scholarly journals Random Forest Classification of Wetland Landcovers from Multi-Sensor Data in the Arid Region of Xinjiang, China

2016 ◽  
Vol 8 (11) ◽  
pp. 954 ◽  
Author(s):  
Shaohong Tian ◽  
Xianfeng Zhang ◽  
Jie Tian ◽  
Quan Sun
Keyword(s):  
Random Forest ◽  
Arid Region ◽  
Sensor Data ◽  
Random Forest Classification ◽  
Forest Classification

scholarly journals Nondestructive Inspection of Reinforced Concrete Utility Poles with ISOMAP and Random Forest

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3463 ◽  
Author(s):  
Saeed Ullah ◽  
Minjoong Jeong ◽  
Woosang Lee
Keyword(s):  
Reinforced Concrete ◽  
Random Forest ◽  
Transmission Lines ◽  
Structural Integrity ◽  
Sensor Data ◽  
Structural Safety ◽  
Portable Devices ◽  
Random Forest Classification ◽  
Safety Issues ◽  
Forest Classification

Reinforced concrete poles are very popular in transmission lines due to their economic efficiency. However, these poles have structural safety issues in their service terms that are caused by cracks, corrosion, deterioration, and short-circuiting of internal reinforcing steel wires. Therefore, they must be periodically inspected to evaluate their structural safety. There are many methods of performing external inspection after installation at an actual site. However, on-site nondestructive safety inspection of steel reinforcement wires inside poles is very difficult. In this study, we developed an application that classifies the magnetic field signals of multiple channels, as measured from the actual poles. Initially, the signal data were gathered by inserting sensors into the poles, and these data were then used to learn the patterns of safe and damaged features. These features were then processed with the isometric feature mapping (ISOMAP) dimensionality reduction algorithm. Subsequently, the resulting reduced data were processed with a random forest classification algorithm. The proposed method could elucidate whether the internal wires of the poles were broken or not according to actual sensor data. This method can be applied for evaluating the structural integrity of concrete poles in combination with portable devices for signal measurement (under development).

Random forest classification of crop type using multi-temporal TerraSAR-X dual-polarimetric data

2014 ◽  
Vol 5 (2) ◽  
pp. 157-164 ◽  
Author(s):  
Rei Sonobe ◽  
Hiroshi Tani ◽  
Xiufeng Wang ◽  
Nobuyuki Kobayashi ◽  
Hideki Shimamura
Keyword(s):  
Random Forest ◽  
Random Forest Classification ◽  
Forest Classification ◽  
Crop Type ◽  
Multi Temporal

scholarly journals SYBA: Bayesian estimation of synthetic accessibility of organic compounds

2020 ◽  
Author(s):  
Milan Voršilák ◽  
Michal Kolář ◽  
Ivan Čmelo ◽  
Daniel Svozil
Keyword(s):  
Random Forest ◽  
Organic Compounds ◽  
General Public ◽  
Bayes Classifier ◽  
Baseline Method ◽  
Random Forest Classification ◽  
Forest Classification ◽  
Synthetic Accessibility ◽  
General Public License

Abstract SYBA (SYnthetic Bayesian Accessibility) is a fragment-based method for the rapid classification of organic compounds as easy- (ES) or hard-to-synthesize (HS). It is based on a Bernoulli naïve Bayes classifier that is used to assign SYBA score contributions to individual fragments based on their frequencies in the database of ES and HS molecules. SYBA was trained on ES molecules available in the ZINC15 database and on HS molecules generated by the Nonpher methodology. SYBA was compared with a random forest, that was utilized as a baseline method, as well as with other two methods for synthetic accessibility assessment: SAScore and SCScore. When used with their suggested thresholds, SYBA improves over random forest classification, albeit marginally, and outperforms SAScore and SCScore. However, upon the optimization of SAScore threshold (that changes from 6.0 to ~4.5), SAScore yields similar results as SYBA. Because SYBA is based merely on fragment contributions, it can be used for the analysis of the contribution of individual molecular parts to compound synthetic accessibility. SYBA is publicly available at https://github.com/lich-uct/syba under the GNU General Public License.

Random Forest classification of Mediterranean land cover using multi-seasonal imagery and multi-seasonal texture

2012 ◽  
Vol 121 ◽  
pp. 93-107 ◽  
Author(s):  
V.F. Rodriguez-Galiano ◽  
M. Chica-Olmo ◽  
F. Abarca-Hernandez ◽  
P.M. Atkinson ◽  
C. Jeganathan
Keyword(s):  
Random Forest ◽  
Land Cover ◽  
Random Forest Classification ◽  
Forest Classification
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