IoT and AI Technologies for Sustainable Living: A Practical Handbook

Cover
Half Title
Title Page
Copyright Page
Preface
Table of Contents
Editors
Contributors
1 Rapid Application Development in Cloud Computing with IoT
	1.1 Introduction to Rapid Application Development
	1.2 Features of Rapid Application Development
	1.3 The Rapid Application Development Model
	1.4 Rapid Application Development Model
	1.5 Steps in the High-Speed Application Development Process
		1.5.1 Phase 1: Planning for Exigency Fulfilment
		1.5.2 Phase 2: User Design
		1.5.3 Phase 3: Rapid Structure
		1.5.4 Phase 4: Cutover
	1.6 RAD Model Pros and Benefits
		1.6.1 Does the RAD Model Suit Your Organization?
	1.7 Benefits of RAD Model
	1.8 RAD vs. Other Software Development Models
		1.8.1 RAD Model vs. Traditional System Development Lifecycle
		1.8.2 RAD vs. Agile
	1.9 When to Use RAD Methodology?
	1.10 A Radical Approach to Traditional Application Development
	1.11 Cloud Platform for RAD
		1.11.1 Mendix, a Cloud Platform That Supports Rapid Developers
		1.11.2 Cloud Platform Function Enables Rapid Application Development
	1.12 IoT with Cloud Computing for Rapid Application Development
	1.13 IoT Cloud Application – Architecture
	1.14 Best Practices for Developing a Robust IoT Cloud Application
		1.14.1 Database Design Issues
		1.14.2 Server Extensions and Application Cloning
		1.14.3 IoT Security Applications in the Cloud
		1.14.4 Thinking about Cloud Database Design
	1.15 Three Ways of Achieving Rapid Application Development in IoT
		1.15.1 Access to the Rapid Development of IoT Applications
			1.15.1.1 Hardware Development vs Toy Development
	1.16 The Ability to Simplify IoT Development
		1.16.1 Three Ways to Quickly Develop IoT Applications
			1.16.1.1 Option 1: Use Existing Hardware Platforms to Meet Application Requirements
			1.16.1.2 Option 2: Use the Hardware Platform to Activate the Application
			1.16.1.3 Option 3 – Use Development Tools to Create Pre-Designed IoT Applications on COTS Hardware
		1.16.2 What Do You Think?
	1.17 Global Rapid Application Development Market Is Expected to Reach USD 95.2 Billion by 2025: FIOR Markets
	Bibliography
2 Integration of IoT with Artificial Intelligence in Health Care
	2.1 Introduction
	2.2 The Terms AI and IoT
	2.3 New Trends in the Healthcare Domain
		2.3.1 Early Contributors
		2.3.2 Current Trends
			2.3.2.1 Patient Care
			2.3.2.2 Diagnosis
			2.3.2.3 Virtual and Augmented Reality with AI and IoT in Healthcare
			2.3.2.4 Applying AI and IoT in Air Quality Assessment (AQA)
	2.4 How COVID 19 Use AI and IoT in Treatment?
	2.5 Disadvantages of AI and IoT in the Healthcare Domain
	2.6 Regulations from the Health Insurance Portability and Accountability Act
		2.6.1 Transport Encryption
		2.6.2 Backup
		2.6.3 Authorization
		2.6.4 Integrity
		2.6.5 Storage Encryption
		2.6.6 Disposal
		2.6.7 Business Associate Agreement
	2.7 Conclusion
	Bibliography
3 Significant Role of IoT in Agriculture for Smart Farming
	3.1 Introduction
	3.2 Why There Is a Need for Smart Farming?
	3.3 Agriculture Sensors
		3.3.1 Location Sensors
		3.3.2 Optical Sensors
		3.3.3 Electro Chemical Sensor
		3.3.4 Mechanical Sensors
		3.3.5 Dielectric Soil Moisture Sensors
		3.3.6 Airflow Sensors
	3.4 Sensor Output Applied
	3.5 Smartphone Apps
	3.6 Applications of IoT in Agriculture
	3.7 Global Implications
	3.8 Conclusion
	Bibliography
4 Next Era of Computing with Machine Learning in Different Disciplines
	4.1 Introduction
	4.2 Overview
		4.2.1 Anaemia Classification
		4.2.2 Introduction to CDSS
