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Special Heft on New Developments in Scalable Computing

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Special Thematic Heft on Optimal Codes and Related Topics

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Special Heft With Selected Papers From The Workshop “Two Years Avitohol: Advanced High Performance Computing Applications 2017

Volumen 17 (2017): Heft 4 (November 2017)

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Special issue with selection of extended papers from 6th International Conference on Logistic, Informatics and Service Science LISS’2016

Volumen 16 (2016): Heft 5 (October 2016)
Heft Title: Special Heft on Application of Advanced Computing and Simulation in Information Systems

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Volumen 15 (2015): Heft 7 (December 2015)
Special Heft on Information Fusion

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Special Heft on Logistics, Informatics and Service Science

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Special Heft on Control in Transportation Systems

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Special Heft

Volumen 14 (2014): Heft 4 (December 2014)

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Volumen 13 (2013): Heft Special-Heft (December 2013)

Volumen 13 (2013): Heft 4 (December 2013)
The publishing of the present issue (Volumen 13, No 4, 2013) of the journal “Cybernetics and Information Technologies” is financially supported by FP7 project “Advanced Computing for Innovation” (ACOMIN), grant agreement 316087 of Call FP7 REGPOT-2012-2013-1.

Volumen 13 (2013): Heft 3 (September 2013)

Volumen 13 (2013): Heft 2 (June 2013)

Volumen 13 (2013): Heft 1 (March 2013)

Volumen 12 (2012): Heft 4 (December 2012)

Volumen 12 (2012): Heft 3 (September 2012)

Volumen 12 (2012): Heft 2 (June 2012)

Volumen 12 (2012): Heft 1 (March 2012)

Zeitschriftendaten
Format
Zeitschrift
eISSN
1314-4081
ISSN
1311-9702
Erstveröffentlichung
13 Mar 2012
Erscheinungsweise
4 Hefte pro Jahr
Sprachen
Englisch

Suche

Volumen 12 (2012): Heft 2 (June 2012)

Zeitschriftendaten
Format
Zeitschrift
eISSN
1314-4081
ISSN
1311-9702
Erstveröffentlichung
13 Mar 2012
Erscheinungsweise
4 Hefte pro Jahr
Sprachen
Englisch

Suche

8 Artikel
Uneingeschränkter Zugang

Some New Information Inequalities Involving f-Divergences

Online veröffentlicht: 16 Mar 2013
Seitenbereich: 3 - 10

Zusammenfassung

Abstract

New information inequalities involving f-divergences have been established using the convexity arguments and some well known inequalities such as the Jensen inequality and the Arithmetic-Geometric Mean (AGM) inequality. Some particular cases have also been discussed.

Schlüsselwörter

  • f-divergence
  • convexity
  • parameterization
  • arithmetic mean
  • geometric mean
Uneingeschränkter Zugang

Secured Multi-Cloud Virtual Infrastructure with Improved Performance

Online veröffentlicht: 16 Mar 2013
Seitenbereich: 11 - 22

Zusammenfassung

Abstract

Cloud computing is a model where software applications and computing resources are accessed over Internet with minimal cost and effort by interacting with the service provider. Along with these benefits there are also some significant security concerns that need to be addressed for handling sensitive data and critical applications.

The simultaneous use of multiple clouds can provide several potential benefits, such as high availability, fault tolerance and reduced infrastructural cost. The model proposed which is the implementation of a secured multi-cloud virtual infrastructure consists of a grid engine on top of the multi-cloud infrastructure to distribute the task among the worker nodes that are supplied with various resources from different clouds to enhance cost efficiency of the infrastructure set up and also to implement high availability feature. The Oracle grid engine is used to schedule the jobs to the worker nodes (in-house and cloud). Worker nodes will be acting like listeners to receive the job from the oracle grid engine master node. High security is provided at this point for data using AES algorithm and also a password protection key for privileged user’s access. Performance analysis, cost analysis and cost-performance ratio analysis are done by comparing different cluster configurations.

