Rivista e Edizione

Volume 22 (2022): Edizione 3 (September 2022)

Volume 22 (2022): Edizione 2 (June 2022)

Volume 22 (2022): Edizione 1 (March 2022)

Volume 21 (2021): Edizione 4 (December 2021)

Volume 21 (2021): Edizione 3 (September 2021)

Volume 21 (2021): Edizione 2 (June 2021)

Volume 21 (2021): Edizione 1 (March 2021)

Volume 20 (2020): Edizione 6 (December 2020)
Special Edizione on New Developments in Scalable Computing

Volume 20 (2020): Edizione 5 (December 2020)
Special issue on Innovations in Intelligent Systems and Applications

Volume 20 (2020): Edizione 4 (November 2020)

Volume 20 (2020): Edizione 3 (September 2020)

Volume 20 (2020): Edizione 2 (June 2020)

Volume 20 (2020): Edizione 1 (March 2020)

Volume 19 (2019): Edizione 4 (November 2019)

Volume 19 (2019): Edizione 3 (September 2019)

Volume 19 (2019): Edizione 2 (June 2019)

Volume 19 (2019): Edizione 1 (March 2019)

Volume 18 (2018): Edizione 5 (May 2018)
Special Thematic Edizione on Optimal Codes and Related Topics

Volume 18 (2018): Edizione 4 (November 2018)

Volume 18 (2018): Edizione 3 (September 2018)

Volume 18 (2018): Edizione 2 (June 2018)

Volume 18 (2018): Edizione 1 (March 2018)

Volume 17 (2017): Edizione 5 (December 2017)
Special Edizione With Selected Papers From The Workshop “Two Years Avitohol: Advanced High Performance Computing Applications 2017

Volume 17 (2017): Edizione 4 (November 2017)

Volume 17 (2017): Edizione 3 (September 2017)

Volume 17 (2017): Edizione 2 (June 2017)

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

Volume 16 (2016): Edizione 6 (December 2016)
Special issue with selection of extended papers from 6th International Conference on Logistic, Informatics and Service Science LISS’2016

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

Volume 16 (2016): Edizione 4 (December 2016)

Volume 16 (2016): Edizione 3 (September 2016)

Volume 16 (2016): Edizione 2 (June 2016)

Volume 16 (2016): Edizione 1 (March 2016)

Volume 15 (2015): Edizione 7 (December 2015)
Special Edizione on Information Fusion

Volume 15 (2015): Edizione 6 (December 2015)
Special Edizione on Logistics, Informatics and Service Science

Volume 15 (2015): Edizione 5 (April 2015)
Special Edizione on Control in Transportation Systems

Volume 15 (2015): Edizione 4 (November 2015)

Volume 15 (2015): Edizione 3 (September 2015)

Volume 15 (2015): Edizione 2 (June 2015)

Volume 15 (2015): Edizione 1 (March 2015)

Volume 14 (2014): Edizione 5 (December 2014)
Special Edizione

Volume 14 (2014): Edizione 4 (December 2014)

Volume 14 (2014): Edizione 3 (September 2014)

Volume 14 (2014): Edizione 2 (June 2014)

Volume 14 (2014): Edizione 1 (March 2014)

Volume 13 (2013): Edizione Special-Edizione (December 2013)

Volume 13 (2013): Edizione 4 (December 2013)
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.

Volume 13 (2013): Edizione 3 (September 2013)

Volume 13 (2013): Edizione 2 (June 2013)

Volume 13 (2013): Edizione 1 (March 2013)

Volume 12 (2012): Edizione 4 (December 2012)

Volume 12 (2012): Edizione 3 (September 2012)

Volume 12 (2012): Edizione 2 (June 2012)

Volume 12 (2012): Edizione 1 (March 2012)

Dettagli della rivista
Formato
Rivista
eISSN
1314-4081
Pubblicato per la prima volta
13 Mar 2012
Periodo di pubblicazione
4 volte all'anno
Lingue
Inglese

Cerca

Volume 17 (2017): Edizione 3 (September 2017)

Dettagli della rivista
Formato
Rivista
eISSN
1314-4081
Pubblicato per la prima volta
13 Mar 2012
Periodo di pubblicazione
4 volte all'anno
Lingue
Inglese

Cerca

12 Articoli
Accesso libero

A Review on Artificial Bee Colony Algorithms and Their Applications to Data Clustering

