Machine Learning and Blockchain – Challenges, Future Trends and Sustainable Technologies

In recent years, blockchain has become one of the most booming technologies. It has completely revolutionized industries and academia by creating a transparent system of trading money. The blockchain industry has started to get significant investments from corporations and tech mega-corporations. In the upcoming years, its net worth is expected to increase more than three times. Blockchain Technology has grown in popularity because of its trust, transparency, and data security. As a result, everyone, either from academics or industry, wants to learn the Blockchain. This chapter provides a detailed overview of Blockchain Technology, which will help researchers and scholars quickly understand Blockchain. Firstly, we introduce blockchain along with its history and a thorough understanding of different types of networks. Then, the definition of Blockchain, fundamentals, categories of Blockchain structure, characteristics, architecture, workflow, components, version, Blockchain protocols, and finally, applications of Blockchain are discussed.

The term “Fintech” (Financial Technology) refers to software and other spearheading technologies adopted by different organizations to automate and enhance financial services. It refers to the technology that improves the backend system at traditional financial institutions. In FY22, $8.53 billion was invested in India's Fintech industry. It has been anticipated that the FinTech industry will generate around $200 billion in revenue by the year 2030 and overall throughput will be $1 trillion. Fintech is expanding quickly, yet there are several problems in the current fintech market including interacting with legacy systems like banks, data and payment security, compliance, lack of end-user awareness, retaining users, and user experience. Due to the development of fintech, more data is now accessible in digital formats, which facilitates analysis and the generation of insights but also increases the risk of security breaches. Blockchain is disruptive technology using which one can securely move money from one account to another without using a bank or any financial organization The term “distributed ledger technology” is often used interchangeably with “blockchain technology” in the financial services corporation. Each transaction has a trustworthy record, thus there is no chance of changing to earlier ones. In essence, blockchain technology can completely ensure the accuracy of every transaction. In this study, the problems facing India's fintech industry are described in detail, and possible solutions employing blockchain distributed ledger technology are suggested. Additionally, it finds blockchain technology has the ability to enhance the security and competence of financial operations in the Indian fintech sector, there are challenges such as regulatory uncertainty and scalability that require to be addressed. The paper concludes with recommendations for the upcoming development and adoption of blockchain technology in the Indian fintech sector. 

Blockchain technology and smart contracts have revolutionized the way financial transactions are conducted in virtual environments. This review paper provides a comprehensive overview of the role of blockchain technology and smart contracts in shaping the future of virtual financial transactions. We explore the fundamentals of blockchain technology, its applications in the financial industry, and the pivotal role that smart contracts play in automating and securing virtual financial transactions. Furthermore, we discuss the benefits, challenges, and prospects of these innovations within the virtual financial landscape.

Through its decentralized architecture, blockchain technology has become a transforming paradigm in the banking sector, revolutionizing established procedures. This study analyzes the dynamic convergence of blockchain and finance, with an emphasis on smart contracts' essential role in altering financial transactions. It adds to the expanding knowledge of the practical uses and possibilities of this technology in the banking industry by investigating the uses, problems, and prospects of smart contracts within the financial environment. This research study digs into the many uses, effects, problems, and prospects of smart contracts in the banking industry. This chapter attempts to give a full knowledge of how smart contracts are revolutionizing financial processes and determining the future of the financial sector through theoretical studies.

Smart Agriculture, also known as Digital Agriculture, has emerged as an essential paradigm in today’s agricultural landscape. The rapid development of this field is fueled by the growth and application of Agricultural Informatics, which significantly enhances various agricultural practices. These practices span from crop and seed cultivation to plant and vegetable farming, livestock management, and postharvest activities.

Advanced Agricultural Information Systems (AIsS) deploy effective methodologies and cutting-edge technologies to optimize cultivation processes, aiming to improve productivity and efficiency. These Agroinformatics technologies are specifically designed to support more commercially intensive agricultural operations and streamline the management of large-scale systems. Agro Information Systems incorporate diverse components of Information Technology (IT), such as databases, networks, web technologies, software solutions, and multimedia systems, all of which contribute to a more connected and data-driven agricultural environment.

