Gingivitis Detection by Fractional Fourier Entropy and Standard Genetic Algorithm

Yan, Yan; Nguyen, Elijah

doi:10.1007/978-3-030-60799-9_53

Cited by 3 publications

(1 citation statement)

References 29 publications

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“…Research in the process of analyzing this disease classification results in optimal machine learning performance and presents new knowledge in a pattern for detecting Gingivitis disease [23]. The same study also reported that ANN with backpropagation algorithm in deep learning concept provides effective performance in the detection of Gingivitis disease [24]. Followed by research adopting the application of backpropagation combined with the grey level co-occurrence matrix (GLCM) method presents a good performance in reading Gingivitis disease [25].…”

Section: Introductionmentioning

confidence: 89%

Optimization artificial neural network classification analysis model diagnosis Gingivitis disease

Ariandi

Yanto²,

Jamhur³

et al. 2023

IJEECS

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<span lang="EN-US">Gingivitis is a disease that can be caused by the buildup of bacteria and plaque caused by leftover food. This disease can attack anyone, especially children who are not aware of maintaining dental and oral health. This study aims to build and optimize the classification analysis model for the diagnosis of Gingivitis. The classification analysis model was built using the artificial neural network (ANN) method which was optimized using fuzzy logic and the multiple linear regression (MRL) method. Optimization with fuzzy aims to develop a pattern of rules in the detection. The MRL method is also used as a process of measuring analysis patterns to ensure the analytical model presents maximum results. The results study indicate that the optimization of fuzzy and MRL methods provides excellent output. These results are based on the fuzzy output which can provide a pattern of 40 rules. The MRL method is can present the level of correlation of each analysis variable with a significant output having an average value of 94.2%. Based on the results of this study, the analysis model that is optimized with the fuzzy logic method and MRL contributes to maximizing the process of diagnosing Gingivitis.</span>

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Section: Introductionmentioning

confidence: 89%

Optimization artificial neural network classification analysis model diagnosis Gingivitis disease

Ariandi

Yanto²,

Jamhur³

et al. 2023

IJEECS

View full text Add to dashboard Cite

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Gingivitis Detection by Wavelet Energy Entropy and Linear Regression Classifier

Yan

2021

Intelligent Computing Theories and Application

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Gingivitis detection by Fractional Fourier Entropy and Particle Swarm Optimization

Yan¹

2021

EAI Endorsed Transactions on e-Learning

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INTRODUCTION: we propose a detection model of gingivitis based on feature extraction based on particle swarm optimization neural network with fractional Fourier entropy. OBJECTIVES: For the sake of reduce the diagnostic burden of doctors' frequent and high concentration. METHODS: Primarily, Fourier transform is applied to the collected image signal, and the entropy is extracted from the gingival image by Shannon entropy so as to get the input value. The particle swarm optimization algorithm was combined with the extraction eigenvector is used to detect whether patients have gingivitis in final. RESULTS: The experimental results show that this method can reduce the unnecessary space of image detection, reduce the complexity of image information, and achieve a sensitivity of 79.00±1.61%, specificity of 80.89±1.87% and accuracy of 79.94±0.96%. CONCLUSION: This optimized algorithm can effectively and accurately cluster the sample data, and the accuracy is also higher than the advanced gingival image diagnosis method, making the gingivitis diagnosis more accurate. Our neural network has good trainability and recognition ability, which makes a unique contribution to medically intelligent detection methods for gingival treatment.

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Gingivitis Detection by Fractional Fourier Entropy and Standard Genetic Algorithm

Cited by 3 publications

References 29 publications

Optimization artificial neural network classification analysis model diagnosis Gingivitis disease

Optimization artificial neural network classification analysis model diagnosis Gingivitis disease

Gingivitis Detection by Wavelet Energy Entropy and Linear Regression Classifier

Gingivitis detection by Fractional Fourier Entropy and Particle Swarm Optimization

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