Schlüsselwörter

  • Grid gain engine
  • map reduce
  • worker nodes
  • passport protection key
  • Monte-Carlo simulation
Uneingeschränkter Zugang

MultiObjective Genetic Modified Algorithm (MOGMA)

Online veröffentlicht: 16 Mar 2013
Seitenbereich: 23 - 33

Zusammenfassung

Abstract

Multiobjective optimization based on genetic algorithms and Pareto based approaches in solving multiobjective optimization problems is discussed in the paper. A Pareto based fitness assignment is used − non-dominated ranking and movement of a population towards the Pareto front in a multiobjective optimization problem. A MultiObjective Genetic Modified Algorithm (MOGMA) is proposed, which is an improvement of the existing algorithm.

Schlüsselwörter

  • Multiobjective optimization
  • genetic algorithms
  • population
  • Pareto based approaches
  • ideal point
  • ideal function
  • MultiObjective Genetic Modified Algorithm
Uneingeschränkter Zugang

Summarizing News Paper Articles: Experiments with Ontology- Based, Customized, Extractive Text Summary and Word Scoring

Online veröffentlicht: 16 Mar 2013
Seitenbereich: 34 - 50

Zusammenfassung

Abstract

The method for filtering information from large volumes of text is called Information Extraction. It is a limited task than understanding the full text. In full text understanding, we express in an explicit fashion about all the information in a given text. But, in Information Extraction, we delimit in advance, as part of the specification of the task and the semantic range of the result. Only extractive summarization method is considered and developed for the study. In this article a model for summarization from large documents using a novel approach has been proposed by considering one of the South Indian regional languages (Kannada). It deals with a single document summarization based on statistical approach. The purpose of summary of an article is to facilitate the quick and accurate identification of the topic of the published document. The objective is to save prospective readers’ time and effort in finding the useful information in a given huge article. Various analyses of results were also discussed by comparing it with the English language.

Schlüsselwörter

  • Information extraction
  • extractive summarization
  • automatic text summarization
  • text summarization
  • stemming
  • word count frequency
  • UTF-8
Uneingeschränkter Zugang

Grinding in Ball Mills: Modeling and Process Control

Online veröffentlicht: 16 Mar 2013
Seitenbereich: 51 - 68

Zusammenfassung

Abstract

The paper presents an overview of the current methodology and practice in modeling and control of the grinding process in industrial ball mills. Basic kinetic and energy models of the grinding process are described and the most commonly used control strategies are analyzed and discussed.

Schlüsselwörter

  • Ball mills
  • grinding circuit
  • process control
Uneingeschränkter Zugang

A New Approach for Mammogram Image Classification Using Fractal Properties

Online veröffentlicht: 16 Mar 2013
Seitenbereich: 69 - 83

Zusammenfassung

Abstract

Accurate classification of images is essential for the analysis of mammograms in computer aided diagnosis of breast cancer. We propose a new approach to classify mammogram images based on fractal features. Given a mammogram image, we first eliminate all the artifacts and extract the salient features such as Fractal Dimension (FD) and Fractal Signature (FS). These features provide good descriptive values of the region. Second, a trainable multilayer feed forward neural network has been designed for the classification purposes and we compared the classification test results with K-Means. The result reveals that the proposed approach can classify with a good performance rate of 98%.

Schlüsselwörter

  • Fractal Dimension
  • Fractal Signature
  • mammograms
  • self-similarity
  • classification
Uneingeschränkter Zugang

Enhanced CNN Based Electron Microscopy Image Segmentation

Online veröffentlicht: 16 Mar 2013
Seitenbereich: 84 - 97

Zusammenfassung

Abstract

Detecting the neural processes like axons and dendrites needs high quality SEM images. This paper proposes an approach using perceptual grouping via a graph cut and its combinations with Convolutional Neural Network (CNN) to achieve improved segmentation of SEM images. Experimental results demonstrate improved computational efficiency with linear running time.

