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Kernel methods for classification is a well-studied area in which data are implicitly mapped from a lower-dimensional space to a higher-dimensional space to improve classification accuracy. However, for most kernel methods, one must still choose a kernel to use for the problem. Since there is, in general, no way of knowing which kernel is the best, multiple kernel learning (MKL) is a technique used...
Mars rover is a robot which explores the Mars surface, is equipped to front-line Panoramic Camera (Pancam). Automatic processing and segmentation of images taken by Pancam is one of the most important and most significant tasks of Mars rover since the transformation cost of images from Mars to earth is extremely high. In this paper, a new feature vector for image pixels will be proposed as well as...
Based on principal component analysis (PCA) and support vector machine (SVM), a new method for the fault diagnosis of TE Process is proposed. The fault recognition based on kernel principal component analysis (KPCA) is analyzed and SVM is employed as a classifier for fault classification. To establish a more efficient SVM model, genetic algorithm (GA) is used to determine the optimal kernel parameter...
Aiming at the problem of low accuracy in intrusion detection system, this paper established a genetic support vector machine (SVM) model according to the features of genetic algorithm and support vector machine algorithm. The model firstly optimizes the support vector parameters according to genetic algorithm, then we build the intrusion detection model with support vector machine optimized and use...
Credit scoring is becoming a competitive issue with rapid growth and significant advance. Building a satisfactory credit model has attracted lots of researchers in the past decades and it is still one of the hottest research topics in the field of credit industry. This paper emphasizes on surveying the development of the artificial intelligence technologies for solving credit scoring. It covers algorithms...
In Bioinformatics, the prediction of protein function is considered a very important task but also difficult. Using a set of enzymes represented by Hydrolase, Isomerase, Ligase, Lyase, Transferase and Oxidoreductase classes, previously used by Dobson et al., this paper proposes a self-learning process able to predict their classes, based on their primary and secondary structures, through a Support...
Multiclass classification is an important technique to many complex biomedicine problems. Genetic algorithms (GA) are proven to be effective to select features prior to multiclass classification by support vector machines (SVM). However, their use is computation intensive. Based on SOA (Service Oriented Architecture) design principles, this paper proposes a cloud computing framework that exploits...
Prediction of the protein structure is one of the most important problems in the computational biology, and it remains one of the biggest challenges in the structural biology. Disulfide bonds play an import structural role in stabilizing protein conformations. For the protein-folding prediction, a correct prediction of disulfide bridges can greatly reduce the search space. The prediction of disulfide...
Consumer credit prediction is considered as an important issue in the credit industry. The credit department often makes decision which depends on intuitive experience with large risk. This study proposed a new model that hybridized the support vector machine (SVM) and particle swarm optimization (PSO) to evaluate the new consumer's credit score. The hybrid model simultaneously optimizes the SVM kernel...
Sleep Apnea (SA) is one of the common symptoms and important part of sleep disorders. It has consequences that affect all daily life activities and present danger to the patient and/or others. The common diagnose procedure is based on an overnight sleep test. The test is usually including recording of several bio-signals that used to detect this syndrome. The conventional approach of detecting the...
The Support Vector Machine method has a good learning and generalization ability. Unfortunately, there are no comprehensive theories to guide the parameter selection of the SVM, which largely limits its application. In order to get the optimal parameters automatically, researchers have tried a variety of methods. Using genetic algorithms to optimize parameters of an SVM Classifier has become one of...
The correct identification of two-phase flow regime is the basis for the accurate measurement of other flow parameters in two-phase flow measurement. A PSO-SVM(Particle Swarm Optimization and Support Vector Machine) model, which can overcome selecting parameters needed in SVM model, was developed to identify the flow regime. The application of PSO-SVM improves the accuracy of flow regime recognition...
Depression is one of the most common mental disorder that at its worst can lead to suicide. Diagnosing depression in the early curable stage is very important. In this paper we study performance of different classification techniques for classifying depression patients from normal subjects. For this aim, power spectrum of three frequency band (alpha, beta, theta) and the whole bands of EEG are used...
Acute hypotension episodes are one of the hemodynamic instabilities with high mortality rate that is frequent among many groups of patients. Prediction of acute hypotension episodes can help clinicians to diagnose the cause of this physiological disorder and select proper treatment based on this diagnosis. In this study new physiological time series are generated based on heart rate, systolic blood...
Microarray data has been widely used to predict different disease condition. But the problem has been the high dimensionality of microarray data, because of very few samples compared to a huge number of genes. To tackle this necessity we have developed EVOL Optimer (Evolutionary Optimization). In our method we used both filter and wrapper based approach for gene selection. The original subsets are...
Reducing power consumption has become a priority in microprocessor design as more devices become mobile and as the density and speed of components lead to power dissipation issues. Power allocation strategies for individual components within a chip are being researched to determine optimal configurations to balance power and performance. Modelling and estimation tools are necessary in order to understand...
In Computer Vision, problem of identifying or classifying the objects present in an image is called Object Categorization. It is challenging problem, especially when the images have clutter background, occlusions or different lighting conditions. Many vision features have been proposed which aid object categorization even in such adverse conditions. Past research has shown that, employing multiple...
Support Vector Machine (SVM) is a useful technique for data classification with successful applications in different fields of bioinformatics, image segmentation, data mining, etc. A key problem of these methods is how to choose an optimal kernel and how to optimize its parameters in the learning process of SVM. The objective of this study is to propose a Genetic Algorithm approach for parameter optimization...
Microarray data contains thousands of genes which are used to evaluate expression level. However, most of them are not associated with cancer diseases and leads to the curse of dimensionality. The challenge based on microarray data is feature selection which searches for subsets of informative genes. At the moment, these techniques focus on filter and wrapper approaches to discover subsets of genes...
This paper presents the results achieved by fault classifier ensembles based on a model-free supervised learning approach for diagnosing faults on oil rigs motor pumps. The main goal is to compare two feature-based ensemble construction methods, and present a third variation from one of them. The use of ensembles instead of single classifier systems has been widely applied in classification problems...
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