Local Adaptive SVM for Object Recognition

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%.

In this paper we use Natural Language Processing techniques to improve different machine learning approaches (Support Vector Machines (SVM), Local SVM, Random Forests) to the problem of automatic keyphrases extraction from scientific papers. For the evaluation we propose a large and high-quality dataset: 2000 ACM papers from the Computer Science domain. We evaluate by comparison with expert-assigned keyphrases. Evaluation shows promising results that outperform state-of-the-art Bayesian learning system KEA improving the average F-Measure from 22% (KEA) to 30% (Random Forest) on the same dataset without the use of controlled vocabularies. Finally, we report a detailed analysis of the effect of the individual NLP features and data set size on the overall quality of extracted keyphrases.KeywordsRandom ForestDataset SizeNatural Language Processing TechniqueKeyphrases ExtractionLexical ChainThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

\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 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.

Support vector machine (SVM) and artificial neural network (ANN) systems were applied to a drug/nondrug classification problem as an example of binary decision problems in early-phase virtual compound filtering and screening. The results indicate that solutions obtained by SVM training seem to be more robust with a smaller standard error compared to ANN training. Generally, the SVM classifier yielded slightly higher prediction accuracy than ANN, irrespective of the type of descriptors used for molecule encoding, the size of the training data sets, and the algorithm employed for neural network training. The performance was compared using various different descriptor sets and descriptor combinations based on the 120 standard Ghose-Crippen fragment descriptors, a wide range of 180 different properties and physicochemical descriptors from the Molecular Operating Environment (MOE) package, and 225 topological pharmacophore (CATS) descriptors. For the complete set of 525 descriptors cross-validated classification by SVM yielded 82% correct predictions (Matthews cc = 0.63), whereas ANN reached 80% correct predictions (Matthews cc = 0.58). Although SVM outperformed the ANN classifiers with regard to overall prediction accuracy, both methods were shown to complement each other, as the sets of true positives, false positives (overprediction), true negatives, and false negatives (underprediction) produced by the two classifiers were not identical. The theory of SVM and ANN training is briefly reviewed.

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.

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

Recent experimental studies have shown that the support vector machine and the random forest are "stand out" models that outperform most other supervised learning methods in benchmarked evaluations on real-world data sets. Studies have also established that both these models are closely related to nearest neighbor classifiers. Due to this connection, one would expect a similar performance from a nearest neighbor classifier; however, the latter lags greatly behind the support vector machine and the random forest in most evaluations. On the contrary, the nearest neighbor classifier has great theoretical potential because of its ability to model complex decision boundaries with low bias. Furthermore, it theoretically guarantees an error of no more than twice the Bayes error rate on an infinite data set. In this paper, we examine this obvious disconnect between the great theoretical promise and empirical reality of the nearest neighbor classifier. An additional point is that the random forest and support vector machine can be considered eager forms of the (lazy) nearest neighbor classifier. Inspired by the connections between nearest neighbor classifiers, support vector machines and random forest models, we propose a nearest neighbor classifier that is competitive to these models.

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%.

This paper presents an efficient approach for classification of the gender of a common fruit fly, Drosophila melanogaster, based on their wing's texture.The novelty of this research effort is that a Modified Local Binary Pattern (MLBP), which combines both the sign and magnitude features for the improvement of fly wing's texture classification performance, is applied.The extracted features are then used to classify the gender of the fruit fly by using the Support Vector Machines (SVMs) and Random Forest (RF).We validate the performance of the proposed scheme on two fly wing datasets.The highest accuracy achieved by the proposed approach is 94%.In this paper, we limit our approach to gender classification; however, this effort can be extended to explore important characteristics of a fly using wing's texture analysis.

Deep learning approaches for COVID-19 detection based on chest X-ray images

Support vector machine is a new and promising technique for pattern classification and regression estimation. The training of support vector machine is characterized by convex optimization problem, up to the determination of a few additional tuning parameters. Moreover, the model complexity follows from this convex optimization problem. In this paper we introduce sequential support vector machine for regression estimation. The support vector machine is trained by the particle filter. By the method of training support vector machine we can design the controller based on support vector machine. Afterwards we discuss feedforward control based on support vector machine. Support vector machine builds the inverse model of plant in the feedforward control based on support vector machine. In order to demonstrate the availability of this new controller designing method, we give a simulation of the simple nonlinear system for output tracking problem. The results of simulation demonstrate this method of new controller designing is very effective.

Support vector machines (SVMs) has been accepted as a fashionable method in machine learning community. However, it cannot be easily scaled to handle large scale problems for its time and space complexity that is around quadratic with respect to the number of training samples. This paper proposes to combine distributed SVMs by simple Bayesian formalism rules (B-SVMs). B-SVMs randomly decomposes a large-scale task into many smaller and simpler sub-tasks in training phase and uses simple Bayesian formalism rules to make decision for final classification in test phase. B-SVMs was compared with single SVMs that is trained on entire training data set, parallel SVMs combined by majority voting (MV-SVMs), and one kind of fast modular SVMs (FM-SVMs). Experimental results on four problems show that B-SVMs can get higher accuracy than MV-SVMs and FM-SVMs does, the proposed algorithm can significantly reduce training and test time. More importantly, it produces test accuracy that is almost the same as single SVMs does.