Semantic analysis of blockchain intelligence with proposed agenda for future issues

Abstract

Integrating blockchain technology with artificial intelligence (AI) i.e., blockchain Intelligence makes an extremely powerful tool that solves many multidimensional problems in several domains. Blockchain technology has the potential to provide links to shared data, transactions, and records in a decentralized, safe, and reliable manner, including the information and decision-making capability of AI which makes machines similar as capable as humans. This study is intended to present an updated systematic review of the integration of Blockchain and AI in various application areas. We have studied and summarized more than 100 research papers to present an updated version of the review. We also discuss the future of Blockchain technologies with AI. By integrating these two technologies results increases the security, efficiency, and productivity of the applications. Past works feature a few possible advantages of integration of Blockchain and AI, yet just give a restricted hypothetical system to depict forthcoming certifiable combination instances of the two advances. We survey and orchestrate surviving exploration on the integration of AI and Blockchain are other ways around to thoroughly build up a future research plan on the fusion of the two innovations. We also proposed an agenda to develop a secure system of cyber threat intelligence information exchange by using features of blockchain and artificial intelligence. This paper mainly focusses on explaining how collaboration of blockchain and AI gives immense boost in latest domains like Cybersecurity, Healthcare, Supply Chain Management, Finance and Banking and Social Media Analytics.

Introduction

Blockchain and Artificial Intelligence (AI) are today’s leading technologies. They are both making infatuated moves in different realms. Recent breakthroughs in Machine Learning (ML), particularly in the field of Deep Learning (DL), are being used for prediction, classification, natural language processing, and image recognition, etc. It is enough to conclude that both AI and Blockchain have their strengths although they have certain limitations as well. Challenges Blockchain faces such as scalability, performance, and stability, and AI issues are the development of false news, safety problems, and massive monopolization of AI. AI and Blockchain will assist each other with their vulnerabilities (Sung-Bong 2019).

Blockchain technology

Stuart Haber and W. Scott Stornetta presented the idea of making sure about the chain of blocks in 1991. Later in 2008, an individual or a group known by the pseudonym "Satoshi Nakamoto" conceptualized and executed the blockchain innovation. They presented the idea of utilizing hashing in the blockchain framework to make it so secure that nobody can make changes or eliminate the records once spared in the blockchain. The Bitcoin digital currency framework utilizes this blockchain plan as its basic or base innovation.

The blockchain is an open, decentralized, distributed, and public digital ledger where transactions are recorded between people across many computers so that the record cannot be altered retroactively without the alteration of all subsequent blocks and the consensus of the network. Blockchain is considered a growing chain of records linked by the power of cryptography. Blockchain is a secure series of time-stamped record chains stored in a database that a group of users manages who are parts of a decentralized network. Figure 1 illustrates the structure of blockchain, how each block is connected.

Fig. 1.

Structure of blockchain

Blockchain is a decentralized or distributed ledger where each node in the network has access to the data or records stored in a blockchain. The encryption of all the important data records in the blockchain is done using cryptographic techniques. This ensures the protection of data in the blockchain (sTeam, D 2019). So, the primary concept behind blockchain technology is having a network of multiple users or computers known as “Nodes” which can have secure and legitimate transactions directly without a third- party mediator. Any authorized node that is a part of the network can access the set of records added as a legitimate block in the blockchain. This makes the blockchain system an immutable, distributed digital public ledger that can record financial as well as other types of transactions (sTeam, D 2019). Although blockchain is a new technology, it already boasts a rich and interesting history. The following is a brief timeline in Table 1 which is explained in https://builtin.com/blockchain of some of the most important and notable events in the development of blockchain (https://builtin.com/blockchain).

Table 1.

Timeline of blockchain

Year	Milestone
2008	Satoshi Nakamoto, a pseudonym for a person or group, publishes “Bitcoin: A Peer-to-Peer Electronic Cash System."
2009	The first successful Bitcoin (BTC) transaction occurs between computer scientist Hal Finney and the mysterious Satoshi Nakamoto
2010	Florida-based programmer Laszlo Hanycez completes the first-ever purchase using Bitcoin—two Papa John’s pizzas. Hanycez transferred 10,000 BTC’s, worth about $60 at the time. Today it is worth $80 million. The market cap of Bitcoin officially exceeds $1 million
2011	1 BTC = $1USD, giving the cryptocurrency parity with the US dollar. Electronic Frontier Foundation, Wikileaks, and other organizations start accepting Bitcoin as donations
2012	Blockchain and cryptocurrency are mentioned in popular television shows like The Good Wife, injecting blockchain into pop culture. Bitcoin Magazine launched by early Bitcoin developer Vitalik Buterin
2013	BTC's market cap surpassed $1 billion. Bitcoin reached $100/BTC for the first time
2014	Gaming company Zynga, The D Las Vegas Hotel, and Overstock.com all start accepting Bitcoin as payment. Buterin’s Ethereum Project is crowdfunded via an Initial Coin Offering (ICO) raising over $18 million in BTC and opening new avenues for blockchain. R3, a group of over 200 blockchain firms, is formed to discover new ways blockchain can be implemented in technology. PayPal announces Bitcoin integration
2015	The number of merchants accepting BTC exceeds 100,000. NASDAQ and San-Francisco blockchain company Chain team up to test the technology for trading shares in private companies
2016	Tech giant IBM announces a blockchain strategy for cloud-based business solutions. The Government of Japan recognizes the legitimacy of blockchain and cryptocurrencies
2017	Bitcoin reaches $1,000/BTC for the first time. The cryptocurrency market cap reaches $150 billion. JP Morgan CEO Jamie Dimon says he believes in blockchain as a future technology, giving the ledger system a vote of confidence from Wall Street. Bitcoin reaches its all-time high at $19,783.21/BTC. Dubai announces its government will be blockchain-powered by 2020
2018	Facebook commits to starting a blockchain group and hints at the possibility of creating its cryptocurrency. IBM develops a blockchain-based banking platform with large banks like Citi and Barclays signing on
2019	Uprising digital assets, focus on privacy, Microsoft is anticipated to roll out the technology via the Digital Identity Foundation

Advantages of Blockchain technology

Blockchain demand grew due to its subtle advantages. Figure 2 depict the benefits of blockchain technology.

1. Immutability of blockchain: One cannot alter a knowledge record or details until processed or inserted as a block in the blockchain. The blockchain data is eternal, nothing can alter it and it gets a permanent location inside the blockchain.

2. Decentralization: The whole blockchain network can be decentralized since it offers single consumer digital independence. No single authority in the network regulates all the opposite users. Per node operates independently.

3. Flexibility: The blockchain network has tight encryption strategies to protect transactions. Blockchain users can preserve their privacy and protection.

4. Security: The encryption mechanism inside the blockchain is cryptography that guarantees hackers cannot alter or modify the info records contained inside blockchain chains. Encrypted hash functions connect all blocks inside the blockchain, then attempting theft or illicit transactions inside the blockchain network is unlikely.

5. No intermediaries: Point-to-point nature of the blockchain network, transactions happen directly between two nodes without a mediator. There is no need for an intermediary like PayPal, any bank, Visa, Western Union, etc. to facilitate transactions between two parties.

6. Transparency: The digital distributed ledger system provides an excellent deal of transparency to all or any of those that are a neighborhood of the network. Each node during a network has its copy of the ledger and has the proper to verify transactions. Thanks to this, nobody can hide their details and transactions from the opposite users ensuring fair trade.

7. Fewer transaction costs: As there are not any intermediaries during a transaction within the blockchain network, the transaction costs also are lowered. If there are intermediaries involved, then they charge an important amount, and your overall transaction cost increases.

8. Consensus-based: The blockchain concept is entirely consensus-based, that is, for each transaction that takes place between two nodes during a blockchain, an invitation for its verification is shipped to all or any of the opposite nodes. In any case, the nodes verify a transaction; it goes into the memory pool to form a replacement block. The memory pool stores numerous such verified transactions.

