Cover
Contents
About the Editors
Foreword
Preface
Acknowledgment
Glossary
1 Introduction to machine learning, blockchain technologies, and Big Data analytics for IoTs: concepts, open issues, and critical challenges
Abstract
1.1 Introduction
1.2 Machine Learning, blockchain, IoT, and Big Data analytics – a useful overview
1.2.1 Securing the IoTs-based applications using AI
1.2.2 Advantages of blockchain solutions
1.2.3 Others
1.3 AI, Machine Learning, blockchain for the IoT: critical challenges and opportunities for future
1.3.1 Critical challenges in the integration of AI and IoT
1.3.2 Challenges in the integration of blockchain and IoT
1.4 Our motivation
1.5 Organization of this work
References
2 Image enhancement on low-light and dark images for object detection using Artificial Intelligence for field practitioners
Abstract
2.1 Introduction
2.2 Related work
2.3 Solution approach for image enhancement
2.3.1 Raw data from camera sensors
2.3.2 Image enhancement on low-light images
2.3.3 Datasets setup for training the U-net architecture
2.3.4 Training with U-net architecture
2.4 Object detection with the enhanced images
2.4.1 Dataset preparation for object detection
2.4.2 Training the object detection models
2.4.3 Testing object detection on low-light-enhanced images
2.4.4 Augmentation
2.5 Application on low-light machine activities
2.6 Conclusions and future works
References
3 Cache memory architecture for the convergence of machine learning, Internet of Things (IoT), and blockchain technologies
