Selective dissemination of XML documents using GAs and SVM

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 Self Adaptive Migration Model Genetic Algorithm (SAMCA)[5] and multi class Support Vector Machine (SVM) are 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.

An agent based adaptive bandwidth allocation scheme for multimedia applications

Bandwidth allocation for multimedia applications in case of network congestion and failure poses technical challenges due to bursty and delay sensitive nature of the applications. The growth of multimedia services on Internet and the development of agent technology have made us to investigate new techniques for resolving the bandwidth issues in multimedia communications. Agent technology is emerging as a flexible promising solution for network resource management and QoS (Quality of Service) control in a distributed environment. In this paper, we propose an adaptive bandwidth allocation scheme for multimedia applications by deploying the static and mobile agents. It is a run-time allocation scheme that functions at the network nodes. This technique adaptively finds an alternate patchup route for every congested/failed link and reallocates the bandwidth for the affected multimedia applications. The designed method has been tested (analytical and simulation)with various network sizes and conditions. The results are presented to assess the performance and effectiveness of the approach. This work also demonstrates some of the benefits of the agent based schemes in providing flexibility, adaptability, software reusability, and maintainability. (C) 2004 Elsevier Inc. All rights reserved.

A Dynamic Migration Model for self-adaptive Genetic Algorithms

In this paper, we propose a self Adaptive Migration Model for Genetic Algorithms, where parameters of population size, the number of points of crossover and mutation rate for each population are fixed adaptively. Further, the migration of individuals between populations is decided dynamically. This paper gives a mathematical schema analysis of the method stating and showing that the algorithm exploits previously discovered knowledge for a more focused and concentrated search of heuristically high yielding regions while simultaneously performing a highly explorative search on the other regions of the search space. The effective performance of the algorithm is then shown using standard testbed functions, when compared with Island model GA(IGA) and Simple GA(SGA).

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.

Mahanadi streamflow: climate change impact assessment and adaptive strategies

Impacts of climate change on hydrology are assessed by downscaling large scale general circulation model (GCM) outputs of climate variables to local scale hydrologic variables. This modelling approach is characterized by uncertainties resulting from the use of different models, different scenarios, etc. Modelling uncertainty in climate change impact assessment includes assigning weights to GCMs and scenarios, based on their performances, and providing weighted mean projection for the future. This projection is further used for water resources planning and adaptation to combat the adverse impacts of climate change. The present article summarizes the recent published work of the authors on uncertainty modelling and development of adaptation strategies to climate change for the Mahanadi river in India.

Why Do Russians Support Censorship of the Media?

This article analyses support for censorship in Russia as part of the democratization process. Censorship has been an important part of Russian history and it was strengthened during the Soviet era. After the collapse of the Soviet system formal censorship was banned even though the reality has been different. Therefore it is not strange that many Russians would like to limit the freedom of the media and to censor certain topics. The views of Russians on censorship have been studied on the basis of a survey carried out in 2007. According to the results, three different dimensions of censorship were found. These dimensions include moral censorship, political censorship, and censorship of religious materials. Support for these dimensions varies on the basis of socio-demographic characteristics and media use. The article concludes that many Russians reject new phenomena, while support for the censorship of political criticism is not as high, but political censorship seems to enjoy more support among elites than among the common people.

Boost Customer Loyalty with Online Support. The Case of Mobile Telecoms Providers.

The paper explores the effect of customer satisfaction with online supporting services on loyalty to providers of an offline core service. Supporting services are provided to customers before, during, or after the purchase of a tangible or intangible core product, and have the purpose of enhancing or facilitating the use of this product. The internet has the potential to dominate all other marketing channels when it comes to the interactive and personalised communication that is considered quintessential for supporting services. Our study shows that the quality of online supporting services powerfully affects satisfaction with the provider and customer loyalty through its effect on online value and enjoyment. Managerial implications are provided.

