scholarly journals A technique for securing big data using k-anonymization with a hybrid optimization algorithm

2021 ◽  
Vol 12 (4) ◽  
pp. 0-0
Keyword(s):  
Big Data ◽  
Privacy Preservation ◽  
Performance Metrics ◽  
Experimental Results ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Swarm Optimization ◽  
Cat Swarm Optimization ◽  
Hybrid Optimization Algorithm ◽  
Big Data Applications

The recent techniques built on cloud computing for data processing is scalable and secure, which increasingly attracts the infrastructure to support big data applications. This paper proposes an effective anonymization based privacy preservation model using k-anonymization criteria and Grey wolf-Cat Swarm Optimization (GWCSO) for attaining privacy preservation in big data. The anonymization technique is processed by adapting k- anonymization criteria for duplicating k records from the original database. The proposed GWCSO is developed by integrating Grey Wolf Optimizer (GWO) and Cat Swarm Optimization (CSO) for constructing the k-anonymized database, which reveals only the essential details to the end users by hiding the confidential information. The experimental results of the proposed technique are compared with various existing techniques based on the performance metrics, such as Classification accuracy (CA) and Information loss (IL). The experimental results show that the proposed technique attains an improved CA value of 0.005 and IL value of 0.798, respectively.

scholarly journals A Technique for Securing Big Data Using K-Anonymization With a Hybrid Optimization Algorithm

2021 ◽  
Vol 12 (4) ◽  
pp. 1-21
Author(s):  
Suman Madan ◽  
Puneet Goswami
Keyword(s):  
Big Data ◽  
Privacy Preservation ◽  
Performance Metrics ◽  
Experimental Results ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Swarm Optimization ◽  
Cat Swarm Optimization ◽  
Hybrid Optimization Algorithm ◽  
Big Data Applications

The recent techniques built on cloud computing for data processing is scalable and secure, which increasingly attracts the infrastructure to support big data applications. This paper proposes an effective anonymization based privacy preservation model using k-anonymization criteria and Grey wolf-Cat Swarm Optimization (GWCSO) for attaining privacy preservation in big data. The anonymization technique is processed by adapting k- anonymization criteria for duplicating k records from the original database. The proposed GWCSO is developed by integrating Grey Wolf Optimizer (GWO) and Cat Swarm Optimization (CSO) for constructing the k-anonymized database, which reveals only the essential details to the end users by hiding the confidential information. The experimental results of the proposed technique are compared with various existing techniques based on the performance metrics, such as Classification accuracy (CA) and Information loss (IL). The experimental results show that the proposed technique attains an improved CA value of 0.005 and IL value of 0.798, respectively.

scholarly journals TV White Space Network Power Allocation Using Hybrid Grey Wolf Optimizer with Firefly Algorithm and Particle Swarm Optimization

2021 ◽  
Vol 20 ◽  
pp. 66-75
Author(s):  
Kennedy Ronoh ◽  
George Kamucha
Keyword(s):  
Particle Swarm Optimization ◽  
Power Allocation ◽  
Firefly Algorithm ◽  
Population Based ◽  
Metaheuristic Algorithms ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Secondary Users ◽  
Swarm Optimization ◽  
Network Power

TV white spaces (TVWS) can be utilized by Secondary Users (SUs) equipped with cognitive radio functionality on the condition that they do not cause harmful interference to Primary Users (PUs). Optimization of power allocation is necessary when there is a high density of secondary users in a network in order to reduce the level of interference among SUs and to protect PUs against harmful interference. Grey Wolf Optimizer (GWO) is relatively recent population based metaheuristic algorithm that has shown superior performance compared to other population based metaheuristic algorithms. Recent trend has been to hybridize population based metaheuristic algorithms in order to avoid the problem of getting trapped in a local optimum. This paper presents the design and analysis of performance of a hybrid grey wolf optimizer and Firefly Algorithm (FA) with Particle Swarm Optimization operators for optimization of power allocation in TVWS network power allocation as a continuous optimization problem. Matlab was used for simulation. The hybrid of GWO, FA and PSO (HFAGWOPSO) reduces sum power by 81.42% compared to GWO and improves sum throughput by 16.41% when compared to GWO. Simulation results also show that the algorithm has better convergence rate.

