scholarly journals Simulating the Stress-Strain Relationship of Geomaterials by Support Vector Machine

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Hongbo Zhao ◽  
Zenghui Huang ◽  
Zhengsheng Zou
Keyword(s):  
Support Vector Machine ◽  
Constitutive Relationship ◽  
Support Vector ◽  
Nonlinear Relationship ◽  
Ann Model ◽  
Stress Strain ◽  
Svm Model ◽  
Strain Relationship ◽  
Artificial Neural Network Ann ◽  
Relationship Of

Stress-strain relationship of geomaterials is important to numerical analysis in geotechnical engineering. It is difficult to be represented by conventional constitutive model accurately. Artificial neural network (ANN) has been proposed as a more effective approach to represent this complex and nonlinear relationship, but ANN itself still has some limitations that restrict the applicability of the method. In this paper, an alternative method, support vector machine (SVM), is proposed to simulate this type of complex constitutive relationship. The SVM model can overcome the limitations of ANN model while still processing the advantages over the traditional model. The application examples show that it is an effective and accurate modeling approach for stress-strain relationship representation for geomaterials.

Detecting SIM Box Fraud by Using Support Vector Machine and Artificial Neural Network

2015 ◽  
Vol 74 (1) ◽  
Author(s):  
Roselina Sallehuddin ◽  
Subariah Ibrahim ◽  
Azlan Mohd Zain ◽  
Abdikarim Hussein Elmi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Model Building ◽  
Computational Time ◽  
Support Vector ◽  
Ann Model ◽  
Svm Model ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Fraud in communication has been increasing dramatically due to the new modern technologies and the global superhighways of communication, resulting in loss of revenues and quality of service in telecommunication providers especially in Africa and Asia.  One of the dominant types of fraud is SIM box bypass fraud whereby SIM cards are used to channel national and multinational calls away from mobile operators and deliver as local calls. Therefore it is important to find techniques that can detect this type of fraud efficiently. In this paper, two classification techniques, Artificial Neural Network (ANN) and Support Vector Machine (SVM) were developed to detect this type of fraud.   The classification uses nine selected features of data extracted from Customer Database Record.  The performance of ANN is compared with SVM to find which model gives the best performance. From the experiments, it is found that SVM model gives higher accuracy compared to ANN by giving the classification accuracy of 99.06% compared with ANN model, 98.71% accuracy. Besides, better accuracy performance, SVM also requires less computational time compared to ANN since it takes lesser amount of time in model building and training.

scholarly journals Wavelet-support vector machine for forecasting palm oil price

2019 ◽  
Vol 15 (3) ◽  
pp. 398-406
Author(s):  
Ani Shabri ◽  
Mohd Fahmi Abdul Hamid
Keyword(s):  
Support Vector Machine ◽  
Wavelet Transform ◽  
Palm Oil ◽  
Oil Price ◽  
Support Vector ◽  
Data Series ◽  
Discrete Wavelet ◽  
Ann Model ◽  
Svm Model ◽  
Artificial Neural Network Ann

This study examines the feasibility of applying Wavelet-Support Vector Machine (W-SVM) model in forecasting palm oil price. The conjunction method wavelet-support vector machine (W-SVM) is obtained by the integration of discrete wavelet transform (DWT) method and support vector machine (SVM). In W-SVM model, wavelet transform is used to decompose data series into two parts; approximation series and details series. This decomposed series were then used as the input to the SVM model to forecast the palm oil price. This study also utilizes the application of partial correlation-based input variable selection as the preprocessing steps in determining the best input to the model. The performance of the W-SVM model was then compared with the classical SVM model and also artificial neural network (ANN) model. The empirical result shows that the addition of wavelet technique in W-SVM model enhances the forecasting performance of classical SVM and performs better than ANN.

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.