Fig. 2.

Advantages of blockchain

Artificial intelligence

According to Yann LeCun said, “Our intelligence is what makes us human, and AI is an extension of that quality” (https://techvidvan.com/tutorials/artificial-intelligence-features/). Artificial Intelligence (AI) is a tool that lets robots learn from experience, respond to new knowledge, and perform human tasks. Figure 3 shows the sharp features of AI.

Fig. 3.

AI features

Although the hype generated around this technology is enormous and unjustified since it is still in its early stages, AI has evolved to provide unique characteristics, including:

1. Profound learning is an ML approach that teaches computers to learn by example by doing what is normal for human beings. Self-driving feature in Tesla (Autopilot).

2. Using biometric mapping, Artificial Intelligence has facilitated the identification of individual faces. This has contributed to developments in security systems breaking through. This, however, has also received a great deal of backlash for the invasion of privacy. For example, Clearview AI.

3. AI can execute the same kind of work repeatedly without breaking a sweat. To understand this feature better, let us take the example of Siri.

4. The data produced by all of us increase exponentially, where AI enters. The AI-enabled does not only collect this information manually but analyses it using its previous experiences. Rather than feeding this data manually. With the aid of neural networks,

5. AI analyses many such data and tries to draw a logical conclusion. Chatbots are software to provide a window for solving customer problems through either audio or textual input. Watson Assistant, an AI- powered assistant, was developed by IBM.

6. AI helps solve complex quantum physics issues with supercomputer accuracy using Quantum Neural Networks. This will lead to breakthrough improvements in the near term. For example, Google AI Quantum is a leader in this field.

7. AI in Cloud—Microsoft Azure is one of the prominent players in the cloud computing industry. It offers to deploy your machine learning models to your data stored in cloud servers without any lock-in.

AI is another area that receives attention, enabling a computer to learn, collect, and change intellectually based on the data collected (Ekramifard et al. 2020). According to Salah et al. (2019) the AI demand will increase to 13 trillion US dollars by 2030. AI refers to the emulation of machine intelligence to think like humans and to duplicate their actions. The word can also be applied to any device where characteristics such as comprehension and problem solving have parallels with a human mind. Artificial intelligence's perfect feature is the capacity to rationalize and perform decisions that have the greatest chance of fulfilling a particular purpose. The emulation of human intelligence in computers applies to artificial intelligence. Artificial intelligence's purposes include comprehension, logic, and interpretation. AI is used in numerous business fields including finance and healthcare. Weak AI appears to be simplistic and one-task-oriented, whereas strong AI executes more complicated and human-like tasks.

Integration of blockchain & AI

Blockchain and AI integration are projected to have different benefits and advantages. It provides a secure, confidential, and distributed forum for sharing a huge quantity of data between nodules without any interference from third parties, for research, learning, and decision making. Innovative software from various fields can also be implemented. Blockchain guarantees that data can be encrypted and is useful for feeding data in AI systems. In the long run, AI assists in identifying, reading at, and recognizing those precedents and databases, instigating independent collaboration, Blockchain is stressed in maintaining correct records, monitoring, and execution. PC-based perspective and Blockchain share a few features that will ensure that, as fast as possible, a predictable affiliation (Sharma and Jain 2019).

As data in Blockchain are public and contain records, Blockchain can provide decentralized AI platforms such as data, computer power, and make AI decisions transparent, making AI less bullying (Dinh and Thai 2018). In turn, AI can support Blockchain design and run for scalability and can also automate Blockchain and optimize it to boost efficiency, shown in Fig. 4. And as Blockchain data is public, AI will help protect privacy and privacy for users (Dinh and Thai 2018). Also, AI and blockchain are two of the main innovations in any field of the market that catalyze the rate of progress and drive fundamental changes. Moreover, each technology has its degree of technical sophistication and business ramifications, but the two can be collectively combined to reinvent from scratch the whole technological system. In Fig. 4 the predictive structure of integration is shown. Cognitive Scale, an AI startup sponsored by IBM, Intel, Microsoft, and USA, among others, aims to use blockchain technology to safely store the results of an AI application that it has developed for regulatory enforcement in the field of financial markets, is part of the organization focused on exploiting these innovations (Hamsa Gayatri et al. 2020). To be able to safely store AI-derived decisions, an industry that is bogged down by a lot of legislation could help market participants keep on top of onerous reporting criteria. IBM is also currently experimenting with the possibility of integrating both its open-source Hyperledger Fabric-based blockchain offering and its Watson AI platform for a variety of industries. Ever ledger, which uses blockchain technologies to trace the provenance of luxury goods, including the diamond trade, is one such project. IBM, for example, is using the data store of individual diamond characteristics from Ever ledger. Similarly, to ensure that diamonds comply with UN decrees prohibiting the export of war minerals, Watson uses knowledge of thousands of regulations. Furthermore, Technology experts believe that Blockchain can also make AI more simplistic and comprehensive by enabling backward planning and better decision making since the ledger of Blockchain records all the data and its variables that are used by machine learning while deciding (Mufti et al. 2020).

Fig. 4.

Blockchain AI integration structure. (Source: https://www.sciencedirect.com/science/article/abs/pii/S0167739X19316474)

Literature survey

We specifically collected the last 5 years data to write the updated version of a systematic literature review on the topic; the following are some appropriate studies on the same field.

Casino et al. in Casino et al. (2019) explains very well how blockchain technology is used in multiple domains with the integration of AI and highlights how specific characteristics of this disruptive technology can revolutionize. This study also shows a clear classification of blockchain across different areas like healthcare, banking, and finance, supply chain management, etc.

The study of Mufti et al. mentions the latest trends and key developments of blockchain (Mufti et al. 2020) in various domains specifically in the finance and banking sector.

In the survey of Ekramifard et al. The study carried out an analysis to determine what applications in real-time can be benefited when we apply Blockchain and AI together. They state that this combination increases the security, efficiency, and productivity of applications (Ekramifard et al. 2020).

JD Harris and B. Waggoner proposed a framework in Harris and Waggoner (2019) for participants to collaboratively build a dataset and use smart contracts to host a continuously updated structure. Ideal learning issues incorporate situations where a model is utilized ordinarily for comparative info, for example, personal assistance, recommendation engine, and so on.

In (Marwala and Xing 2018; Sung-Bong 2019), and (Zheng 2019), authors give analysis to describe how we can change the upcoming trend of technology by integrating Blockchain and AI which is the future of the latest era.

V. M Ambika et al., B Aruna et al., Sri PSGA et al., A. Yashaswini et al. and Bhargava et.al. all the authors describe in their respective articles (Ammbika and Rao xxxxa; xxxb; Aruna et al. xxxx; Sri and Bhaskari 2018; Yasaswini et al. 2018) and (Bhargava and Rao 2018) about the security issues and methods to resolve those challenges which help in further study in that field.

In article (Pratuisha et al. 2017) and (Sahu and Swain 2018) proposed system on how we can use artificial-neural network techniques for estimation of coronary- artery disease, this helps in healthcare, similarly, an improved data hiding technique using bit differencing and LSB matching is given with detailed knowledge and work on enhanced artificial bee colony optimization for solving economic load dispatch is described in Raghav et al. (2017) which gives the idea of how a influence in all realms.

In article (Ragavan and Prabu 2022; Behera and Gangopadhyay 2021; Pravin et al. 2019; Elavarasan and Vincent 2021; Talaat and Gamel 2022; Priyadharsini and Chitra 2022) author gives new ideas on data security using cryptography along with various artificial intelligence algorithms, which may help in analyzing in terms of security aspect of network.

