Blockchain Papers

Merve Kır, Süphan Nasır

No abstract is available for this record.

Artor Nuhiu, Florin Aliu

The need for transparency, efficiency and trust in business operations and financial mechanisms continues to highlight the role and importance of blockchain technology for optimizing digital business models. As an emerging technology, it is gradually expanding through its applications in various aspects of the financial industry. The chapter investigates the potential synergies between blockchain technology and digital finance for addressing digital security concerns and inefficiencies in financial transactions. The development of decentralized finance (DeFi) platforms and their possible integration with AI and machine learning represent a shift towards significant advancement in the digital finance sector. These combined technologies have the potential to provide decentralized access to financial services, which would be especially beneficial for those who are financially excluded. The chapter sheds light on Blockchain's transformative role in digital finance by providing an overview of the mechanisms needed to enhance digital security and ensure a robust financial system.

Daniela Matušíková, Jaroslava Gburová, Andrea Vadkertiová

This study examines consumer attitudes toward cryptocurrencies in Slovakia, focusing on the perceived adequacy of their promotion and the influence of demographic factors such as education, gender, and age. The findings reveal that a significant majority of respondents view cryptocurrency promotion as insufficient, with 77.77% expressing dissatisfaction. Demographic factors were found to have minimal impact on attitudes, suggesting that universal barriers—such as trust, technological literacy, and perceived risks—play a more critical role. Social media emerged as a key platform for engaging consumers, particularly younger demographics, provided that campaigns are well-targeted and informative. These results highlight the need for innovative promotional strategies emphasizing transparency, education, and trust-building to bridge the gap between cryptocurrencies and broader consumer adoption. The study contributes to the growing literature on cryptocurrency marketing by providing actionable insights for addressing challenges in emerging markets like Slovakia.

Georgios Rigopoulos

One of the primary uses of blockchain technology is cryptocurrencies, which have grown significantly in popularity in recent years due to investors looking for alternatives to traditional currencies for value storage or speculation. But not every investor makes big financial commitments or does so for the same reasons. However, it appears that peer pressure and word-of-mouth are significant factors in the adoption of cryptocurrencies. This empirical study focuses on the factors that affect the adoption of cryptocurrencies from prospective investors. The study employs qualitative methodology and semi-structured interviews with investors from EU and UK that have digitally advanced economies when it comes to payment mediums. Purposive sampling was used to choose participants who were interviewed via video conference. Data analysis was done using grounded theory technique in conjunction with thematic analysis. Two dimensions can summarize the main findings. According to the first dimension, the perceived return on investment has a major role in influencing the adoption of cryptocurrencies, and the second dimension refers to peer and friend influence as a powerful effect. The present study contributes to the existing body of research and sets the stage for further quantitative investigation as policymakers can use the empirical findings to understand public perceptions of cryptocurrencies and safeguard investors with appropriate regulatory actions.

Zhuohuan Hu, F. Richard Yu, Zizhou Zhang, Haoran Zheng · 6 authors

This paper leverages machine learning algorithms to forecast and analyze financial time series. The process begins with a denoising autoencoder to filter out random noise fluctuations from the main contract price data. Then, one-dimensional convolution reduces the dimensionality of the filtered data and extracts key information. The filtered and dimensionality-reduced price data is fed into a GANs network, and its output serve as input of a fully connected network. Through cross-validation, a model is trained to capture features that precede large price fluctuations. The model predicts the likelihood and direction of significant price changes in real-time price sequences, placing trades at moments of high prediction accuracy. Empirical results demonstrate that using autoencoders and convolution to filter and denoise financial data, combined with GANs, achieves a certain level of predictive performance, validating the capabilities of machine learning algorithms to discover underlying patterns in financial sequences. Keywords - CNN;GANs; Cryptocurrency; Prediction.

