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Soft computing in data science : 4th International Conference, SCDS 2018, Bangkok, Thailand, August 15-16, 2018, Proceedings

Author: Yap Bee Wah; Azlinah Hj Mohamed; Michael W Berry
Publisher: Singapore : Springer, [2019]
Series: Communications in computer and information science, 937.
Edition/Format:   eBook : Document : Conference publication : EnglishView all editions and formats
Summary:

This book constitutes the refereed proceedings of the 4th International Conference on Soft Computing in Data Science, SCDS 2018, held in Bangkok, Thailand, in August 2018.The 30 revised full papers  Read more...

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Genre/Form: Electronic books
Conference papers and proceedings
Congresses
Additional Physical Format: Print version:
SCDS (Conference) (4th : 2018 : Bangkok, Thailand).
Soft computing in data science.
Singapore : Springer, [2019]
(OCoLC)1059268938
Material Type: Conference publication, Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Yap Bee Wah; Azlinah Hj Mohamed; Michael W Berry
ISBN: 9789811334412 9811334412
OCLC Number: 1080340076
Notes: International conference proceedings.
Includes author index.
Description: 1 online resource (xviii, 404 pages) : illustrations (some color)
Contents: Intro; Preface; Organization; Contents; Machine and Deep Learning; A Hybrid Singular Spectrum Analysis and Neural Networks for Forecasting Inflow and Outflow Currency of Bank Indonesia; Abstract; 1 Introduction; 2 Materials and Methods; 2.1 ARIMAX; 2.2 Singular Spectrum Analysis (SSA); 2.3 Neural Networks; 2.4 Hybrid Singular Spectrum Analysis and Neural Network; 2.5 Model Evaluation; 3 Results; 3.1 Simulation Study; 3.2 Inflow and Outflow Data; 3.3 Forecasting Inflow and Outflow Data Using ARIMAX; 3.4 Forecasting Inflow and Outflow Data Using SSA-NN; 4 Conclusion; Acknowledgements Simulation Study of Feature Selection on Survival Least Square Support Vector Machines with Application to Health DataAbstract; 1 Introduction; 2 Literature Review; 2.1 Least Square SVM for Survival Analysis; 2.2 Performance Measure; 3 Data Description and Methodology; 3.1 Data Description; 3.2 Methodology; 4 Empirical Results; 4.1 Simulation Study; 4.2 Application on Health Data; 5 Conclusion; Acknowledgement; References; VAR and GSTAR-Based Feature Selection in Support Vector Regression for Multivariate Spatio-Temporal Forecasting; Abstract; 1 Introduction; 2 Literature Review 2.1 Vector Autoregressive (VAR) Model2.2 Generalized Space Time Autoregressive (GSTAR) Model; 2.3 Support Vector Regression (SVR); 2.4 Model Selection; 3 Data and Method; 4 Empirical Results; 4.1 Descriptive Statistics; 4.2 VAR Modeling; 4.3 GSTAR Modeling; 4.4 Forecasting Using VAR-SVR and GSTAR-SVR Model; 5 Conclusion; Acknowledgement; References; Feature and Architecture Selection on Deep Feedforward Network for Roll Motion Time Series Prediction; Abstract; 1 Introduction; 2 Time Series Analysis: Concept and Methods 2.1 Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF)2.2 The ARIMA Model; 2.3 Artificial Neural Network; 2.4 Deep Feedforward Network; 3 Dataset and Methodology; 3.1 Dataset; 3.2 Methodology; 4 PACF Based Deep Neural Network Implementation; 4.1 Preliminary Analysis; 4.2 Feature Selection; 4.3 Optimal Neural Architecture: Hyperparameters Tuning; 4.4 Test Set Prediction; 5 ARIMA Based Deep Neural Network; 5.1 Procedure Implementation; 6 Discussion and Future Works; Acknowledgements; References
Series Title: Communications in computer and information science, 937.
Other Titles: SCDS 2018
Responsibility: Bee Wah Yap, Azlinah Hj Mohamed, Michael W. Berry (eds.).

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