Abstract
3.1 Introduction
3.1.1 Literature review
3.1.2 Role of cache memory design in IoT
3.1.3 Need of cache memory for convergence of machine learning (ML)
3.1.4 Need of cache memory for convergence of blockchain technologies
3.1.5 Cache memory
3.2 Single-bit STSRAMC SA architectures
3.2.1 Single-bit STSRAMC LSA architecture
3.3 Description of proposed architectures and techniques of power reduction
3.3.1 Single-bit STSRAMC VLSA architecture
3.3.2 Techniques of power reduction
3.3.3 Power reduction sleep transistor technique
3.3.4 Power reduction sleep stack technique
3.3.5 Power reduction dual sleep technique
3.4 Simulated resulted of proposed architecture with discussion
3.4.1 Results and discussion
3.4.2 Simulation of CWD
3.4.3 Simulation of STSRAMC
3.4.4 Simulation of LSA
3.4.5 Comparison table
3.5 Conclusion and future scope
3.5.1 Conclusion
3.5.2 Future scope
References
4 Machine learning algorithms for Big Data analytics including deep learning
Abstract
4.1 Introduction
4.1.1 Machine Learning (ML)
4.1.2 Deep learning
4.1.3 Big Data
4.2 Related work
4.3 Motivation
4.4 Importance of Big Data analytics in near future
4.5 Benefits of Machine Learning and deep learning algorithms in Big Data analytics
4.6 Weaknesses identified in (of) Machine Learning, deep learning algorithms during analysis of Big Data
4.7 Critical issues raised during Big Data analytics
4.8 Challenges faced by Users/Organization (with respect to Big Data Analytics)
4.9 Future research directions (with respect to Big Data analytics, Machine Learning and deep learning algorithms)
4.10 Conclusion
References
5 Machine learning-based blockchain technologies for data storage: challenges, and opportunities
Abstract
5.1 Introduction
5.2 Machine Learning (ML) data storage
5.3 Machine Learning (ML) techniques
5.4 Healthcare sector
5.5 Various sector of Machine Learning (ML)
5.6 Conclusion
References
6 Clustering crowdsourced healthcare data from drones using Big Data analytics
Abstract
6.1 Introduction
6.2 Problem statement and analysis
6.3 Technical background
6.3.1 Machine Learning
6.3.2 Types of learning
6.3.3 Big Data analytics
6.3.4 The R
6.3.5 The data lakes
6.3.6 No SQL database
6.3.7 G. Apache Spark
6.3.8 Crowdsensing with drone
6.4 Methodology
6.4.1 Investigate objectives
6.4.2 Research context
6.4.3 Investigate right consent
6.4.4 Proposed framework
6.5 Implementation
6.5.1 Internet of drone thing
6.5.2 Big Data analytics
6.5.3 Use cases
6.5.4 Analysis of outcomes
6.5.5 Methods description
6.6 Clustering technique
6.6.1 K-means clustering
6.6.2 Hierarchical clustering
6.6.3 Grid-based method
6.6.4 Density-based methods
6.6.5 Architectural approach
6.7 Analysis
6.7.1 Data for the study
6.7.2 Study results
6.8 Comparison of results
6.9 Conclusion
References
7 Authentication and authorization in cloud computing using blockchain
Abstract
7.1 Introduction
7.2 Cloud computing
7.2.1 Cloud computing architecture
7.2.2 Deployment strategies
7.2.3 Cloud service models
7.2.4 Security risks of cloud computing
7.3 Review of various authentication and authorization in cloud computing
7.3.1 Existing authentication techniques
7.3.2 Review of authentication methods
7.4 Introduction to blockchain as authentication and authorization method in cloud environment
7.4.1 Blockchain technology
7.4.2 Working mechanism
7.4.3 Types of blockchain
7.5 Integration of cloud computing and blockchain
7.5.1 Structure of blockchain
7.5.2 Characteristics of blockchain
7.5.3 Requirements of cloud
7.6 Review of blockchain-based approaches
7.7 Conclusion
References
8 Fundamentals of machine learning and blockchain technologies for applications in cybersecurity
Abstract
8.1 Introduction
8.2 Research method
8.3 Cybersecurity
8.4 The importance of cybersecurity
8.5 Artificial Intelligence (AI) for cybersecurity
8.6 Machine Learning (ML) for cybersecurity
8.7 Blockchain for cybersecurity
8.8 Discussion
8.9 Trends
8.10 Conclusions
References
9 Real-world applications of generative adversarial networks and their role in blockchain technology
Abstract
9.1 Introduction
