Predict Coordinated Development Degree of County Eco-Environment System Using GA-SVM

2018 ◽  
Vol 26 (3) ◽  
pp. 1-10 ◽  
Author(s):  
Jing Zhao ◽  
Zhen Jin
Keyword(s):  
Economic Development ◽  
Lift Coefficient ◽  
Solution Space ◽  
Support Vector ◽  
Coordinated Development ◽  
Slowing Down ◽  
Svm Model ◽  
Environment Sustainability ◽  
Positive Effect ◽  
Coordinated Development Degree

This article describes how economic development has had a significant impact on the environment. County eco-environment coordinated development has contributed to regional coordinated development in China. A support vector machine (SVM) model was constructed to classify and predict coordinated development degrees of the county eco-environment system. In order to improve the discrimination precision of SVM in classification, a Genetic Algorithm (GA) was used to optimize SVM parameters in the solution space. The method was compared with artificial neural network, decision tree, logistic regression and naive Bayesian classifier regarding coordinated development degree of county eco-environment system prediction for Guanzhong urban agglomeration. It found that the method has the best accuracy rate, hit rate, covering rate and lift coefficient. The simulation indicates that the county slowing-down of economic development would not have positive effect on the environment sustainability. GA-SVM provides an effective measurement for region eco-environment system classification and prediction.

Research on Coordination Degree of Economic Development and Human Settlements Environment Based on PCA for Guizhou

2014 ◽  
Vol 962-965 ◽  
pp. 2055-2060 ◽  
Author(s):  
Xian Gui Xue ◽  
Lu Li
Keyword(s):  
Principal Component Analysis ◽  
Economic Development ◽  
Quantitative Analysis ◽  
Principal Component ◽  
Fuzzy Mathematics ◽  
Coordinated Development ◽  
Human Settlements ◽  
The Index System ◽  
Coordinated Development Degree ◽  
Coordination Degree

Established the index system of human settlements environment and economic system for Guizhou recently 8 years, using principal component analysis and fuzzy mathematics，obtained coordinated development degree of two systems through the quantitative analysis,and put forward some suggestions about the problems appeared.

scholarly journals Spatiotemporal Relationship Between Ecological Environment and Economic Development in Tropical and Subtropical Regions of Asia

2019 ◽  
Vol 12 ◽  
pp. 194008291987896 ◽  
Author(s):  
Tao Shi ◽  
Tian Weiteng ◽  
Wei Zhang ◽  
Qian Zhou
Keyword(s):  
Economic Development ◽  
Entropy Method ◽  
Ecological Environment ◽  
Policy Recommendations ◽  
Negative Effects ◽  
Coordinated Development ◽  
Positive Effects ◽  
The North ◽  
Positive Effect ◽  
Better Than

This article is devoted to study the coordination coupling relationship between economic development and ecological environment in tropical and subtropical regions of Asia to reflect the spatiotemporal heterogeneity of the coordination in different regions. Using the entropy method and Geographically and Temporally Weighted Regression model, we empirically analyze 14 tropical and subtropical countries in Asia from 2003 to 2016. The empirical results show that most of the tropical and subtropical sample countries in Asia are at an intermediate coordination coupling level between economic development and ecological environment; the economic development lag type is the main one, and the ecological development lag type is less. At the same time, the positive effects between economic development and ecological environment in most sample countries are more obvious. Spatially, the ecological environment in the north of the Asian tropical and subtropical countries has a positive effect on economic development rather than that in the south and tends to be positive. The positive effect of economic development on ecological environment in the faster economic development areas is better than that in the slower economic development areas, and more areas tend to play negative effects. The research in this article provides a basis for strengthening ecological environment protection in tropical and subtropical regions of Asia, promoting the coordinated development of economic and ecological environment. Further, we put forward some corresponding policy recommendations.

The Spinning Quality Control Management Based on Decision Making by Data Mining Techniques

2018 ◽  
Vol 7 (1) ◽  
pp. 72
Author(s):  
Khalid AA Abakar ◽  
Chongwen Yu
Keyword(s):  
Data Mining ◽  
Kernel Functions ◽  
Support Vector ◽  
Ann Model ◽  
Data Mining Techniques ◽  
Yarn Quality ◽  
Yarn Properties ◽  
Svm Model ◽  
Rbf Kernel

This work demonstrated the possibility of using the data mining techniques such as artificial neural networks (ANN) and support vector machine (SVM) based model to predict the quality of the spinning yarn parameters. Three different kernel functions were used as SVM kernel functions which are Polynomial and Radial Basis Function (RBF) and Pearson VII Function-based Universal Kernel (PUK) and ANN model were used as data mining techniques to predict yarn properties. In this paper, it was found that the SVM model based on Person VII kernel function (PUK) have the same performance in prediction of spinning yarn quality in comparison with SVM based RBF kernel. The comparison with the ANN model showed that the two SVM models give a better prediction performance than an ANN model.