Pubblicato online: 04 Oct 2017
Pagine: 3 - 28

Astratto

Abstract

Data clustering is an important data mining technique being widely used in numerous applications. It is a method of creating groups (clusters) of objects, in such a way that objects in one cluster are very similar and objects in different clusters are quite distinct, i.e. intra-cluster distance is minimized and inter-cluster distance is maximized. However, the popular conventional clustering algorithms have shortcomings such as dependency on center initialization, slow convergence rate, local optima trap, etc. Artificial Bee Colony (ABC) algorithm is one of the popular swarm based algorithm inspired by intelligent foraging behaviour of honeybees that helps to minimize these shortcomings. In the past, many swarm intelligence based techniques for clustering were introduced and proved their performance. This paper provides a literature survey on ABC, its variants and its applications in data clustering.

Parole chiave

  • Data clustering
  • swarm intelligence
  • artificial bee colony
  • meta-heuristics
  • optimization
Accesso libero

Multicriteria Fuzzy Sets Application in Economic Clustering Problems

Pubblicato online: 04 Oct 2017
Pagine: 29 - 46

Astratto

Abstract

This paper presents an approach for small and medium-sized enterprises selection in economic clusters, where the problem of integration is defined as “ill structured under condition of uncertainty”. The proposed solution demonstrates applying several fuzzy multi-criteria decision making algorithms along with discussion over specific input data requirements. The results are compared with classical multi-criteria decision-making algorithm PROMETHEE II.

Parole chiave

  • Multi-criteria decision
  • fuzzy sets algorithms
  • intercriteria analysis
  • small and medium-sized enterprises
  • economic clustering
Accesso libero

Energy Efficient Resource Allocation for Virtual Services Based on Heterogeneous Shared Hosting Platforms in Cloud Computing

Pubblicato online: 04 Oct 2017
Pagine: 47 - 58

Astratto

Abstract

This paper is an extended and updated version, presented at the INDIA 2017 conference. Optimal resource provisioning for virtual services in the Cloud computing is one of the concerns nowadays. For cloud computing service providers, reducing the number of physical machines providing resources for virtual services in cloud computing is one of the efficient ways to reduce the amount of energy consumption, which in turn enhances the performance of data centers. Multi-dimensional resource provisioning on a Heterogeneous Shared Hosting Platforms for virtual services is known as a NP-hard problem. Therefore, it is necessary to apply the metaheuristic algorithms for estimating the outcome of the problem. In this study, we propose the resource allocation problem for reducing the energy consumption. ECRA-SA algorithms were designed to solve it and were evaluated through CloudSim simulation tool compared with FFD algorithm. The experimental results show that the proposed ECRA-SA algorithm yields a better performance than FFD algorithm.

Parole chiave

  • Resource allocation
  • simulated annealing
  • virtual service
  • cloud computing
  • energy consumption
Accesso libero

Particle Swarm Optimization Based on Smoothing Approach for Solving a Class of Bi-Level Multiobjective Programming Problem

Pubblicato online: 04 Oct 2017
Pagine: 59 - 74

Astratto

Abstract

As a metaheuristic, Particle Swarm Optimization (PSO) has been used to solve the Bi-level Multiobjective Programming Problem (BMPP). However, in the existing solving approach based on PSO for the BMPP, the upper level and the lower level problem are solved interactively by PSO. In this paper, we present a different solving approach based on PSO for the BMPP. Firstly, we replace the lower level problem of the BMPP with Kuhn-Tucker optimality conditions and adopt the perturbed Fischer-Burmeister function to smooth the complementary conditions. After that, we adopt PSO approach to solve the smoothed multiobjective programming problem. Numerical results show that our solving approach can obtain the Pareto optimal front of the BMPP efficiently.

Parole chiave

  • Bi-level multiobjective programming problem
  • scalarization method
  • optimality conditions
  • smoothing method
  • particle swarm optimization
Accesso libero

Bi-Level Model for Public Rail Transportation under Incomplete Data

Pubblicato online: 04 Oct 2017
Pagine: 75 - 91

Astratto

Abstract

The increase of the utilization of public rail transportations is searched in directions for redistribution of the passenger travels between rail and bus transportation. The rail transport benefits by redistribution of the transportation flows on paths, predominantly supported by rails. The redistribution of the transportation is formalized by bi-level optimization problem. The upper level optimization estimates the maximal flow, which can be transported through a transportation network, supported both by bus and rail transports. The lower level optimization gives priority to the rail transport by decreasing the costs of flow distribution, using rail transport. This bi-level optimization problem was applied for the case of optimization of the rail exploitation in Bulgaria, defining priorities in transportation of the National transport scheme.