	4.3 Problem Statement
	4.4 Literature Review
	4.5 Agent-Based CDSS for Anaemia Prediction
		4.5.1 Agent Systems
		4.5.2 Agents
		4.5.3 Multi-Agent Systems (MAS)
		4.5.4 Intra-Agent Communication
		4.5.5 JADE (Java Agent Development Framework)
			4.5.5.1 Agent Class
			4.5.5.2 JADE Agent
			4.5.5.3 Agents Behaviour
			4.5.5.4 Unlock an Agent
	4.6 Agent-Based Architecture
	4.7 Experimentation and Exploration
	4.8 Conclusion and Future Work
	Bibliography
5 Self-Diagnosis in Healthcare Systems Using AI Chatbots
	5.1 Introduction
	5.2 Healthcare Chatbots
	5.3 Healthcare Chatbots in Use
	5.4 Developing Healthcare Chatbots
		5.4.1 Data Pre-Processing
		5.4.2 Model: Training
			5.4.2.1 Custom Models
			5.4.2.2 Deep Learning
			5.4.2.3 NLP
	5.5 Need for Chatbots
	5.6 Research Works
	5.7 Limitations
	5.8 Conclusions
	Bibliography
6 Digital Water: New Approach to Build Efficient Water Management Systems
	6.1 Introduction
	6.2 Artificial Intelligence
	6.3 Applications of AI
		6.3.1 Categories of AI
	6.4 Considerations While Using AI
	6.5 Water Resource Management
	6.6 Digital Water
	6.7 What AI Requires
	6.8 Technologies Used by AI for Effective Water Management
	6.9 Benefits of Working with AI
	6.10 Conclusion
	Bibliography
7 Online Recommendation Using Machine Learning (ML) and NLP
	7.1 Introduction
	7.2 Content-Base Methods
	7.3 Collaborative Filtering
	7.4 Knowledge-Based
	7.5 Hybrid Recommendation System
	7.6 Deep Learning Models for Recommendation Systems
	7.7 Recommendation System Pitfalls
	7.8 NLP-Based RS without User Preferences
		7.8.1 Practical Aspect: The Data
	7.9 Conclusion
	Bibliography
8 Natural Language Processing and Translation Using Machine Learning
	8.1 Introduction to Natural Language Processing
		8.1.1 Examples of NLP
		8.1.2 Stages of NLP
			8.1.2.1 Lexical Analysis and Morphological
			8.1.2.2 Syntactic Analysis (Parsing)
			8.1.2.3 Semantic Analysis
			8.1.2.4 Discourse Integration
			8.1.2.5 Pragmatic Analysis
	8.2 Machine Translation
	8.3 Machine Learning for Natural Language Processing
		8.3.1 Supervised Learning
		8.3.2 Unsupervised Learning
		8.3.3 Semi-Supervised Learning/Reinforced Learning
	8.4 Machine Learning and Natural Language Processing
		8.4.1 Supervised Machine Learning for NLP and Text Analytics
			8.4.1.1 Tokenization
			8.4.1.2 Part-of-Speech Tagging
			8.4.1.3 Named Entity Recognition
			8.4.1.4 Sentiment Analysis
			8.4.1.5 Categorization and Classification
		8.4.2 Unsupervised Machine Learning for Natural Language Processing and Text Analytics
		8.4.3 Using Machine Learning on Natural Language Sentences
		8.4.4 Hybrid Machine Learning Systems for NLP
	8.5 Machine Translation
		8.5.1 Neural MT’s Evolution
		8.5.2 Replacing an Algorithm with a System
		8.5.3 MT with Neural Networks
			8.5.3.1 Google Translate
			8.5.3.2 Translator by Microsoft
			8.5.3.3 Facebook Translator
	8.6 Conclusion
	Bibliography
9 Text and Multimedia Mining through Machine Learning
	9.1 Introduction
		9.1.1 About Text Mining
		9.1.2 About Multimedia Mining
		9.1.3 What Exactly Is Machine Learning
	9.2 Text Mining and Machine Learning
		9.2.1 Text Mining Fundamental Principles
		9.2.2 Text Mining Architecture and Its Process
			9.2.2.1 Information Retrieval
			9.2.2.2 Information Extraction
			9.2.2.3 Choosing ML Algorithms
		9.2.3 Text Mining Techniques
			9.2.3.1 Word Frequency Analysis
			9.2.3.2 Collocation Analysis
			9.2.3.3 Concordance Analysis
		9.2.4 Feature Selection Using Machine Learning
			9.2.4.1 Multivariate Relative Discrimination Criterion
