scholarly journals Ferrography Wear Particles Image Recognition Based on Extreme Learning Machine

2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Qiong Li ◽  
Tingting Zhao ◽  
Lingchao Zhang ◽  
Wenhui Sun ◽  
Xi Zhao
Keyword(s):  
Neural Network ◽  
Extreme Learning Machine ◽  
Image Recognition ◽  
Rapid Development ◽  
Texture Features ◽  
Wear Particle ◽  
Training Algorithms ◽  
Wear Particles ◽  
Learning Machine ◽  
Elm Classifier

The morphology of wear particles reflects the complex properties of wear processes involved in particle formation. Typically, the morphology of wear particles is evaluated qualitatively based on microscopy observations. This procedure relies upon the experts’ knowledge and, thus, is not always objective and cheap. With the rapid development of computer image processing technology, neural network based on traditional gradient training algorithm can be used to recognize them. However, the feedforward neural network based on traditional gradient training algorithms for image segmentation creates many issues, such as needing multiple iterations to converge and easy fall into local minimum, which restrict its development heavily. Recently, extreme learning machine (ELM) for single-hidden-layer feedforward neural networks (SLFN) has been attracting attentions for its faster learning speed and better generalization performance than those of traditional gradient-based learning algorithms. In this paper, we propose to employ ELM for ferrography wear particles image recognition. We extract the shape features, color features, and texture features of five typical kinds of wear particles as the input of the ELM classifier and set five types of wear particles as the output of the ELM classifier. Therefore, the novel ferrography wear particle classifier is founded based on ELM.

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 Identifying of unripe Ambon and Hijau banana fruits using computer vision and extreme learning machine classifier

2022 ◽  
Vol 951 (1) ◽  
pp. 012031
Author(s):  
C Dewi ◽  
E Arisoesilaningsih ◽  
W F Mahmudy ◽  
Solimun
Keyword(s):  
Computer Vision ◽  
Extreme Learning Machine ◽  
Texture Feature ◽  
Texture Features ◽  
Test Results ◽  
Skin Texture ◽  
Morphological Descriptor ◽  
Combined Use ◽  
Learning Machine ◽  
Elm Classifier

Abstract The unripe Indonesian cultivar bananas of ambon kuning (Ambon) and ambon hijau (Hijau) after harvesting show a very close looking, green colour, similar size and shape, even Ambon one is costly than the Hijau. Hence in this study, identification was conducted using computer vision utilizing banana finger image taken with a mobile phone camera. The feature used as a differentiating feature is the shape feature and the skin texture feature of the fruit. The shape features were then extracted using morphological descriptor and convex hull, while the texture features were extracted using local binary pattern (LBP). The extreme learning machine (ELM) classifier was used to recognize both cultivars. A total of 76 banana finger imagery data were used in 3-fold testing. The test results showed that the combined use of shape and LBP features resulted in the highest accuracy, precision and recall values more than 93%. These results showed that the combination of the two features can effectively be used to distinguish the unripe Ambon and Hijau bananas.

scholarly journals Deep Convolutional Neural Network Based Extreme Learning Machine Image Classification

2021 ◽  
pp. 30-38
Author(s):  
G. D. Praveenkumar ◽  
Dr. R. Nagaraj
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Extreme Learning Machine ◽  
Deep Convolutional Neural Network ◽  
Computational Time ◽  
Machine Model ◽  
Proposed Model ◽  
Benchmark Datasets ◽  
Learning Machine ◽  
Elm Classifier

In this paper, we introduce a new deep convolutional neural network based extreme learning machine model for the classification task in order to improve the network's performance. The proposed model has two stages: first, the input images are fed into a convolutional neural network layer to extract deep-learned attributes, and then the input is classified using an ELM classifier. The proposed model achieves good recognition accuracy while reducing computational time on both the MNIST and CIFAR-10 benchmark datasets.

scholarly journals Protein Sequence Classification with Improved Extreme Learning Machine Algorithms

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Jiuwen Cao ◽  
Lianglin Xiong
Keyword(s):  
Extreme Learning Machine ◽  
Protein Sequence ◽  
Protein Sequences ◽  
Activation Function ◽  
Majority Voting ◽  
Training Algorithms ◽  
Sequence Classification ◽  
Protein Sequence Classification ◽  
Learning Machine ◽  
Majority Voting Method

Precisely classifying a protein sequence from a large biological protein sequences database plays an important role for developing competitive pharmacological products. Comparing the unseen sequence with all the identified protein sequences and returning the category index with the highest similarity scored protein, conventional methods are usually time-consuming. Therefore, it is urgent and necessary to build an efficient protein sequence classification system. In this paper, we study the performance of protein sequence classification using SLFNs. The recent efficient extreme learning machine (ELM) and its invariants are utilized as the training algorithms. The optimal pruned ELM is first employed for protein sequence classification in this paper. To further enhance the performance, the ensemble based SLFNs structure is constructed where multiple SLFNs with the same number of hidden nodes and the same activation function are used as ensembles. For each ensemble, the same training algorithm is adopted. The final category index is derived using the majority voting method. Two approaches, namely, the basic ELM and the OP-ELM, are adopted for the ensemble based SLFNs. The performance is analyzed and compared with several existing methods using datasets obtained from the Protein Information Resource center. The experimental results show the priority of the proposed algorithms.

scholarly journals A microarray gene expressions with classification using extreme learning machine

Genetika ◽  
2015 ◽  
Vol 47 (2) ◽  
pp. 523-534
Author(s):  
M. Yasodha ◽  
P. Ponmuthuramalingam
Keyword(s):  
Extreme Learning Machine ◽  
High Speed ◽  
Dna Microarrays ◽  
Research Work ◽  
The Body ◽  
Cancer Classification ◽  
Medical Sciences ◽  
Gene Expressions ◽  
Learning Machine ◽  
Elm Classifier

In the present scenario, one of the dangerous disease is cancer. It spreads through blood or lymph to other location of the body, it is a set of cells display uncontrolled growth, attack and destroy nearby tissues, and occasionally metastasis. In cancer diagnosis and molecular biology, a utilized effective tool is DNA microarrays. The dominance of this technique is recognized, so several open doubt arise regarding proper examination of microarray data. In the field of medical sciences, multicategory cancer classification plays very important role. The need for cancer classification has become essential because the number of cancer sufferers is increasing. In this research work, to overcome problems of multicategory cancer classification an improved Extreme Learning Machine (ELM) classifier is used. It rectify problems faced by iterative learning methods such as local minima, improper learning rate and over fitting and the training completes with high speed.

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