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Introduction to IoT Analytics [Kõva köide]

  • Formaat: Hardback, 354 pages, kõrgus x laius: 254x178 mm, kaal: 840 g, 24 Tables, color; 186 Illustrations, color
  • Sari: Chapman & Hall/CRC Data Science Series
  • Ilmumisaeg: 04-Mar-2021
  • Kirjastus: Chapman & Hall/CRC
  • ISBN-10: 0367687828
  • ISBN-13: 9780367687823
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Introduction to IoT AnalyticsSuurem pilt
  • Formaat: Hardback, 354 pages, kõrgus x laius: 254x178 mm, kaal: 840 g, 24 Tables, color; 186 Illustrations, color
  • Sari: Chapman & Hall/CRC Data Science Series
  • Ilmumisaeg: 04-Mar-2021
  • Kirjastus: Chapman & Hall/CRC
  • ISBN-10: 0367687828
  • ISBN-13: 9780367687823
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Märksõnad:
"An Introduction to IoT Analytics covers techniques that can be used to analyze data from IoT sensors and also addresses questions regarding the performance of an IoT system. It strikes a balance between practice and theory so that one can learn how to apply these tools in practice with a good understanding of their inner workings. It is an introductory book for readers that have no familiarity with these techniques. The techniques presented in the book come from the areas of Machine Learning, Statistics, and Operations Research. Machine Learning techniques are described that can be used to analyze IoT data generated from sensors for clustering, classification, and regression. The statistical techniques described can be used to carry out regression and forecasting of IoT sensor data, and dimensionality reduction of data sets. Operations Research is concerned with the performance of an IoT system by constructing a model of a system under study, and then carry out what-if analysis. The book also describes simulation techniques. Key features: IoT analytics is not just Machine Learning but it also involves other tools, such as, forecasting and simulation techniques. Many diagrams and examples are given throughout the book to better explain the material presented. At the end of each chapter, there is a project designed to help the reader to better understand the techniques described in the chapter. The material is this book has been class tested over several semesters"--

An Introduction to IoT Analytics covers techniques that can be used to analyze data from IoT sensors and also addresses questions regarding the performance of an IoT system. It strikes a balance between practice and theory so that one can learn how to apply these tools in practice with a good understanding of their inner workings. It is an introductory book for readers that have no familiarity with these techniques.

The techniques presented in the book come from the areas of Machine Learning, Statistics, and Operations Research. Machine Learning techniques are described that can be used to analyze IoT data generated from sensors for clustering, classification, and regression. The statistical techniques described can be used to carry out regression and forecasting of IoT sensor data, and dimensionality reduction of data sets. Operations Research is concerned with the performance of an IoT system by constructing a model of a system under study, and then carry out what-if analysis. The book also describes simulation techniques.

Key features:

  • IoT analytics is not just Machine Learning but it also involves other tools, such as, forecasting and simulation techniques.

  • Many diagrams and examples are given throughout the book to better explain the material presented.

  • At the end of each chapter, there is a project designed to help the reader to better understand the techniques described in the chapter.

  • The material is this book has been class tested over several semesters.

  • Contains practice exercises, with solutions provided online at www.routledge.com/9780367686314