			9.2.4.2 Minimal Redundancy-Maximal New Classification Information
		9.2.5 Feature Extraction Using Machine Learning
			9.2.5.1 Bag of Words (BOW)
			9.2.5.2 TF-IDF
			9.2.5.3 Word2Vec
		9.2.6 Machine Learning Algorithms for Text Mining
		9.2.7 Accuracy, Precision, Recall, F1 Score, and Cross-Validation
		9.2.8 Challenges of ML Text Analysis
	9.3 Multimedia Mining and Machine Learning
		9.3.1 Multimedia Mining Process
		9.3.2 Machine Learning Algorithms for Multimedia Mining
	9.4 Conclusion
	Bibliography
10 Application of IoT and Block Chaining for Business Analysis
	10.1 Introduction
	10.2 IoT
	10.3 Introduction to Collaborating Technologies
	10.4 Blockchain Technology
		10.4.1 Blockchain Technology: Powering the Business of the Future
		10.4.2 New Wave of Economic Opportunity and Digital Innovation
	10.5 Advantages of Blockchain and IoT Collaboration
	10.6 Business Analysis
	10.7 Business Analyst
	10.8 Application of IoT and Blockchain Technology for Business Analysis
		10.8.1 Publicity
		10.8.2 Decentralization
		10.8.3 Resiliency
		10.8.4 Security and Speed
		10.8.5 Cost Saving and Immutability
		10.8.6 Privacy
	10.9 Conclusion
	Bibliography
11 Applications of Body Sensor Network in Healthcare
	11.1 Introduction
		11.1.1 Sensor Network
		11.1.2 Wireless Sensor Networks
		11.1.3 Body Sensor Network
	11.2 Wireless BSN Architecture
	11.3 Sensors Used for Treatment and Health Observing
		11.3.1 An Introduction To Sensors in Healthcare
		11.3.2 Non-Invasive Applications
			11.3.2.1 Electrophysiological Measurement
			11.3.2.2 Environmental, Biochemical and Biophysical Sensors
	11.4 Future Scope in Healthcare
	11.5 Future Trends
	11.6 Conclusion
	Bibliography
12 Sentimental Analysis with Web Engineering and Web Mining
	12.1 Introduction
	12.2 Constituents and Approaches
		12.2.1 Literature Aspects
	12.3 Proposed Methodology
	12.4 Outcomes and Explanations
		12.4.1 Movie Review Dataset
		12.4.2 OHSUMED Dataset
		12.4.3 Outcomes
	12.5 Conclusion
	Bibliography
13 Big Data in Cloud Computing - A Defense Mechanism
	13.1 Introduction
	13.2 Overview of Cloud
		13.2.1 Important Characteristics
			13.2.1.1 On-Demand Self-Service
			13.2.1.2 Broad Network Access
			13.2.1.3 Resource Pooling
			13.2.1.4 Rapid Elasticity
			13.2.1.5 Measured Service
		13.2.2 Deployment Models
			13.2.2.1 Private Cloud
			13.2.2.2 Community Cloud
			13.2.2.3 Public Cloud
			13.2.2.4 Hybrid Cloud
		13.2.3 Service Models
			13.2.3.1 Software as a Service (SaaS)
			13.2.3.2 Platform as a Service (PaaS)
			13.2.3.3 Infrastructure as a Service (IaaS)
	13.3 Big Data: Overview
		13.3.1 Characteristics of Big Data
			13.3.1.1 Volume
			13.3.1.2 Veracity
			13.3.1.3 Value
			13.3.1.4 Variety
			13.3.1.5 Velocity
		13.3.2 Significance of Big Data
		13.3.3 Big Data in Cloud
	13.4 Security Issues Faced by the Big Data in Cloud
		13.4.1 Confidentiality
		13.4.2 Integrity
		13.4.3 Authenticity
		13.4.4 Availability
		13.4.5 DoS and DDoS Attacks
		13.4.6 MitM Attack
		13.4.7 Sniffer Attacks
		13.4.8 Spoofing
		13.4.9 SQL Injection Attack
		13.4.10 Cross-Site Scripting (XSS)
		13.4.11 Vulnerability in Data Security
		13.4.12 Data Breach
	13.5 Security Measures for Big Data in Cloud
		13.5.1 Encryption
		13.5.2 Hashing
		13.5.3 Digital Signature
		13.5.4 DDoS Prevention
		13.5.5 Secure Sockets Layer (SSL)/Transport Layer Security (TLS)
		13.5.6 Prevention of SQL Injection
		13.5.7 Prevention of Cross-Site Scripting (XSS) Attacks
		13.5.8 Physical Server Security
		13.5.9 Virtual Machine (VM) Security
	13.6 Conclusion
	Bibliography
