Selective dissemination of XML documents based on genetically learned user model and Support Vector Machines

Extensible Markup Language ( XML) has emerged as a medium for interoperability over the Internet. As the number of documents published in the form of XML is increasing, there is a need for selective dissemination of XML documents based on user interests. In the proposed technique, a combination of Adaptive Genetic Algorithms and multi class Support Vector Machine ( SVM) is used to learn a user model. Based on the feedback from the users, the system automatically adapts to the user's preference and interests. The user model and a similarity metric are used for selective dissemination of a continuous stream of XML documents. Experimental evaluations performed over a wide range of XML documents, indicate that the proposed approach significantly improves the performance of the selective dissemination task, with respect to accuracy and efficiency.

Sequential split vector quantization of LSF parameters using conditional PDF

A better performing product code vector quantization (VQ) method is proposed for coding the line spectrum frequency (LSF) parameters; the method is referred to as sequential split vector quantization (SeSVQ). The split sub-vectors of the full LSF vector are quantized in sequence and thus uses conditional distribution derived from the previous quantized sub-vectors. Unlike the traditional split vector quantization (SVQ) method, SeSVQ exploits the inter sub-vector correlation and thus provides improved rate-distortion performance, but at the expense of higher memory. We investigate the quantization performance of SeSVQ over traditional SVQ and transform domain split VQ (TrSVQ) methods. Compared to SVQ, SeSVQ saves 1 bit and nearly 3 bits, for telephone-band and wide-band speech coding applications respectively.

Support vector machine approach for the switching transient peak overvoltages during line energization

At the time of restoration transmission line switching is one of the major causes, which creates transient overvoltages. Though detailed Electro Magnetic Transient studies are carried out extensively for the planning and design of transmission systems, such studies are not common in a day-today operation of power systems. However it is important for the operator to ensure during restoration of supply that peak overvoltages resulting from the switching operations are well within safe limits. This paper presents a support vector machine approach to classify the various cases of line energization in the category of safe or unsafe based upon the peak value of overvoltage at the receiving end of line. Operator can define the threshold value of voltage to assign the data pattern in either of the class. For illustration of proposed approach the power system used for switching transient peak overvoltages tests is a 400 kV equivalent system of an Indian southern gri

Improved switching frequency variation control of hysteresis controlled voltage source inverter-fed IM drives using current error space vector

A constant switching frequency current error space vector-based hysteresis controller for two-level voltage source inverter-fed induction motor (IM) drives is proposed in this study. The proposed controller is capable of driving the IM in the entire speed range extending to the six-step mode. The proposed controller uses the parabolic boundary, reported earlier, for vector selection in a sector, but uses simple, fast and self-adaptive sector identification logic for sector change detection in the entire modulation range. This new scheme detects the sector change using the change in direction of current error along the axes jA, jB and jC. Most of the previous schemes use an outer boundary for sector change detection. So the current error goes outside the boundary six times during sector change, in one cycle,, introducing additional fifth and seventh harmonic components in phase current. This may cause sixth harmonic torque pulsations in the motor and spread in the harmonic spectrum of phase voltage. The proposed new scheme detects the sector change fast and accurately eliminating the chance of introducing additional fifth and seventh harmonic components in phase current and provides harmonic spectrum of phase voltage, which exactly matches with that of constant switching frequency voltage-controlled space vector pulse width modulation (VC-SVPWM)-based two-level inverter-fed drives.

Characterization of the human SLC22A18 gene promoter and its regulation by the transcription factor Sp1

SLC22A18, a poly-specific organic cation transporter, is paternally imprinted in humans and mice. It shows loss-of-heterozygosity in childhood and adult tumors, and gain-of-imprinting in hepatocarcinomas and breast cancers. Despite the importance of this gene, its transcriptional regulation has not been studied, and the promoter has not yet been characterized. We therefore set out to identify the potential cis-regulatory elements including the promoter of this gene. The luciferase reporter assay in human cells indicated that a region from -120 by to +78 by is required for the core promoter activity. No consensus TATA or CHAT boxes were found in this region, but two Sp1 binding sites were conserved in human, chimpanzee, mouse and rat. Mutational analysis of the two Sp1 sites suggested their requirement for the promoter activity. Chromatin-immunoprecipitation showed binding of Sp1 to the promoter region in vivo. Overexpression of Sp1 in Drosophila Sp1-null SL2 cells suggested that Sp1 is the transactivator of the promoter. The human core promoter was functional in mouse 3T3 and monkey COS7 cells. We found a CpG island which spanned the core promoter and exon 1. COBRA technique did not reveal promoter methylation in 10 normal oral tissues, 14 oral tumors, and two human cell lines HuH7 and A549. This study provides the first insight into the mechanism that controls expression of this imprinted tumor suppressor gene. A COBRA-based assay has been developed to look for promoter methylation in different cancers. The present data will help to understand the regulation of this gene and its role in tumorigenesis. (C) 2008 Elsevier B.V. All rights reserved.