All the reviews till time give the analysis of many future aspects to be explored; we also give some more future trends to explore in healthcare, cybersecurity, finance and banking, supply chain management, and one emerging application social media.

Research goals

The aim is to analyze the prior works and their findings to summarize the efforts of integration of Blockchain and artificial intelligence in discussed application domains. By narrowing the study, we have developed four research questions as follows:

• RQ1: What are the latest trends in technology and how the integration of blockchain and artificial intelligence helps to improve the latest trends of applications?

• RQ2: What is the involvement of artificial intelligence use cases in blockchain?

• RQ3: What are the current research gaps to explore the combination of blockchain and AI in different areas?

• RQ4: What methods/solutions available/proposed for major areas?

Contribution and paper organization

We identify 24 primary study articles related to our topic up to early 2022, other scholars and researchers can use this list of studies for their work in this specific area. After shortlisting 24 articles as a primary study that are closer to the topic and provide a more desired level of quality for comparative analysis. By doing comprehensive reviews within the subset of primary studies and present it as an idea in similar research. Meta-analysis regarding the possibilities in which blockchain and AI can be implemented to improve the structure. We also propose procedure guidelines to support future possibilities in this area.

Our paper is organized as follows: Sect. 2, describes methods with which the primary studies were systematically selected for analysis. Section 3 presents the findings of all the primary studies selected. Section 4 Discuss the finding related to the research questions we develop. In Sect. 5, we proposed an agenda for our future research direction lastly, in Sect. 6 we conclude the research with future investigations.

Research methodologies

To accomplish the target of responding to the research questions, we conducted the survey with the help of guidance published by Kitchenham and Charters (Kitchenham et al. 2009) and Andrew S. Denney and Richard Tewksbury (Denney and Tewksbury 2013), we sought to move through the planning, conducting, and reporting phases of the review in iterations to allow for a thorough evaluation of the Literature survey.

Selection of primary studies

To address our essential research question, a precise review was completed during September 2020 without time limitations and the outcomes were thus refreshed during January 2022. The following platforms are used for the survey-

• Scopus

• Web of Science (SCI/SSCI)

• IEEE Xplore Digital Library

• ScienceDirect

• SpringerLink

• ACM Digital Library

• Google Scholar

• Web Searches (gray literature)

Inclusion and exclusion criterion

The Table 2 elaborates on the criteria of inclusion and exclusion of selected articles and Fig. 5 shows the process flow of inclusion. Based on the specific topic and domain and setting the time frame for the latest 3–5 years, only peer-reviewed articles from the above-mentioned sources are taken. Exclusion is typically based on the old methodologies, different domains, and reports that are not showing significant results.

Table 2.

Criterion of inclusion/exclusion of article

Selection criteria	Scientific database		Grey literature
Inclusion	Peer-reviewed research articles, conference. Proceedings papers, book chapters, review papers, short surveys, serials, etc With time-frame restrictions. 3–5 years		English reports No time-frame restriction
Exclusion	Before importation to a bibliographic manager	Non-English articles, articles with missing abstracts, notes, editorials	Generic reports related to Blockchain technology without describing specific applications
	During title screening	Generic articles related to Blockchain technology and/or blockchain architecture
	During abstract screening	Software-oriented articles related to the blockchain technology
	During full-text screening	Articles addressing technical aspects of blockchain technology

Fig. 5.

Inclusion process. (Col1: Type of studies, Col2: Database Searched, Col3: Primary Keywords, Col4: Secondary Keywords, Col5: Total Selected Papers, Col6: Primary Study Identified)

Selection results

After evaluating the studies under the inclusion/exclusion criterion, these papers were read in full, with the inclusion/exclusion requirements being re-applied, and 24 papers remained, which became the primary study for our survey.

Findings

Each primary research paper was read in full and the related qualitative and quantitative data were extracted and summarized in Table 3. All the primary studies had an emphasis or theme on how blockchain and AI were coping with a specific issue. The emphasis of each paper is documented as well. Figure 6 shows the graphical analysis of the finding table. The subject of each paper was further grouped into wider categories to allow for a simpler classification of the themes of the primary studies. The themes found in the primary studies highlight that cybersecurity, social media, healthcare, supply chain management, finance, and banking applications of blockchain are most concerned with AI.

Table 3.

Key findings from primary studies

Primary studies	Key qualitative and quantitative data reported	Strategy	Domain
Casino et al. (2019)	A systematic literature review of blockchain-based applications: Current status, classification, and open issues	Analytical	Combined
Gorkhali et al. (2020)	Blockchain: a literature review	Analytical	Combined
Taylor et al. (2020)	A systematic literature review of blockchain cybersecurity	Analytical	Security
Ekramifard et al. (2020)	A Systematic Literature Review of Integration of Blockchain and Artificial Intelligence	Analytical	Combined
Harris and Waggoner (2019)	Decentralized and Collaborative AI on Blockchain	Empirical	Combined
Marwala and Xing (2018)	Blockchain and Artificial Intelligence	Empirical	Combined
Sung-Bong (2019)	A Survey of Blockchain and Its Applications,	Analytical	Combined
Zheng (2019)	Blockchain Intelligence: When Blockchain Meets Artificial Intelligence	Empirical	Combined
Salah et al. (2019)	Blockchain for AI: Review and Open Research Challenges	Analytical	Combined
Jaoude and Saade (2019)	Blockchain Applications – Usage in Different Domains	Empirical	Combined
Pandl et al. (2020)	On the Convergence of Artificial Intelligence and Distributed Ledger Technology: A Scoping Review and Future Research Agenda	Analytical	Combined
Hughes et al. (xxxx)	Blockchain research, practice, and policy: Applications, benefits, limitations, emerging research themes, and research agenda	Empirical	Combined
Goyal (xxxx)	Moving from centralized to decentralized social sites using blockchain technology,	Empirical	Social media
Sgantzos (2019)	Artificial Intelligence Implementations on the Blockchain. Use Cases and Future Applications	Empirical	Combined
Rath (2019)	A review of Artificial Intelligence Emerging technologies and challenges in Blockchain Technology	Analytical	Combined
Sharma and Jain (2019)	An Integration of Blockchain and Artificial intelligence: A Concept	Empirical	Combined
Queiroz et al. (2019b)	Blockchain and supply chain management integration: A systematic review of the literature	Analytical	Combined
Khezr et al. (2019)	Blockchain Technology in Healthcare: A Comprehensive Review and Directions for Future Research	Analytical	Healthcare
Kaur et al. (2020)	Banking 4.0: “The Influence of Artificial Intelligence on The Banking Industry & How Ai Is Changing the Face of Modern Day Banks”	Empirical	Finance
Siyal et al. (2019)	Applications of Blockchain Technology in Medicine and Healthcare: Challenges and Future Perspectives	Empirical	Healthcare
Gurtu and Johny (2019)	Potential of blockchain technology in supply chain management: A literature review	Analytical	Supply chain
Labazova et al. (2019)	From Hype to Reality: A Taxonomy of Blockchain Applications	Empirical	Combined
Dinh and Thai (2018)	AI and Blockchain: A Disruptive Integration	Empirical	Combined
Hamsa Gayatri et al. (2020)	A convergence of IoT, AI, and Blockchain. Blockchain, Big Data, and Machine Learning	Empirical	Combined

Fig. 6.

Graphical analysis from primary studies

Blockchain framework

A key blockchain platform includes distributed headlines, encryption, consensus protocol, and an intelligent agreement (Gorkhali et al. 2020). The blockchain system was protected by five papers in the SCI/SSCI database. The results are discussed hereafter in Table 4.

Table 4.

