Big Data, IoT, and Machine Learning: Tools and Applications (Internet of Everything (IoE))

Cover
Half Title
Series Page
Title Page
Copyright Page
Table of Contents
Preface
Acknowledgement
Editors
Contributors
Section I Applications of Machine Learning
	Chapter 1 Machine Learning Classifiers
		1.1 Introduction
		1.2 Machine Learning Overview
			1.2.1 Steps in Machine Learning
			1.2.2 Performance Measures for Machine Learning Algorithms
				1.2.2.1 Confusion Matrix
		1.3 Machine Learning Approaches
		1.4 Types of Machine Learning
			1.4.1 Supervised Learning
			1.4.2 Unsupervised Learning
			1.4.3 Semi-Supervised Learning
			1.4.4 Reinforcement Learning
		1.5 A Taste of Classification
			1.5.1 Binary Classification
			1.5.2 Multiclass Classification
			1.5.3 Multilabel Classification
			1.5.4 Linear Classification
			1.5.5 Non-Linear Classification
		1.6 Machine Learning Classifiers
			1.6.1 Python for Machine Learning Classification
			1.6.2 Decision Tree
				1.6.2.1 Building a Decision Tree
				1.6.2.2 Induction
				1.6.2.3 Best Attribute Selection
				1.6.2.4 Pruning
			1.6.3 Random Forests
				1.6.3.1 Evaluating Random Forest
				1.6.3.2 Tuning Parameters in Random Forest
				1.6.3.3 Splitting Rule
			1.6.4 Support Vector Machine
			1.6.5 Neural Networks
				1.6.5.1 Back Propagation Algorithm
			1.6.6 Logistic Regression
			1.6.7 k-Nearest Neighbor
				1.6.7.1 The k-NN Algorithm
		1.7 Model Selection and Validation
			1.7.1 Hyperparameter Tuning and Model Selection
			1.7.2 Bias, Variance and Model Selection
			1.7.3 Model Validation
		Conclusion
		References
	Chapter 2 Dimension Reduction Techniques
		2.1 Dimension Reduction
		2.2 Dimension Reduction Techniques
			2.2.1 Feature Selection
			2.2.2 Feature Extraction
		2.3 Linear Dimension Reduction Techniques
			2.3.1 Principal Component Analysis
			2.3.2 Singular Value Decomposition
			2.3.3 Latent Discriminant Analysis
			2.3.4 Independent Component Analysis
			2.3.5 Projection Pursuits
			2.3.6 Latent Semantic Analysis
			2.3.7 Locality Preserving Projection
		2.4 Nonlinear Dimension Reduction Techniques
			2.4.1 Kernel Principal Component Analysis
			2.4.2 Isomap
			2.4.3 Locally Linear Embedding
			2.4.4 Self Organising Map
			2.4.5 Learning Vector Quantisation
			2.4.6 t-Stochastic Neighbor Embedding
		2.5 Conclusion and Future Directions
		References
	Chapter 3 Reviews Analysis of Apple Store Applications Using Supervised Machine Learning
		3.1 Introduction
		3.2 Literature Review
			3.2.1 Machine Learning Algorithms
			3.2.2 Feature Extraction Algorithms
