scholarly journals Optimized visual stimuli for BCI with hessenberg decomposition based extreme learning machine

2019 ◽  
Author(s):  
Apdullah Yayık ◽  
Yakup Kutlu ◽  
Gökhan Altan
Keyword(s):  
Machine Learning ◽  
Extreme Learning Machine ◽  
Performance Measures ◽  
Visual Stimulus ◽  
Decomposition Method ◽  
Visual Stimuli ◽  
Human Beings ◽  
Related Potentials ◽  
Learning Machine

AbstractBackground and ObjectivesBrain-computer interfaces (BCIs) aim to provide neuroscientific communication platform for human-beings, in particular locked-in patients. In most cases event-related potentials (ERPs), averaged voltage responses to a specific target stimuli over time, have key roles in designing BCIs. With this reason, for the last several decades BCI researchers heavily have focused on signal processing methods to improve quality of ERPs. However, designing visual stimulus with considering their physical properties with regard to rapid and also reliable machine learning algorithms for BCIs remain relatively unexplored. Addressing the issues explained above, in summary the main contributions of this study are as follows: (1) optimizing visual stimulus in terms of size, color and background and, (2) to enhance learning capacity of conventional extreme learning machine (ELM) using advanced linear algebra techniques.MethodsTwo different sized (small and big), three different colored (blue, red and colorful) images with four different backgrounds (white, black and concentric) for each of them were designed and utilized as single object paradigm. Hessenberg decomposition method was proposed for learning process and compared with conventional ELM and multi-layer perceptron in terms of training duration and performance measures.ResultsPerformance measures of small colorful images with orange-concentric background were statistically higher than those of others. Visual stimulus with white background led to relatively higher performance measures than those with black background. Blue colored images had much more impact on improvement of P300 waves than red colored ones had. Hessenberg decomposition method provided 1.5 times shortened training duration than conventional ELM, in addition with comparable performance measures.ConclusionsHerein, a visual stimuli model based on improving quality of ERP responses and machine learning algorithm relies on hessenberg decomposition method are introduced with demonstration of their advantages in the context of BCI. Methods and findings described in this study may pave the way for widespread applications, particularly in clinical health-informatics.

Comparison of Several Extreme Learning Machine Algorithm for Modeling Concrete Compressive Strength

2014 ◽  
Vol 548-549 ◽  
pp. 1735-1738 ◽  
Author(s):  
Jian Tang ◽  
Dong Yan ◽  
Li Jie Zhao
Keyword(s):  
Compressive Strength ◽  
Extreme Learning Machine ◽  
Prediction Accuracy ◽  
Partial Least Square ◽  
Least Square ◽  
Extreme Learning Machines ◽  
Concrete Compressive Strength ◽  
Learning Machines ◽  
Learning Machine

Modeling concrete compressive strength is useful to ensure quality of civil engineering. This paper aims to compare several Extreme learning machines (ELMs) based modeling approaches for predicting the concrete compressive strength. Normal ELM algorithm, Partial least square-based extreme learning machines (PLS-ELMs) algorithm and Kernel ELM (KELM) algorithm are used and evaluated. Results indicate that the normal ELMs algorithm has the highest modeling speed, and the KELM has the best prediction accuracy. Every method is validated for modeling concrete compressive strength. The appropriate modeling approach should be selected according different purposes.

scholarly journals Assessing the Applicability of Random Forest, Stochastic Gradient Boosted Model, and Extreme Learning Machine Methods to the Quantitative Precipitation Estimation of the Radar Data: A Case Study to Gwangdeoksan Radar, South Korea, in 2018

2019 ◽  
Vol 2019 ◽  
pp. 1-17
Author(s):  
Ju-Young Shin ◽  
Yonghun Ro ◽  
Joo-Wan Cha ◽  
Kyu-Rang Kim ◽  
Jong-Chul Ha
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
South Korea ◽  
Extreme Learning Machine ◽  
Radar Data ◽  
Machine Learning Algorithms ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation ◽  
Learning Machine ◽  
Estimation Models

Machine learning algorithms should be tested for use in quantitative precipitation estimation models of rain radar data in South Korea because such an application can provide a more accurate estimate of rainfall than the conventional ZR relationship-based model. The applicability of random forest, stochastic gradient boosted model, and extreme learning machine methods to quantitative precipitation estimation models was investigated using case studies with polarization radar data from Gwangdeoksan radar station. Various combinations of input variable sets were tested, and results showed that machine learning algorithms can be applied to build the quantitative precipitation estimation model of the polarization radar data in South Korea. The machine learning-based quantitative precipitation estimation models led to better performances than ZR relationship-based models, particularly for heavy rainfall events. The extreme learning machine is considered the best of the algorithms used based on evaluation criteria.

