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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.

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Detalles de la revista
Formato
Revista
eISSN
1314-4081
Publicado por primera vez
13 Mar 2012
Periodo de publicación
4 veces al año
Idiomas
Inglés

Buscar

Volumen 18 (2018): Edición 2 (June 2018)

Detalles de la revista
Formato
Revista
eISSN
1314-4081
Publicado por primera vez
13 Mar 2012
Periodo de publicación
4 veces al año
Idiomas
Inglés

Buscar

11 Artículos
Acceso abierto

Mining Fuzzy Sequential Patterns with Fuzzy Time-Intervals in Quantitative Sequence Databases

Publicado en línea: 30 Jun 2018
Páginas: 3 - 19

Resumen

Abstract

The main objective of this paper is to introduce fuzzy sequential patterns with fuzzy time-intervals in quantitative sequence databases. In the fuzzy sequential pattern with fuzzy time-intervals, both quantitative attributes and time distances are represented by linguistic terms. A new algorithm based on the Apriori algorithm is proposed to find the patterns. The mined patterns can be applied to market basket analysis, stock market analysis, and so on.

Palabras clave

  • Data mining
  • fuzzy sequential pattern
  • fuzzy time-interval
  • sequence database
Acceso abierto

Genetic Fuzzy System for Financial Management

Publicado en línea: 30 Jun 2018
Páginas: 20 - 35

Resumen

Abstract

This paper discusses genetic fuzzy systems – hybrid systems of artificial intelligence combining the potential of fuzzy sets for modeling approximate reasoning with the abilities of genetic algorithms for finding optimal solutions. The use of genetic algorithms for optimizing the parameters of a fuzzy system is demonstrated on GFSSAM.

Palabras clave

  • Genetic fuzzy system
  • intelligent hybrid systems
  • financial management
  • artificial intelligence
Acceso abierto

A New Opinion Mining Method based on Fuzzy Classifier and Particle Swarm Optimization (PSO) Algorithm

Publicado en línea: 30 Jun 2018
Páginas: 36 - 50

Resumen

Abstract

Opinion Mining or Sentiment Analysis is the task of extracting people final opinion about something through their unstructured sentiments. The Opinion Mining process is as follows: first, product features which are most important to a user are extracted from his/her comments. Then, sentiments will be emotionally classified using their emotional implications. In this paper we propose an opinion classification method based on Fuzzy Logic. Up to now, a few methods have taken advantage of fuzzy logic in opinion classification and all of them have imported fuzzy rules into system as background knowledge. But the main challenge here is finding the fuzzy rules. Our contribution is to automatically extract fuzzy rules and their parameters from training data. Here we have used the Particle Swarm Optimization (PSO) algorithm to extract fuzzy rules from training data. Also, for better results we have devised a mutation-based PSO. All proposed methods have been implemented and tested on relevant data. Results confirm that our method can reach better accuracy than current state of the art methods in this domain.

Palabras clave

  • Opinion mining
  • sentiment analysis
  • Particle Swarm Optimization Algorithm
  • fuzzy classification algorithm
Acceso abierto

Group Decision Analysis Algorithms with EDAS for Interval Fuzzy Sets

Publicado en línea: 30 Jun 2018
Páginas: 51 - 64

Resumen

Abstract

The purpose of this paper is proposing, analyzing and assessing two new algorithms of the EDAS method for group multi-criteria decision making with fuzzy sets. In the first proposed EDAS extension for distance measure between two interval Type-2 fuzzy numbers is applied Graded Mean Integration Representation (GMIR). The second algorithm takes into account the proximity between the fuzzy alternatives and its similarity measure is Map Distance Operator (MDO). The two new algorithms are verified by a numerical example. Comparative analysis of obtained rankings demonstrates that GMIR extension is more reliable as an interval Type-2 fuzzy alternative to Evaluation based on Distance from Average Solution (EDAS). In case that time is of the essence, the MDO EDAS could be preferred.