With the rapid evolution of IT, new technologies such as cloud computing, data analytics, big data, the Internet of Things (IoT), and Blockchain are becoming increasingly vital in modern agriculture. Among these, Blockchain technology is revolutionizing agriculture by enabling faster, more secure, and highly efficient systems for agricultural development. Blockchain applications in agriculture ensure transparency, traceability, and security in various processes, from farm-to-fork supply chains to smart contract-based transactions.

In this context, the integration of Blockchain and Machine Learning (ML) technologies plays a pivotal role in shaping Agriculture 4.0. By combining Blockchain's secure and decentralized nature with ML’s predictive analytics and data-driven decision-making capabilities, these technologies offer unprecedented opportunities for improving farming practices. This paper explores the current applications of Blockchain and Machine Learning in agriculture, focusing on their potential and prospects in transforming the industry. Furthermore, it delves into how blockchain and Machine learning-based agrosystems can foster sustainable agricultural practices, supporting the future of farming by enhancing productivity, reducing waste, and promoting environmental stewardship.

Security and privacy concerns, which are made worse by the growing number of Internet-connected devices, are the primary obstacles to the Internet of Things (IoT) widespread implementation. Everyone is now very concerned about Internet of Things security, including businesses, governments, and consumers. Even though no system can ever be completely protected from attacks, effective system defense depends on real-time threat detection. There is a dearth of studies on intrusion detection systems that work well in IoT-based blockchain systems. In this study, we present a novel approach to intrusion detection in the Internet of Things-based blockchain systems by employing the DL (Deep Learning) subfield of machine learning to identify security abnormalities. This detection platform provides security as a service and allows interoperability with numerous network communication protocols utilized by the Internet of Things. We go into great details about the suggested system's architecture and intrusion detection method. Real network traces are evaluated along with simulated data to illustrate the scalability of the proposed intrusion detection system to prove its viability. Our results confirm that the proposed intrusion detection system can correctly detect real intrusions.

Social media has completely changed how people communicate on a worldwide scale by offering a robust platform for idea sharing, contract negotiations, and the submission of fresh business concepts. However, a number of problems, such as inaccurate information, inadequate content screening, copyright infringement, hacking, identity theft, and fake news, limit the use of social media. This paper presents a proof-of-credibility (PoC) and notarization service-based blockchain strategy for identifying fake news and preventing its spread through social media. Social media platforms are using machine learning methods as part of their marketing strategies. On social media, however, deliberately created screenshots and bogus news are constantly created and shared. Blockchain technology is a good platform for notarizing online activity because it can store data in a safe, unchangeable manner. The Proof-ofConcept methodology was tested on two datasets of notable tweets gathered from various news sources on Twitter. We propose a framework for the authenticated archiving of social media content using blockchain technology. A text message scenario is given as a proof-of-concept based on the suggested strategy, and the results demonstrate how well the suggested approach works in identifying rumors and halting their spread.

The rapid uptake of blockchain and artificial intelligence (AI) technologies like machine learning (ML) in particular has brought about a paradigm change that is elevating the digital ecosystem for smart cities. The development of new technology can be used to create intelligent societies within smart cities in this era of rapid digital communication. Uses of blockchain technology and ML technologies are proliferating, guaranteeing solutions for issues in a multitude of sectors, such as financial services, cryptocurrency, threat intelligence, social and public services, and the Internet of Things. These “smart cities” are constructed using various technologies, including artificial intelligence and blockchain. As a result, the way these technologies are applied in smart cities both present and future will alter not only the character of governance and human interaction but also the way business is done. The research suggests doing an experimental study to find out how blockchain technology and artificial intelligence are affecting the creation of smart cities. It tries to set the scene for queries like how traditional business models are getting ready for this disruption, what obstacles they might encounter, and what effects both technologies might have on the organization's growth. Blockchain technology and artificial intelligence have a lot of potential to help create smart cities. The results of the research will provide corporations with the rationale to start concentrating on these technologies and putting early adoption strategies into place, which will eventually cause them to change their smart city business models advance.

