A. Salcedo-Bernal ◽  
M.P. Villamil-Giraldo ◽  
A.D. Moreno-Barbosa
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Clinical Data ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Clinical Data Analysis

scholarly journals Possible Solutions to the Problems of Microfinance Organizations with the Application of Intelligent Methods of Machine Learning

2018 ◽  
Vol 12 (2) ◽  
pp. 66-71
Author(s):  
A. V. Zolotaryuk ◽  
I. A. Chechneva
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Subject Area ◽  
Microfinance Institutions ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Economic Information ◽  
The Subject ◽  
Intelligent Methods

The authors consider the problems associated with the activities of microfinance organizations, and directions to eliminate them. The subject of the study is the need to introduce machine learning to solve urgent problems. Machine learning methods are increasingly being implemented to analyze financial and economic information, which reduces and eliminates some of the difficulties. Although currently these methods are not widely used in the field of microfinance institutions (MFIs), there are opportunities for their application. The aim of the work is to determine the prospects for the use of these methods in MFOs. The article describes the subject area of research, associated with MFIs. The authors identify the main groups of problems related to MFOs, consider the possibility of introducing machine learning for data analysis in this area and determine the main directions of the possible use of machine learning for MFIs. The authors concluded that such methods are applicable for assessing the performance of MFIs.

scholarly journals Pharmacometrics and Machine Learning Partner to Advance Clinical Data Analysis

2020 ◽  
Vol 107 (4) ◽  
pp. 926-933 ◽  
Author(s):  
Gilbert Koch ◽  
Marc Pfister ◽  
Imant Daunhawer ◽  
Melanie Wilbaux ◽  
Sven Wellmann ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Clinical Data ◽  
Clinical Data Analysis

scholarly journals Enhanced Defect Detection in Carbon Fiber Reinforced Polymer Composites via Generative Kernel Principal Component Thermography

Polymers ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 825
Author(s):  
Kaixin Liu ◽  
Zhengyang Ma ◽  
Yi Liu ◽  
Jianguo Yang ◽  
Yuan Yao
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Carbon Fiber ◽  
Defect Detection ◽  
Principal Component ◽  
Fiber Reinforced Polymer ◽  
Carbon Fiber Reinforced Polymer ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Reinforced Polymer

Increasing machine learning methods are being applied to infrared non-destructive assessment for internal defects assessment of composite materials. However, most of them extract only linear features, which is not in accord with the nonlinear characteristics of infrared data. Moreover, limited infrared images tend to restrict the data analysis capabilities of machine learning methods. In this work, a novel generative kernel principal component thermography (GKPCT) method is proposed for defect detection of carbon fiber reinforced polymer (CFRP) composites. Specifically, the spectral normalization generative adversarial network is proposed to augment the thermograms for model construction. Sequentially, the KPCT method is used by feature mapping of all thermogram data using kernel principal component analysis, which allows for differentiation of defects and background in the dimensionality-reduced data. Additionally, a defect-background separation metric is designed to help the performance evaluation of data analysis methods. Experimental results on CFRP demonstrate the feasibility and advantages of the proposed GKPCT method.

scholarly journals Machine Learning Applications for Mass Spectrometry-Based Metabolomics

Metabolites ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. 243 ◽  
Author(s):  
Ulf W. Liebal ◽  
An N. T. Phan ◽  
Malvika Sudhakar ◽  
Karthik Raman ◽  
Lars M. Blank
Keyword(s):  
Machine Learning ◽  
Mass Spectrometry ◽  
Data Analysis ◽  
Metabolic Engineering ◽  
Data Representation ◽  
Heterogeneous Data ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Learning Methods ◽  
Machine Learning Methods

