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The publishing of the present issue (Volume 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.

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Détails du magazine
Format
Magazine
eISSN
1314-4081
Première publication
13 Mar 2012
Période de publication
4 fois par an
Langues
Anglais

Chercher

Volume 17 (2017): Edition 1 (March 2017)

Détails du magazine
Format
Magazine
eISSN
1314-4081
Première publication
13 Mar 2012
Période de publication
4 fois par an
Langues
Anglais

Chercher

12 Articles
Accès libre

An Optimization of Closed Frequent Subgraph Mining Algorithm

Publié en ligne: 06 Apr 2017
Pages: 3 - 15

Résumé

Abstract

Graph mining isamajor area of interest within the field of data mining in recent years. Akey aspect of graph mining is frequent subgraph mining. Central to the entire discipline of frequent subgraph mining is the concept of subgraph isomorphism. One major issue in early subgraph isomorphism research concerns computational complexity. Normally, the subgraph isomorphism problem is NP-complete. Previous studies of frequent subgraph mining have not solved NP-complete problem in the subgraph isomorphism. In this paper, we proposeanew algorithm which can deal with this problem. The proposed algorithm can solve the subgraph isomorphism in polynomial time in some settings. Moreover, the new algorithm is proved theoretically more effective than previous studies in closed frequent subgraph mining.

Mots clés

  • Frequent patterns
  • closed frequent subgraph
  • frequent subgraphs
  • subgraph mining
  • subgraph isomorphism
Accès libre

Fast Matrix Multiplication with Big Sparse Data

Publié en ligne: 06 Apr 2017
Pages: 16 - 30

Résumé

Abstract

Big Data becameabuzz word nowadays due to the evolution of huge volumes of data beyond peta bytes. This article focuses on matrix multiplication with big sparse data. The proposed FASTsparse MULalgorithm outperforms the state-of-the-art big matrix multiplication approaches in sparse data scenario.

Mots clés

  • Sparse data
  • sparse matrices multiplication
  • Big Data
  • Mapreduce
Accès libre

Group Decision Analysis with Interval Type-2 Fuzzy Numbers

Publié en ligne: 06 Apr 2017
Pages: 31 - 44

Résumé

Abstract

This paper presentsagroup multi-criteria DEMATELand VIKORdecision analysis method with interval type-2 fuzzy sets. In order to compare normal fuzzy trapezoidal numbers, we convert them into crisp values using graded mean integration representation. Byacase study for selection of business intelligence platform, we prove that the proposed combination isafeasible solution that can work with benefits and costs criteria, while also reducing uncertainty in experts’assessments.

Mots clés

  • Group decision making
  • multi-criteria decision analysis
  • DEMATEL
  • VIKOR
  • interval type-2 fuzzy sets
  • business intelligence
Accès libre

On Improving the Classification of Imbalanced Data

Publié en ligne: 06 Apr 2017
Pages: 45 - 62

Résumé

Abstract

Mining of imbalanced data isachallenging task due to its complex inherent characteristics. The conventional classifiers such as the nearest neighbor severely bias towards the majority class, as minority class data are under-represented and outnumbered. This paper focuses on building an improved Nearest Neighbor Classifier foratwo class imbalanced data. Three oversampling techniques are presented, for generation of artificial instances for the minority class for balancing the distribution among the classes. Experimental results showed that the proposed methods outperformed the conventional classifier.