Companies in the financial industry are among those who are implementing their operations online as a result of the internet's rapid growth in usage. Because of the enormous economic damages that arise from financial fraud, it is becoming an important concern as financial frauds are becoming more common and sophisticated globally. An economic fraud detection system (FDS) must be able to identify risks such as unusual assaults and unauthorized entry. The last few decades have seen a widespread application of data mining and machine learning (ML) methods to address this problem. These techniques still require improvement, though, to handle big data quickly and recognize unidentified trends in attacks. The importance of data safety and evaluation systems for big data has changed recently due to the enormous volume of data and its continuous growth. Big data is defined as information that is difficult to handle, store, and evaluate using standard software tools and databases. Big databases exhibit considerable quantity, speed, and diversity, necessitating the development of novel methods for handling them. Thus, employing blockchain and a deep learningbased (DL) approach for FDS is suggested in this work for credit card frauds. The objective of this framework is to improve the accuracy of identification in the context of big data in a private-permissioned blockchain network, while also improving the existing detection methods. A present DL algorithm called the Auto-encoder algorithm and a few other ML algorithms are contrasted with the outcomes of the suggested framework's evaluation using a real database of credit card frauds. According to the study findings, the LSTM performed flawlessly, achieving 99.9% accuracy in roughly a minute.

The Internet of Things (IoT) is utilized to enhance conventional healthcare organizations in a variety of ways, such as monitoring patient habits. Sensor data from the IoTs is critical for medical facilities. Due to confidentiality and safety concerns, data should be safeguarded from unwanted alterations. On the contrary, Blockchain innovation offers a variety of ways to protect data from alterations. Deep learning (DL), as a subsection of machine learning (ML), has the revolutionary possibility to reliably analyze enormous amounts of data at very fast rates, provide insightful conclusions, and effectively resolve complex problems. Preventive treatment and prompt intervention for individuals at risk depend heavily on rapid and precise disease prediction. The capacity to handle consecutive time-series data with recurrent neural network variations of DL depends on developing prediction systems with improved accuracy, which is crucial given the increased usage of electronic clinical documents. Prediction analysis is used for the electronic clinical data kept in the cloud regarding patient records by the proposed framework, which acquires data from IoT gadgets. With an accuracy of 98.8 percent, specificity of 98.8 percent, precision of 98.9 percent, and F-value of 98.8 percent, the bidirectional long short-term memory-based intelligence medical network for monitoring and precisely predicting heart disease threat outperforms the current Intelligent cardiovascular illnesses prediction structures.

 The Internet of Things (IoT) has advanced toward smart houses as a result of the widespread detection and supply administration brought about by the advancement of technological advances in the field of sensing devices advancements. Many IoT gadgets in smart houses are represented by gateway links, the safety of which is dependent on the centralized framework. The blockchain structure is thought of as a smart house gateway to handle safety concerns in this system by fending off potential threats and utilizing the machine learning algorithm Deep Reinforcement Learning (DRL). The safety and dependability of the suggested blockchain-oriented smart house strategy were thoroughly assessed in terms of reach, confidentiality, and authenticity. In the data storage and transfer of blocks, blockchain is used to circumvent conventional centralized design. The capacity of networked users to authenticate is caused by the data authenticity within and outside of the smart house. The system that is being exhibited is built on the Ethereum blockchain, and its safety, responsiveness, and accuracy are measured. The results of the study demonstrate that the suggested fix outperforms more current, published works. The most successful parts of the suggested method to enhance structure performance oriented on appropriate values and integrate with blockchain in terms of smart house safety oriented on smart gadgets to prevent sharing and confidentiality hackers are found in DRL, a machine learning-based method. This chapter tested the suggested approach using two different kinds of databases and then contrasted it to other state-of-the-art systems. In the subsequent phase, when there are sixteen percent disparities in terms of enhancing the accuracy of smart houses, a DRL with an accuracy of 96.7 percent operates better and produces more powerful results compared to Artificial Neural Networks with an accuracy of 80.05%.