Blockchain Papers

jaemin jung, Byoung Ki Seo

No abstract is available for this record.

Mehmet Kaygusuzoglua, Yakup Durmaza, Miraç Yücel Basera

This study aims to examine the use of blockchain technology and smart contracts in accounting. Accounting is one of the sectors that is suitable for the use of different technologies because of both human and system errors. Blockchain technology, which is decentralised and not affected by malicious interference by parties, is the most likely technology to be used with accounting. Compared to traditional accounting records, owing to the three-entry accounting system, records can now be followed instantly rather than for specific periods. In addition, blockchain technology, which provides benefits such as the inability of personnel and auditors to manipulate records, enables various activities, from automating business processes in accounting to creating a more transparent accounting ecosystem thanks to smart contracts. In this vein, we aim to examine the potential benefits that this approach will provide to the field of accounting by researching smart contracts.

Xihui Haviour Chen, Provash Kumer Sarker, Chi Keung Marco Lau

No abstract is available for this record.

Amrita Jain, Savi Jain, Shruti Lashkari, Sweta Gupta · 5 authors

The overall objective of the study is to understand the current status of blockchain applications and evaluate their ability to satisfy the growing demand for blockchain knowledge in the applications industry.In order to determine which would be the superior option in each situation, it also evaluated the respective advantages of Ethereum and Hyperledger.The study's extensive data set allowed it to offer priceless insights into the intricate workings of a blockchain application.The materials used included reports, journals, and periodicals.The "smart contract" refers to a digital transaction that runs on its own, logs the pertinent dynamic activity on a distributed ledger, and uses predefined criteria to demonstrate its legitimacy.The key component of a blockchain that enables its use as a platform for use cases beyond currency is a smart contract.Voting, education, entertainment, real estate, the Internet of Things (IoT), The development of blockchain technology has advanced significantly in recent years, with a particular emphasis on smart contracts; yet, little research has been done on the idea.Notwithstanding the many advantages of smart contracts, a number of obstacles have prevented their widespread use, including as security holes, coverage gaps, and the difficulties of lawfully enforcing contracts.

Vasiliki Basdekidou

Abstract Digital transformation and the adoption of blockchain in corporate operations and financial services introduces a new set of significant policy issues and concerns about security, competition, and regulatory rules ensuring healthy competition and level playing fields in the FinTech business and financial ecosystem. From the disrupting adoption of blockchain and the impact of FinTech, one possible outcome concerning market competitiveness, concentration, and competition is a business and financial world consisting of numerous specialized businesses and just a few major providers. Hence, to handle trade-offs between stability, trust, integrity, knowledge, information, data sharing, competitiveness, efficiency, consumer protection, cyber-hacking, security, and privacy, authorities must collaborate across financial regulation, competition, and industry regulatory organizations. This paper follows a simple literature review methodology for demonstrating knowledge and understanding of the academic literature on the disruptive power of FinTech and the importance of blockchain and distributed ledger technologies in digital transformation processes.

Mahdi Ijlal, Amr Youssef

No abstract is available for this record.

Ammoon Birzoim

No abstract is available for this record.

Chittaranjan Pradhan

No abstract is available for this record.

Imelda T. Angeles

No abstract is available for this record.

Pooja Patil, Aryan Singh, Aryan Raut, Akash Sharma · 5 authors

The increasing reliance on e-commerce platforms has amplified challenges related to transparency, trust, fraud, and inefficiencies in reward distribution systems. Existing centralized architectures fail to address these issues effectively. This paper proposes BlockArc, a blockchain-based smart contract framework designed to revolutionize reward systems in e-commerce. The system leverages a permissioned blockchain and smart contracts to automate reward distribution, enhance security, and ensure transaction transparency. The framework consists of four layers: Blockchain Layer, Smart Contract Layer, Application Layer, and User Roles, each addressing key challenges such as reward fragmentation, fraudulent transactions, and inefficient refund processes. Smart contracts autonomously handle reward issuance, redemption, and expiration while integrating oracles for real-world data validation. Security is reinforced using cryptographic hashing, Zero-Knowledge Proofs (ZKPs), and Role-Based Access Control (RBAC) to prevent fraudulent activities. A performance evaluation demonstrated 112 transactions per second (TPS) under moderate load, fraud detection accuracy of 100%, and a 37% reduction in operational costs by eliminating intermediaries. User satisfaction surveys indicated high levels of trust and transparency. The study concludes that BlockArc enhances e-commerce reward systems by improving efficiency, security, and decentralization, paving the way for scalable and interoperable blockchain applications in digital commerce.