scholarly journals Review Process on URL Phishing

2021 ◽  
pp. 241-244
Author(s):  
Vivek Sharma S ◽  
Hemalatha R ◽  
Kavyashree Y B
Keyword(s):  
Machine Learning ◽  
Extreme Learning Machine ◽  
Review Process ◽  
Learning Machine

Phishing is that the most typical and most dangerous attack among cybercrimes. The aim of these attacks is to steal the data that’s utilized by people and organizations to perform transactions or any vital info. The goal of this is often to perform an Extreme Learning Machine (ELM) primarily based upon the classification of options together with Phishing Websites information among the UC Irvine Machine Learning Repository information. For results assessment, ELM was compared with different machine learning (SVM), Naive Thomas Bayes (NB) strategies and detected to possess the best possible accuracy.

scholarly journals An introductory study to machine learning and its application to employee turnover prediction

2019 ◽  
Author(s):  
João Pedro Pazinato Cruz de Oliveira ◽  
Leonardo Tomazeli Duarte
Keyword(s):  
Machine Learning ◽  
Extreme Learning Machine ◽  
Test Data ◽  
Employee Turnover ◽  
Learning Machine ◽  
Elm Classifier

The objective of this paper is to study the problem of employee turnover prediction and to develop a classifier that uses employee's data to identify those who have a greater tendency to leave the company voluntarily. For such purpose, the data of 8724 employees from a real Brazilian beverage company was used to train an Extreme Learning Machine (ELM) classifier, assigning to each sample a weight inversely proportional to the size of the respective class. After the training, the classifier displayed an overall accuracy of 79% of the test data.

Designing Extreme Learning Machine Network Structure Based on Tolerance Rough Set

2017 ◽  
Vol 13 (4) ◽  
pp. 38-55 ◽  
Author(s):  
Han Ke
Keyword(s):  
Breast Cancer ◽  
Extreme Learning Machine ◽  
Rough Set ◽  
Network Structure ◽  
High Efficiency ◽  
Breast Cancer Dataset ◽  
Intelligent Technique ◽  
Tolerance Rough Set ◽  
Learning Machine

In this paper, we present a new extreme learning machine network structure on the basis of tolerance rough set. The purpose of this paper is to realize the high-efficiency and multi-dimensional ELM network structure. Various published algorithms have been applied to breast cancer datasets, but rough set is a fairly new intelligent technique that applies to predict breast cancer recurrence. We analyze Ljubljana Breast Cancer Dataset, firstly, obtain lower and upper approximations and calculate the accuracy and quality of the classification. The high values of the quality of classification and accuracy prove that the attributes selected can well approximate the classification. Rough sets approach is established to solve the prolem of tolerance.

scholarly journals Text Classification Based on Weighted Extreme Learning Machine

2019 ◽  
Vol 32 (1) ◽  
pp. 203 ◽  
Author(s):  
Hayder Mahmood Salman
Keyword(s):  
Machine Learning ◽  
Extreme Learning Machine ◽  
Text Classification ◽  
The Internet ◽  
Huge Amount ◽  
Text Documents ◽  
Feature Weights ◽  
Weighted Extreme Learning Machine ◽  
Proposed Model ◽  
Learning Machine

The huge amount of documents in the internet led to the rapid need of text classification (TC). TC is used to organize these text documents. In this research paper, a new model is based on Extreme Machine learning (EML) is used. The proposed model consists of many phases including: preprocessing, feature extraction, Multiple Linear Regression (MLR) and ELM. The basic idea of the proposed model is built upon the calculation of feature weights by using MLR. These feature weights with the extracted features introduced as an input to the ELM that produced weighted Extreme Learning Machine (WELM). The results showed   a great competence of the proposed WELM compared to the ELM. 

Designing Extreme Learning Machine Network Structure Based on Tolerance Rough Set

2020 ◽  
pp. 263-282
Author(s):  
Han Ke
Keyword(s):  
Breast Cancer ◽  
Extreme Learning Machine ◽  
Rough Set ◽  
Network Structure ◽  
High Efficiency ◽  
Breast Cancer Dataset ◽  
Tolerance Rough Set ◽  
Learning Machine ◽  
Lower And Upper Approximations

In this paper, we present a new extreme learning machine network structure on the basis of tolerance rough set. The purpose of this paper is to realize the high-efficiency and multi-dimensional ELM network structure. Various published algorithms have been applied to breast cancer datasets, but rough set is a fairly new intelligent technique that applies to predict breast cancer recurrence. We analyze Ljubljana Breast Cancer Dataset, firstly, obtain lower and upper approximations and calculate the accuracy and quality of the classification. The high values of the quality of classification and accuracy prove that the attributes selected can well approximate the classification. Rough sets approach is established to solve the prolem of tolerance.

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