Blockchain Papers

Antonios Giatzis, Elvira-Maria Arvanitou, Danai Papadopoulou, Theodoros Maikantis · 11 authors

No abstract is available for this record.

Hoda Hamouda, Victoria L. Lemieux

The decentralized applications and distributed ledgers of the blockchain technology (BT) make the exchange of health records more secure, allow users to be the primary owners of their health records, and provide higher protection of users’ records due to an ability to exchange information without revealing their identifiable information. The paper discusses what human-centered design (HCD) methods revealed about the user experience of individuals interacting with a BT-based solution that lets users contribute their de-identified health data to research projects in precision medicine. The methods revealed challenges in the user experience and presented the solutions carried out throughout the iteration phases of the solution’s user experience. Despite the privacy-preserving benefits of blockchain-based platforms, the complicated architecture of the technology and management of BT wallets constitute a real challenge to designing a user-friendly experience. This negatively impacts the adoption and implementation of BT-based solutions in health records management.

D. Tulasiram, S. Kanmani Jebaseeli, Myasar Mundher Adnan, Arelli Madhavi · 6 authors

Increased regulation of the supply chain is another important issue of concern to industries in distinct countries as they struggle to implement the regulation in a network setting. This paper aims to explore ways to improve regulatory compliance through the application of the blockchain and machine learning model, namely Gaussian process regression. Blockchain is an accurate and permanent record-keeping system that can be applied to track the flow of items and associated activities in the supply chain. Compliance can also be enforced, and through smart contracts, analyses of various parameters such as obligations can be monitored and adjusted automatically through GPR which involves the analysis of data from systems that use blockchain to model compliance risks and predict compliance issues. Thus, using the above-mentioned technologies, a company can prevent compliance failures, meet the necessary standards and set up a constant process of improvement in the supply chain area. This research specifically defines the theoretical foundations, application, and advantages of blockchain and GPR in enhancing the compliance of supply chains with the regulations in this work. In this approach, case-study and use of examples show how this approach works in handling compliance issues in several industries.

Naila Iqbal Qureshi

Due to the current day's financial landscape, which is data-driven and changing rapidly, the latest use of technology has become an order of the day to further increase efficiency in the decision-making process within accounting environments. This paper explores edge computing combined with artificial intelligence (AI) in redefining the financial decision-making process. On one side, edge computing is decentralized data processing much closer to its source; therefore, it reduces latency and increases efficiency. On the other side, artificial intelligence is an overarching term that refers to a collection of a number of different techniques that allow machines to imitate human cognitive functions. This coupled with AI presents vast opportunities for the revolution in accounting environments through real-time insights, accuracy, and efficient resource utilization. In this regard, it is thereby established how these two technologies complement one another in an attempt to make these capabilities strong relative to financial decisions in the different areas. Another benefit of the integration of edge computing with AI in the accounting environment is its ability to process huge loads of financial data with very little to zero latency. Edge computing reduces the latencies between the data generation, processing of the data, and decision-making process by moving the computation closer to where data is created, enabling almost real-time analysis and decision-making. This is complemented by AI algorithms that autonomously analyze patterns in the data, detect anomalies, and generate actionable insights toward empowering the professionals of finance to take informed decisions promptly.

Giovanni De Gasperis, Sante Dino Facchini

No abstract is available for this record.

Farha Masroor, Adarsh Gopalakrishnan, Neena Goveas

Our EHR Management System (EMS) empowers patients to control their electronic health records (EHR), en-hancing data privacy and access control. The system allows patients to carry their data as modular units on cost-effective, resource-constrained devices like Raspberry Pi, Beaglebone, and ESP32. We implemented EMS in two scenarios: one device per patient and one device shared among multiple patients. Using Blockchain-based Non-Fungible Tokens (NFTs), our system ensures secure access and authentication for authorized users. We evaluated our EMS by measuring delays in accessing data and verifying NFTs in a hospital scenario where two types of patients are scheduled to general and specialized doctors hourly. Despite using low-cost devices, scheduling delays were minimal. Among the tested scheduling techniques-Modified Queue-based, Reinforcement Learning (RL), and Deep Reinforcement Learning (Deep RL)-the Modified Queue-based method showed the least delay, proving efficient for our EMS.

