Smart Systems for Industrial Applications (Artificial Intelligence and Soft Computing for Industrial Transformation)

Cover
Half-Title Page
Series Page
Title Page
Copyright Page
Contents
Preface
1 AI-Driven Information and Communication Technologies, Services, and Applications for Next-Generation Healthcare System
	1.1 Introduction: Overview of Communication Technology and Services for Healthcare
	1.2 AI-Driven Communication Technology in Healthcare
		1.2.1 Technologies Empowering in Healthcare
		1.2.2 AI in Diagnosis
		1.2.3 Conversion Protocols
		1.2.4 AI in Treatment Assistant
		1.2.5 AI in the Monitoring Process
		1.2.6 Challenges of AI in Healthcare
	1.3 AI-Driven mHealth Communication System and Services
		1.3.1 Embedding of Handheld Imaging Platforms With mHealth Devices
		1.3.2 The Adaptability of POCUS in Telemedicine
	1.4 AI-Driven Body Area Network Communication Technologies and Applications
		1.4.1 Features
		1.4.2 Communication Architecture of Wireless Body Area Networks
		1.4.3 Role of AI in WBAN Architecture
		1.4.4 Medical Applications
		1.4.5 Nonmedical Applications
		1.4.6 Challenges
	1.5 AI-Driven IoT Device Communication Technologies and Healthcare Applications
		1.5.1 AI’s and IoT’s Role in Healthcare
		1.5.2 Creating Efficient Communication Framework for Remote Healthcare Management
		1.5.3 Developing Autonomous Capability is Key for Remote Healthcare Management
		1.5.4 Enabling Data Privacy and Security in the Field of Remote Healthcare Management
	1.6 AI-Driven Augmented and Virtual Reality–Based Communication Technologies and Healthcare Applications
		1.6.1 Clinical Applications of Communication-Based AI and Augmented Reality
		1.6.2 Surgical Applications of Communication-Based on Artificial Intelligence and Augmented Reality
	References
2 Pneumatic Position Servo System Using Multi-Variable Multi-Objective Genetic Algorithm–Based Fractional-Order PID Controller
	2.1 Introduction
	2.2 Pneumatic Servo System
	2.3 Existing System Analysis
	2.4 Proposed Controller and Its Modeling
		2.4.1 Modeling of Fractional-Order PID Controller
			2.4.1.1 Fractional-Order Calculus
			2.4.1.2 Fractional-Order PID Controller
	2.5 Genetic Algorithm
		2.5.1 GA Optimization Methodology
			2.5.1.1 Initialization
			2.5.1.2 Fitness Function
			2.5.1.3 Evaluation and Selection
			2.5.1.4 Crossover
			2.5.1.5 Mutation
		2.5.2 GA Parameter Tuning
	2.6 Simulation Results and Discussion
		2.6.1 MATLAB Genetic Algorithm Tool Box
		2.6.2 Simulation Results
			2.6.2.1 Reference = 500 (Error)
			2.6.2.2 Reference = 500
			2.6.2.3 Reference = 1,500
			2.6.2.4 Analysis Report
	2.7 Hardware Results
		2.7.1 Reference = 500
		2.7.2 Reference = 1,500
	2.8 Conclusion
	References
3 Improved Weighted Distance Hop Hyperbolic Prediction–Based Reliable Data Dissemination (IWDH-HP-RDD) Mechanism for Smart Vehic
	3.1 Introduction
	3.2 Related Work
		3.2.1 Extract of the Literature
	3.3 Proposed Improved Weighted Distance Hop Hyperbolic Prediction–Based Reliable Data Dissemination (IWDH-HP-RDD) Mechanism for
