SAV decoupled ensemble algorithms for fast computation of Stokes–Darcy flow ensembles

2021 ◽  
Vol 387 ◽  
pp. 114150
Author(s):  
Nan Jiang ◽  
Huanhuan Yang
Keyword(s):  
Darcy Flow ◽  
Fast Computation ◽  
Ensemble Algorithms

Fast Computation of Electrical Property of Microstrip Array with AIM

2011 ◽  
Vol 30 (1) ◽  
pp. 238-240
Author(s):  
Xiu-lian He ◽  
Wen-jun Gao ◽  
Yi-cai Ji ◽  
Hong Lei ◽  
Shu-xi Gong
Keyword(s):  
Electrical Property ◽  
Fast Computation ◽  
Microstrip Array

Easy programming for fast computation of S-matrix sensitivities

1988 ◽  
Vol 24 (12) ◽  
pp. 764
Author(s):  
J.-S. Hong
Keyword(s):  
Fast Computation ◽  
S Matrix

Numerical Investigation of liquefaction in earth dams: A Comparison of Darcy and Non-Darcy flow models

2019 ◽  
Vol 116 ◽  
pp. 103182 ◽  
Author(s):  
Farideh Hosseinejad ◽  
Farhoud Kalateh ◽  
Alireza Mojtahedi
Keyword(s):  
Numerical Investigation ◽  
Darcy Flow ◽  
Earth Dams ◽  
Flow Models

Customer lifetime value prediction for gaming industry: fuzzy clustering based approach

2021 ◽  
pp. 1-10
Author(s):  
Ahmet Tezcan Tekin ◽  
Tolga Kaya ◽  
Ferhan Cebi
Keyword(s):  
Machine Learning ◽  
Fuzzy Clustering ◽  
Customer Lifetime Value ◽  
Gaming Industry ◽  
Value Prediction ◽  
Customer Lifetime ◽  
Lifetime Value ◽  
Membership Value ◽  
Different Characteristics ◽  
Ensemble Algorithms

The use of fuzzy logic in machine learning is becoming widespread. In machine learning problems, the data, which have different characteristics, are trained and predicted together. Training the model consisting of data with different characteristics can increase the rate of error in prediction. In this study, we suggest a new approach to assembling prediction with fuzzy clustering. Our approach aims to cluster the data according to their fuzzy membership value and model it with similar characteristics. This approach allows for efficient clustering of objects with more than one cluster characteristic. On the other hand, our approach will enable us to combine boosting type ensemble algorithms, which are various forms of assemblies that are widely used in machine learning due to their excellent success in the literature. We used a mobile game’s customers’ marketing and gameplay data for predicting their customer lifetime value for testing our approach. Customer lifetime value prediction for users is crucial for determining the marketing cost cap for companies. The findings reveal that using a fuzzy method to ensemble the algorithms outperforms implementing the algorithms individually.

scholarly journals The Lagrangian kinematics of three-dimensional Darcy flow

2021 ◽  
Vol 918 ◽  
Author(s):  
Daniel R. Lester ◽  
Marco Dentz ◽  
Aditya Bandopadhyay ◽  
Tanguy Le Borgne
Keyword(s):  
Three Dimensional ◽  
Darcy Flow

Abstract

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