Youli Xu, Muddassar Sarfraz, Jianmin Sun, Larisa Ivaşcu · 5 authors

Abstract In today's business landscape, the paramount focus of firms is to achieve advanced corporate sustainability. This objective has led to the development of big data analytics, reliable big and cloud data analytics capabilities, and blockchain technology as powerful tools worldwide that have helped firms increase sustainable performance. Capitalizing on these technologies, this study provides new insight into the role data‐driven competitive sustainability, data‐driven culture, and organizational management information in steering firms' performance. Using a quantitative research approach, data was collected through a structured survey administered to managers and IT professionals across various industries. The relationships between the variables were analyzed using structural equation modeling (SEM), confirming that all proposed hypotheses were supported. The results demonstrate the significant positive impact of big data analytics capabilities, reliable big and cloud data analytics capabilities, and blockchain technology on corporate sustainable performance and data‐driven competitive sustainability. Furthermore, data‐driven competitive sustainability was found to mediate the relationships between BDA, RBCDA, blockchain technology, and corporate sustainable performance. Additionally, OMIS‐fit and data‐driven culture were identified as critical moderators that enhance the effects of these technological capabilities on competitive sustainability and corporate performance. These findings provide valuable insights for organizations seeking to leverage advanced technological capabilities to achieve a sustainable competitive advantage in emerging economies.

Odunayo Akindotei, Igba Emmanuel, Babatunde Olusola Awotiwon, Adah Otakwu

Blockchain technology has garnered significant attention for its potential to revolutionize critical systems by enhancing transparency, efficiency, and data security. This review examines the integration of blockchain in three essential domains: Agile Project Management, Decentralized Finance (DeFi), and Cold Chain Management. By leveraging decentralized ledgers and smart contracts, blockchain provides a robust framework for real-time tracking, data integrity, and automated compliance, addressing long-standing challenges across these sectors. In Agile Project Management, blockchain fosters seamless collaboration and transparent decision-making, minimizing bottlenecks and improving accountability. In DeFi, blockchain strengthens security for digital transactions and identity verification while offering financial autonomy and mitigating fraud risks. Within Cold Chain Management, blockchain ensures traceability, reduces data tampering risks, and enhances visibility throughout supply chain processes, safeguarding temperature-sensitive goods. This paper evaluates existing blockchain-based applications and frameworks, identifies current limitations, and discusses future opportunities for optimizing critical systems through blockchain. The findings highlight blockchain's transformative role in driving operational efficiency, security, and data transparency across diverse applications, providing a roadmap for industries to harness its full potential in critical environments.

Jinchun Lu, Rachsuda Setthawong, Pisal Setthawong

A main challenge of cryptocurrency trading is selecting technical indicators which fits the dynamic nature of the cryptocurrency market. This research proposes a framework that integrates a genetic algorithm with a neural network to effectively explore the efficacy of traditional technical indicators in cryptocurrency. It optimizes both the selection of technical indicators and neural network parameters through tailored genetic operations such as mutation and crossover, allowing for enhanced exploration of the solution space. Through rigorous testing on historical cryptocurrency market data in two distinct periods, the proposed model demonstrates superior predictive accuracy and improved trading performance compared to traditional methods, generating a 19.33% profit in the first period and 7.13% in the second period, outperforming the buy-and-hold benchmark. The results highlight the robustness of the model, which consistently delivered positive returns across varying market conditions, including both bullish and bearish phases.

Prity Khastgir, Shweta Shalini

This chapter outlines diverse AI tools for philanthropic operations, substantiating the discussion with other technological breakthroughs such as blockchain and Decentralized Autonomous Organizations (DAOs) for improved governance. A comprehensive overview of how AI is revolutionizing fundraising, outreach, engagement, and Human Resources (HR) is provided, presenting real-world examples of AI tools in enhancing such internal and external operations. The chapter emphasizes AI’s ability to enhance organizational growth and innovation through automation and efficiency. Furthermore, it explores how blockchain and DAOs can revolutionize philanthropic governance by improving efficiency, grantmaking strategies, and promoting accountability and transparency. It then touches on the joint role of AI and philanthropy in advancing the Sustainable Development Goals (SDGs), addressing global challenges with enhanced innovation. Through case studies and tool examples, this chapter underscores the significant impact of AI, DAOs, and blockchain in reshaping operational landscapes and advancing social good initiatives with transparency, efficiency, and innovation.