Palabras clave

  • Multi-criteria group decision making
  • fuzzy decision making
  • Interval Type-2 fuzzy sets
  • EDAS
Acceso abierto

Combinatorial Optimization Model for Group Decision-Making

Publicado en línea: 30 Jun 2018
Páginas: 65 - 73

Resumen

Abstract

In the article a combinatorial optimization model for group decision-making problem is proposed. The described model relies on extended simple additive weighting model. A distinctive feature of the proposed model is consideration of the importance of experts’ opinions by introducing weighted coefficient for each of experts. This allows flexible adjustment of differences in knowledge and experience of the group members responsible to determine most preferable alternative to be achieved. The numerical application is illustrated by an example for software engineering adopted from D. Krapohl. The obtained results show the practical applicability of the proposed combinatorial optimization model for group decision-making.

Palabras clave

  • Combinatorial optimization
  • group decision-making
  • multi-attribute decision making
  • simple additive weighting
  • experts’ weights
Acceso abierto

An Enhanced LSB-Image Steganography Using the Hybrid Canny-Sobel Edge Detection

Publicado en línea: 30 Jun 2018
Páginas: 74 - 88

Resumen

Abstract

The Internet is a public network with many issues of data transfer security. Steganography is a data transmission security technique that is done by hiding the message in a container media, such as an image. The media certainly has its limited payload to accommodate the embedded data. This paper proposes a method for increasing the payload of secret messages in an image. The edge area is used to accommodate more message bits because the image edge area can better tolerate pixel value changes. In this research paper, Canny and Sobel detectors are combined to get a wider edge area. This two-detector combined method provides a larger edge area for greater payload of messages while maintaining imperceptibility of stego-images.

Palabras clave

  • Image steganography
  • LSB
  • Hybrid edge detection
  • Canny
  • Sobel
Acceso abierto

New Proposed Fusion between DCT for Feature Extraction and NSVC for Face Classification

Publicado en línea: 30 Jun 2018
Páginas: 89 - 97

Resumen

Abstract

Feature extraction is an interactive and iterative analysis process of a large dataset of raw data in order to extract meaningful knowledge. In this article, we present a strong descriptor based on the Discrete Cosine Transform (DCT), we show that the new DCT-based Neighboring Support Vector Classifier (DCT-NSVC) provides a better results compared to other algorithms for supervised classification. Experiments on our real dataset named BOSS, show that the accuracy of classification has reached 99%. The application of DCT-NSVC on MIT-CBCL dataset confirms the performance of the proposed approach.

Palabras clave

  • Supervised learning
  • DCT
  • NSVC
  • shape recognition
  • SVM
  • feature extraction
Acceso abierto

Energy-Aware Task Scheduling Using Hybrid Firefly-BAT (FFABAT) in Big Data

Publicado en línea: 30 Jun 2018
Páginas: 98 - 111

Resumen

Abstract

In modern times there is an increasing trend of applications for handling Big data. However, negotiating with the concepts of the Big data is an extremely difficult issue today. The MapReduce framework has been in focus recently for serious consideration. The aim of this study is to get the task-scheduling over Big data using Hadoop. Initially, we prioritize the tasks with the help of k-means clustering algorithm. Then, the MapReduce framework is employed. The available resource is optimally selected using optimization technique in map-phase. The proposed method uses the FireFly Algorithm and BAT algorithms (FFABAT) for choosing the optimal resource with minimum cost value. The bat-inspired algorithm is a meta-heuristic optimization method developed by Xin-She Yang (2010). This bat algorithm is established on the echo-location behaviour of micro-bats with variable pulse rates of emission and loudness. Finally, the tasks are scheduled with the optimal resource in reducer-phase and stored in the cloud. The performance of the algorithm is analysed, based on the total cost, time and memory utilization.

Palabras clave

  • Map reduce framework
  • task scheduling
  • firefly algorithm
  • BAT Algorithm
  • Hadoop
  • k-Means clustering
  • Hadoop
  • HDFS
Acceso abierto

Evaluation of Two-Dimensional Angular Orientation of a Mobile Robot by a Modified Algorithm Based on Hough Transform

Publicado en línea: 30 Jun 2018
Páginas: 112 - 122

Resumen

Abstract

This paper proposes an algorithm that assesses the angular orientation of a mobile robot with respect to its referential position or a map of the surrounding space. In the framework of the suggested method, the orientation problem is converted to evaluating a dimensional rotation of the object that is abstracted as a polygon (or a closed polygonal chain). The method is based on Hough transform, which transforms the measurement space to a parametric space (in this case, a two-dimensional space [θ, r] of straight-line parameters). The Hough transform preserves the angles between the straight lines during rotation, translation, and isotropic scaling transformations. The problem of rotation assessment then becomes a one-dimensional optimization problem. The suggested algorithm inherits the Hough method’s robustness to noise.

