Multilayer extreme learning machine: a systematic review

Kaur, Ravneet; Roul, Rajendra Kumar; Batra, Shalini

doi:10.1007/s11042-023-14634-4

Cited by 9 publications

(5 citation statements)

References 142 publications

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“…Accuracy (%) Support vector regression (SVR) [29] 89.2924 Random forest (RF) [30] 84.5236 Extreme learning machine (ELM) [31] 84.1132 General regression neural network (GRNN) [4] 74.4805 Proposed technique 91.9802…”

Section: Methodsmentioning

confidence: 99%

“…To verify the proposed ensemble learning strategy in the application of BGL detection, we have drawn a comparison with other classical machine learning methods in the field of few-shot learning. The same dataset (300 samples based on img1) is used to train and test these methods, such as SVR, RF, extreme learning machine (ELM) [31], and general regression…”

Section: Bgl Estimation With the Bgl Approach Of Ensemble Learningmentioning

confidence: 99%

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Accurate blood glucose level monitoring using microwave imaging

Wang,

Xiao,

Pang

et al. 2024

Meas. Sci. Technol.

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Painless, and non-invasive detection techniques are needed to replace finger-pick blood for diabetic. In this paper, a first-of-its-kind, noninvasive, and continuous blood glucose level (BGL) detection method based on microwave imaging is introduced. This method avoids the complex task of frequency choice for the design of electromagnetic sensor. A radar-based microwave imaging technology combined with improved very-deep super resolution (VDSR-BL) method is introduced to obtain high-resolution (HR) microwave images. After image super-resolution reconstruction by VDSR-BL, the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) of HR images reach 35.4461 dB and 0.9761, respectively. Then an ensemble learning strategy based on support vector regression (SVR) and random forest algorithms is proposed to identify HR microwave images for BGL estimation. The developed detection system has been verified on the medium under tests (MUT) with different glucose solution. The final detection results obtain the root mean squared error (RMSE) of 0.1394 mg/mL and the mean absolute relative difference (MARD) of 8.02%, which show well accuracy with clinical acceptance. Meanwhile, we also conducted human trials. The high correlation coefficient (R) of 0.9254 was achieved between the results of microwave imaging and invasive BGL. Together, these results show that microwave imaging offers a promising new approach for noninvasive BGL monitoring.

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Section: Methodsmentioning

confidence: 99%

Section: Bgl Estimation With the Bgl Approach Of Ensemble Learningmentioning

confidence: 99%

Accurate blood glucose level monitoring using microwave imaging

Wang,

Xiao,

Pang

et al. 2024

Meas. Sci. Technol.

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“…These highlight the need to investigate further the number of neurons in the hidden layer in future studies. Recently, two reviews of multilayer ELM neural networks [13,14] have been presented, highlighting the importance of implementing parallel and distributed computing to address big data problems with this variant. Finally, Patil and Sharma [12] provide a review of theories, algorithms, and applications of ELM, while Huérfano-Maldonado et al [11] present a comprehensive review of medical image processing with ELM.…”

Section: Elm Based On Metaheuristicsmentioning

confidence: 99%

A Review on Large-Scale Data Processing with Parallel and Distributed Randomized Extreme Learning Machine Neural Networks

Gelvez-Almeida,

Mora,

Barrientos

et al. 2024

MCA

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The randomization-based feedforward neural network has raised great interest in the scientific community due to its simplicity, training speed, and accuracy comparable to traditional learning algorithms. The basic algorithm consists of randomly determining the weights and biases of the hidden layer and analytically calculating the weights of the output layer by solving a linear overdetermined system using the Moore–Penrose generalized inverse. When processing large volumes of data, randomization-based feedforward neural network models consume large amounts of memory and drastically increase training time. To efficiently solve the above problems, parallel and distributed models have recently been proposed. Previous reviews of randomization-based feedforward neural network models have mainly focused on categorizing and describing the evolution of the algorithms presented in the literature. The main contribution of this paper is to approach the topic from the perspective of the handling of large volumes of data. In this sense, we present a current and extensive review of the parallel and distributed models of randomized feedforward neural networks, focusing on extreme learning machine. In particular, we review the mathematical foundations (Moore–Penrose generalized inverse and solution of linear systems using parallel and distributed methods) and hardware and software technologies considered in current implementations.

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“…Unlike traditional neural network training methods which iteratively adjust all parameters, ELM uniquely fixes the hidden layer parameters and only optimizes the output weights, leading to significantly faster training times (Huang et al, 2019). ELM aims to address the limitations of traditional neural networks, such as slow convergence and local minima entrapment (Kaur et al, 2023). The core principle behind ELM is the random assignment of input weights and biases, followed by the determination of output weights through a straightforward linear optimization process (Deng et al, 2015).…”

Section: Extreme Learning Machine (Elm)mentioning

confidence: 99%

Maximizing hydropower station safety against earthquake through extreme learning machine-enabled shear waves velocity prediction

Song,

Guan,

Wang

et al. 2024

Front. Environ. Sci.

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Hydropower stations are important infrastructures for generating clean energy. However, they are vulnerable to natural disasters such as earthquakes, which can cause severe damage and even lead to catastrophic failures. Therefore, it is essential to develop effective strategies for maximizing hydropower station safety against earthquakes. To evaluate the potential shear rate of surrounding rock layers, the shear wave velocity (Vs) parameter can be used as a useful tool. This parameter helps to determine the velocity at which shear waves travel through the rock layers, which can indicate their stability and susceptibility to earthquakes. This study will investigate the significance of the Vs parameter in evaluating the potential shear rate of rock layers surrounding hydropower stations and how it can be used to ensure their safety and efficiency in earthquake-prone regions. Furthermore, a novel approach is proposed in this research, which involves using extreme learning machine (ELM) technology to predict Vs and enhance the seismic safety of hydropower stations. The ELM model predicts the Vs of the soil layers around the hydropower station, a crucial factor in determining the structure’s seismic response. The predicted Vs is then used to assess seismic hazard and design appropriate safety measures. The ML-ELM model outperformed both the ELM and empirical models, with an RMSE of 0.0432 μs/ft and R2 of 0.9954, as well as fewer outlier data predictions. This approach shows promise for predicting Vs in similar environments, and future research could explore its effectiveness in other datasets and practical applications.

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Multilayer extreme learning machine: a systematic review

Cited by 9 publications

References 142 publications

Accurate blood glucose level monitoring using microwave imaging

Accurate blood glucose level monitoring using microwave imaging

A Review on Large-Scale Data Processing with Parallel and Distributed Randomized Extreme Learning Machine Neural Networks

Maximizing hydropower station safety against earthquake through extreme learning machine-enabled shear waves velocity prediction

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