Multi-kernel SVM Research Articles

Performance Evaluation of Biometric Authentication Using Fragment Jaya Optimizer-Based Deep CNN with Multi-kernel SVM

Performance Evaluation of Biometric Authentication Using Fragment Jaya Optimizer-Based Deep CNN with Multi-kernel SVM

Detection of Simultaneous Bearing Faults Fusing Cross Correlation With Multikernel SVM

Detection of simultaneous bearing faults for condition monitoring (CM) of bearings using time-domain analysis is quite challenging and open area particularly in noisy environment. This work presents a new scheme for simultaneous bearing fault detection using vibration signal, in cases where single-point localized bearing fault and multiple-point compound fault coexist. Bearings of a 415 V, 3kW, 3-phase Squirrel Cage Induction Motor (SCIM) have been used for data collection, while the loading arrangement is done using a 110V, 4kW DC generator connected with a load box and coupled to the motor. A cross-correlation-based time-domain feature extraction approach has been introduced. The neighborhood component analysis (NCA) technique has been applied to the cross-correlation-based features to reduce the complexity of the proposed model. Furthermore, the selected features have been fed into a multi-kernel support vector machine (MKSVM) to classify simultaneous bearing faults. This method has also been tested on signals contaminated with white Gaussian noise to verify reliability in the industrial environment. It is found that with only five features, the proposed model yields 100% classification performance metrics for raw signal and under noisy environments with signal-to-noise ratio (SNR) of 20 dB to 50 dB for both full load and no-load conditions. Whereas at 10 dB SNR value, performance decreases slightly, still an overall classification performance metrics of more than 99% is achieved by this method. Furthermore, this method has enhanced performance when compared to earlier studies with publicly available databases for localized bearing failure identification.

Optimal multi-kernel SVM classifier with rotation, illumination and scale invariant hybrid DWT-Shearlet based GLCM feature descriptor and its application to face recognition

In computer vision, we must handle with the various structural aspects of image or video data. The texture is one of the most important aspects of this type of data, which is utilised to identify objects or regions of interest in an image. As imaging conditions change, textures inside actual images significantly change in brightness, contrast, size, and skew. To recognise textures in real-world images, a similarity measure that is invariant to these features must be used. Existing recognition techniques did not perform well due to issues such as illumination, scale, and subject rotation. To address this issue, invariant feature representation methods are being developed to generate features that are insensitive to such variations. In this paper, we proposed a robust hybrid feature descriptor and predicted the faces under illumination, scale, and pose variations using an optimum multi-kernel support vector machine. Additionally, the suggested robust hybrid feature descriptor is enhanced by combining a hybrid transform composed of discrete wavelet and discrete shearlet transforms with some image statistical textural data. The proposed face recognition system is implemented in MATLAB, and analysed using various parameters to show proposed methods improved performance as compared to the state of the art methods.

MKL-SING: A data-driven approach of sign recognition for managing and improving public services

The detection and identification of traffic signs is a fundamental function of an intelligent transportation system. The extraction or identification of a road sign poses the same problems as object identification in natural contexts: conditions of illumination are variable and uncontrollable, and various objects frequently surround road signs. These difficulties make the extraction of features difficult. The fusion of time and space features of traffic signs is important for improving the performance of sign recognition. Deep learning-based algorithms are time-consuming to train based on a large amount of data. They are difficult to deploy on resource-constrained portable devices and conduct sign detection in real time. The accuracy of sign detection should be further improved, which is related to the safety of traffic participants. To improve the accuracy of feature extraction and classification of traffic signs, we propose MKL-SING, a hybrid approach based on multi-kernel support vector machine (MKL-SVM) for public transportation SIGN recognition. It contains three main components: a principal component analysis for image dimension reduction, a fused feature extractor, and a multi-kernel SVM-based classifier. The fused feature extractor extracts and fuses the time and space features of traffic signs. The multi-kernel SVM then classifies the traffic signs based on the fused features. Different kernel functions in the multi-kernel SVM are fused based on a feature weighting procedure. Compared with single-core SVM, multi-kernel SVM can better process massive data because it can project each kernel function into high-dimensional feature space to get global solutions. Finally, the performance of SVM-TSR is validated based on three traffic sign datasets. Experiment results show that SVM-TSR performs better than state-of-the-art methods in terms of dynamic traffic sign identification and recognition.

