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