Xiang He

With the development of the global economy and the progress of science and technology, the importance of supply chain management in modern economic activities has become increasingly prominent. However, traditional supply chain management is characterized by problems such as information silos, low transparency, and difficulties in traceability, which seriously affect the efficiency and security of the supply chain. The introduction of blockchain technology provides a new technical path and solution to solve these problems. This paper explores the application of blockchain technology, especially smart contracts, in supply chain management. By developing Python-based smart contracts, this paper verifies the effectiveness of blockchain technology in enhancing supply chain transparency, efficiency, security, and trust. In addition, this paper demonstrates the current status and challenges of the application of blockchain technology in supply chain management through literature review and case study analysis, and points out the direction of future research. The findings of this paper show that the application of blockchain technology in supply chain management has significant advantages, but it is still necessary to further optimize the performance and security of smart contracts and explore more application scenarios.

Stav Cohen, Barak Fishbain

• Decision Support System enhances player decision-making in the dynamic Metaverse. • Blockchain and NFTs can be used for accurate asset representation in the Metaverse. • Genetic algorithms optimize character traits under constraints in the Metaverse. • Genetic algorithms dominate over greedy in repeated games simulation. The Metaverse, a virtual world enabling user interaction with digital environments and assets, increasingly utilizes blockchain and Non-Fungible Tokens (NFTs) to represent unique characters and items. Representing and valuing individual digital characters is crucial in the Metaverse as it establishes ownership, identity, and status within these virtual worlds. This research investigates decision-making within collaborative online games, where players face the challenge of optimizing their character's value through strategic NFT acquisition. This challenge is analogous to the Knapsack Problem, aiming to maximize the value of items selected within a limited capacity, just as players seek to maximize character rarity within budget constraints. We propose a Decision Support System (DSS) employing genetic algorithms to assist players in tackling this complex optimization problem. A simulation framework, based on the "SunflowerLand" Metaverse, demonstrates that players using the DSS achieve significantly higher character rarity, highlighting the potential of this approach to enhance player experience.

Somnath Mondal, Sujan Das, Shib Shankar Golder, Rajesh Bose · 6 authors

As Artificial Intelligence (AI) and Big Data continue to evolve at a rapid pace, personalised medicine in healthcare has undergone revolutionary changes. Despite these advancements, current AI-driven systems encounter substantial hurdles in security, scalability, and privacy, particularly when handling sensitive patient data across decentralised networks. This paper introduces an innovative AI-Driven Big Data Analytics Framework that incorporates Federated Learning, Blockchain technology, and Quantum Computing to enhance personalised medicine. The proposed architecture ensures robust data privacy, real-time analytics, and secure patient data sharing, leading to significant improvements in healthcare diagnostics, treatment planning, and prognosis prediction.

U A Lanjewar, Ganesh Khekare, Amitabh Wahi

A smart city is a fast-moving terrain that requires efficient and smart security mechanisms with resilience for solving the intricate challenges of modern urbanism. The current paper presents the critical review of machine learning, blockchain, and deep learning models in strengthening security and making the urban environment at smart cities more resilient. Not all the review articles heretofore successfully integrated these three pivotal domains, which lowered their practical applicability and insight depth. This paper reviews recent state-of-the-art models, including supervised and unsupervised machine learning algorithms, blockchain frameworks, and deep advanced learning architectures. The important machine learning models reviewed include Random Forest, Support Vector Machines, and K-means clustering. These were chosen for their already proved effectiveness in anomaly detection, predictive analytics, and classification tasks. On the other hand, blockchain models of Ethereum and Hyperledger Fabric would be evaluated for decentralized security features, immutability, and capability to improve data integrity and transparency. Deep learning models can handle large-scaled, unstructured data and extract complex patterns. Their integration, therefore, shows great promise in enhancing the security and resilience of smart cities. This paper contributes to a holistic, multidisciplinary viewpoint, filling literature gaps by providing a basic framework for further research and implementation of smart city initiatives. Such insights synthesized across these domains set a course for innovation in the formulation of solutions that fortify urban infrastructures against emerging threats and enhance overall urban resilience. (Abstract)