Characterization of the human SLC22A18 gene promoter and its regulation by the transcription factor Sp1

SLC22A18, a poly-specific organic cation transporter, is paternally imprinted in humans and mice. It shows loss-of-heterozygosity in childhood and adult tumors, and gain-of-imprinting in hepatocarcinomas and breast cancers. Despite the importance of this gene, its transcriptional regulation has not been studied, and the promoter has not yet been characterized. We therefore set out to identify the potential cis-regulatory elements including the promoter of this gene. The luciferase reporter assay in human cells indicated that a region from -120 by to +78 by is required for the core promoter activity. No consensus TATA or CHAT boxes were found in this region, but two Sp1 binding sites were conserved in human, chimpanzee, mouse and rat. Mutational analysis of the two Sp1 sites suggested their requirement for the promoter activity. Chromatin-immunoprecipitation showed binding of Sp1 to the promoter region in vivo. Overexpression of Sp1 in Drosophila Sp1-null SL2 cells suggested that Sp1 is the transactivator of the promoter. The human core promoter was functional in mouse 3T3 and monkey COS7 cells. We found a CpG island which spanned the core promoter and exon 1. COBRA technique did not reveal promoter methylation in 10 normal oral tissues, 14 oral tumors, and two human cell lines HuH7 and A549. This study provides the first insight into the mechanism that controls expression of this imprinted tumor suppressor gene. A COBRA-based assay has been developed to look for promoter methylation in different cancers. The present data will help to understand the regulation of this gene and its role in tumorigenesis. (C) 2008 Elsevier B.V. All rights reserved.

Site characterization model using least-square support vector machine and relevance vector machine based on corrected SPT data (N-c)

Statistical learning algorithms provide a viable framework for geotechnical engineering modeling. This paper describes two statistical learning algorithms applied for site characterization modeling based on standard penetration test (SPT) data. More than 2700 field SPT values (N) have been collected from 766 boreholes spread over an area of 220 sqkm area in Bangalore. To get N corrected value (N,), N values have been corrected (Ne) for different parameters such as overburden stress, size of borehole, type of sampler, length of connecting rod, etc. In three-dimensional site characterization model, the function N-c=N-c (X, Y, Z), where X, Y and Z are the coordinates of a point corresponding to N, value, is to be approximated in which N, value at any half-space point in Bangalore can be determined. The first algorithm uses least-square support vector machine (LSSVM), which is related to aridge regression type of support vector machine. The second algorithm uses relevance vector machine (RVM), which combines the strengths of kernel-based methods and Bayesian theory to establish the relationships between a set of input vectors and a desired output. The paper also presents the comparative study between the developed LSSVM and RVM model for site characterization. Copyright (C) 2009 John Wiley & Sons,Ltd.

Stringent constraints on the scalar K pi form factor from analyticity, unitarity and low-energy theorems

We investigate the scalar K pi form factor at low energies by the method of unitarity bounds adapted so as to include information on the phase and modulus along the elastic region of the unitarity cut. Using at input the values of the form factor at t = 0 and the Callan-Treiman point, we obtain stringent constraints on the slope and curvature parameters of the Taylor expansion at the origin. Also, we predict a quite narrow range for the higher-order ChPT corrections at the second Callan-Treiman point.

An intelligent approach using support vector machines for monitoring and identification of faults on transmission systems

Power system disturbances are often caused by faults on transmission lines. When faults occur in a power system, the protective relays detect the fault and initiate tripping of appropriate circuit breakers, which isolate the affected part from the rest of the power system. Generally Extra High Voltage (EHV) transmission substations in power systems are connected with multiple transmission lines to neighboring substations. In some cases mal-operation of relays can happen under varying operating conditions, because of inappropriate coordination of relay settings. Due to these actions the power system margins for contingencies are decreasing. Hence, power system protective relaying reliability becomes increasingly important. In this paper an approach is presented using Support Vector Machine (SVM) as an intelligent tool for identifying the faulted line that is emanating from a substation and finding the distance from the substation. Results on 24-bus equivalent EHV system, part of Indian southern grid, are presented for illustration purpose. This approach is particularly important to avoid mal-operation of relays following a disturbance in the neighboring line connected to the same substation and assuring secure operation of the power systems.

Cluster based core vector machine

Core Vector Machine(CVM) is suitable for efficient large-scale pattern classification. In this paper, a method for improving the performance of CVM with Gaussian kernel function irrespective of the orderings of patterns belonging to different classes within the data set is proposed. This method employs a selective sampling based training of CVM using a novel kernel based scalable hierarchical clustering algorithm. Empirical studies made on synthetic and real world data sets show that the proposed strategy performs well on large data sets.

Minimum enclosing spheres formulations for support vector ordinal regression

We present two new support vector approaches for ordinal regression. These approaches find the concentric spheres with minimum volume that contain most of the training samples. Both approaches guarantee that the radii of the spheres are properly ordered at the optimal solution. The size of the optimization problem is linear in the number of training samples. The popular SMO algorithm is adapted to solve the resulting optimization problem. Numerical experiments on some real-world data sets verify the usefulness of our approaches for data mining.

Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network

In this paper. we propose a novel method using wavelets as input to neural network self-organizing maps and support vector machine for classification of magnetic resonance (MR) images of the human brain. The proposed method classifies MR brain images as either normal or abnormal. We have tested the proposed approach using a dataset of 52 MR brain images. Good classification percentage of more than 94% was achieved using the neural network self-organizing maps (SOM) and 98% front support vector machine. We observed that the classification rate is high for a Support vector machine classifier compared to self-organizing map-based approach.

Scalable non-linear Support Vector Machine using hierarchical clustering

This paper discusses a method for scaling SVM with Gaussian kernel function to handle large data sets by using a selective sampling strategy for the training set. It employs a scalable hierarchical clustering algorithm to construct cluster indexing structures of the training data in the kernel induced feature space. These are then used for selective sampling of the training data for SVM to impart scalability to the training process. Empirical studies made on real world data sets show that the proposed strategy performs well on large data sets.