Genetic Optimization of Neural Networks for Person Recognition Based on the Iris

Melín, Patricia; Herrera, Víctor Manuel Velasco; Romero, Danniela; Valdez, Fevrier; Castillo, Oscar

doi:10.12928/telkomnika.v10i2.800

Cited by 7 publications

(3 citation statements)

References 15 publications

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“…Research by Woodward & Kelleher [34] used genetic algorithm applied machine learning principles, paving the way for optimizing 'smart' optical technologies. Then Karegowda et al [35] and Melin et al [36] they are using genetic algorithm approach for class distribution problem.…”

Section: Related Workmentioning

confidence: 99%

“…1) Preprocessing Preprocessing the data before modeling, and preparing the initialization of used parameters as follows: population size, number of genetic algorithm generations, crossover rate, mutation rate, and learning rate. [36]- [42] In this research, the number of weights generated was equal to the class in the data and generated the learning vector quantization method weights from the class randomly. The length of the chromosome used was 45.…”

Section: A Data Collectionmentioning

confidence: 99%

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Hybrid Architecture Model of Genetic Algorithm and Learning Vector Quantization Neural Network for Early Identification of Ear, Nose, and Throat Diseases

Hayat,

Soenandi

2024

J. Inf. Syst. Eng. Bus. Intell.

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Background: In 2020, the World Health Organization (WHO) estimated that 466 million people worldwide are affected by hearing loss, with 34 million of them being children. Indonesia is identified as one of the four Asian countries with a high prevalence of hearing loss, specifically at 4.6%. Previous research was conducted to identify diseases related to the Ear, Nose, and Throat, utilizing the certainty factor method with a test accuracy rate of 46.54%. The novelty of this research lies in the combination of two methods, the use of genetic algorithms for optimization and learning vector quantization to improve the level of accuracy for early identification of Ear, Nose, and Throat diseases. Objective: This research aims to produce a hybrid model between the genetic algorithm and the learning vector quantization neural network to be able to identify Ear, Nose, and Throat diseases with mild symptoms to improve accuracy. Methods: Implementing a 90:10 ratio means that 90% (186 data) of the data from the initial sequence is assigned for training purposes, while the remaining 10% (21 data) is allocated for testing. The procedural stages of genetic algorithm-learning vector quantization are population initialization, crossover, mutation, evaluation, selection elitism, and learning vector quantization training. Results The optimum hybrid genetic algorithm-learning vector quantization model for early identification of Ear, Nose, and Throat diseases was obtained with an accuracy of 82.12%. The parameter values with the population size 10, cr 0.9, mr 0.1, maximum epoch of 5000, error goal of 0.01, and learning rate (alpha) of 0.5. Better accuracy was obtained compared to backpropagation (64%), certainty factor 46.54%), and radial basic function (72%). Conclusion: Experiments in this research, successed identifying models by combining genetic algorithm-learning vector quantization to perform the early identification of Ear, Nose, and Throat diseases. For further research, it's very challenging to develop a model that automatically adapts the bandwidth parameters of the weighting functions during trainin Keywords: Early Identification, Ear-Nose-Throat Diseases, Genetic Algorithm, Learning Vector Quantization

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Section: Related Workmentioning

confidence: 99%

Section: A Data Collectionmentioning

confidence: 99%

Hybrid Architecture Model of Genetic Algorithm and Learning Vector Quantization Neural Network for Early Identification of Ear, Nose, and Throat Diseases

Hayat,

Soenandi

2024

J. Inf. Syst. Eng. Bus. Intell.

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“…It is easy like both algorithms Particle Swarm Optimization (PSO) and Differential Evolution (DE) and utilizes usual control variables like the size of colony and maximum number of cycles. ABC as an optimization instrument supplies a population-based search technique where individuals termed as locations are altered by the artificial bees with time and the bee's project in discovering the food source areas with more nectar amount and lastly the one with the greatest nectar [37][38][39][40][41][42][43][44][45] Karaboga [36] suggested the ABC algorithm prompted by this foraging character of honeybees. This algorithm considers an area that provides food considered as a candidate outcome for the problem of optimization and the outcome of the fitness is constituted by the quantity of nectar in the food origin.…”

Section: Artificial Bee Colony (Abc)mentioning

confidence: 99%

Robust watermarking scheme based LWT and SVD using artificial bee colony optimization

Abdulazeez

Zeebaree

Hajy

et al. 2021

IJEECS

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<span>This paper presents a watermarking scheme for grayscale images, in which lifting wavelet transform and singular value decomposition are exploited based on multi-objective artificial bee colony optimization to produce a robust watermarking method. Furthermore, for increasing security encryption of the watermark is done prior to the embedding operation. In the proposed scheme, the actual image is altered to four sub-band over three levels of lifting wavelet transform then the singular value of the watermark image is embedded to the singular value of LH sub-band of the transformed original image. In the embedding operation, multiple scaling factors are utilized on behalf of the single scaling element to get the maximum probable robustness without changing watermark lucidity. Multi-objective artificial bee colony optimization is utilized for the determination of the optimal values for multiple scaling components, which are examined against various types of attacks. For making the proposed scheme more secure, the watermark is encrypted chaotically by logistic chaotic encryption before embedding it to the host (original) image. The experimental results show excellent imperceptibility and good resiliency against a wide range of image processing attacks.</span>

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Recent Trends in Face Recognition Using Metaheuristic Optimization

Chalabi

Attia

Bouziane

et al. 2022

Studies in Systems, Decision and Control

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Genetic Optimization of Neural Networks for Person Recognition Based on the Iris

Cited by 7 publications

References 15 publications

Hybrid Architecture Model of Genetic Algorithm and Learning Vector Quantization Neural Network for Early Identification of Ear, Nose, and Throat Diseases

Hybrid Architecture Model of Genetic Algorithm and Learning Vector Quantization Neural Network for Early Identification of Ear, Nose, and Throat Diseases

Robust watermarking scheme based LWT and SVD using artificial bee colony optimization

Recent Trends in Face Recognition Using Metaheuristic Optimization

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