Intelligent Web caching using Adaptive Regression Trees, Splines, Random Forests and Tree Net

2011 ◽  
Author(s):  
Sarina Sulaiman ◽  
Siti Mariyam Shamsuddin ◽  
Ajith Abraham
Keyword(s):  
Random Forests ◽  
Regression Trees ◽  
Web Caching ◽  
Adaptive Regression

scholarly journals PREDICTION OF STUDENTS’ SCIENCE ACHIEVEMENT: AN APPLICATION OF MULTIVARIATE ADAPTIVE REGRESSION SPLINES AND REGRESSION TREES

2018 ◽  
Vol 17 (5) ◽  
pp. 887-903 ◽  
Author(s):  
Serpil Kilic Depren
Keyword(s):  
Science Achievement ◽  
Student Assessment ◽  
Regression Trees ◽  
Machine Learning Algorithms ◽  
Multivariate Adaptive Regression Splines ◽  
Regression Splines ◽  
Factors Affecting ◽  
International Student Assessment ◽  
Adaptive Regression ◽  
Adaptive Regression Splines

Turkey is ranked at the 54th out of 72 countries in terms of science achievement in the Programme for International Student Assessment (PISA) survey conducted in 2015, which is a very big disappointment for that country. The aim of this research was to determine factors affecting Turkish students’ science achievements in order to identify the improvement areas using PISA 2015 dataset. To achieve this aim, Multivariate Adaptive Regression Splines (MARS) and Classification and Regression Trees (CART) approaches were used and these approaches were compared in terms of model accuracy statistics. Since Singapore was the top performer country in terms of science achievement in PISA 2015 survey, the analysis results of Turkey and Singapore were compared to each other to understand the differences. The results showed that MARS outperforms the CART in terms of measuring the prediction of students’ science achievement. Furthermore, the most important factors affecting science achievements were environmental optimism, home possessions and science learning time (minutes per week) for Turkey, while the index of economic, social and cultural status, environmental awareness and enjoyment of science for Singapore. Keywords: higher education, machine learning algorithms, PISA, science achievement.

scholarly journals Application of Selected Methods of Black Box for Modelling the Settleability Process in Wastewater Treatment Plant

2017 ◽  
Vol 24 (1) ◽  
pp. 119-127
Author(s):  
Bartosz Szeląg ◽  
Alicja Gawdzik ◽  
Andrzej Gawdzik
Keyword(s):  
Wastewater Treatment ◽  
Activated Sludge ◽  
Random Forests ◽  
Wastewater Treatment Plant ◽  
Treatment Plant ◽  
Black Box ◽  
Multivariate Adaptive Regression Splines ◽  
Regression Splines ◽  
Adaptive Regression ◽  
Adaptive Regression Splines

Abstract The paper described how the results of measurements of inflow wastewater temperature in the chamber, a degree of external and internal recirculation in the biological-mechanical wastewater treatment plant (WWTP) in Cedzyna near Kielce, Poland, were used to make predictions of settleability of activated sludge. Three methods, namely: multivariate adaptive regression splines (MARS), random forests (RF) and modified random forests (RF + SOM) were employed to compute activated sludge settleability. The results of analysis indicate that modified random forests demonstrate the best predictive abilities.

scholarly journals Social, economic, and legislative factors and global road traffic fatalities

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Mohammad Reza Rahmanian Haghighi ◽  
Mohammad Sayari ◽  
Sulmaz Ghahramani ◽  
Kamran Bagheri Lankarani
Keyword(s):  
Human Development ◽  
Human Development Index ◽  
Road Traffic ◽  
Regression Trees ◽  
Classification And Regression Trees ◽  
Multivariate Adaptive Regression Splines ◽  
Development Index ◽  
Adaptive Regression ◽  
Classification And Regression ◽  
Three Components

Abstract Background Road traffic fatalities (RTF) is the 8th cause of mortality around the world. At the end of the Decade of Action, it would be of utmost importance to revisit our knowledge on the determinants of RTF. The aim of this study is to assess factors related to RTF at global level. Methods We used road safety development index which accounts for the interactions between system, human and products to assess the RTF in 115 and 113 countries in 2013 and 2016, respectively. To analyze data, three statistical procedures (linear regression, classification and regression trees, and multivariate adaptive regression splines) were employed. Results Classification and regression trees has the best performance amongst all others followed by multivariate adaptive regression splines for 2013 and 2016 data set with an R2 around 0.83. Results show that any increase in human development index was associated with RTF reduction. Comparing RTF data of 2013 and 2016, 8 countries experienced a change of more than 30%, which demonstrated a significant relationship with GINI index (named after Corrado Gini). Considering the three components of human development index, it is revealed that education explained most of RTF variation in classification and regression trees model followed by income and life expectancy. Conclusion Policymakers should consider road collisions as a socio-economic issue. In this regard, they can make provisions to reduce RTF in the long run by focusing on enhancing the three components of human development index, mainly education. However, there is a need to investigate the causation pathway among these three components with RTF with different time-trend procedures.

scholarly journals Social, economic, and legislative factors and global road traffic fatalities

2020 ◽  
Author(s):  
Mohammad Reza Rahmanian Haghighi ◽  
Mohammad Sayari ◽  
Sulmaz Ghahramani ◽  
Kamran Bagheri Lankarani
Keyword(s):  
Human Development ◽  
Human Development Index ◽  
Road Traffic ◽  
Regression Trees ◽  
Classification And Regression Trees ◽  
Multivariate Adaptive Regression Splines ◽  
Development Index ◽  
Adaptive Regression ◽  
Classification And Regression ◽  
Three Components

Abstract Background: Road traffic fatalities (RTF) is the 8th cause of mortality around the world. At the end of the Decade of Action, it would be of utmost importance to revisit our knowledge on the determinants of RTF. The aim of this study is to assess factors related to RTF at global level.Methods: We used road safety development index which accounts for the interactions between system, human and products to assess the RTF in 115 and 113 countries in 2013 and 2016, respectively. To analyze data, three statistical procedures (linear regression, classification and regression trees, and multivariate adaptive regression splines) were employed. Results: Classification and regression trees has the best performance amongst all others followed by multivariate adaptive regression splines for 2013 and 2016 data set with an R2 around 0.83. Results show that any increase in human development index was associated with RTF reduction. Comparing RTF data of 2013 and 2016, 8 countries experienced a change of more than 30%, which demonstrated a significant relationship with GINI index (named after Corrado Gini). Considering the three components of human development index, it is revealed that education explained most of RTF variation in classification and regression trees model followed by income and life expectancy. Conclusion: Policymakers should consider road collisions as a socio-economic issue. In this regard, they can make provisions to reduce RTF in the long run by focusing on enhancing the three components of human development index, mainly education. However, there is a need to investigate the causation pathway among these three components with RTF with different time-trend procedures.

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