Mots clés

  • Imbalance data
  • nearest neighbor classifier
  • oversampling
  • synthetic data
  • Data Mining
Accès libre

Axiomatic Design Approach for Nonlinear Multiple Objective Optimizaton Problem and Robustness in Spring Design

Publié en ligne: 06 Apr 2017
Pages: 63 - 71

Résumé

Abstract

This paper gives general information about multi-objective, axiomatic and robust design approaches and considersasolution model of nonlinear multi-objective optimization problem based on applyinganew robust design approach. Both axiomatic and robust design approaches were used complementarily inacase study with distinct multi-objectives. In this case study, the main target was achieving each objective optimum to minimize the mass and the shear stress ofaspring by integrating robustness and durability at the design stage due to trade off between objectives. This spring problem was examined using the independence axiom of the axiomatic design methodology. Also, semangularity and reangularity concepts were used and design matrices were formed to find coupled and decoupled solutions. It was observed that there were some acceptable design parameter values for which the design became decoupled. Graphical and numerical results were checked to see if they were compatible with each other. Finally, this decoupled design was given appropriate tolerances by using robust design method. This way,arobust and durable spring was designed which would satisfy the given specifications with minimum cost in the existing literature from the view point of axiomatic design approach.

Mots clés

  • Axiomatic design
  • multi objective design
  • robustness
Accès libre

Mutation: A New Operator in Gravitational Search Algorithm Using Fuzzy Controller

Publié en ligne: 06 Apr 2017
Pages: 72 - 86

Résumé

Abstract

Gravitational Search Algorithm (GSA) isanovel meta-heuristic algorithm. Despite it has high exploring ability, this algorithm faces premature convergence and gets trapped in some problems, therefore it has difficulty in finding the optimum solution for problems, which is considered as one of the disadvantages of GSA. In this paper, this problem has been solved through definingamutation function which uses fuzzy controller to control mutation parameter. The proposed method has been evaluated on standard benchmark functions including unimodal and multimodal functions; the obtained results have been compared with Standard Gravitational Search Algorithm (SGSA), Gravitational Particle Swarm algorithm (GPS), Particle Swarm Optimization algorithm (PSO), Clustered Gravitational Search Algorithm (CGSA) and Real Genetic Algorithm (RGA). The observed experiments indicate that the proposed approach yields better results than other algorithms compared with it.

Mots clés

  • Gravitational search algorithm
  • heuristic search algorithm
  • mutation function
  • exploration and exploitation
  • fuzzy controller
Accès libre

A Two-Stage Placement Algorithm with Multi-Objective Optimization and Group Decision Making

Publié en ligne: 06 Apr 2017
Pages: 87 - 103

Résumé

Abstract

Atwo-stage placement algorithm with multi-objective optimization and group decision making is proposed. The first stage aims to determineaset of design alternatives for objects placement by multi-objective combinatorial optimization. The second stage relies on business intelligence via group decision-making based on solution of optimization task to makeachoice of the most suitable alternative. The design alternatives are determined by means of weighted sum and lexicographic methods. The group decision making is used to evaluate determined design alternatives toward the design parameters. The described algorithm is used for wind farm layout optimization problem. The results of numerical testing demonstrate the applicability of the proposed algorithm.

Mots clés

  • Placement algorithm
  • multi objective alternatives determination
  • business intelligence
  • group decision making
  • wind farm layout design
Accès libre

Multi-Band Spectrum Allocation Algorithm Based on First-Price Sealed Auction

Publié en ligne: 06 Apr 2017
Pages: 104 - 112

Résumé

Abstract

Auction is often applied in cognitive wireless networks due to its fairness properties and efficiency. To solve the allocation issues of cognitive wireless network inamulti-band spectrum, multi-item auction mechanism and models were discussed in depth. Multi-item highest price sealed auction was designed for cognitive wireless networks’multi-band spectrum allocation algorithm. This algorithm divided the spectrum allocation process into several stages which was along with low complexity. Experiments show that the algorithm improves the utilization of spectrum frequency, because it takes into account the spectrum owner’s economic efficiency and the users’equity.

