Tea Category Classification Based on Feed-Forward Neural Network and Two-Dimensional Wavelet Entropy

Zhou, Xingxing; Zhang, Guangshuai; Dong, Zhengchao; Wang, Shuihua; Zhang, Yudong

doi:10.1007/978-3-319-32557-6_5

Cited by 9 publications

(2 citation statements)

References 25 publications

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“…Entropy is the uncertainty degree of information that Shannon quoted from thermodynamics. Entropy is a measure of disorder, which is used to represent the average value of information of probability distribution [ 21 – 25 ].…”

Section: Methodsmentioning

confidence: 99%

Gingivitis Classification via Wavelet Entropy and Support Vector Machine

2020

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Gingivitis is usually detected by a series of oral examinations. In this process, the dental record plays a very important role. However, it often takes a lot of physical and mental effort to accurately detect gingivitis in a large number of dental records. Therefore, it is of great significance to study the classification technology of gingivitis. In this study, a new gingivitis classification method based on wavelet entropy and support vector machine is proposed to help diagnose gingivitis. The feature of the image is extracted by wavelet entropy, and then the image is classified by support vector machine. The experimental results show that the average sensitivity, specificity, precision and accuracy of this method are 75.17%, 75.29%, 75.35% and 75.24% respectively, which are superior to the other three methods This method is proved to be effective in the classification of gingivitis.

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Section: Methodsmentioning

confidence: 99%

Gingivitis Classification via Wavelet Entropy and Support Vector Machine

2020

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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“…By applying wavelet entropy [5][6][7][8][9], we can extract a better expression of image signal from original image, which will be viewed as features of the image. As a result, extracted features will be the inputs of PSO-SVM.…”

Section: Introductionmentioning

confidence: 99%

Vehicle Identification Using Wavelet Entropy and Particle Swarm Optimization Support Vector Machine

Bao¹,

Nakamura²

2018

Proceedings of the 2018 3rd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 201

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Abstract:In order to identify Ford vehicles from non-Ford vehicles, this paper proposed a novel method based on the combination of wavelet entropy, particle swarm optimization, and support vector machine. We collect a 100-image dataset, 50 are Ford vehicles and the rest 50 are non-Ford vehicles. The results show that our method obtained a sensitivity of 82.20± 3.94%, a specificity of 81.60± 3.50%, and an accuracy of 81.90± 0.74%. In all, this method is promising in vehicle identification.

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Tea category identification based on optimal wavelet entropy and weighted k-Nearest Neighbors algorithm

Yang

Wang

2016

Multimed Tools Appl

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Tea Category Classification Based on Feed-Forward Neural Network and Two-Dimensional Wavelet Entropy

Cited by 9 publications

References 25 publications

Gingivitis Classification via Wavelet Entropy and Support Vector Machine

Gingivitis Classification via Wavelet Entropy and Support Vector Machine

Vehicle Identification Using Wavelet Entropy and Particle Swarm Optimization Support Vector Machine

Tea category identification based on optimal wavelet entropy and weighted k-Nearest Neighbors algorithm

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