Schlüsselwörter

  • Convolutional Neural Network
  • SEM image
  • perceptual grouping constraints via a graph cut
  • segmentation using a graph cut
  • graph cut optimization
  • affinity graph
  • backpropagation
  • Maxflow/Mincut algorithm
  • Neuronal segmentation
Uneingeschränkter Zugang

Prediction of e-Learning Efficiency by Neural Networks

Online veröffentlicht: 16 Mar 2013
Seitenbereich: 98 - 108

Zusammenfassung

Abstract

A model for prediction of the outcome indicators of e-Learning, based on Balanced ScoreCard (BSC) by Neural Networks (NN) is proposed. In the development of NN models the problem of a small sample size of the data arises. In order to reduce the number of variables and increase the examples of the training sample, preprocessing of the data with the help of the methods Interpolation and Principal Component Analysis (PCA) is performed. A method for optimizing the structure of the neural network is applied over linear and nonlinear neural network architectures. The highest accuracy of prognosis is obtained applying the method of Optimal Brain Damage (OBD) over the nonlinear neural network. The efficiency and applicability of the method suggested is proved by numerical experiments on the basis of real data.

Schlüsselwörter

  • e-Learning efficiency
  • Balanced ScoreCard
  • Neural Networks
8 Artikel
Uneingeschränkter Zugang

Some New Information Inequalities Involving f-Divergences

Online veröffentlicht: 16 Mar 2013
Seitenbereich: 3 - 10

Zusammenfassung

Abstract

New information inequalities involving f-divergences have been established using the convexity arguments and some well known inequalities such as the Jensen inequality and the Arithmetic-Geometric Mean (AGM) inequality. Some particular cases have also been discussed.

Schlüsselwörter

  • f-divergence
  • convexity
  • parameterization
  • arithmetic mean
  • geometric mean
Uneingeschränkter Zugang

Secured Multi-Cloud Virtual Infrastructure with Improved Performance

Online veröffentlicht: 16 Mar 2013
Seitenbereich: 11 - 22

Zusammenfassung

Abstract

Cloud computing is a model where software applications and computing resources are accessed over Internet with minimal cost and effort by interacting with the service provider. Along with these benefits there are also some significant security concerns that need to be addressed for handling sensitive data and critical applications.

The simultaneous use of multiple clouds can provide several potential benefits, such as high availability, fault tolerance and reduced infrastructural cost. The model proposed which is the implementation of a secured multi-cloud virtual infrastructure consists of a grid engine on top of the multi-cloud infrastructure to distribute the task among the worker nodes that are supplied with various resources from different clouds to enhance cost efficiency of the infrastructure set up and also to implement high availability feature. The Oracle grid engine is used to schedule the jobs to the worker nodes (in-house and cloud). Worker nodes will be acting like listeners to receive the job from the oracle grid engine master node. High security is provided at this point for data using AES algorithm and also a password protection key for privileged user’s access. Performance analysis, cost analysis and cost-performance ratio analysis are done by comparing different cluster configurations.

Schlüsselwörter

  • Grid gain engine
  • map reduce
  • worker nodes
  • passport protection key
  • Monte-Carlo simulation
Uneingeschränkter Zugang

MultiObjective Genetic Modified Algorithm (MOGMA)

Online veröffentlicht: 16 Mar 2013
Seitenbereich: 23 - 33

Zusammenfassung

Abstract

Multiobjective optimization based on genetic algorithms and Pareto based approaches in solving multiobjective optimization problems is discussed in the paper. A Pareto based fitness assignment is used − non-dominated ranking and movement of a population towards the Pareto front in a multiobjective optimization problem. A MultiObjective Genetic Modified Algorithm (MOGMA) is proposed, which is an improvement of the existing algorithm.