PEDOT-PSSA as an alternative support for Pt electrodes in PEFCs

Poly (3,4-ethylenedioxythiophene) (PEDOT) and poly (styrene sulphonic acid) (PSSA) supported platinum (Pt) electrodes for application in polymer electrolyte fuel cells (PEFCs) are reported. PEDOT-PSSA support helps Pt particles to be uniformly distributed on to the electrodes, and facilitates mixed electronic and ionic (H+-ion) conduction within the catalyst, ameliorating Pt utilization. The inherent proton conductivity of PEDOT-PSSA composite also helps reducing Nation content in PEFC electrodes. During prolonged operation of PEFCs, Pt electrodes supported onto PEDOT-PSSA composite exhibit lower corrosion in relation to Pt electrodes supported onto commercially available Vulcan XC-72R carbon. Physical properties of PEDOT-PSSA composite have been characterized by X-ray diffraction, Fourier transform infrared spectroscopy, scanning electron microscopy and transmission electron microscopy. PEFCs with PEDOT-PSSA-supported Pt catalyst electrodes offer a peak power-density of 810 mW cm(-2) at a load current-density of 1800 mA cm(-2) with Nation content as low as 5 wt.% in the catalyst layer. Accordingly, the present study provides a novel alternative support for platinized PEFC electrodes.

PEDOT-PSSA as an alternative support for Pt electrodes in PEFCs

Poly (3,4-ethylenedioxythiophene) (PEDOT) and poly (styrene sulphonic acid) (PSSA) supported platinum (Pt) electrodes for application in polymer electrolyte fuel cells (PEFCs) are reported. PEDOT-PSSA support helps Pt particles to be uniformly distributed on to the electrodes, and facilitates mixed electronic and ionic (H+-ion) conduction within the catalyst, ameliorating Pt utilization. The inherent proton conductivity of PEDOT-PSSA composite also helps reducing Nation content in PEFC electrodes. During prolonged operation of PEFCs, Pt electrodes supported onto PEDOT-PSSA composite exhibit lower corrosion in relation to Pt electrodes supported onto commercially available Vulcan XC-72R carbon. Physical properties of PEDOT-PSSA composite have been characterized by X-ray diffraction, Fourier transform infrared spectroscopy, scanning electron microscopy and transmission electron microscopy. PEFCs with PEDOT-PSSA-supported Pt catalyst electrodes offer a peak power-density of 810 mW cm(-2) at a load current-density of 1800 mA cm(-2) with Nation content as low as 5 wt.% in the catalyst layer. Accordingly, the present study provides a novel alternative support for platinized PEFC electrodes

Decision support requirements for intelligent operation of southern grid in India

Control centers (CC) play a very important role in power system operation. An overall view of the system with information about all existing resources and needs is implemented through SCADA (Supervisory control and data acquisition system) and an EMS (energy management system). As advanced technologies have made their way into the utility environment, the operators are flooded with huge amount of data. The last decade has seen extensive applications of AI techniques, knowledge-based systems, Artificial Neural Networks in this area. This paper focuses on the need for development of an intelligent decision support system to assist the operator in making proper decisions. The requirements for realization of such a system are recognized for the effective operation and energy management of the southern grid in India The application of Petri nets leading to decision support system has been illustrated considering 24 bus system that is a part of southern grid.

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.

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.

Effective treatment of infectious diseases: A nonlinear adaptive control theoretic approach

A nonlinear adaptive system theoretic approach is presented in this paper for effective treatment of infectious diseases that affect various organs of the human body. The generic model used does not represent any specific disease. However, it mimics the generic immunological dynamics of the human body under pathological attack, including the response to external drugs. From a system theoretic point of view, drugs can be interpreted as control inputs. Assuming a set of nominal parameters in the mathematical model, first a nonlinear controller is designed based on the principle of dynamic inversion. This treatment strategy was found to be effective in completely curing "nominal patients". However, in some cases it is ineffective in curing "realistic patients". This leads to serious (sometimes fatal) damage to the affected organ. To make the drug dosage design more effective, a model-following neuro-adaptive control design is carried out using neural networks, which are trained (adapted) online. From simulation studies, this adaptive controller is found to be effective in killing the invading microbes and healing the damaged organ even in the presence of parameter uncertainties and continuing pathogen attack.

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.