Parole chiave

  • Bi-level optimization
  • rail transportation
  • max-flow problem
  • network design
Accesso libero

Effective Gene Patterned Association Rule Hiding Algorithm for Privacy Preserving Data Mining on Transactional Database

Pubblicato online: 04 Oct 2017
Pagine: 92 - 108

Astratto

Abstract

Association Rule Hiding methodology is a privacy preserving data mining technique that sanitizes the original database by hide sensitive association rules generated from the transactional database. The side effect of association rules hiding technique is to hide certain rules that are not sensitive, failing to hide certain sensitive rules and generating false rules in the resulted database. This affects the privacy of the data and the utility of data mining results. In this paper, a method called Gene Patterned Association Rule Hiding (GPARH) is proposed for preserving privacy of the data and maintaining the data utility, based on data perturbation technique. Using gene selection operation, privacy linked hidden and exposed data items are mapped to the vector data items, thereby obtaining gene based data item. The performance of proposed GPARH is evaluated in terms of metrics such as number of sensitive rules generated, true positive privacy rate and execution time for selecting the sensitive rules by using Abalone and Taxi Service Trajectory datasets.

Parole chiave

  • Association Rule Hiding
  • Data Mining
  • Gene Pattern
  • Transactional database
  • Multiplicative perturbation
  • Additive perturbation
Accesso libero

Modules for Rapid Application Development of Web-Based Information Systems (RADWIS)

Pubblicato online: 04 Oct 2017
Pagine: 109 - 127

Astratto

Abstract

This paper describes a model of modular system for Rapid Application Development of Web-based Information Systems (RADWIS). The existing modular systems on technology, framework and platform level does not fully solve the problems of functionality reuse, rapid application development and balance between the complexity, size and functionality. The proposed modular system addresses these problems in a new way. The current work fills the gap between the modular systems on the framework and platform level. The model uses flexible, reusable modules, which can be built with different technologies. They are installable and shareable with the standard dependency manager of the technology and can communicate using web services. The modules use NoSQL approaches in SQL databases. A workflow engine module, based on the Petri Nets theory, allows a graphical and formal mathematical solution for a wide variety of problems.

Parole chiave

  • Modules
  • Web-based information systems
  • Petri Nets
  • NoSQL in SQL
Accesso libero

Secret Image Enhanced Sharing Using Visual Cryptography

Pubblicato online: 04 Oct 2017
Pagine: 128 - 139

Astratto

Abstract

In the conventional visual cryptographic scheme, an image is divided into several image shares, which are distributed among the members of a group, and the original image is retrieved by combining the shares of all the members. This secret image becomes accessible to every individual member and there is an inherent risk of any one of the members in the group using the valuable information for illegal purposes as an intruder. To overcome this problem, the proposed algorithm Secret Image Enhanced Sharing using Visual Cryptography (SIESVC) diligently facilitates any member in the group to retrieve either only a part or the complete secret image based purely on his access privilege rights only.

Parole chiave

  • Visual Cryptography
  • Secret Image Enhanced Sharing using Visual Cryptography (SIESVC)
Accesso libero

Approach for Analysis and Improved Usage of Digital Cultural Assets for Learning Purposes

Pubblicato online: 04 Oct 2017
Pagine: 140 - 151

Astratto

Abstract

This paper presents an approach for analysis and improved usage of digital cultural assets for non-formal learning purposes in digital culture ecosystems. The digital culture ecosystems, their features, and an exemplar are discussed.