Delamination Depth Evaluation in Near-Surface of GFRP Laminates Based on RQA-MKLSVM Model

The increasing use of glass fiber reinforced polymer (GFRP) laminates in aircraft creates an urgent demand for nondestructive testing to identify defects such as impact damage and delamination in the materials’ near-surface structure. The High-frequency ultrasonic technique is an effective nondestructive testing technique capable of detecting such defects. However, with the increased delamination depth, it is difficult to accurately characterize the delamination defects in GFRP laminates by traditional characterization methods. And the conventional defect depth detection method takes much time and requires professional discrimination, which is easily affected by the level of inspectors and subjective factors. To address the issue of inaccurate feature extraction and low detection efficiency, we propose a new method for delamination depth recognition in the near-surface of GFRP laminates based on the RQA-MKLSVM model by combining the recurrence quantitative analysis (RQA) feature extraction method with the multi-kernel learning SVM technique to improve the capability of detecting delamination defects on GFRP laminates. Additionally, an image-enhanced method based on the nonlinear transformation function and wavelet multiscale product is used to locate the delaminated areas. Results show that the proposed method achieves accurate recognition of different delamination defects within depths ranging from 0.8 mm to 4.0 mm, and the average recognition rate has reached 95.63%. Compared with the training models based on discrete wavelet transform(DWT) and empirical mode decomposition (EMD), the recognition rate of our method is improved by 6.25% and 4.38%, respectively. And Compared with the two single-core models, the recognition accuracy of our method is improved by 8.55% and 5.00%, respectively.

Hybrid multi-kernel SVM algorithm for microaneurysm recognition in colour fundus images

Hybrid multi-kernel SVM algorithm for microaneurysm recognition in colour fundus images

Retraction Note to: Optimal feature-based multi-kernel SVM approach for thyroid disease classification

Retraction Note to: Optimal feature-based multi-kernel SVM approach for thyroid disease classification

An Adaptive Firefly Optimization (AFO) with Multi-Kernel SVM (MKSVM) Classification for Big Data Dimensionality Reduction

The data's dimensionality had already risen sharply in the last several decades. The "Dimensionality Curse" (DC) is a problem for conventional learning techniques when dealing with "Big Data (BD)" with a higher level of dimensionality. A learning model's performance degrades when there is a numerous range of features present. "Dimensionality Reduction (DR)" approaches are used to solve the DC issue, and the field of "Machine Learning (ML)" research is significant in this regard. It is a prominent procedure to use "Feature Selection (FS)" to reduce dimensions. Improved learning effectiveness such as greater classification precision, cheaper processing costs, and improved model comprehensibility are all typical outcomes of this approach that selects an optimal portion of the original features based on some relevant assessment criteria. An "Adaptive Firefly Optimization (AFO)" technique based on the "Map Reduce (MR)" platform is developed in this research. During the initial phase (mapping stage) the whole large "DataSet (DS)" is first subdivided into blocks of contexts. The AFO technique is then used to choose features from its large DS. In the final phase (reduction stage), every one of the fragmentary findings is combined into a single feature vector. Then the "Multi Kernel Support Vector Machine (MKSVM)" classifier is used as classification in this research to classify the data for appropriate class from the optimal features obtained from AFO for DR purposes. We found that the suggested algorithm AFO combined with MKSVM (AFO-MKSVM) scales very well to high-dimensional DSs which outperforms the existing approach "Linear Discriminant Analysis-Support Vector Machine (LDA-SVM)" in terms of performance. The evaluation metrics such as Information-Ratio for Dimension-Reduction, Accuracy, and Recall, indicate that the AFO-MKSVM method established a better outcome than the LDA-SVM method.

