Machine learning integrated blockchain model for Industry 4.0 smart applications
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Saikat Samanta
Abstract
In the last few years, machine learning (ML) and blockchain are the most prominent innovations. Blockchain’s potential has been widely explored in literature and media, especially in finance and payment industries. Data confidentiality and privacy are prioritized in blockchain’s decentralized database. However, this procedure is time consuming and inconvenient, which is one of the explanations why blockchain technology has yet to gain widespread acceptance. To solve the invalid dataset, we used integrated blockchain and ML approaches to secure system transactions and manage a dataset. Mostly, blockchain can greatly facilitate the exchange of training data and ML models, as well as decentralized information, stability, anonymity, and trustworthy ML decision making. We study the literature on integrating blockchain and ML systems in this paper and show how they can work together efficiently and effectively. We will go through the problems that each industry faces when it comes to implementing blockchain. We present a systematic report on ML and blockchain-based smart Industry 4.0 applications more robust to attacks in this article. Finally, we suggest some potential research avenues and anticipate further studies into the deeper convergence of the two promising technologies. We hope that our results will help decision-makers embrace blockchain technology and invest in Industry 4.0 by empowering and promoting research in this field.
Abstract
In the last few years, machine learning (ML) and blockchain are the most prominent innovations. Blockchain’s potential has been widely explored in literature and media, especially in finance and payment industries. Data confidentiality and privacy are prioritized in blockchain’s decentralized database. However, this procedure is time consuming and inconvenient, which is one of the explanations why blockchain technology has yet to gain widespread acceptance. To solve the invalid dataset, we used integrated blockchain and ML approaches to secure system transactions and manage a dataset. Mostly, blockchain can greatly facilitate the exchange of training data and ML models, as well as decentralized information, stability, anonymity, and trustworthy ML decision making. We study the literature on integrating blockchain and ML systems in this paper and show how they can work together efficiently and effectively. We will go through the problems that each industry faces when it comes to implementing blockchain. We present a systematic report on ML and blockchain-based smart Industry 4.0 applications more robust to attacks in this article. Finally, we suggest some potential research avenues and anticipate further studies into the deeper convergence of the two promising technologies. We hope that our results will help decision-makers embrace blockchain technology and invest in Industry 4.0 by empowering and promoting research in this field.
Chapters in this book
- Frontmatter I
- Contents V
- Knowledge engineering for industrial expert systems 1
- Machine learning integrated blockchain model for Industry 4.0 smart applications 13
- Prototyping the expectancy disconfirmation theory model for quality service delivery in federal university libraries in Nigeria 26
- Design of chatbot using natural language processing 60
- Algorithm development based on an integrated approach for identifying cause and effect relationships between different factors 80
- Risk analysis and management in projects 96
- Assessing and managing risks in smart computing applications 122
- COVID-19 visualization and exploratory data analysis 145
- Business intelligence and decision support systems: business applications in the modern information system era 156
- Business intelligence implementation in different organizational setup evidence from reviewed literatures 173
- Conceptualization of a modern digital-driven health-care management information system (HMIS) 187
- Knowledge engine for a Hindi text-to-scene generation system 201
- Index 229
Chapters in this book
- Frontmatter I
- Contents V
- Knowledge engineering for industrial expert systems 1
- Machine learning integrated blockchain model for Industry 4.0 smart applications 13
- Prototyping the expectancy disconfirmation theory model for quality service delivery in federal university libraries in Nigeria 26
- Design of chatbot using natural language processing 60
- Algorithm development based on an integrated approach for identifying cause and effect relationships between different factors 80
- Risk analysis and management in projects 96
- Assessing and managing risks in smart computing applications 122
- COVID-19 visualization and exploratory data analysis 145
- Business intelligence and decision support systems: business applications in the modern information system era 156
- Business intelligence implementation in different organizational setup evidence from reviewed literatures 173
- Conceptualization of a modern digital-driven health-care management information system (HMIS) 187
- Knowledge engine for a Hindi text-to-scene generation system 201
- Index 229
