Classification of Internet Traffic Data Using Ensemble Method

2020 ◽  
pp. 397-405
Author(s):  
N. Manju ◽  
B. S. Harish
Keyword(s):  
Internet Traffic ◽  
Ensemble Method ◽  
Traffic Data

scholarly journals Classifying Internet Traffic using an Efficient Classifier

2019 ◽  
Vol 8 (3) ◽  
pp. 577-582
Keyword(s):  
Network Optimization ◽  
Computation Time ◽  
Internet Traffic ◽  
The Internet ◽  
Traffic Data ◽  
Internet Applications ◽  
Hybrid Classifier ◽  
New Development ◽  
P2p Applications

The new development in the architecture of Internet has increased internet traffic. The introduction of Peer to Peer (P2P) applications are affecting the performance of traditional internet applications. Network optimization is used to monitor and manage the internet traffic and improve the performance of internet applications. The existing optimizations methods are not able to provide better management for networks. Machine learning (ML) is one of the familiar techniques to handle the internet traffic. It is used to identify and reduce the traffic. The lack of relevant datasets have reduced the performance of ML techniques in classification of internet traffic. The aim of the research is to develop a hybrid classifier to classify the internet traffic data and mitigate the traffic. The proposed method is deployed in the classification of traffic traces of University Technology Malaysia. The method has produced an accuracy of 98.3% with less computation time

scholarly journals Machine learning: Enhancing the Perception of the Computer

2020 ◽  
Vol 9 (4) ◽  
pp. 758-761
Keyword(s):  
Machine Learning ◽  
Computer Training ◽  
Internet Traffic ◽  
The Internet ◽  
Popular Method ◽  
Exponential Increase ◽  
Computer Teaching ◽  
Traffic Detection ◽  
The Web

The advancement of technology and networking allows the use of the Web incredibly important. There is thus an exponential increase in data and information via the Internet. This flow thus is a beneficial field of study which can be defined accurately. Internet traffic detection is a very popular method of identifying information. Although so many methods have been successfully developed for classifying internet traffic, computer training technology among them is most popular. A short study of the classification of Internet traffic on various managed and non-regulated computer teaching systems was undertaken by many researchers. This paper will give various ideas to the other researcher’s and help them to learn a lot about machine learning

scholarly journals Accurate Recovery of Internet Traffic Data Under Variable Rate Measurements

2018 ◽  
Vol 26 (3) ◽  
pp. 1137-1150 ◽  
Author(s):  
Kun Xie ◽  
Can Peng ◽  
Xin Wang ◽  
Gaogang Xie ◽  
Jigang Wen ◽  
...  
Keyword(s):  
Internet Traffic ◽  
Variable Rate ◽  
Traffic Data

Solder joint defect classification based on ensemble learning

2017 ◽  
Vol 29 (3) ◽  
pp. 164-170 ◽  
Author(s):  
Hao Wu
Keyword(s):  
Random Forest ◽  
Solder Joint ◽  
Decision Trees ◽  
Printed Circuit Board ◽  
Solder Joints ◽  
High Accuracy ◽  
Ensemble Method ◽  
Circuit Board ◽  
Content Type

Purpose This paper aims to inspect the defects of solder joints of printed circuit board in real-time production line, simple computing and high accuracy are primary consideration factors for feature extraction and classification algorithm. Design/methodology/approach In this study, the author presents an ensemble method for the classification of solder joint defects. The new method is based on extracting the color and geometry features after solder image acquisition and using decision trees to guarantee the algorithm’s running executive efficiency. To improve algorithm accuracy, the author proposes an ensemble method of random forest which combined several trees for the classification of solder joints. Findings The proposed method has been tested using 280 samples of solder joints, including good and various defect types, for experiments. The results show that the proposed method has a high accuracy. Originality/value The author extracted the color and geometry features and used decision trees to guarantee the algorithm's running executive efficiency. To improve the algorithm accuracy, the author proposes using an ensemble method of random forest which combined several trees for the classification of solder joints. The results show that the proposed method has a high accuracy.

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