Sabreen Ahmadjee, Carlos Mera‐Gómez, Siamak Farshidi, Rami Bahsoon · 5 authors

Smart contract-based applications are executed in a blockchain environment, and they cannot directly access data from external systems, which is required for the service provision of these applications. Instead, smart contracts use agents known as blockchain oracles to collect and provide data feeds to the contracts. The functionality and compatibility with smart contract applications need to be considered when selecting the best-fit oracle platform. As the number of oracle alternatives and their features increases, the decision-making process becomes increasingly complex. Selecting the wrong or sub-optimal oracle is costly and may lead to severe security risks. This article provides a decision support model for the oracle selection problem. The model supports smart contract decision-makers in selecting a secure, cost-effective, and feasible oracle platform for their applications. We interviewed oracle co-founders and smart contracts experts to refine and validate the decision model. Two real-world smart contract application case studies were used to evaluate the model. Our model prioritises and suggests more than one possible oracle platform based on the developer’s required criteria, security assessment and cost analysis. Moreover, this guided decision model serves to reveal issues that may go unnoticed if done haphazardly, reduce decision-making efforts and provide a cost-effective solution.

Richard Hobeck, Christopher Klinkmüller, H. M. N. Dilum Bandara, Ingo Weber · 5 authors

Abstract Blockchain technology is known for its transparency properties due to its publicly available, immutable data. Yet, as data availability does not inherently ensure transparency, further analytical methods may be required for human interpretation of data traces. Process mining has emerged as a popular toolbox for understanding processes and how they are executed in practice. The paper studies process mining as a method to enhance the transparency of blockchain data. To this end, two popular Ethereum applications were analyzed using process mining: the prediction and betting marketplace Augur and the network marketing platform Forsage . Observations from the process-mining analyses are used to discuss if process mining can serve as a method to establish transparency of a blockchain. For both applications, new insights are generated for usage scenarios such as application redesign, security analysis, user behavior analysis, and revealing blind spots in Augur’s and Forsage’s documentation. The paper concludes that there is evidence that process mining can serve as a method to enhance transparency in blockchains at the cost of technical setup and knowledge acquisition.

Raja Krishnamoorthy, K. P. Kaliyamurthie

The safe and smooth transfer of data across intercepting nodes is a crucial component of data processing in the medical field. Transmitting error-free, unduplicated data is achievable when third-party entities are effectively eliminated and direct connections between the patient and healthcare provider are maintained. Blockchain technology offers a secure method for exchanging information through nodes and connections, ensuring the safety of transactions and potentially addressing current limitations. Currently, the medical data exchange is provider-centric, insecure, sluggish, and often incomplete. These issues arise from fundamental, structural, and semantic inoperability, which impede data interchange. By utilizing blockchain technology with the appropriate markers, patient data security during transfer can be ensured. This research assesses the possibility of future use of distributed ledger technology in mobile healthcare settings.

Ruchi Arora, Meera Kapoor, Nidhi Singh, Muhammad Zafar Yaqub

Abstract This study critically evaluates the extant research on green cryptocurrency (GC). It incorporates the systematic literature review (SLR) approach of research executed through the analysis and compilation of 54 relevant studies. The focus is on identifying and interpreting the thematic foci and existing gaps about GC to inform potential areas of future research. The SLR findings aggregated the research around key themes: interconnectedness, portfolio diversification, environmental impact, green blockchain, behavioral impact, and regulatory policy. Additionally, the study develops the research profile of the selected studies in terms of data analysis methodology, research findings, variables investigated, and critical knowledge gaps in the literature corpus. The study proffers an integrated framework and actionable inferences for supporting the merits of novel research and regulatory project applications in GC. It is crucial for understanding and advancing research in the GC domain, which currently has scant academic literature and efforts toward sustainable investment.

Abbey Ngochindo Igwe, Chikezie Paul-Mikki Ewim, Onyeka Chrisanctus Ofodile, Ngodoo Joy Sam-Bulya