14 Sound and Precise Analysis of Web Applications for Injection Vulnerabilities
	14.1 Introduction
	14.2 Related Work
		14.2.1 Injection Vulnerabilities
		14.2.2 SQL Injection
		14.2.3 Roslyn: Microsoft.NET Compiler Platform
		14.2.4 Microsoft Azure Machine Learning (Azure ML)
	14.3 Proposed Architecture
	14.4 Data Collection and Preparation
		14.4.1 Independent Variable
		14.4.2 Dependent Variable
		14.4.3 Feature Selection
	14.5 The Implementation of the Framework
	14.6 Experimental Results
		14.6.1 Evaluation of the Models
		14.6.2 Verification and Validation of the Compiler Platform
	14.7 Conclusions and Future Work
	Bibliography
15 Multimedia Applications in Forensics, Security and Intelligence
	15.1 Introduction
	15.2 Multimedia Application and Its Need
	15.3 Forensics
	15.4 Multimedia Applications in Security and Intelligence
	15.5 Multimedia Encryption
	15.6 Biometrics in Digital Rights Management
	15.7 Digital Millennium Copyright Act
	15.8 Secure Media Streaming and Secure Transcoding
	15.9 Approaches to Multimedia Authentication
		15.9.1 Active Image Authentication
		15.9.2 Passive Image Authentication
	15.10 Security Intelligence
	15.11 A Glimpse at the Future
	15.12 Conclusion
	Bibliography
16 Advancements and Innovation in Digital Marketing and SEO
	16.1 Introduction
	16.2 Marketing
		16.2.1 Shift from Traditional Marketing to Digital Marketing
	16.3 Digital Marketing
		16.3.1 Digital Marketing: Then and Now
		16.3.2 AI in Digital Marketing
	16.4 Omni-Channel Marketing
		16.4.1 Augmented Reality (AR) and Immersive Technologies
		16.4.2 Augmented and Predictive Analytics
	16.5 Marketing Automation
	16.6 Social Media Marketing
		16.6.1 Social Media Stories
	16.7 Mobile Marketing
		16.7.1 Mobile Website
		16.7.2 Mobile Applications
	16.8 Geo-Fencing Marketing
	16.9 Influencer Marketing
	16.10 Digital Advertising
		16.10.1 Display Advertising
		16.10.2 Audience Targeting
		16.10.3 Programmatic Advertising
		16.10.4 Search Advertising
			16.10.4.1 Visual Search
			16.10.4.2 Voice Search, Voice Assistants, and Smart Speakers
		16.10.5 Banner and Video Advertising
		16.10.6 Video Advertising
		16.10.7 Social Media Advertising
			16.10.7.1 Precise Targeting
			16.10.7.2 Ad Placement
			16.10.7.3 Ad Bidding
		16.10.8 Mobile Advertising
	16.11 Search Engine Optimization
		16.11.1 SEO: Then and Now
			16.11.1.1 Google Panda: The Game Changer Algorithm for Content
			16.11.1.2 Google Penguin
			16.11.1.3 Google Hummingbird
			16.11.1.4 On-Site SEO
			16.11.1.5 Off-Site SEO
			16.11.1.6 SEO Best Practices
			16.11.1.7 Title Tag
			16.11.1.8 Meta Descriptions
			16.11.1.9 URL
			16.11.1.10 Content of Page
			16.11.1.11 Image ALT Text
			16.11.1.12 Page Ranking Factors
	16.12 Benefits
	Bibliography
17 Advanced Wireless Solutions (Case Studies on Application Scenarios)
	17.1 Foreword and Preamble to Wireless Technologies
	17.2 Applications of Wireless Networks
	17.3 Internet of Things and Advanced Scenarios
	17.4 Key Cases and Applications of IoT
		17.4.1 Smart Homes
		17.4.2 Healthcare System
		17.4.3 Traffic Management
		17.4.4 Smart Farming
		17.4.5 Business Automation
		17.4.6 Defense Application
		17.4.7 Woman Security Bands
		17.4.8 Connected Cars
	17.5 Key Technologies and Standards with Wireless Environment
	17.6 Key Features of Wireless Environment
	17.7 Advanced Cases and Technologies with Internet of Things (IoT)
	17.8 Cloud Platforms for MQTT
		17.8.1 CloudMQTT
		17.8.2 DIoTY
		17.8.3 Cloud Integration with Node-RED
		17.8.4 Dynamic Key-Based Communication in IoT Scenario
	17.9 Conclusion
	Bibliography