The metabolome of an organism depends on environmental factors and intracellular regulation and provides information about the physiological conditions. Metabolomics helps to understand disease progression in clinical settings or estimate metabolite overproduction for metabolic engineering. The most popular analytical metabolomics platform is mass spectrometry (MS). However, MS metabolome data analysis is complicated, since metabolites interact nonlinearly, and the data structures themselves are complex. Machine learning methods have become immensely popular for statistical analysis due to the inherent nonlinear data representation and the ability to process large and heterogeneous data rapidly. In this review, we address recent developments in using machine learning for processing MS spectra and show how machine learning generates new biological insights. In particular, supervised machine learning has great potential in metabolomics research because of the ability to supply quantitative predictions. We review here commonly used tools, such as random forest, support vector machines, artificial neural networks, and genetic algorithms. During processing steps, the supervised machine learning methods help peak picking, normalization, and missing data imputation. For knowledge-driven analysis, machine learning contributes to biomarker detection, classification and regression, biochemical pathway identification, and carbon flux determination. Of important relevance is the combination of different omics data to identify the contributions of the various regulatory levels. Our overview of the recent publications also highlights that data quality determines analysis quality, but also adds to the challenge of choosing the right model for the data. Machine learning methods applied to MS-based metabolomics ease data analysis and can support clinical decisions, guide metabolic engineering, and stimulate fundamental biological discoveries.

scholarly journals Machine learning methods for analyzing genomic and phenomic data in psychiatry

2019 ◽  
pp. 127
Author(s):  
T. I. Nurgaliev
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Learning Methods ◽  
Machine Learning Methods

This review briefly describes modern approaches of data analysis in psychiatry using machine learning and gives possible prospects and common obstacles of this approach.

DATA ANALYSIS OF MEDICAL-SOCIOLOGICAL MONITORING WITH MACHINE LEARNING METHODS

2019 ◽  
Vol 1 (88) ◽  
pp. 27-38
Author(s):  
G.G. Rapakov ◽  
G.T. Banshchikov ◽  
V.A. Gorbunov ◽  
L.L. Malygin ◽  
I.M. Revelev
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Learning Methods ◽  
Machine Learning Methods

scholarly journals Bongard Problems: A Topological Data Analysis Approach

2020 ◽  
Vol 15 ◽  
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Persistent Homology ◽  
Topological Data Analysis ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Unique Rule ◽  
Thinking Processes ◽  
The Right ◽  
Minimal Information

Bongard problems are a set of 100 visual puzzles posed by M. M. Bongard, where each puzzle consists of twelve images separated into two groups of six images. The task is to find the unique rule separating the two classes in each given problem. The problems were first posed as a challenge for the AI community to test machines ability to imitate complex, context-depending thinking processes using only minimal information. Although some work was done to solve these problems, none of the previous approaches could automatically solve all of them. The present paper is a contribution to attack these problems with a different approach, combining the tools of persistent homology alongside with machine learning methods. In this work, we present an algorithm and show that it is able to solve problems involving differences in connectivity and size as examples, we also show that it can solve problems involving a much larger set of differences provided the right G-equivariant operators

scholarly journals Machine Learning in Early Genetic Detection of Multiple Sclerosis Disease: A Survey

2020 ◽  
Vol 12 (5) ◽  
pp. 1-18
Author(s):  
Nehal M. Ali ◽  
Mohamed Shaheen ◽  
Mai S. Mabrouk ◽  
Mohamed A. AboRezka
Keyword(s):  
Machine Learning ◽  
Multiple Sclerosis ◽  
Data Analysis ◽  
Genetic Disease ◽  
Next Generation Sequencing Data ◽  
Disease Detection ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Multiple Sclerosis Disease ◽  
Genetic Detection

Multiple sclerosis disease is a main cause of non-traumatic disabilities and one of the most common neurological disorders in young adults over many countries. In this work, we introduce a survey study of the utilization of machine learning methods in Multiple Sclerosis early genetic disease detection methods incorporating Microarray data analysis and Single Nucleotide Polymorphism data analysis and explains in details the machine learning methods used in literature. In addition, this study demonstrates the future trends of Next Generation Sequencing data analysis in disease detection and sample datasets of each genetic detection method was included .in addition, the challenges facing genetic disease detection were elaborated.

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