Palabras clave

  • Simultaneous Localization And Mapping (SLAM)
  • path planning
  • Hough transform
  • angular orientation
  • mobile robot
Acceso abierto

Using Agent-Based Methodologies in Healthcare Information Systems

Publicado en línea: 30 Jun 2018
Páginas: 123 - 132

Resumen

Abstract

This paper carries out a comparative analysis to determine the advantages and the stages of two agent-based methodologies: Multi-agent Systems Engineering (MaSE) methodology, which is designed specifically for an agent-based and complete lifecycle approach, while also being appropriate for understanding and developing complex open systems; Agent Systems Methodology (ASEME) suggests a modular Multi-Agent System (MAS) development approach and uses the concept of intra-agent control. We also examine the strengths and weaknesses of these methodologies and the dependencies between their models and their processes. Both methodologies are applied to develop The Guardian Angle: Patient-Centered Health Information System (GA: PCHIS), which is an example of agent-based applications used to improve health care information systems.

Palabras clave

  • Agent-based Methodologies
  • MaSE
  • ASEME
Acceso abierto

Mobile Edge Services for Quality of Service Control and Access to Terminal Status

Publicado en línea: 30 Jun 2018
Páginas: 133 - 150

Resumen

Abstract

An important ingredient of fifth generation (5G) networks will be Multi-access Edge Computing (MEC). MEC brings the computational intelligence of the cloud within the Radio Access Network (RAN). The virtualized functionality is accessible through Application Programming Interfaces (APIs). In this paper, we study capabilities of reuse existing time-tested Web Services to provide mobile edge middleware services. The focus is on mobile edge services that can be used by applications for bandwidth management and access to user contextual information. An extension of Web Service functionality is proposed. Implementation issues of Web Services in RAN are considered.

Palabras clave

  • Multi-access Edge Computing
  • Application Programming Interfaces
  • Radio resource control
  • Bandwidth management
  • Terminal activity
11 Artículos
Acceso abierto

Mining Fuzzy Sequential Patterns with Fuzzy Time-Intervals in Quantitative Sequence Databases

Publicado en línea: 30 Jun 2018
Páginas: 3 - 19

Resumen

Abstract

The main objective of this paper is to introduce fuzzy sequential patterns with fuzzy time-intervals in quantitative sequence databases. In the fuzzy sequential pattern with fuzzy time-intervals, both quantitative attributes and time distances are represented by linguistic terms. A new algorithm based on the Apriori algorithm is proposed to find the patterns. The mined patterns can be applied to market basket analysis, stock market analysis, and so on.

Palabras clave

  • Data mining
  • fuzzy sequential pattern
  • fuzzy time-interval
  • sequence database
Acceso abierto

Genetic Fuzzy System for Financial Management

Publicado en línea: 30 Jun 2018
Páginas: 20 - 35

Resumen

Abstract

This paper discusses genetic fuzzy systems – hybrid systems of artificial intelligence combining the potential of fuzzy sets for modeling approximate reasoning with the abilities of genetic algorithms for finding optimal solutions. The use of genetic algorithms for optimizing the parameters of a fuzzy system is demonstrated on GFSSAM.

Palabras clave

  • Genetic fuzzy system
  • intelligent hybrid systems
  • financial management
  • artificial intelligence
Acceso abierto

A New Opinion Mining Method based on Fuzzy Classifier and Particle Swarm Optimization (PSO) Algorithm

Publicado en línea: 30 Jun 2018
Páginas: 36 - 50

Resumen

Abstract

Opinion Mining or Sentiment Analysis is the task of extracting people final opinion about something through their unstructured sentiments. The Opinion Mining process is as follows: first, product features which are most important to a user are extracted from his/her comments. Then, sentiments will be emotionally classified using their emotional implications. In this paper we propose an opinion classification method based on Fuzzy Logic. Up to now, a few methods have taken advantage of fuzzy logic in opinion classification and all of them have imported fuzzy rules into system as background knowledge. But the main challenge here is finding the fuzzy rules. Our contribution is to automatically extract fuzzy rules and their parameters from training data. Here we have used the Particle Swarm Optimization (PSO) algorithm to extract fuzzy rules from training data. Also, for better results we have devised a mutation-based PSO. All proposed methods have been implemented and tested on relevant data. Results confirm that our method can reach better accuracy than current state of the art methods in this domain.

