Artificial Intelligence in IoT and Cyborgization

✍ Scribed by Rajesh Kumar Dhanaraj; Bharat S. Rawal; Sathya Krishnamoorthi; Balamurugan Balusamy

• Publisher: Springer Nature Singapore
• Year: 2023
• Tongue: English
• Leaves: 191
• Series: Studies in Computational Intelligence
• Edition: 1
• Category: Library

✦ Synopsis

This book introduces the concept of combining artificial intelligence (AI) and Internet of things (IoT) with real human organs to form a cybernetic organism or cyborg. It is a concept of man–machine mixture which helps in restoring or enhancing the ability of a body part by integrating some technology or artificial component with that body part. These smart artificial organs act as a substitute for real organs having various capabilities like scanning the body, detecting and transmitting the diagnostic data to machines. For example, an artificial heart is capable of monitoring the overall health of a person, and lungs can inform the doctor of abnormalities. This book benefits academic researchers and industrialist who work in the field cyborgization and IoT within human bodies.

✦ Table of Contents

Preface
Contents
Introduction to Cyborgization Systems
1 Introduction: Definitions of Cyborg
2 Cyborgs—Biosocial Elements
2.1 Cyborg Tissues in Engineering
3 Remarkable Cyborgization Attempts
3.1 Eyesight Restoration
4 Cyborgs—Bodily Networks
4.1 Cyborg Animals
4.2 In Medicine
4.3 Brain Computer Interface (BCI)
4.4 Deep Brain Stimulation
4.5 Retinal Implantation
4.6 Implantation of Vocal Cords
4.7 Artificial Heart Beat Generator
4.8 Artificial Pancreas
4.9 Cyborgs in Military
5 Hybrid-Insect-MEMS
5.1 Role of Cyborgs in Sports
5.2 Action of Cyborgs in Art
5.3 Artists as Cyborgs
5.4 In Body Modification
5.5 In Space
6 Future Scope of Implantable Technologies
References
AI Based Smart IoT Systems
1 Introduction
2 What’s IoT?
2.1 How Does It Work?
3 Elements of IoT
3.1 Identification
3.2 Sensing
3.3 Gateway
3.4 Cloud
3.5 User Interface
3.6 Services
4 Framework of IoT
4.1 DeviceHive
4.2 ThingSpeak
4.3 Mainflux
4.4 Thinger.io
4.5 Zetta
5 Architecture of IoT
6 Need for IoT
6.1 Disaster Management
6.2 Urban Management
6.3 Smart Healthcare
6.4 Interactive Performance
6.5 Superior Functionalities
6.6 Convenient Usage
7 AI in IoT
8 Artificial Intelligence for Intelligent Sensing
9 Artificial Intelligence in Analytical Skills (IoT)
10 Edge Computing in IoT
11 Neural Networks
12 Reliability of AI in IoT Systems
13 AI Tools for IoT
14 Applications of IoT
14.1 Virtual Reality
14.2 Augmented Reality
14.3 Agriculture
14.4 Smart Locks
14.5 Intelligent Road Toll
14.6 Use of IoT in Factories
14.7 Waste Management
14.8 IoT in Healthcare
14.9 Disaster Management
15 Conclusions
References
Role of Machine Learning and Deep Learning Applications in the Internet of Things (IoT) Security
1 Data Security in IoT
2 IoT Security Challenges
2.1 Attacks in IoT
3 Machine Learning (Ml) to Tackle Challenges of Security in IoT
3.1 ML Techniques
3.2 Solutions for IoT Security Based on ML
4 Deep Learning (DL) Based Security Solutions in IoT
4.1 Convolution Neural Network (CNN)
4.2 Recurrent Neural Networks (RNNs)
4.3 Deep Belief Network (DBN)
4.4 Deep Auto Encoders (AEs)
4.5 Restricted Boltzmann Machine (RBM)
4.6 Ensemble of DL Networks (EDLNs)
4.7 Generative Adversarial Network (GAN)
5 Current Development in IoT Security, Challenges
6 Summary
References
IOT Based Experimental Relaying System for Smart Grid