Blockchain frameworks (K. K. 2018)

Hyperledger	Supported by Linux Foundation and IBM
Ethereum	A private blockchain framework from Ethereum
Multichain	An open platform for building blockchains
Eris Industries	low-cost blockchain implementation framework
R3 Corda	Corda is a blockchain framework designed for the BFSI industry
Open Blockchain	An open blockchain fabric code framework

Blockchain algorithms

For the development of blockchain technology, so many algorithms are continuously developed which aim to solve the faults of existing algorithms like the Proof-of-Work (PoW) and Proof-of-Stake (PoS) system. They are both existing Consensus algorithms. They allow all blockchain nodes to agree and prevent double-spending attacks which attempt to spend the same coins more than once (Ali 2020). Here is a closer look at various algorithms.

Consensus algorithms: Consensus algorithms are difficult but assist when buying coins or running a node. On networks containing several nodes, consensus algorithms achieve durability, meaning that all nodes adhere to the said rule or action. In bitcoin, nodes describe consensus, not miners. The chain with the most work determines Agreement. You will not have the mining capacity to protect it if you fork and change the POW. Nodes accept transactions, verify transactions, replicate transactions, block validation, replicate blocks, serve the network, and store the database. The Proof-of-Work algorithm that miners must employ is already described by nodes. Nodes preserve, not miners, the protocol. The development of Consensus algorithms was initiated with the implementation of blockchain, and several further algorithms were developed to solve the faults of the Proof-of-Work (PoW) algorithm method, the first-ever.

Mining algorithms: There are three key components of data mining: clustering or grouping, rules of the association, and sequence analysis. Clustering/classification is the study of a data set and the generation of a set of rules for grouping that can be used for classifying potential data. Association law is a rule that indicates association relationships in a database between collections of objects. The study of a sequence is the study of patterns that appear in sequence. To incorporate certain facets of data mining, there are several algorithms suggested. Miners use machines in the blockchain to guess responses constantly and very easily to a challenge before one of them wins. More explicitly, miners would run the unique header metadata of the block (including time stamp and program version) via a hash function that returns a random number string of fixed lengths, while changing the nonce value to influence the value of the hash. The miner is rewarded in cryptocurrency if a miner discovers a hash that suits the target, and the block is transmitted across the network for each node to verify and connect to their copy of the ledger. When a miner starts to solve the puzzle faster or slower, the algorithm changes itself automatically so that miners can stay within the 12–15 s solution time.

Traceability algorithms: Traceability shows the sources and procedures of a contract while obtaining additional details to enhance the efficiency of the internal mechanism and the preparation of each node in the supply chain. As transaction data streams data and high-dimensional data from distributed computing networks, Blockchain operates on big data analytics. The primary purpose of algorithms in the traceability chain is to easily make traceability decisions. Such an operation, however, introduces meaningless data issues and improperly optimizes blockchain traceability. Therefore, because of an inference process, the artificial intelligence of a blockchain mining algorithm,

like the traceability chain algorithm, operates faster than a consensus algorithm. Blockchain traceability facilitates and enhances supply chain communication, offering more accurate information on the location and status of transactions so that this information can be used by a node as a reference for its planning operation. Traceability algorithms consist of three main sub-processes (Ali 2020):

• Identification and labeling of products to facilitate product identification.

• Data capturing and recording scanning capabilities with electronic information flow to optimize retrieval of data.

• Linkages and communication to optimize data sharing between supply chain partners and protocols.

Implementation of blockchain

Using an algorithm from one of the groups of distinct consensus algorithms, blockchain technologies can be applied. Besides, some algorithms are less cost-effective, while others have limits on bandwidth and latency.

(Non-proof-of-work) blockchain: Sections of the blockchain algorithm that require costly mathematical operations are replaced with easier, but less protected alternatives when used in enabled networks (networks where all peers are identified and trusted). Algorithms of the non-PoW blockchain also have a master. Therefore, DoS attacks are vulnerable to the non-PoW blockchain but could provide a better solution in democratized transactions.

(Proof-of-work) blockchain: Blockchain uses Proof-of-Work in open networks (networks where anyone can interact without limitation as a peer; peers are both untrusted and theoretically unknown) that mitigates the vulnerability to DoS attacks but results in a high cost per transaction. Each algorithm in the blockchain has its own set of benefits and disadvantages. You should select the best-suited algorithm for your organization based on the type of business and the system you create.

Taxonomy of application based on the integration of blockchain with AI

Our survey is typically based on the latest literature available in reputed and accurate databases like Scopus, WOS, etc. We are creating a blockchain technology taxonomy that encompasses five fields of a blockchain application that are divided into eight functional dimensions. The taxonomy is based on current research literature, company studies, and classifications of prior blockchains. Our taxonomy is distinct because it combines knowledge of blockchain and AI technologies that can direct the implementation of blockchain-based systems. This research adds to the scientific knowledge base in three respects. Next, we build a review of current literature on areas of blockchain use. Second, we recognize new AI aspects of relevance to blockchain implementations, which supplement extant work in the scientific literature. Third, we connected blockchain implementation areas and AI with blockchain features that can direct blockchain-based system development. For developers, the taxonomy offers an analysis of popular blockchain implementations for potential blockchain-based projects that can reduce implementation challenges (Labazova et al. 2019). According to the authors of Casino et al. (2019); Ekramifard et al. 2020; Hughes et al. xxxx), the taxonomy of blockchain-based applications is broadly divided into eight categories as shown in Fig. 7.

Fig. 7.

Taxonomy of applications

In this review as the title indicates “emerging” in the same way we take the top five sectors which are seeking most of the attention of researchers and practitioners i.e., Healthcare, Finance & Banking, Cyber Security, Supply chain Management and social media.

Healthcare

It is not an emphasis if we call this 2020 a year of COVID-19, Healthcare industry plays a very crucial role in this pandemic. According to S. Chakraborty et al. in Chakraborty et al. (2019) Healthcare and Biomedical advancement have consistently been significantly concerned to be elevated in all possible ways with the technological progression that is seaming out all through the globe. Upgrading the structure, trust, strategy, and productivity of medical care administrations and supporting the patients with qualified sustenance and care is of the sole significance. The world currently has been watching an individual being more hesitant towards individual medical services until a significant difficulty appears. Something like this can be regularly viewed as a fragment of over-commitment to the costumed occupied life and tuned way of life structure. In this manner, if a framework is created so that measures or distinguishes normal inconsistencies in the wellbeing set-up of a human and can answer to the assigned individual medical care boss of the individual, at that point the entire situation would be substantially more helpful, and an easy meeting should be possible on the patient in the correct second and inside a shielded length.

Applications and their utilization

This hierarchy shown in Fig. 8 of the healthcare industry shows the entire variant used in this domain where we can apply blockchain technology to enhance productivity.

Fig. 8.

Healthcare applications

Application areas are not limited as they include data management to IoMT (Internet of Medical Things). As shown in Fig. 9 below.

Fig. 9.

Expanded applications of healthcare

Existing work

In (Khezr et al. 2019), a blockchain-based healthcare technology workflow. The workflow shown in Fig. 10 consists of four key layers including healthcare raw data, blockchain technology, healthcare implementation, and stakeholders. As a shared technology, the blockchain helps different parties to profit from healthcare applications.

Fig. 10.