Zhao Yu, Yangguang Tian, Chunbo Wang, Xiaoqiang Di · 5 authors

With the development of vehicular networks and cloud-edge collaborative technologies, a large amount of vehicle data is collected at edge nodes (Edge Node, EN) for analysis and decision-making. However, edge data faces challenges in terms of integrity and security. Data owners (Data Owner, DO) should delegate auditors to periodically verify the integrity of the data. However, existing verification methods have not yet addressed issues related to verifiers forging evidence and fair payment. This paper proposes a smart contract-based edge data integrity verification scheme. An audit tree based on lattice hashing is designed, allowing the smart contract to initiate multiple verification challenges while only storing a complete label, thus reducing storage overhead. The homomorphic additivity of lattice hashing supports arbitrary data segmentation as challenges, effectively preventing EN from forging evidence. This scheme also designs two smart contract arbitration algorithms to ensure fair payment. Experimental comparisons show that this scheme effectively resolves the trust issues related to EN and ensures fair payment among EN, CSP, and DO.

Dr. Carol P. Osborne, Noah Levin

As immersive technology becomes more streamlined and accessible to larger audiences, it is imperative for advertisers and businesses to understand and engage in new spaces where prospective consumers are gathered. The introduction of the metaverse, a virtual hub of experiences, commerce, and impressions, for consumers through revolutionary peripherals such as virtual and augmented reality represents a shift in how online users and brands are positioned within the digital space. This thesis analyses the industry space and how brands, researchers, and consumers are utilizing revolutionary technologies to reconceptualize the idea of commerce, interactivity, and self-expression. This report and research have largely been conducted by interviewing industry experts across the globe and attending Web3 conferences, including DCentral 2022 in Miami, Florida, and serves as a detailed guide for marketers and brands to the utility of the metaverse found in the decentralized layout, incorporation of digital assets and product authenticity, as well as the increased opportunities found in innovative marketing research and development.

Kavish Bhatia

The accelerating convergence of Cloud computing and the Internet of Things (IoT) has revolutionized data-driven services, yet it has also introduced a significant trust deficit in highly regulated sectors such as healthcare and finance. Traditional architectures, characterized by static security protocols and reactive monitoring, are increasingly inadequate for protecting sensitive medical records and financial assets against sophisticated cyber-threats and operational anomalies. This review article proposes a "Cognitive Cloud–IoT Architecture" that integrates human-like reasoning, self-learning, and context-aware decision-making into the data exchange process. We evaluate a multi-layered framework comprising an intelligent perception layer, a cognitive middleware reasoning engine, and a secure cloud core designed to establish objective trust through continuous verification. The study analyzes key mechanisms for trustworthy exchange, including Zero-Knowledge Proofs (ZKP), blockchain-enabled immutable ledgers, and privacy-preserving federated learning. In the healthcare domain, we examine the application of "cognitive patients" through remote monitoring systems that differentiate between sensor noise and clinical emergencies. In the financial sector, we explore the "cognitive ledger" for autonomous fraud forensics and secure cross-border settlements. Furthermore, the article addresses critical strategic challenges, such as the computational overhead of running cognitive models on edge devices and the legal necessity of algorithmic explainability. By synthesizing future trends, including quantum-safe hybridization and sovereign cognitive clouds, this research provides a comprehensive roadmap for developing resilient, intelligent ecosystems. Ultimately, we demonstrate that cognitive architecture is the essential bridge to an "invisible intelligence" that ensures the integrity of human life and global financial stability in an increasingly connected world.

Sharda Haryani, Sukhjeet Kaur Matharu, Chetan Nagar, Ankush Verma

The optimization algorithms in Bitcoin and blockchain technology have attracted significant interest and debate due to their convergence with the financial sector, known as Fintech. Cryptocurrency and blockchains are comprehensively examined in this paper, along with their evolution, adoption trends, and implications for financial transactions. The potential for blockchain technology to revolutionize traditional banking and monetary services is discussed, as well as the role it can play in facilitating decentralized and secure transactions. In addition, we discuss regulatory considerations and technological advancements associated with integrating cryptocurrency into mobile payments and e-commerce. In a multidisciplinary review, this review explores cryptocurrency's potential to reshape the financial industry through a multidisciplinary lens.