Palabras clave

  • Opinion mining
  • sentiment analysis
  • Particle Swarm Optimization Algorithm
  • fuzzy classification algorithm
Acceso abierto

Group Decision Analysis Algorithms with EDAS for Interval Fuzzy Sets

Publicado en línea: 30 Jun 2018
Páginas: 51 - 64

Resumen

Abstract

The purpose of this paper is proposing, analyzing and assessing two new algorithms of the EDAS method for group multi-criteria decision making with fuzzy sets. In the first proposed EDAS extension for distance measure between two interval Type-2 fuzzy numbers is applied Graded Mean Integration Representation (GMIR). The second algorithm takes into account the proximity between the fuzzy alternatives and its similarity measure is Map Distance Operator (MDO). The two new algorithms are verified by a numerical example. Comparative analysis of obtained rankings demonstrates that GMIR extension is more reliable as an interval Type-2 fuzzy alternative to Evaluation based on Distance from Average Solution (EDAS). In case that time is of the essence, the MDO EDAS could be preferred.

Palabras clave

  • Multi-criteria group decision making
  • fuzzy decision making
  • Interval Type-2 fuzzy sets
  • EDAS
Acceso abierto

Combinatorial Optimization Model for Group Decision-Making

Publicado en línea: 30 Jun 2018
Páginas: 65 - 73

Resumen

Abstract

In the article a combinatorial optimization model for group decision-making problem is proposed. The described model relies on extended simple additive weighting model. A distinctive feature of the proposed model is consideration of the importance of experts’ opinions by introducing weighted coefficient for each of experts. This allows flexible adjustment of differences in knowledge and experience of the group members responsible to determine most preferable alternative to be achieved. The numerical application is illustrated by an example for software engineering adopted from D. Krapohl. The obtained results show the practical applicability of the proposed combinatorial optimization model for group decision-making.

Palabras clave

  • Combinatorial optimization
  • group decision-making
  • multi-attribute decision making
  • simple additive weighting
  • experts’ weights
Acceso abierto

An Enhanced LSB-Image Steganography Using the Hybrid Canny-Sobel Edge Detection

Publicado en línea: 30 Jun 2018
Páginas: 74 - 88

Resumen

Abstract

The Internet is a public network with many issues of data transfer security. Steganography is a data transmission security technique that is done by hiding the message in a container media, such as an image. The media certainly has its limited payload to accommodate the embedded data. This paper proposes a method for increasing the payload of secret messages in an image. The edge area is used to accommodate more message bits because the image edge area can better tolerate pixel value changes. In this research paper, Canny and Sobel detectors are combined to get a wider edge area. This two-detector combined method provides a larger edge area for greater payload of messages while maintaining imperceptibility of stego-images.

Palabras clave

  • Image steganography
  • LSB
  • Hybrid edge detection
  • Canny
  • Sobel
Acceso abierto

New Proposed Fusion between DCT for Feature Extraction and NSVC for Face Classification

Publicado en línea: 30 Jun 2018
Páginas: 89 - 97

Resumen

Abstract

Feature extraction is an interactive and iterative analysis process of a large dataset of raw data in order to extract meaningful knowledge. In this article, we present a strong descriptor based on the Discrete Cosine Transform (DCT), we show that the new DCT-based Neighboring Support Vector Classifier (DCT-NSVC) provides a better results compared to other algorithms for supervised classification. Experiments on our real dataset named BOSS, show that the accuracy of classification has reached 99%. The application of DCT-NSVC on MIT-CBCL dataset confirms the performance of the proposed approach.