	3.4 Simulation Results and Analysis of the Proposed IWDH-HP-RDD Scheme
	3.5 Conclusion
	References
4 Remaining Useful Life Prediction of Small and Large Signal Analog Circuits Using Filtering Algorithms
	4.1 Introduction
	4.2 Literature Survey
	4.3 System Architecture
	4.4 Remaining Useful Life Prediction
		4.4.1 Initialization
		4.4.2 Proposal Distribution
		4.4.3 Time Update
		4.4.4 Relative Entropy in Particle Resampling
		4.4.5 RUL Prediction
	4.5 Results and Discussion
	4.6 Conclusion
	References
5 AI in Healthcare
	5.1 Introduction
		5.1.1 What is Artificial Intelligence?
		Machine Learning – Neural Networks and Deep Learning
		Natural Language Processing
	5.2 Need of AI in Electronic Health Record
		5.2.1 How Does AI/ML Fit Into EHR?
		5.2.2 Natural Language Processing (NLP)
		5.2.3 Data Analytics and Representation
		5.2.4 Predictive Investigation
		5.2.5 Administrative and Security Consistency
	5.3 The Trending Role of AI in Pharmaceutical Development
		5.3.1 Drug Discovery and Design
		5.3.2 Diagnosis of Biomedical and Clinical Data
		5.3.3 Rare
		and Epidemic Prediction
		5.3.4 Applications of AI in Pharma
		5.3.5 AI in Marketing
		5.3.6 Review of the Companies That Use AI
	5.4 AI in Surgery
		5.4.1 3D Printing
		5.4.2 Stem Cells
		5.4.3 Patient Care
		5.4.4 Training and Future Surgical Team
	5.5 Artificial Intelligence in Medical Imaging
		5.5.1 In Cardio Vascular Abnormalities
		5.5.2 In Fractures and Musculoskeletal Injuries
		5.5.3 In Neurological Diseases and Thoracic Complications
		5.5.4 In Detecting Cancers
	5.6 AI in Patient Monitoring and Wearable Health Devices
		Monitoring Health Through Wearable’s and Personal Devices
		5.6.2 Making Smartphone Selfies Into Powerful Diagnostic Tools
	5.7 Revolutionizing of AI in Medicinal DecisionMaking at the Bedside
	5.8 Future of AI in Healthcare
	5.9 Conclusion
	References
6 Introduction of Artificial Intelligence
	6.1 Introduction
		6.1.1 Intelligence
		6.1.2 Types of Intelligence
		6.1.3 A Brief History of Artificial Intelligence From 1923 till 2000
	6.2 Introduction to the Philosophy Behind Artificial Intelligence
		6.2.1 Programming With and Without AI
	6.3 Basic Functions of Artificial Intelligence
		6.3.1 Categories of Artificial Intelligence
			6.3.1.1 Reactive Machines
			6.3.1.2 Limited Memory
			6.3.1.3 Theory of Mind
			6.3.1.4 Self-Awareness
	6.4 Existing Technology and Its Review
		6.4.1 Tesla’s Autopilot
		6.4.2 Boxever
		6.4.3 Fin Gesture
		6.4.4 AI Robot
		6.4.5 Vinci
		6.4.6 AI Glasses
		6.4.7 Affectiva
		6.4.8 AlphaGo Beats
		6.4.9 Cogito
		6.4.10 Siri and Alexa
		6.4.11 Pandora’s
	6.5 Objectives
		6.5.1 Major Goals
		6.5.2 Need for Artificial Intelligence
		6.5.3 Distinction Between Artificial Intelligence and Business Intelligence
	6.6 Significance of the Study
		6.6.1 Segments of Master Frameworks
			6.6.1.1 User Interface
			6.6.1.2 Expert Systems
			6.6.1.3 Inference Engine
			6.6.1.4 Voice Recognition
			6.6.1.5 Robots
	6.7 Discussion
		6.7.1 Artificial Intelligence and Design Practice
	6.8 Applications of AI
		6.8.1 AI Has Been Developing a Huge Number of Tools Necessary to Find a Solution to the Most Challenging Problems in Computer Sc
		6.8.2 Future of AI
	6.9 Conclusion
	References
7 Artificial Intelligence in Healthcare: Algorithms and Decision Support Systems
	7.1 Introduction
	7.2 Machine Learning Work Flow and Applications in Healthcare
		7.2.1 Formatting and Cleaning Data
		7.2.2 Supervised and Unsupervised Learning
