Wavelet extreme learning machine and deep learning for data classification

Yahia, Siwar; Said, Salwa; Zaied, Mourad

doi:10.1016/j.neucom.2020.04.158

Cited by 37 publications

(10 citation statements)

References 44 publications

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“…However, due to the characteristics of massive and fast data streams emerging in the practical application field, it is difficult to obtain them all at once. At the same time, when new data arrives, these batch-processing algorithms continue to retrain new data and discard old models, resulting in a large loss of effective historical data [ 21 ]. Therefore, learning models that can handle data stream environments are receiving more and more attention.…”

Section: Related Workmentioning

confidence: 99%

Towards an Optimal KELM Using the PSO-BOA Optimization Strategy with Applications in Data Classification

et al. 2023

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The features of the kernel extreme learning machine—efficient processing, improved performance, and less human parameter setting—have allowed it to be effectively used to batch multi-label classification tasks. These classic classification algorithms must at present contend with accuracy and space–time issues as a result of the vast and quick, multi-label, and concept drift features of the developing data streams in the practical application sector. The KELM training procedure still has a difficulty in that it has to be repeated numerous times independently in order to maximize the model’s generalization performance or the number of nodes in the hidden layer. In this paper, a kernel extreme learning machine multi-label data classification method based on the butterfly algorithm optimized by particle swarm optimization is proposed. The proposed algorithm, which fully accounts for the optimization of the model generalization ability and the number of hidden layer nodes, can train multiple KELM hidden layer networks at once while maintaining the algorithm’s current time complexity and avoiding a significant number of repeated calculations. The simulation results demonstrate that, in comparison to the PSO-KELM, BBA-KELM, and BOA-KELM algorithms, the PSOBOA-KELM algorithm proposed in this paper can more effectively search the kernel extreme learning machine parameters and more effectively balance the global and local performance, resulting in a KELM prediction model with a higher prediction accuracy.

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Section: Related Workmentioning

confidence: 99%

Towards an Optimal KELM Using the PSO-BOA Optimization Strategy with Applications in Data Classification

et al. 2023

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“…These models generally consist of deep artificial neural networks, convolutional neural networks, artificial neural networks and other models. The open-source image processing models using these networks are models such as googleNet, VGG16, VGG19, ResNet and Inception [32,33]. Another difficulty seen in the studies in the literature is related to the image data.…”

Section: Literature Reviewmentioning

confidence: 99%

Deep Learning Based Convolutional Neural Network Structured New Image Classification Approach for Eye Disease Identification

Topaloğlu

2022

Scientia Iranica

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A deep learning-based convolutional artificial neural networks structured a new image classification method approach was implemented in the study. Sample application was carried out with Diabetic Retinopathy disease. Obtaining information about the blood vessels and any abnormal patterns from the rest of the phonoscopic image and assessing the degree of retinopathy is the problem itself. To solve this problem developed methodology and algorithmic structure of this new approach is presented in the study. An approach called care model was used in this study different from the classical CNN structure. The care approach is based on the idea that the best solution will be taken from the new data obtained by rescale the available data according to total number of pixels before the average data pool is created and then CNN processes will continue. In the care model approach, all data is multiplied by the number of elements by the number of epoch time eight tensors. The purposed care model includes VGG19 image classification model and developed mathematical model presented. Pre-trained model and all image dataset taken from kaggle and keras for implementation of case study. The purposed model provides train accuracy 87%, test accuracy 88%, precision 93% and recall 83%.

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“…Recently, the ELM has attracted considerable attention from researchers because of its high generalization performance and remarkably fast learning rate compared with traditional methods. The minimal requirement for human intervention is another advantage of the ELM approach, where most parameters can be randomly generated (Yahia et al, 2021). In particular, the ELM can adaptively determine the number of nodes in the hidden layer, randomly assign the input weights and hidden layer biases using an activation function, and obtain output layer weights through the least squares method; these abilities appreciably enhance the learning speed and generalization ability (Ding et al, 2015).…”

Section: Extreme Learning Machinementioning

confidence: 99%

Application of Machine Learning Algorithms for Geogenic Radon Potential Mapping in Danyang-Gun, South Korea

Rezaie

Kim

Alizadeh

et al. 2021

Front. Environ. Sci.

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Continuous generation of radon gas by soil and rocks rich in components of the uranium chain, along with prolonged inhalation of radon progeny in enclosed spaces, can lead to severe respiratory diseases. Detection of radon-prone areas and acquisition of detailed knowledge regarding relationships between indoor radon variations and geogenic factors can facilitate the implementation of more appropriate radon mitigation strategies in high-risk residential zones. In the present study, 10 factors (i.e., lithology; fault density; mean soil calcium oxide [CaO], copper [Cu], lead [Pb], and ferric oxide [Fe2O3] concentrations; elevation; slope; valley depth; and the topographic wetness index [TWI]) were selected to map radon potential areas based on measurements of indoor radon levels in 1,452 dwellings. Mapping was performed using three machine learning methods: long short-term memory (LSTM), extreme learning machine (ELM), and random vector functional link (RVFL). The results were validated in terms of the area under the receiver operating characteristic curve (AUROC), root mean square error (RMSE), and standard deviation (StD). The prediction abilities of all models were satisfactory; however, the ELM model had the best performance, with AUROC, RMSE, and StD values of 0.824, 0.209, and 0.207, respectively. Moreover, approximately 40% of the study area was covered by very high and high-risk radon potential zones that mainly included populated areas in Danyang-gun, South Korea. Therefore, the map can be used to establish more appropriate construction regulations in radon-priority areas, and identify more cost-effective remedial actions for existing buildings, thus reducing indoor radon levels and, by extension, radon exposure-associated effects on human health.

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Wavelet extreme learning machine and deep learning for data classification

Cited by 37 publications

References 44 publications

Towards an Optimal KELM Using the PSO-BOA Optimization Strategy with Applications in Data Classification

Towards an Optimal KELM Using the PSO-BOA Optimization Strategy with Applications in Data Classification

Deep Learning Based Convolutional Neural Network Structured New Image Classification Approach for Eye Disease Identification

Application of Machine Learning Algorithms for Geogenic Radon Potential Mapping in Danyang-Gun, South Korea

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