Mohideen AbdulKader M, Kalaichelvi Nallusamy, Jebamalar, Zulaiha Maryam M

In the field of financial technology, the creation of an automated decision system for the decentralized execution of stock market operations is a noteworthy development. The challenges of the generic approach, such as transparency, security, and efficiency in stock trading, are effectively solved with the help of predicting analytics combined with smart contracts based on blockchain technology. In this research, a Blockchain Smart Contract Adoption with Quaternion Generative Adversarial Networks, (BSCA-QGAN), is presented for decentralized stock market prediction over Yahoo Finance data. The first pre-processing is data cleaning and normalization of data collected and then feature extraction is done by the Spike Driven Transformer (SDT) which is the integration of Transformers and Spiking Neural Networks. Also, the Quaternion Generative Adversarial Networks (QGANs) forecast the stock prices whereby time series with built-in quaternion math is modelled. The QGAN's hyperparameters are optimized using the White Shark Optimizer (WSO). An automated decision system for decentralized stock markets is integrated with blockchain smart contracts. The system achieved 99.72% prediction accuracy and minimal memory usage of 10MB.

Christy Dwita Mariana, Zaäfri A. Husodo, Irwan Adi Ekaputra, Mochammad Fahlevi

Metaverse, a virtual space created as a digital version of the real world, is currently under development. Over the past two years, there has been a significant rise in scientific research on the metaverse. However, prior research necessitates a methodical examination of the literature on developing the metaverse environment, particularly with regard to payment ecology. To bridge this gap, we reviewed 40 journal articles related to the metaverse digital payment ecosystem and offer recommendations for future research. We refer to the Scientific Procedures and Rationales for Systematic Literature Reviews” (“SPAR-4-SLR”) procedure for carrying out a thorough review of the literature. Bibliometric analysis was also performed for the thematic observations. The results reveal five clusters in the Metaverse digital payment ecosystem: (1) metaverse reality, (2) technology, (3) data analytics, (4) blockchain, (5) non-fungible tokens (NFTs), and other tokens. Metaverse digital payment ecosystem research is still somewhat scattered and has no significant theme. Furthermore, this study provides a path for future research on the stability of the payment ecosystem and the metaverse regulatory framework.

Krishnaveni Subramani, Geetha Manoharan

Digital technology increases diagnosis, treatment, and efficiency, transforming healthcare. This article uses real-life business examples to demonstrate how blockchain technology, AI, and IoT are changing health care and how they may help. AI algorithms speed up and enhance diagnostic, prognostic, and therapeutic decision-making. IoT devices can monitor patients, offer remote health care, and collect data instantly, improving outcomes and resource use. Blockchain technology protects patient privacy and promotes healthcare system collaboration by securely and transparently managing medical records. As seen in this chapter, these technologies work well. Using AI in imaging and genetics simplifies early diagnosis and treatment planning. Wearable health monitoring, smart implants, and telemedicine can improve patient treatment. Blockchain technology will secure EHRs, manage medicine supply chains, and facilitate clinical trials. The Internet of Things, blockchain technology, and AI are improving data privacy, patient participation, and treatment availability in healthcare.

Mahmoud Allahham, Atheer Ahmed Alrashed, Omaia Al-Omari, Wasef Ibrahim Almajali

This justifies the feature of blockchain in decision-making where companies predominantly use data processing and managing methods or have intricate supply chains demanding authenticity and relying on blockchain technology. This study is motivated by increasing convolutions and demands for secure speedy supply chain system as it is important in nurturing trustworthiness and accountability during commercial transactions, so it focuses on showing why current supply chain authentication methods are inefficient and unreliable leading to potential frauds and mistrust. This research starts by reviewing existing supply chain authentication methods and their shortcomings. The Basics of Blockchain refer to How Decentralized and Secure it really is, explaining what blockchain technology is. This blog post discusses how blockchain can be used to enhance decision-making processes, providing an unchangeable, auditable way to keep track of and authenticate the supply chain, and briefly covers various use cases of blockchain in supply chains and explains how they are transforming the security and efficiency of operations. The objective of this study is to suggest a structure that combines blockchain and machine learning for better supply chain decisions and authentication systems. It makes use of decentralized system architecture from methodological point of view by which the immutability aspect of blockchain and machine learning analytics can be integrated together in forecasting of demand while at the same time ensuring safety for transactions. Overall, blockchain technology enables significant disruption in supply chain authentication by helping partner systems and organizations make better decisions and reinforcing security requirements between partners. Combined with blockchain, RFIDs provide improved transparency and cost savings and ensure data is secure and trustworthy, thereby greatly enhancing supply chain networks. Indeed, the findings have shown clearly that coherent use of these technologies in supply chain management yields tangible improvements in such areas as fraud prevention, operational efficiency and even customer trust.