Mots clés

  • Cognitive radio networks
  • spectrum allocation
  • auction
  • secondary user
Accès libre

Improvement of Range Estimation with Microphone Array

Publié en ligne: 06 Apr 2017
Pages: 113 - 125

Résumé

Abstract

This paper presentsanew approach for the three-dimensional (3-D) localization of sound sources. An acoustic camera uses an angular beamforming to measure the Direction of Arrival (Do A) of an incoming signal, to localize the emission source. The acoustic sensor used in this article is the Brüel & Kjaer acoustic camera transformed to operate inabistatic mode. The transformation consists inaplacing of one of the microphones of the acoustic camera outside of its microphone array. This allows simultaneous estimation of the Do Aand the Time Difference of Arrival (TDo A) of the incoming signal(s). Such sensors were not found. The paper proposes emitter localization in range - cross range - elevation coordinates by combining estimates of TDo Aand Do Aand presents the signal processing method for that purpose. The range resolution of 0.2mwas achieved in an experiment. Experimental results were obtained using different emission sources. Adescription of resolution cell limitations is presented. The obtained results show acoustic noise source localization without the pre-metering of the range of the imaging plane, i.e., withoutaneed to use the additional range meter which is notapart of the acoustic camera. The latter is important in tasks of non-destructive testing.

Mots clés

  • Acoustic noise source localization
  • acoustic camera
  • bistatic reception
  • time difference of arrival
  • stationary acoustic noise signal
Accès libre

Data Receiving Method Based on Multimedia Timing in Real- Time System

Publié en ligne: 06 Apr 2017
Pages: 126 - 134

Résumé

Abstract

Several methods, such as polling, multithread, timing, and so on, can be used in data receiving course. Low-efficiency and high level of system resource consumption may bring about data loss in polling and multithread methods when the data transmission rate is very high. Software timing methods are discussed and analyzed in Visual C++. Timing method would improve the system resource availability and decrease the risk of data loss. According to the demand ofatesting system,areal-time monitoring system based on memory sharing and multimedia timer is presented. After testing, the average timing error of the multimedia timer in the given instance is not bigger than 0.05%, so the continuity and integrity of data receiving can be assured under the conditions of high-speed data transmission.

Mots clés

  • Real-time monitoring system
  • data receiving
  • timing
  • multimedia timer
  • memory sharing
Accès libre

Research on Quick Response Code Defect Detection Algorithm

Publié en ligne: 06 Apr 2017
Pages: 135 - 145

Résumé

Abstract

Defect Detection is one of the most important parts of Automatic Identification and Data transmission. Quick Response code (QRcode) is one of the most popular types of two-dimensional barcodes. It isachallenge to detect defect of various QRcode images efficiently and accurately. In this paper, we propose the procedure byaserial of carefully designed preprocessing methods. The defect detection procedure consists of QRcode identification, QRcode reconstruction, perspective transformation, image binarization, morphological operation, image matching, and Blob analysis. By these steps, we can detect defect of different types of QRcode images. The experiment results show that our method has stronger robustness and higher efficiency. Moreover, experiment results on QRcode images show that the prediction accuracy of proposed method reaches 99.07%with an average execution time of 6.592 ms. This method can detect defect of these images in real time.

Mots clés

  • QR Code
  • defect detection
  • perspective transformation
  • blob analysis
  • correlation matching
Accès libre

Empirical Study of Job Scheduling Algorithms in Hadoop MapReduce

Publié en ligne: 06 Apr 2017
Pages: 146 - 163

Résumé

Abstract

Several Job scheduling algorithms have been developed for Hadoop-Map Reduce model, which vary widely in design and behavior for handling different issues such as locality of data, user share fairness, and resource awareness. This article focuses on empirically evaluating the performance of three schedulers: First In First Out (FIFO), Fair scheduler, and Capacity scheduler. To carry out the experimental evaluation, we implement our own Hadoop cluster testbed, consisting of four machines, in which one of the machines works as the master node and all four machines work as slave nodes. The experiments include variation in data sizes, use of two different data processing applications, and variation in the number of nodes used in processing. The article analyzes the performance of the job scheduling algorithms based on various relevant performance measures. The results of the experiments are evident of the performance being affected by the job scheduling parameters, the type of applications, the number of nodes in the cluster, and size of the input data.

