Chaotic Map Model-Based Interference Employed in Quantum-Inspired Genetic Algorithm to Determine the Optimum Gray Level Image Thresholding

2014 ◽  
pp. 68-110 ◽  
Author(s):  
Sandip Dey ◽  
Siddhartha Bhattacharyya ◽  
Ujjwal Maulik
Keyword(s):  
Genetic Algorithm ◽  
Quantum Computing ◽  
Chaotic Map ◽  
Threshold Value ◽  
Evaluation Metrics ◽  
Gray Level ◽  
Linear Index ◽  
Gray Level Image ◽  
Basic Features ◽  
Conventional Genetic Algorithm

In this chapter, a Quantum-Inspired Genetic Algorithm (QIGA) is presented. The QIGA adopted the inherent principles of quantum computing and has been applied on three gray level test images to determine their optimal threshold values. Quantum random interference based on chaotic map models and later quantum crossover, quantum mutation, and quantum shift operation have been applied in the proposed QIGA. The basic features of quantum computing like qubit, superposition of states, coherence and decoherence, etc. help to espouse parallelism and time discreteness in QIGA. Finally, the optimum threshold value has been derived through the quantum measurement phase. In the proposed QIGA, the selected evaluation metrics are Wu’s algorithm, Renyi’s algorithm, Yen’s algorithm, Johannsen’s algorithm, Silva’s algorithm, and finally, linear index of fuzziness, and the selected gray level images are Baboon, Peppers, and Corridor. The conventional Genetic Algorithm (GA) and Quantum Evolutionary Algorithm (QEA) proposed by Han et al. have been run on the same set of images and evaluation metrics with the same parameters as QIGA. Finally, the performance analysis has been made between the proposed QIGA with the conventional GA and later with QEA proposed by Han et al., which reveals its time efficacy compared to GA along with the drawbacks in QEA.

Chaotic Map Model-Based Interference Employed in Quantum-Inspired Genetic Algorithm to Determine the Optimum Gray Level Image Thresholding

2015 ◽  
pp. 503-542
Author(s):  
Sandip Dey ◽  
Siddhartha Bhattacharyya ◽  
Ujjwal Maulik
Keyword(s):  
Genetic Algorithm ◽  
Quantum Computing ◽  
Chaotic Map ◽  
Threshold Value ◽  
Evaluation Metrics ◽  
Gray Level ◽  
Linear Index ◽  
Gray Level Image ◽  
Basic Features ◽  
Conventional Genetic Algorithm

In this chapter, a Quantum-Inspired Genetic Algorithm (QIGA) is presented. The QIGA adopted the inherent principles of quantum computing and has been applied on three gray level test images to determine their optimal threshold values. Quantum random interference based on chaotic map models and later quantum crossover, quantum mutation, and quantum shift operation have been applied in the proposed QIGA. The basic features of quantum computing like qubit, superposition of states, coherence and decoherence, etc. help to espouse parallelism and time discreteness in QIGA. Finally, the optimum threshold value has been derived through the quantum measurement phase. In the proposed QIGA, the selected evaluation metrics are Wu's algorithm, Renyi's algorithm, Yen's algorithm, Johannsen's algorithm, Silva's algorithm, and finally, linear index of fuzziness, and the selected gray level images are Baboon, Peppers, and Corridor. The conventional Genetic Algorithm (GA) and Quantum Evolutionary Algorithm (QEA) proposed by Han et al. have been run on the same set of images and evaluation metrics with the same parameters as QIGA. Finally, the performance analysis has been made between the proposed QIGA with the conventional GA and later with QEA proposed by Han et al., which reveals its time efficacy compared to GA along with the drawbacks in QEA.

Optimum Gray Level Image Thresholding using a Quantum Inspired Genetic Algorithm

2016 ◽  
pp. 349-377 ◽  
Author(s):  
Sandip Dey ◽  
Siddhartha Bhattacharyya ◽  
Ujjwal Maulik
Keyword(s):  
Genetic Algorithm ◽  
Chaotic Map ◽  
Population Diversity ◽  
Gray Level ◽  
The Novel ◽  
Image Thresholding ◽  
Gray Level Image ◽  
Quantum Operators ◽  
Pass Through ◽  
Gray Level Images

In this article, a genetic algorithm inspired by quantum computing is presented. The novel algorithm referred to as quantum inspired genetic algorithm (QIGA) is applied to determine optimal threshold of two gray level images. Different random chaotic map models exhibit the inherent interference operation in collaboration with qubit and superposition of states. The random interference is followed by three different quantum operators viz., quantum crossover, quantum mutation and quantum shifting produce population diversity. Finally, the intermediate states pass through the quantum measurement for optimization of image thresholding. In the proposed algorithm three evaluation metrics such as Brinks's, Kapur's and Pun's algorithms have been applied to two gray level images viz., Lena and Barbara. These algorithms have been applied in conventional GA and Han et al.'s QEA. A comparative study has been made between the proposed QIGA, Han et al.'s algorithm and conventional GA that indicates encouraging avenues of the proposed QIGA.