		7.2.3 Linear Discriminant Analysis
		7.2.4 K-Nearest Neighbor
		7.2.5 K-Means Clustering
		7.2.6 Random Forest
		7.2.7 Decision Tree
		7.2.8 Support Vector Machine
		7.2.9 Artificial Neural Network
		7.2.10 Natural Language Processing
		7.2.11 Deep Learning
		7.2.12 Ensembles
	7.3 Commercial Decision Support Systems Based on AI
		7.3.1 Personal Genome Diagnostics
		7.3.2 Tempus
		7.3.3 iCarbonX—Manage Your Digital Life
		7.3.4 H2O.ai
		7.3.5 Google DeepMind
		7.3.6 Buoy Health
		7.3.7 PathAI
		7.3.8 Beth Israel Deaconess Medical Center
		7.3.9 Bioxcel Therapeutics
		7.3.10 BERG
		7.3.11 Enlitic
		7.3.12 Deep Genomics
		7.3.13 Freenome
		7.3.14 CloudMedX
		7.3.15 Proscia
	7.4 Conclusion
	References
8 Smart Homes and Smart Cities
	8.1 Smart Homes
		8.1.1 Introduction
		8.1.2 Evolution of Smart Home
		8.1.3 Smart Home Architecture
			8.1.3.1 Smart Electrical Devices or Smart Plugs
			8.1.3.2 Home Intelligent Terminals or Home Area Networks
			8.1.3.3 Master Network
		8.1.4 Smart Home Technologies
		8.1.5 Smart Grid Technology
		8.1.6 Smart Home Applications
			8.1.6.1 Smart Home in the Healthcare of Elderly People
			8.1.6.2 Smart Home in Education
			8.1.6.3 Smart Lighting
			8.1.6.4 Smart Surveillance
		8.1.7 Advantages and Disadvantages of Smart Homes
	8.2 Smart Cities
		8.2.1 Introduction
		8.2.2 Smart City Framework
		8.2.3 Architecture of Smart Cities
		8.2.4 Components of Smart Cities
			8.2.4.1 Smart Technology
			8.2.4.2 Smart Infrastructure
			8.2.4.3 Smart Mobility
			8.2.4.4 Smart Buildings
			8.2.4.5 Smart Energy
			8.2.4.6 Smart Governance
			8.2.4.7 Smart Healthcare
		8.2.5 Characteristics of Smart Cities
		8.2.6 Challenges in Smart Cities
		8.2.7 Conclusion
	References
9 Application of AI in Healthcare
	9.1 Introduction
		9.1.1 Supervised Learning Process
		9.1.2 Unsupervised Learning Process
		9.1.3 Semi-Supervised Learning Process
		9.1.4 Reinforcement Learning Process
		9.1.5 Healthcare System Using ML
		9.1.6 Primary Examples of ML’s Implementation in the Healthcare
			9.1.6.1 AI-Assisted Radiology and Pathology
			9.1.6.2 Physical Robots for Surgery Assistance
			9.1.6.3 With the Assistance of AI/ML Techniques, Drug Discovery
			9.1.6.4 Precision Medicine and Preventive Healthcare in the Future
	9.2 Related Works
		9.2.1 In Healthcare, Data Driven AI Models
		9.2.2 Support Vector Machine
		9.2.3 Artificial Neural Networks
		9.2.4 Logistic Regression
		9.2.5 Random Forest
		9.2.6 Discriminant Analysis
		9.2.7 Naïve Bayes
		9.2.8 Natural Language Processing
		9.2.9 TF-IDF
		9.2.10 Word Vectors
		9.2.11 Deep Learning
		9.2.12 Convolutional Neural Network
	9.3 DL Frameworks for Identifying Disease
		9.3.1 TensorFlow
		9.3.2 High Level APIs
		9.3.3 Estimators
		9.3.4 Accelerators
		9.3.5 Low Level APIs
	9.4 Proposed Work
		9.4.1 Application of AI in Finding Heart Disease
		9.4.2 Data Pre-Processing and Classification of Heart Disease
	9.5 Results and Discussions
	9.6 Conclusion
	References
10 Battery Life and Electric Vehicle Range Prediction
	10.1 Introduction
	10.2 Different Stages of Electrification of Electric Vehicles
		10.2.1 Starting and Stopping
		10.2.2 Regenerative Braking
		10.2.3 Motor Control
		10.2.4 EV Drive
	10.3 Estimating SoC
		10.3.1 Cell Capacity
		10.3.2 Calendar Life
		10.3.3 Cycling Life
		10.3.4 SoH Based on Capacity Fade
		10.3.5 SoH Based on Power Fade
		10.3.6 Open Circuit Voltage
		10.3.7 Impedance Spectroscopy
		10.3.8 Model-Based Approach
