Uncertainty Quantification Techniques in Statistics, Machine Learning and FinTech
Special Issue Information
Dear Colleagues,
Uncertainty Quantification (UQ) is a mainstream research topic in applied mathematics and statistics. To identify UQ problems, diverse modern techniques for large and complex data analysis have been developed in applied mathematics, computer science, and statistics.
To promote these modern data analysis methods in biology, economics, environmental studies, finance, mathematics, operational research, science, and statistics, a Special Issue of Mathematics (ISSN 2227-7390), the Science Citation Index Expanded (SCIE) Journal, will be devoted to “Uncertainty Quantification Techniques in Statistics, Machine Learning and FinTech”.
Prof. Dr. Jong-Min Kim
Guest Editor
Manuscript Submission Information
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Keywords
- Artificial intelligence
- Bayesian statistics
- Bioinformatics
- Biostatics
- Blockchain
- Big data
- Change-point detection
- Computer model
- Cryptocurrencies
- Cyber security
- Data analytics
- Data mining
- Deep learning
- Electronic data interchange (EDI)
- e-Learning
- Expert systems
- Financial time series
- Functional data analysis
- Fuzzy logic
- Internet security
- Internet of things
- Machine learning
- Mobile applications
- Mobile learning
- Neural networks
- Quality control
- Security
- Sentiment analysis
- Spatial statistics
- Support vector machines
- Web services and performance
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Related Special Issues
Published Papers
Mathematics - ISSN 2227-7390