Improved Support Vector Machine Classifier Research Articles

Multi-Classification of Lung Infections Using Improved Stacking Convolution Neural Network

Lung disease is a respiratory disease that poses a high risk to people worldwide and includes pneumonia and COVID-19. As such, quick and precise identification of lung disease is vital in medical treatment. Early detection and diagnosis can significantly reduce the life-threatening nature of lung diseases and improve the quality of life of human beings. Chest X-ray and computed tomography (CT) scan images are currently the best techniques to detect and diagnose lung infection. The increase in the chest X-ray or CT scan images at the time of training addresses the overfitting dilemma, and multi-class classification of lung diseases will deal with meaningful information and overfitting. Overfitting deteriorates the performance of the model and gives inaccurate results. This study reduces the overfitting issue and computational complexity by proposing a new enhanced kernel convolution function. Alongside an enhanced kernel convolution function, this study used convolution neural network (CNN) models to determine pneumonia and COVID-19. Each CNN model was applied to the collected dataset to extract the features and later applied these features as input to the classification models. This study shows that extracting deep features from the common layers of the CNN models increased the performance of the classification procedure. The multi-class classification improves the diagnostic performance, and the evaluation metrics improved significantly with the improved support vector machine (SVM). The best results were obtained using the improved SVM classifier fed with the features provided by CNN, and the success rate of the improved SVM was 99.8%.

An enhanced segmentation technique and improved support vector machine classifier for facial image recognition

PurposeOne of the challenging issues in computer vision and pattern recognition is face image recognition. Several studies based on face recognition were introduced in the past decades, but it has few classification issues in terms of poor performances. Hence, the authors proposed a novel model for face recognition.Design/methodology/approachThe proposed method consists of four major sections such as data acquisition, segmentation, feature extraction and recognition. Initially, the images are transferred into grayscale images, and they pose issues that are eliminated by resizing the input images. The contrast limited adaptive histogram equalization (CLAHE) utilizes the image preprocessing step, thereby eliminating unwanted noise and improving the image contrast level. Second, the active contour and level set-based segmentation (ALS) with neural network (NN) or ALS with NN algorithm is used for facial image segmentation. Next, the four major kinds of feature descriptors are dominant color structure descriptors, scale-invariant feature transform descriptors, improved center-symmetric local binary patterns (ICSLBP) and histograms of gradients (HOG) are based on clour and texture features. Finally, the support vector machine (SVM) with modified random forest (MRF) model for facial image recognition.FindingsExperimentally, the proposed method performance is evaluated using different kinds of evaluation criterions such as accuracy, similarity index, dice similarity coefficient, precision, recall and F-score results. However, the proposed method offers superior recognition performances than other state-of-art methods. Further face recognition was analyzed with the metrics such as accuracy, precision, recall and F-score and attained 99.2, 96, 98 and 96%, respectively.Originality/valueThe good facial recognition method is proposed in this research work to overcome threat to privacy, violation of rights and provide better security of data.

An Image Recognition Algorithm of Bolt Loss in Underground Pipelines Based on Local Binary Pattern Operator

This paper mainly designs an image recognition algorithm of bolt loss in underground pipelines. Firstly, the local binary pattern (LBP) operator was improved to optimize the information content of eigenvectors and enhance the discriminability. Next, the patterns were selected through weighting and ranking, thereby optimizing the original features in each channel of the image. Meanwhile, the main patterns of each channel were classified and identified with the support vector machine (SVM) classifier. The radial basis function (RBF) was taken as the kernel function for the SVM, and the teaching-learning-based optimization (TLBO) algorithm was improved to optimize the SVM parameters. Finally, the improved SVM classifier assigns suitable weights to the predicted class tags of different channels, facilitating the recognition of bolt loss. The research results shed new light on the application of swarm intelligence in image recognition.

An improved support vector machine classifier based on artificial bee colony algorithm

Support vector machine (SVM) has unique advantages in the classification of small sample data. The selection of parameters has an important impact on the classification accuracy and generalization ability of SVM. Since the selection of parameters of SVM is usually based on experience, an improved SVM classification model based on artificial bee colony algorithm (GOABC-SVM) is proposed in this paper. In this model, firstly, the traditional artificial bee colony algorithm is optimized using the ideas of global optimal solution guidance and opposite learning. Secondly, we set the reciprocal of classification error rate as the fitness function, and use the improved artificial bee colony algorithm to obtain the optimal parameter combination of SVM. Experiments on a set of datasets of UCI show that the proposed model has higher classification accuracy and better generalization ability.

