A novel outlier-insensitive local support vector machine for robust data-driven forecasting in engineering

This paper uses a system that can recognize and detect skin diseases such as hives, eczema and psoriasis and provide temporary relief using RGH-HSV Color Space, Local Binary Pattern, Color Histogram and Support Vector Machine (SVM). These algorithms were used to train images for the testing process of the system. For the controlled testing the researchers gathered 30 images for each disease, eczema, hives and psoriasis, from dermnet.com and images from people that have been already diagnosed with the mentioned diseases in both training the prototype and testing the system's accuracy. The system identifying eczema, hives, and psoriasis have the following accuracies respectively, 96.0560%, 95.8041%, and 98.5609%.

Based on the nonlinear time series local prediction method and the relevance vector machine regression model, the local relevance vector machine prediction method is proposed and applied to predict the small scale traffic measurement data, and the BIC-based neighbor point selection method is used to choose the number of nearest-neighbor points for the local relevance vector machine regression model. We also compare the performance of the local relevance vector machine regression model with the feed-forward neural network optimized by particle swarm optimization for the same problem. Experimental results show that the local relevance vector machine prediction method whose neighboring points have been optimized can effectively predict the small scale traffic measurement data, can reproduce the statistical features of real small scale traffic measurements, and the prediction accuracy of the local relevance vector machine regression model is superior to that of the feedforward neural network optimized by PSO and the local linear prediction method.

\nIn order to effectively identify the bearing running condition, this paper proposed a new method which combines local mean decomposition (LMD) and support vector machine (SVM) together for ball bearing fault identification. Firstly, the gathered vibration signals were decomposed into a number of product functions (PFs) by LMD, with each PF corresponding to an instantaneous amplitude (IA) signal and instantaneous frequency (IF) signal. Then, introduce the concept of fault characteristic amplitude ratios which can be used to construct fault feature vectors; the extracted characteristic features were input into SVM to train and construct the fault identification model; the bearing running state identification was thereby realized. Cases of normal and fault were analyzed. Experimental results show that the proposed algorithm can diagnose the bearing failures reasonable and efficient.\n

This paper presents an effective approach for aero‐engine fault diagnosis with focus on rub‐impact, through combination of improved local discriminant bases (LDB) with support vector machine (SVM). The improved LDB algorithm, using both the normalized energy difference and the relative entropy as quantification measures, is applied to choose the optimal set of orthogonal subspaces for wavelet packet transform‐ (WPT‐) based signal decomposition. Then two optimal sets of orthogonal subspaces have been obtained and the energy features extracted from those subspaces appearing in both sets will be selected as input to a SVM classifier to diagnose aero‐engine faults. Experiment studies conducted on an aero‐engine rub‐impact test system have verified the effectiveness of the proposed approach for classifying working conditions of aero‐engines.

A DC series arc fault is harmful and not easy to distinguish, so its detection technology needs to be continuously improved. This research establishes a DC series fault arc generation and experiment platform. Firstly, this research basically analyzes and summarizes the variations of line current and supply output voltage caused by an arc fault. Then, a DC series arc fault detection method, based on comprehensively using line current and supply output voltage signals, is proposed. In order to extract the characteristic frequency band of the line current, the Local mean decomposition (LMD) is employed which has a better decomposition effect. Then, fuzzy entropy and harmonic power of the corresponding frequency-band signals are calculated to reflect the variation of the line current complexity. Moreover, the standard deviation of the supply output voltage is selected as another characteristic for identifying arc faults. Finally, the support vector machines (SVM) is employed to classify the extracted features. Experiment data and simulation results illustrate that the hybrid method not only has a higher recognition rate, but also can avoid misjudgment and invalid operation of the program to a certain extent.

Silhouette-Based Gender Recognition in Smart Environments Using Fuzzy Local Binary Patterns and Support Vector Machines

This paper proposes a novel face representation approach, local Gabor binary mapping pattern (LGBMP), for multi-view gender classification. In this approach, a face image is first represented as a series of Gabor magnitude pictures (GMP) by applying multi-scale and multi-orientation Gabor filters. Each GMP is then encoded as a LGBP image where a uniform local binary pattern (LBP) operator is used. After that, each LGBP image is divided into non-overlapping rectangular regions, from which spatial histograms are extracted. Although an LGBP feature vector can be obtained by fitting together the regional histograms, it can not be employed in pattern classification due to its high dimension. We propose that each regional LGBP feature be mapped onto a one-dimensional subspace independently before they are concatenated as a whole feature vector. This is attractive since we reduce the feature dimension and also preserve the spatial information of LGBP image. Two ways have been proposed to map the regional LGBP feature in this paper. One is so-called LGBMP-LDA using linear discriminant analysis (LDA) for dimensionality reduction while the other is to project the regional LGBP feature onto the class center connecting line, namely, LGBMP-CCL. As a result, despite several decades of Gabor filters, the final feature dimension is even less than that of the feature extracted by using LBP directly on gray-scale images. The classification tasks in our work are performed by support vector machines (SVM). The experimental results on the CAS-PEAL face database indicate that the proposed approach achieves higher accuracy than the others such as SVMs+Gray-scale pixel, SVMs+Gabor and SVMs+LBP approach, more particularly, it has the lowest dimension of feature vector.