Workflow diagram. (Source: https://www.mdpi.com/2076-3417/9/9/1736/htm)

A digital patient report containing critical information, medical record information, test reports, medication reports, medication information, and diagnostic effects. The Database is a digital patient report. The blockchain facilitates access by HDG, MedRec, PSN & BBDS to medical institutions, patients, and other related stakeholders on electronic health data. The decentralized architecture of Blockchain saves the stored data securely and allows medical data to be updated on each network node in real-time. Cloud server persistence reduces the possibility of a loss of health data and/or sensitive information and increases the availability and reliability of medical information. For patients, clinical data and clinical reports are used to check the health status of an individual so they then determine their state of health based on their medical information as they detect a specific condition. For doctors, a clinical program should be employed to enable quicker awareness of medical contraindications and diagnosis of instruments. The Repository is intended for large-scale data collection, care, and equal monitoring by hospitals and medical investigations departments, drugs, and so forth. The integrated intelligent contracts of blockchain are designed to construct an intelligent medical management system through medical contracts and vouchers (Lu 2019). Initially, all the data from medical equipment, hospitals, social media, and many other channels are consolidated to generate raw data that eventually expands in size to big data. This data is the vital ingredient of the entire blockchain-based healthcare, and it is the principal component that generates the first layer of the stack. Blockchain infrastructure is at the top of the raw data layer and is perceived to be the central system in search of developing a four-component protected healthcare architecture. Every blockchain platform has different features such as consensus algorithms and protocols. Blockchain networks enable the development and control of transactions by users (Khezr et al. 2019). Some existing studies are there (Padmaja and Seshadri 2021; Srivastava et al. 2019; Kumar et al. 2021; Chinnasamy and Deepalakshmi 2021; Parameswari and Ranjani 2022) which indirectly take a dig into this to enhance the area of working in healthcare.

Future investigation

• Cross-border sharing of health data where a separate and sometimes contradictory authority occurs can impede the benefits of blockchain sharing. Based on government legislation, the presumption of the privacy of a person varies from one country to another. Work on legislation, standardization, and cross-border communication of health data recovery strategies, including the purpose to maintain and use health data, is also urgent (Khezr et al. 2019).

• The ability of the blockchain to store and manage large data access transactions promptly. The delay of mining blocks in the private or public blockchain will increase exponentially as the number of transactions increases, requiring creative algorithms to reduce mining delays.

• The applicability of the blockchain to combat belief erosion and to improve clinical trial data integrity.

• Supply chain control of prescription tracking pharmaceuticals, comprehensive surveillance systems to regulate the registration of the required drugs.

• The existing method is RFID and barcode, which are not quite resistant to data tampering as these codes are sent as constant values that can be changed in the SC process.

• Through using the blockchain, patient reports and billing can be made more accurate so that they cannot be misused.

• Build tamper-resistant systems (IoMT) utilizing the blockchain to ensure the fidelity of medical records.

• Blockchain efficiency tests in dynamic and varied communications networks.

Finance & banking

In a recent scenario, blockchain is extended to a broad variety of financial sectors, including business management, money- related resource repayment, forecasting markets, and financial exchanges. Blockchain is expected to make a fundamental contribution to the feasible change of the global economy, bringing benefits to shoppers, to the existing financial system, and society. The global budgetary system investigates strategies for the use of blockchain-powered applications for financial services, such as security, fiat cash, and derivative contracts. For example, blockchain innovation provides a huge shift in the capital business sectors and a more productive route for carrying out tasks such as security and derivative transactions, digital payments, loan management, general financial administration, monetary auditing, or cryptocurrency exchange and trading (i.e., e-wallets). Strikingly, many of the world's largest banks, including Barclays and Goldman Sachs, have joined forces with R3 to create a functioning blockchain-based monetary market infrastructure. Another instance of bank cooperation is the Global Payments Steering Group (GPSG), which includes Santander, Bank of America, and UniCredit, among others.

Other financial-oriented areas may include the processing of commercial property and casualty claims, syndicated loans, contingent convertible bonds, electronic enforcement, proxy voting, the redemption of properties, and the over-the-counter business. Finally, blockchain adoption by the financial sector would potentially lead to cost savings in areas such as central finance reporting, arbitration, centralized operations, and business operations.

Issues in finance and banking

Blockchain is a successful apparatus for comprehending the issues of the financial industry. For instance: The compromise and settlement costs between money-related organizations are exceptionally high and there exist numerous perplexing cycles. In the protection market, the exchange cycle takes quite a while with significant expenses. Assets Management is essentially overseen by go-betweens, which builds exchange costs and the danger of forging. User distinguishing proof. Client information between various monetary organizations is hard to collaborate viably. For the situation of cross-border exchanges, the two players regularly have lacking trust, and the requirement for a middle person ensures.

The blockchain can set up the exact, ideal, and multifaceted oversight. For example, point-to-point esteem move, dispersed innovations, and computerized resources setting up instruments through savvy agreements to guarantee consistency with contracts, advanced character acknowledgment.

Blockchain in finance

Blockchain technology will help in finance and banking in the following aspects:

• Better transaction processing,

• Sustainable banking and finance,

• Enhance financial security.

• Privacy as well as automated financial contracts (Jaoude and Saade 2019).

AI in financial service

There is also a range of improvements (Fig. 11) in the way communications, customer support, and recruiting and asset management take place throughout the financial sector. Today, for example, stock investing and finance are all about technical skills and divine luck. Yet in the future, with the aid of sentiment analysis, crowd-sourced data, and algorithms, we will be able to handle money in a much different way (Kaur et al. 2020).

Fig. 11.

Range of improvements in the finance sector

Existing work

As of now, the financial business has begun putting resources into a wide range of ventures and new companies giving Blockchain-based arrangements as this innovation gives a significant level of wellbeing for putting away and sending information, dispersed, and straightforward organization framework, decentralization, and minimal effort of activities. Banks have expanded directing the trial of decentralized resource innovation and executing blockchain in the business cycle. As blockchain itself holds a changeless record that records all exchanges in the chain if countless exchanges are being prepared by the organization, a gigantic volume of information is gathered, and AI methods can be utilized to measure and characterize the information. Telecoin is digital money dependent on the Ethereum.

blockchain which will be conveyed and acknowledged by telecom administrators, empowering budgetary installments, settlements, credit, and different monetary administrations on the blockchain. Telecoin recommends that the consolidated highlights of blockchain and AI can add to different applications like foreseeing illegal tax avoidance as AI is better in design grouping and identification of abnormalities in a lot of information can be taken care of with blockchain innovation. Figure 12 shows:

Fig. 12.

Architecture of Finance Sector in Blockchain. (Source: https://www.semanticscholar.org/paper/Blockchain-for-AI)

Figure 12 presents a model of AI and blockchain innovation that can be utilized in the banking and fund foundations. Another wonderful result of consolidating the two innovations is the treatment of a fluctuating scope of digital forms of money where AI procedures can help diminish the natural unpredictability of digital forms of money built up a neural organization model that saddles the capability of profound learning money-related time arrangement within the sight of solid clamor which demonstrated that, with enormous volumes of datasets, AI strategies perform superior to other conventional models. AI procedures can investigate the cost and subtleties of different stock trades and foresee the future figures precisely and decentralized agreements can be utilized to freeze the cost of cash for a fixed measure of time.

Future investigation

Data privacy: Data security is one of the main challenges in the business space in today's era, where many structures experience information penetrating, breaking personal data, unwanted surveillance, and eavesdropping, penetrating access control privileges, taking and spilling information. Information is put away in a changeless way with the circulated Blockchain breakthrough, having ensured time-stepping, public audit, and consensus, making the system effective against security problems.

Backup and disaster recovery: In any business application, information storage and reinforcement are essential. More computing power is needed to put away and hold up a huge amount of information, which brings about the expansion of the general cost. Furthermore, if the data is stored on a collected structure, the probability of a single point failure is increased. For the dispersed treatment of knowledge, the blockchain component utilizes decentralized frameworks. For data storage and replication, a clustered hierarchy is used that reduces the risk of losing the data (Bodkhe et al. 2020).

Smart contracts: The contracting process requires pricing, confirmation, and approval to enable the next steps to be taken. The implementation of a conventional agreement may require human involvement that requires a third party as a service. Also, during conflicts, the same is expected and leads to greater time for resource use and the contract's high cost. Smart automation is produced without human interference using blockchains, which removes the involvement of third-party transactions and unnecessary delays in time (Bodkhe et al. 2020).

