Extreme Learning Machine Denoising Algorithm-Based Transvaginal Three-dimensional Ultrasound Imaging on the Diagnosis Efficiency of Intrauterine Adhesions

The study was intended to eliminate the noise in three-dimensional transvaginal ultrasound (3D-TVS) images and improve the diagnostic accuracy in intrauterine adhesion (IUA). The extreme learning machine (ELM) algorithm was introduced first for statement. One hundred and thirty cases of suspected IUA patients were taken as the research subjects. The denoising effects of ELM algorithm were evaluated in terms of mean square errors (MSE), peak signal-to-noise ratio (PSNR), and running time, and its diagnostic efficiency of IUA was identified from precise, specificity, and sensitivity. Furthermore, the support vector machine (SVM) algorithm was introduced for comparison. It was found that the MSE and PSNR of the ELM algorithm were 0.0021 and 64.5, respectively, and its average operation time was 11.22 ± 0.89s, that the MSE values of SVM algorithm and ELM algorithm were 0.0045 and 0.0021 and the PSNR values were 52.3 and 64.5, respectively, and that the average running time of SVM algorithm was 16.35 ± 1.33s, and the average running time of ELM algorithm was 11.22 ± 0.89s, superior to the SVM algorithm in denoising effects. Moreover, the ELM algorithm showed excellent diagnostic efficiency for patients with various degrees of IUA. In conclusion, ELM can effectively eliminate noise in 3D-TVS images and demonstrates excellent diagnostic efficiency on IUA, which is worthy of clinical application.

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An efficient computational method for predicting drug-target interactions using weighted extreme learning machine and speed up robot features

BioData Mining ◽

10.1186/s13040-021-00242-1 ◽

2021 ◽

Vol 14 (1) ◽

Author(s):

Ji-Yong An ◽

Fan-Rong Meng ◽

Zi-Ji Yan

Keyword(s):

Ion Channel ◽

Extreme Learning Machine ◽

Nuclear Receptor ◽

Drug Target ◽

Computational Method ◽

Evolutionary Information ◽

Support Vector ◽

Weighted Extreme Learning Machine ◽

Speed Up ◽

Learning Machine

Abstract Background Prediction of novel Drug–Target interactions (DTIs) plays an important role in discovering new drug candidates and finding new proteins to target. In consideration of the time-consuming and expensive of experimental methods. Therefore, it is a challenging task that how to develop efficient computational approaches for the accurate predicting potential associations between drug and target. Results In the paper, we proposed a novel computational method called WELM-SURF based on drug fingerprints and protein evolutionary information for identifying DTIs. More specifically, for exploiting protein sequence feature, Position Specific Scoring Matrix (PSSM) is applied to capturing protein evolutionary information and Speed up robot features (SURF) is employed to extract sequence key feature from PSSM. For drug fingerprints, the chemical structure of molecular substructure fingerprints was used to represent drug as feature vector. Take account of the advantage that the Weighted Extreme Learning Machine (WELM) has short training time, good generalization ability, and most importantly ability to efficiently execute classification by optimizing the loss function of weight matrix. Therefore, the WELM classifier is used to carry out classification based on extracted features for predicting DTIs. The performance of the WELM-SURF model was evaluated by experimental validations on enzyme, ion channel, GPCRs and nuclear receptor datasets by using fivefold cross-validation test. The WELM-SURF obtained average accuracies of 93.54, 90.58, 85.43 and 77.45% on enzyme, ion channels, GPCRs and nuclear receptor dataset respectively. We also compared our performance with the Extreme Learning Machine (ELM), the state-of-the-art Support Vector Machine (SVM) on enzyme and ion channels dataset and other exiting methods on four datasets. By comparing with experimental results, the performance of WELM-SURF is significantly better than that of ELM, SVM and other previous methods in the domain. Conclusion The results demonstrated that the proposed WELM-SURF model is competent for predicting DTIs with high accuracy and robustness. It is anticipated that the WELM-SURF method is a useful computational tool to facilitate widely bioinformatics studies related to DTIs prediction.

