This book provides an overview of a wide range of relevant applications and reveals how to solve them. Many of the latest applications in finance, technology, education, medicine and other important and relevant fields are data-driven. The volumes of data are enormous. Specific methods need to be developed or adapted to solve a particular problem. It illustrates data science in applications. These applications have in common the discovery of knowledge in data and the use of this knowledge to make real decisions. The set of examples presented serves as a recipe book for their direct application to similar problems or as a guide for the development of new, more sophisticated approaches. The intended readership is data scientists looking for appropriate solutions to their problems. In addition, the examples provided serves as material for lectures at universities.
Author(s): Gintautas Dzemyda, Jolita Bernatavičienė, Janusz Kacprzyk
Series: Studies in Computational Intelligence, 1084
Publisher: Springer
Year: 2023
Language: English
Pages: 259
City: Cham
978-3-031-24453-7
1
Preface
Contents
978-3-031-24453-7_1
Computational Thinking Design Application for STEAM Education
1 Introduction
2 Background
2.1 Computational Thinking Approaches
2.2 CT in STEAM Context
2.3 Design Thinking
2.4 Design Thinking in STEAM Context
3 Computational Thinking Design: Proposed Framework
3.1 Applying the Framework
3.2 Implementation Examples
4 Discussion and Conclusions
References
978-3-031-24453-7_2
Education Data for Science: Case of Lithuania
1 Introduction
2 International Surveys Versus National Testing?
3 Sources of Education Data in Lithuania
4 How Much Data Do We Explore?
5 Does National Data is in Line with ILSAs?
6 Conclusions
References
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Imbalanced Data Classification Approach Based on Clustered Training Set
1 Introduction
2 Literature Review
3 Our Approach
4 Experimental Results
4.1 Used Data
4.2 Data Cleaning and Preparation
4.3 Finding the Best Collection of Features and Number of Clusters
4.4 Undersampling and Model Fitting
4.5 Training Results
4.6 Classification Results
5 Conclusions
6 Future Work
References
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Baltic States in Global Value Chains: Quantifying International Production Sharing at Bilateral and Sectoral Levels
1 Introduction
2 Input–Output Model for Quantifying International Production Sharing
3 Breakdown of Gross Exports into Separate Value-Added Components
4 Results of the Study on the Involvement of the Baltic States in GVCs
5 Conclusions
Appendix: Decomposition of Gross Exports of Baltic States into Value-Added Components in 2000 and 2014
References
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The Soft Power of Understanding Social Media Dynamics: A Data-Driven Approach
1 Introduction
2 Motivation—Why Should We Care?
3 Data
4 Methodology
5 Results
6 Discussion and Conclusion
7 Further Developments
References
978-3-031-24453-7_6
Bootstrapping Network Autoregressive Models for Testing Linearity
1 Modelling Network Time Series
1.1 The Case of Discrete Responses
1.2 Nonlinear Models
1.3 Testing for Linearity
1.4 Outline
2 Network Autoregressive Models
2.1 Examples of Specific Models of Interest
2.2 Inference
3 Linearity Test
3.1 The Case of Non Identifiable Parameters
3.2 Bootstrapping Test Statistics
4 Applications
4.1 Simulation Results
4.2 New COVID-19 Cases on Italian Provinces
References
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Novel Data Science Methodologies for Essential Genes Identification Based on Network Analysis
1 Introduction
2 State-of-the-Art
3 Materials
3.1 Gene Integrated Network: PPI+MET
3.2 Biological Attributes
3.3 Network Attributes
3.4 Data Pre-processing
3.5 Gene Essentiality Labeling
4 Methods
5 Results
6 Discussion
7 Conclusions
References
978-3-031-24453-7_8
Acoustic Analysis for Vocal Fold Assessment—Challenges, Trends, and Opportunities
1 Introduction
2 Related Studies
3 Dimensions of Research
4 Research Method
5 Inference and Results of the SMS
6 Discussion
7 Conclusions
References
978-3-031-24453-7_9
The Paradigm of an Explainable Artificial Intelligence (XAI) and Data Science (DS)-Based Decision Support System (DSS)
1 Introduction
2 Systemic Approach to the Functional Organization of the DSS Paradigm
2.1 SWOT Analysis
2.2 Elements of CWW
2.3 SWOT and FCM Combination
2.4 Risk Evaluation and Actions
2.5 Systemic Structure of DSS
3 Experimental Studies, Validation and Applications
3.1 Description of a Real Case Under Investigation
3.2 FCM for Analysis of Culture, Politics and Economy
3.3 Notes on the Scaling of FSM Variables
3.4 Results of Simulation of the Cultural, Political and Economic Interactions
3.5 Risk Evaluation
3.6 Recommendations, Leverages and Actions
4 Conclusions
References
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Stock Portfolio Risk-Return Ratio Optimisation Using Grey Wolf Model
1 Introduction
2 Mean-Variance (M-V) Based Portfolio Selection
3 Self-Organizing Map
4 Grey Wolf Optimization Algorithm
5 Simulation (Numerical) Experiment
6 Main Results and Conclusion
Appendix: GWO Algorithm in Matlab for the Set of Selected Equities
References
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Towards Seamless Execution of Deep Learning Application on Heterogeneous HPC Systems
1 Introduction
1.1 Related Work
1.2 Scope
2 Background
2.1 Deep Learning
2.2 HPC Architectures
2.3 Gaps Between HPC and Deep Learning
2.4 Frameworks
2.5 Distributed Training
3 Adopting Deep Learning for HPC
3.1 Environment Setup
3.2 Hybrid Workflow
4 Case Study
4.1 Case Study I: Material Characteristic Identification
4.2 Case Study II: Satellite Image Segmentation
5 Conclusion
References