1 Introduction
1.1 Introduction
1.2 Components Used in the Proposed Module
2 Proposed Circuit Description
3 Experimental Results
4 Conclusion
References
Environment Twin Based Deep Learning Model Using Reconfigurable Holographic Surface for User Location Prediction
1 Introduction
2 Literature Survey
3 Proposed Work
3.1 System Model
3.2 Reconfigurable Holographic Surface
3.3 Deep Learning Based Environmental Twin Model
4 Results and Discussion
5 Conclusion
References
Surveillance of Robotic Boat Using Iot and Image Processing
1 Introduction
2 Related Work
3 Proposed Methodology
3.1 Relevant Moisture Terms
3.2 ADC
3.3 Protocols
4 Result and Discussions
5 Conclusion and Future Scope
References
Advanced Human–Computer Interaction Technology in Digital Twins
1 Introduction
2 Application Status of DTs
3 Intelligent HAR Based on HCI in Industrial DTs
3.1 DTs and HCI in IM
3.2 HPE Based on Improved HRNet
3.3 Human Action Intelligent Recognition of Wearable Devices Based on Deep Learning
4 Results
4.1 HPE Results Based on HRNet
4.2 Performance Evaluation of ADLC
5 Conclusion
References
CNN Architecture and Classification of Miosis and Mydriasis Clinical Conditions
1 Introduction
2 Disease Description
3 General Image Processing Approaches Used to Detect and Classify Eye Diseases
4 CNN Architecture Data Flow for Classification of Miosis and Mydriasis Clinical Conditions
5 Image as an Input
6 CNN Architectural Steps for Image Classification
7 Dense Layer Interactions
8 Summary
References
Role of Object Detection for Brain Tumor Identification Using Magnetic Resonance Image Scans
1 Introduction
2 Disease Description
3 Algorithms in Object Detection
3.1 RCNN Family
4 Single Shot Detector Algorithm
5 YOLO Architecture—(You-Only-Look-Once)
5.1 How Object Detection is Implemented in YOLO?
6 Image Segmentation & Applications in Processing Medical Images
7 Role of Object Detection in Medical Processing
8 3D Medical Images and Deep Learning
9 Summary
References
Deep Learning Model for Predicting Diabetes Disease Using SVM
1 Introduction
1.1 Data Set
1.2 Detailed Data Information
2 Methodology
2.1 Data Preprocessing
2.2 Rectified Linear Activation Function or ReLU
2.3 Deep Convolution Neural Network (DCNN)
2.4 Performance Measures
2.5 K-Fold Cross-Validation
2.6 SVM Classifier
2.7 Decision Tree
2.8 Optimizer
3 Implementation
3.1 Implementation
3.2 Training and Testing
3.3 Comparison of Different Methodology
4 Conclusion
References
Deep Learning for Targeted Treatment
1 Introduction
2 Working Methodology of Deep Learning
3 Layers in Deep Learning
4 How Deep Learning Helps to Improve Targeted Treatment
5 Revolution of Deep Learning
6 Deep Neural Networks
7 Non-linear Complex Connections
8 Graph Neural Networks
9 Applications of Deep Learning
9.1 Automatic Speech Recognition
9.2 Image Recognition
9.3 Drug Discovery And Toxicology
10 Analysation of Medical Images
10.1 Generative Models
10.2 Deep Belief Networks
10.3 Targeted Treatment—Introduction
10.4 Treatment Prediction by Deep Learning
11 Deep Learning Algorithms for Radiotherapy
12 Molecularly Targeted Treatment
13 Cancer Diagnosis
14 Biomolecular Targets
15 Drug Design
16 Targeted Treatment for Lung Cancer
17 Conclusion
References

✦ Subjects

Engineering; Cyber-physical systems; Internet of things; IoT; Professional Computing; Artificial Intelligence; Computational Intelligence; Cybernetic Organism; Bionic Systems