Franklin

Big Data and Differential Privacy : Analysis Strategies for Railway Track Engineering.

Other records:
Author/Creator:
Attoh-Okine, Nii O.
Publication:
New York : John Wiley & Sons, Incorporated, 2017.
Series:
Wiley Series in Operations Research and Management Science Ser.
Wiley Series in Operations Research and Management Science Ser.
Format/Description:
Book
1 online resource (270 pages)
Subjects:
Railroad tracks -- Mathematical models.
Form/Genre:
Electronic books.
Contents:
Cover
Title Page
Copyright
Contents
Preface
Acknowledgments
Chapter 1 Introduction
1.1 General
1.2 Track Components
1.3 Characteristics of Railway Track Data
1.4 Railway Track Engineering Problems
1.5 Wheel-Rail Interface Data
1.5.1 Switches and Crossings
1.6 Geometry Data
1.7 Track Geometry Degradation Models
1.7.1 Deterministic Models
1.7.1.1 Linear Models
1.7.1.2 Nonlinear Models
1.7.2 Stochastic Models
1.7.3 Discussion
1.8 Rail Defect Data
1.9 Inspection and Detection Systems
1.10 Rail Grinding
1.11 Traditional Data Analysis Techniques
1.11.1 Emerging Data Analysis
1.12 Remarks
References
Chapter 2 Data Analysis - Basic Overview
2.1 Introduction
2.2 Exploratory Data Analysis (EDA)
2.3 Symbolic Data Analysis
2.3.1 Building Symbolic Data
2.3.2 Advantages of Symbolic Data
2.4 Imputation
2.5 Bayesian Methods and Big Data Analysis
2.6 Remarks
References
Chapter 3 Machine Learning: A Basic Overview
3.1 Introduction
3.2 Supervised Learning
3.3 Unsupervised Learning
3.4 Semi-Supervised Learning
3.5 Reinforcement Learning
3.6 Data Integration
3.7 Data Science Ontology
3.7.1 Kernels
3.7.1.1 General
3.7.1.2 Learning Process
3.7.2 Basic Operations with Kernels
3.7.3 Different Kernel Types
3.7.4 Intuitive Example
3.7.5 Kernel Methods
3.7.5.1 Support Vector Machines
3.8 Imbalanced Classification
3.9 Model Validation
3.9.1 Receiver Operating Characteristic (ROC) Curves
3.9.1.1 ROC Curves
3.10 Ensemble Methods
3.10.1 General
3.10.2 Bagging
3.10.3 Boosting
3.11 Big P and Small N (P ≫ N)
3.11.1 Bias and Variances
3.11.2 Multivariate Adaptive Regression Splines (MARS)
3.12 Deep Learning
3.12.1 General
3.12.2 Deep Belief Networks.
3.12.2.1 Restricted Boltzmann Machines (RBM)
3.12.2.2 Deep Belief Nets (DBN)
3.12.3 Convolutional Neural Networks (CNN)
3.12.4 Granular Computing (Rough Set Theory)
3.12.5 Clustering
3.12.5.1 Measures of Similarity or Dissimilarity
3.12.5.2 Hierarchical Methods
3.12.5.3 Non-Hierarchical Clustering
3.12.5.4 k-Means Algorithm
3.12.5.5 Expectation-Maximization (EM) Algorithms
3.13 Data Stream Processing
3.13.1 Methods and Analysis
3.13.2 LogLog Counting
3.13.3 Count-Min Sketch
3.13.3.1 Online Support Regression
3.14 Remarks
References
Chapter 4 Basic Foundations of Big Data
4.1 Introduction
4.2 Query
4.3 Taxonomy of Big Data Analytics in Railway Track Engineering
4.4 Data Engineering
4.5 Remarks
References
Chapter 5 Hilbert-Huang Transform, Profile, Signal, and Image Analysis
5.1 Hilbert-Huang Transform
5.1.1 Traditional Empirical Mode Decomposition
5.1.1.1 Side Effect (Boundary Effect)
5.1.1.2 Example
5.1.1.3 Stopping Criterion
5.1.2 Ensemble Empirical Mode Decomposition (EEMD)
5.1.2.1 Post-Processing EEMD
5.1.3 Complex Empirical Mode Decomposition (CEMD)
5.1.4 Spectral Analysis
5.1.5 Bidimensional Empirical Mode Decomposition (BEMD)
5.1.5.1 Example
5.2 Axle Box Acceleration
5.2.1 General
5.3 Analysis
5.4 Remarks
References
Chapter 6 Tensors - Big Data in Multidimensional Settings
6.1 Introduction
6.2 Notations and Definitions
6.3 Tensor Decomposition Models
6.3.1 Nonnegative Tensor Factorization
6.4 Application
6.5 Remarks
References
Chapter 7 Copula Models
7.1 Introduction
7.1.1 Archimedean Copulas
7.1.1.1 Concordance Measures
7.1.2 Multivariate Archimedean Copulas
7.2 Pair Copula: Vines
7.3 Computational Example
7.3.1 Results
7.4 Remarks
References.
Chapter 8 Topological Data Analysis
8.1 Introduction
8.2 Basic Ideas
8.2.1 Topology
8.2.2 Homology
8.2.2.1 Simplicial Complex
8.2.2.2 Cycles, Boundaries, and Homology
8.2.3 Persistent Homology
8.2.3.1 Filtration
8.2.4 Persistence Visualizations
8.2.4.1 Persistence Diagrams
8.3 A Simple Railway Track Engineering Application
8.3.1 Embedding Method
8.4 Remarks
References
Chapter 9 Bayesian Analysis
9.1 Introduction
9.1.1 Prior and Posterior Distributions
9.2 Markov Chain Monte Carlo (MCMC)
9.2.1 Gibbs Sampling
9.2.2 Metropolis-Hastings
9.3 Approximate Bayesian Computation
9.3.1 ABC - Rejection algorithm
9.3.2 ABC Steps
9.4 Markov Chain Monte Carlo Application
9.5 ABC Application
9.6 Remarks
References
Chapter 10 Basic Bayesian Nonparametrics
10.1 General
10.2 Dirichlet Family
10.2.1 Moments
10.2.1.1 Marginal Distribution
10.3 Dirichlet Process
10.3.1 Stick-Breaking Construction
10.3.2 Chinese Restaurant Process
10.3.3 Chinese Restaurant Process (CRP) for Infinite Mixture
10.3.4 Nonparametric Clustering and Dirichlet Process
10.4 Finite Mixture Modeling
10.5 Bayesian Nonparametric Railway Track
10.6 Remarks
References
Chapter 11 Basic Metaheuristics
11.1 Introduction
11.1.1 Particle Swarm Optimization
11.1.2 PSO Algorithm Parameters
11.2 Remarks
References
Chapter 12 Differential Privacy
12.1 General
12.2 Differential Privacy
12.2.1 Differential Privacy: Hypothetical Track Application
12.3 Remarks
References
Index
EULA.
Notes:
Description based on publisher supplied metadata and other sources.
Local notes:
Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2021. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
Other format:
Print version: Attoh-Okine, Nii O. Big Data and Differential Privacy
ISBN:
9781119229056
9781119229049
OCLC:
974487431
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