Multiple Support Vector Machine Models Research Articles

Sensor-Embedded Tissue Phantom for Magnetic Resonance Elastography Mechanical Failure Testing

Abstract Magnetic Resonance Elastography (MRE) is an imaging technique capable of quantifying the stiffness of in vivo tissue by applying and imaging shear waves produced by an MRE actuator. Poor image acquisition may result from the MRE procedure if there is insufficient contact between the MRE actuator and the patient. An experimental test setup outside of the clinic will aid in reducing the number of failed acquisitions by enabling the development of advanced actuators and actuator systems. This work presents the development and testing of a sensor-embedded tissue phantom setup paired with a support vector machine (SVM) classifier to automate the MRE actuator testing process. MRE actuation of soft tissue is simulated by utilizing a voice coil positioning stage that interfaces with a phantom. To capture the resulting vibrations, accelerometers are embedded inside the phantom. Subsequent characterization experiments verify the functionality of the developed phantoms to capture wave propagation. A secondary investigation was performed by utilizing the developed setup to collect acceleration measurements at varying contact distances. We provide an overview of feature analysis and selection to develop SVM models for contact detection. Multiple SVM models are reported, and the best-performing model displayed almost perfect validation (94.53%) and test (90.91%) accuracy. The pairing of sensor-embedded phantom with an SVM for detection demonstrates potential improvements to the MRE actuator developmental process by automatically assessing contact-related issues prior to clinical testing.

An SVM-Based AdaBoost Cascade Classifier for Sonar Image

This paper proposes an improved AdaBoost classifier for sonar images with low resolution ratio and noise. First, Histogram of oriented gradient (HOG) is used to perform feature extraction, and a weak classifier is obtained by Support vector machine (SVM) at the same time. Then, multiple SVM models are constructed for target classification based on the AdaBoost cascade classification framework. A new function for updating sample weights has been designed in this paper to improve the accuracy of the classifier. And new iteration rules of classifier have been made to reduce the training time of the proposed method. The experimental results on the sonar dataset which are proposed for improving the generalization ability in this paper show that the classification accuracy of the proposed algorithm is about 92%, and the accuracy on Cifar-10 dataset is also higher than general methods.

A Study on DS-SVM-Based Forecast Model from the View of Information Fusion

Dempster-Shafer Theory is specially advantaged in information fusion, while Support Vector Machine (SVM) can well deal with high-dimensional limited sample data. This Article firstly forecasts the data samples by categories with multiple SVMs, and hence based thereon, fuses the resulting information from multiple SVM models by using DS theory. At the end, Anderson's Iris data set is used to simulate the system of the created DS-SVM model, which shows that the approaches proposed in this Article can not only increase the accuracy rate of the SVM forecasting model, but also improve the propensity scores of the SVM forecasting model.

Combining Cluster Analysis, Feature Selection and Multiple Support Vector Machine Models for the Identification of Human Ether‐a‐go‐go Related Gene Channel Blocking Compounds

Blockade of the human ether-a-go-go related gene potassium channel is regarded as a major cause of drug toxicity and associated with severe cardiac side-effects. A variety of in silico models have been reported to aid in the identification of compounds blocking the human ether-a-go-go related gene channel. Herein, we present a classification approach for the detection of diverse human ether-a-go-go related gene blockers that combines cluster analysis of training data, feature selection and support vector machine learning. Compound learning sets are first divided into clusters of similar molecules. For each cluster, independent support vector machine models are generated utilizing preselected MACCS structural keys as descriptors. These models are combined to predict human ether-a-go-go related gene inhibition of our large compound data set with consistent experimental measurements (i.e. only patch clamp measurements on mammalian cell lines). Our combined support vector machine model achieves a prediction accuracy of 85% on this data set and performs better than alternative methods used for comparison. We also find that structural keys selected on the basis of statistical criteria are associated with molecular substructures implicated in human ether-a-go-go related gene channel binding.