Palabras clave

  • Supervised learning
  • DCT
  • NSVC
  • shape recognition
  • SVM
  • feature extraction
Acceso abierto

Energy-Aware Task Scheduling Using Hybrid Firefly-BAT (FFABAT) in Big Data

Publicado en línea: 30 Jun 2018
Páginas: 98 - 111

Resumen

Abstract

In modern times there is an increasing trend of applications for handling Big data. However, negotiating with the concepts of the Big data is an extremely difficult issue today. The MapReduce framework has been in focus recently for serious consideration. The aim of this study is to get the task-scheduling over Big data using Hadoop. Initially, we prioritize the tasks with the help of k-means clustering algorithm. Then, the MapReduce framework is employed. The available resource is optimally selected using optimization technique in map-phase. The proposed method uses the FireFly Algorithm and BAT algorithms (FFABAT) for choosing the optimal resource with minimum cost value. The bat-inspired algorithm is a meta-heuristic optimization method developed by Xin-She Yang (2010). This bat algorithm is established on the echo-location behaviour of micro-bats with variable pulse rates of emission and loudness. Finally, the tasks are scheduled with the optimal resource in reducer-phase and stored in the cloud. The performance of the algorithm is analysed, based on the total cost, time and memory utilization.

Palabras clave

  • Map reduce framework
  • task scheduling
  • firefly algorithm
  • BAT Algorithm
  • Hadoop
  • k-Means clustering
  • Hadoop
  • HDFS
Acceso abierto

Evaluation of Two-Dimensional Angular Orientation of a Mobile Robot by a Modified Algorithm Based on Hough Transform

Publicado en línea: 30 Jun 2018
Páginas: 112 - 122

Resumen

Abstract

This paper proposes an algorithm that assesses the angular orientation of a mobile robot with respect to its referential position or a map of the surrounding space. In the framework of the suggested method, the orientation problem is converted to evaluating a dimensional rotation of the object that is abstracted as a polygon (or a closed polygonal chain). The method is based on Hough transform, which transforms the measurement space to a parametric space (in this case, a two-dimensional space [θ, r] of straight-line parameters). The Hough transform preserves the angles between the straight lines during rotation, translation, and isotropic scaling transformations. The problem of rotation assessment then becomes a one-dimensional optimization problem. The suggested algorithm inherits the Hough method’s robustness to noise.

Palabras clave

  • Simultaneous Localization And Mapping (SLAM)
  • path planning
  • Hough transform
  • angular orientation
  • mobile robot
Acceso abierto

Using Agent-Based Methodologies in Healthcare Information Systems

Publicado en línea: 30 Jun 2018
Páginas: 123 - 132

Resumen

Abstract

This paper carries out a comparative analysis to determine the advantages and the stages of two agent-based methodologies: Multi-agent Systems Engineering (MaSE) methodology, which is designed specifically for an agent-based and complete lifecycle approach, while also being appropriate for understanding and developing complex open systems; Agent Systems Methodology (ASEME) suggests a modular Multi-Agent System (MAS) development approach and uses the concept of intra-agent control. We also examine the strengths and weaknesses of these methodologies and the dependencies between their models and their processes. Both methodologies are applied to develop The Guardian Angle: Patient-Centered Health Information System (GA: PCHIS), which is an example of agent-based applications used to improve health care information systems.

Palabras clave

  • Agent-based Methodologies
  • MaSE
  • ASEME
Acceso abierto

Mobile Edge Services for Quality of Service Control and Access to Terminal Status

Publicado en línea: 30 Jun 2018
Páginas: 133 - 150

Resumen

Abstract

An important ingredient of fifth generation (5G) networks will be Multi-access Edge Computing (MEC). MEC brings the computational intelligence of the cloud within the Radio Access Network (RAN). The virtualized functionality is accessible through Application Programming Interfaces (APIs). In this paper, we study capabilities of reuse existing time-tested Web Services to provide mobile edge middleware services. The focus is on mobile edge services that can be used by applications for bandwidth management and access to user contextual information. An extension of Web Service functionality is proposed. Implementation issues of Web Services in RAN are considered.

Palabras clave

  • Multi-access Edge Computing
  • Application Programming Interfaces
  • Radio resource control
  • Bandwidth management
  • Terminal activity

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