Letter of credit (lc) payment: A buyer and dealer can make transactions with an international letter of credit payment and use a paper-based letter of credit. Each party needs to submit the required documents through postal or courier services in this scenario. The time and cost involved in the process are much higher and not easy for the exporters because of the specifications. Blockchain technology can remove the time delay when used in this domain by delivering cost-effective, quicker services. The blockchain enables the transactions to be transparent and incorporated with the ledger’s electronic bill (Bodkhe et al. 2020).

Cybersecurity

Cybersecurity has become an issue of great importance recently due to various cyberattacks on almost every domain. As 2020 becomes a year of challenges so far, industries are also facing the big picture of cyber threats as no one is prepared for this pandemic (COVID-19) and this gave an immense opportunity to the hackers to create new threats. Cybercrime has now escalated more and more, causing significant government and company casualties, not just because hackers are more vulnerable to humans. Cybercrime is an unauthorized network connection to computers to obtain data, destroy the operating device, hardware, program by an attack. Cybersecurity is an essential tool to protect data, sensitive information, and computer devices from the new technology large amounts of data are gathered with low estimate power which fails in protection, privacy, and interoperability. In cyberspace, IoT and CPS will play a primary role. The Internet of Things (IoT) is a structured network infrastructure that is more prone to malware attacks and other assaults due to Internet interconnection. Advanced Persistent Threats (APTs) Artificial Intelligence is a tool that plays a key role in data defense to defend against multiple disruptive activities. To find bugs in the network and applications, AI is used (Sridevi and Kumar 2019).

Solution through blockchain & AI

For cyber protection problems, Blockchain and AI technologies give no magic bullet. If anything, they bolster current efforts for safe networks, communications, and records. Blockchain uses encryption and cryptography to store permanent documents and many of the current cyber protection technologies use very similar techniques as well. Figure 13 showing most of the security mechanisms in effect rely on a single trustworthy authority to validate information or store encrypted data.

Fig. 13.

Cyber Security in AI. (Source: https://www.xenonstack.com/blog/artificial-intelligence-cyber-security/)

This makes the infrastructure open to attack, and many bad actors might concentrate their energies on a single objective of refusing service services, injecting malicious data, and extorting information via fraud or extortion. In that real blockchains are autonomous and may not need the jurisdiction or support of a particular member of the community or network, blockchains have the upper hand over existing protection measures. The mechanism does not need trust because each node or participant has a full copy of all existing past information and more details will be added to the chain of previous information only by reaching the consent of the majority, so the bottom line is this: multiple members of a group who have access to the same information will be able to defend the group way more than a group made.

Future investigation

Data is collected from various sources in the modern computing world and distributed through networks between devices (e.g., IoT). Artificial Intelligence (AI) and its derivatives have been used as essential methods for analyzing and manipulating the data gathered to achieve efficient reasoning in solving security issues. While AI is efficient and can be involved with distributed computation, when manipulated or deceptive data is purposely or accidentally introduced by a malicious third party based on adversarial inputs, misleading analysis can be produced. Blockchain has the potential to be leveraged in various areas of cyberspace as a mainstream ledger framework. Thanks to its features such as decentralization, encryption, and immutability, to ensure the accuracy, accountability, and honesty of data, Blockchain aims to reduce transaction risks and financial exploitation. When data integrity and reliability can be ensured, AI can produce more stable and trustworthy outcomes. The use of the blockchain for the security of AI data in B2B and M2 M environments could be a potential research direction. Blockchain cybersecurity research is divided between academia and the developer group by publishing open-source applications and datasets and engaging with the community. In (Priyanka et al. 2021; Shobanadevi et al. 2021; Mishra et al. 2022; Kiraz 2016; Mousavi et al. 2020) authors mention so many areas to expand as future research and showed some gaps, to close this gap, academic researchers are required to make efforts to release more open-source tools, services, and datasets to engage industry and start-ups. Blockchain analysis includes a large population evidenced by the proliferation of open-source tools such as bitcoin so that academic researchers can actively engage the community in developing, validating, and preserving the findings of their study (Taylor et al. 2020).

Supply chain management (SCM)

The latest trends, directly related to Industry 4.0, are creating substantial disruptions, and are forcing the Supply Chain Management (SCM) sector to create innovative market strategy models. Among these technologies, blockchains are one of the most successful (Queiroz et al. 2019b). Blockchain technology is supposed to increase the efficiency and accountability of supply chain networks, thereby facilitating more effective value chains. Blockchain-based implementations have the potential to generate breakthroughs in three areas of the supply chain: visibility, optimization, and demand (IBM Company, 2016) (Casino et al. 2019). It is feasible to use blockchain in logistics, identify counterfeit products, minimize paper load handling, enable origin monitoring, and enable buyers and sellers to transact directly without interference. Besides, it has been shown that the use of blockchain-based applications in supply chain networks can protect the security, lead to more robust contract management frameworks for third- party and fourth-party logistics (3PL, 4PL) to address information asymmetry, strengthen tracking mechanisms, and assurance of traceability, provide better information management throughout the supply chain.

Advantages of using blockchain technology

Blockchain technology coupled with the ability to program business logic with the use of smart contracts enables the following:

Transparency into the provenance of consumer good—from the source point to end consumption Precise asset monitoring Improved licensing of facilities, materials, and applications Also in today's technologically advanced environment, supply chains could significantly increase performance, audible monitoring, and restrict exploitative activities. Paperwork can account for half the cost of transportation in the container industry. Mica, which is used in cosmetics, electronics, and car painting, is frequently mined by child laborers from illicit mines shown in Fig. 14.

Fig. 14.

Blockchain in SCM. (Source: https://www.3i-infotech.com/must-modernize-supply-chain-management-blockchain/)

Furthermore, consumer products, particularly appliances, pharmaceuticals, and luxury brands, are susceptible to counterfeiting and fraud. PwC currently reports that counterfeiting sales account for more than 2 percent of global economic production. The introduction of public, private, and hybrid blockchains will bring the movement of goods and commodities to traceability, transparency, and accountability. The system will be used in logistics to render manufacturing operations more effective and to reduce the expense of infrastructure in the supply chain.

Existing work

The SCM will use a blockchain to classify players executing each operation. This blockchain enables the assessment of findings and output of the core SCM processes to be true and efficient. Once the data is in a blockchain database, it is immutable. Shipments, progress on-road, and deliveries can also be monitored by other vendors in the cell. For instance: “Drakes” Supermarket, Australia's largest 100% family-owned meat producer, Thomas Foods International, and its largest independent foodstuff retailer, have signed into the IBM Food Trust foodstuff blockchain-based ecosystem. The two South Australian organizations are the first in Australia to use the IBM Blockchain Network to pilot IBM Food Trust, reducing traceability from 3 days to three seconds. This creates the trust of suppliers between blockchain. The quality of audits may be increased, and expenditures minimized by removing intermediaries. Individual providers may execute their inspections and balance sheets in real-time. The blockchain also offers a precise form of calculation of commodity consistency. Through the blockchain network, the shipping industry gradually aims to streamline the global supply phase also said in Bhargava et al. (2022). This is because 90% of the world's commodities are shipped by ships and shipping transactions require thousands of individuals and organizations and create more than 200 separate partnerships and interactions between them. The rest of the maritime agreements are purchase contracts, charter contracts, landing bills, certificates of origin, harbor papers, LC, and a variety of other vessels- and cargo papers. The internet allows the international sharing of information faster, but it happens bilaterally and hence creates gaps in the supply chain. Application for shippers, importers, and traders is open. It does not need information technology or operating modifications (Gurtu and Johny 2019). Also supply chain can be a part of healthcare as medical supply chain (Khatter and DevanjaliRelan 2021).

Future investigation

From the academic point of view, this research provides students with experience in these hot topic’s critical perspectives. In any sense of the supply chain, Blockchain technologies may be utilized. In publications on this topic, we expect an increasing pattern in the near term. This is also an interesting agenda for potential research (Queiroz et al. 2019a). We suggest a cohesive and articulated agenda for potential studies as a significant contribution.