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Three-Dimensional Site Characterization Model of Bangalore Using Support Vector Machine

ISRN Soil Science ◽

10.5402/2012/346439 ◽

2012 ◽

Vol 2012 ◽

pp. 1-10

Author(s):

Pijush Samui

Keyword(s):

Support Vector Machine ◽

Three Dimensional ◽

Site Characterization ◽

Standard Penetration Test ◽

Support Vector ◽

Characterization Model ◽

Svm Model ◽

Input Variables ◽

Learning Machine ◽

A Site

The main objective of site characterization is the prediction of in situ soil properties at any half-space point at a site based on limited tests. In this study, the Support Vector Machine (SVM) has been used to develop a three dimensional site characterization model for Bangalore, India based on large amount of Standard Penetration Test. SVM is a novel type of learning machine based on statistical learning theory, uses regression technique by introducing ε-insensitive loss function. The database consists of 766 boreholes, with more than 2700 field SPT values () spread over 220 sq km area of Bangalore. The model is applied for corrected () values. The three input variables (, , and , where , , and are the coordinates of the Bangalore) were used for the SVM model. The output of SVM was the data. The results presented in this paper clearly highlight that the SVM is a robust tool for site characterization. In this study, a sensitivity analysis of SVM parameters (σ, , and ε) has been also presented.

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Pressure Prediction of Coal Slurry Transportation Pipeline Based on Particle Swarm Optimization Kernel Function Extreme Learning Machine

Mathematical Problems in Engineering ◽

10.1155/2015/542182 ◽

2015 ◽

Vol 2015 ◽

pp. 1-7

Author(s):

Xue-cun Yang ◽

Xiao-ru Yan ◽

Chun-feng Song

Keyword(s):

Support Vector Machine ◽

Prediction Model ◽

Extreme Learning Machine ◽

Kernel Function ◽

Particle Swarm ◽

Support Vector ◽

Particle Swarm Algorithm ◽

Coal Slurry ◽

Swarm Optimization ◽

Learning Machine

For coal slurry pipeline blockage prediction problem, through the analysis of actual scene, it is determined that the pressure prediction from each measuring point is the premise of pipeline blockage prediction. Kernel function of support vector machine is introduced into extreme learning machine, the parameters are optimized by particle swarm algorithm, and blockage prediction method based on particle swarm optimization kernel function extreme learning machine (PSOKELM) is put forward. The actual test data from HuangLing coal gangue power plant are used for simulation experiments and compared with support vector machine prediction model optimized by particle swarm algorithm (PSOSVM) and kernel function extreme learning machine prediction model (KELM). The results prove that mean square error (MSE) for the prediction model based on PSOKELM is 0.0038 and the correlation coefficient is 0.9955, which is superior to prediction model based on PSOSVM in speed and accuracy and superior to KELM prediction model in accuracy.

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Multi-Classification of Fetal Health Status Using Extreme Learning Machine

ICONTECH INTERNATIONAL JOURNAL ◽

10.46291/icontechvol5iss2pp62-70 ◽

2021 ◽

Vol 5 (2) ◽

pp. 62-70

Author(s):

Ömer KASIM

Keyword(s):