Parole chiave

  • Digital culture ecosystem
  • analysis
  • improved usage
  • digital cultural assets
  • non-formal learning
Accesso libero

Learned Features are Better for Ethnicity Classification

Pubblicato online: 04 Oct 2017
Pagine: 152 - 164

Astratto

Abstract

Ethnicity is a key demographic attribute of human beings and it plays a vital role in automatic facial recognition and have extensive real world applications such as Human Computer Interaction (HCI); demographic based classification; biometric based recognition; security and defense to name a few. In this paper, we present a novel approach for extracting ethnicity from the facial images. The proposed method makes use of a pre trained Convolutional Neural Network (CNN) to extract the features, then Support Vector Machine (SVM) with linear kernel is used as a classifier. This technique uses translational invariant hierarchical features learned by the network, in contrast to previous works, which use hand crafted features such as Local Binary Pattern (LBP); Gabor, etc. Thorough experiments are presented on ten different facial databases, which strongly suggest that our approach is robust to different expressions and illuminations conditions. Here we consider ethnicity classification as a three class problem including Asian, African-American and Caucasian. Average classification accuracy over all databases is 98.28%, 99.66% and 99.05% for Asian, African-American and Caucasian respectively. All the codes are available for reproducing the results on request.

Parole chiave

  • Ethnicity recognition
  • race classification
  • Convolutional Neural Network (CNN)
  • VGG Face
  • Support Vector Machine (SVM)
Accesso libero

Emphasis on Cloud Optimization and Security Gaps: A Literature Review

Pubblicato online: 04 Oct 2017
Pagine: 165 - 185

Astratto

Abstract

Cloud computing is emerging as a significant new paradigm in the fields of Service-oriented computing, software engineering, etc. The paper aims to characterize the cloud environment and to study the cloud optimization problems. About 50 papers are collected from the standard journals, and it is first reviewed chronologically to find out the contributions in cloud security. After reviewing, the various challenges addressed in the cloud environment and its performance analysis is discussed. In the next section, the exploration of the meta-heuristic study of cloud optimization is done. The algorithms used in the cloud security challenges are discussed and reviewed. The algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) are exploited for the finding the cloud security problems. As the research outcome, case studies are taken and reviewed. Cloud computing is a vast field, and a lot of problems in it had to be addressed and solved.

Parole chiave

  • Cloud computing
  • optimization
  • metaheuristic
  • Cloud environment
Accesso libero

Erratum to the paper “Two-Dimensional l1-Norm Minimization in SAR Image Reconstriction”

Pubblicato online: 04 Oct 2017
Pagine: 186 - 186

Astratto

12 Articoli
Accesso libero

A Review on Artificial Bee Colony Algorithms and Their Applications to Data Clustering

Pubblicato online: 04 Oct 2017
Pagine: 3 - 28

Astratto

Abstract

Data clustering is an important data mining technique being widely used in numerous applications. It is a method of creating groups (clusters) of objects, in such a way that objects in one cluster are very similar and objects in different clusters are quite distinct, i.e. intra-cluster distance is minimized and inter-cluster distance is maximized. However, the popular conventional clustering algorithms have shortcomings such as dependency on center initialization, slow convergence rate, local optima trap, etc. Artificial Bee Colony (ABC) algorithm is one of the popular swarm based algorithm inspired by intelligent foraging behaviour of honeybees that helps to minimize these shortcomings. In the past, many swarm intelligence based techniques for clustering were introduced and proved their performance. This paper provides a literature survey on ABC, its variants and its applications in data clustering.

Parole chiave

  • Data clustering
  • swarm intelligence
  • artificial bee colony
  • meta-heuristics
  • optimization
Accesso libero

Multicriteria Fuzzy Sets Application in Economic Clustering Problems

Pubblicato online: 04 Oct 2017
Pagine: 29 - 46

Astratto

Abstract

This paper presents an approach for small and medium-sized enterprises selection in economic clusters, where the problem of integration is defined as “ill structured under condition of uncertainty”. The proposed solution demonstrates applying several fuzzy multi-criteria decision making algorithms along with discussion over specific input data requirements. The results are compared with classical multi-criteria decision-making algorithm PROMETHEE II.

