This book, entitled Computational Intelligence of Blockchain Systems, emphasizes concepts of computational intelligence and blockchain-based applications. It also presents a detailed discussion on the incorporation of blockchain technology and computational intelligence in implementing smart real-time applications. Furthermore, this book examines the convergence of blockchain and computational intelligence from a research perspective that helps students/research scholars to carry out their research in this direction. This book comprises chapters that explain the role of blockchain and computational intelligence in Industry 4.0, fake news detection, privacy preservation, voting systems, etc. in a precise way. This precise explanation motivates students to develop their own secure and smart applications in various domains.
Author(s): Ch. Rupa, MD. Sirajuddin
Series: Computer Science, Technology and Applications
Publisher: Nova Science Publishers
Year: 2022
Language: English
Pages: 112
City: New York
Preface
Acknowledgement
Chapter 1
Convergence of Blockchain and Computational Intelligence for Industry 4.0: A Survey
Abstract
Introduction
1. Blockchain in Industry 4.0
1.1. Types of Blockchain
1.2. Blockchain Applications for Industry 4.0
2. Computational Intelligence for Industry 4.0
3. Empowering Industry 4.0 with Integration of Blockchain and Computational Intelligence
3.1. Proposed Architecture
3.2. Advantages of the Proposed Architecture in the Context of Industry 4.0
4. Future Research Challenges
Conclusion
References
Chapter 2
Essential Computational Intelligence in Real-Time Applications
Abstract
Background
Introduction to Computational Intelligence
1. Definition of Intelligence
1.1. What Is Intelligence?
1.2. What Is Computational Intelligence?
1.3. Artificial Neural Networks (ANN)
1.4. Evolutionary Computation (EC)
1.5. Artificial Immune System (AIS)
1.6. Fuzzy System (FS)
1.7. Swarm Intelligence (SI)
2. Role of Computational Intelligence
3. Case Studies
3.1. Learning, Tracking of Learning
3.2. Blackbox AI
Conclusion
References
Chapter 3
Intelligent Automation in Blockchain
Abstract
Introduction
Computational Intelligence
1. Intelligent Automation
1.2. Advantages of Intelligent Automation
1.3. Applications of Intelligent Automation
1.3.1. Automotive
1.3.2. Life Sciences
1.3.3. Healthcare
1.3.4. Insurance
2. Integration of Blockchain and Artificial Intelligence
2.1. Fairness
2.1.1. Decentralization
2.2. Privacy Protection
2.2.1. Distributed Computing
3. Blockchain in BPM
3.1. The Relationship between Blockchain and BPM [9]
3.1.1. Collaboration
3.1.2. Payments
3.1.3. Security
4. Role of Blockchain in Robotic processes automation
4.1. Benefits of RPA
4.2. Components of RPA
4.3. Business use cases integrating RPA and Blockchain
4.4. Function of RPA
4.4.1. Compliance Management
4.4.2. Automated and Trusted Data Processing
4.4.3. Trade and Contracts
4.4.4. File and Data Storage
Conclusion
References
Chapter 4
Biometric-Based Image Privacy Preservation Using Haar Cascade and Blockchain
Abstract
Introduction
1. Related Work
2. Smile Detection Using Haar Classifiers
3. Incorporating gTTS API
4. Results
Conclusion
References
Chapter 5
A BCT Applied Technique for Fake News Detection Using Deep Logistic Regression
Abstract
Introduction
Deep Learning
Natural Language Processing (NLP)
1. Literature Survey
2. Dataset
3. Experimental Analysis
3.1. Logistic Regression
3.2. Analysis
Conclusion and Future Scope
References
Chapter 6
Blockchain in the Modern World
Abstract
Introduction
1. Blockchain in HealthCare
2. Blockchain in Finance
3. Blockchain in Energy
4. Blockchain on the Internet
5. Blockchain in Supply Chain
6. Blockchain in Real-Estate
7. Blockchain in Retail
8. Blockchain in Education
9. Blockchain in Data Science
10. Blockchain in Government
Conclusion
References
Chapter 7
An Exhaustive Study on Fuzzy Sets and Artificial Neural Networks of Computational Intelligence
Abstract
Introduction
1. Techniques of Computational Intelligence
2. Sets with a Lot of Fuzziness
2.1. S-function
3. Artificial Neural Networks
3.1. Back-Propagation Neural Networks (BPNNs)
3.2. Self-Organizing Maps (SOM)
3.3. a. Training for Self-Organizing Maps
3.3. Hopfield Neural Networks
3.3.1. Network of Discrete Hopfields
3.3.2. Architecture
3.3.3. Algorithm for Training
3.3.4. Algorithm for Testing
3.3.5. Evaluation of Energy Function
3.4. Feed Forward Neural Networks
4. Algorithms for Computational Intelligence
Conclusion
References
About the Editors
Index
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