Fingerprint Iris Palmprint Multimodal Biometric Watermarking System Using Genetic Algorithm-Based Bacterial Foraging Optimization Algorithm

2016 ◽  
pp. 347-376
Author(s):  
S. Anu H. Nair ◽  
P. Aruna
Keyword(s):  
Genetic Algorithm ◽  
Wavelet Transform ◽  
Optimization Algorithm ◽  
Principal Component ◽  
Biometric System ◽  
Bacterial Foraging Optimization ◽  
Multimodal Biometric System ◽  
Bacterial Foraging ◽  
Fused Image ◽  
Bacterial Foraging Optimization Algorithm

With the wide spread utilization of Biometric identification systems, establishing the authenticity of biometric data itself has emerged as an important issue. In this chapter, a novel approach for creating a multimodal biometric system has been suggested. The multimodal biometric system is implemented using the different fusion schemes such as Average Fusion, Minimum Fusion, Maximum Fusion, Principal Component Analysis Fusion, Discrete Wavelet Transform Fusion, Stationary Wavelet Transform Fusion, Intensity Hue Saturation Fusion, Laplacian Gradient Fusion, Pyramid Gradient Fusion and Sparse Representation Fusion. In modality extraction level, the information extracted from different modalities is stored in vectors on the basis of their modality. These are then blended to produce a joint template which is the basis for the watermarking system. The fused image is applied as input along with the cover image to the Genetic Algorithm based Bacterial Foraging Optimization Algorithm watermarking system. The standard images are used as cover images and performance was compared.

scholarly journals A Crossover Bacterial Foraging Optimization Algorithm

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
Rutuparna Panda ◽  
Manoj Kumar Naik
Keyword(s):  
Genetic Algorithm ◽  
Performance Evaluation ◽  
Objective Function ◽  
Optimization Algorithm ◽  
Bacterial Foraging Optimization ◽  
Bacterial Foraging ◽  
Objective Function Values ◽  
Algorithm Search ◽  
Bacterial Foraging Optimization Algorithm

This paper presents a modified bacterial foraging optimization algorithm called crossover bacterial foraging optimization algorithm, which inherits the crossover technique of genetic algorithm. This can be used for improvising the evaluation of optimal objective function values. The idea of using crossover mechanism is to search nearby locations by offspring (50 percent of bacteria), because they are randomly produced at different locations. In the traditional bacterial foraging optimization algorithm, search starts from the same locations (50 percent of bacteria are replicated) which is not desirable. Seven different benchmark functions are considered for performance evaluation. Also, comparison with the results of previous methods is presented to reveal the effectiveness of the proposed algorithm.

Bacterial Foraging Optimization Algorithm with Quorum Sensing Mechanism

2014 ◽  
Vol 556-562 ◽  
pp. 3844-3848
Author(s):  
Hai Shen ◽  
Mo Zhang
Keyword(s):  
Quorum Sensing ◽  
Optimization Algorithm ◽  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Cell Communication ◽  
Bacterial Foraging Optimization ◽  
Sensing Mechanism ◽  
Bacterial Foraging ◽  
Bacterial Quorum ◽  
Bacterial Foraging Optimization Algorithm

Quorum sensing is widely distributed in bacteria and make bacteria are similar to complex adaptive systems, with intelligent features such as emerging and non-linear, the ultimate expression of the adaptive to changes in the environment. Based on the phenomenon of bacterial quorum sensing and Bacterial Foraging Optimization Algorithm, some new optimization algorithms have been proposed. In this paper, it presents research situations, such as environment-dependent quorum sensing mechanism, quorum sensing mechanism with quantum behavior, cell-to-cell communication, multi-colony communication, density perception mechanism. Areas of future emphasis and direction in development were also pointed out.

A Clustering Approach Using Fractional Calculus-Bacterial Foraging Optimization Algorithm for k-Anonymization in Privacy Preserving Data Mining

2019 ◽  
pp. 587-608
Author(s):  
Pawan R. Bhaladhare ◽  
Devesh C. Jinwala
Keyword(s):  
Data Mining ◽  
Fractional Calculus ◽  
Optimization Algorithm ◽  
Information Loss ◽  
Bacterial Foraging Optimization ◽  
Bacterial Foraging ◽  
Individual Privacy ◽  
Clustering Approach ◽  
Bacterial Foraging Optimization Algorithm ◽  
Optimal Cluster

A tremendous amount of personal data of an individual is being collected and analyzed using data mining techniques. Such collected data, however, may also contain sensitive data about an individual. Thus, when analyzing such data, individual privacy can be breached. Therefore, to preserve individual privacy, one can find numerous approaches proposed for the same in the literature. One of the solutions proposed in the literature is k-anonymity which is used along with the clustering approach. During the investigation, the authors observed that the k-anonymization based clustering approaches all the times result in the loss of information. This paper presents a fractional calculus-based bacterial foraging optimization algorithm (FC-BFO) to generate an optimal cluster. In addition to this, the authors utilize the concept of fractional calculus (FC) in the chemotaxis step of a bacterial foraging optimization (BFO) algorithm. The main objective is to improve the optimization ability of the BFO algorithm. The authors also evaluate their proposed FC-BFO algorithm, empirically, focusing on information loss and execution time as a vital metric. The experimental evaluations show that our proposed FC-BFO algorithm generates an optimal cluster with lesser information loss as compared with the existing clustering approaches.

A Clustering Approach Using Fractional Calculus-Bacterial Foraging Optimization Algorithm for k-Anonymization in Privacy Preserving Data Mining

2016 ◽  
Vol 10 (1) ◽  
pp. 45-65 ◽  
Author(s):  
Pawan R. Bhaladhare ◽  
Devesh C. Jinwala
Keyword(s):  
Data Mining ◽  
Fractional Calculus ◽  
Optimization Algorithm ◽  
Information Loss ◽  
Bacterial Foraging Optimization ◽  
Bacterial Foraging ◽  
Individual Privacy ◽  
Clustering Approach ◽  
Bacterial Foraging Optimization Algorithm ◽  
Optimal Cluster

A tremendous amount of personal data of an individual is being collected and analyzed using data mining techniques. Such collected data, however, may also contain sensitive data about an individual. Thus, when analyzing such data, individual privacy can be breached. Therefore, to preserve individual privacy, one can find numerous approaches proposed for the same in the literature. One of the solutions proposed in the literature is k-anonymity which is used along with the clustering approach. During the investigation, the authors observed that the k-anonymization based clustering approaches all the times result in the loss of information. This paper presents a fractional calculus-based bacterial foraging optimization algorithm (FC-BFO) to generate an optimal cluster. In addition to this, the authors utilize the concept of fractional calculus (FC) in the chemotaxis step of a bacterial foraging optimization (BFO) algorithm. The main objective is to improve the optimization ability of the BFO algorithm. The authors also evaluate their proposed FC-BFO algorithm, empirically, focusing on information loss and execution time as a vital metric. The experimental evaluations show that our proposed FC-BFO algorithm generates an optimal cluster with lesser information loss as compared with the existing clustering approaches.

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