Identification of conveyor belt injury based on image texture SVM classification method

2017 ◽  
Author(s):  
Chen Yang ◽  
Yajun Song ◽  
Jinbao Yang ◽  
Yachao Liu
Keyword(s):  
Conveyor Belt ◽  
Classification Method ◽  
Image Texture ◽  
Svm Classification

scholarly journals New KF-PP-SVM classification method for EEG in brain-computer interfaces

2014 ◽  
Vol 24 (6) ◽  
pp. 3665-3673
Author(s):  
Banghua Yang ◽  
Zhijun Han ◽  
Peng Zan ◽  
Qian Wang
Keyword(s):  
Classification Method ◽  
Brain Computer Interfaces ◽  
Svm Classification ◽  
Computer Interfaces

scholarly journals A Multi-Classification Method of Improved SVM-based Information Fusion for Traffic Parameters Forecasting

2016 ◽  
Vol 28 (2) ◽  
pp. 117-124 ◽  
Author(s):  
Hongzhuan Zhao ◽  
Dihua Sun ◽  
Min Zhao ◽  
Senlin Cheng
Keyword(s):  
Support Vector Machine ◽  
Information Fusion ◽  
Binary Tree ◽  
Classification Method ◽  
Traffic Information ◽  
Support Vector ◽  
Cyber Physical System ◽  
Svm Classification ◽  
Forecasting Performance ◽  
Multi Classification

With the enrichment of perception methods, modern transportation system has many physical objects whose states are influenced by many information factors so that it is a typical Cyber-Physical System (CPS). Thus, the traffic information is generally multi-sourced, heterogeneous and hierarchical. Existing research results show that the multisourced traffic information through accurate classification in the process of information fusion can achieve better parameters forecasting performance. For solving the problem of traffic information accurate classification, via analysing the characteristics of the multi-sourced traffic information and using redefined binary tree to overcome the shortcomings of the original Support Vector Machine (SVM) classification in information fusion, a multi-classification method using improved SVM in information fusion for traffic parameters forecasting is proposed. The experiment was conducted to examine the performance of the proposed scheme, and the results reveal that the method can get more accurate and practical outcomes.

scholarly journals DNS Tunneling Detection Method Based on Multilabel Support Vector Machine

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Ahmed Almusawi ◽  
Haleh Amintoosi
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Detection Method ◽  
Binary Classification ◽  
Experimental Results ◽  
Classification Method ◽  
Support Vector ◽  
Class Label ◽  
Svm Classification ◽  
Different Types

DNS tunneling is a method used by malicious users who intend to bypass the firewall to send or receive commands and data. This has a significant impact on revealing or releasing classified information. Several researchers have examined the use of machine learning in terms of detecting DNS tunneling. However, these studies have treated the problem of DNS tunneling as a binary classification where the class label is either legitimate or tunnel. In fact, there are different types of DNS tunneling such as FTP-DNS tunneling, HTTP-DNS tunneling, HTTPS-DNS tunneling, and POP3-DNS tunneling. Therefore, there is a vital demand to not only detect the DNS tunneling but rather classify such tunnel. This study aims to propose a multilabel support vector machine in order to detect and classify the DNS tunneling. The proposed method has been evaluated using a benchmark dataset that contains numerous DNS queries and is compared with a multilabel Bayesian classifier based on the number of corrected classified DNS tunneling instances. Experimental results demonstrate the efficacy of the proposed SVM classification method by obtaining an f-measure of 0.80.

scholarly journals Analysis of rainfall classification over Tanah Laut disrict based on global climate indicators using support vector machine method

2021 ◽  
Vol 2106 (1) ◽  
pp. 012009
Author(s):  
N Hayah ◽  
O Soesanto ◽  
M A Rahman
Keyword(s):  
Support Vector Machine ◽  
Global Climate ◽  
Climatic Conditions ◽  
Training Data ◽  
Classification Method ◽  
Support Vector ◽  
Rainfall Forecasting ◽  
Machine Method ◽  
Svm Classification ◽  
The Relationship

Abstract The Support Vector Machine (SVM) classification method can be applied in various fields, one of which is meteorology and climatology in rainfall forecasting. Thus, a study was conducted by classifying rainfall to recognize the relationship between global phenomena and rainfall and the results of applying the classification using the SVM method to rainfall in the Tanah Laut Regency. The analysis is carried out using the SVM Multiclass concept with 4 categories of rainfall classification: low, medium, high, and Extreme. The kernel used in SVM is the RBF kernel with optimization parameters used, namely Cost (C) 1,5,10,15 and Gamma (γ) 1,5,10,15. The dataset formed is based on the annual period, climatic conditions, and seasonality. The Spearman Rank correlation test describes the relationship between global phenomena and rainfall with a correlation range of (−0.1456 ) − (0.43144) for the entire dataset. The implementation of the SVM classification method shows that the Cost (C) 10 and Gamma (γ) ≥ 5 parameters obtained the highest accuracy of 100% on the training data. In contrast, in testing the data testing, the accuracy was good, namely the accuracy of 78.00% in La Nina and 81.38% in seasonal periods.

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