scholarly journals Moving Object Detection and Tracking using Genetic Algorithm Enabled Extreme Learning Machine

2018 ◽  
Vol 13 (2) ◽  
pp. 162-174 ◽  
Author(s):  
G. Jemilda ◽  
S. Baulkani
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Extreme Learning Machine ◽  
Probabilistic Neural Network ◽  
Moving Object ◽  
Support Vector ◽  
Feed Forward Neural Network ◽  
Shape Information ◽  
Speeded Up Robust Features ◽  
Learning Machine

In this proposed work, the moving object is localized using curvelet transform, soft thresholding and frame differencing. The feature extraction techniques are applied on to the localized object and the texture, color and shape information of objects are considered. To extract the shape information, Speeded Up Robust Features (SURF) is used. To extract the texture features, the Enhanced Local Vector Pattern (ELVP) and to extract color features, Histogram of Gradient (HOG) are used and then reduced feature set obtained using genetic algorithm are fused to form a single feature vector and given into the Extreme Learning Machine (ELM) to classify the objects. The performance of the proposed work is compared with Naive Bayes, Support Vector Machine, Feed Forward Neural Network and Probabilistic Neural Network and inferred that the proposed method performs better.

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.

AUTOMATED RECOGNITION OF SINGLE & HYBRID POWER QUALITY DISTURBANCES USING WAVELET TRANSFORM BASED SUPPORT VECTOR MACHINE

2016 ◽  
Vol 79 (1) ◽  
Author(s):  
Suhail Khokhar ◽  
A. A. Mohd Zin ◽  
M. A. Bhayo ◽  
A. S. Mokhtar
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Wavelet Transform ◽  
Power Quality ◽  
Probabilistic Neural Network ◽  
Recognition Rate ◽  
Classification Performance ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Feed Forward Neural Network

The monitoring of power quality (PQ) disturbances in a systematic and automated way is an important issue to prevent detrimental effects on power system. The development of new methods for the automatic recognition of single and hybrid PQ disturbances is at present a major concern. This paper presents a combined approach of wavelet transform based support vector machine (WT-SVM) for the automatic classification of single and hybrid PQ disturbances. The proposed approach is applied by using synthetic models of various single and hybrid PQ signals. The suitable features of the PQ waveforms were first extracted by using discrete wavelet transform. Then SVM classifies the type of PQ disturbances based on these features. The classification performance of the proposed algorithm is also compared with wavelet based radial basis function neural network, probabilistic neural network and feed-forward neural network. The experimental results show that the recognition rate of the proposed WT-SVM based classification system is more accurate and much better than the other classifiers. 

AUTOMATIC DETECTION OF CARDIOVASCULAR DISEASE USING DEEP KERNEL EXTREME LEARNING MACHINE

2018 ◽  
Vol 30 (06) ◽  
pp. 1850038
Author(s):  
Dongping Li
Keyword(s):  
Neural Network ◽  
Cardiovascular Disease ◽  
Feature Extraction ◽  
Deep Learning ◽  
Extreme Learning Machine ◽  
Learning Process ◽  
Support Vector ◽  
Learning Models ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine

The electrocardiogram (ECG) is a principal signal employed to automatically diagnose cardiovascular disease in shallow and deep learning models. However, ECG feature extraction is required and this may reduce diagnosis accuracy in traditional shallow learning models, while backward propagation (BP) algorithm used by the traditional deep learning models has the disadvantages of local minimization and slow convergence rate. To solve these problems, a new deep learning algorithm called deep kernel extreme learning machine (DKELM) is proposed by combining the extreme learning machine auto-encoder (ELM-AE) and kernel ELM (KELM). In the new DKELM architecture with [Formula: see text] hidden layers, ELM-AEs are employed by the front [Formula: see text] hidden layers for feature extraction in the unsupervised learning process, which can effectively extract abstract features from the original ECG signal. To overcome the “dimension disaster” problem, the kernel function is introduced into ELM to act as classifier by the [Formula: see text]th hidden layer in the supervised learning process. The experiments demonstrate that DKELM outperforms the BP neural network, support vector machine (SVM), extreme learning machine (ELM), deep auto-encoder (DAE), deep belief network (DBN) in classification accuracy. Though the accuracy of convolutional neural network (CNN) is almost the same as DKELM, the computing time of CNN is much longer than DKELM.

scholarly journals An Effective Turkey Marble Classification System: Convolutional Neural Network with Genetic Algorithm -Wavelet Kernel - Extreme Learning Machine

2021 ◽  
Vol 38 (4) ◽  
pp. 1229-1235
Author(s):  
Derya Avci ◽  
Eser Sert
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Convolutional Neural Network ◽  
Extreme Learning Machine ◽  
Optical Illusions ◽  
Factors Affecting ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine ◽  
Hidden Layer ◽  
Elm Classifier