Extreme Learning Machine ◽

Classification Accuracy ◽

Binary Classification ◽

Clinical Decision ◽

Support Vector ◽

Data Set ◽

Multiple Classification ◽

Learning Machine ◽

Multi Class Classification ◽

The University

Cardiotocography (CTG) is used for monitoring the fetal heart rate signals during pregnancy. Evaluation of these signals by specialists provides information about fetal status. When a clinical decision support system is introduced with a system that can automatically classify these signals, it is more sensitive for experts to examine CTG data. In this study, CTG data were analysed with the Extreme Learning Machine (ELM) algorithm and these data were classified as normal, suspicious and pathological as well as benign and malicious. The proposed method is validated with the University of California International CTG data set. The performance of the proposed method is evaluated with accuracy, f1 score, Cohen kappa, precision, and recall metrics. As a result of the experiments, binary classification accuracy was obtained as 99.29%. There was only 1 false positive. When multi-class classification was performed, the accuracy was obtained as 98.12%. The amount of false positives was found as 2. The processing time of the training and testing of the ELM algorithm were quite minimized in terms of data processing compared to the support vector machine and multi-layer perceptron. This result proved that a high classification accuracy was obtained by analysing the CTG data both binary and multiple classification.

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Risk Stratification with Extreme Learning Machine: A Retrospective Study on Emergency Department Patients

Mathematical Problems in Engineering ◽

10.1155/2014/248938 ◽

2014 ◽

Vol 2014 ◽

pp. 1-6 ◽

Cited By ~ 3

Author(s):

Nan Liu ◽

Jiuwen Cao ◽

Zhi Xiong Koh ◽

Pin Pin Pek ◽

Marcus Eng Hock Ong

Keyword(s):

Emergency Department ◽

Risk Stratification ◽

Extreme Learning Machine ◽

Tertiary Hospital ◽

Support Vector ◽

Characteristic Analysis ◽

Scoring Method ◽

Stratification Method ◽

Emergency Department Patients ◽

Learning Machine

This paper presents a novel risk stratification method using extreme learning machine (ELM). ELM was integrated into a scoring system to identify the risk of cardiac arrest in emergency department (ED) patients. The experiments were conducted on a cohort of 1025 critically ill patients presented to the ED of a tertiary hospital. ELM and voting based ELM (V-ELM) were evaluated. To enhance the prediction performance, we proposed a selective V-ELM (SV-ELM) algorithm. The results showed that ELM based scoring methods outperformed support vector machine (SVM) based scoring method in the receiver operation characteristic analysis.

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Short-Term Traffic Flow Forecasting Based on Data-Driven Model

Mathematics ◽

10.3390/math8020152 ◽

2020 ◽

Vol 8 (2) ◽

pp. 152 ◽

Cited By ~ 2

Author(s):

Su-qi Zhang ◽

Kuo-Ping Lin

Keyword(s):

Support Vector Machine ◽

Traffic Flow ◽

Extreme Learning Machine ◽

Support Vector ◽

Local Optimum ◽

Short Term ◽

Swarm Optimization ◽

Traffic Flow Forecasting ◽

Learning Machine ◽

The One

Short-term traffic flow forecasting is the technical basis of the intelligent transportation system (ITS). Higher precision, short-term traffic flow forecasting plays an important role in alleviating road congestion and improving traffic management efficiency. In order to improve the accuracy of short-term traffic flow forecasting, an improved bird swarm optimizer (IBSA) is used to optimize the random parameters of the extreme learning machine (ELM). In addition, the improved bird swarm optimization extreme learning machine (IBSAELM) model is established to predict short-term traffic flow. The main researches in this paper are as follows: (1) The bird swarm optimizer (BSA) is prone to fall into the local optimum, so the distribution mechanism of the BSA optimizer is improved. The first five percent of the particles with better fitness values are selected as producers. The last ten percent of the particles with worse fitness values are selected as beggars. (2) The one-day and two-day traffic flows are predicted by the support vector machine (SVM), particle swarm optimization support vector machine (PSOSVM), bird swarm optimization extreme learning machine (BSAELM) and IBSAELM models, respectively. (3) The prediction results of the models are evaluated. For the one-day traffic flow sequence, the mean absolute percentage error (MAPE) values of the IBSAELM model are smaller than the SVM, PSOSVM and BSAELM models, respectively. The experimental analysis results show that the IBSAELM model proposed in this study can meet the actual engineering requirements.

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