9.2 Application of Generative Adversarial Networks (GANs)
9.2.1 GAN in self-driving
9.2.2 Credit card fraud
9.2.3 GAN in medical field
9.3 Blockchain basics
9.3.1 Blockchain
9.3.2 Security of blockchain-based wireless network
9.3.3 Applications of GAN in blockchain
9.4 Conclusion
References
10 Internet of Things (IoTs)-enabled security using artificial intelligence and blockchain technologies
Abstract
10.1 Overview
10.2 Blockchain technology with IoT
10.2.1 Need for blockchain in IoT
10.2.2 Architecture
10.3 Safe future with blockchain IoT
10.3.1 Security issues and challenges
10.3.2 Security solutions with AI
10.3.3 Blockchain safety countermeasures with IoT
10.4 Cloud-based blockchain with IoT
10.4.1 Blockchain security in cloud environment
10.4.2 Blockcloud—a service-centric networking
10.4.3 A dew-blockcloud model
10.4.4 Incentive mechanism for edge servers
10.4.5 Blockchain as a Service (BaaS)
10.5 Blockchain IoT real-time applications
10.6 Conclusion
References
11 Blockchain network with artificial intelligence—DeFi affair management
Abstract
11.1 Introduction
11.1.1 Role of artificial intelligence and blockchain in various applications
11.2 Types of services in finance market
11.2.1 Centralized finance
11.2.2 Decentralized finance
11.2.3 Back-end
11.2.4 Front-end
11.3 Decentralized bank app
11.3.1 Creation of custom ERC20 token
11.3.2 Creation of staking functionality
11.3.3 Creation of loan functionality
11.3.4 Deploying smart contract to the blockchain
11.3.5 Interaction with client-side application
11.3.6 Applications
11.4 Security token application
11.4.1 Creation of security token
11.4.2 KYC and general verification process
11.4.3 AML verification process
11.4.4 Creation of required roles
11.4.5 Upgradable smart contract
11.5 Conclusion
11.6 Future work
References
12 Vulnerabilities of smart contracts and solutions
Abstract
12.1 Introduction about blockchain and smart contract
12.1.1 Smart contract
12.1.2 Why do we need smart contracts?
12.2 Related work
12.2.1 Y2K problem
12.2.2 Decentralized autonomous blockchain (DAO) hack
12.3 Motivation
12.4 Scope of smart contracts today and tomorrow
12.4.1 Advantages of smart contracts
12.5 Necessity of finding vulnerabilities in smart contract
12.6 Vulnerabilities in smart contract
12.7 Available techniques/mechanism in secure smart contract
12.8 Problem raised during protecting smart contract
12.8.1 Disadvantages of smart contracts
12.9 Opportunities for future research communities toward smart contract
12.10 Conclusion
Glossary
References
13 Data analytics for socio-economic factors affecting crime rates
Abstract
13.1 Introduction
13.1.1 Polynomial regression
13.1.2 Data visualization and analysis tool
13.1.3 Literature survey
13.2 Proposed system
13.2.1 Crime rate analysis
13.2.2 Socio-economic attributes of generalized dataset
13.3 Crime rate model implementation
13.3.1 Literacy rate
13.3.2 State wise improvement in literacy rate
13.3.3 Poverty Index
13.3.4 Unemployment rate
13.3.5 Per capita income
13.3.6 Prohibition of child marriage
13.3.7 Crime against children
13.4 Result analysis
13.4.1 Methodology
13.4.2 Using Jupyter (Python)
13.4.3 Prediction model analysis
13.4.4 Algorithm for linear regression model
13.4.5 Algorithm for logistic regression model
13.4.6 Lasso regression
13.4.7 Decision tree model
13.4.8 Root mean square
13.4.9 Data visualization analysis
13.5 Conclusion
References
14 Deployment of automated teller machinery for e-polling
Abstract
14.1 Introduction
14.2 Background
14.2.1 Blockchain
14.2.2 ATM machine
14.2.3 Literature survey
14.2.4 Problem definition
14.2.5 Existing voting equipment
14.2.6 Review of e-voting schemes
14.2.7 Contribution of the work
14.3 Proposed model
14.3.1 General constraints for the system
14.3.2 Detailed architecture
14.3.3 Authentication stage
14.3.4 Voting stage
14.3.5 Completion stage
14.4 Analysis
14.5 Conclusion and future work
References
15 Machine learning-based blockchain technology for protection and privacy against intrusion attacks in intelligent transportation systems
