scholarly journals Influential Factor Analysis and Projection of Industrial CO2 Emissions in China Based on Extreme Learning Machine Improved by Genetic Algorithm

2020 ◽  
Vol 29 (3) ◽  
pp. 2259-2271
Author(s):  
Yanmei Li ◽  
Hongdan Hu
Keyword(s):  
Genetic Algorithm ◽  
Factor Analysis ◽  
Extreme Learning Machine ◽  
Influential Factor ◽  
Learning Machine

scholarly journals An Expert Diagnosis System for Parkinson Disease Based on Genetic Algorithm-Wavelet Kernel-Extreme Learning Machine

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Derya Avci ◽  
Akif Dogantekin
Keyword(s):  
Genetic Algorithm ◽  
Parkinson Disease ◽  
Extreme Learning Machine ◽  
Classification Accuracy ◽  
Disease Diagnosis ◽  
Major Public Health Problem ◽  
Diagnosis System ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine ◽  
Hidden Neurons

Parkinson disease is a major public health problem all around the world. This paper proposes an expert disease diagnosis system for Parkinson disease based on genetic algorithm- (GA-) wavelet kernel- (WK-) Extreme Learning Machines (ELM). The classifier used in this paper is single layer neural network (SLNN) and it is trained by the ELM learning method. The Parkinson disease datasets are obtained from the UCI machine learning database. In wavelet kernel-Extreme Learning Machine (WK-ELM) structure, there are three adjustable parameters of wavelet kernel. These parameters and the numbers of hidden neurons play a major role in the performance of ELM. In this study, the optimum values of these parameters and the numbers of hidden neurons of ELM were obtained by using a genetic algorithm (GA). The performance of the proposed GA-WK-ELM method is evaluated using statical methods such as classification accuracy, sensitivity and specificity analysis, and ROC curves. The calculated highest classification accuracy of the proposed GA-WK-ELM method is found as 96.81%.

scholarly journals Inverse kinematics solution for robotic manipulator based on extreme learning machine and sequential mutation genetic algorithm

2018 ◽  
Vol 15 (4) ◽  
pp. 172988141879299 ◽  
Author(s):  
Zhiyu Zhou ◽  
Hanxuan Guo ◽  
Yaming Wang ◽  
Zefei Zhu ◽  
Jiang Wu ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Extreme Learning Machine ◽  
Inverse Kinematics ◽  
Robotic Manipulator ◽  
Computational Time ◽  
Improved Genetic Algorithm ◽  
Intelligent Algorithm ◽  
End Effector ◽  
Training Time ◽  
Learning Machine

This article presents an intelligent algorithm based on extreme learning machine and sequential mutation genetic algorithm to determine the inverse kinematics solutions of a robotic manipulator with six degrees of freedom. This algorithm is developed to minimize the computational time without compromising the accuracy of the end effector. In the proposed algorithm, the preliminary inverse kinematics solution is first computed by extreme learning machine and the solution is then optimized by an improved genetic algorithm based on sequential mutation. Extreme learning machine randomly initializes the weights of the input layer and biases of the hidden layer, which greatly improves the training speed. Unlike classical genetic algorithms, sequential mutation genetic algorithm changes the order of the genetic codes from high to low, which reduces the randomness of mutation operation and improves the local search capability. Consequently, the convergence speed at the end of evolution is improved. The performance of the extreme learning machine and sequential mutation genetic algorithm is also compared with that of a hybrid intelligent algorithm, and the results showed that there is significant reduction in the training time and computational time while the solution accuracy is retained. Based on the experimental results, the proposed extreme learning machine and sequential mutation genetic algorithm can greatly improve the time efficiency while ensuring high accuracy of the end effector.

scholarly journals Optimised genetic algorithm-extreme learning machine approach for automatic COVID-19 detection

