Machine learning in cognitive IoT

✍ Scribed by Kumar, Neeraj; Makkar, Aaisha

• Publisher: CRC Press
• Year: 2020
• Tongue: English
• Leaves: 319
• Category: Library

✦ Synopsis

This book covers the different technologies of Internet, and machine learning capabilities involved in Cognitive Internet of Things (CIoT). Machine learning is explored by covering all the technical issues and various models used for data analytics during decision making at different steps. It initiates with IoT basics, its history, architecture and applications followed by capabilities of CIoT in real world and description of machine learning (ML) in data mining. Further, it explains various ML techniques and paradigms with different phases of data pre-processing and feature engineering. Each chapter includes sample questions to help understand concepts of ML used in different applications. Explains integration of Machine Learning in IoT for building an efficient decision support system Covers IoT, CIoT, machine learning paradigms and models Includes implementation of machine learning models in R Help the analysts and developers to work efficiently with emerging technologies such as data analytics, data processing, Big Data, Robotics Includes programming codes in Python/Matlab/R alongwith practical examples, questions and multiple choice questions

✦ Table of Contents

Cover......Page 1
Half Title......Page 2
Title Page......Page 4
Copyright Page......Page 5
Dedication......Page 6
Table of Contents......Page 8
Preface......Page 14
Acknowledgements......Page 16
List of Figures......Page 18
List of Tables......Page 22
1 Internet of Things......Page 24
1.1 IoT History......Page 26
1.2 IoT Architecture......Page 27
1.3.1 Wireless Sensor Networks (WSN)......Page 28
1.3.2 Radio Frequency Identification (RFID)......Page 29
1.3.2.1 RFID Applications......Page 30
1.3.3 Data Storage......Page 31
1.4.1.1 Industrial Data......Page 32
1.4.2.1 Data Acquisition......Page 33
1.4.3 IoT Technologies......Page 34
1.4.4 Optimization Techniques......Page 35
1.5.2.1 Duplicate Observations......Page 36
1.5.3 Data Reduction Schemes......Page 37
1.5.3.2 Feature Selection......Page 38
1.6.1 Infrastructure Layer (Network/Transport Layer)......Page 39
1.6.3 Physical Layer......Page 40
1.7 IoT Applications......Page 41
1.7.3 Personal and Social......Page 42
1.8 Book Outline......Page 43
1.10 Summary and What’s Next?......Page 45
1.11 Exercises......Page 46
2.1 Cognitive Devices......Page 50
2.2 Cognitive in IoT......Page 51
2.3 CIoT Background......Page 52
2.4.2 Machine Learning......Page 54
2.5 How Do Cognitive Devices Act as Human Assistants?......Page 55
2.6.1 Language......Page 57
2.6.2 Interpersonal Relationship......Page 58
2.8 Machine-to-web Communication(M2W)......Page 59
2.9 CIoT Applications......Page 60
2.9.1 Cognitive Living......Page 61
2.9.3 Cognitive Health......Page 62
2.9.4 Auto-casting and Auto-reacting Cognition Systems......Page 63
2.11 Exercises......Page 64
3.1 Search Engines as a Medium......Page 70
3.2 Data Creation and Retrieval Scheme......Page 71
3.3 Data Mining......Page 73
3.3.1 Data Mining Functions......Page 74
3.3.1.1 Classification......Page 75
3.3.2 Relation of Data Science with Machine Learning......Page 79
3.4 Data Mining in IoT......Page 80
3.5 Machine Learning in IoT......Page 81
3.6 Summary and What’s Next?......Page 83
3.7 Exercises......Page 84
4 Machine Learning Techniques......Page 90
4.1 Tools to Implement Machine Learning......Page 92
4.1.2 R......Page 93
4.2 Experiments......Page 94
4.3 Supervised Learning......Page 98
4.3.1 Unsupervised Learning......Page 99