Combination of Support Vector Machine and K-Fold cross-validation for prediction of long-term degradation of the compressive strength of marine concrete

2018 ◽  
Vol 1 (1) ◽  
pp. 120-130 ◽  
Author(s):  
Chunxiang Qian ◽  
Wence Kang ◽  
Hao Ling ◽  
Hua Dong ◽  
Chengyao Liang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Environmental Factors ◽  
Cross Validation ◽  
Concrete Strength ◽  
Simulation Method ◽  
Support Vector ◽  
Svm Model ◽  
Artificial Neural Network Ann ◽  
Influence Degree ◽  
Fold Cross Validation

Support Vector Machine (SVM) model optimized by K-Fold cross-validation was built to predict and evaluate the degradation of concrete strength in a complicated marine environment. Meanwhile, several mathematical models, such as Artificial Neural Network (ANN) and Decision Tree (DT), were also built and compared with SVM to determine which one could make the most accurate predictions. The material factors and environmental factors that influence the results were considered. The materials factors mainly involved the original concrete strength, the amount of cement replaced by fly ash and slag. The environmental factors consisted of the concentration of Mg2+, SO42-, Cl-, temperature and exposing time. It was concluded from the prediction results that the optimized SVM model appeared to perform better than other models in predicting the concrete strength. Based on SVM model, a simulation method of variables limitation was used to determine the sensitivity of various factors and the influence degree of these factors on the degradation of concrete strength.

MK-FSVM-SVDD: A Multiple Kernel-based Fuzzy SVM Model for Predicting DNA-binding Proteins via Support Vector Data Description

2020 ◽  
Vol 15 ◽  
Author(s):  
Yi Zou ◽  
Hongjie Wu ◽  
Xiaoyi Guo ◽  
Li Peng ◽  
Yijie Ding ◽  
...  
Keyword(s):  
Dna Binding ◽  
Binding Proteins ◽  
Detection Efficiency ◽  
Dna Binding Proteins ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Data Description ◽  
Multiple Kernel ◽  
Svm Model

Background: Detecting DNA-binding proetins (DBPs) based on biological and chemical methods is time consuming and expensive. Objective: In recent years, the rise of computational biology methods based on Machine Learning (ML) has greatly improved the detection efficiency of DBPs. Method: In this study, Multiple Kernel-based Fuzzy SVM Model with Support Vector Data Description (MK-FSVM-SVDD) is proposed to predict DBPs. Firstly, sex features are extracted from protein sequence. Secondly, multiple kernels are constructed via these sequence feature. Than, multiple kernels are integrated by Centered Kernel Alignment-based Multiple Kernel Learning (CKA-MKL). Next, fuzzy membership scores of training samples are calculated with Support Vector Data Description (SVDD). FSVM is trained and employed to detect new DBPs. Results: Our model is test on several benchmark datasets. Compared with other methods, MK-FSVM-SVDD achieves best Matthew's Correlation Coefficient (MCC) on PDB186 (0.7250) and PDB2272 (0.5476). Conclusion: We can conclude that MK-FSVM-SVDD is more suitable than common SVM, as the classifier for DNA-binding proteins identification.

scholarly journals Correlation between the structure and skin permeability of compounds

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ruolan Zeng ◽  
Jiyong Deng ◽  
Limin Dang ◽  
Xinliang Yu
Keyword(s):  
Large Data ◽  
Qsar Model ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Skin Permeability ◽  
Data Set ◽  
Test Set ◽  
Svm Algorithm ◽  
Svm Model ◽  
Toxicity Relationship

AbstractA three-descriptor quantitative structure–activity/toxicity relationship (QSAR/QSTR) model was developed for the skin permeability of a sufficiently large data set consisting of 274 compounds, by applying support vector machine (SVM) together with genetic algorithm. The optimal SVM model possesses the coefficient of determination R2 of 0.946 and root mean square (rms) error of 0.253 for the training set of 139 compounds; and a R2 of 0.872 and rms of 0.302 for the test set of 135 compounds. Compared with other models reported in the literature, our SVM model shows better statistical performance in a model that deals with more samples in the test set. Therefore, applying a SVM algorithm to develop a nonlinear QSAR model for skin permeability was achieved.