Varun Bodepudi, Purna Chandra Rao Chinta

The increasing adoption of Bitcoin as a digital asset has led to significant interest in accurately predicting its price movements. However, the highly volatile and speculative nature of Bitcoin presents substantial challenges for traditional financial models, which often struggle to capture the complex and nonlinear patterns that influence its price fluctuations. This study proposes a novel approach to enhancing financial predictions related to Bitcoin prices by leveraging the power of big data analytics and deep learning techniques. The integration of large-scale historical market data, social sentiment analysis, blockchain transaction metrics, and macroeconomic indicators allows for a more comprehensive understanding of Bitcoin’s market behavior.To achieve this, deep learning architectures such as Long Short-Term Memory (LSTM) networks and Transformer-based models are employed due to their superior ability to capture long-range dependencies and dynamic trends in time-series data. These models are trained on high-frequency trading data, order book information, real-time market indicators, and sentiment data derived from news sources and social media platforms. By utilizing a data-driven approach, the proposed model aims to improve the robustness and accuracy of Bitcoin price predictions.Extensive experiments and comparative analyses are conducted to evaluate the effectiveness of the deep learning-based framework against traditional statistical models and classical machine learning techniques. The results demonstrate that the proposed approach significantly outperforms conventional methods in terms of predictive accuracy, stability, and generalization capabilities. The findings highlight the potential of deep learning and big data analytics in enhancing cryptocurrency market predictions and risk assessment strategies.The insights derived from this study provide valuable implications for traders, investors, and policymakers seeking to develop more informed trading strategies and risk management frameworks. By harnessing the power of deep learning and big data, this research contributes to the growing field of financial technology and underscores the importance of advanced predictive models in navigating the rapidly evolving cryptocurrency market.

Ahmad Alsuwaidi, Irenee Dondjio

No abstract is available for this record.

Jie Zhang

With the rapid development of blockchain technology, smart contracts are becoming increasingly important in various applications. Aiming at the problems of uneven resource allocation, low execution efficiency and insufficient security that are still faced in their management and optimization, a smart contract management system based on ant colony optimization algorithm is proposed. First, we build a smart contract management framework, define the key parameters in the contract execution process, and use the ant colony optimization algorithm to simulate the intelligent behavior of multiple ants in problem solving, and continuously optimize the contract execution strategy through pheromone updates and path selection. Then, we use simulation experiments to compare the effects of traditional management methods and ant colony optimization algorithms. After using the ant colony optimization algorithm, the number of vulnerabilities is generally less than 5. The ant colony optimization algorithm shows a significant optimization effect in smart contract management, which provides new ideas for efficient and secure management of smart contracts in the future.

Ibtisam El Gaddafi, M. Z. Rashad, Amal Abou Eleneen

Modeling is the process of developing models representing software systems from different perspectives using a modeling language that includes graphical notation. This process assists software developers in understanding the system functionality, evaluating design proposals, and documenting the software to be implemented. Blockchain is a popular, decentralized, efficient, and secure technology that enables transparent and tamper-resistant transactions across distributed networks. However, blockchain systems lack a dedicated modeling language to represent Blockchain-Based Applications (BBAs) and related Smart Contracts (SCs). Recent research has introduced adaptations of well-known graphical notations to meet the unique modeling needs of blockchain systems. This paper proposes a structured review of graphical models, techniques, and languages to enhance Blockchain-Oriented Software Engineering (BOSE) modeling. A detailed analysis of 36 studies published between 2018 and 2023 highlights the trends and developments in blockchain modeling, and a classification of modified modeling techniques and languages for BBAs and SCs is presented. A modeling framework that guides blockchain developers in describing, designing, and documenting BBAs and related SCs is developed, and an example is provided to demonstrate how this framework is used. These insights can help software engineers select suitable modeling strategies to improve the reliability and quality of BBAs.

Baharudin Osman, Hapini Awang, Nur Suhaili Mansor, Alawiyah Abd Wahab · 9 authors

No abstract is available for this record.

Mohd Basheer, Luma Abuorabi

No abstract is available for this record.

Vamsee Pamisetty, Lahari Pandiri, Sneha singreddy, Venkata Narasareddy Annapareddy · 5 authors

No abstract is available for this record.

G. Nithin, H. P. Ramya, B. R. Manjunath

No abstract is available for this record.

Bill X. Hu, Chang Liu, Rachel Washam, Zeng Li

Environmental concerns may influence cryptocurrency prices. Investors' information acquisition regarding environmental issues linked to cryptocurrency mining plays an important role in the pricing of cryptocurrencies. The volume of Google searches for environmental issues related to cryptocurrency mining significantly and negatively affects future cryptocurrency returns. This impact is more pronounced for Proof-of-Work (PoW) cryptocurrencies, which are known for their high energy consumption, compared to Proof-of-Stake (PoS) cryptocurrencies. This study provides important insights into how environmental awareness influences cryptocurrency markets, offering a novel perspective on market prices in the context of sustainability.

