Multi-factors Body Surface Area Calculation Model Based on G-CNN(Grey Correlation Analysis-Convolution Neural Network)

2021 ◽  
Author(s):  
Yulu Wang ◽  
Lei Jin ◽  
Jing Wang ◽  
Li Han ◽  
Fei Li
Keyword(s):  
Neural Network ◽  
Correlation Analysis ◽  
Surface Area ◽  
Body Surface Area ◽  
Body Surface ◽  
Calculation Model ◽  
Convolution Neural Network ◽  
Grey Correlation ◽  
Model Based ◽  
Grey Correlation Analysis

scholarly journals Construction and Drug Evaluation Based on Convolutional Neural Network System Optimized by Grey Correlation Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Hui Teng
Keyword(s):  
Neural Network ◽  
Correlation Analysis ◽  
Drug Evaluation ◽  
Convolution Neural Network ◽  
Network System ◽  
Grey Correlation ◽  
Mental Diseases ◽  
Neural Network System ◽  
Grey Correlation Analysis ◽  
Deep Convolution Neural Network

Incidence rate of mental illness is increasing year by year with the development of city. The amount of modern medical data is huge and complex. In many cases, it is difficult to realize the rational allocation of resources, which puts forward an urgent demand for the artificial intelligence of modern medicine and brings great pressure to the development of the medical industry. The purpose of this study is to develop and construct a grey correlation analysis and related drug evaluation system of mental diseases based on deep convolution neural network. The establishment of the system can effectively improve the automation and intelligence of modern psychiatric treatment process. In this article, the grey correlation analysis of patient data is carried out, and then, the optimized deep convolution neural network is constructed. Combined with the medical knowledge base, the analysis of disease results is realized, and on this basis, the efficacy of related drugs in the treatment of mental diseases is evaluated. The results show that the advantage of the deep convolution neural network system is to effectively improve the induction rate. What’s more, compared with other algorithms, this algorithm has higher accuracy and efficiency. It improves the comprehensiveness and informatization of disease screening methods, improves the accuracy of screening, reduces the consumption of doctors’ human resources, and provides a theoretical basis for the digitization of the medical industry in the future.

The Selection Model of Equipment Resource Based on AHP and Grey Correlation Analysis

2011 ◽  
Vol 130-134 ◽  
pp. 1226-1229
Author(s):  
Jun Ying Wei ◽  
Pei Si Zhong
Keyword(s):  
Correlation Analysis ◽  
Influencing Factors ◽  
Index System ◽  
Selection Index ◽  
Selection Model ◽  
Grey Correlation ◽  
Model Based ◽  
Grey Correlation Analysis ◽  
Manufacturing Resources ◽  
Resources Sharing

This paper mainly researches the problem that enterprises select equipment resource in manufacturing resources sharing system. There are many influencing factors and some are uncertain or unqualified in the selection, therefore this paper using AHP and grey correlation analysis to evaluate and select. Firstly, the selection index system of equipment resource according to AHP is established, and then selection model based on multi-hierarchical grey correlation analysis is constructed. Applying this model to help enterprise select equipment can avoid errors resulted from human objective cognition difference and equalitarianism. The select result is more scientific and reasonable, and more in line with the enterprise requirements.

scholarly journals Safety Risk Assessment of a Pb-Zn Mine Based on Fuzzy-Grey Correlation Analysis

Electronics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 130 ◽  
Author(s):  
Guofang Dong ◽  
Wei Wei ◽  
Xu Xia ◽  
Marcin Woźniak ◽  
Robertas Damaševičius
Keyword(s):  
Risk Assessment ◽  
Correlation Analysis ◽  
Risk Evaluation ◽  
Assessment Model ◽  
Mine Safety ◽  
Property A ◽  
Safety Risk ◽  
Grey Correlation ◽  
Model Based ◽  
Grey Correlation Analysis

