Acceptability analysis method for evaluate data on twitter using support vector machine

Traditional analysis methods of reliability in the foundation pit engineering have larger error and larger amount of calculation. Therefore, the response surface method has attracted much attention because it can effectively use the finite element analysis method (FEAM) and reduce the number of the numerical simulation. This paper combines uniform design (UD) with support vector machine (SVM). On this base, a reliability analysis method of the foundation pit is put forward based on the response surface of support vector machine (RSSVM). The UD structures random samples and the FEAM is used to obtain corresponding response parameters including the lateral displacement of wall, settlement of ground, safety factor of overall stability and safety factor of against overturning. Then, SVM trains the above random samples and corresponding response parameters to get response surface (RS) respectively. The probability density distribution of each response parameter is obtained by combining the Monte Carlo method with RSSVM. The instance analysis shows that the method has high computing efficiency and less amount of calculation, and the result is reasonable. It provides an effective way for the reliability analysis of the foundation pit engineering.

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Determining damages in ceramic plates by using discrete wavelet packet transform and support vector machine

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v10i5.pp4759-4769 ◽

2020 ◽

Vol 10 (5) ◽

pp. 4759

Author(s):

Mehmet Yumurtaci ◽

Gokhan Gokmen ◽

Tahir Cetin Akinci

Keyword(s):

Support Vector Machine ◽

Wavelet Packet ◽

Wavelet Packet Transform ◽

Support Vector ◽

Discrete Wavelet ◽

Experimental Equipment ◽

Analysis Method ◽

Discrete Wavelet Packet Transform ◽

Cracked Plates ◽

Svm Model

In this study, an analysis was conducted by using discrete wavelet packet transform (DWPT) and support vector machine (SVM) methods to determine undamaged and cracked plates. The pendulum was used to land equal impacts on plates in this experimental study. Sounds, which emerge from plates as a result of the impacts applied to undamaged and cracked plates, are sound signals used in the analysis and DWPT of these sound signals were obtained with 128 decompositions for feature extraction. The first four components, reflecting the characteristics of undamaged and cracked plates within these 128 components, were selected for enhancing the performance of the classifier and energy values were used as feature vectors. In the study, the SVM model was created by selecting appropriate C and γ parameters for the classifier. Undamaged and cracked plates were seen to be successfully identified by an analysis of the training and testing phases. Undamaged and cracked statuses of the plates that are undamaged and have the analysis had identified different cracks. The biggest advantage of this analysis method used is that it is high-precision, is relatively low in cost regarding experimental equipment and requires hardware.

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Texture classification of fabric defects using machine learning

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v10i4.pp4390-4399 ◽

2020 ◽

Vol 10 (4) ◽

pp. 4390

Author(s):

Yassine Ben Salem ◽

Mohamed Naceur Abdelkrim

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Comparative Study ◽

Texture Classification ◽

Support Vector ◽

Defect Classification ◽

Analysis Method ◽

Fabric Defect ◽

Novel Algorithm

In this paper, a novel algorithm for automatic fabric defect classification was proposed, based on the combination of a texture analysis method and a support vector machine SVM. Three texture methods were used and compared, GLCM, LBP, and LPQ. They were combined with SVM’s classifier. The system has been tested using TILDA database. A comparative study of the performance and the running time of the three methods was carried out. The obtained results are interesting and show that LBP is the best method for recognition and classification and it proves that the SVM is a suitable classifier for such problems. We demonstrate that some defects are easier to classify than others.

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Auto Classification of Biomass through Characterization of their Pyrolysis Behaviors by using Thermogravimetric Analysis with Support Vector Machine Algorithm: Case Study for Tobacco

10.21203/rs.3.rs-112676/v1 ◽

2020 ◽

Author(s):

Chao Yin ◽

Xiaohua Deng ◽

Zhiqiang Yu ◽

Ruting Chen ◽

Hongxiang Zhong ◽

...

Keyword(s):

Support Vector Machine ◽

Thermogravimetric Analysis ◽

Oil Quality ◽

Support Vector ◽

Analysis Method ◽

Biomass Pyrolysis ◽

Svm Algorithm ◽

Bio Oil

Abstract Background: During the biomass-to-bio-oil conversion process, many researches focus on the study of the association between the biomass and the bio-products by using near infrared spectra (NIR) and chemical analysis method. However, the characterization of biomass pyrolysis behaviors by using thermogravimetric analysis (TGA) with support vector machine (SVM) algorithm has not been reported. In this study, tobacco was chosen as the object for biomass, because the cigarette smoke (including water, tar and gases) released by tobacco pyrolysis reactions decide the sensory quality, which is similar to the use of biomass as a renewable resource through the pyrolysis process. Results: Support vector machine (SVM) has been employed to automatically classify the planting area and growing position of tobacco leaves by using thermogravimetric analysis data as the information source for the first time. 88 single-grade tobacco samples belonging to 4 grades and 8 categories were split into the training, validation and blind testing set. Our model showed excellent performances in both the training and validation set as well as in the blind test, with accuracy over 91.67%. Throughout the whole dataset of 88 samples, our model not only provides precise results on the planting area of tobacco leave, but also accurately distinguishes the major grades among the upper, lower and middle positions. Error only occurs in the classification of subgrades of the middle position. Conclusions: Our results not only validated the feasibility of using thermogravimetric analysis with SVM algorithm as an objective and rapid method for automatic classification of tobacco planting area and growing position, but also showed this new analysis method would be a promising way to exploring bio-oil quality prior to biomass pyrolysis production.

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An information security analysis method of Internet of things based on balanced double SVM

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189259 ◽

2020 ◽

Vol 39 (6) ◽

pp. 8633-8642

Author(s):

Zhu, Hongwei ◽

Wang, Xuesong

Keyword(s):

Support Vector Machine ◽

Information Security ◽

Internet Of Things ◽

Security Analysis ◽

Reference Value ◽

Support Vector ◽

The Internet ◽

Analysis Method ◽

Network Intrusion ◽

The Internet Of Things

With the continuous progress of social science and technology, the development of the Internet of things is growing. With the development of Internet of things, security problems emerge in endlessly. During the period of COVID-19, the Internet of Things have been widely used to fight virus outbreak. However, the most serious security problem of the Internet of things is network intrusion. This paper proposes a balanced quadratic support vector machine information security analysis method for Internet of things. Compared with the traditional support vector machine Internet of things security analysis method, this method has a higher accuracy, and can shorten the detection time, with efficient and powerful characteristics. The method proposed in this paper has certain reference value to the Internet of things network intrusion problem. It provides better security for the Internet of things during the protection period of covid-19.

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