Sathishkumar Veerappampalayam Easwaramoorthy, S. Neelakandan, Angela Lee

The COVID-19 epidemic has negatively affected features of human beings and diverse sectors, particularly the healthcare industry. This modality led to the formation of novel life patterns that people need to face to reduce the spreading of the pandemic by promising social distance, amongst others. For that reason, investigators have discovered several deep learning (DL) and machine learning (ML) based techniques to quickly diagnose COVID-19 patients using X-ray images. ML-based approaches can decrease costs and take less time for treatment. Nevertheless, maintaining patient privacy poses challenges inside such third-person-controlled models, potentially decreasing to protect patients from possible discomfort and disgrace. However, BC technology provides the potential to safely store complex HealthCare data anonymously, without needing third-person interference. In this study, we introduced a Leveraging Blockchain Technology and Artificial Intelligence using a Smart Contract-Driven COVID-19 Pandemic Detection (LBCTAI-SCCPD) model. The main aim of the LBCTAI-SCCPD model is to investigate the absence or presence of COVID-19 from medical images. To accomplish that, the LBCTAI-SCCPD model is a blockchain-based COVID-19 recognition infrastructure, which contains a smart contracting method for uploading COVID-19-positive case-oriented information with blockchain. At the initial step, the LBCTAI-SCCPD technique employs an adaptive median filtering (AMF) approach to pre-process the input image. In addition, the complex patterns and features in the images can be derived from CapsNet model. Furthermore, the radial basis function neural network (RBFNN) model can be used for a precise COVID-19 process of classification. Lastly, the dung beetle optimization (DBO) algorithm is used for optimal hyperparameter selection of the RBFNN model. To demonstrate the better performance of the LBCTAI-SCCPD technique, a sequence of simulations is executed on the benchmark dataset. The stimulated outcomes stated the improved results of the LBCTAI-SCCPD system on the other techniques.

S. L. Trofimov, Leonid Voskov, Mikhail Komarov

The development of the Intelligent Transportation System (ITS) is penetrating many economies around the globe. Various researchers in both industry and academia are looking into more efficient management of both vehicles and related data processing aspects. A vast trend related to the latter part is the distributed data processing of the transmitted data. This article discusses approaches to the use of blockchain technology in ITS. It explores the use of blockchain in modern transport industries. Particularly, the paper proposes a novel approach to the maintenance of public transportation vehicles or buses. The specificity of the proposed approach is the autonomous control of the technical condition using information systems. When using blockchain technology, building a transparent vehicle fleet management system is possible. The specificity of the proposed approach lies in the approach to data processing: in the organization, confidence in data increases, the possibility of manipulating transportation is eliminated, and the decision-making chain is reduced. As a result, the system can manage itself. It also helps to increase the service life of vehicles, makes it possible to predict their malfunctions and improves the quality of data on their technical condition.

Shanthi Makka, Mahadeshwara Prasad, ReddySaiSIndhu Theja, Parves Kamal · 5 authors

No abstract is available for this record.

