Machine learning in cognitive IoT / by Neeraj Kumar, Aaisha Makkar.

Format:

Book

Author/Creator:

Kumar, Neeraj (Computer scientist), author.

Makkar, Aaisha, author.

Language:

English

Subjects (All):

Embedded computer systems.

Physical Description:

1 online resource (xxii, 296 pages) : illustrations

Edition:

1st ed.

Place of Publication:

Boca Raton, FL : CRC Press, Taylor & Francis Group, [2020]

Summary:

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

Contents:

Cover

Half Title

Title Page

Copyright Page

Dedication

Table of Contents

Preface

Acknowledgements

List of Figures

List of Tables

1 Internet of Things

1.1 IoT History

1.2 IoT Architecture

1.3 IoT Elements

1.3.1 Wireless Sensor Networks (WSN)

1.3.2 Radio Frequency Identification (RFID)

1.3.2.1 RFID Applications

1.3.3 Data Storage

1.3.4 Challenging Issues

1.4 Data Analytics

1.4.1 IoT Data Sources

1.4.1.1 Industrial Data

1.4.1.2 Business Applications

1.4.1.3 Sensors and Devices

1.4.1.4 Smartphones

1.4.2 Data Processing

1.4.2.1 Data Acquisition

1.4.2.2 Data Transport

1.4.2.3 Data Preprocessing

1.4.3 IoT Technologies

1.4.3.1 Wireless Communication Technologies

1.4.3.2 Data Analysis Schemes

1.4.3.3 Machine Learning

1.4.4 Optimization Techniques

1.4.4.1 Edge Computing Technology

1.4.4.2 Refining the Results

1.5 Steps of Data Preprocessing

1.5.1 Data Formatting

1.5.2 Data Cleaning

1.5.2.1 Duplicate Observations

1.5.2.2 Irrelevant Observations

1.5.2.3 Handling Missing Data

1.5.2.4 Detecting Outliers

1.5.3 Data Reduction Schemes

1.5.3.1 Feature Extraction

1.5.3.2 Feature Selection

1.6 IoT Protocols

1.6.1 Infrastructure Layer (Network/Transport Layer)

1.6.2 Data Protocols

1.6.3 Physical Layer

1.6.4 LPWAN: Low Power Wide Area Network

1.7 IoT Applications

1.7.1 Logistics and Transportation

1.7.2 Home and Workplace

1.7.3 Personal and Social

1.7.4 Health Domain

1.8 Book Outline

1.9 Target Audience

1.10 Summary and What's Next?

1.11 Exercises

2 Cognitive Internet of Things

2.1 Cognitive Devices

2.2 Cognitive in IoT

2.3 CIoT Background

2.4 CIoT Elements

2.4.1 Sensors

2.4.2 Machine Learning

2.4.3 Cloud Storage

2.5 How Do Cognitive Devices Act as Human Assistants?.

2.6 Machine-to-machine Interfaces

2.6.1 Language

2.6.2 Interpersonal Relationship

2.7 Man-to-machine Communication

2.8 Machine-to-web Communication(M2W)

2.9 CIoT Applications

2.9.1 Cognitive Living

2.9.2 Cognitive Cities

2.9.3 Cognitive Health

2.9.4 Auto-casting and Auto-reacting Cognition Systems

2.10 Summary and What's Next?

2.11 Exercises

3 Data Mining in IoT

3.1 Search Engines as a Medium

3.2 Data Creation and Retrieval Scheme

3.3 Data Mining

3.3.1 Data Mining Functions

3.3.1.1 Classification

3.3.2 Relation of Data Science with Machine Learning

3.4 Data Mining in IoT

3.5 Machine Learning in IoT

3.6 Summary and What's Next?

3.7 Exercises

4 Machine Learning Techniques

4.1 Tools to Implement Machine Learning

4.1.1 Python

4.1.2 R

4.1.3 Matlab

4.1.4 Weka

4.2 Experiments

4.2.1 Dataset

4.3 Supervised Learning

4.3.1 Unsupervised Learning

4.4 Classification

4.5 Regression

4.6 Clustering

4.7 Summary and What's Next?

4.8 Exercises

5 R Programming

5.1 Introduction

5.1.1 Basis of the R programming

5.1.2 Installing R

5.1.3 Working in R

5.2 Basic Commands

5.2.1 Assignment

5.2.2 Comments