Mots clés

  • Big Data
  • Hadoop
  • Map Reduce
  • job scheduling
  • analysis
  • experimental evaluation
12 Articles
Accès libre

An Optimization of Closed Frequent Subgraph Mining Algorithm

Publié en ligne: 06 Apr 2017
Pages: 3 - 15

Résumé

Abstract

Graph mining isamajor area of interest within the field of data mining in recent years. Akey aspect of graph mining is frequent subgraph mining. Central to the entire discipline of frequent subgraph mining is the concept of subgraph isomorphism. One major issue in early subgraph isomorphism research concerns computational complexity. Normally, the subgraph isomorphism problem is NP-complete. Previous studies of frequent subgraph mining have not solved NP-complete problem in the subgraph isomorphism. In this paper, we proposeanew algorithm which can deal with this problem. The proposed algorithm can solve the subgraph isomorphism in polynomial time in some settings. Moreover, the new algorithm is proved theoretically more effective than previous studies in closed frequent subgraph mining.

Mots clés

  • Frequent patterns
  • closed frequent subgraph
  • frequent subgraphs
  • subgraph mining
  • subgraph isomorphism
Accès libre

Fast Matrix Multiplication with Big Sparse Data

Publié en ligne: 06 Apr 2017
Pages: 16 - 30

Résumé

Abstract

Big Data becameabuzz word nowadays due to the evolution of huge volumes of data beyond peta bytes. This article focuses on matrix multiplication with big sparse data. The proposed FASTsparse MULalgorithm outperforms the state-of-the-art big matrix multiplication approaches in sparse data scenario.

Mots clés

  • Sparse data
  • sparse matrices multiplication
  • Big Data
  • Mapreduce
Accès libre

Group Decision Analysis with Interval Type-2 Fuzzy Numbers

Publié en ligne: 06 Apr 2017
Pages: 31 - 44

Résumé

Abstract

This paper presentsagroup multi-criteria DEMATELand VIKORdecision analysis method with interval type-2 fuzzy sets. In order to compare normal fuzzy trapezoidal numbers, we convert them into crisp values using graded mean integration representation. Byacase study for selection of business intelligence platform, we prove that the proposed combination isafeasible solution that can work with benefits and costs criteria, while also reducing uncertainty in experts’assessments.

Mots clés

  • Group decision making
  • multi-criteria decision analysis
  • DEMATEL
  • VIKOR
  • interval type-2 fuzzy sets
  • business intelligence
Accès libre

On Improving the Classification of Imbalanced Data

Publié en ligne: 06 Apr 2017
Pages: 45 - 62

Résumé

Abstract

Mining of imbalanced data isachallenging task due to its complex inherent characteristics. The conventional classifiers such as the nearest neighbor severely bias towards the majority class, as minority class data are under-represented and outnumbered. This paper focuses on building an improved Nearest Neighbor Classifier foratwo class imbalanced data. Three oversampling techniques are presented, for generation of artificial instances for the minority class for balancing the distribution among the classes. Experimental results showed that the proposed methods outperformed the conventional classifier.

Mots clés

  • Imbalance data
  • nearest neighbor classifier
  • oversampling
  • synthetic data
  • Data Mining
Accès libre

Axiomatic Design Approach for Nonlinear Multiple Objective Optimizaton Problem and Robustness in Spring Design

Publié en ligne: 06 Apr 2017
Pages: 63 - 71

Résumé

Abstract

This paper gives general information about multi-objective, axiomatic and robust design approaches and considersasolution model of nonlinear multi-objective optimization problem based on applyinganew robust design approach. Both axiomatic and robust design approaches were used complementarily inacase study with distinct multi-objectives. In this case study, the main target was achieving each objective optimum to minimize the mass and the shear stress ofaspring by integrating robustness and durability at the design stage due to trade off between objectives. This spring problem was examined using the independence axiom of the axiomatic design methodology. Also, semangularity and reangularity concepts were used and design matrices were formed to find coupled and decoupled solutions. It was observed that there were some acceptable design parameter values for which the design became decoupled. Graphical and numerical results were checked to see if they were compatible with each other. Finally, this decoupled design was given appropriate tolerances by using robust design method. This way,arobust and durable spring was designed which would satisfy the given specifications with minimum cost in the existing literature from the view point of axiomatic design approach.