		3.3 Proposed Methodology
			3.3.1 Data Collection
			3.3.2 Feature Extraction
			3.3.3 Data Analysis and Sentiment Analysis
				Text Processing
			3.3.4 Text Normalisation
		3.4 Feature Extraction Algorithm
			3.4.1 CountVectorizer
			3.4.2 TfidfVectorizer (TF–IDF)
		3.5 Supervised ML Classification
		3.6 Experiment Design
		3.7 Experimental Results and Analysis
		3.8 Recommendation and Future Work
		3.9 Conclusion
		References
	Chapter 4 Machine Learning for Biomedical and Health Informatics
		4.1 Introduction
		4.2 Overview of Machine Learning Applications
		4.3 Impact of Machine Learning in Healthcare
		4.4 Recent Trends in Machine Learning in the Biomedical Field
		4.6 Supervised Learning Methods
		4.7 Unsupervised Learning Methods
		4.8 Reinforcement Learning (RL)
		4.9 Semi-Supervised Learning
			4.9.1 K-Nearest Neighbor (KNN)
			4.9.2 Naive Bayes (NB)
			4.9.3 Decision Trees (DT)
			4.9.4 Support Vector Machine (SVM)
			4.9.5 Artificial Neural Network (ANN)
		4.10 Deep Learning (DL)
			4.10.1 Recurrent Neural Network (RNN)
			4.10.2 Convolutional Neural Network (CNN)
			4.10.3 Deep Learning in Healthcare
		4.11 Existing Works on ML for Biomedical and Health Informatics
		4.12 Conclusion and Future Issues
		References
	Chapter 5 Meta-Heuristic Algorithms: A Concentration on the Applications in Text Mining
		5.1 Introduction
		5.2 Literature Review of Meta-Heuristic Algorithms
			5.2.1 Genetic Algorithms (GA)
			5.2.2 Ant Colony Optimisation (ACO)
			5.2.3 Ant Lion Optimiser (ALO)
			5.2.4 Bat Algorithm (BA)
			5.2.5 Cat Swarm Optimisation Algorithm (CSO)
			5.2.6 Crow Search Algorithm (CSA)
			5.2.7 Cuckoo Optimisation Algorithm (COA)
			5.2.8 Bee Colony Optimisation (BCO)
			5.2.9 Particle Swarm Optimisation (PSO)
			5.2.10 Firefly Algorithm (FA)
			5.2.11 Tabu Search Algorithm (TS)
		5.3 Proposed Model for Application of Meta-Heuristic in Text Mining
		5.4 Future Research
		5.5 Conclusion
		References
	Chapter 6 Optimizing Text Data in Deep Learning: An Experimental Approach
		6.1 Introduction
		6.2 Existing Structure of Deep Learning
			6.2.1 Neural Networks
		6.3 Problems in Existing Definition
		6.4 Research Trust
		6.5 Text Classification
			6.5.1 Steps of Text Classification
			6.5.2 Developing a GUI-Based Deep Learning Application to Perform Text Classification on Reuters Dataset
		6.6 Experimentation
			6.6.1 Code
		6.7 Conclusion and Future Scope
		References
Section II Big Data, Cloud and Internet of Things
	Chapter 7 Latest Data and Analytics Technology Trends That Will Change Business Perspectives