Prediction of the optimal FSW process parameters for joints using machine learning techniques

2021 ◽  
Vol 63 (12) ◽  
pp. 1104-1111
Author(s):  
Furkan Sarsilmaz ◽  
Gürkan Kavuran
Keyword(s):  
Mechanical Properties ◽  
Tensile Strength ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Ann Model ◽  
Friction Stir ◽  
Learning Techniques ◽  
Svm Model ◽  
Artificial Neural Network Ann ◽  
Fsw Parameters

Abstract In this work, a couple of dissimilar AA2024/AA7075 plates were experimentally welded for the purpose of considering the effect of friction-stir welding (FSW) parameters on mechanical properties. First, the main mechanical properties such as ultimate tensile strength (UTS) and hardness of welded joints were determined experimentally. Secondly, these data were evaluated through modeling and the optimization of the FSW process as well as an optimal parametric combination to affirm tensile strength and hardness using a support vector machine (SVM) and an artificial neural network (ANN). In this study, a new ANN model, including the Nelder-Mead algorithm, was first used and compared with the SVM model in the FSW process. It was concluded that the ANN approach works better than SVM techniques. The validity and accuracy of the proposed method were proved by simulation studies.

scholarly journals Use of GA-ANN and GA-SVM for a QSPR study on aqueous solubility of pesticides

2020 ◽  
pp. 66-66
Author(s):  
Amel Bouakkadia ◽  
Noureddine Kertiou ◽  
Rana Amiri ◽  
Youssouf Driouche ◽  
Djelloul Messadi
Keyword(s):  
Pearson Correlation ◽  
Chemical Properties ◽  
Aqueous Solubility ◽  
Important Variable ◽  
Support Vector ◽  
Ann Model ◽  
Experimental Procedure ◽  
Svm Model ◽  
Artificial Neural Network Ann ◽  
Selection Of

The partitioning tendency of pesticides, in these study herbicides in particular, into different environmental compartments depends mainly of the physic-chemical properties of the pesticides itself. Aqueous solubility (S) indicates the tendency of a pesticide to be removed from soil by runoff or irrigation and to reach surface water. The experimental procedure determining aqueous solubility of pesticides is very expensive and difficult. QSPR methods are often used to estimate the aqueous solubility of herbicides. The artificial neural network (ANN) and support vector machine (SVM) methods, every time associated with genetic algorithm (GA) selection of the most important variable, were used to develop QSPR models to predict the aqueous solubility of a series 80 herbicides. The values of log S of the studied compounds were well correlated with de descriptors. Considering the pertinent descriptors, a Pearson Correlation Squared (R2) coefficient of 0.8 was obtained for the ANN model with a structure of 5-3-1 and 0.8 was obtained for the SVM model using the RBF function for the optimal parameters values: C = 11.12; ? = 0.1111 and ? = 0.222.

A Novel SVM-Based Method for Seismic First-Arrival Detecting

2010 ◽  
Vol 29-32 ◽  
pp. 973-978 ◽  
Author(s):  
Ming Chen ◽  
Yong Li ◽  
Jun Xie
Keyword(s):  
Support Vector ◽  
The Novel ◽  
Ann Model ◽  
Seismic Record ◽  
Feature Vectors ◽  
Svm Model ◽  
First Arrival ◽  
Ann Models ◽  
Novel Method ◽  
Artificial Neural Network Ann

First arrivals detecting on seismic record is important at all times. A novel support vector machine (SVM)-based method for seismic first-arrival pickup is proposed in this research. Firstly, the multi-resolution wavelet decomposition is used to de-noise the seismic record. And then, feature vectors are extracted from the denoise data. Finally, both SVM and artificial neural network (ANN) models are employed to train and predict the feature vectors. Experimental results demonstrate that the SVM model gives better accuracy than the ANN model. It is promising that the novel method is very prospective.

Experimental study on residual strength of concrete confined with corroded stirrups

2016 ◽  
Vol 43 (6) ◽  
pp. 583-590 ◽  
Author(s):  
Guanghui Zhang ◽  
Xinhu Cao ◽  
Qiuni Fu
Keyword(s):  
Experimental Study ◽  
Weight Loss ◽  
Confined Concrete ◽  
Constitutive Relationship ◽  
Stress Strain ◽  
Model Modification ◽  
Chloride Corrosion ◽  
Strain Relationship ◽  
Corrosion Of Steel ◽  
Relationship Of

Corrosion of steel bars is a serious issue compromising the reliability of reinforced concrete structures. Previous research has proven that confined concrete has increased confined strength, ductility, energy dissipation capacity, etc. However, existing confined concrete models cannot accurately predict the strength and stress–strain relationship of concrete confined with corroded stirrups. This paper presents an experimental study on the behavior of eight square columns confined with stirrups damaged by accelerated chloride corrosion. The weight loss of the stirrup ranges from 5% to 31.7%. The stress–strain curves of confined concrete are obtained from the concentric compression loading tests. Based on the well-established Mander model, modification factors are introduced to account for the effect of corrosion on the confined strength, confined strain, and descending branch of the curves. These modification factors predict the constitutive relationship of concrete confined with square stirrups as a function of the weight loss and localization levels of corrosion.