5.3 Data Types

5.3.1 Basic Data Types

5.3.1.1 Numbers

5.3.1.2 String

5.3.2 Structural Data Type

5.3.2.1 Matrices

5.3.2.2 Arrays

5.3.2.3 Data Frames

5.3.2.4 Lists

5.3.2.5 Factors

5.3.2.6 Vectors

5.3.3 Operators

5.3.3.1 Arithmetic Operators

5.3.3.2 Relational Operators

5.3.3.3 Logical Operators

5.3.4 Graphics

5.3.4.1 Histograms

5.3.4.2 Scatter Diagrams

5.3.4.3 Pie Chart

5.3.5 Basic Statistics

5.3.5.1 Descriptive Statistics

5.3.5.2 Correlation Testing

5.3.6 Packages

5.3.7 Input Parameters Formats for R

5.4 Summary and What's Next?

5.5 Exercises.

6 Machine Learning Paradigms

6.1 Introduction

6.2 Generalizing Input

6.3 Generalizing Output

6.3.1 Decision Tree

6.4 Classification Rules

6.5 Numeric Prediction

6.6 Instance-based Learning

6.6.1 Distance Metric

6.7 Summary and What's Next

6.8 Exercises

7 Different Machine Learning Models

7.1 Linear Method for Regression

7.2 Linear Method for Classification

7.3 Kernel Smoothing Models

7.4 Back Propagation

7.4.1 Radial Basis Function Networks

7.5 Neural Network

7.5.1 The Perceptron

7.6 Bayesian Methods

7.6.1 Bayesian Statistics

7.6.2 Bayesian Inference

7.7 Summary and What's Next

7.8 Exercises

8 Data Processing

8.1 Input Preparation

8.2 Data Preprocessing

8.3 Data Cleaning

8.3.1 The Condensed Nearest Neighbor Rule

8.3.2 Tomek

8.3.3 One-sided Selection

8.3.4 SMOTE

8.3.5 ADASYN Algorithm

8.3.6 SOTU

8.4 Summary and What's Next?

8.5 Exercises

9 Feature Engineering and Optimization

9.1 Feature Reduction

9.1.1 Principal Component Analysis

9.2 Feature Selection

9.2.1 Feature Importance

9.2.1.1 Chi-squared Filter

9.2.1.2 Consistency-based Filter

9.2.1.3 Correlation Filter

9.2.1.4 Entropy-based Filter

9.2.1.5 OneR Algorithm

9.2.1.6 RandomForest Filter

9.2.1.7 RReliefF Filter

9.2.2 Recursive Feature Elimination

9.3 Machine Learning Models

9.3.1 Experiments

9.4 Bagging and Boosting Techniques

9.4.1 Bagging

9.4.2 Boosting

9.5 Ensemble Approach

9.6 Summary and What's Next

9.7 Exercises

10 Evaluation and Validation of Results

10.1 Confusion Matrix

10.2 Correlation

10.2.1 Covariance

10.2.2 Pearson's Correlation

10.2.3 Spearman's Correlation

10.2.4 Matthews' Correlation Coefficient (MCC)

10.3 Coefficient of Determinant: R2

10.4 Accuracy (ACC)

10.5 ROC and AUC.

10.6 Error in Regression

10.6.1 Root Mean Squared Error (RMSE)

10.6.2 Mean Absolute Error (MAE)

10.6.3 Relative Squared Error (RSE)

10.6.4 Relative Absolute Error (RAE)

10.7 Measuring Rates

10.7.1 Sensitivity, Recall, Hit Rate, or True Positive Rate (TPR)

10.7.2 Specificity, Selectivity, or True Negative Rate (TNR)

10.7.3 Precision, Positive Predictive Value (PPV)

10.7.4 Recall, Sensitivity, Hit Rate, or True Positive Rate (TPR)

10.7.5 Fallout, False Positive Rate (FPR)

10.7.6 Miss Rate or False Negative Rate (FNR)

10.7.7 False Discovery Rate (FDR)

10.7.8 False Omission Rate (FOR)

10.8 F Measure

10.9 Summary and What's Next?

10.10 Exercises

11 Solutions

11.1 Chapter 1

11.2 Chapter 2

11.3 Chapter 3

11.4 Chapter 4

11.5 Chapter 5

11.6 Chapter 6

11.7 Chapter 7

11.8 Chapter 8

11.9 Chapter 9

11.10 Chapter 10

12 Dataset

Bibliography

Index.

Notes:

Includes bibliographical references and index.

Description based on print version record.

ISBN:

1-000-76759-0

0-429-34261-6

1-000-76797-3

9780429342615

OCLC:

1190725826