Mots clés

  • Axiomatic design
  • multi objective design
  • robustness
Accès libre

Mutation: A New Operator in Gravitational Search Algorithm Using Fuzzy Controller

Publié en ligne: 06 Apr 2017
Pages: 72 - 86

Résumé

Abstract

Gravitational Search Algorithm (GSA) isanovel meta-heuristic algorithm. Despite it has high exploring ability, this algorithm faces premature convergence and gets trapped in some problems, therefore it has difficulty in finding the optimum solution for problems, which is considered as one of the disadvantages of GSA. In this paper, this problem has been solved through definingamutation function which uses fuzzy controller to control mutation parameter. The proposed method has been evaluated on standard benchmark functions including unimodal and multimodal functions; the obtained results have been compared with Standard Gravitational Search Algorithm (SGSA), Gravitational Particle Swarm algorithm (GPS), Particle Swarm Optimization algorithm (PSO), Clustered Gravitational Search Algorithm (CGSA) and Real Genetic Algorithm (RGA). The observed experiments indicate that the proposed approach yields better results than other algorithms compared with it.

Mots clés

  • Gravitational search algorithm
  • heuristic search algorithm
  • mutation function
  • exploration and exploitation
  • fuzzy controller
Accès libre

A Two-Stage Placement Algorithm with Multi-Objective Optimization and Group Decision Making

Publié en ligne: 06 Apr 2017
Pages: 87 - 103

Résumé

Abstract

Atwo-stage placement algorithm with multi-objective optimization and group decision making is proposed. The first stage aims to determineaset of design alternatives for objects placement by multi-objective combinatorial optimization. The second stage relies on business intelligence via group decision-making based on solution of optimization task to makeachoice of the most suitable alternative. The design alternatives are determined by means of weighted sum and lexicographic methods. The group decision making is used to evaluate determined design alternatives toward the design parameters. The described algorithm is used for wind farm layout optimization problem. The results of numerical testing demonstrate the applicability of the proposed algorithm.

Mots clés

  • Placement algorithm
  • multi objective alternatives determination
  • business intelligence
  • group decision making
  • wind farm layout design
Accès libre

Multi-Band Spectrum Allocation Algorithm Based on First-Price Sealed Auction

Publié en ligne: 06 Apr 2017
Pages: 104 - 112

Résumé

Abstract

Auction is often applied in cognitive wireless networks due to its fairness properties and efficiency. To solve the allocation issues of cognitive wireless network inamulti-band spectrum, multi-item auction mechanism and models were discussed in depth. Multi-item highest price sealed auction was designed for cognitive wireless networks’multi-band spectrum allocation algorithm. This algorithm divided the spectrum allocation process into several stages which was along with low complexity. Experiments show that the algorithm improves the utilization of spectrum frequency, because it takes into account the spectrum owner’s economic efficiency and the users’equity.