	10.4 Kalman Filter
		10.4.1 Sigma Point Kalman Filter
		10.4.2 Six Step Process
	10.5 Estimating SoH
	10.6 Results and Discussion
	10.7 Conclusion
	References
11 AI-Driven Healthcare Analysis
	11.1 Introduction
	11.2 Literature Review
	11.3 Feature Extraction
		11.3.1 GLCM Feature Descriptors
	11.4 Classifiers
		11.4.1 Stochastic Gradient Descent Classifier
		11.4.2 Naïve Bayes Classifier
		11.4.3 K-Nearest Neighbor Classifier
		11.4.4 Support Vector Machine Classifier
		11.4.5 Random Forest Classifier
		11.4.6 Working of Random Forest Algorithm
		11.4.7 Convolutional Neural Network
			11.4.7.1 Activation Function
			11.4.7.2 Pooling Layer
			11.4.7.3 Fully Connected Layer (FC)
	11.5 Results and Conclusion
		11.5.1 5,000 Images
		11.5.2 10,000 Images
	References
12 A Novel Technique for Continuous Monitoring of Fuel Adulteration
	12.1 Introduction
		12.1.1 Literature Review
		12.1.2 Overview
		12.1.3 Objective
	12.2 Existing Method
		12.2.1 Module-1 Water
		12.2.2 Module-2 Petrol
		12.2.3 Petrol Density Measurement
		12.2.4 Block Diagram
		12.2.5 Components of the System
			12.2.5.1 Pressure Instrument
			12.2.5.2 Sensor
		12.2.6 Personal Computer
		12.2.7 Petrol Density Measurement Instrument Setup
			12.2.7.1 Setup 1
			12.2.7.2 Setup 2
			12.2.7.3 Setup 3
			12.2.7.4 Setup 4
			12.2.7.5 Final Setup
	12.3 Interfacing MPX2010DP with INA114
		12.3.1 I2C Bus Configuration for Honeywell Sensor
		12.3.2 Pressure and Temperature Output Through I2C
	12.4 Results and Discussion
	12.5 Conclusion
	References
13 Improved Merkle Hash and Trapdoor Function–Based Secure Mutual Authentication (IMH-TF-SMA) Mechanism for Securing Smart Home
	13.1 Introduction
	13.2 Related Work
	13.3 Proposed Improved Merkle Hash and Trapdoor Function–Based Secure Mutual Authentication (IMH-TF-SMA) Mechanism for Securing
		13.3.1 Threat Model
		13.3.2 IMH-TF-SMA Mechanism
			13.3.2.1 Phase of Initialization
			13.3.2.2 Phase of Addressing
			13.3.2.3 Phase of Registration
			13.3.2.4 Phase of Login Authentication
			13.3.2.5 Phase of Session Agreement
	13.4 Results and Discussion
	13.5 Conclusion
	References
14 Smart Sensing Technology
	14.1 Introduction
		14.1.1 Sensor
			14.1.1.1 Real-Time Example of Sensor
			14.1.1.2 Definition of Sensors
			14.1.1.3 Characteristics of Sensors
			14.1.1.4 Classification of Sensors
			14.1.1.5 Types of Sensors
		14.1.2 IoT (Internet of Things)
			14.1.2.1 Trends and Characteristics
			14.1.2.2 Definition
			14.1.2.3 Flow Chart of IoT
			14.1.2.4 IoT Phases
			14.1.2.5 Phase Chart
			14.1.2.6 IoT Protocol
		14.1.3 WPAN
			14.1.3.1 IEEE 802.15.1 Overview
			14.1.3.2 Bluetooth
			14.1.3.3 History of Bluetooth
			14.1.3.4 How Bluetooth Works
			14.1.3.5 Bluetooth Specifications
			14.1.3.6 Advantages of Bluetooth Technology
			14.1.3.7 Applications
		14.1.4 Zigbee (IEEE 802.15.4)
			14.1.4.1 Introduction
			14.1.4.2 Architecture of Zigbee
			14.1.4.3 Zigbee Devices
			14.1.4.4 Operating Modes of Zigbee
			14.1.4.5 Zigbee Topologies
			14.1.4.6 Applications of Zigbee Technology
		14.1.5 WLAN
			14.1.5.1 Introduction
			14.1.5.2 Advantages of WLANs
			14.1.5.3 Drawbacks of WLAN
		14.1.6 GSM
			14.1.6.1 Introduction
			14.1.6.2 Composition of GSM Networks
			14.1.6.3 Security
		14.1.7 Smart Sensor
			14.1.7.1 Development History of Smart Sensors
			14.1.7.2 Internal Parts of Smart Transmitter
			14.1.7.3 Applications
		14.1.8 Conclusion
	References
Index
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