scholarly journals Rural Financial Development Impacts on Agricultural Technology Innovation: Evidence from China

2021 ◽  
Vol 18 (3) ◽  
pp. 1110
Author(s):  
Yuyu Liu ◽  
Duan Ji ◽  
Lin Zhang ◽  
Jingjing An ◽  
Wenyan Sun
Keyword(s):  
Economic Development ◽  
Financial Development ◽  
Agricultural Development ◽  
Technology Innovation ◽  
Agricultural Technology ◽  
Rural Finance ◽  
Rural Economic Development ◽  
High Degree ◽  
The Impact ◽  
Positive Effect

Agricultural technology innovation is key for improving productivity, sustainability, and resilience in food production and agriculture to contribute to public health. Using panel data of 31 provinces in China from 2003 to 2015, this study examines the impact of rural financial development on agricultural technology innovation from the perspective of rural financial scale and rural finance efficiency. Furthermore, it examines how the effects of rural financial development vary in regions with different levels of marketization and economic development. The empirical results show that the development of rural finance has a significant and positive effect on the level of agricultural technology innovation. Rural finance efficiency has a significantly positive effect on innovation in regions with a low degree of marketization, while the rural financial scale has a significantly positive effect on technological innovation in regions with a high degree of marketization. Further analysis showed that improving the level of agricultural technology innovation is conducive to rural economic development. This study provides new insights into the effects of rural financial development on sustainable agricultural development from the perspective of agricultural technology innovation.

New Multi-View Classification Method with Uncertain Data

2021 ◽  
Vol 16 (1) ◽  
pp. 1-23
Author(s):  
Bo Liu ◽  
Haowen Zhong ◽  
Yanshan Xiao
Keyword(s):  
Learning Strategy ◽  
State Of The Art ◽  
Uncertain Data ◽  
Real Life ◽  
Support Vector ◽  
Classification Methods ◽  
Complementary Information ◽  
Novel Approach ◽  
Svm Model ◽  
Iterative Framework

Multi-view classification aims at designing a multi-view learning strategy to train a classifier from multi-view data, which are easily collected in practice. Most of the existing works focus on multi-view classification by assuming the multi-view data are collected with precise information. However, we always collect the uncertain multi-view data due to the collection process is corrupted with noise in real-life application. In this case, this article proposes a novel approach, called uncertain multi-view learning with support vector machine (UMV-SVM) to cope with the problem of multi-view learning with uncertain data. The method first enforces the agreement among all the views to seek complementary information of multi-view data and takes the uncertainty of the multi-view data into consideration by modeling reachability area of the noise. Then it proposes an iterative framework to solve the proposed UMV-SVM model such that we can obtain the multi-view classifier for prediction. Extensive experiments on real-life datasets have shown that the proposed UMV-SVM can achieve a better performance for uncertain multi-view classification in comparison to the state-of-the-art multi-view classification methods.

scholarly journals Three-Dimensional Site Characterization Model of Bangalore Using Support Vector Machine

2012 ◽  
Vol 2012 ◽  
pp. 1-10
Author(s):  
Pijush Samui
Keyword(s):  
Support Vector Machine ◽  
Three Dimensional ◽  
Site Characterization ◽  
Standard Penetration Test ◽  
Support Vector ◽  
Characterization Model ◽  
Svm Model ◽  
Input Variables ◽  
Learning Machine ◽  
A Site

The main objective of site characterization is the prediction of in situ soil properties at any half-space point at a site based on limited tests. In this study, the Support Vector Machine (SVM) has been used to develop a three dimensional site characterization model for Bangalore, India based on large amount of Standard Penetration Test. SVM is a novel type of learning machine based on statistical learning theory, uses regression technique by introducing ε-insensitive loss function. The database consists of 766 boreholes, with more than 2700 field SPT values () spread over 220 sq km area of Bangalore. The model is applied for corrected () values. The three input variables (, , and , where , , and are the coordinates of the Bangalore) were used for the SVM model. The output of SVM was the data. The results presented in this paper clearly highlight that the SVM is a robust tool for site characterization. In this study, a sensitivity analysis of SVM parameters (σ, , and ε) has been also presented.

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