		7.1 Introduction
		7.2 Strategic Planning Assumptions and Analysis
		7.3 Driving Factors for Latest Data and Analytics Technology Trends
			7.3.1 Trend 1: Augmented Analytics
				7.3.1.1 What Does It Enable?
				7.3.1.2 Use Cases
				7.3.1.3 Recommendations
			7.3.2 Trend 2: Augmented Data Management
				7.3.2.1 What Does It Enable?
				7.3.2.2 How Does This Impact Your Organisation and Skills?
				7.3.2.3 Use Cases
				7.3.2.4 Recommendations
			7.3.3 Trend 3: NLP and Conversational Analytics
				7.3.3.1 What Does It Enable?
				7.3.3.2 How Does This Impact Your Organisation and Skills?
				7.3.3.3 Use Cases
				7.3.3.4 Recommendations
			7.3.4 Trend 4: Graph Analytics
				7.3.4.1 What Does It Enable?
				7.3.4.2 How Does This Impact Your Organisation and Skills?
				7.3.4.3 Use Cases
			7.3.5 Trend 5: Commercial AI/ML Will Dominate the Market over Open Source
				7.3.5.1 What Does It Enable?
				7.3.5.2 How Does This Impact Your Organisation and Skills?
				7.3.5.3 Use Cases
				7.3.5.4 Recommendations
			7.3.6 Trend 6: Data Fabric
				7.3.6.1 What Does It Enable?
				7.3.6.2 How Does This Impact Your Organisation and Skills?
				7.3.6.3 Use Cases
				7.3.6.4 Recommendations
			7.3.7 Trend 7: Explainable AI
				7.3.7.1 What Does It Enable?
				7.3.7.2 How Does This Impact Your Organisation and Skills?
				7.3.7.3 Use Cases
				7.3.7.4 Recommendations
			7.3.8 Trend 8: Blockchain in Data and Analytics
				7.3.8.1 What Does It Enable?
				7.3.8.2 How Does This Impact Your Organisation and Skills?
				7.3.8.3 Use Cases
				7.3.8.4 Recommendations
			7.3.9 Trend 9: Continuous Intelligence
				7.3.9.1 What Does It Enable?
				7.3.9.2 How Does This Impact Your Organisation and Skills?
				7.3.9.3 Use Cases
				7.3.9.4 Recommendations
			7.3.10 Trend 10: Persistent Memory Servers
				7.3.10.1 What Does It Enable?
				7.3.10.2 How Does This Impact Your Organisation and Skills?
				7.3.10.3 Use Cases
				7.3.10.4 Recommendations
		References
	Chapter 8 A Proposal Based on Discrete Events for Improvement of the Transmission Channels in Cloud Environments and Big Data
		8.1 Introduction
		8.2 Big Data
		8.3 Cloud Computing
		8.4 Big Data and Cloud Computing
		8.5 Discrete Event, Communication Channel and Modulation
		8.6 Methodology
		8.7 Results and Discussion
		8.8 Future Research Directions
		8.9 Conclusion
		References
	Chapter 9 Heterogeneous Data Fusion for Healthcare Monitoring: A Survey
		9.1 Introduction
		9.2 Sensor Data Fusion
			9.2.1 Sensor Data Fusion in the Healthcare Environment
		9.3 Healthcare Data Fusion: Opportunities and Challenges
			9.3.1 Healthcare Data Fusion: Opportunities
			9.3.2 Healthcare Data Fusion: Challenges
		9.4 Evaluation Framework
			9.4.1 Middleware Architecture Type
			9.4.2 Context Awareness
			9.4.3 Semantic Interaction
			9.4.4 Dynamic Configuration
			9.4.5 Fusion Complexity
			9.4.6 Actuation Management
			9.4.7 Data Processing Type
			9.4.8 Cross Domain Portability
			9.4.9 Implementation
			9.4.10 Performance Evaluation
			9.4.11 Data Security and Privacy
		9.5 Application of Data Fusion for Health Monitoring
		9.6 Conclusion
		References
	Chapter 10 Discriminative and Generative Model Learning for Video Object Tracking
		10.1 Introduction: Artificial Intelligence and Computer Vision
		10.2 Computer Vision
		10.3 Introduction to Video Object Tracking
		10.4 Appearance Model of the Target
			10.4.1 Construction of Generative Appearance Model
			10.4.2 Generative Appearance Model
		10.5 Motion Model
		10.6 Proposed Method of Online Parameter Learning
		10.7 Experimental Results
		10.8 Conclusion
		References
