A multilevel analysis of hiv1-miR-H1 miRNA using KPCA, K-means, Random Forest and online target tools

2021 ◽  
Vol 17 (2) ◽  
pp. 111
Author(s):  
Vinai George Biju ◽  
Blessy Baby Mathew ◽  
C.M. Prashanth
Keyword(s):  
Random Forest ◽  
Multilevel Analysis

Simultaneous multilevel analysis.

1994 ◽  
Vol 49 (2) ◽  
pp. 144-145 ◽  
Author(s):  
Alex B. Caldwell
Keyword(s):  
Multilevel Analysis

When Organizational Culture Makes Respecful Leadership Crucial: A Multilevel Analysis

2012 ◽  
Author(s):  
Suzanne van Gils ◽  
Niels Van Quaquebeke ◽  
Jan Borkowski ◽  
Daan van Knippenberg
Keyword(s):  
Organizational Culture ◽  
Multilevel Analysis

A Multilevel Analysis of Hostility, Job Attitudes, and Workplace Deviance

2005 ◽  
Author(s):  
Brent A. Scott ◽  
Timothy A. Judge ◽  
Remus Ilies
Keyword(s):  
Multilevel Analysis ◽  
Job Attitudes ◽  
Workplace Deviance

Streaming, tracking and reading achievement: A multilevel analysis of students in 40 countries.

2017 ◽  
Vol 109 (7) ◽  
pp. 915-934 ◽  
Author(s):  
Ming Ming Chiu ◽  
Bonnie Wing-Yin Chow ◽  
Sung Wook Joh
Keyword(s):  
Reading Achievement ◽  
Multilevel Analysis

Implementation of data mining as a support of business application strategy

2018 ◽  
Vol 5 (1) ◽  
pp. 47-55
Author(s):  
Florensia Unggul Damayanti
Keyword(s):  
Data Mining ◽  
Random Forest ◽  
Business Strategy ◽  
Input Parameter ◽  
Data Mining Algorithm ◽  
Complex Data ◽  
Business Decision ◽  
Marketing Department ◽  
Business Application ◽  
Complex Data Sets

Data mining help industries create intelligent decision on complex problems. Data mining algorithm can be applied to the data in order to forecasting, identity pattern, make rules and recommendations, analyze the sequence in complex data sets and retrieve fresh insights. Yet, increasing of technology and various techniques among data mining availability data give opportunity to industries to explore and gain valuable information from their data and use the information to support business decision making. This paper implement classification data mining in order to retrieve knowledge in customer databases to support marketing department while planning strategy for predict plan premium. The dataset decompose into conceptual analytic to identify characteristic data that can be used as input parameter of data mining model. Business decision and application is characterized by processing step, processing characteristic and processing outcome (Seng, J.L., Chen T.C. 2010). This paper set up experimental of data mining based on J48 and Random Forest classifiers and put a light on performance evaluation between J48 and random forest in the context of dataset in insurance industries. The experiment result are about classification accuracy and efficiency of J48 and Random Forest , also find out the most attribute that can be used to predict plan premium in context of strategic planning to support business strategy.

Database-Driven Modeling based on Variable Selection using Random Forest and Its Application for Linear Air Fuel Ratio Sensor Output Prediction

2019 ◽  
Vol 139 (8) ◽  
pp. 850-857
Author(s):  
Hiromu Imaji ◽  
Takuya Kinoshita ◽  
Toru Yamamoto ◽  
Keisuke Ito ◽  
Masahiro Yoshida ◽  
...  
Keyword(s):  
Random Forest ◽  
Variable Selection ◽  
Sensor Output ◽  
Fuel Ratio
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