Marble is one of the most popular decorative elements. Marble quality varies depending on its vein patterns and color, which are the two most important factors affecting marble quality and class. The manual classification of marbles is likely to lead to various mistakes due to different optical illusions. However, computer vision minimizes these mistakes thanks to artificial intelligence and machine learning. The present study proposes the Convolutional Neural Network- (CNN-) with genetic algorithm- (GA) Wavelet Kernel- (WK-) Extreme Learning Machine (ELM) (CNN–GA-WK-ELM) approach. Using CNN architectures such as AlexNet, VGG-19, SqueezeNet, and ResNet-50, the proposed approach obtained 4 different feature vectors from 10 different marble images. Later, Genetic Algorithm (GA) was used to optimize adjustable parameters, i.e. k, 1, and m, and hidden layer neuron number in Wavelet Kernel (WK) – Extreme Learning Machine (ELM) and to increase the performance of ELM. Finally, 4 different feature vector parameters were optimized and classified using the WK-ELM classifier. The proposed CNN–GA-WK-ELM yielded an accuracy rate of 98.20%, 96.40%, 96.20%, and 95.60% using AlexNet, SequeezeNet, VGG-19, and ResNet-50, respectively.

scholarly journals Toward deep neural networks: Mirror extreme learning machines for pattern classification

Filomat ◽  
2020 ◽  
Vol 34 (15) ◽  
pp. 4985-4996
Author(s):  
Bolin Liao ◽  
Chuan Ma ◽  
Meiling Liao ◽  
Shuai Li ◽  
Zhiguan Huang
Keyword(s):  
Neural Network ◽  
Extreme Learning Machine ◽  
Pattern Classification ◽  
Simple Structure ◽  
Layer Structure ◽  
Feed Forward Neural Network ◽  
Learning Machines ◽  
S Parameter ◽  
Learning Machine ◽  
Hidden Layer

In this paper, a novel type of feed-forward neural network with a simple structure is proposed and investigated for pattern classification. Because the novel type of forward neural network?s parameter setting is mirrored with those of the Extreme Learning Machine (ELM), it is termed the mirror extreme learning machine (MELM). For the MELM, the input weights are determined by the pseudoinverse method analytically, while the output weights are generated randomly, which are completely different from the conventional ELM. Besides, a growing method is adopted to obtain the optimal hidden-layer structure. Finally, to evaluate the performance of the proposed MELM, abundant comparative experiments based on different real-world classification datasets are performed. Experimental results validate the high classification accuracy and good generalization performance of the proposed neural network with a simple structure in pattern classification.

Extreme Learning Machine for Fault Diagnosis of Rotating Machinery

2014 ◽  
Vol 960-961 ◽  
pp. 1400-1403 ◽  
Author(s):  
Rui Yu ◽  
Rui Xiang ◽  
Shi Wei Yao
Keyword(s):  
Neural Network ◽  
Power Spectrum ◽  
Extreme Learning Machine ◽  
Probabilistic Neural Network ◽  
Wavelet Packet ◽  
Noise Signal ◽  
Rotating Machinery ◽  
Machine Method ◽  
Learning Machine ◽  
Hidden Layer

The authors present extreme learning machine (ELM) as a novel mechanism for diagnosing the faults of rotating machinery, which is reflected from the power spectrum of the vibration signals. Extreme learning machine was originally developed for the single-hidden layer feedforward neural network (SLFN) and then extended to the generalized SLFN. We obtained the fault feature table of rotating machinery by wavelet packet analysis of the power spectrum, then trained and diagnosed the fault feature table with extreme learning machine. Diagnostic results show that the extreme learning machine method achieves higher diagnostic accuracy than the probabilistic neural network (PNN) method, exhibiting superior diagnostic performance. In addition, the diagnosis of fault feature table adding noise signal indicates the extreme learning machine method provides satisfactory generalization performance.

Comparison of the Extreme Learning Machine with the BP Neural Network for Short-Term Prediction of Wind Power

2012 ◽  
Vol 608-609 ◽  
pp. 564-568 ◽  
Author(s):  
Yi Hui Zhang ◽  
He Wang ◽  
Zhi Jian Hu ◽  
Meng Lin Zhang ◽  
Xiao Lu Gong ◽  
...  
Keyword(s):  
Neural Network ◽  
Wind Power ◽  
Extreme Learning Machine ◽  
Bp Neural Network ◽  
Learning Algorithm ◽  
Power Prediction ◽  
Feed Forward Neural Network ◽  
Wind Power Prediction ◽  
Learning Machine ◽  
Hidden Layer

Extreme learning machine (ELM) is a new and effective single-hidden layer feed forward neural network learning algorithm. Extreme learning machine only needs to set the number of hidden layer nodes of the network, and there is no need to adjust the neural network input weights and the hidden units bias, and it generates the only optimum solution, so it has the advantage of fast learning and good generalization ability. And the back propagation (BP) neural network is the most maturely applied. This paper has introduced the extreme learning machine into the wind power prediction. By comparing the wind power prediction method using the BP neural network. Study shows that the extreme learning machine has better prediction accuracy and shorter model training time.

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