Retraction Note to: Effective segmentation and classification of brain tumor using rough K means algorithm and multi kernel SVM in MR images

Retraction Note to: Effective segmentation and classification of brain tumor using rough K means algorithm and multi kernel SVM in MR images

Asso approach for classifying gene expression data based on optimal features

Gene expression profiles are sequences of numbers, and the need to analyze them has now increased significantly. Gene expression data contain a large number of genes and models used for cancer classification. As the wealth of these data being produced, new prediction, classification and clustering techniques are applied to the analysis of the data. Although there are a number of proposed methods with good results, there is still limited diagnostics and a lot of problems still to be solved. To solve the difficulty, in this paper, an efficient gene expression data classification is proposed. To predict the cancer class of patients from the gene expression profile, this paper presents a novel classification framework in the manner of three steps namely, Pre-processing, feature selection and classification. In pre-processing, missing value is filled and redundant data are removed. To attain the enhanced classification outcomes, the important features are selected from the database with the help of Adaptive Salp Swarm Optimization (ASSO) algorithm. Then, the selected features are given to the multi kernel SVM (MKSVM) to classify the gene expression data namely, BRCA, KIRC, COAD, LUAD and PRAD. The performance of proposed methodology is analyzed in terms of different metrics namely, accuracy, sensitivity and specificity. The performance of proposed methodology is 4.5% better than existing method in terms of accuracy.

Hybrid multi-kernel SVM algorithm for detection of microaneurysm in color fundus images.

Diabetic retinopathy (DR) is a chronic disease that may cause vision loss in diabetic patients. Microaneurysms which are characterized by small red spots on the retina due to fluid or blood leakage from the weak capillary wall often occur during the early stage of DR, making screening at this stage is essential. In this paper, an automatic screening system for early detection of DR in retinal images is developed using a combined shape and texture features. Due to minimum number of hand-crafted features, the computational burden is much reduced. The proposed hybrid multi-kernel support vector machine classifier is constructed by learning a kernel model formed as a combination of the base kernels. This approach outperforms the recent deep learning techniques in terms of the evaluation metrics. The efficiency of the proposed scheme is experimentally validated on three public datasets - Retinopathy Online Challenge, DIARETdB1, MESSIDOR, and AGAR300 (developed for this study). Studies reveal that the proposed model produced the best results of 0.503 in ROC dataset, 0.481 in DIARETdB1, and 0.464 in the MESSIDOR dataset in terms of FROC score. The AGAR300 database outperforms the existing MA detection algorithm in terms of FROC, AUC, F1 score, precision, sensitivity, and specificity which guarantees the robustness of this system.

Object recognition based on topology preserving skeleton features

Object recognition is a procedure for recognising a particular object in an advanced video or image. Appearance-based or feature-based techniques are used for object recognition. An object skeleton is the useful cue for object recognition, which provides a structural representation to specify the relationship among object parts. The shape's geometry and topology can be efficiently encoded. In this paper, an effective object recognition method is introduced with the help of multi kernel SVM using the skeleton derived from topology preserving skeleton features method.

Multivariate Information Fusion for Identifying Antifungal Peptides with Hilbert-Schmidt Independence Criterion

Background: Antifungal Peptides (AFP) have been found to be effective against many fungal infections. Objective: However, it is difficult to identify AFP. Therefore, it is great practical significance to identify AFP via machine learning methods (with sequence information). Method: In this study, a Multi-Kernel Support Vector Machine (MKSVM) with Hilbert-Schmidt Independence Criterion (HSIC) is proposed. Proteins are encoded with five types of features (188-bit, AAC, ASDC, CKSAAP, DPC), and then construct kernels using Gaussian kernel function. HSIC are used to combine kernels and multi-kernel SVM model is built. Results: Our model performed well on three AFPs datasets and the performance is better than or comparable to other state-of-art predictive models. Conclusion: Our method will be a useful tool for identifying antifungal peptides.