Abstract
15.1 Introduction
15.1.1 Background
15.1.2 Internet of Vehicles (IoV) and Intelligent Transportation System (ITS)
15.1.3 Security and privacy
15.1.4 Current ITS trends
15.2 Related works
15.3 Proposed system
15.3.1 Maintaining privacy with the use of blockchain technology and smart contracts
15.3.2 Collaborative IDS based on Machine Learning
15.3.3 IoV based on blockchain
15.3.4 CatBoost
15.3.5 Random forest (RF)
15.3.6 AdaBoost classifier
15.3.7 Extra tree (ET)
15.3.8 Quadratic discriminant analysis
15.4 Results and discussion
15.4.1 Performance of the models with 25% training data of UNSW-NB15
15.4.2 Performance of the models with 30% training data of UNSW-NB 15
15.4.3 Comparison of attacks in existing methods and attacks considered in this research
15.5 Conclusion and future work
References
16 Blockchain-enabled Internet of Things (IoTs) platforms for vehicle sensing and transportation monitoring
Abstract
16.1 Introduction
16.2 The security issues and challenges in IoV
16.2.1 Disadvantages of centralized security & simple replication protocols in IoV
16.3 The security threats and countermeasures in IoV
16.3.1 Main differences of blockchain systems from past systems in IoV
16.3.2 Tamper-proofness of blockchain against malicious attacks in IoV
16.4 Blockchain mechanisms or algorithms in IoV
16.5 Security issues in Internet of Drones (IoD) and unmanned traffic management (UTM) systems
16.5.1 Cyber attacks on Unmanned Aerial Vehicle (UAV)
16.5.2 Attacks on GCS
16.5.3 Attacks on data communication links
16.5.4 Attacks on cyber systems
16.6 Innovative blockchain solutions in IoV
16.7 Blockchain solutions for IoD
16.8 Blockchain applications in IoV
16.9 Future possible directions
16.10 Conclusions
References
17 Blockchain-enabled Internet of Things (IoTs) platforms for the healthcare sector
Abstract
17.1 Introduction
17.1.1 Why blockchain for healthcare?
17.1.2 Why IoT for healthcare?
17.1.3 Innovative projects of IoT and blockchain in 2019
17.1.4 Workflow
17.2 Theoretical foundations
17.2.1 Blockchain
17.2.2 Advantages of blockchain
17.2.3 Blockchain types
17.2.4 Real time
17.2.5 Key characteristics of blockchain
17.2.6 Blockchain in the healthcare sector
17.3 Internet of Things (IoTs)
17.3.1 IoT for healthcare
17.4 Blockchain, IoT and healthcare systems
17.4.1 Why both blockchain and IoT?
17.4.2 Shortfall in existing systems
17.5 SimplyVitalHealth
17.5.1 Blockchain and IoT-based architecture
17.5.2 Comparison of existing approaches for layer-oriented use cases
17.5.3 Combined efforts of blockchain and IoT: benefits
17.6 Future research and challenges
17.6.1 Healthcare sector: challenges
17.6.2 Healthcare sector: future research
17.7 Conclusions
References
18 An integrated dimensionality reduction model for classifying IoT-enabled smart healthcare genomic data
Abstract
18.1 Introduction
18.2 Related works
18.3 Methodology
18.4 Dataset
18.5 Algorithms
18.5.1 PLS
18.5.2 Linear discriminant analysis (LDA)
18.5.3 Support vector machine (SVM)
18.5.4 KNN
18.5.5 Evaluation
18.6 Potential healthcare domains
18.7 Results and discussions
18.8 Conclusion
References
19 Blockchain-based learning automated analytics platform in telemedicine
Abstract
19.1 Introduction
19.2 Literature review
19.3 Telemedicine market segmentation
19.3.1 Market segmentation based on components
19.3.2 Technology-based segmentation
19.3.3 Specialty-based segmentation
19.3.4 Region-based segmentation
19.4 Automated analytics
19.4.1 All about analytics
19.4.2 Growth of automation in analytics
19.4.3 Why automated analytics in telemedicine?
19.4.4 Leveraging the potential of Machine Learning in automated analytics
19.5 Blockchain in telemedicine
19.5.1 All about blockchain
19.5.2 Architectural design
19.5.3 Data security and its cloud storage with the aid of blockchain
19.5.4 Democratization of blockchain in telemedicine and beyond
19.5.5 Pros and cons of blockchain in telemedicine
19.5.6 Use cases in telemedicine from India and overseas
19.6 Telemedicine and the law: worldwide perspectives
19.7 Future of telemedicine: disruptive technologies
19.8 Conclusion
References
20 A sensor-based architecture for telemedical and environmental air pollution monitoring system using 5G and blockchain
Abstract
20.1 Introduction
20.1.1 Architecture—IoT
20.2 Background
20.3 Research methodology—measurement of biological signals