Preface xv
Author xvii
Chapter 1 Introduction
1(14)
1.1 The Internet of Things (IOT)
1(1)
1.2 IOT Application Domains
1(3)
1.3 IOT Reference Model
4(1)
1.4 Performance Evaluation and Modeling of IOT Systems
5(4)
1.5 Machine Learning and Statistical Techniques For IOT
9(2)
1.6 Overview of the Book
11(4)
Exercises
13(1)
References
13(2)
Chapter 2 Review of Probability Theory
15(24)
2.1 Random Variables
15(1)
2.2 Discrete Random Variables
16(4)
2.2.1 The Binomial Random Variable
16(1)
2.2.2 The Geometric Random Variable
17(1)
2.2.3 The Poisson Random Variable
17(1)
2.2.4 The Cumulative Distribution
18(2)
2.3 Continuous Random Variables
20(8)
2.3.1 The Uniform Random Variable
21(1)
2.3.2 The Exponential Random Variable
21(3)
2.3.3 Mixtures of Exponential Random Variables
24(2)
2.3.4 The Normal Random Variable
26(2)
2.4 The Joint Probability Distribution
28(3)
2.4.1 The Marginal Probability Distribution
29(1)
2.4.2 The Conditional Probability
30(1)
2.5 Expectation and Variance
31(8)
2.5.1 The Expectation and Variance of Some Random Variables
33(3)
Exercises
36(1)
References
37(2)
Chapter 3 Simulation Techniques
39(36)
3.1 Introduction
39(1)
3.2 The Discrete-Event Simulation Technique
40(6)
3.2.1 Recertification of IoT Devices: A Simple Model
40(3)
3.2.2 Recertification of IoT Devices: A More Complex Model
43(3)
3.3 Generating Random Numbers
46(8)
3.3.1 Generating Pseudo-Random Numbers
46(2)
3.3.2 Generating Random Variates
48(6)
3.4 Simulation Designs
54(4)
3.4.1 The Event List
56(2)
3.4.2 Selecting the Unit Time
58(1)
3.5 Estimation Techniques
58(13)
3.5.1 Collecting Endogenously Created Data
58(2)
3.5.2 Transient-State versus Steady-State Simulation
60(1)
3.5.3 Estimation of the Confidence Interval of the Mean
61(8)
3.5.4 Estimation of the Confidence Interval of a Percentile
69(1)
3.5.5 Estimation of the Confidence Interval of a Probability
70(1)
3.5.6 Achieving a Required Accuracy
70(1)
3.6 Validation of a Simulation Model
71(1)
3.7 Simulation Languages
71(4)
Exercises
72(1)
Simulation Project
73(1)
Task 1 (to be completed after you read Section 3.2)
73(1)
Task 2 (to be completed after you read Section 3.3)
73(1)
Task 3 (to be completed after you read Section 3.5)
73(1)
References
74(1)
Chapter 4 Hypothesis Testing
75(14)
4.1 Statistical Hypothesis Testing For a Mean
75(7)
4.1.1 The p-Value
78(1)
4.1.2 Hypothesis Testing for the Difference between Two Population Means
79(1)
4.1.3 Hypothesis Testing for a Proportion
80(1)
4.1.4 Type I and Type II Errors
81(1)
4.2 Analysis Of Variance (Anova)
82(7)
4.2.1 Degrees of Freedom
85(1)
Exercises
86(1)
References
87(2)
Chapter 5 Multivariable Linear Regression
89(28)
5.1 Simple Linear Regression
90(3)
5.2 Multivariable Linear Regression
93(8)
5.2.1 Significance of the Regression Coefficients
94(1)
5.2.2 Residual Analysis
95(4)
5.2.3 K-Squared
99(1)
5.2.4 Multicollinearity
100(1)
5.2.5 Data Transformations
100(1)
5.3 An Example
101(4)
5.4 Polynomial Regression
105(2)
5.5 Confidence and Prediction Intervals
107(1)
5.6 Ridge, Lasso, and Elastic Net Regression
108(9)
5.6.1 Ridge Regression
109(2)
5.6.2 Lasso Regression
111(1)
5.6.3 Elastic Net Regression
111(1)
Exercises
112(1)
Regression Project
113(1)
Data Set Generation
113(1)
Task 1 Basic Statistics Analysis
114(1)
Task 2 Simple Linear Regression
114(1)
Task 3 Linear Multivariable Regression
114(1)