In the pursuit of sustainable supply chains, the importance of data interoperability and fusion has become increasingly evident. Distributed Ledger Technologies (DLT) have emerged as a transformative solution, enabling enhanced data sharing, transparency, and accountability among diverse stakeholders. This review explores the role of DLT in improving data interoperability within sustainable supply chains, addressing key challenges and opportunities that arise from its implementation. The first section provides an overview of sustainable supply chains, highlighting the necessity for effective data interoperability to achieve operational efficiency and meet sustainability goals. The challenges related to data fragmentation, disparate formats, and security concerns are discussed, emphasizing the need for a cohesive approach to data management. Next, the review delves into the core principles of DLT, including decentralization, immutability, and consensus mechanisms. It outlines how these principles facilitate the development of standardized data formats and promote secure, transparent data sharing among supply chain participants. The integration of DLT with legacy systems and its capacity to enhance cross-border data exchange are also examined, showcasing how DLT can bridge existing gaps in data interoperability. Moreover, the benefits of DLT in sustainable supply chains are explored, including enhanced traceability, increased efficiency, better compliance with sustainability standards, and the establishment of trust among stakeholders. Real-world case studies from sectors such as food, textiles, and energy illustrate successful implementations of DLT and the resultant improvements in sustainability outcomes. The review also discusses future directions and innovations in the application of DLT. The potential integration of artificial intelligence (AI) for predictive analytics and decision-making, as well as the incorporation of Internet of Things (IoT) devices for real-time data capture, are highlighted as pivotal developments that can further enhance data interoperability. This review underscores the critical role of DLT in fostering data interoperability and fusion within sustainable supply chains. It calls for collaborative efforts among stakeholders to harness the full potential of DLT, paving the way for more resilient, efficient, and sustainable supply chain systems in the future. Through this exploration, the review aims to contribute to the ongoing discourse on the intersection of technology and sustainability in supply chain management. Keywords: Ledger Technologies, Data Interoperability, Supply Chains, Review.

Tarek Zaarour, Ahmed Khalid, Preeja Pradeep, Ahmed H. Zahran

Knowledge graphs have proven vital for efficient data management, enhanced search capabilities, and improved decision-making in various information technology domains. However, constructing reliable knowledge graphs in decentralized ecosystems, with distributed autonomous actors, poses significant challenges related to asynchronous transmission, out-of-order knowledge-sharing, device heterogeneity, and trust issues. These challenges are also present in resource orchestration within multi-cloud edge ecosystems where multiple stakeholders must collaborate and share information to enable next-gen smart applications. In this paper, we propose a novel system design that utilizes Distributed Ledger Technology to build knowledge graphs. This approach ensures consistent and trustworthy knowledge sharing among orchestrators in a cloud-edge continuum. Our solution accommodates diverse requirements of both cloud and edge servers, allowing clients to construct complete historic graphs or build filtered sub-graphs. We deploy our solution in a multi-cloud edge environment and construct knowledge graphs representing the system state, including clusters, servers, microservices, and various resources. We validate the feasibility and performance of our solution through a real-world deployment and experiments in a smart shopping use case. Results demonstrate that the proposed solution achieves the claimed benefits with minimal or acceptable delays in comparison to traditional event streaming services.

Vasiliki Balaska, Symeon Symeonidis, Sarantis Antoniou, Thomas Fotiadis · 6 authors

No abstract is available for this record.

Eric Joshua Nyato, Emmanuel Charles Kimito, Jaehun Yang, Doyeop Lee · 5 authors

No abstract is available for this record.

Ankita Vashisth, Kolawolé Valère Salako, Pramitha Pinto

Blockchain-based digital assets, particularly cryptocurrencies and non-fungible tokens (NFTs), have gained significant popularity in recent years due to their unique attributes, benefits and challenges. This book chapter aims to provide a comprehensive understanding of the financial reporting and valuation of digital assets. The chapter is divided into three sections, with the first section presenting an overview of digital assets, highlighting the attributes, benefits and challenges of cryptocurrencies and NFTs. The second section delves into the valuation and financial reporting of digital assets, including the regulatory frameworks for cryptocurrencies and NFTs, tax implications for these assets and the types of valuation methodology used. The final section explores the future prospects of cryptocurrencies and NFTs, considering their global impact and adoption on the economy. The chapter discusses the similarities and differences between cryptocurrencies and NFTs. Both types of digital assets rely on blockchain technology; but cryptocurrencies represent fungible digital assets that can be exchanged for other assets or currencies, while NFTs are unique digital assets that represent ownership of a specific asset, such as artwork or music. In terms of valuation, the chapter discusses various methodologies used to value digital assets, including the cost, market, and income approaches and how these methods may be adapted to account for the unique characteristics of digital assets. Furthermore, the chapter provides insights into the financial and corporate reporting requirements for digital assets and how they may differ from traditional financial reporting standards. The regulatory framework for digital assets is also discussed with a focus on the current regulatory environment and the need for clarity and consistency in regulations to promote investor confidence and protect consumers. The chapter also explores the impact of taxation on cryptocurrencies and NFTs, highlighting the complexities of tax regulations and compliance considerations for digital assets. It examines the current tax regulations and their potential impact on the valuation and reporting of digital assets. The chapter also discusses the future prospects of cryptocurrencies and NFTs, considering their potential impact on the global economy, including their role in disrupting traditional financial systems and promoting financial inclusion. Finally, the chapter provides recommendations and best practices for stakeholders to consider when investing, valuing or reporting digital assets.

