Soft measurement of dioxin emission concentration based on deep forest regression algorithm

2021 ◽  
Vol 3 (3) ◽  
pp. 208
Author(s):  
Tang Jian ◽  
Xia Heng ◽  
Qiao Junfei ◽  
Guo Zihao
Keyword(s):  
Soft Measurement ◽  
Dioxin Emission ◽  
Deep Forest

A Comprehensive Study on Sentiment Analysis Using Deep Forest

2018 ◽  
Vol 6 (8) ◽  
pp. 115-123 ◽  
Author(s):  
Krishna Priya S ◽  
Shaksham Kapoor ◽  
Kavita S Oza ◽  
R.K. Kamat
Keyword(s):  
Sentiment Analysis ◽  
Deep Forest ◽  
Comprehensive Study

Structured behaviour prediction of on‐road vehicles via deep forest

2019 ◽  
Vol 55 (8) ◽  
pp. 452-455 ◽  
Author(s):  
Luntian Mou ◽  
Shasha Mao ◽  
Haitao Xie ◽  
Yanyan Chen
Keyword(s):  
Road Vehicles ◽  
Deep Forest

scholarly journals Multi-Feature Fusion for Weak Target Detection on Sea-Surface Based on FAR Controllable Deep Forest Model

2021 ◽  
Vol 13 (4) ◽  
pp. 812
Author(s):  
Jiahuan Zhang ◽  
Hongjun Song
Keyword(s):  
Target Detection ◽  
Feature Fusion ◽  
Feature Space ◽  
Sea Surface ◽  
Detection Accuracy ◽  
Forest Model ◽  
High Profile ◽  
Deep Forest ◽  
Positive Effect ◽  
Weak Target

Target detection on the sea-surface has always been a high-profile problem, and the detection of weak targets is one of the most difficult problems and the key issue under this problem. Traditional techniques, such as imaging, cannot effectively detect these types of targets, so researchers choose to start by mining the characteristics of the received echoes and other aspects for target detection. This paper proposes a false alarm rate (FAR) controllable deep forest model based on six-dimensional feature space for efficient and accurate detection of weak targets on the sea-surface. This is the first attempt at the deep forest model in this field. The validity of the model was verified on IPIX data, and the detection probability was compared with other proposed methods. Under the same FAR condition, the average detection accuracy rate of the proposed method could reach over 99.19%, which is 9.96% better than the results of the current most advanced method (K-NN FAR-controlled Detector). Experimental results show that multi-feature fusion and the use of a suitable detection framework have a positive effect on the detection of weak targets on the sea-surface.

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