Task 4 Lasso Regression
115(1)
References
115(2)
Chapter 6 Time Series Forecasting
117(44)
6.1 A Stationary Time Series
117(9)
6.1.1 How to Recognize Seasonality
120(3)
6.1.2 Techniques for Removing Non-Stationary Features
123(3)
6.2 Moving Average Or Smoothing Models
126(3)
6.2.1 The Simple Average Model
126(1)
6.2.2 The Exponential Moving Average Model
127(1)
6.2.3 The Average Age of a Model
128(1)
6.2.4 Selecting the Best Value for k and a
129(1)
6.3 The Moving Average MA(q) Model
129(5)
6.3.1 Derivation of the Mean and Variance of Xt
131(1)
6.3.2 Derivation of the Autocorrelation Function of the MA(1)
132(1)
6.3.3 Invertibility of MA(q)
133(1)
6.4 The Autoregressive Model
134(7)
6.4.1 The AR(1) Model
134(3)
6.4.2 Stationarity Condition of AR(p)
137(1)
6.4.3 Derivation of the Coefficients ai, i = 1, 2, ..., p
138(2)
6.4.4 Determination of the Order of AR(p)
140(1)
6.5 The Non-Seasonal ARIMA (p, d, q) Model
141(4)
6.5.1 Determination of the ARIMA Parameters
143(2)
6.6 Decomposition Models
145(3)
6.6.1 Basic Steps for the Decomposition Model
146(2)
6.7 Forecast Accuracy
148(1)
6.8 Prediction Intervals
149(2)
6.9 Vector Autoregression
151(10)
6.9.1 Fitting a VAR(p)
154(3)
Exercises
157(1)
Forecasting Project
157(1)
Data Set
157(1)
Task 1 Check for Stationarity
158(1)
Task 2 Fit a Simple Moving Average Model
158(1)
Task 3 Fit an Exponential Smoothing Model
158(1)
Task 4 Fit an Arma(p, q) Model
158(1)
Task 5 Comparison of All the Models
159(1)
References
159(2)
Chapter 7 Dimensionality Reduction
161(18)
7.1 A Review of Eigenvalues and Eigenvectors
161(2)
7.2 Principal Component Analysis (PCA)
163(8)
7.2.1 The PCA Algorithm
165(6)
7.3 Linear and Multiple Discriminant Analysis
171(8)
7.3.1 Linear Discriminant Analysis (LDA)
172(4)
7.3.2 Multiple Discriminant Analysis (MDA)
176(2)
Exercises
178(1)
References
178(1)
Chapter 8 Clustering Techniques
179(24)
8.1 Distance Metrics
179(3)
8.2 Hierarchical Clustering
182(5)
8.2.1 The Hierarchical Clustering Algorithm
184(1)
8.2.2 Linkage Criteria
185(2)
8.3 The k-Means Algorithm
187(5)
8.3.1 The Algorithm
188(1)
8.3.2 Determining the Number k of Clusters
189(3)
8.4 The Fuzzy c-Means Algorithm
192(1)
8.5 The Gaussian Mixture Decomposition
193(2)
8.6 The Dbscan Algorithm
195(8)
8.6.1 Determining MinPts and ε
198(1)
8.6.2 Advantages and Disadvantages of DBSCAN
198(1)
Exercises
199(1)
Clustering Project
200(1)
Data Set Generation
200(1)
Task 1 Hierarchical Clustering
201(1)
Task 2 k-Means Algorithm
201(1)
Task 3 DBSCAN Clustering
201(1)
Task 4 Conclusions
201(1)
References
201(2)
Chapter 9 Classification Techniques
203(40)
9.1 The k-Nearest Neighbor (K-NN) Method
203(7)
9.1.1 Selection of k
205(1)
9.1.2 Using Kernels with the k-NN Method
206(2)
9.1.3 Curse of Dimensionality
208(1)
9.1.4 Voronoi Diagrams
209(1)
9.1.5 Advantages and Disadvantages of the k-NN Method
210(1)
9.2 The Naive Bayes Classifier
210(7)
9.2.1 The Simple Bayes Classifier
211(1)
9.2.2 The Naive Bayes Classifier
212(2)
9.2.3 The Gaussian Naive Bayes Classifier
214(2)
9.2.4 Advantages and Disadvantages
216(1)
9.2.5 The k-NN Method Using Bayes' Theorem
216(1)
9.3 Decision Trees
217(15)
9.3.1 Regression Trees
219(1)
9.3.2 Classification Trees
220(6)
9.3.3 Pre-Pruning and Post-Pruning
226(4)
9.3.4 Advantages and Disadvantages of Decision Trees
230(1)
9.3.5 Decision Trees Ensemble Methods