Improving safety management and risk evaluation methods is important for the global mining industry, which is the backbone of the industrial development of our society. To prevent any accidental loss or harm to human life and property, a safety risk assessment method is needed to perform the continuous risk assessment of mines. Based on the requirements of mine safety evaluation, this paper proposes the Pb-Zn mine safety risk evaluation model based on the fuzzy-grey correlation analysis method. The model is compared with the risk assessment model based on the fuzzy TOPSIS method. Through the experiments, our results demonstrate that the proposed fuzzy-grey correlation model is more sensitive to risk and has less effect on the evaluation results under different scoring attitudes (cautious, rational, and relaxed).

scholarly journals Artificial Neural Network Estimation of Thermal Insulation Value of Children’s School Wear in Kuwait Classroom

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Khaled Al-Rashidi ◽  
Radhi Alazmi ◽  
Mubarak Alazmi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Surface Area ◽  
Body Surface Area ◽  
Thermal Insulation ◽  
Body Surface ◽  
The Body ◽  
Artificial Neural ◽  
Thermal Insulation Value ◽  
Insulation Value

Artificial neural network (ANN) was utilized to predict the thermal insulation values of children’s school wear in Kuwait. The input thermal insulation data of the different children’s school wear used in Kuwait classrooms were obtained from study using thermal manikins. The lowest mean squared error (MSE) value for the validation data was 1.5 × 10−5 using one hidden layer of six neurons and one output layer. The R2 values for the training, validation, and testing data were almost equal to 1. The values from ANN prediction were compared with McCullough’s equation and the standard tables’ methods. Results suggested that the ANN is able to give more accurate prediction of the clothing thermal insulation values than the regression equation and the standard tables methods. The effect of the different input variables on the thermal insulation value was examined using Garson algorithm and sensitivity analysis and it was found that the cloths weight, the body surface area nude (BSA0), and body surface area covered by one layer of clothing (BSAC1) have the highest effect on the thermal insulation value with about 29%, 27%, and 23%, respectively.

A Multi-Factor Comprehensive Evaluation Method for Pipeline Integrity

2019 ◽  
Author(s):  
Jing Yang ◽  
Xiaolin Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Correlation Analysis ◽  
Evaluation Method ◽  
Residual Life ◽  
Comprehensive Evaluation ◽  
Comprehensive Evaluation Method ◽  
Grey Correlation ◽  
Grey Correlation Analysis ◽  
Artificial Neural

Abstract Pipeline integrity management is widely used as an effective means for pipeline safety management, in which integrity evaluation is an important part. To some extent, pipeline integrity can be interpreted as the safety condition of the pipeline, while safety is an eternal topic for pipeline operators. In numerous recent studies, the evaluation of pipeline integrity generally focuses on the evaluation of remaining strength and/or residual life, which is based on the defect size such as corrosion, dents, etc., obtained during inspection. However, pipeline integrity is not only related to the pipe body, all factors that may threaten the operation safety of the pipe should be considered, including the pipe body, ancillary facilities, the pipe security system, and the surrounding environment, etc.. Although some comprehensive models have been established recently to assess pipeline condition, there still exist limitations for practical application, such as quantification of integrity and complexity of analysis. Therefore this paper presents the development of a comprehensive integrity evaluation method based on multi-factor analysis. The method is developed by an integrated application of fuzzy mathematics, grey correlation analysis theory, and the artificial neural network technique. After establishing integrity evaluating indexes, fuzzy analysis is used to quantify and classify pipeline integrity, and grey correlation analysis to screen key influence indicators. Then a comprehensive predictive evaluation model can be generated using large amount of relevant sample data based on the artificial neural network technique. In the end of the paper, a simple case is applied to validate feasibility of this comprehensive integrity evaluation method. The comprehensive evaluation method is expected to be applied to determine the condition of pipeline integrity, and to grade and rank the integrity condition of pipes, so as to assist and optimize pipeline maintenance decision for pipeline operators.

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