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0242899
Author(s):  
Musatafa Abbas Abbood Albadr ◽  
Sabrina Tiun ◽  
Masri Ayob ◽  
Fahad Taha AL-Dhief ◽  
Khairuddin Omar ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Ct Scan ◽  
Extreme Learning Machine ◽  
Experimental Result ◽  
Support Vector ◽  
X Ray ◽  
Chest X Ray ◽  
Learning Machine

The coronavirus disease (COVID-19), is an ongoing global pandemic caused by severe acute respiratory syndrome. Chest Computed Tomography (CT) is an effective method for detecting lung illnesses, including COVID-19. However, the CT scan is expensive and time-consuming. Therefore, this work focus on detecting COVID-19 using chest X-ray images because it is widely available, faster, and cheaper than CT scan. Many machine learning approaches such as Deep Learning, Neural Network, and Support Vector Machine; have used X-ray for detecting the COVID-19. Although the performance of those approaches is acceptable in terms of accuracy, however, they require high computational time and more memory space. Therefore, this work employs an Optimised Genetic Algorithm-Extreme Learning Machine (OGA-ELM) with three selection criteria (i.e., random, K-tournament, and roulette wheel) to detect COVID-19 using X-ray images. The most crucial strength factors of the Extreme Learning Machine (ELM) are: (i) high capability of the ELM in avoiding overfitting; (ii) its usability on binary and multi-type classifiers; and (iii) ELM could work as a kernel-based support vector machine with a structure of a neural network. These advantages make the ELM efficient in achieving an excellent learning performance. ELMs have successfully been applied in many domains, including medical domains such as breast cancer detection, pathological brain detection, and ductal carcinoma in situ detection, but not yet tested on detecting COVID-19. Hence, this work aims to identify the effectiveness of employing OGA-ELM in detecting COVID-19 using chest X-ray images. In order to reduce the dimensionality of a histogram oriented gradient features, we use principal component analysis. The performance of OGA-ELM is evaluated on a benchmark dataset containing 188 chest X-ray images with two classes: a healthy and a COVID-19 infected. The experimental result shows that the OGA-ELM achieves 100.00% accuracy with fast computation time. This demonstrates that OGA-ELM is an efficient method for COVID-19 detecting using chest X-ray images.

Classification of catenary equipment based on a coupled genetic algorithm-extreme learning machine method

2020 ◽  
Vol 62 (1) ◽  
pp. 15-21
Author(s):  
Changdong Wu
Keyword(s):  
Genetic Algorithm ◽  
Extreme Learning Machine ◽  
Monitoring System ◽  
Classification Accuracy ◽  
Activation Function ◽  
Machine Method ◽  
Learning Machine ◽  
Hidden Layer ◽  
Low Dimensional

In an online monitoring system for an electrified railway, it is important to classify the catenary equipment successfully. The extreme learning machine (ELM) is an effective image classification algorithm and the genetic algorithm (GA) is a typical optimisation method. In this paper, a coupled genetic algorithm-extreme learning machine (GA-ELM) technique is proposed for the classification of catenary equipment. Firstly, the GA is used to search for optimal features by reducing the initial multi-dimensional features to low-dimensional features. Next, the optimised features are used as the input to the ELM. The ELM algorithm is then used to classify the catenary equipment. In this process, the impacts of the activation function, the number of hidden layer neurons and different models on the performance of the ELM are discussed in turn. Finally, the proposed method is compared with traditional methods in terms of classification accuracy and efficiency. Experimental results show that the number of feature dimensions decreases to 58% of the original number and the computational complexity is greatly decreased. Moreover, the reduced features and the few steps of the ELM improve the classification accuracy and speed. Noticeably, when the performance of the GA-ELM method is compared with that of the ELM method, the classification accuracy rate is 93.33% compared with 85.83% and the time consumption is 2.25 s compared with 8.85 s, respectively. That is to say, the proposed method not only decreases the number of features but also increases the classification accuracy and efficiency. This meets the needs of a real-time online condition monitoring system.

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