CDN Domain Name Detection Method Based on J48 Decision Tree

2021 ◽  
Vol 11 (07) ◽  
pp. 1982-1993
Author(s):  
飞 杜
Keyword(s):  
Decision Tree ◽  
Detection Method ◽  
Domain Name ◽  
J48 Decision Tree

Landslide susceptibility mapping using J48 Decision Tree with AdaBoost, Bagging and Rotation Forest ensembles in the Guangchang area (China)

CATENA ◽  
2018 ◽  
Vol 163 ◽  
pp. 399-413 ◽  
Author(s):  
Haoyuan Hong ◽  
Junzhi Liu ◽  
Dieu Tien Bui ◽  
Biswajeet Pradhan ◽  
Tri Dev Acharya ◽  
...  
Keyword(s):  
Decision Tree ◽  
Landslide Susceptibility ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Rotation Forest ◽  
J48 Decision Tree

scholarly journals Classifying the Level of Energy-Environmental Efficiency Rating of Brazilian Ethanol

Energies ◽  
2020 ◽  
Vol 13 (8) ◽  
pp. 2067
Author(s):  
Nilsa Duarte da Silva Lima ◽  
Irenilza de Alencar Nääs ◽  
João Gilberto Mendes dos Reis ◽  
Raquel Baracat Tosi Rodrigues da Silva
Keyword(s):  
Decision Tree ◽  
High Efficiency ◽  
Rating Scale ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Environmental Efficiency ◽  
Classification Model ◽  
Bayes Algorithm ◽  
J48 Decision Tree

The present study aimed to assess and classify energy-environmental efficiency levels to reduce greenhouse gas emissions in the production, commercialization, and use of biofuels certified by the Brazilian National Biofuel Policy (RenovaBio). The parameters of the level of energy-environmental efficiency were standardized and categorized according to the Energy-Environmental Efficiency Rating (E-EER). The rating scale varied between lower efficiency (D) and high efficiency + (highest efficiency A+). The classification method with the J48 decision tree and naive Bayes algorithms was used to predict the models. The classification of the E-EER scores using a decision tree using the J48 algorithm and Bayesian classifiers using the naive Bayes algorithm produced decision tree models efficient at estimating the efficiency level of Brazilian ethanol producers and importers certified by the RenovaBio. The rules generated by the models can assess the level classes (efficiency scores) according to the scale discretized into high efficiency (Classification A), average efficiency (Classification B), and standard efficiency (Classification C). These results might generate an ethanol energy-environmental efficiency label for the end consumers and resellers of the product, to assist in making a purchase decision concerning its performance. The best classification model was naive Bayes, compared to the J48 decision tree. The classification of the Energy Efficiency Note levels using the naive Bayes algorithm produced a model capable of estimating the efficiency level of Brazilian ethanol to create labels.

scholarly journals Network Software Vulnerability Identifier using J48 decision tree algorithm

2021 ◽  
Vol 9 (8) ◽  
pp. 1889-1892
Author(s):  
Shankar Murthy J
Keyword(s):  
Decision Tree ◽  
Cyber Security ◽  
Decision Tree Algorithm ◽  
Software Vulnerability ◽  
Tree Algorithm ◽  
Software Vulnerabilities ◽  
Security Issues ◽  
Modern Era ◽  
Vulnerability Classifications ◽  
J48 Decision Tree

Abstract: Software vulnerabilities are the primary causes of different security issues in the modern era. When vulnerability is exploited by malicious assaults, it substantially jeopardizes the system's security and may potentially result in catastrophic losses. As a result, automatic classification methods are useful for successfully managing software vulnerabilities, improving system security performance, and lowering the chance of the system being attacked and destroyed. In the software industry and in the field of cyber security, the ever-increasing number of publicly reported security flaws has become a major source of concern. Because software security flaws play such a significant part in cyber security attacks, relevant security experts are conducting an increasing number of vulnerability classification studies, this project can predict the software vulnerability means the software's in the device are authorized or not and who scan the system multiple times, to identify the vulnerability j48 decision tree algorithm was used. Keywords: Malicious assaults, catastrophic losses, Security flaws, Cyber security, Vulnerability Classifications.

scholarly journals An effective frequency-domain feature of atrial fibrillation based on time-frequency analysis

2020 ◽  
Author(s):  
Yusong Hu ◽  
Yantao Zhao ◽  
Jihong Liu ◽  
Jin Pang ◽  
Chen Zhang ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Decision Tree ◽  
Sinus Rhythm ◽  
Frequency Domain ◽  
Frequency Spectrum ◽  
Detection Method ◽  
Maximum Amplitude ◽  
Time Frequency ◽  
Electrocardiogram Signals ◽  
Cart Algorithm

Abstract Background: Atrial fibrillation is a type of persistent arrhythmia that can lead to serious complications. Therefore, accurate and quick detection of atrial fibrillation by surface electrocardiogram has great importance on further treatment. The practical electrocardiogram signals contain various interferences in different frequencies, such as myoelectricity interference, power interference and so on. Detection speed and accuracy largely depend on the atrial fibrillation signal features extracted by the algorithm. But some of the discovered atrial fibrillation features are not well distinguishable, resulting in poor classification effect. Methods: This paper proposed a high distinguishable frequency feature - the frequency corresponding to the maximum amplitude in the frequency spectrum. We used the R-R interval detection method optimized with the mathematical morphology method and combined with the wavelet transform method for analysis. According to the two features - the maximum amplitude in the frequency spectrum and R-R interval irregular, we could recognize atrial fibrillation signals in electrocardiogram signals by decision tree classification algorithm. Results: The data used in the experiment come from the MIT-BIH database, which is publicly accessible via the web and with ethical approval and consent. Based on the input of time-domain and frequency-domain features, we classified sinus rhythm signals and AF signals using the decision tree generated by classification and regression tree (CART) algorithm. From the confusion matrix, we got the accuracy was 98.9%, sensitivity was 97.93% and specificity was 99.63%. Conclusions: The experimental results can prove the validity of the maximum amplitude in the frequency spectrum and the practicability and accuracy of the detection method, which applied this frequency-domain feature. Through the detection method, we obtained good accuracy of classifying sinus rhythm signals and atrial fibrillation signals. And the sensitivity and specificity of our method were pretty good by comparison with other studies.

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