20.3.1 Measurement of ECG signals through cloth material provided
20.3.2 Measurement of BCG signals through cloth material provided
20.3.3 Measurement of PPG signals through cloth material provided
20.3.4 HR monitoring and BP estimation
20.4 Research methodologies—non-invasive blood glucose monitoring device
20.4.1 Ultra-thin compact flexible antenna
20.4.2 CPW-PIFA antenna design
20.4.3 Dielectric properties of fresh human blood
20.5 Wireless stethoscope using Wi-Fi technology for remote access of patients
20.5.1 Chest-piece
20.5.2 Analysis—display unit
20.5.3 Ear piece
20.6 IoT-enabled environmental air pollution monitoring and rerouting system using supervised learning algorithm
20.6.1 NB-IoT transmission module
20.6.2 Updation in Google maps and rerouting
20.7 Results and discussion
20.8 Conclusion
References
21 Blockchain-enabled Internet of Things (IoT) platforms for financial services
Abstract
21.1 Blockchain era
21.2 Blockchain in finance
21.2.1 Banks and insurance agencies augment the capability of smart agreements
21.2.2 Computerized monetary standards
21.2.3 Identifying security holders inside blockchain
21.2.4 Blockchain use for file retaining
21.3 Blockchain in commercial banking
21.3.1 Blockchain strengthens threat control
21.4 Blockchain platforms
21.4.1 Existing blockchain platforms
21.4.2 Future blockchain platforms
21.5 Blockchain-based IoT platforms
21.6 Blockchain-based artificial intelligence platforms in finance
21.7 Conclusion
References
22 Blockchain and machine learning: an approach for predicting the commodity prices
Abstract
22.1 Introduction
22.2 State-of-the-art works
22.3 Leveraging Machine Learning on BCT
22.4 Steps/process of proposed methodology
22.5 Discussion and experimental setup
22.5.1 Model 1—linear regression
22.5.2 Debugging gradient descent
22.5.3 Model 1—SVM
22.6 Results
22.7 Conclusion
References
23 Knowledge extraction from abnormal stock returns: evidence from Indian stock market
Abstract
23.1 Introduction
23.2 Literature survey
23.2.1 Data and period of study
23.3 Methodology
23.4 Results and discussions
23.5 Conclusions
23.6 Future scope
Appendix 1
Appendix 2
Appendix 3
References
24 Impact of influence analysis of social media fake news—a machine learning perspective
Abstract
24.1 Introduction
24.2 Background
24.3 Related work
24.4 About influence analytics
24.4.1 The influence of counterfeit news
24.4.2 Main factors that affect fake news
24.5 Methodology
24.6 Implementation results
24.6.1 Data pre-processing
24.6.2 Bagging classifier
24.6.3 Random forest classifier
24.6.4 Boosting algorithms: Ada boosting, gradient boosting
24.7 Discussion
24.8 Conclusion
24.9 Future enhancement
References
25 Application of machine learning techniques
based on real-time images for site specific insect
pest and disease management of crops
Abstract
25.1 Introduction
25.2 Literature survey and related works
25.3 Resources and approaches
25.3.1 Image data-set
25.3.2 Methodologies
25.4 Experiments
25.5 Outcomes
25.5.1 Object identification network models
25.5.2 DA processing
25.5.3 Impression of dropout on model evaluation
25.6 Design and execution of mung bean pest & disease
identification system
25.6.1 Software information
25.6.2 Proposed Android-based application structural design
25.6.3 Software testing
25.6.4 Discussion
25.7 Conclusion
25.8 Tools used
25.9 Acknowledgment
References
26 A prioritized potential framework for combined computing technologies: IoT, Machine Learning, and blockchain
Abstract
26.1 Introduction
26.2 A review on combined technologies
26.3 Research gaps
26.4 Case studies
26.5 Convergence of IoT, Machine Learning, and blockchain methods
26.5.1 Framework
26.5.2 Futuristic applications for combined computing technology
26.5.3 Issues in combined computing technologies
26.6 Pioneering results for combined computing
26.6.1 Impact of IoT, Machine Learning, and blockchain in intelligent transport system
26.6.2 Impact of IoT, Machine Learning, and blockchain in healthcare system
26.7 Potential of computing technologies
26.8 Future highlights
26.9 Conclusion and future scope
References
27 Conclusion to this book
27.1 Challenges in the integration of Machine Learning and IoT
27.2 Challenges in the integration of blockchain and Big Data
27.3 Opportunities with Machine Learning, blockchain, and IoT
27.4 Conclusion
References
Index
Back Cover