Multi-biomarkers-Base Alzheimer’s Disease Classification

Various anatomical MRI imaging biomarkers for Alzheimer’s Disease (AD) identification have been recognized so far. Cortical and subcortical volume, hippocampal, amygdala volume, and genetics patterns have been utilized successfully to diagnose AD patients from healthy. These fundamental sMRI bio-measures have been utilized frequently and independently. The entire possibility of anatomical MRI imaging measures for AD diagnosis might thus still to analyze fully. Thus, in this paper, we merge different structural MRI imaging biomarkers to intensify diagnostic classification and analysis of Alzheimer’s. For 54 clinically pronounce Alzheimer’s patients, 58 cognitively healthy controls, and 99 Mild Cognitive Impairment (MCI); we calculated 1. Cortical and subcortical features, 2. The hippocampal subfield, amygdala nuclei volume using Freesurfer (6.0.0) and 3. Genetics (APoE ε4) biomarkers were obtained from the ADNI database. These three measures were first applied separately and then combined to predict the AD. After feature combination, we utilize the sequential feature selection [SFS (wrapper)] method to select the top-ranked features vectors and feed them into the Multi-Kernel SVM for classification. This diagnostic classification algorithm yields 94.33% of accuracy, 95.40% of sensitivity, 96.50% of specificity with 94.30% of AUC for AD/HC; for AD/MCI propose method obtained 85.58% of accuracy, 95.73% of sensitivity, and 87.30% of specificity along with 91.48% of AUC. Similarly, for HC/MCI, we obtained 89.77% of accuracy, 96.15% of sensitivity, and 87.35% of specificity with 92.55% of AUC. We also presented the performance comparison of the proposed method with KNN classifiers.

Sparse coded spatial pyramid matching and multi-kernel integrated SVM for non-linear scene classification

AbstractSupport vector machine (SVM) techniques and deep learning have been prevalent in object classification for many years. However, deep learning is computation-intensive and can require a long training time. SVM is significantly faster than Convolution Neural Network (CNN). However, the SVM has limited its applications in the mid-size dataset as it requires proper tuning. Recently the parameterization of multiple kernels has shown greater flexibility in the characterization of the dataset. Therefore, this paper proposes a sparse coded multi-scale approach to reduce training complexity and tuning of SVM using a non-linear fusion of kernels for large class natural scene classification. The optimum features are obtained by parameterizing the dictionary, Scale Invariant Feature Transform (SIFT) parameters, and fusion of multiple kernels. Experiments were conducted on a large dataset to examine the multi-kernel space capability to find distinct features for better classification. The proposed approach founds to be promising than the linear multi-kernel SVM approaches achieving 91.12 % maximum accuracy.

SVM Kernels comparison for brain tumor diagnosis using MRI

Magnetic Resonance Image (MRI) brain images have an essential role in medical analysis and cancer identification .In this paper multi kernel SVM algorithm is used for MRI brain tumor detection. The proposed work is involving the following stages: image acquisition, image preprocessing, feature extraction and tumor classification. An automatic threshold selection region based segmentation method called Otsu is used for thresholding during preprocessing stage. SVM classification algorithm with four different kernels are used to determine the normal and abnormal images. SVM with quadratic kernel results in best classification accuracy of 86.5%.

Analyzing brain structural differences among undergraduates with different grades of self-esteem using multiple anatomical brain network

