Random Forest Algorithm-Based Lightweight Comprehensive Evaluation for Wireless User Perception

The method of pedestrian navigation in the cities illustrated by the example of Saint-Petersburg was investigated. The factors influencing people when they choose a route for their walk were determined. Based on acquired factors corresponding data was collected and used to develop model determining attractiveness of a street in the city using Random Forest algorithm. The results obtained shows that routes provided by the method are 14% more attractive and just 6% longer compared with the shortest ones.

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MetalExplorer, a Bioinformatics Tool for the Improved Prediction of Eight Types of Metal-Binding Sites Using a Random Forest Algorithm with Two- Step Feature Selection

Current Bioinformatics ◽

10.2174/2468422806666160618091522 ◽

2017 ◽

Vol 12 (6) ◽

Cited By ~ 6

Author(s):

Jiangning Song ◽

Chen Li ◽

Cheng Zheng ◽

Jerico Revote ◽

Ziding Zhang ◽

...

Keyword(s):

Feature Selection ◽

Random Forest ◽

Metal Binding ◽

Binding Sites ◽

Random Forest Algorithm ◽

Bioinformatics Tool ◽

Metal Binding Sites

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Prediction model based on the Laplacian eigenmap method combined with a random forest algorithm for rainstorm satellite images during the first annual rainy season in South China

Natural Hazards ◽

10.1007/s11069-021-04585-0 ◽

2021 ◽

Author(s):

Xiao-yan Huang ◽

Li He ◽

Hua-sheng Zhao ◽

Ying Huang ◽

Yu-shuang Wu

Keyword(s):

Random Forest ◽

Prediction Model ◽

South China ◽

Rainy Season ◽

Satellite Images ◽

Random Forest Algorithm ◽

Model Based

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Classification and photometric redshift estimation of quasars in photometric surveys

Proceedings of the International Astronomical Union ◽

10.1017/s1743921320001829 ◽

2020 ◽

Vol 15 (S359) ◽

pp. 40-41

Author(s):

L. M. Izuti Nakazono ◽

C. Mendes de Oliveira ◽

N. S. T. Hirata ◽

S. Jeram ◽

A. Gonzalez ◽

...

Keyword(s):

Machine Learning ◽

Random Forest ◽

Nearest Neighbour ◽

Random Forest Algorithm ◽

Photometric Redshift ◽

Using Data

AbstractWe present a machine learning methodology to separate quasars from galaxies and stars using data from S-PLUS in the Stripe-82 region. In terms of quasar classification, we achieved 95.49% for precision and 95.26% for recall using a Random Forest algorithm. For photometric redshift estimation, we obtained a precision of 6% using k-Nearest Neighbour.

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