scholarly journals Geomechanical characterization of volcanic rocks using empirical systems and data mining techniques

2018 ◽  
Vol 10 (1) ◽  
pp. 138-150 ◽  
Author(s):  
T. Miranda ◽  
L.R. Sousa ◽  
A.T. Gomes ◽  
J. Tinoco ◽  
C. Ferreira
Keyword(s):  
Data Mining ◽  
Volcanic Rocks ◽  
Data Mining Techniques ◽  
Geomechanical Characterization

Selective and comprehensive characterization of the quinochalcone C-glycoside homologs in Carthamus tinctorius L. by offline comprehensive two-dimensional liquid chromatography/LTQ-Orbitrap MS coupled with versatile data mining strategies

RSC Advances ◽  
2016 ◽  
Vol 6 (1) ◽  
pp. 495-506 ◽  
Author(s):  
Wenzhi Yang ◽  
Wei Si ◽  
Jingxian Zhang ◽  
Min Yang ◽  
Huiqin Pan ◽  
...  
Keyword(s):  
Data Mining ◽  
Liquid Chromatography ◽  
Carthamus Tinctorius ◽  
Two Dimensional ◽  
Data Mining Techniques ◽  
Comprehensive Characterization ◽  
Orbitrap Ms

An offline 2D LC/LTQ-Orbitrap MS approach and versatile data mining techniques were developed to characterize new QCGs from C. tinctorius.

scholarly journals A data mining approach to the relationships between landslides and open-pit mining activity: a case study in Soacha (Cundinamarca)

DYNA ◽  
2021 ◽  
Vol 88 (217) ◽  
pp. 111-119
Author(s):  
Yolanda Calderón Larrañaga ◽  
CESAR AUGUSTO GARCIA UBAQUE ◽  
Jorge Arturo Pineda Jaimes
Keyword(s):  
Data Mining ◽  
Developing Economies ◽  
Mining Activity ◽  
Open Pit ◽  
Open Pit Mining ◽  
Mining Sites ◽  
Data Mining Approach ◽  
Indirect Relationship ◽  
Geomechanical Characterization

Landslides caused by changes in land use, or by anthropic activities such as open-pit mining, constitute one of the most important socio-economic risk factors in countries with developing economies. This article presents an approach to the relationships between mining activity and the development of landslides in a pilot area located in Soacha, Cundinamarca. Through data mining analysis and the use of Geographic Information Systems (GIS), an evaluation of the possible relationships of these factors was carried out, including socioeconomic aspects. From an inventory of open-pit mining sites, the geomechanical characterization of soil and rock units, and the characterization of environmental and social variables, data were obtained to define variables whose relationships were determined by algorithms programmed in the GIS. The results show that there is an indirect relationship between open-pit mining activity and landslides development over the last four decades in the studied zone.

KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

2019 ◽  
Vol 15 (2) ◽  
pp. 275-280
Author(s):  
Agus Setiyono ◽  
Hilman F Pardede
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Spam Detection ◽  
Support Vector Machine Algorithm ◽  
Data Mining Techniques ◽  
To Receive

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam.  One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.

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