Rizky Parlika, R. Rizal Isnanto, Basuki Rahmat

Since Satoshi Nakamoto first proposed the idea of bitcoin in 2009, the cryptocurrency and prediction methods for it have grown and changed exceptionally quickly. The Patterned Dataset Model was a valuable tool in earlier studies to explain how changes in the price of Bitcoin affect the movements of other cryptocurrencies in a digital trading market. Three different kinds of datasets are generated by this model: patterned datasets under full conditions, patterned datasets under dropping prices (Crash), and patterned datasets under rising prices (Moon). The K-means approach was then used to cluster these three datasets. Specifically, each dataset was split into two clusters, and the clustering score was determined by utilizing eight unique clustering metrics. Consequently, the best clustering score was found in the patterned dataset in the crash situation. Additionally, from 2022 to 2024, the raw data from this crash-condition-patterned dataset is used to determine the possibility of reaching maximum profit and return on investment (ROI) daily and monthly. According to the calculation results, the range computed over the course of a whole month (30 to 31 days) is significantly larger than the daily range (24 hours multiplied by one month), which represents the most significant profit and ROI attained before the emergence of the first diamond crash level. This research also covers the application of a deep learning model to forecast patterned datasets for crash scenarios that may occur many days in advance. The ConvLSTM2D Model performs better in predicting pattern dataset values for the subsequent crash scenario, according to the hyperparameter comparison between the Gated Recurrent Unit (GRU) Model and the 2D Convolutional Long Short-Term Memory Model.

Vincent W.J. van Gerven Oei, Pieter Effendy, Lili Ayu Wulandhari, Islam Nur Alam

Cryptocurrencies have recently become popular among many people, young and old, with various backgrounds. The popularity of cryptocurrency shines due to several things, one of which is Bitcoin, the largest cryptocurrency ever to exist. However, even though Bitcoin is well-known and considered as the largest cryptocurrency, Bitcoin still experienced major price fluctuations over the years, seen in daily trades and yearly valuations. It is because in this digital era, the whole market can be said to be vulnerable because news and social media posts can easily be accessed on the internet. Therefore, it can create sentiments and trends across society. Due to the possibility that sentiment and trends can influence the volatility movements of cryptocurrencies such as Bitcoin, this research wants to see whether the use of sentiment analysis and trends in one of Machine Learning algorithms, namely Random Forest, can predict the Bitcoin price action well.

Walaa Hassan, Habiba Mohamed

In the growing field of artificial intelligence, training models often involve collecting large datasets in a centralized way. This raises concerns about data privacy and security. This chapter explores a novel approach called federated learning (FL). It proposes a shift towards decentralized and collaborative training. Instead of centralizing data, federated learning allows individual devices like smartphones, laptops, or private servers to act as training nodes. Using local data, each node helps improve the model without directly sharing sensitive information. FL has demonstrably enhanced many applications. This chapter presents a comprehensive classification and clustering analysis of the ongoing advancements in FL, encompassing its application to a diverse array of technologies and real-world use cases. Specifically, the analysis delves into the integration of FL with various applications and technologies, such as artificial intelligence (AI) and the Internet of Things (IoT), healthcare (patient data analysis for disease prediction and personalized medicine), mobile devices (on-device personalization of AI features), voice assistants (enhanced speech recognition while safeguarding privacy), finance (collaborative fraud detection and personalized recommendations), autonomous vehicles (safer and more efficient driving through decentralized learning), and cross-domain learning (gaining broader insights while respecting data privacy boundaries). By analyzing the theoretical foundations, practical applications, and ongoing advancements of FL in these areas, this chapter illuminates its potential as a cornerstone for decentralized, privacy-preserving AI development.

Medina Ayta Mohammed, Carmen De‐Pablos‐Heredero, José Luis Montes Botella

Blockchain technology and its business applications have attracted considerable scholarly interest, leading to a surge in academic studies. While this wealth of research is beneficial, it also poses challenges in identifying the most relevant publications. Despite the availability of survey articles, research on this topic remains fragmented and concentrates on specific industrial sectors. This review addresses this gap by providing a detailed literature analysis, highlighting key themes, recent advancements, the benefits of blockchain adoption for businesses, and associated challenges. This study employs a multi-method literature review approach called bibliometric systematic literature review (B-SLR), combining bibliometric analysis with systematic literature review (SLR) techniques. This review critically examines studies of blockchain adoption in modern business from 2017 to 2023. Our findings reveal a decline in academic publications on blockchain for businesses since 2023, along with a shift in core themes from traditional supply chains to exploring blockchain’s role in environmentally sustainable supply chains, such as reverse logistics, green supply chains, and the circular economy. Additionally, there is an emerging focus on the role of blockchain in virtual environments, such as the metaverse and digital twins. Drawing from our analysis, we also present a theoretical framework and highlight ten crucial areas for future research.