Schlüsselwörter

  • Multiobjective optimization
  • genetic algorithms
  • population
  • Pareto based approaches
  • ideal point
  • ideal function
  • MultiObjective Genetic Modified Algorithm
Uneingeschränkter Zugang

Summarizing News Paper Articles: Experiments with Ontology- Based, Customized, Extractive Text Summary and Word Scoring

Online veröffentlicht: 16 Mar 2013
Seitenbereich: 34 - 50

Zusammenfassung

Abstract

The method for filtering information from large volumes of text is called Information Extraction. It is a limited task than understanding the full text. In full text understanding, we express in an explicit fashion about all the information in a given text. But, in Information Extraction, we delimit in advance, as part of the specification of the task and the semantic range of the result. Only extractive summarization method is considered and developed for the study. In this article a model for summarization from large documents using a novel approach has been proposed by considering one of the South Indian regional languages (Kannada). It deals with a single document summarization based on statistical approach. The purpose of summary of an article is to facilitate the quick and accurate identification of the topic of the published document. The objective is to save prospective readers’ time and effort in finding the useful information in a given huge article. Various analyses of results were also discussed by comparing it with the English language.

Schlüsselwörter

  • Information extraction
  • extractive summarization
  • automatic text summarization
  • text summarization
  • stemming
  • word count frequency
  • UTF-8
Uneingeschränkter Zugang

Grinding in Ball Mills: Modeling and Process Control

Online veröffentlicht: 16 Mar 2013
Seitenbereich: 51 - 68

Zusammenfassung

Abstract

The paper presents an overview of the current methodology and practice in modeling and control of the grinding process in industrial ball mills. Basic kinetic and energy models of the grinding process are described and the most commonly used control strategies are analyzed and discussed.

Schlüsselwörter

  • Ball mills
  • grinding circuit
  • process control
Uneingeschränkter Zugang

A New Approach for Mammogram Image Classification Using Fractal Properties

Online veröffentlicht: 16 Mar 2013
Seitenbereich: 69 - 83

Zusammenfassung

Abstract

Accurate classification of images is essential for the analysis of mammograms in computer aided diagnosis of breast cancer. We propose a new approach to classify mammogram images based on fractal features. Given a mammogram image, we first eliminate all the artifacts and extract the salient features such as Fractal Dimension (FD) and Fractal Signature (FS). These features provide good descriptive values of the region. Second, a trainable multilayer feed forward neural network has been designed for the classification purposes and we compared the classification test results with K-Means. The result reveals that the proposed approach can classify with a good performance rate of 98%.

Schlüsselwörter

  • Fractal Dimension
  • Fractal Signature
  • mammograms
  • self-similarity
  • classification
Uneingeschränkter Zugang

Enhanced CNN Based Electron Microscopy Image Segmentation

Online veröffentlicht: 16 Mar 2013
Seitenbereich: 84 - 97

Zusammenfassung

Abstract

Detecting the neural processes like axons and dendrites needs high quality SEM images. This paper proposes an approach using perceptual grouping via a graph cut and its combinations with Convolutional Neural Network (CNN) to achieve improved segmentation of SEM images. Experimental results demonstrate improved computational efficiency with linear running time.

Schlüsselwörter

  • Convolutional Neural Network
  • SEM image
  • perceptual grouping constraints via a graph cut
  • segmentation using a graph cut
  • graph cut optimization
  • affinity graph
  • backpropagation
  • Maxflow/Mincut algorithm
  • Neuronal segmentation
Uneingeschränkter Zugang

Prediction of e-Learning Efficiency by Neural Networks

Online veröffentlicht: 16 Mar 2013
Seitenbereich: 98 - 108

Zusammenfassung

Abstract

A model for prediction of the outcome indicators of e-Learning, based on Balanced ScoreCard (BSC) by Neural Networks (NN) is proposed. In the development of NN models the problem of a small sample size of the data arises. In order to reduce the number of variables and increase the examples of the training sample, preprocessing of the data with the help of the methods Interpolation and Principal Component Analysis (PCA) is performed. A method for optimizing the structure of the neural network is applied over linear and nonlinear neural network architectures. The highest accuracy of prognosis is obtained applying the method of Optimal Brain Damage (OBD) over the nonlinear neural network. The efficiency and applicability of the method suggested is proved by numerical experiments on the basis of real data.

Schlüsselwörter

  • e-Learning efficiency
  • Balanced ScoreCard
  • Neural Networks

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