Webshell Detection Based on Random Forest–Gradient Boosting Decision Tree Algorithm

2018 ◽  
Author(s):  
Handong Cui ◽  
Delu Huang ◽  
Yong Fang ◽  
Liang Liu ◽  
Cheng Huang
Keyword(s):  
Random Forest ◽  
Decision Tree ◽  
Gradient Boosting ◽  
Decision Tree Algorithm ◽  
Tree Algorithm

scholarly journals Health Data Driven on Continuous Blood Pressure Prediction Based on Gradient Boosting Decision Tree Algorithm

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 32423-32433 ◽  
Author(s):  
Bing Zhang ◽  
Jiadong Ren ◽  
Yongqiang Cheng ◽  
Bing Wang ◽  
Zhiyao Wei
Keyword(s):  
Blood Pressure ◽  
Decision Tree ◽  
Health Data ◽  
Data Driven ◽  
Gradient Boosting ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Continuous Blood Pressure

scholarly journals CLASSIFICATION OF ORCHARD CROP USING SENTINEL-1A SYNTHETIC APERTURE RADAR DATA

2018 ◽  
Vol XLII-5 ◽  
pp. 335-338 ◽  
Author(s):  
H. Sahu ◽  
D. Haldar ◽  
A. Danodia ◽  
S. Kumar
Keyword(s):  
Maximum Likelihood ◽  
Random Forest ◽  
Decision Tree ◽  
Decision Tree Algorithm ◽  
Random Forest Algorithm ◽  
Synthetic Aperture Radar Data ◽  
Tree Algorithm ◽  
Maximum Likelihood Classifier ◽  
Sar Data

<p><strong>Abstract.</strong> A study was conducted in Saharanpur District of Uttar Pradesh to asses the potential of Sentinel-1A SAR Data in orchard crop classification. The objective of the study was to evaluate three different classifiers that are maximum likelihood classifier, decision tree algorithm and random forest algorithm in Sentinel-1A SAR Data. An attempt is made to study Sentinel-1A SAR Data to classify orchard crop using this approach. Here the rule-based classifiers such as decision tree algorithm and random forest algorithm are compared with conventional maximum likelihood classifier. Statistical analysis of the classification show that the distribution of the crop, forest orchard, settlement and waterbody was 17.47<span class="thinspace"></span>%, 0.47<span class="thinspace"></span>%, 28.3<span class="thinspace"></span>%, 28.3<span class="thinspace"></span>% and 25.5<span class="thinspace"></span>% respectively in all the classification algorithm but root mean square error for maximum likelihood classifier (1.278) is more than decision tree algorithm (1.196) and random forest algorithm (1.193). Out of three, a percentage correct prediction is highest in case of decision tree algorithm (73.4) than random forest algorithm (72.5) and least for maximum likelihood classifier (66.8) in December 2017. The accuracy for orchard class is 0.81 for maximum likelihood classifier, 0.80 for decision tree algorithm and 0.78 for random forest algorithm. Thus Sentinel-1A SAR Data was effectively utilized for the classification of orchard crops.</p>

scholarly journals Secondary structural characterization of the nucleic acids from circular dichroism spectra using extreme gradient boosting decision-tree algorithm

2020 ◽  
Author(s):  
Chakkarai Sathyaseelan ◽  
V Vinothini ◽  
Thenmalarchelvi Rathinavelan
Keyword(s):  
Circular Dichroism ◽  
Decision Tree ◽  
Nucleic Acids ◽  
Secondary Structures ◽  
Gradient Boosting ◽  
Decision Tree Algorithm ◽  
Cd Spectra ◽  
Tree Algorithm ◽  
Extreme Gradient Boosting ◽  
Circular Dichroïsm

AbstractNucleic acids exhibit a repertoire of conformational preference depending on the sequence and environment. Circular dichroism (CD) is an important and valuable tool for monitoring such secondary structural conformations of nucleic acids. Nonetheless, the CD spectral diversity associated with these structures poses a challenge in obtaining the quantitative information about the secondary structural content of a given CD spectrum. To this end, the competence of extreme gradient boosting decision-tree algorithm has been exploited here to predict the diverse secondary structures of nucleic acids. A curated library of 610 CD spectra corresponding to 16 different secondary structures of nucleic acids has been developed and used as a training dataset. For a test dataset of 242 CD spectra, the algorithm exhibited the prediction accuracy of 99%. For the sake of accessibility, the entire process is automated and implemented as a webserver, called CD-NuSS (CD to nucleic acids secondary structure) and is freely accessible at https://www.iith.ac.in/cdnuss/. The XGBoost algorithm presented here may also be extended to identify the hybrid nucleic acid topologies in future.

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