Filtering Random Valued Impulse Noise from Grayscale Images through Support Vector Machine and Markov Chain

This paper presents a context-based filter to denoise grayscale images affected by random valued impulse noise. A support vector machine classifier is used for noise detection and two Markov filter variants are evaluated for their denoising capacity. The classifier needs to be trained on a set of training images. The experiments performed on another set of test images have shown that the support vector machine with the radial basis function kernel combined with the Markov+ filter is the best configuration, providing the highest noise detection accuracy. Our filter was compared with existing denoising methods, it being better on some images and comparable with them on others.

Adaptive data-driven nonlinear synchro squeezed transform with single class radial basis function kernel support vector machine applied to wind turbine planetary gearbox fault diagnostics

Planetary stage gears operated at low rotational speed and varying wind speed result variation in load. Variable speed and variable load induce nonstationary operating conditions. Vibration signal measured from Wind power gear transmission systems are embedded with multiple sources of vibration and attenuated considerably as it travels from source of vibration to measuring point. Efficacious multi-component decomposition without mode mixing ensures the accurate fault signature recognition. Synchro squeezing transform is the promising tool that represents the ridges with high resolution in time as well as in frequency axis. An efficient vibration analysis technique, short windowed Fourier synchro squeezing transform with nonlinear radial basis function kernel support vector machine is proposed to detect the mechanical faults in low speed planetary stage of wind turbines. Raw vibration is modeled in time–frequency plane to extract fault pattern signatures effectively with high resolution by adapting an empirical nonlinear tool synchro squeezing transforms. Amplitude modulation and frequency modulation parameters are sculpted from instantaneous amplitude and instantaneous phase, frequency. Hybrid feature space with signal attributes, statistical moments, and randomness measures are extricated from amplitude modulation-frequency modulation components. Single class radial basis function support vector machine is trained with hybrid features. The fault detection accuracy of the proposed method is compared with the standard variants of empirical mode decomposition. The proposed short windowed Fourier synchro squeezing transform-radial basis function kernel support vector machine shows 98.2% accuracy, 98% sensitivity, and 98% specificity.

Performance of Support Vector Machine Kernels (SVM-K) on Breast Cancer (BC) Dataset

Breast cancer (BC) most diagnosed invasive disorder and important cause of casualty for women worldwide. Indian contest BC most commonly spread disease among females. This problem is more alarming to economically developing country like India. Government of India made a lot of effort to make aware the women of the country, but despite of availability of diagnostic tool, prediction of disease in real situation is still a puzzle for researchers. Timely detection and categorization of BC using the evolving techniques like Machine Learning (ML) can show a significant role in BC identification and this could be a preventive policy which effectively reduces the risk of BC patients. Although there are four Kernels in ML, are widely in use but their performance varies with the kind of data available. In this study we, apply four different Kernels such as Linear Kernel (LK), Polynomial Kernel (PK), Sigmoid Kernel (SK) and Radial Basis Function Kernel (RBFK) on BC dataset. We estimated the performance of Support Vector Machine Kernels (SVM-K) on BC dataset .The basic idea is to check the exactness of SVM-K to classify WBCD in terms of effectiveness with respect to accuracy, runtime, specificity and precision. The investigations outcome displays that RBFK provides greater accuracy with minimal errors

Three Dimensional Analysis of SPECT Images for Diagnosing Early Parkinson’s Disease using Radial Basis Function Kernel − Extreme Learning Machine

Background: Parkinson’s Disease (PD) is caused by the deficiency of dopamine, the neurotransmitter that has an effect on specific uptake region of the substantia nigra. Identification of PD is quite tough at an early stage. Objective: The present work proposes an expert system for three dimensional Single-Photon Emission Computed Tomography (SPECT) image to diagnose the early PD. Methods: The transaxial image slices are selected on the basis of their high specific uptake region. The processing techniques like preprocessing, segmentation and feature extraction are implemented to extract the quantification parameters like Intensity, correlation, entropy, skewness and kurtosis of the images. The Support Vector Machine (SVM) and Extreme Learning Machine (ELM) classifiers using Radial Basis Function kernel (RBF) are implemented and their results are compared in order to achieve better performance of the system. The performance of the system is evaluated in terms of sensitivity, specificity analysis, accuracy, Receiver Operating Curve (ROC) and Area Under the Curve (AUC). Results: It is found that RBF-ELM provides high accuracy of 98.2% in diagnosing early PD. In addition, the similarity among the features is found out using K-means clustering algorithm to compute the threshold level for early PD. The computed threshold level is validated using Analysis of Variance (ANOVA). Conclusion: The proposed system has a great potential to assist the clinicians in the early diagnosis process of PD.