Our research showed a lack of analytical studies that reported on the lessons learned from implementation and the major challenges. To overcome this, we propose research aimed at recognizing the existing stage of blockchain-SCM maturity integration and mapping the major issues. Empirical research reporting diverse viewpoints on blockchain implementations in various supply chain environments is also a possible subject.

Social media

Internet networking as well as social networks and social media management are now an integral part of online existence with so little time, as are Facebook, WhatsApp, YouTube, WeChat, Instagram, Qzone, Snapchat, Viber, Pinterest, LINE, Telegram, Talk on, Skype, Baidu, Tieba, LinkedIn, Reddit, and so forth. These sites are used for personal, private, political, social, work posting, educational, financial, crowdsourcing, dating, governance, health, and medical, community-specific, and real-world activities. The word social networking is described as the word "Web-based and mobile technology for collaborative discourse." (Goyal xxxx).

Issues in SMA (Social Media Analytics)

In (Choi et al. 2020) the author presents related problems and other conventional SMA social networking networks. More specifics are discussed below:

1. The processing of data: Web crawling is not completely accessible and completely public with social network site data.

2. Incentive: Traditional social media platforms are a free place to meet and have fun at the very beginning. Consequently, conventional social networking sites are typically not a good opportunity to inspire people to connect and contribute honestly. Today, YouTube and Facebook Live will have incentives, but the sharing of rewards is possibly relatively minimal and open.

3. Fehling info: Social network info with several missed pieces of information could be incomplete. Due to the veracity of social network results, some statistics are of low consistency. These all create challenges that are not trivial. Some traditional methods to ignore the missing data lead to partialities. There are also suggested advanced methods to assist. There are still missing data, however, and this is a critical problem.

4. Authentication problem: "bot" issues are not necessary for social media information posted online. Companies may use machines by paying some individuals to add comments or create posts. False information and counterfeit data are therefore quite common. This is an important topic and Forbes notes that Facebook has lately dealt with the problems of bogus profiles.

5. High data volume and data speed: Social media data is a typical case in big data, with high volume and high speed in application requirements as the most important features. As a tradition, this challenge must be overcome by the right software architecture and storage technology.

6. Unstructured information: social media is a repository of numerous forms of data, including messages, photographs, videos, and sound. Data is not given in nearly all cases in an organized way. The use of data, particularly for technically sound organizational analyses, is thus rendered more difficult. Indeed, the bulk of social network research software presently employing concentrates primarily on texts but not on graphics or photographs. They still just look at icons for those who examine images or pictures. This indicates that the whole image of the scene is incomplete.

Solution through Blockchain & AI

1. Combining centralization and decentralization: All details flow into a common authority within a centralized structure- based network, making it easier to monitor knowledge outlets such as Facebook and Google's email service. Therefore, the Spam Directory idea is given by E-Mail providers to filter anything that passes via a single centralized stage. It ensures that on each mail you will see anything. Likewise, Facebook is immense in terms of the amount of data that provides the largest share of networking media for the user and the volume of data. Because centralized structures quickly capture data, the government and conventional business models that track any operation and the behavior of all are a good choice (Goyal xxxx). The distributed authority of the system enhances the privacy of Blockchain to a single node. This ensures that knowledge cannot be monitored from a single point or several nodes that render it impossible to follow on the network in location. It helps to secure online identities, to build trust, authenticate and avoid the portability of info. (Choi et al. 2020).

2. Concerning challenges in accessing records: Blockchain technology aims to establish a permanent, freely accessible data record. This increases the transparency of data, promotes SMA, which also allows the usage of data in the supply chain simpler. This is key to the improvement of social networking activities. For unstructured info, social network researchers should think about a way to analyze them utilizing the correct tool by holding databases in the blockchain. The amount of data supplied by the BSM platform helps to increase performance by utilizing data if learning is required. Moreover, most databases with blockchain technologies organize data more structured to enable the use and review of the related data.

3. For the lack of evidence problem: blockchain technologies may have a long background of authentication data. The volume of data accessible is more adequate, which serves to dampen the scarcity of data.

4. Addressing the problem of high volume and high speed: This includes the usage of large data analytic techniques. In this relation, the BSM network may also boost the SMA as blockchain allows the dissemination of knowledge more conveniently feasible. The compilation and compilation of data for further analysis in SMA will therefore be encouraged.

5. For verification of data and the problem of false information: blockchain technology solves this difficult question. The user identification is checked and is real across several BSM platforms. Data and user-built contents are often registered with permanent records in the open blockchain framework. Both features will make data true and accurate. The SMA findings would be more reliable in favor of company decisions.

6. For the Data Island problem: each organization has its data. Data between participants may be exchanged more transparently with the usage of blockchain technologies. This contributes directly to more effective consumer characterization (e.g., Facebook user). It aims to improve accuracy for focused consumers across multiple businesses-oriented practices from demand forecasts to publicity.

7. Ensure freedom of speech: what we hear from the publications by users will potentially be censored on certain conventional social networking sites. On certain occasions where the released material hurts a party's rights or harms its reputation, including when the contents are true and trustworthy. This will contribute to partial databases for SMA. Freedom of expression is therefore not entirely supported. Social networking networks supported by blockchain technologies improve expression freedom by ensuring that the data are authentic, uncensored, and evident. It, therefore, enables social network consumers to genuinely express themselves without central network regulation. The quality of recommendations by SMA can be improved by using the respective data.

8. Security of privacy: The solution to the central database is embraced by conventional social networking sites. Users' sensitive information is destroyed when they are targeted by cyber-criminals. This raises questions as to whether actual data on social networking sites can be preserved. Blockchain technology is a decentralized ledger that uses the distributed data storage network. It is proven to be stable because cyber hackers cannot strike. It is stable. With the security of the privacy of the consumers, they are happy to have more true details they would like to post on social networking and better-quality data that can be used by SMA.

Existing work

Hayat et al. explain in Hayat et al. (2019) how deep learning can be used in social media for recommendation, sentimental analysis, classification, and ranking in a very effective way. As per (Dwivedi et al. 2019; Jayavadivel and Prabaharan 2021) fingerprints, human identification through face and iris identification how it can be used in social media and how we can secure them, analysis is given.

Future investigation

There is far more methodological research on SMA than the observational equivalents, as the assessment findings indicate. There are currently few SMA studies that apply to scientific SCOM hypotheses and that focus on them. Of necessity, SMA is aligned with certain classical SCOM ideas from the organization's point of view, such as the capital-dependent hypothesis or the hypothesis of acquisition costs. This is the theoretical foundation for potential scientific SCOM SMA studies. Almost all studies on TSM platforms such as Facebook and Twitter currently focus on SMA applications. While we argue that blockchain technology will offer multiple advantages, it is important to analyze whether there are challenges or negative impacts.

Innovative business models: several innovative business models are funded by SMA. Groupon is one sector that is highly connected with the analytics of social media. Innovative business strategies such as Groupon and others are under-explored and warrant more study on how blockchain applications are funded on social Networking sites.

Research methods: The management guidelines focused on SMA for the same dataset which currently are completely different. Analytical methods: Many of the methods used today clearly emphasize "speed," but ignore the value of precision and data quality. The study on how methodological approaches might be selected for the consistency and precision of the suggestions could be given greater importance in future research. Besides, it needs more study as to how blockchain technologies may play a part.

Challenges in collaboration

1. Real-time and automated blockchain security: Future smart running blockchain systems are expected to conduct real-time analysis on numerous production metrics of blockchain systems instead of performance monitoring and fault detection, and to achieve automated crash spot recovery.

