A Wavelet Support Vector Machine-Based Neural Network Metamodel for Structural Reliability Assessment

2017 ◽  
Vol 32 (4) ◽  
pp. 344-357 ◽  
Author(s):  
Hongzhe Dai ◽  
Zhenggang Cao
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Structural Reliability ◽  
Reliability Assessment ◽  
Support Vector

scholarly journals Structural Reliability Assessment by Integrating Sensitivity Analysis and Support Vector Machine

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Shao-Fei Jiang ◽  
Da-Bao Fu ◽  
Si-Yao Wu
Keyword(s):  
Sensitivity Analysis ◽  
Support Vector Machine ◽  
Structural Reliability ◽  
Reliability Assessment ◽  
Assessment Method ◽  
Reliability Theory ◽  
Performance Comparison ◽  
Support Vector ◽  
Performance Function ◽  
Small Influence

To reduce the runtime and ensure enough computation accuracy, this paper proposes a structural reliability assessment method by the use of sensitivity analysis (SA) and support vector machine (SVM). The sensitivity analysis is firstly applied to assess the effect of random variables on the values of performance function, while the small-influence variables are rejected as input vectors of SVM. Then, the trained SVM is used to classify the input vectors, which are produced by sampling the residual variables based on their distributions. Finally, the reliability assessment is implemented with the aid of reliability theory. A 10-bar planar truss is used to validate the feasibility and efficiency of the proposed method, and a performance comparison is made with other existing methods. The results show that the proposed method can largely save the runtime with less reduction of the accuracy; furthermore, the accuracy using the proposed method is the highest among the methods employed.

scholarly journals One-Class Support Vector Machine Based Schemes for Structural Reliability Assessment Under Imbalanced Sample Conditions

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 184350-184359 ◽  
Author(s):  
Li Zhan ◽  
Jie Liu ◽  
Mian Zhang ◽  
Chengning Zhou ◽  
Lin Zhang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Structural Reliability ◽  
Reliability Assessment ◽  
Support Vector

Analysis of Feature Reduction Techniques for Online News Popularity Prediction

2018 ◽  
Vol 4 (10) ◽  
pp. 6
Author(s):  
Shivangi Bhargava ◽  
Dr. Shivnath Ghosh
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Naive Bayes ◽  
Particle Swarm ◽  
Naïve Bayes ◽  
Online News ◽  
Feature Reduction ◽  
Support Vector ◽  
Swarm Optimization

News popularity is the maximum growth of attention given for particular news article. The popularity of online news depends on various factors such as the number of social media, the number of visitor comments, the number of Likes, etc. It is therefore necessary to build an automatic decision support system to predict the popularity of the news as it will help in business intelligence too. The work presented in this study aims to find the best model to predict the popularity of online news using machine learning methods. In this work, the result analysis is performed by applying Co-relation algorithm, particle swarm optimization and principal component analysis. For performance evaluation support vector machine, naïve bayes, k-nearest neighbor and neural network classifiers are used to classify the popular and unpopular data. From the experimental results, it is observed that support vector machine and naïve bayes outperforms better with co-relation algorithm as well as k-NN and neural network outperforms better with particle swarm optimization.

Single Channel EEG signal for Automatic Detection of Absence Seizure using Convolutional Neural Network

2019 ◽  
Vol 13 ◽  
Author(s):  
Niha Kamal Basha ◽  
Aisha Banu Wahab
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Convolutional Neural Network ◽  
Monitoring System ◽  
Single Channel ◽  
Confusion Matrix ◽  
Sudden Change ◽  
Automatic Detection ◽  
Support Vector ◽  
Absence Seizure

: Absence seizure is a type of brain disorder in which subject get into sudden lapses in attention. Which means sudden change in brain stimulation. Most of this type of disorder is widely found in children’s (5-18 years). These Electroencephalogram (EEG) signals are captured with long term monitoring system and are analyzed individually. In this paper, a Convolutional Neural Network to extract single channel EEG seizure features like Power, log sum of wavelet transform, cross correlation, and mean phase variance of each frame in a windows are extracted after pre-processing and classify them into normal or absence seizure class, is proposed as an empowerment of monitoring system by automatic detection of absence seizure. The training data is collected from the normal and absence seizure subjects in the form of Electroencephalogram. The objective is to perform automatic detection of absence seizure using single channel electroencephalogram signal as input. Here the data is used to train the proposed Convolutional Neural Network to extract and classify absence seizure. The Convolutional Neural Network consist of three layers 1] convolutional layer – which extract the features in the form of vector 2] Pooling layer – the dimensionality of output from convolutional layer is reduced and 3] Fully connected layer–the activation function called soft-max is used to find the probability distribution of output class. This paper goes through the automatic detection of absence seizure in detail and provide the comparative analysis of classification between Support Vector Machine and Convolutional Neural Network. The proposed approach outperforms the performance of Support Vector Machine by 80% in automatic detection of absence seizure and validated using confusion matrix.

