Big-Data Analytics for Cloud, IoT and Cognitive Computing

Kai Hwang

ISBN: 9788126570430

432 pages

INR 839


This book blends together big-data theories with emerging technologies on smart clouds over the Internet of Things (IoT). Data analysts and computer scientists must learn how to use clouds and IoT effectively to discover new knowledge, or to make critical decisions intelligently. This book aims to close the gaps between these learning groups, and encourages mutual learning and collaborative work between data scientists and cloud programmers. The world-renowned authors take a technological fusion approach to integrating big-data theories, cloud design principles, IoT sensing, machine learning, data analytics, and Hadoop and Spark programming in a single volume.

About the Authors


About the Companion Website


Part 1 Big Data, Clouds and Internet of Things

1. Big Data Science and Machine Intelligence

1.1 Enabling Technologies for Big Data Computing  

1.2 Social-Media, Mobile Networks and Cloud Computing

1.3 Big Data Acquisition and Analytics Evolution

1.4 Machine Intelligence and Big Data Applications

1.5 Conclusions


2. Smart Clouds, Virtualization and Mashup Services

2.1 Cloud Computing Models and Services

2.2 Creation of Virtual Machines and Docker Containers

2.3 Cloud Architectures and Resources Management

2.4 Case Studies of IaaS, PaaS and SaaS Clouds

2.5 Mobile Clouds and Inter-Cloud Mashup Services

2.6 Conclusions


3. IoT Sensing, Mobile and Cognitive Systems

3.1 Sensing Technologies for Internet of Things

3.2 IoT Interactions with GPS, Clouds and Smart Machines

3.3 Radio Frequency Identification (RFID)

3.4 Sensors, Wireless Sensor Networks and GPS Systems

3.5 Cognitive Computing Technologies and Prototype Systems

3.6 Conclusions


Part 2 Machine Learning and Deep Learning Algorithms

4. Supervised Machine Learning Algorithms

4.1 Taxonomy of Machine Learning Algorithms

4.2 Regression Methods for Machine Learning

4.3 Supervised Classification Methods

4.4 Bayesian Network and Ensemble Methods

4.5 Conclusions


5. Unsupervised Machine Learning Algorithms

5.1 Introduction and Association Analysis

5.2 Clustering Methods without Labels

5.3 Dimensionality Reduction and Other Algorithms

5.4 How to Choose Machine Learning Algorithms?

5.5 Conclusions


6. Deep Learning with Artificial Neural Networks

6.1 Introduction

6.2 Artificial Neural Networks (ANN)

6.3 Stacked Auto Encoder and Deep Belief Network

6.4 Convolutional Neural Networks (CNN) and Extensions

6.5 Conclusions


Part 3 Big Data Analytics for Health-Care and Cognitive Learning

7. Machine Learning for Big Data in Healthcare Applications

7.1 Healthcare Problems and Machine Learning Tools

7.2 IoT-based Healthcare Systems and Applications

7.3 Big Data Analytics for Healthcare Applications

7.4 Emotion-Control Healthcare Applications

7.5 Conclusions 335


8. Deep Reinforcement Learning and Social Media Analytics

8.1 Deep Learning Systems and Social Media Industry

8.2 Text and Image Recognition using ANN and CNN

8.3 DeepMind with Deep Reinforcement Learning

8.4 Data Analytics for Social-Media Applications

8.5 Conclusions


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