231(1)
9.4 Logistic Regression
232(11)
9.4.1 The Binary Logistic Regression
233(4)
9.4.2 Multinomial Logistics Regression
237(1)
9.4.3 Ordinal Logistic Regression
238(2)
Exercises
240(1)
Classification Project
241(1)
References
242(1)
Chapter 10 Artificial Neural Networks
243(36)
10.1 The Feedforward Artificial Neural Network
243(3)
10.2 Other Artificial Neural Networks
246(1)
10.3 Activation Functions
247(2)
10.4 Calculation of the Output Value
249(2)
10.5 Selecting the Number of Layers and Nodes
251(1)
10.6 The Backpropagation Algorithm
252(14)
10.6.1 The Gradient Descent Algorithm
254(2)
10.6.2 Calculation of the Gradients
256(10)
10.7 Stochastic, Batch, Mini-Batch Gradient Descent Methods
266(1)
10.8 Feature Normalization
267(1)
10.9 Overfitting
268(4)
10.9.1 The Early Stopping Method
269(1)
10.9.2 Regularization
270(2)
10.9.3 The Dropout Method
272(1)
10.10 Selecting The Hyper-Parameters
272(7)
10.10.1 Selecting the Learning Rate γ
273(1)
10.10.2 Selecting the Regularization Parameter λ
274(1)
Exercises
275(1)
Neural Network Project
276(1)
Data Set Generation
276(1)
Task 1 Train a Feedforward Neural Network
277(1)
Task 2 Automatic Grid Search
277(1)
Task 3 Compare the Best Trained Neural Network Model with Multivariable Regression
277(1)
References
277(2)
Chapter 11 Support Vector Machines
279(24)
11.1 Some Basic Concepts
279(4)
11.2 The SVM Algorithm: Linearly Separable Data
283(4)
11.3 Soft-Margin SVM (C-SVM)
287(2)
11.4 The SVM Algorithm: Non-Linearly Separable Data
289(4)
11.5 Other SVM Methods
293(1)
11.6 Multiple Classes
294(2)
11.7 Selecting the Best Values for C and γ
296(2)
11.8 ε-Support Vector Regression (ε-SVR)
298(5)
Exercises
300(1)
SVM Project
301(1)
Data Set Generation
301(1)
Tasks
302(1)
References
302(1)
Chapter 12 Hidden Markov Models
303(34)
12.1 Markov Chains
303(4)
12.2 Hidden Markov Models-An Example
307(1)
12.3 The Three Basic HMM Problems
308(2)
12.3.1 Problem 1 -- The Evaluation Problem
309(1)
12.3.2 Problem 2 -- The Decoding Problem
309(1)
12.3.3 Problem 3 -- The Learning Problem
309(1)
12.4 Mathematical Notation
310(1)
12.5 Solution to Problem 1
310(7)
12.5.1 A Brute Force Solution
311(1)
12.5.2 The Forward-Backward Algorithm
312(5)
12.6 Solution to Problem 2
317(7)
12.6.1 The Heuristic Solution
317(2)
12.6.2 The Viterbi Algorithm
319(5)
12.7 Solution to Problem 3
324(4)
12.8 Selection of the Number of States N
328(2)
12.9 Forecasting OT+t
330(1)
12.10 Continuous Observation Probability Distributions
331(1)
12.11 Autoregressive HMMS
332(5)
Exercises
333(1)
HMM Project
333(1)
Data Set Generation
333(1)
Task 1 Estimate p(O\λ)
334(1)
Task 2 Estimate the Most Probable Sequence Q
334(1)
Task 3 Train the HMM
335(1)
References
335(2)
APPENDIX A SOME BASIC CONCEPTS OF QUEUEING THEORY
337(6)
APPENDIX B MAXIMUM LIKELIHOOD ESTIMATION (MLE)
343(8)
B.1 The MLE Method
343(3)
B.2 Relation of MLE to Bayesian Inference
346(1)
B.3 MLE and the Least Squares Method
347(1)
B.4 MLE of the Gaussian MA(1)
347(2)
B.5 MLE of the Gaussian AR(1)
349(2)
Index 351
Harry G. Perros is a Professor of Computer Science at North Carolina State University, an Alumni Distinguished Graduate Professor, and an IEEE Fellow. He has published extensively in the area of performance modelling of computer and communication systems, and in his free time he likes to go sailing and play the bouzouki.