	Chapter 11 Feature, Technology, Application, and Challenges of Internet of Things
		11.1 Introduction
		11.2 About the Web of Things
		11.3 IoT as Tool for Change in Technology
		11.4 Characteristics of IoT
			11.4.1 Heterogeneity
			11.4.2 Interconnectivity
			11.4.3 Dynamic Changes
			11.4.4 Enormous Scale
			11.4.5 Safety
			11.4.6 Connectivity
		11.5 Applications of IoT
			11.5.1 Smarter Cities
			11.5.2 Smarter Home
			11.5.3 Smart Energy
			11.5.4 Smart Health
			11.5.5 Environmental Observation (Smart Appliances)
			11.5.6 Smart Vesture and Good Accessories (Wearable)
			11.5.7 Hobbyists
		11.6 Challenges
			11.6.1 Scalability
			11.6.2 Self-Organizing
			11.6.3 Data Volumes
			11.6.4 Data Interpretation
			11.6.5 Interoperability
			11.6.6 Automatic Discovery
			11.6.7 Software Complexity
			11.6.8 Security and Privacy
			11.6.9 Wireless Communications
		11.7 The Problem of Overlays
			11.7.1 Redundant Overlay Networks
			11.7.2 Management Complexity
			11.7.3 Precious Physical Space
		11.8 Rise of Converged APs
		11.9 Addressing Convergence Challenges
			11.9.1 Radiofrequency (RF) Interference
			11.9.2 Packet Coordination
			11.9.3 Antenna Design
		11.10 Future Technologies of IoT
			11.10.1 Cloud Computing
			11.10.2 Shared Computing
			11.10.3 Cloud Computing
			11.10.4 Wireless Fidelity (Wi-Fi)
			11.10.5 Bluetooth
			11.10.6 ZigBee
		11.11 Cloud Computing in IoT
			11.11.1 Remote Process Power
			11.11.2 Lowers the Entry Bar for Suppliers Who Lack the Infrastructure
			11.11.3 Analytics and Observation
			11.11.4 User Security and Privacy
		11.12 Challenges in Integration of Cloud Computing and IoT
			11.12.1 No Uniformity
			11.12.2 Performance
			11.12.3 Dependableness
			11.12.4 Massive Scale
			11.12.5 Big Data
		11.13 Conclusion
		References
	Chapter 12 Analytical Approach to Sustainable Smart City Using IoT and Machine Learning
		12.1 Introduction to Smart City
		12.2 Background
		12.3 Smart City Architecture
			12.3.1 Sensing Layer
			12.3.2 Transmission Layer
			12.3.3 Data Management Layer
			12.3.4 Application Layer
		12.4 Major Smartest Cities in the World
			12.4.1 Reykjavik
			12.4.2 Tokyo
			12.4.3 Paris
			12.4.4 London
			12.4.5 New York City
		12.5 Role of Fog Computing in Smart City
		12.6 Analytical Approach to Sustainability in the Smart City
		12.7 Enabling Technologies for Sustainability
			12.7.1 IoT
			12.7.2 Machine Learning
			12.7.3 Big Data
		12.8 Proposed Model for the Analytical Framework of a Sustainable Smart City
		12.9 Conclusion
		References
	Chapter 13 Traffic Flow Prediction with Convolutional Neural Network Accelerated by Spark Distributed Cluster
		13.1 Introduction
		13.2 Background and Related Studies
			13.2.1 Machine Learning
			13.2.2 Deep Learning
			13.2.3 CNN
			13.2.4 Spark Cluster and Distributed Environment Acceleration
		13.3 Existing Machine Learning and Deep Learning Methods
			13.3.1 Decision Tree
			13.3.2 Random Forest
			13.3.3 SVM
			13.3.4 KNN
			13.3.5 CNN
			13.3.6 Comparison of Performance
		13.4 Proposed Method: CNN with Spark
			13.4.1 Workflow
			13.4.2 CNN Model Design and Modification
				13.4.2.1 Learning Rate
				13.4.2.2 Activation Function
				13.4.2.3 Pooling Layer
				13.4.2.4 Final CNN Model
			13.4.3 Spark Cluster Configuration
				13.4.3.1 Four Modes
				13.4.3.2 Memory Layout
		13.5 Performance Evaluation
			13.5.1 Experiment Setup
				13.5.1.1 Dataset
				13.5.1.2 Profiling Tool
				13.5.1.3 Four Measures
			13.5.2 Results and Analysis
				13.5.2.1 CNN Model Optimisation
				13.5.2.2 Spark Cluster Tuning
			13.5.3 Summary of Performance Evaluation
		13.6 Conclusion and Future Directions
		References
Index