BackgroundSelf-esteem is the individual evaluation of oneself. People with high self-esteem grade have mental health and can bravely cope with the threats from the environment. With the development of neuroimaging techniques, researches on cognitive neural mechanisms of self-esteem are increased. Existing methods based on brain morphometry and single-layer brain network cannot characterize the subtle structural differences related to self-esteem.MethodTo solve this issue, we proposed a multiple anatomical brain network based on multi-resolution region of interest (ROI) template to study the brain structural connections of self-esteem. The multiple anatomical brain network consists of ROI features and hierarchal brain network features that are extracted from structural MRI. For each layer, we calculated the correlation relationship between pairs of ROIs. In order to solve the high-dimensional problem caused by the large amount of network features, feature selection methods (t-test, mRMR, and SVM-RFE) are adopted to reduce the number of features while retaining discriminative information to the maximum extent. Multi-kernel SVM is employed to integrate the various types of features by appropriate weight coefficient.ResultThe experimental results show that the proposed method can improve classification accuracy to 97.26% compared with single-layer brain network.ConclusionsThe proposed method provides a new perspective for the analysis of brain structural differences of self-esteem, which also has potential guiding significance in other researches involved brain cognitive activity and brain disease diagnosis.

RETRACTED ARTICLE: Effective segmentation and classification of brain tumor using rough K means algorithm and multi kernel SVM in MR images

From the classifications, an effective brain tumor classification and segmentation is the curious part for identifying the tumor and non-tumor cells in brain and the cell levels are evaluated. The brain tumor segmentation and classification is established on their experiences. The accuracy of tumor segmentation is very crucial to diagnosis accuracy. So, in our work we are align and improve an approach for tumor identification applying brain MR image segmentation. With an efficient, accurate and reproducible manner, the aim of our suggested method is to evaluate the tumor. Then the brain tumor is separated by using the effective techniques. For segmentation process, first the MRI image must be preprocessed. Next, the process of feature extraction is done by using preprocessed images. In feature extraction process, a raised Gabor wavelet transform (IGWT) is applied. In this research, the means of optimization technique is changed from the traditional Gabor wavelet transform. And the effectiveness of that optimization technique is aligned by using an oppositional fruit fly algorithm. At the end of the process, feature values are transferred in to the clustering process for segmentation. In this article we are introduced an algorithm called as rough k means clustering algorithm for segmentation. Here, we are applying an oppositional fruit fly algorithm to develop an effectiveness of the Gabor filter. Further to raise the classification accuracy of brain tumor we are introduced a multi kernel support vector machine algorithm.

Optimal region growing and multi-kernel SVM for fault detection in electrical equipments using infrared thermography images

Infrared thermography (IRT) has played an essential part in observing and examining thermal defects of electrical equipment without ending, which has vital enormity for the dependability of electrical recorded. This paper dissected the electrical parts are faulted or non-faulted with the help of segmentation and classification model. The features are calculated from the input thermal images and regions of interest (ROI) is segmented by utilising optimal region growing (ORG) technique and faults are classified using multi kernel support vector machine (MKSVM). In the tests, the classification performances from different input features are assessed. For enhancing the performance of the segmentation investigation optimisation procedure that is whale optimisation (WO) is used. Before classifying, the extracted electrical components are fused by using feature level fusion (FLF) procedure to fused vector in all images. These multi kernel classification performance indices, including sensitivity, specificity and accuracy are utilised to recognise the most appropriate input feature and the best arrangement of classifiers. The performance of SVM is contrasted with a neural network. The correlation comes about demonstrating that our technique can accomplish a superior performance with accuracy at 98.21%.

RETRACTED ARTICLE: Prediction of pre-term groups from EHG signals using optimal multi-kernel SVM

Pre-term birth is the birth that carries out before the baby’s expected date. Sometimes, it augments the possibility of health problems or death. In this research, the abdominal EHG signal incorporates both the maternal heart beat signal and Fetal ECG signal. The removal of fetal ECG signal from the heart beat signal is difficult in pre-term detection. In this work, the EHG signal is pre-processed using wiener filter that is applied to enhance the signal quality. Then, the attributes are removed from the pre-processed signal to find the distinctive class. In addition, Opposition based Ant Lion Optimization is used to select the multi-kernel Support Vector Machine and training algorithms for predicting the pre-term birth. The proposed methodology is simulated by using MATLAB software and the results are investigated to verify the classification accuracy. From the experimental study, the proposed work enhanced the classification accuracy upto 3–19% related to the existing works.