Garbage is one of the problems that always arise in Indonesia and even in the world. Increasingly, the production of waste is increased along with the increase in population and consumption. Therefore, need a prevention to stop wasting or producing garbage through recycle. This research do garbage recycle classification of cardboard, glass, metal, paper and plastic by using Local Binary Pattern (LBP) texture feature extraction methode and Support Vector Machine (SVM) as classification methode. For examination technic and dataset distribution is using K-Fold Cross Validation methode type Leave One Out (LOO). From examination result had been done were using fold 5 until fold 10. Polynomial kernel get highest accuracy result from every fold used with mean point 87.82%. Based on SVM classification examination result whether linear kernel, polynomial nor gaussian by using fold 5 until fold 10. The best accuracy point for cardboard garbage is 96.01%. For glass garbage, the best accuracy point is 90.62%. Then, metal garbage get the best accuracy point 89.72%. While paper garbage with highest accuracy point 96.01%. And plastic garbage with highest accuracy point 87.64%.

Diabetic Retinopathy (DR) is a severe and widely spread eye disease. Exudates are one of the most prevalent signs during the early stage of DR and an early detection of these lesions is vital to prevent the patient’s blindness. Hence, detection of exudates is an important diagnostic task of DR, in which computer assistance may play a major role. In this paper, a system based on local feature extraction and Support Vector Machine (SVM) classification is used to develop and compare different strategies for automated detection of exudates. The main novelty of this work is allowing the detection of exudates using non-regular regions to perform the local feature extraction. To accomplish this objective, different methods for generating superpixels are applied to the fundus images of E-OPHTA database and texture and morphological features are extracted for each of the resulting regions. An exhaustive comparison among the proposed methods is also carried out.

Detecting pedestrians accurately is the first fundamental step for many computer vision applications such as video surveillance, smart vehicles, intersection traffic analysis and so on. The authors present an experimental study on pedestrian detection using state-of-the-art local feature extraction and support vector machine (SVM) classifiers. The performance of pedestrian detection using region covariance, histogram of oriented gradients (HOG) and local receptive fields (LRF) feature descriptors is experimentally evaluated. The experiments are performed on the DaimlerChrysler benchmarking data set, the MIT CBCL data set and ’Intitut National de Recherche en Informatique et Automatique (INRIA) data set. All can be publicly accessed. The experimental results show that region covariance features with radial basis function kernel SVM and HOG features with quadratic kernel SVM outperform the combination of LRF features with quadratic kernel SVM. Furthermore, the results reveal that both covariance and HOG features perform very well in the context of pedestrian detection.

Recognition of animated facial expression is an important and a fundamental task for automatically implementing analysis and evaluation of animation movie products. In this work, an improved algorithm was proposed for recognizing facial expression of animated human characters based on the Local Binary Pattern (LBP) and Support Vector Machine (SVM). The proposed method was tested on an animated facial expression database which contained human characters from top rated animations. The experimental results showed that the recognition accuracy of facial expression was significantly improved compared with other methods.

Banana plantation is a commercial agricultural practice of huge significance especially in Asian and African countries. Banana production is affected by natural calamities and plant diseases. But plant diseases present a constant threat to the farmers affecting the quantity and quality of the banana cultivation. From the last decade, the image processing techniques and machine learning algorithms have been broadly used for identification and classification of infections in plants. In this work, texture pattern techniques for identification and classification of diseases in banana plants is introduced. The proposed methodology consists of two primary phases; (a) extraction of texture features from using local binary pattern (LBP); (b) classification of banana plant diseases and healthy banana plant. The texture features using LBP are extracted from an enhanced input image. The extracted features are fed to Support Vector Machine (SVM) and K-nearest neighbor (KNN) for final banana plant disease classification. The proposed technique is tested on the Plant Village dataset for the classification of two different experimental cases (i) Healthy-Black Sigatoka and (ii) Healthy-Cordana leaf spot. The proposed methodology attained an accuracy of 89.1 % and 90.9% for two experimental cases using SVM classifier.

An application for video data analysis based on computer vision and machine learning methods is presented. Novel gender and age classifiers based on adaptive features, local binary patterns and support vector machines are proposed. More than 94% accuracy of viewer's gender recognition is achieved. Our age estimation algorithm provides world-quality results for MORTH database, but focused on real-life audience measurement videodata in which faces can be looks more or less similar to RUS-FD private database. In this case we can reach total mean absolute error score less than 7. All the video processing stages are united into a real-time system of audience analysis. The system allows to extract all the possible information about people from the input video stream, to aggregate and analyze this information in order to measure different statistical parameters. The promising practical application of such algorithms can be human-computer interaction, surveillance monitoring, video content analysis, targeted advertising, biometrics, and entertainment.

The ability to detect pedestrians is a first important step in many computer vision applications such as video surveillance. This paper presents an experimental study on pedestrian detection using state-of-the-art local feature extraction and support vector machine (SVM) classifiers. The performance of pedestrian detection using region covariance, histogram of oriented gradients (HOG) and local receptive fields (LRF) feature descriptors is experimentally evaluated. The experiments are performed on both the benchmarking dataset used in [1] and the MIT CBCL dataset. Both can be publicly accessed. The experimental results show that region covariance features with radial basis function (RBF) kernel SVM and HOG features with quadratic kernel SVM outperform the combination of LRF features with quadratic kernel SVM reported in [1].

In this paper, we present a real-time face recognition system for home security service robot which can be applied to recognize the person's face in front and give a warning when the identity of the person is a stranger. Considering the complexity of the actual situation, there might be some errors causing by the following factors like the angle, the size, the environment and the illumination of the human face, which can hardly be avoided. Our system is designed to reduce and eliminate the influence of the factors above. This article first used Local Binary Patterns (LBP) to detect face and cut out the face region. Then, based on the collected and pretreated face database, the target face could be recognized utilizing Support Vector Machine (SVM). However, the given face database are always irregular in size, position of the face area, scale and some other factors. The proposed face recognition system has been tested in practical application for home security service robot. The results indicate that our face recognition system is effective and real-time. It works well in multi-face recognition and stranger identification, which meet the demand of robot.