scholarly journals Immovable Cultural Relics Disease Prediction Based on Relevance Vector Machine

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Bao Liu ◽  
Kun Mu ◽  
Fei Ye ◽  
Jun Deng ◽  
Jingting Wang
Keyword(s):  
Prediction Accuracy ◽  
Principal Component ◽  
Relevance Vector Machine ◽  
Likelihood Method ◽  
Support Vector ◽  
Disease Prediction ◽  
Key Factors ◽  
Factors Affecting ◽  
Prediction Approach ◽  
Cultural Relics

The preventive cultural relics protection is one of the most concerned contents in archaeology, which includes environmental monitoring and accurate prediction of cultural relics diseases. In view of the deficiency of the analysis of cultural relics data and the prediction of cultural relics diseases, a prediction model of immovable cultural relics diseases based on relevance vector machine (RVM) is proposed. The key factors affecting the disease of immovable cultural relics are found out by the principal component analysis method, and the dimension reduction of data is realized; then, the RVM model under the framework of Bayesian theory is constructed, and the super parameters are estimated by the maximum edge likelihood method; finally, the prediction accuracy of the model is compared with the traditional diseases prediction methods. The experiment results demonstrate that the proposed RVM-based immovable cultural relics disease prediction approach not only has the advantages of more sparse model but also has better prediction accuracy than the traditional radial basis function neural network-based and support vector machine-based methods.

Using Stratified Sample and Grid Search to Improve Disease Prediction Accuracy of SVM

2013 ◽  
Vol 295-298 ◽  
pp. 644-647 ◽  
Author(s):  
Yu Kai Yao ◽  
Hong Mei Cui ◽  
Ming Wei Len ◽  
Xiao Yun Chen
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Classification Accuracy ◽  
Prediction Accuracy ◽  
Support Vector ◽  
Disease Prediction ◽  
Data Mining Algorithm ◽  
Grid Search ◽  
Mining Algorithm ◽  
Stratified Sample

SVM (Support Vector Machine) is a powerful data mining algorithm, and is mainly used to finish classification or regression tasks. In this literature, SVM is used to conduct disease prediction. We focus on integrating with stratified sample and grid search technology to improve the classification accuracy of SVM, thus, we propose an improved algorithm named SGSVM: Stratified sample and Grid search based SVM. To testify the performance of SGSVM, heart-disease data from UCI are used in our experiment, and the results show SGSVM has obvious improvement in classification accuracy, and this is very valuable especially in disease prediction.

scholarly journals Nondestructive Testing and Visualization of Catechin Content in Black Tea Fermentation Using Hyperspectral Imaging

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8051
Author(s):  
Chunwang Dong ◽  
Chongshan Yang ◽  
Zhongyuan Liu ◽  
Rentian Zhang ◽  
Peng Yan ◽  
...  
Keyword(s):  
Spectral Data ◽  
Prediction Accuracy ◽  
Visual Analysis ◽  
Population Analysis ◽  
Scatter Correction ◽  
Principal Component ◽  
Black Tea ◽  
Support Vector ◽  
Epicatechin Gallate ◽  
Variable Combination

Catechin is a major reactive substance involved in black tea fermentation. It has a determinant effect on the final quality and taste of made teas. In this study, we applied hyperspectral technology with the chemometrics method and used different pretreatment and variable filtering algorithms to reduce noise interference. After reduction of the spectral data dimensions by principal component analysis (PCA), an optimal prediction model for catechin content was constructed, followed by visual analysis of catechin content when fermenting leaves for different periods of time. The results showed that zero mean normalization (Z-score), multiplicative scatter correction (MSC), and standard normal variate (SNV) can effectively improve model accuracy; while the shuffled frog leaping algorithm (SFLA), the variable combination population analysis genetic algorithm (VCPA-GA), and variable combination population analysis iteratively retaining informative variables (VCPA-IRIV) can significantly reduce spectral data and enhance the calculation speed of the model. We found that nonlinear models performed better than linear ones. The prediction accuracy for the total amount of catechins and for epicatechin gallate (ECG) of the extreme learning machine (ELM), based on optimal variables, reached 0.989 and 0.994, respectively, and the prediction accuracy for EGC, C, EC, and EGCG of the content support vector regression (SVR) models reached 0.972, 0.993, 0.990, and 0.994, respectively. The optimal model offers accurate prediction, and visual analysis can determine the distribution of the catechin content when fermenting leaves for different fermentation periods. The findings provide significant reference material for intelligent digital assessment of black tea during processing.

scholarly journals Research on key influence factors of creativity development of applied undergraduates based on PCA