Parole chiave

  • Multi-criteria decision
  • fuzzy sets algorithms
  • intercriteria analysis
  • small and medium-sized enterprises
  • economic clustering
Accesso libero

Energy Efficient Resource Allocation for Virtual Services Based on Heterogeneous Shared Hosting Platforms in Cloud Computing

Pubblicato online: 04 Oct 2017
Pagine: 47 - 58

Astratto

Abstract

This paper is an extended and updated version, presented at the INDIA 2017 conference. Optimal resource provisioning for virtual services in the Cloud computing is one of the concerns nowadays. For cloud computing service providers, reducing the number of physical machines providing resources for virtual services in cloud computing is one of the efficient ways to reduce the amount of energy consumption, which in turn enhances the performance of data centers. Multi-dimensional resource provisioning on a Heterogeneous Shared Hosting Platforms for virtual services is known as a NP-hard problem. Therefore, it is necessary to apply the metaheuristic algorithms for estimating the outcome of the problem. In this study, we propose the resource allocation problem for reducing the energy consumption. ECRA-SA algorithms were designed to solve it and were evaluated through CloudSim simulation tool compared with FFD algorithm. The experimental results show that the proposed ECRA-SA algorithm yields a better performance than FFD algorithm.

Parole chiave

  • Resource allocation
  • simulated annealing
  • virtual service
  • cloud computing
  • energy consumption
Accesso libero

Particle Swarm Optimization Based on Smoothing Approach for Solving a Class of Bi-Level Multiobjective Programming Problem

Pubblicato online: 04 Oct 2017
Pagine: 59 - 74

Astratto

Abstract

As a metaheuristic, Particle Swarm Optimization (PSO) has been used to solve the Bi-level Multiobjective Programming Problem (BMPP). However, in the existing solving approach based on PSO for the BMPP, the upper level and the lower level problem are solved interactively by PSO. In this paper, we present a different solving approach based on PSO for the BMPP. Firstly, we replace the lower level problem of the BMPP with Kuhn-Tucker optimality conditions and adopt the perturbed Fischer-Burmeister function to smooth the complementary conditions. After that, we adopt PSO approach to solve the smoothed multiobjective programming problem. Numerical results show that our solving approach can obtain the Pareto optimal front of the BMPP efficiently.

Parole chiave

  • Bi-level multiobjective programming problem
  • scalarization method
  • optimality conditions
  • smoothing method
  • particle swarm optimization
Accesso libero

Bi-Level Model for Public Rail Transportation under Incomplete Data

Pubblicato online: 04 Oct 2017
Pagine: 75 - 91

Astratto

Abstract

The increase of the utilization of public rail transportations is searched in directions for redistribution of the passenger travels between rail and bus transportation. The rail transport benefits by redistribution of the transportation flows on paths, predominantly supported by rails. The redistribution of the transportation is formalized by bi-level optimization problem. The upper level optimization estimates the maximal flow, which can be transported through a transportation network, supported both by bus and rail transports. The lower level optimization gives priority to the rail transport by decreasing the costs of flow distribution, using rail transport. This bi-level optimization problem was applied for the case of optimization of the rail exploitation in Bulgaria, defining priorities in transportation of the National transport scheme.

Parole chiave

  • Bi-level optimization
  • rail transportation
  • max-flow problem
  • network design
Accesso libero

Effective Gene Patterned Association Rule Hiding Algorithm for Privacy Preserving Data Mining on Transactional Database

Pubblicato online: 04 Oct 2017
Pagine: 92 - 108

Astratto

Abstract

Association Rule Hiding methodology is a privacy preserving data mining technique that sanitizes the original database by hide sensitive association rules generated from the transactional database. The side effect of association rules hiding technique is to hide certain rules that are not sensitive, failing to hide certain sensitive rules and generating false rules in the resulted database. This affects the privacy of the data and the utility of data mining results. In this paper, a method called Gene Patterned Association Rule Hiding (GPARH) is proposed for preserving privacy of the data and maintaining the data utility, based on data perturbation technique. Using gene selection operation, privacy linked hidden and exposed data items are mapped to the vector data items, thereby obtaining gene based data item. The performance of proposed GPARH is evaluated in terms of metrics such as number of sensitive rules generated, true positive privacy rate and execution time for selecting the sensitive rules by using Abalone and Taxi Service Trajectory datasets.

Parole chiave

  • Association Rule Hiding
  • Data Mining
  • Gene Pattern
  • Transactional database
  • Multiplicative perturbation
  • Additive perturbation
Accesso libero

Modules for Rapid Application Development of Web-Based Information Systems (RADWIS)

Pubblicato online: 04 Oct 2017
Pagine: 109 - 127

Astratto

Abstract

This paper describes a model of modular system for Rapid Application Development of Web-based Information Systems (RADWIS). The existing modular systems on technology, framework and platform level does not fully solve the problems of functionality reuse, rapid application development and balance between the complexity, size and functionality. The proposed modular system addresses these problems in a new way. The current work fills the gap between the modular systems on the framework and platform level. The model uses flexible, reusable modules, which can be built with different technologies. They are installable and shareable with the standard dependency manager of the technology and can communicate using web services. The modules use NoSQL approaches in SQL databases. A workflow engine module, based on the Petri Nets theory, allows a graphical and formal mathematical solution for a wide variety of problems.

