Hearing loss detection by discrete wavelet transform and multi-layer perceptron trained by nature-inspired algorithms

Yang, Jingyuan; Govindaraj, Vishnuvarthanan; Yang, Ming; Wang, Shuihua

doi:10.1007/s11042-019-08344-z

Cited by 6 publications

(10 citation statements)

References 34 publications

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“…Step 3: Adjust the weights. If the actual output of the network differs significantly from the desired output, the connection weights parameter is adjusted, and the adjustment process relies on the perceptron learning algorithm automatically [ 19 ]. Step 4: Perform step 3 repeatedly until the difference between the actual and desired outputs of the network meets the predesigned requirements.…”

Section: Product Styling Design Evaluation Methods Based On Multilayer Perceptron Genetic Algorithm Neural Network Algorithmmentioning

confidence: 99%

A Product Styling Design Evaluation Method Based on Multilayer Perceptron Genetic Algorithm Neural Network Algorithm

Wu¹

2021

Computational Intelligence and Neuroscience

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Products no longer exist simply as carriers of useful functions, but more and more consumers are beginning to pay attention to the spiritual aspects of the feelings brought by products. This paper brings machine learning algorithms to the discipline of industrial design and proposes a method to evaluate the design of product shapes using a multilayer perceptron genetic algorithm neural network (GA-MLP-NN) algorithm, quantifying the product shape, using computer-aided design technology to achieve shape optimization, shape, and color scheme generation, and using interactive feedback with users to finally generate a product shape with market demand. In this paper, we use the combinatorial innovation method to arrange and combine the detail elements in the solution library to generate the modeling solution, combine the multilayer perceptron genetic algorithm neural network algorithm with product modeling, and establish the interactive genetic modeling system for the product, use this system to design the product modeling solution, and finally get the product modeling solution satisfied by the target users; using the multilayer perceptron genetic algorithm neural network method to evaluate the product modeling items. The mapping relationship model between morphological feature space and imagery cognitive space was constructed based on multiple linear regression equations, and the multiple regression model for each affective dimension was ideal. The results show that the model performance is reliable. The weights are calculated, and the appropriate people are selected to score and calculate the modeling scheme, and finally, the satisfactory product modeling scheme is obtained.

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Section: Product Styling Design Evaluation Methods Based On Multilayer Perceptron Genetic Algorithm Neural Network Algorithmmentioning

confidence: 99%

A Product Styling Design Evaluation Method Based on Multilayer Perceptron Genetic Algorithm Neural Network Algorithm

Wu¹

2021

Computational Intelligence and Neuroscience

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“…In this section, we propose the WE with four layers to extract features, which includes four steps: wavelet family and decomposition level selection, selection, discrete wavelet decomposition, and WE calculation. First, followed by paper [7], we choose the 4‐level db3 from the wavelet family. Next, we decompose the wavelet on preprocessed MRI images by discrete wavelet transform.…”

Section: Proposed Modelmentioning

confidence: 99%

“…In particular, MRI scans are efficiently employed to detect structural reason for HL, which not only diagnose congenital HL but also diagnose acquired diseases caused by an auto‐immune disease or bacterial infection [4]. However, manual detection of MRI scanning may be influenced by many subjective factors, which leads to a biased result [5–7]. Meanwhile, the manual one is time‐consuming and unpredictable.…”

Section: Introductionmentioning

confidence: 99%

“…Yang et al. [7] use the multi‐layer perceptron optimised by an improved artificial bee colony algorithm (IABC) for SNHL detection. Chen et al.…”

Section: Introductionmentioning

confidence: 99%

“…However, FNN trained by gradient‐based algorithms may cause over‐fitting, premature convergence, and slow convergence. To solve these limitations of FNN, in papers [6, 7, and 9], researchers use evolutionary algorithms to train FNN, respectively. Nevertheless, they overlook the optimization problem of the parameters of FNN as a non‐separable problem.…”

Section: Introductionmentioning

confidence: 99%

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APPSO‐NN: An adaptive‐probability particle swarm optimization neural network for sensorineural hearing loss detection

Yang

Zhang

2023

IET Biometrics

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As a hearing disorder, sensorineural hearing loss (SNHL) can be effectively detected by magnetic resonance imaging (MRI). However, the manual detection of MRI scanning is subjective, time-consuming, and unpredictable. An accurate and automatic computeraided diagnosis system is proposed for SNHL detection, providing reliable references for professionals. The system first employs a wavelet entropy layer to extract features of MRI images. Then, a neural network layer is proposed as the classifier consisting of a feedforward neural network (FNN) and an adaptive-probability PSO (APPSO) algorithm. The authors prove the rotation-variant property of the basic particle swarm optimization (PSO) by the algebraic property of matrix transformation. The property is unsuitable for optimising parameters of neural networks. Thus, in APPSO, the authors integrate the new update rules based on all-dimensional variation and adaptive-probability mechanism into the basic PSO, which can improve its searching ability without losing population diversity. The authors compare APPSO-NN with FNN trained by five popular evolutionary algorithms. The simulation results show that APPSO performs best in training FNN. The method also compares with six state-of-the-art methods. The simulation results show that the best performance in sensitivity and overall accuracy of hearing loss classification, which proves that the method is effective and promising for SNHL detection.

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SLICACO: An automated novel hybrid approach for dermatoscopic melanocytic skin lesion segmentation

Singh

Janghel

Sahu

2021

Int J Imaging Syst Tech

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Low contrast images and blurriness pose challenge in the over‐segmentation of image, which increases model complexities. In this work, a novel hybrid dermoscopic skin‐lesion segmentation method, namely SLICACO, is proposed incorporating the simple linear iterative clustering (SLIC) and ant colony optimization (ACO) algorithms. The working of proposed method is multifold. First, over‐segmentation of preprocessed image is generated using SLIC super‐pixel technique. Second, clusters of super‐pixels generated by SLIC are used by ACO with the pixels of similar intensity for edge detection and seek for the optimum pathway in a strained zone. Third, lesion area is segmented using the Convex Hull and Thresholding. Fourth, Erosion Filtering is used to obtain the final segmented image. The performance of SLICACO is assessed on five benchmark dermatoscopic datasets and compared with deep learning models to test its generalizing behavior. Promising results are obtained on the PH2 archive data set with an accuracy of 95.9%.

show abstract

Hearing loss detection by discrete wavelet transform and multi-layer perceptron trained by nature-inspired algorithms

Cited by 6 publications

References 34 publications

A Product Styling Design Evaluation Method Based on Multilayer Perceptron Genetic Algorithm Neural Network Algorithm

A Product Styling Design Evaluation Method Based on Multilayer Perceptron Genetic Algorithm Neural Network Algorithm

APPSO‐NN: An adaptive‐probability particle swarm optimization neural network for sensorineural hearing loss detection

SLICACO: An automated novel hybrid approach for dermatoscopic melanocytic skin lesion segmentation

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