Artificial bee colony algorithm for feature selection and improved support vector machine for text classification

PurposeOwing to the huge volume of documents available on the internet, text classification becomes a necessary task to handle these documents. To achieve optimal text classification results, feature selection, an important stage, is used to curtail the dimensionality of text documents by choosing suitable features. The main purpose of this research work is to classify the personal computer documents based on their content.Design/methodology/approachThis paper proposes a new algorithm for feature selection based on artificial bee colony (ABCFS) to enhance the text classification accuracy. The proposed algorithm (ABCFS) is scrutinized with the real and benchmark data sets, which is contrary to the other existing feature selection approaches such as information gain and χ2 statistic. To justify the efficiency of the proposed algorithm, the support vector machine (SVM) and improved SVM classifier are used in this paper.FindingsThe experiment was conducted on real and benchmark data sets. The real data set was collected in the form of documents that were stored in the personal computer, and the benchmark data set was collected from Reuters and 20 Newsgroups corpus. The results prove the performance of the proposed feature selection algorithm by enhancing the text document classification accuracy.Originality/valueThis paper proposes a new ABCFS algorithm for feature selection, evaluates the efficiency of the ABCFS algorithm and improves the support vector machine. In this paper, the ABCFS algorithm is used to select the features from text (unstructured) documents. Although, there is no text feature selection algorithm in the existing work, the ABCFS algorithm is used to select the data (structured) features. The proposed algorithm will classify the documents automatically based on their content.

A Hybridization Approach for Optimal Feature Subset Selection in High Dimensional Data

Feature subset selection assumes an essential part in the fields of data mining and machine learning. A good feature subset selection algorithm can adequately expel unimportant and repetitive elements and consider feature interaction. This not just paves the way to an understanding comprehension of the information, additionally enhances the execution of a learner by improving the generalization capacity and the interpretability of the learning model. Initially, the input micro array dataset is selected from the medical database. Then preprocessing step is done in the input micro array dataset. The resultant output is fed to the second step; here the features are optimally selected using clustering and optimization process. In our proposed technique, the optimal hybrid fuzzy c-means clustering algorithm with artificial bee colony algorithm is applied on the high dimensional micro array dataset to select the important features. Here the proposed method is optimally select the features with the help of social spider optimization algorithm. After that, the classification is done through improved support vector machine classifier. At last, the experimentation is performed by means of different micro array dataset. Experimental results indicate that the proposed classification framework has outperformed by having better accuracy of 93.19% for GLA-BRA-180 dataset when compared existing SVM and neuro fuzzy classifier only achieved 90.69% and 89%.

SVM Classification of MRI Brain Images for Computer-Assisted Diagnosis

Magnetic Resonance Imaging is a powerful technique that helps in the diagnosis of various medical conditions. MRI Image pre-processing followed by detection of brain abnormalities, such as brain tumors, are considered in this work. These images are often corrupted by noise from various sources. The Discrete Wavelet Transforms (DWT) with details thresholding is used for efficient noise removal followed by edge detection and threshold segmentation of the denoised images. Segmented image features are then extracted using morphological operations. These features are finally used to train an improved Support Vector Machine classifier that uses a Gausssian radial basis function kernel. The performance of the classifier is evaluated and the results of the classification show that the proposed scheme accurately distinguishes normal brain images from the abnormal ones and benign lesions from malignant tumours. The accuracy of the classification is shown to be 100% which is superior to the results reported in the literature.

An improved SVM classifier based on double chains quantum genetic algorithm and its application in analogue circuit diagnosis

Support Vector Machine (SVM) classifier is widely used in analogue circuit diagnosis. However, the penalty parameter C and the kernel parameter γ of SVM classifier with the radial basis function (RBF) affect the classification performance seriously. A double-chains-quantum-genetic-algorithm (DCQGA) based method is proposed to optimize C and γ. In DCQGA, each chromosome carries two gene chains, and each of gene chains represents an optimization solution, which can accelerate the search process and help to find the global solution. Thereafter, the optimal parameters C and γ are obtained by optimizing the parameter searching process with DCQGA. Two common datasets named Iris and Wine from UCI Machine Learning Repository are used to test the performance of the presented SVM classifier. The simulation results illustrate that the population's best fitness and the classifying accuracy of the proposed DCQGA-SVM are higher than that of the Particle-Swarm-Optimization based SVM (PSO-SVM), the Quantum Genetic Algorithm based SVM (QGA-SVM) and the classifier based on grid search method (GS-SVM). Finally, the proposed DCQGA-SVM is applied to analogue circuit diagnosis, a Sallen–Key bandpass filter circuit and a four-opamp biquad high-pass filter are chosen as circuits under test (CUT). Wavelet packet analysis is performed to extract the fault features before classifying. The experimental results show that the SVM parameters selected by DCQGA-SVM contribute to higher diagnosis accuracy than other methods referred in this paper.

