scholarly journals MULTIDIMENSIONAL MODELING TO FIND THE MOST EFFICIENT FACTORS OF COVID-19 PANDEMIC OVER THE PUBLIC HEALTH WITH PRINCIPAL COMPONENTS ANALYSIS METHOD

2021 ◽  
Vol 18 (1) ◽  
pp. 19-50
Author(s):  
Müzeyyen Cömert Aksu ◽  
Adnan Mazmanoğlu
Keyword(s):  
Public Health ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Analysis Method ◽  
The Public ◽  
Multidimensional Modeling ◽  
Components Analysis

Evaluation on Restoration of Ecological City of Ha’erbin

2010 ◽  
Vol 113-116 ◽  
pp. 938-942
Author(s):  
Mu Hua Cui
Keyword(s):  
Quantitative Analysis ◽  
Qualitative Analysis ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Index System ◽  
Analysis Method ◽  
Components Analysis ◽  
Made In

This article is designed to carry out design of index system for evaluation of ecological city which is applicable to features of city of Ha’erbin on basis of actual conditions of Ha’erbin in principle of combination of qualitative analysis and quantitative analysis and to conduct evaluation on effect of restoration of ecological city of Ha’erbin with principal components analysis method. Results of evaluation show that some accomplishment has been made in terms of construction of ecological city of Ha’erbin and sub-system of environment, economy and society of Ha’erbin has been greatly improved since 2002.

Kernel principal components based cascade forest towards disease identification with human microbiota

2020 ◽  
Author(s):  
Jiayu Zhou ◽  
Xuwen Wang ◽  
Yanqing Ye ◽  
Jiang Jiang
Keyword(s):  
Principal Components Analysis ◽  
Principal Components ◽  
Human Disease ◽  
Analysis Method ◽  
Human Microbiota ◽  
Disease States ◽  
Disease Identification ◽  
Deep Forest ◽  
Components Analysis ◽  
Kernel Principal Components Analysis

Abstract Numerous pieces of clinical evidence have shown that many phenotypic traits of human disease are related to their gut microbiome. Through supervised classification, it is feasible to determine the human disease states by revealing the intestinal microbiota compositional information. However, the abundance matrix of microbiome data is so sparse, an interpretable deep model is crucial to further represent and mine the data for expansion, such as the deep forest. What's more, overfitting can still exist in the original deep forest model when dealing with such “large p, small n” biology data. Feature reduction is considered to improve the ensemble forest model especially towards the disease identification in the human microbiota. In this work, we propose the kernel principal components based cascade forest method, so-called KPCCF, to classify the disease states of patients by using taxonomic profiles of the microbiome at the family level. In detail, the kernel principal components analysis method is first used to reduce the original dimension of human microbiota datasets. Besides, the processed data is fed into the cascade forest to preliminarily discriminate the disease state of the samples. Thus, the proposed KPCCF algorithm can represent the small-scale and high-dimension human microbiota datasets with the sparse feature matrix. Systematic comparison experiments demonstrate that our method consistently outperforms the state-of-the-art methods with the comparative study on 4 datasets. Additionally, compared to other dimensionality reduction methods, the kernel principal components analysis method is more suitable for microbiota datasets.

Kernel principal components based cascade forest towards disease identification with human microbiota

2020 ◽  
Author(s):  
Jiayu Zhou ◽  
Xuwen Wang ◽  
Yanqing Ye ◽  
Jiang Jiang
Keyword(s):  
Principal Components Analysis ◽  
Principal Components ◽  
Human Disease ◽  
Analysis Method ◽  
Human Microbiota ◽  
Disease States ◽  
Disease Identification ◽  
Deep Forest ◽  
Components Analysis ◽  
Kernel Principal Components Analysis

Abstract Numerous pieces of clinical evidence have shown that many phenotypic traits of human disease are related to their gut microbiome. Through supervised classification, it is feasible to determine the human disease states by revealing the intestinal microbiota compositional information. However, the abundance matrix of microbiome data is so sparse, an interpretable deep model is crucial to further represent and mine the data for expansion, such as the deep forest. What's more, overfitting can still exist in the original deep forest model when dealing with such “large p, small n” biology data. Feature reduction is considered to improve the ensemble forest model especially towards the disease identification in the human microbiota. In this work, we propose the kernel principal components based cascade forest method, so-called KPCCF, to classify the disease states of patients by using taxonomic profiles of the microbiome at the family level. In detail, the kernel principal components analysis method is first used to reduce the original dimension of human microbiota datasets. Besides, the processed data is fed into the cascade forest to preliminarily discriminate the disease state of the samples. Thus, the proposed KPCCF algorithm can represent the small-scale and high-dimension human microbiota datasets with the sparse feature matrix. Systematic comparison experiments demonstrate that our method consistently outperforms the state-of-the-art methods with the comparative study on 4 datasets. Additionally, compared to other dimensionality reduction methods, kernel principal components analysis method is more suitable for microbiota datasets.

scholarly journals Study on Low-carbon Urban Development Path and Strategy Based on Principal Components Analysis

2015 ◽  
Vol 9 (1) ◽  
pp. 81-86 ◽  
Author(s):  
Wang Hongping ◽  
Su Tianbao ◽  
Sun Hailing
Keyword(s):  
Urban Development ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Evaluation Model ◽  
Low Carbon ◽  
Analysis Method ◽  
Development Path ◽  
Government Decision ◽  
Components Analysis ◽  
Low Carbon City

Scholars at home and abroad always focus on the low-carbon urban development. This paper described the basic theory of low-carbon city, analyzed the main factors effect the urban carbon emissions based on principal components analysis method, and established evaluation model to adapt to the development of low-carbon city. Finally, reasonable proposals have been proposed to provide scientific and theoretical basis for government decision making.

Research on the Method of Wool Knitted Fabric Style Evaluation

2012 ◽  
Vol 627 ◽  
pp. 338-342
Author(s):  
Xi Gu ◽  
Ling Cheng ◽  
Jin Qiu Zhang
Keyword(s):  
Principal Components Analysis ◽  
Principal Components ◽  
Testing System ◽  
Knitted Fabric ◽  
Analysis Method ◽  
Components Analysis

This study determined the method which can be used to evaluate the wool knitted fabric style basing on KES system. 20 wool knitted fabric samples are tested by the KES fabric style testing system. The sixteen kinds of mechanical indexes of every fabric samples are acquired and their influences on the fabric style of wool knitted fabric were investigated. Ten indexes which have remarkable influences on the fabric style of wool knitted fabric were analyzed by using the principal components analysis method. The result showed that there are four principal components, which are defined as smoothness-limpness, softness, elasticity and fullness, can be used to characterize the wool knitted fabric style exactly.

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