A novel feature descriptor based coral image classification using extreme learning machine with ameliorated chimp optimization algorithm

Annalakshmi, G.; Murugan, S. Sakthivel

doi:10.1016/j.ecoinf.2021.101527

Cited by 15 publications

(6 citation statements)

References 45 publications

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“…Extreme learning machines are a type of machine learning algorithm that has been used for various multimedia applications, including signal coding. In signal coding, ELMs can be trained to learn compressed representations of multimedia signals such as images or video [22,23]. In this work, the ELM-enabled coding unit partitioning algorithm is proposed to reduce the computational complexity of VVC inter-prediction.…”

Section: Extreme Learning Machinementioning

confidence: 99%

Extreme Learning Machine-Enabled Coding Unit Partitioning Algorithm for Versatile Video Coding

Jiang,

Xiang,

Jin

et al. 2023

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The versatile video coding (VVC) standard offers improved coding efficiency compared to the high efficiency video coding (HEVC) standard in multimedia signal coding. However, this increased efficiency comes at the cost of increased coding complexity. This work proposes an efficient coding unit partitioning algorithm based on an extreme learning machine (ELM), which can reduce the coding complexity while ensuring coding efficiency. Firstly, the coding unit size decision is modeled as a classification problem. Secondly, an ELM classifier is trained to predict the coding unit size. In the experiment, the proposed approach is verified based on the VVC reference model. The results show that the proposed method can reduce coding complexity significantly, and good image quality can be obtained.

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Section: Extreme Learning Machinementioning

confidence: 99%

Extreme Learning Machine-Enabled Coding Unit Partitioning Algorithm for Versatile Video Coding

Jiang,

Xiang,

Jin

et al. 2023

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“…Feedforward neural network is one of the most widely used neural networks today. It consists of input layer, hidden layer and output layer [30]. Each layer consists of several neurons, each neuron receives the output of the previous layer as input, and then generates output to the next layer [31], which belongs to one-way propagation [32].…”

Section: Extreme Learning Machinementioning

confidence: 99%

COVID-19 Diagnosis by Wavelet Entropy and Extreme Learning Machine

Han

Wang

2022

EAI Endorsed Trans e-Learn

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In recent years, COVID-19 has spread rapidly among humans. Chest CT is an effective means of diagnosing COVID-19. However, the diagnosis of CT images still depends on the doctor's visual judgment and medical experience. This takes a certain amount of time and may lead to misjudgment. In this paper, a new algorithm for automatic diagnosis of COVID-19 based on chest CT image data was proposed. The algorithm comprehensively uses WE to extract image features, uses ELM for training, and finally passes k-fold CV validation. After evaluating and detecting performance on 296 chest CT images, our proposed method is superior to state-of-the-art approaches in terms of sensitivity, specificity, precision, accuracy, F1, MCC and FMI.

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“…Consequently, the ELM has significant advantages over traditional neural networks and support vector machines and is characterized by swift learning, straightforward implementation, and minimal human intervention. Notably, ELMs have garnered widespread application owing to their exceptional generalization performance and operational efficiency 24 . Numerous enhanced ELM algorithms have been presented along with an expansion in the range of supervised 25 , semisupervised 26 , and unsupervised learning 27 methods that can be used to execute the standard ELM 28 .…”

Section: Introductionmentioning

confidence: 99%

Integrated metaheuristic algorithms with extreme learning machine models for river streamflow prediction

Van Thieu,

Nguyen,

Sherif

et al. 2024

Sci Rep

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Accurate river streamflow prediction is pivotal for effective resource planning and flood risk management. Traditional river streamflow forecasting models encounter challenges such as nonlinearity, stochastic behavior, and convergence reliability. To overcome these, we introduce novel hybrid models that combine extreme learning machines (ELM) with cutting-edge mathematical inspired metaheuristic optimization algorithms, including Pareto-like sequential sampling (PSS), weighted mean of vectors (INFO), and the Runge–Kutta optimizer (RUN). Our comparative assessment includes 20 hybrid models across eight metaheuristic categories, using streamflow data from the Aswan High Dam on the Nile River. Our findings highlight the superior performance of mathematically based models, which demonstrate enhanced predictive accuracy, robust convergence, and sustained stability. Specifically, the PSS-ELM model achieves superior performance with a root mean square error of 2.0667, a Pearson’s correlation index (R) of 0.9374, and a Nash–Sutcliffe efficiency (NSE) of 0.8642. Additionally, INFO-ELM and RUN-ELM models exhibit robust convergence with mean absolute percentage errors of 15.21% and 15.28% respectively, a mean absolute errors of 1.2145 and 1.2105, and high Kling-Gupta efficiencies values of 0.9113 and 0.9124, respectively. These findings suggest that the adoption of our proposed models significantly enhances water management strategies and reduces any risks.

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A novel feature descriptor based coral image classification using extreme learning machine with ameliorated chimp optimization algorithm

Cited by 15 publications

References 45 publications

Extreme Learning Machine-Enabled Coding Unit Partitioning Algorithm for Versatile Video Coding

Extreme Learning Machine-Enabled Coding Unit Partitioning Algorithm for Versatile Video Coding

COVID-19 Diagnosis by Wavelet Entropy and Extreme Learning Machine

Integrated metaheuristic algorithms with extreme learning machine models for river streamflow prediction

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