scholarly journals Skin Cancer Classification Using Random Forest Algorithm

SISFOTENIKA ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 137
Author(s):  
Nurul Khasanah ◽  
Rachman Komarudin ◽  
Nurul Afni ◽  
Yana Iqbal Maulana ◽  
Agus Salim
Keyword(s):  
Skin Cancer ◽  
Random Forest ◽  
Cancer Classification ◽  
Random Forest Algorithm

scholarly journals Skin Cancer Classification using Random Forest

2019 ◽  
Vol 4 (3) ◽  
pp. 39-42
Keyword(s):  
Machine Learning ◽  
Skin Cancer ◽  
Random Forest ◽  
Old Age ◽  
Age Groups ◽  
Cancer Classification ◽  
Health Issue ◽  
Data Set ◽  
Training Images ◽  
Different Types

Skin cancer is a very big health issue in today’s fastgrowing population not only for old age people but for all age groups. We are classifying skin cancer of a person according to dermatoscopic images into seven different types. We handle this issue utilizing the HAM10000 (Human-Against-Machine with 10000 training images) data-set. The finalized dataset includes 10001 dermatoscopic pictures which are released as a readiness set for academic machine learning purposes and are openly available through the ISIC archive. We are classifying skin cancer of a person according to dermatoscopic images into seven different types.Through this research a person will get to know that if he/she suffering from any kind of skin cancer or not, so before going to consult any doctor a person will have some assurance about skin cancer.

METHOD SUGGESTING CITY WALKING ROUTES FOR PEDESTRIANS USING AN EXAMPLE OF SAINT-PETERSBURG

2019 ◽  
pp. 71-76
Author(s):  
A.E. Semenov
Keyword(s):  
Random Forest ◽  
Develop Model ◽  
Random Forest Algorithm ◽  
Saint Petersburg ◽  
Pedestrian Navigation ◽  
Factors Influencing ◽  
The City

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.

Classification and photometric redshift estimation of quasars in photometric surveys

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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