Research of Bus Protection Based on Support Vector Machine with Parameter Optimization

2011 ◽  
Vol 271-273 ◽  
pp. 823-828
Author(s):  
Han Han ◽  
Hou Jun Wang ◽  
Cheng Ti Huang ◽  
Bin Long
Keyword(s):  
Support Vector Machine ◽  
Parameter Optimization ◽  
Power Plants ◽  
Power Transformer ◽  
Normal Operation ◽  
Electrical Network ◽  
Support Vector ◽  
Ann Model ◽  
Protection Method ◽  
Artificial Neural Network Ann

Bus reliability is essential for maintaining the steady state of electrical network in power plants and power transformer stations. Researches on techniques and methods to achieve highly reliable and intelligent bus protection are very important. A new bus protection method based on parameter optimization of the support vector machine (SVM) is provided in this study. Various types of fault data were collected and used as samples for sorting comparison and analysis of different bus faults. Data were obtained by comparing the SVM method with the artificial neural network (ANN) method. The results suggest that the SVM model using RBF kernel can better distinguish among bus normal operation and different bus fault types than the ANN model, and meet the precision requirements for bus protection.

scholarly journals Screening for Prediabetes Using Machine Learning Models

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Soo Beom Choi ◽  
Won Jae Kim ◽  
Tae Keun Yoo ◽  
Jee Soo Park ◽  
Jai Won Chung ◽  
...  
Keyword(s):  
External Validation ◽  
Systematic Evaluation ◽  
Support Vector ◽  
Ann Model ◽  
Internal Validation ◽  
Health And Nutrition ◽  
Svm Model ◽  
Score Model ◽  
Artificial Neural Network Ann ◽  
Better Than

The global prevalence of diabetes is rapidly increasing. Studies support the necessity of screening and interventions for prediabetes, which could result in serious complications and diabetes. This study aimed at developing an intelligence-based screening model for prediabetes. Data from the Korean National Health and Nutrition Examination Survey (KNHANES) were used, excluding subjects with diabetes. The KNHANES 2010 data(n=4685)were used for training and internal validation, while data from KNHANES 2011(n=4566)were used for external validation. We developed two models to screen for prediabetes using an artificial neural network (ANN) and support vector machine (SVM) and performed a systematic evaluation of the models using internal and external validation. We compared the performance of our models with that of a screening score model based on logistic regression analysis for prediabetes that had been developed previously. The SVM model showed the areas under the curve of 0.731 in the external datasets, which is higher than those of the ANN model (0.729) and the screening score model (0.712), respectively. The prescreening methods developed in this study performed better than the screening score model that had been developed previously and may be more effective method for prediabetes screening.

Prediction of friction capacity of driven piles in clay using the support vector machine

2008 ◽  
Vol 45 (2) ◽  
pp. 288-295 ◽  
Author(s):  
Pijush Samui
Keyword(s):  
Support Vector Machine ◽  
Model Complexity ◽  
Support Vector ◽  
Ann Model ◽  
Driven Piles ◽  
Learning Technique ◽  
Practical Tool ◽  
Artificial Neural Network Ann ◽  
Friction Capacity ◽  
Better Than

The support vector machine (SVM) is an emerging machine learning technique where prediction error and model complexity are simultaneously minimized. This paper examines the potential of SVM to predict the friction capacity of driven piles in clay. This SVM is firmly based on the statistical learning theory and uses the regression technique by introducing accuracy (ε) insensitive loss function. The results are compared with those from a widely used artificial neural network (ANN) model. Overall, the SVM showed good performance and is proven to be better than ANN model. A sensitivity analysis has been also performed to investigate the importance of the input parameters. The study shows that SVM has the potential to be a useful and practical tool for prediction of friction capacity of driven piles in clay.

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