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Xiangmin Ren ◽  
Jingwei Yu
Keyword(s):  
Principal Component Analysis ◽  
Influence Factors ◽  
Principal Component ◽  
Heilongjiang Province ◽  
Key Factors ◽  
Factors Affecting ◽  
The Core ◽  
Creativity Development ◽  
Teachers And Students ◽  
Core Characteristics

Abstract Creativity is one of the core characteristics of talent; for this reason, the creativity development of applied undergraduates should be one of the basic components of their education. This article gives an overview of the meaning of the creativity of applied undergraduates and makes a literature knowledge-mining and expert investigation on the factors affecting the creativity development. We obtained more than 100 influencing factors, filtered out the duplicative factors, and formed the remaining factors into a questionnaire. A survey was conducted among 1460 teachers and students of some applied undergraduates in Heilongjiang Province. By using principal component analysis (PCA) to analyse the questionnaire, the key factors that affect the creativity development of applied undergraduates are obtained, and the key factors are systematically analysed. According to the results of the analysis, the specific ways and methods of the creativity development of applied undergraduates are put forward.

scholarly journals Weight-Selected Attribute Bagging for Credit Scoring

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Jianwu Li ◽  
Haizhou Wei ◽  
Wangli Hao
Keyword(s):  
Credit Risk ◽  
Prediction Accuracy ◽  
Financial Risk ◽  
Evaluation Method ◽  
Credit Scoring ◽  
Principal Component ◽  
Support Vector ◽  
Training Samples ◽  
Attribute Evaluation ◽  
Bagging Method

Assessment of credit risk is of great importance in financial risk management. In this paper, we propose an improved attribute bagging method, weight-selected attribute bagging (WSAB), to evaluate credit risk. Weights of attributes are first computed using attribute evaluation method such as linear support vector machine (LSVM) and principal component analysis (PCA). Subsets of attributes are then constructed according to weights of attributes. For each of attribute subsets, the larger the weights of the attributes the larger the probabilities by which they are selected into the attribute subset. Next, training samples and test samples are projected onto each attribute subset, respectively. A scoring model is then constructed based on each set of newly produced training samples. Finally, all scoring models are used to vote for test instances. An individual model that only uses selected attributes will be more accurate because of elimination of some of redundant and uninformative attributes. Besides, the way of selecting attributes by probability can also guarantee the diversity of scoring models. Experimental results based on two credit benchmark databases show that the proposed method, WSAB, is outstanding in both prediction accuracy and stability, as compared to analogous methods.

scholarly journals Developing a Hyperparameter Tuning Based Machine Learning Approach of Heart Disease Prediction

2020 ◽  
Vol 7 (2) ◽  
pp. 631-647
Author(s):  
Emrana Kabir Hashi ◽  
Md. Shahid Uz Zaman
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Prediction Models ◽  
Traditional Approach ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
K Nearest Neighbor ◽  
Traditional System ◽  
Prediction Approach

Machine learning techniques are widely used in healthcare sectors to predict fatal diseases. The objective of this research was to develop and compare the performance of the traditional system with the proposed system that predicts the heart disease implementing the Logistic regression, K-nearest neighbor, Support vector machine, Decision tree, and Random Forest classification models. The proposed system helped to tune the hyperparameters using the grid search approach to the five mentioned classification algorithms. The performance of the heart disease prediction system is the major research issue. With the hyperparameter tuning model, it can be used to enhance the performance of the prediction models. The achievement of the traditional and proposed system was evaluated and compared in terms of accuracy, precision, recall, and F1 score. As the traditional system achieved accuracies between 81.97% and 90.16%., the proposed hyperparameter tuning model achieved accuracies in the range increased between 85.25% and 91.80%. These evaluations demonstrated that the proposed prediction approach is capable of achieving more accurate results compared with the traditional approach in predicting heart disease with the acquisition of feasible performance.

scholarly journals Toward a Survey-Based Assessment of Wind Turbine Noise: The Impacts on Wellbeing of Local Residents

Energies ◽  
2020 ◽  
Vol 13 (21) ◽  
pp. 5845
Author(s):  
Lida Liao ◽  
Yuliang Ling ◽  
Bin Huang ◽  
Xu Zhou ◽  
Hongbo Luo ◽  
...  
Keyword(s):  
Energy Harvesting ◽  
Wind Energy ◽  
Wind Turbine ◽  
Principal Component ◽  
Well Being ◽  
Key Factors ◽  
Factors Affecting ◽  
Wind Turbine Noise ◽  
Local Residents ◽  
Assessment Results