Some Words About Nature-Inspired Computing

2022 ◽  
pp. 1-9
Author(s):  
Mohamed Arezki Mellal
Keyword(s):  
Artificial Intelligence ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Swarm Optimization ◽  
Plant Propagation ◽  
Cuckoo Optimization Algorithm ◽  
Propagation Algorithm ◽  
Wide Range ◽  
Nature Inspired Computing ◽  
Computing Approach

The use of artificial intelligence (AI) in various domains has drastically increased during the last decade. Nature-inspired computing is a strong computing approach that belongs to AI and covers a wide range of techniques. It has successfully tackled many complex problems and outperformed several classical techniques. This chapter provides the original ideas behind some nature-inspired computing techniques and their applications, such as the genetic algorithms, particle swarm optimization, grey wolf optimizer, ant colony optimization, plant propagation algorithm, cuckoo optimization algorithm, and artificial neural networks.

scholarly journals Grey-Wolf-Based Wang’s Demons for Retinal Image Registration

Entropy ◽  
2020 ◽  
Vol 22 (6) ◽  
pp. 659 ◽  
Author(s):  
Sayan Chakraborty ◽  
Ratika Pradhan ◽  
Amira S. Ashour ◽  
Luminita Moraru ◽  
Nilanjan Dey
Keyword(s):  
Image Registration ◽  
Retinal Image ◽  
Firefly Algorithm ◽  
Cuckoo Search ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Swarm Optimization ◽  
Registration Error ◽  
Registration Process ◽  
Fast Processing

Image registration has an imperative role in medical imaging. In this work, a grey-wolf optimizer (GWO)-based non-rigid demons registration is proposed to support the retinal image registration process. A comparative study of the proposed GWO-based demons registration framework with cuckoo search, firefly algorithm, and particle swarm optimization-based demons registration is conducted. In addition, a comparative analysis of different demons registration methods, such as Wang’s demons, Tang’s demons, and Thirion’s demons which are optimized using the proposed GWO is carried out. The results established the superiority of the GWO-based framework which achieved 0.9977 correlation, and fast processing compared to the use of the other optimization algorithms. Moreover, GWO-based Wang’s demons performed better accuracy compared to the Tang’s demons and Thirion’s demons framework. It also achieved the best less registration error of 8.36 × 10−5.

scholarly journals Grey Wolf Based Wang’s Demons for Retinal Image Registration

2020 ◽  
Author(s):  
Sayan Chakraborty ◽  
Ratika Pradhan ◽  
Amira S. Ashour ◽  
Luminita Moraru ◽  
Nilanjan Dey
Keyword(s):  
Image Registration ◽  
Retinal Image ◽  
Firefly Algorithm ◽  
Cuckoo Search ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Swarm Optimization ◽  
Registration Error ◽  
Registration Process ◽  
Fast Processing

Image registration has an imperative role in medical imaging. In this work, a grey-wolf optimizer (GWO) based non-rigid demons registration is proposed to support the retinal image registration process. A comparative study of the proposed GWO-based demons registration framework with cuckoo search, firefly algorithm, and particle swarm optimization- based demons registration is conducted. In addition, a comparative analysis of different demons registration methods, such as Wang’s demons, Tang’s demons, and Thirion’s demons which are optimized using the proposed GWO is carried out. The results established the superiority of the GWO-based framework which achieved 0.9977 correlation, and fast processing compared to the use of the other optimization algorithms. Moreover, GWO-based Wang’s demons performed better accuracy compared to the Tang’s demons and Thirion’s demons framework. It also achieved the best less registration error of 8.36×10-5.

Fusion of Gravitational Search Algorithm, Particle Swarm Optimization, and Grey Wolf Optimizer for Odor Source Localization

2018 ◽  
pp. 276-302
Author(s):  
Upma Jain ◽  
Ritu Tiwari ◽  
W. Wilfred Godfrey
Keyword(s):  
Particle Swarm Optimization ◽  
Mobile Robots ◽  
Source Localization ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Grey Wolf Optimizer ◽  
Odor Source ◽  
Grey Wolf ◽  
Swarm Optimization ◽  
Gravitational Search

This chapter concerns the problem of odor source localization by a team of mobile robots. A brief overview of odor source localization is given which is followed by related work. Three methods are proposed for odor source localization. These methods are largely inspired by gravitational search algorithm, grey wolf optimizer, and particle swarm optimization. Objective of the proposed approaches is to reduce the time required to localize the odor source by a team of mobile robots. The intensity of odor across the plume area is assumed to follow the Gaussian distribution. Robots start search from the corner of the workspace. As robots enter in the vicinity of plume area, they form groups using K-nearest neighbor algorithm. To avoid stagnation of the robots at local optima, search counter concept is used. Proposed approaches are tested and validated through simulation.

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