AI for Big Data-Based Engineering Applications from Security Perspectives

✍ Scribed by Balwinder Raj (editor), Brij B. Gupta (editor), Shingo Yamaguchi (editor), Sandeep Singh Gill (editor)

• Publisher: CRC Press
• Year: 2023
• Tongue: English
• Leaves: 261
• Edition: 1
• Category: Library

✦ Synopsis

Artificial intelligence (AI), machine learning, and advanced electronic circuits involve learning from every data input and using those inputs to generate new rules for future business analytics. AI and machine learning are now giving us new opportunities to use big data that we already had, as well as unleash a whole lot of new use cases with new data types. With the increasing use of AI dealing with highly sensitive information such as healthcare, adequate security measures are required to securely store and transmit this information. This book provides a broader coverage of the basic aspects of advanced circuits design and applications.

AI for Big Data-Based Engineering Applications from Security Perspectives is an integrated source that aims at understanding the basic concepts associated with the security of advanced circuits. The content includes theoretical frameworks and recent empirical findings in the field to understand the associated principles, key challenges, and recent real-time applications of advanced circuits, AI, and big data security. It illustrates the notions, models, and terminologies that are widely used in the area of Very Large Scale Integration (VLSI) circuits, security, identifies the existing security issues in the field, and evaluates the underlying factors that influence system security. This work emphasizes the idea of understanding the motivation behind advanced circuit design to establish the AI interface and to mitigate security attacks in a better way for big data. This book also outlines exciting areas of future research where already existing methodologies can be implemented. This material is suitable for students, researchers, and professionals with research interest in AI for big data–based engineering applications, faculty members across universities, and software developers.