Monks, CA, USA, Shafeeq Ur Rahaman, P. Sudheer, Samsung, CA, USA · 5 authors

The rapidly evolving landscape of cryptocurrency markets presents unique challenges and opportunities. The significant daily variations in cryptocurrency exchange rates lead to substantial risks associated with investments in crypto assets. This study aims to forecast the prices of cryptocurrencies using advanced machine learning models. Among seven models that were tested for their prediction and validation efficiency, Neutral Networks performed the best with minimum error. Thus, Long Short-Term Memory (LSTM) neural networks were used for predicting future trends. LSTM model is well-suited for analyzing complex dependencies in financial data. Starting with historical data collection, data preprocessing, feature engineering, normalization and integrative binning, a comprehensive Exploratory Data Analysis (EDA) was conducted on 50 cryptocurrencies. Top performers were identified based on criteria such as trading volume, market capitalization, and price trends. The LSTM model was implemented using Python to predict 90-day price movements data to check intricate patterns and relationships. Model performance was validated by performance metrics such as MAE and RMSE. The findings align with the Adaptive Market Hypothesis (AMH) which suggests that cryptocurrency markets exhibit dynamic efficiency influenced by evolving market conditions and investor behavior. The study shows the potential of machine learning models in financial economics and their role in enhancing risk management strategies and investment decision-making processes.

Mikhalov Alojo

The increasing complexity of data management systems, coupled with the evolving nature of cybersecurity threats, necessitates innovative approaches to ensure data integrity, confidentiality, and availability. This paper explores recent studies on advanced data management strategies and their intersection with cybersecurity practices. Key insights are drawn from the latest research on topics such as distributed ledger technologies, artificial intelligence-driven threat detection, and privacy-preserving data management frameworks. The analysis highlights how these emerging technologies are reshaping the landscape of data management while addressing cybersecurity challenges. Additionally, this paper examines the role of regulation and policy in fostering secure data ecosystems. The findings offer a comprehensive overview of current trends, challenges, and opportunities in the field, with recommendations for future research directions.

Akhil Verma, Ranjan Walia, Vaneet Kumar

Cryptocurrency is a digital or virtual currency that uses a decentralized system through cryptography for secure financial transaction and record keeping. This process is widely known as mining, where the decentralized nature of the system works without central issuance. One of the objectives is to the end that the spread of the influence of institutions and organizations should be up to the users/investors with enhanced authority. In India, this has been under deployment and substantial deliberation in the relevance and importance it has come to assume in the lives of people. This is largely due to the fact that availability has increased, and it also would need high utilization of processor by things like mining. The recent past has increasingly seen popularity and importance to which cryptocurrencies hold relevance. In fact, this was manifest in the massive growth of rapid advances and that which had led many global businesses to acknowledge the importance of using this technology to gain access to myriad quantitative benefits electronically. The concept of rice forecasting has been very pivotal in different industries, like in cryptocurrency and stock prices. It greatly challenges the current financial market since indeed, traders have had the dilemma of selling their cryptocurrencies or buying them.

Pushan Kumar Dutta, Pronaya Bhattacharya, Kammari Sriram, K. Vijayakumar

No abstract is available for this record.

Roman Karl, Valentina Mazzonello, Bernardo Pacheco, João Paulo Borges da Silveira · 7 authors

The publication focuses on exploring ways of integrating smart contracts within a data space dedicated to the public security domain. This data space shall facilitate involving various stakeholders, such as law enforcement agencies (LEAs), research facilities and universities as well as and research-oriented companies by taking their needs and requirements into account. On the one hand, we discuss the benefits of smart contracts in this context, particularly how blockchain-backed processes can contribute to building a trustworthy data sharing ecosystem. On the other hand, we also investigate challenges and costs associated with this approach.