Deep and wide feature based extreme learning machine for image classification

Qing, Yuanyuan; Zeng, Yijie; Li, Yue; Huang, Guang-Bin

doi:10.1016/j.neucom.2020.06.110

Cited by 31 publications

(24 citation statements)

References 27 publications

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“…ELM is a feedforward neural network which is much faster in training. It can generate input weights randomly and directly find the optimal solution of the output weight matrix without iterative optimization [39].…”

Section: Classifiermentioning

confidence: 99%

Prediction of Epilepsy Based on Tensor Decomposition and Functional Brain Network

et al. 2021

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Epilepsy is a chronic neurological disorder which can affect 65 million patients worldwide. Recently, network based analyses have been of great help in the investigation of seizures. Now graph theory is commonly applied to analyze functional brain networks, but functional brain networks are dynamic. Methods based on graph theory find it difficult to reflect the dynamic changes of functional brain network. In this paper, an approach to extracting features from brain functional networks is presented. Dynamic functional brain networks can be obtained by stacking multiple functional brain networks on the time axis. Then, a tensor decomposition method is used to extract features, and an ELM classifier is introduced to complete epilepsy prediction. In the prediction of epilepsy, the accuracy and F1 score of the feature extracted by tensor decomposition are higher than the degree and clustering coefficient. The features extracted from the dynamic functional brain network by tensor decomposition show better and more comprehensive performance than degree and clustering coefficient in epilepsy prediction.

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Section: Classifiermentioning

confidence: 99%

Prediction of Epilepsy Based on Tensor Decomposition and Functional Brain Network

et al. 2021

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“…The convolution operation is used to extract features [27,28]. The network used all 3 × 3 convolution kernels for convolution.…”

Section: Green Plum Defect Detection Network Structurementioning

confidence: 99%

“…The local minimum generated at the end of each learning rate cycle tends to accumulate in the edge area of the loss surface. The loss value on these The convolution operation is used to extract features [27,28]. The network used all 3 × 3 convolution kernels for convolution.…”

Section: Swa Optimizermentioning

confidence: 99%

Defect Classification of Green Plums Based on Deep Learning

Zhou

Zhuang

Liu

et al. 2020

Sensors

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The green plum is rich in amino acids, lipids, inorganic salts, vitamins, and trace elements. It has high nutritional value and medicinal value and is very popular among Chinese people. However, green plums are susceptible to collisions and pests during growth, picking, storage, and transportation, causing surface defects, affecting the quality of green plums and their products and reducing their economic value. In China, defect detection and grading of green plum products are still performed manually. Traditional manual classification has low accuracy and high cost, which is far from meeting the production needs of green plum products. In order to improve the economic value of green plums and their products and improve the automation and intelligence level of the product production process, this study adopted deep learning methods based on a convolutional neural network and cost-effective computer vision technology to achieve efficient classification of green plum defects. First, a camera and LEDs were used to collect 1240 green plum images of RGB, and the green plum experimental classification standard was formulated and divided into five categories, namely, rot, spot, scar, crack, and normal. Images were randomly divided into a training set and test set, and the number of images of the training set was expanded. Then, the stochastic weight averaging (SWA) optimizer and w-softmax loss function were used to improve the VGG network, which was trained and tested to generate a green plum defect detection network model. The average recognition accuracy of green plum defects was 93.8%, the test time for each picture was 84.69 ms, the recognition rate of decay defect was 99.25%, and the recognition rate of normal green plum was 95.65%. The results were compared with the source VGG network, resnet18 network, and green lemon network. The results show that for the classification of green plum defects, the recognition accuracy of the green plum defect detection network increased by 9.8% and 16.6%, and the test speed is increased by 1.87 and 6.21 ms, respectively, which has certain advantages.

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“…At present, fault diagnosis methods of motor bearing include artificial neural network, support vector machine, and extreme learning machine. [1][2][3], among which extreme learning machine [4][5][6][7][8][9] has a wide application in classification and prediction fields due to its advantages of fast network training speed and good generalization performance, and extreme learning machine is a promising fault diagnosis method of motor bearing. However, it is necessary to set the number of hidden layer nodes in the training process of extreme learning machine.…”

Section: Introductionmentioning

confidence: 99%

A Novel Fault Diagnosis Method for Motor Bearing Based on DTCWT and AFSO‐KELM

2021

Shock and Vibration

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Aiming at the defects of wavelet transform-based feature extraction and extreme learning machine-based classification, a novel fault diagnosis method for motor bearing, based on dual tree complex wavelet transform and artificial fish swarm optimization-kernel extreme learning machine (DTCWT-AFSO-KELM), is proposed in this paper. Firstly, the dual tree complex wavelet transform instead of the discrete wavelet transform is used to decompose the motor bearing signal; then, the features with large differentiation of motor-bearing fault are extracted; finally, the states of motor bearing are classified by using artificial fish swarm optimization-kernel extreme learning machine. In order to better prove the superiority of this method, four kinds of state data of motor bearing under the conditions of 0 HP (horsepower) load, 1 HP load, 2 HP load, and 3 HP load are used to test. The experimental results indicate that the diagnosis accuracies of DTCWT-AFSO-KELM are obviously better than those of discrete wavelet transform and artificial fish swarm optimization-kernel extreme learning machine (DWT-AFSO-KELM) or discrete wavelet transform and extreme learning machine (DWT-ELM) under different loads.

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Deep and wide feature based extreme learning machine for image classification

Cited by 31 publications

References 27 publications

Prediction of Epilepsy Based on Tensor Decomposition and Functional Brain Network

Prediction of Epilepsy Based on Tensor Decomposition and Functional Brain Network

Defect Classification of Green Plums Based on Deep Learning

A Novel Fault Diagnosis Method for Motor Bearing Based on DTCWT and AFSO‐KELM

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