✦ Table of Contents

Cover
Half Title
Title Page
Copyright Page
Table of Contents
Preface
Acknowledgments
About the Editors
Contributors
Chapter 1 Artificial Intelligence-Empowered 3D Bioprinting
1.1 Introduction
1.2 Applications of 3D Printing
1.2.1 Aeronautics Industry
1.2.2 Automobile Industry
1.2.3 Catering Industry
1.2.4 Health Care/Medical Industry
1.2.5 Textile Industry
1.2.6 Construction Industry
1.2.7 Electronics Industry
1.3 3D Bioprinting
1.3.1 Pre-Processing Phase
1.3.2 Material Selection and Bioinks
1.3.3 Bioprinting Techniques
1.4 Artificial Intelligence and 3D Bioprinting
1.4.1 Machine Learning Methods
1.4.2 Deep Learning in 3D Bioprinting Workflow
1.4.3 Image Attainment and Segmentation
1.4.4 Material Selection and Cell Quality
1.4.5 Printing of Anatomical Structure
1.5 Summary
References
Chapter 2 AI and IoT in Smart Healthcare
2.1 Introduction
2.1.1 Emerging Technologies
2.1.2 Architecture of IoT
2.1.3 IoT Ecosystem
2.2 Communication Protocol
2.2.1 Transport Control Protocol (TCP)/IP Stack
2.2.2 CoAP
2.3 Wearable Sensors
2.4 Smart Healthcare System
2.4.1 Prerequisites
2.4.2 Benefits of Smart Healthcare Ecosystem
2.5 Conclusion
References
Chapter 3 Spintronic Technology Applications for Artificial Intelligence
3.1 Introduction
3.2 History of Spintronic Technology
3.3 Theory
3.3.1 Main Spintronic Effects
3.3.1.1 Giant Magnetoresistance Effect
3.3.1.2 CIP GMR
3.3.1.3 CPP-GMR
3.3.2 Tunnel Magnetoresistance Effect
3.3.3 Spin Transfer Torque (STT)
3.3.4 Spin Hall Effect (SHE)
3.3.5 Spin Seebeck Effect
3.4 Low Power Spintronic Technologies
3.4.1 Active Devices
3.4.1.1 Spin Valve
3.4.1.2 Magnetic Tunnel Junction
3.4.1.3 Ferroelectric Tunnel Junction (FTJ)
3.4.1.4 DW in Magnetic Nanowires
3.4.2 Passive Devices
3.4.2.1 Monolithic Spintronics
3.4.2.2 Hybrid Spintronics
3.4.3 Organic Spintronics
3.5 Why is Spintronics Better than Electronics?
3.6 Working Principle of Spintronic Devices
3.7 Issues in Spintronics
3.7.1 Spin Injection
3.7.2 Tunnel Injection
3.7.3 Electrical Injection
3.8 Spintronic Material
3.8.1 Magnetic Metals
3.8.1.1 Ferromagnetic Metals
3.8.1.2 Half-Metallic Ferromagnets (HMFs)
3.8.1.3 Half-Metallic Anti-Ferromagnets
3.8.2 Topological Insulators (TIs)
3.8.3 Magnetic Semiconductors
3.9 Spintronic Devices and Instruments
3.10 Sensors
3.11 MRAM
3.12 Unipolar Spin Diode
3.13 Spin Solar Cell
3.14 Spin LEDs
3.15 Summary and Future Scope
References
Chapter 4 AI-Based ECG Signal Monitoring System for Arrhythmia Detection Using IoMT
4.1 Introduction
4.1.1 IoT-Enabled Healthcare System
4.1.2 IoMT-Based Heart Monitoring
4.1.3 Artificial Intelligence in Healthcare
4.1.3.1 Decision-Making
4.1.3.2 Maintaining Electronic Medical Records
4.1.3.3 Clinical Documentation
4.1.3.4 Disease Diagnosis
4.2 Literature Survey
4.3 Conclusion
References
Chapter 5 Fast Image Desmogging for Road Safety Using Optimized Dark Channel Prior
5.1 Introduction
5.1.1 Atmospheric Scattering Model
5.2 Related Work
5.3 Experimental Work
5.3.1 Conventional Dark Channel Prior
5.3.2 Feature Enhancement
5.3.3 Desmogged Output
5.4 Results and Discussion
5.4.1 Qualitative Analysis
5.4.2 Quantitative Analysis
5.5 Summary
Acknowledgment
References
Chapter 6 Face Mask Detection Using Artificial
6.1 Introduction
6.2 Techniques for Face Mask Detection
6.2.1 Multi-Stage Detectors
6.3 Artificial Intelligence (AI)
6.3.1 Features of AI Programming
6.3.2 Components of AI
6.4 Expert System
6.4.1 Benefits of Expert Systems
6.5 Convolutional Neural Network
6.5.1 Image Classification and Recognition
6.6 Deep Learning
6.6.1 Supervised Machine Learning
6.6.2 Unsupervised Machine Learning
6.6.3 Reinforcement Machine Learning
6.7 Artificial Neural Networks
6.8 Evolution of Convolutional Neural Networks
6.8.1 CNN Architecture
6.8.2 Deep Learning Using Convolutional Neural Network for Computer Vision
6.8.3 Basic CNN Components
6.8.4 Applications of Deep Learning
6.8.4.1 Automatic Speech Recognition (ASR)
6.8.4.2 Image Recognition
6.8.4.3 Natural Image Processing
6.9 AI Algorithms and Models
6.10 Summary
References
Chapter 7 Emerging Nonvolatile Memories for AI Applications
7.1 Introduction
7.2 Solid-State Memory Technology Classification
7.3 Emerging Nonvolatile Memory Devices
7.4 Nanowire-Based NVM
7.4.1 MNW
7.5 Si Nanowire-Based PCM
7.6 Ge-NW-Based FET Memory
7.7 GaN Nanowire-Based Memory
7.8 FeFET-Based Memory
7.9 Market Memory Technologies by Applications
7.10 Product Suggestions
7.11 Conclusion
Acknowledgments
References
Chapter 8 Intelligent Irrigation Systems
8.1 Introduction
8.2 Design and Implementation
8.2.1 Water Planting Section
8.2.2 Temperature Monitoring Section
8.3 Hardware Components Description
8.3.1 Microcontroller (AT89S52)
8.3.1.1 Reset Circuit
8.3.1.2 Clock Generator
8.3.2 GSM Modem (SIM300)
8.3.3 Relay and its Driver Unit
8.3.4 Moisture Sensor
8.3.5 Temperature Sensor
8.3.6 Analog to Digital Converter
8.3.7 Power Supply
8.3.7.1 Transformer
8.3.7.2 Diode
8.3.7.3 Voltage Regulators
8.4 Conclusion
References
Chapter 9 Reversible Logic Gates Using Quantum Dot Cellular Automata (QCA) Nanotechnology
9.1 Introduction
9.2 Reversible Gates
9.3 Performance Comparison
9.4 Conclusion
References