Cyber Attack Detection System based on Improved Support Vector Machine

This paper presents a novel cyber attack classification approach using improved Support Vector Machine (iSVM) by modifying Gaussian kernel. The Support Vector Machine (SVM) is based on machine learning technique known to perform well at various pattern recognition tasks; such as image classification, text categorization and handwritten character recognition. The cyber attack detection is basically a pattern classification problem, in which classification of normal pattern is done from the abnormal pattern (attack). Although, traditional SVM is better classifier in terms of fast training, scalable and generalization capability. Performance of traditional SVM is enhanced in this work by modifying Gaussian kernel to enlarge the spatial resolution around the margin by a conformal mapping, so that the separability between attack classes is increased. It is based on the Riemannian geometrical structure induced by the kernel function. In the proposed method, class specific Cyber Attack Detection System which combines feature reduction technique and improved support vector machine classifier. This technique has two phases, in the first phase we reduced the redundant features of the original KDDCUP2009 dataset by Generalized Discriminant Analysis (GDA). In the second phase we used improved Support Vector Machine (iSVM) classifier to classify the reduced dataset obtained from first phase. Result shows that iSVM gives 100% detection accuracy for Normal and Denial of Service (DOS) classes and comparable to false alarm rate, training, and testing times.

Sparse representation‐based feature extraction combined with support vector machine for sense‐through‐foliage target detection and recognition

Owing to multipath propagation effects of rough surfaces, scattering from trees and ground tend to overwhelm the weak backscattering of targets, which makes it more difficult for sense-through-foliage target detection and recognition. In this study, a novel method to detect and recognise targets obscured by foliage based on sparse representation (SR) and support vector machine (SVM) is proposed. SR theory is applied to analysing the components of received radar signals and sparse coefficients are used to describe target features, the dimension of the sparse coefficients is reduced using principal component analysis (PCA). Then, an improved SVM classifier is developed to perform target detection and recognition. A chaotic differential evolution optimisation approach using tent map is developed to determine the parameters of SVM. The experimental results indicate that the proposed approach is an effective method for sense-through-foliage target detection and recognition, which can achieve higher accuracy than that of the differential evolution-optimised SVM, SVM, k-nearest neighbour and BP neural network (BPNN).

Histogram Based Bacteria Colony Features Analysis

Features analysis is an important task which can significantly affect the performance of automatic bacteria colony picking. This paper presents a novel approach for adaptive colony segmentation by classifying the detected peaks of intensity histograms of images. The relevance and importance of these features can be determined in an improved support vector machine classifier using unascertained least square estimation. Experimental results show that the proposed unascertained support vector machine (USVM) has better recognition accuracy than the other state of the art techniques, and its training process takes less time than most of the traditional approaches presented in this paper.

Adaptive bacteria colony picking in unstructured environments using intensity histogram and unascertained LS-SVM classifier.

Features analysis is an important task which can significantly affect the performance of automatic bacteria colony picking. Unstructured environments also affect the automatic colony screening. This paper presents a novel approach for adaptive colony segmentation in unstructured environments by treating the detected peaks of intensity histograms as a morphological feature of images. In order to avoid disturbing peaks, an entropy based mean shift filter is introduced to smooth images as a preprocessing step. The relevance and importance of these features can be determined in an improved support vector machine classifier using unascertained least square estimation. Experimental results show that the proposed unascertained least square support vector machine (ULSSVM) has better recognition accuracy than the other state-of-the-art techniques, and its training process takes less time than most of the traditional approaches presented in this paper.

改进的LFP算法在白细胞图像纹理特征提取与识别中的应用

Leukocyte image feature extraction and classification are studied to improve the correct recognition rate of leukocyte image.For cell texture feature extraction,leukocyte image texture features are extracted by using the improved local fuzzy pattern,and the texture feature extraction method based on local fussy pattern(LFP)was proposed by making the threshold parameter fuzzy in local binary pattern(LBP).The algorithm introduces in uniform pattern to make the extracted feature dimension decrease to 10 with rotation invariance.The classification of 100CellAtlas' s white blood cells images was tested with a support vector machine combination classifier established by a directed acyclic graph method.Experimental results show that:the improved local fuzzy pattern algorithm simplifies texture feature quantity to realize"discard the false and retain the true".The leukocyte image classification and recognition with noise exhibits excellent performance,so that the extracted features have better Robustness.And it has a short running time,high efficiency,leukocyte correct recognition rate is up to 93%.Improved support vector machine classifier shows efficient classification effect,and has better characteristics to small sample analysis.

A method of salt-affected soil information extraction based on a support vector machine with texture features

This paper describes an effort to apply an improved support vector machine classifier to classify salt-affected soil. In this study, we used the support vector machine with texture features to extract thematic information for salt-affected soil. The SVM classification was conducted using a combination of multi-spectral features and texture features as the data source. We used mean, variance and homogeneity features, which were the best texture features, to improve the classification. In addition, we provided a contrast between the proposed SVM method and other SVM methods. The results revealed that the SVM classification used here can effectively extract salinization soil thematic information for the Yinchuan Plain. Specifically, the accuracy of this method was 84.6974% and the kappa coefficient was 0.8202, which indicated superiority over other classification methods.