Hearing Loss via Wavelet Entropy and Particle Swarm Optimized Trained Support Vector Machine

Bao, Fangzhou; Nakamura, Koji

doi:10.12783/dtetr/ecae2018/27724

Cited by 4 publications

(1 citation statement)

References 15 publications

(15 reference statements)

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“…Their earlier application of genetic algorithm for hearing loss detection system provides ideas for many subsequent researchers. Fang-zhou BAO (2018) et al [11] In the process of referring to previous studies, we found there are common problems in defecting hearing loss:…”

Section: Introductionmentioning

confidence: 68%

Hearing loss classification via stationary wavelet entropy and Biogeography-based optimization

Yao¹,

Tan²,

Sun³

2020

EAI Endorsed Transactions on e-Learning

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Introduction: Sensorineural hearing loss is associated with many complications and needs timely detection and diagnosis. Objectives: Optimize the sensorineural hearing loss detection system to improve the accuracies of image detection. Method: The stationary wavelet entropy was used to extract the features of NMR images, the single hidden layer neural network was used for classification, and the BBO algorithm was used for optimization to avoid the dilemma of local optimum. We used two-level SWE as input to the classifier to enhance the identify and classify ability of hearing loss. Results: The results of 10-fold cross validation show that the accuracies of HC, LHL and RHL are 91.83± 3.09%, 92.67±2.38% and 91.17±2.61%, respectively. The overall accuracy is 91.89±0.70%. Conclusion: This model has good performance in detecting hearing loss.

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Section: Introductionmentioning

confidence: 68%

Hearing loss classification via stationary wavelet entropy and Biogeography-based optimization

Yao¹,

Tan²,

Sun³

2020

EAI Endorsed Transactions on e-Learning

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Hearing Loss Identification via Fractional Fourier Entropy and Direct Acyclic Graph Support Vector Machine

Wang

2020

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

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With the risk of hearing loss being higher than before since the digital device is more popular, it becomes more urgent to identify the sensorineural hearing loss from the view of changes in internal brain structure. Based on 180 brain MRI of three categories of hearing loss balanced dataset, one schema with fractional Fourier transform entropy and direct acyclic graph support vector machine is proposed and applied to identify the features and predict the categories of hearing loss. The experiments prove this schema rather promising when the dataset is not large since the overall accuracy is up to 94.06 ± 1.08% which is higher than those of some previous methods in scope of traditional machine learning.

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Hearing loss classification via stationary wavelet entropy and cat swarm optimization

Yao

2022

Cognitive Systems and Signal Processing in Image Processing

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Hearing Loss via Wavelet Entropy and Particle Swarm Optimized Trained Support Vector Machine

Cited by 4 publications

References 15 publications

Hearing loss classification via stationary wavelet entropy and Biogeography-based optimization

Hearing loss classification via stationary wavelet entropy and Biogeography-based optimization

Hearing Loss Identification via Fractional Fourier Entropy and Direct Acyclic Graph Support Vector Machine

Hearing loss classification via stationary wavelet entropy and cat swarm optimization

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