Mots clés

  • Cognitive radio networks
  • spectrum allocation
  • auction
  • secondary user
Accès libre

Improvement of Range Estimation with Microphone Array

Publié en ligne: 06 Apr 2017
Pages: 113 - 125

Résumé

Abstract

This paper presentsanew approach for the three-dimensional (3-D) localization of sound sources. An acoustic camera uses an angular beamforming to measure the Direction of Arrival (Do A) of an incoming signal, to localize the emission source. The acoustic sensor used in this article is the Brüel & Kjaer acoustic camera transformed to operate inabistatic mode. The transformation consists inaplacing of one of the microphones of the acoustic camera outside of its microphone array. This allows simultaneous estimation of the Do Aand the Time Difference of Arrival (TDo A) of the incoming signal(s). Such sensors were not found. The paper proposes emitter localization in range - cross range - elevation coordinates by combining estimates of TDo Aand Do Aand presents the signal processing method for that purpose. The range resolution of 0.2mwas achieved in an experiment. Experimental results were obtained using different emission sources. Adescription of resolution cell limitations is presented. The obtained results show acoustic noise source localization without the pre-metering of the range of the imaging plane, i.e., withoutaneed to use the additional range meter which is notapart of the acoustic camera. The latter is important in tasks of non-destructive testing.

Mots clés

  • Acoustic noise source localization
  • acoustic camera
  • bistatic reception
  • time difference of arrival
  • stationary acoustic noise signal
Accès libre

Data Receiving Method Based on Multimedia Timing in Real- Time System

Publié en ligne: 06 Apr 2017
Pages: 126 - 134

Résumé

Abstract

Several methods, such as polling, multithread, timing, and so on, can be used in data receiving course. Low-efficiency and high level of system resource consumption may bring about data loss in polling and multithread methods when the data transmission rate is very high. Software timing methods are discussed and analyzed in Visual C++. Timing method would improve the system resource availability and decrease the risk of data loss. According to the demand ofatesting system,areal-time monitoring system based on memory sharing and multimedia timer is presented. After testing, the average timing error of the multimedia timer in the given instance is not bigger than 0.05%, so the continuity and integrity of data receiving can be assured under the conditions of high-speed data transmission.

Mots clés

  • Real-time monitoring system
  • data receiving
  • timing
  • multimedia timer
  • memory sharing
Accès libre

Research on Quick Response Code Defect Detection Algorithm

Publié en ligne: 06 Apr 2017
Pages: 135 - 145

Résumé

Abstract

Defect Detection is one of the most important parts of Automatic Identification and Data transmission. Quick Response code (QRcode) is one of the most popular types of two-dimensional barcodes. It isachallenge to detect defect of various QRcode images efficiently and accurately. In this paper, we propose the procedure byaserial of carefully designed preprocessing methods. The defect detection procedure consists of QRcode identification, QRcode reconstruction, perspective transformation, image binarization, morphological operation, image matching, and Blob analysis. By these steps, we can detect defect of different types of QRcode images. The experiment results show that our method has stronger robustness and higher efficiency. Moreover, experiment results on QRcode images show that the prediction accuracy of proposed method reaches 99.07%with an average execution time of 6.592 ms. This method can detect defect of these images in real time.

Mots clés

  • QR Code
  • defect detection
  • perspective transformation
  • blob analysis
  • correlation matching
Accès libre

Empirical Study of Job Scheduling Algorithms in Hadoop MapReduce

Publié en ligne: 06 Apr 2017
Pages: 146 - 163

Résumé

Abstract

Several Job scheduling algorithms have been developed for Hadoop-Map Reduce model, which vary widely in design and behavior for handling different issues such as locality of data, user share fairness, and resource awareness. This article focuses on empirically evaluating the performance of three schedulers: First In First Out (FIFO), Fair scheduler, and Capacity scheduler. To carry out the experimental evaluation, we implement our own Hadoop cluster testbed, consisting of four machines, in which one of the machines works as the master node and all four machines work as slave nodes. The experiments include variation in data sizes, use of two different data processing applications, and variation in the number of nodes used in processing. The article analyzes the performance of the job scheduling algorithms based on various relevant performance measures. The results of the experiments are evident of the performance being affected by the job scheduling parameters, the type of applications, the number of nodes in the cluster, and size of the input data.

Mots clés

  • Big Data
  • Hadoop
  • Map Reduce
  • job scheduling
  • analysis
  • experimental evaluation

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