Parole chiave

  • Modules
  • Web-based information systems
  • Petri Nets
  • NoSQL in SQL
Accesso libero

Secret Image Enhanced Sharing Using Visual Cryptography

Pubblicato online: 04 Oct 2017
Pagine: 128 - 139

Astratto

Abstract

In the conventional visual cryptographic scheme, an image is divided into several image shares, which are distributed among the members of a group, and the original image is retrieved by combining the shares of all the members. This secret image becomes accessible to every individual member and there is an inherent risk of any one of the members in the group using the valuable information for illegal purposes as an intruder. To overcome this problem, the proposed algorithm Secret Image Enhanced Sharing using Visual Cryptography (SIESVC) diligently facilitates any member in the group to retrieve either only a part or the complete secret image based purely on his access privilege rights only.

Parole chiave

  • Visual Cryptography
  • Secret Image Enhanced Sharing using Visual Cryptography (SIESVC)
Accesso libero

Approach for Analysis and Improved Usage of Digital Cultural Assets for Learning Purposes

Pubblicato online: 04 Oct 2017
Pagine: 140 - 151

Astratto

Abstract

This paper presents an approach for analysis and improved usage of digital cultural assets for non-formal learning purposes in digital culture ecosystems. The digital culture ecosystems, their features, and an exemplar are discussed.

Parole chiave

  • Digital culture ecosystem
  • analysis
  • improved usage
  • digital cultural assets
  • non-formal learning
Accesso libero

Learned Features are Better for Ethnicity Classification

Pubblicato online: 04 Oct 2017
Pagine: 152 - 164

Astratto

Abstract

Ethnicity is a key demographic attribute of human beings and it plays a vital role in automatic facial recognition and have extensive real world applications such as Human Computer Interaction (HCI); demographic based classification; biometric based recognition; security and defense to name a few. In this paper, we present a novel approach for extracting ethnicity from the facial images. The proposed method makes use of a pre trained Convolutional Neural Network (CNN) to extract the features, then Support Vector Machine (SVM) with linear kernel is used as a classifier. This technique uses translational invariant hierarchical features learned by the network, in contrast to previous works, which use hand crafted features such as Local Binary Pattern (LBP); Gabor, etc. Thorough experiments are presented on ten different facial databases, which strongly suggest that our approach is robust to different expressions and illuminations conditions. Here we consider ethnicity classification as a three class problem including Asian, African-American and Caucasian. Average classification accuracy over all databases is 98.28%, 99.66% and 99.05% for Asian, African-American and Caucasian respectively. All the codes are available for reproducing the results on request.

Parole chiave

  • Ethnicity recognition
  • race classification
  • Convolutional Neural Network (CNN)
  • VGG Face
  • Support Vector Machine (SVM)
Accesso libero

Emphasis on Cloud Optimization and Security Gaps: A Literature Review

Pubblicato online: 04 Oct 2017
Pagine: 165 - 185

Astratto

Abstract

Cloud computing is emerging as a significant new paradigm in the fields of Service-oriented computing, software engineering, etc. The paper aims to characterize the cloud environment and to study the cloud optimization problems. About 50 papers are collected from the standard journals, and it is first reviewed chronologically to find out the contributions in cloud security. After reviewing, the various challenges addressed in the cloud environment and its performance analysis is discussed. In the next section, the exploration of the meta-heuristic study of cloud optimization is done. The algorithms used in the cloud security challenges are discussed and reviewed. The algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) are exploited for the finding the cloud security problems. As the research outcome, case studies are taken and reviewed. Cloud computing is a vast field, and a lot of problems in it had to be addressed and solved.

Parole chiave

  • Cloud computing
  • optimization
  • metaheuristic
  • Cloud environment
Accesso libero

Erratum to the paper “Two-Dimensional l1-Norm Minimization in SAR Image Reconstriction”

Pubblicato online: 04 Oct 2017
Pagine: 186 - 186

Astratto

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