A Quantum-Inspired Genetic Algorithm for Extractive Text Summarization

2021 ◽  
Vol 10 (2) ◽  
pp. 42-60
Author(s):  
Khadidja Chettah ◽  
Amer Draa
Keyword(s):  
Genetic Algorithm ◽  
State Of The Art ◽  
Text Summarization ◽  
Automated System ◽  
Evaluation Metrics ◽  
Document Summarization ◽  
Automatic Text Summarization ◽  
Reference Methods ◽  
Textual Data ◽  
Automatic Text

Automatic text summarization has recently become a key instrument for reducing the huge quantity of textual data. In this paper, the authors propose a quantum-inspired genetic algorithm (QGA) for extractive single-document summarization. The QGA is used inside a totally automated system as an optimizer to search for the best combination of sentences to be put in the final summary. The presented approach is compared with 11 reference methods including supervised and unsupervised summarization techniques. They have evaluated the performances of the proposed approach on the DUC 2001 and DUC 2002 datasets using the ROUGE-1 and ROUGE-2 evaluation metrics. The obtained results show that the proposal can compete with other state-of-the-art methods. It is ranked first out of 12, outperforming all other algorithms.

scholarly journals Double stages of feature extarction-based GFPMI for colored finger vein identification

2020 ◽  
Vol 18 (2) ◽  
pp. 927
Author(s):  
Dawlat Mustafa Sulaiman ◽  
Adnan Mohsin Abdulazeez ◽  
Habibollah Haron
Keyword(s):  
Feature Extraction ◽  
Texture Features ◽  
Biometric Identification ◽  
Gray Level ◽  
Image Detection ◽  
Structure Information ◽  
Finger Vein ◽  
Gray Level Image ◽  
Vein Recognition ◽  
Finger Vein Recognition

Today, finger vein recognition has a lot of attention as a promising approach of biometric identification framework and still does not meet the challenges of the researchers on this filed. To solve this problem, we propose s double stage of feature extraction schemes based localized finger fine image detection. We propose Globalized Features Pattern Map Indication (GFPMI) to extract the globalized finger vein line features basede on using two generated vein image datasets: original gray level color, globalized finger vein line feature, original localized gray level image, and the colored localized finger vein images. Then, two kinds of features (gray scale and texture features) are extracted, which tell the structure information of the whole finger vein pattern in the whole dataset. The recurrent based residual neural network (RNN) is used to identify the finger vein images. The experimental show that the localized colored finger vein images based globalized feature extraction has achieved the higher accuracy (93.49%) while the original image dataset achieved less accuracy by (69.86%).

scholarly journals AN EFFICIENT CHAOS-BASED OPTIMIZATION ALGORITHM APPROACH FOR CRYPTOGRAPHY

2012 ◽  
pp. 75-79
Author(s):  
RASHI VOHRA ◽  
BRAJESH PATEL
Keyword(s):  
Genetic Algorithm ◽  
Optimization Algorithm ◽  
Negative Impact ◽  
Chaotic Map ◽  
Pseudorandom Sequence ◽  
Secure Communications ◽  
Security Threats ◽  
Suggested Approach ◽  
Novel Approach ◽  
Algorithm Operation

The utmost negative impact of advancement of technology is an exponential increase in security threats, due to which tremendous demand for effective electronic security is increasing importantly. The principles of any security mechanism are confidentiality, authentication, integrity, non-repudiation, access control and availability. Cryptography is an essential aspect for secure communications. Many chaotic cryptosystem has been developed, as a result of the interesting relationship between the two field chaos and cryptography phenomenological behavior. In this paper, an overview of cryptography, optimization algorithm and chaos theory is provided and a novel approach for encryption and decryption based on chaos and optimization algorithms is discussed. In this article, the basic idea is to encrypt and decrypt the information using the concept of genetic algorithm with the pseudorandom sequence further used as a key in genetic algorithm operation for encryption: which is generated by application of chaotic map. This attempt result in good desirable cryptographic properties as a change in key will produce undesired result in receiver side. The suggested approach complements standard, algorithmic procedures, providing security solutions with novel features.

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