2. Providing smart contracts for shared intelligence: The new QoS assurance strategies of smart contracts rely primarily on researching contract patterns (i.e., recognizing and analyzing unstable contracts) at a single blockchain peer. Mutual intelligence, unlike a single intelligent individual, will enable all participants to engage in analyzing and discussing, while exchanging their mutual knowledge to make wiser decisions in a global sense. In the future, mutual wisdom is required to communicate with autonomous blockchain systems and include a stable and knowledgeable blockchain application.

3. Integrating various methods to machine learning to track and report blockchain results: Blockchain decentralization allows it difficult to track and monitor transactions on blockchain platforms, resulting in several illegal activities. Meanwhile, blockchain proof is also heterogeneous and pseudonymous, exacerbating this scenario. To extract main features from different forms of blockchain info, future several machine learning approaches should be merged. Moreover, a hierarchical graph (or network) of transactions also senses the interaction between various accounts to identify malicious behaviors (Zheng 2019).

4. Confidentiality: Digital blockchain ledgers allow secure and authentic data collection, but the data collected remains freely available to all users. IoT’s omnipresent sensing systems often actively capture sensitive and confidential consumer details and putting this information on transparent ledgers will lead to privacy concerns. Private blockchain ledgers may guarantee privacy security by enabling confidentiality and providing controlled access to ledgers. However, such private blockchain networks will limit access and dissemination of a large amount of knowledge AI would use to process and perform relevant and accurate decision-making and analytics (Salah et al. 2019).

5. Scalability: Scalability is a big problem for today's blockchain network. Bitcoin network can execute an average of 4 transactions per second on cryptocurrency blockchain networks, while Ethereum can execute an average of 12 transactions per second. This performance is rather unacceptable relative to Facebook, which processes millions of interactions per second, including likes, notifications, and messages Sidechains (also known as side channels) are used to accelerate blockchain performance, where transactions are quickly settled beyond the main chain and settled on the main chain just once a day (Queiroz et al. 2019b).

6. Vulnerabilities in smart contracts: It is necessary to ensure that a smart contract is implemented free of failures and weaknesses and safe from attacks. Because they can be susceptible to attacks, network code and information must be protected. Testing smart bug contracts became essential, and several methods were created to decide a smart contract message's security status. In contrast, the results of smart contracts are all deterministic as of now and might not be probabilistic. This can pose a main issue for decentralized AI in which decision-making algorithms based on AI and machine learning are conducted by mining nodes as smart contracts in which output outcomes are not necessarily deterministic, but rather random, uncertain, and most often estimated. To depend on findings with a strong degree of trust, quality or durability, and data input that can be extremely fluctuating as IoT and sensory readings, this involves a different method to deal with approximate computation and devise consensus protocols for mining nodes.

7. Smart contracts are for external activities or external tasks invoked by blockchain users: Smart contracts are not designed to automatically trigger events or allow data collection alone. Forcing information and actions into contracts is significant. Trusted oracles (which are trusted external entities or nodes) are proposed as substitutes to address these deficiencies which are used to drive occurrences of information into smart contracts. To maintain and preserve confidence. Typically, voting among trustworthy oracles is used to create an agreement.

8. Emerging AI-only consensus protocols: Present consensus protocols accept network and middleware levels in blockchain networks through different proofing protocols. For prospective students, a large range of study opportunities open to investigating whether application-level consensus protocols can be developed on evidence- centered on the consistency of learning models, efficient search techniques, data consistency and provenance, and performance enhancement.

9. Fog-computing paradigm: Fog computing is a newly emerging computing paradigm that allows customers or IoT devices to deliver localized computing and storage near to data sources. Future fog nodes need to be configured with AI and machine learning capability in the sense of AI and blockchain, as well as enabled with a blockchain interface, whereby fog nodes conduct localized data security, entry, and control.

10. Interoperability, requirements, and regulations: Blockchain implementation specifications are yet to be formulated. Research proceeds to set blockchain interoperability, openness, integration, and architecture requirements through IEEE, NIST, ITU, and other standards bodies. This involves software-based studies and proof of concepts that can play a key role in denying the right set of technical specifications for blockchain architecture systems, services, deployment, and interoperability.

11. Quantum computing: Future quantum computing should have the ability to public-key cryptography where private keys can be measured. The new blockchain utilizes cryptographic signatures using public-key cryptography. This involves extreme quantity-safe, robust blockchain research that can survive such breakability while retaining strong performance and scalability.

12. Governance: Deploying, designing, and sustaining a blockchain network among different participants and stakeholders is tedious. Including with a private blockchain consortium, major problems emerge with the kind of blockchain to be deployed (e.g., Hyperledger or Ethereum), which manages and troubleshoots the blockchain, position of blockchain nodes, writing smart contracts, dispute settlement, selection of trustworthy oracles, off-chain operating processes, implementation of side channels, regulation of side channels

Other than that, blockchain technology weakness is clarified in Casino et al. (2019), which offers us space to develop efficiently-

• Blockchain suitability

• Scalability and latency

• Sustainability of protocol for blockchain

• Resilience of quantum

• Adoption of blockchains and interoperability

• Data protection and options for privacy & security

• Big Data and AI

Several of the key studies (Taylor et al. 2020; Parizi et al. 2018; Episode 10: Bringing together the best talent in cybersecurity 2020) have preferred to use the Ethereum smart contracts and network to pursue solutions to their safety issues furtherer potential analysis may involve a study of the diverse contexts in which disruptive cybersecurity technologies have been or can be used, by Ethereum and/or other permissionless authorized blockchain systems.

Proposed agenda

From the above discussion on various domains, Cybersecurity needs more attention in terms of making it less vulnerable to society, here we proposed an agenda to develop a secure system of cyber threat intelligence information exchange by using features of blockchain and artificial intelligence. The idea is to make the industry integrated robust against cybersecurity attacks. Where the responsibility of countering these attacks does not only lay on an individual organization but with secure information exchange about the cyber-attacks and their countermeasures among various stakeholders this responsibility becomes a common challenge and a goal shared by these collaborative partners. We have staken this as our future work.

Conclusion and future aspect

We contribute to a systematic literature review of various blockchain and AI implementations in different fields. recognizes four research questions and checks for those questions in information databases. Our analysis is focused on studies that use artificial intelligence to add applications suitability meantime use blockchain as a hyper ledger to add automation. The most common types of applications are security and productivity enhancement, prediction, and decision-making (Ekramifard et al. 2020) First of all, we are extending prior research that considers blockchain in AI integration. Second, all views, and the many different definitions of their convergence, are considered. Third, by drawing theoretical conclusions from practical research and outlining possible practical research possibilities from theory, we bridge the gap between theory practices. Fourth, we explain how convergence produces innovation (Pandl et al. 2020). Fifth, we propose an agenda to look at one of the core principles of cyber threat intelligence information exchange in cybersecurity. As the study indicates, the hottest subjects in recent developments are cybersecurity, social media, healthcare, supply chain management, and finance/banking. On the way to the future, the alliance between blockchain and AI would provide our community with limitless inventions and revolutions (Dinh and Thai 2018) The convergence of blockchain and AI will provide a bunch of innovations in the future to enhance human life, but it is still in the development stage, with a lot of unexplored areas to be tackled, such as scalability, lack of standards, problems with consensus protocols, etc. For future studies, it is an exceptional open door.

As per journal guidelines I am as an author of manuscript titled “Semantic analysis of blockchain intelligence with proposed agenda for future issues” with submission id- IJSA-D-22-00063R1, declares following points as follows:

Declarations

Conflict of interest

The authors have no relevant financial or non-financial interests to disclose. The authors have no conflicts of interest to declare that are relevant to the content of this article.

Research involving Human Participants and/or Animals

This is an observational study. The Research Ethics Committee has confirmed that no ethical approval is required.

Informed consent

Informed consent was obtained from all individual participants included in the study.