scholarly journals SEOpinion: Summarization and Exploration of Opinion from E-Commerce Websites

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 636
Author(s):  
Alhassan Mabrouk ◽  
Rebeca P. Díaz Redondo ◽  
Mohammed Kayed
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Cognitive Processing ◽  
Support Vector ◽  
Processing Capacity ◽  
Customer Reviews ◽  
Aspect Extraction ◽  
Descriptive Information ◽  
Opinion Summarization ◽  
Second Phases

Recently, it has been found that e-commerce (EC) websites provide a large amount of useful information that exceed the human cognitive processing capacity. In order to help customers in comparing alternatives when buying a product, previous research authors have designed opinion summarization systems based on customer reviews. They ignored the template information provided by manufacturers, although its descriptive information has the most useful product characteristics and texts are linguistically correct, unlike reviews. Therefore, this paper proposes a methodology coined as SEOpinion (summarization and exploration of opinions) to summarize aspects and spot opinion(s) regarding them using a combination of template information with customer reviews in two main phases. First, the hierarchical aspect extraction (HAE) phase creates a hierarchy of aspects from the template. Subsequently, the hierarchical aspect-based opinion summarization (HAOS) phase enriches this hierarchy with customers’ opinions to be shown to other potential buyers. To test the feasibility of using deep learning-based BERT techniques with our approach, we created a corpus by gathering information from the top five EC websites for laptops. The experimental results showed that recurrent neural network (RNN) achieved better results (77.4% and 82.6% in terms of F1-measure for the first and second phases, respectively) than the convolutional neural network (CNN) and the support vector machine (SVM) technique.

A visual terrain classification method for mobile robots’ navigation based on convolutional neural network and support vector machine

2021 ◽  
pp. 014233122098791
Author(s):  
Wanli Wang ◽  
Botao Zhang ◽  
Kaiqi Wu ◽  
Sergey A Chepinskiy ◽  
Anton A Zhilenkov ◽  
...  
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Mobile Robots ◽  
Convolutional Neural Network ◽  
Hybrid Method ◽  
Classification Accuracy ◽  
Support Vector ◽  
High Classification Accuracy ◽  
Enhance Efficiency ◽  
Multi Class Classification

In this paper, a hybrid method based on deep learning is proposed to visually classify terrains encountered by mobile robots. Considering the limited computing resource on mobile robots and the requirement for high classification accuracy, the proposed hybrid method combines a convolutional neural network with a support vector machine to keep a high classification accuracy while improve work efficiency. The key idea is that the convolutional neural network is used to finish a multi-class classification and simultaneously the support vector machine is used to make a two-class classification. The two-class classification performed by the support vector machine is aimed at one kind of terrain that users are mostly concerned with. Results of the two classifications will be consolidated to get the final classification result. The convolutional neural network used in this method is modified for the on-board usage of mobile robots. In order to enhance efficiency, the convolutional neural network has a simple architecture. The convolutional neural network and the support vector machine are trained and tested by using RGB images of six kinds of common terrains. Experimental results demonstrate that this method can help robots classify terrains accurately and efficiently. Therefore, the proposed method has a significant potential for being applied to the on-board usage of mobile robots.

scholarly journals Research on Automated Defect Classification Based on Visual Sensing and Convolutional Neural Network-Support Vector Machine for GTA-Assisted Droplet Deposition Manufacturing Process

Metals ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 639
Author(s):  
Chen Ma ◽  
Haifei Dang ◽  
Jun Du ◽  
Pengfei He ◽  
Minbo Jiang ◽  
...  
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Convolutional Neural Network ◽  
Manufacturing Process ◽  
Support Vector ◽  
Defect Classification ◽  
Droplet Deposition ◽  
Network Support ◽  
Visual Sensing ◽  
Low Efficiency

This paper proposes a novel metal additive manufacturing process, which is a composition of gas tungsten arc (GTA) and droplet deposition manufacturing (DDM). Due to complex physical metallurgical processes involved, such as droplet impact, spreading, surface pre-melting, etc., defects, including lack of fusion, overflow and discontinuity of deposited layers always occur. To assure the quality of GTA-assisted DDM-ed parts, online monitoring based on visual sensing has been implemented. The current study also focuses on automated defect classification to avoid low efficiency and bias of manual recognition by the way of convolutional neural network-support vector machine (CNN-SVM). The best accuracy of 98.9%, with an execution time of about 12 milliseconds to handle an image, proved our model can be enough to use in real-time feedback control of the process.

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