As a renewable energy source, wind energy harvesting provides a desirable solution to address the environmental concerns associated with energy production to satisfy the increasingly global demand. Over the years, the penetration of wind turbines has experienced a rapid growth, however, the impacts of turbine noise correspondingly become a major concern in wind energy harvesting. Recent studies indicate that the noise emitted by turbine operating could increase the risk of nuisance, which might further affect the well-being of local residents. However, the main factors affecting turbine noise assessment and to what extent they contribute to the assessment are still unclear. In this study, a survey-based approach is developed to identify these major factors and to explore the interactions between the factors and assessment results. Principal component analysis method was adapted to extract key factors; followed by reliability assessment, validity analysis, descriptive assessment, and correlation analysis were conducted to test the robust of the proposed methodology, as well as to examine the interactions between variables. Regression analysis was finally employed to measure the impacts on results contributed by the key factors. Findings of this study indicate that key factors including physical conditions, control capacity, and subjective opinions are of significant impact on residents’ response to wind turbine noise, while the factor of subjective opinions contributes predominately to the assessment results. Further validations also indicate that the proposed approach is robust and can be extensively applied in survey-based assessments for other fields.

The PCA index for measuring functional diversity and its application to Juglans mandshurica communities in the Beijing mountains, China

2017 ◽  
Vol 10 (07) ◽  
pp. 1750097
Author(s):  
Naiqi Song ◽  
Jin-Tun Zhang ◽  
Fenggu Zhao
Keyword(s):  
Functional Diversity ◽  
Plant Communities ◽  
Environmental Variables ◽  
Principal Component ◽  
Functional Trait ◽  
Effective Index ◽  
Key Factors ◽  
Juglans Mandshurica ◽  
Factors Affecting ◽  
Functional Studies

Methods for measuring functional diversity are essential for functional studies of plant communities. A useful method, the PCA index, based on principal component analysis ordination of functional trait data was introduced and applied to functional diversity analysis of Juglans mandshurica communities in the Beijing Mountains. Thirty-five [Formula: see text] quadrats were established in Juglans mandshurica communities. Species composition, functional traits and environmental factors were measured and recorded. The four common indices, FAD, MFAD, FDp and FDc, were used and compared with the PCA index in the analysis. The results showed that the PCA index was successful in quantifying functional diversity and describing its relationships with environmental variables; therefore, it was an effective index in functional diversity analyses. Functional diversity in Juglans mandshuricacommunities varied widely. Elevation and aspect were the key factors affecting functional diversity in communities. Functional diversity increased with elevation increases and with the change in aspect from North to South. Functional diversity was significantly correlated with species richness and heterogeneity.

Research on Optimum Processing Parameter of EDM Based on Support Vector Machine

2015 ◽  
Vol 775 ◽  
pp. 229-233
Author(s):  
Hao Jiang ◽  
Yuan Lin ◽  
Shuo Wang ◽  
Xiu Wu Sui
Keyword(s):  
Support Vector Machine ◽  
Prediction Accuracy ◽  
Electrical Discharge Machining ◽  
Support Vector ◽  
Processing Quality ◽  
Processing Parameter ◽  
Electric Parameter ◽  
Factors Affecting ◽  
Average Accuracy ◽  
Algorithm Stability

The processing mechanism of electrical discharge machining (EDM) is complex and there are many factors affecting it, therefore the process parameter is very important for processing quality. This paper analyses the relationship between electric parameter and processing quality, then uses support vector machine (SVM) to predict the optimum electric parameter. The simulation result shows that the highest prediction accuracy is 96.10%, the lowest is 89.20%, average accuracy is 94.28%, indicating that the algorithm stability and generalization ability are outstanding. Further verified by experiment, the highest prediction accuracy can amount to 92.65%, the lowest is 81.5%, average accuracy is 89.38%, and electric parameter optimized by SVM can guarantee the expected processing effect better. The exploration in EDM intelligent machining will be convenient for operators to determine the most effective machining conditions.

Multi-Classification Combination Algorithm Based on Logit Model and Support Vector Machine

2013 ◽  
Vol 734-737 ◽  
pp. 2978-2982 ◽  
Author(s):  
Xin Lei Zhang ◽  
Meng Gang Li ◽  
Zuo Quan Zhang
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Machine ◽  
Regression Analysis ◽  
Logit Model ◽  
Prediction Accuracy ◽  
Principal Component ◽  
Composite Indicator ◽  
Support Vector ◽  
Logit Regression ◽  
Multi Classification

According to the basic theories of Logit regression analysis and support vector machine, this article involves improved multi-classification combination algorithm. When applying this model, there are some innovations. First, choose optimized composite indicator as a variable through principal component analysis and get more information. Second, introduce Logit parameter model to the quadratic to increase prediction accuracy. Third, put forward a multi-classification combination model of improved Logit model with SVM to increase prediction accuracy.

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