4.4 Classification......Page 100
4.5 Regression......Page 101
4.7 Summary and What’s Next?......Page 102
4.8 Exercises......Page 104
5.1 Introduction......Page 110
5.1.1 Basis of the R programming......Page 111
5.2.1 Assignment......Page 112
5.3 Data Types......Page 114
5.3.2 Structural Data Type......Page 115
5.3.2.1 Matrices......Page 117
5.3.2.2 Arrays......Page 118
5.3.2.4 Lists......Page 120
5.3.2.5 Factors......Page 121
5.3.3.1 Arithmetic Operators......Page 122
5.3.3.3 Logical Operators......Page 123
5.3.4 Graphics......Page 124
5.3.4.2 Scatter Diagrams......Page 125
5.3.4.3 Pie Chart......Page 127
5.3.5 Basic Statistics......Page 128
5.3.5.2 Correlation Testing......Page 129
5.3.6 Packages......Page 133
5.3.7 Input Parameters Formats for R......Page 134
5.4 Summary and What’s Next?......Page 139
5.5 Exercises......Page 140
6.1 Introduction......Page 146
6.2 Generalizing Input......Page 148
6.3.1 Decision Tree......Page 150
6.4 Classification Rules......Page 152
6.5 Numeric Prediction......Page 154
6.6 Instance-based Learning......Page 158
6.6.1 Distance Metric......Page 160
6.7 Summary and What’s Next......Page 161
6.8 Exercises......Page 162
7.1 Linear Method for Regression......Page 168
7.2 Linear Method for Classification......Page 169
7.3 Kernel Smoothing Models......Page 172
7.4 Back Propagation......Page 173
7.5 Neural Network......Page 175
7.5.1 The Perceptron......Page 176
7.6.1 Bayesian Statistics......Page 177
7.6.2 Bayesian Inference......Page 178
7.7 Summary and What’s Next......Page 179
7.8 Exercises......Page 180
8.1 Input Preparation......Page 184
8.2 Data Preprocessing......Page 188
8.3.1 The Condensed Nearest Neighbor Rule......Page 189
8.3.2 Tomek......Page 191
8.3.3 One-sided Selection......Page 193
8.3.4 SMOTE......Page 195
8.3.5 ADASYN Algorithm......Page 197
8.3.6 SOTU......Page 200
8.4 Summary and What’s Next?......Page 202
8.5 Exercises......Page 203
9 Feature Engineering and Optimization......Page 206
9.1.1 Principal Component Analysis......Page 207
9.2 Feature Selection......Page 217
9.2.1 Feature Importance......Page 219
9.2.1.1 Chi-squared Filter......Page 220
9.2.1.2 Consistency-based Filter......Page 222
9.2.1.3 Correlation Filter......Page 223
9.2.1.4 Entropy-based Filter......Page 224
9.2.1.5 OneR Algorithm......Page 228
9.2.1.6 RandomForest Filter......Page 229
9.2.1.7 RReliefF Filter......Page 230
9.2.2 Recursive Feature Elimination......Page 232
9.3 Machine Learning Models......Page 234
9.4.1 Bagging......Page 237
9.4.2 Boosting......Page 242
9.5 Ensemble Approach......Page 243
9.6 Summary and What’s Next......Page 244
9.7 Exercises......Page 245
10 Evaluation and Validation of Results......Page 250
10.1 Confusion Matrix......Page 251
10.2 Correlation......Page 252
10.2.2 Pearson’s Correlation......Page 253
10.2.3 Spearman’s Correlation......Page 254
10.2.4 Matthews’ Correlation Coefficient (MCC)......Page 256
10.4 Accuracy (ACC)......Page 257
10.6.1 Root Mean Squared Error (RMSE)......Page 258
10.6.3 Relative Squared Error (RSE)......Page 259
10.7.6 Miss Rate or False Negative Rate (FNR)......Page 260
10.9 Summary and What’s Next?......Page 261
10.10 Exercises......Page 262
11.2 Chapter 2......Page 268
11.3 Chapter 3......Page 269
11.5 Chapter 5......Page 270
11.7 Chapter 7......Page 271
11.9 Chapter 9......Page 272
11.10 Chapter 10......Page 273
12 Dataset......Page 274
Bibliography......Page 312
Index......Page 316

✦ Subjects

COMPUTERS / Computer Engineering;COMPUTERS / Machine Theory;Embedded computer systems;Internet of things;Machine learning;MATHEMATICS / General;Electronic books