Mfon Akpan

This study investigates the correlation between the number of views and non-fungible tokens (NFTs) valuation, explicitly focusing on Sandbox land assets. This study uses data from the OpenSea marketplace to examine various valuation metrics, including current price, offer price, and floor price. It develops a digital investment valuation and analysis (DIVA) model to predict NFT valuations. This study employs a quantitative research design, incorporating descriptive statistics, correlation analysis, and multiple regression analysis to analyze the data. The findings reveal significant positive correlations between views, offers, and current prices, highlighting the critical role of attention in NFT valuation (Wang et al., 2021). The validated DIVA model demonstrates strong predictive power, explaining 75 percent of the variance in current prices. These insights are crucial for investors, creators, and platform operators, emphasizing the importance of visibility and engagement in maximizing NFT values (Sun, 2024). This study aims to contribute to the literature on digital asset valuation and offers insights that may inform investment strategies and market efficiency in the evolving NFT market. Future research should consider more extensive and diverse samples and explore additional variables to refine the valuation model.

Sharjil Ahmed

No abstract is available for this record.

Ishtiaq Ahmad Bajwa

Digital currencies, including Bitcoins, Ethereum, and others, are an established alternative asset class opted by individual and institutional investors worldwide. However, this digital asset class carries several inherent risks. This study investigates the influence of three key risks, including liquidity risk, cyber risk, and regulatory risk, on the reinvestment intentions of investors. Data was collected from global crypto investors via a questionnaire and analyzed using PLS structural equation modeling. The study further used Investors' risk tolerance as a moderating variable. The findings of the study reveal that a significant and negative relationship exists between liquidity risk, cyber risk, regulatory risk, and investors' reinvestment intentions. Interestingly, investors with high-risk tolerance levels seem less affected by cyber security concerns, as they are more capable of absorbing or overlooking such risks. On the other hand, liquidity and regulatory risks appear to affect all types of investors irrespective of their risk tolerance level. This study bridges an important research gap by providing clear evidence on how these specific risks influence reinvestment decisions in the growing crypto market. It highlights the critical need for effective risk management strategies and stronger regulatory frameworks to address cyber vulnerabilities, tackle liquidity issues, and offer regulatory clarity. Taking these steps are essential for boosting investor confidence and encouraging their sustained involvement in the cryptocurrency ecosystem.

Siti Hajar Mohd Yusof, R. Zahilah, Siti Hajar Othman

This research explores the development of a hybrid consensus algorithm that combines the benefits of Proof of Authority (PoA), Delegated Proof of Stake (DPoS), and threshold cryptography to create a secure, efficient, and scalable consensus mechanism for resource-constrained devices. The proposed algorithm addresses traditional consensus algorithms' limitations in resource-constrained environments, where energy efficiency, security, and decentralisation are crucial. By leveraging the strengths of PoA, DPoS, and threshold cryptography, this hybrid approach is anticipated to provide a robust and adaptable consensus mechanism to support many applications in IoT, edge computing, and other resource-constrained domains. The research aims to investigate the feasibility, performance, and security of this hybrid consensus algorithm and its potential to enable secure, decentralised, and scalable blockchain-based systems for resource-constrained devices.

Tomáš Klieštik, Robert gabriel Dragomir, Aurelian Virgil Băluță, Iulia Grecu · 17 authors

Research background: Enterprise generative AI system-based worker behavior tracking and monitoring, socially responsible organizational practices, employee performance management satisfaction, and human resource management procedures, relationships, and outcomes develop on hiring and objective performance assessment algorithms in terms of human resource management activities, functions, processes, practices, policies, and productivity. Deep reinforcement and machine learning techniques, operational and analytical generative AI and cloud capabilities, and real-time anomalous behavior recognition systems further fintech development for credit and lending services, payment analytics processes, and risk assessment, monitoring, and mitigation. Generative AI tools can bolster predictive analytics by collaborative and interconnected sensor and machine data for tailored, seamless, and fine-tuned product, operational process, and organizational workflow development, efficiency, and innovation, driving agile transformative changes in digital twin industrial metaverse. Purpose of the article: We show that enterprise generative AI-driven schedule prediction tools, job search and algorithmic hiring systems, and synthetic training data can improve team selection, job performance and firing decisions, hiring decision processes, and workforce productivity in terms of prediction and decision-making by use of algorithmic management, system performance, and production process tracking tools. Blockchain-based fintech operations can shape cloud-based financial and digital banking services, quote-to-cash process automation, cash-settled crypto futures, digital loan decisioning, asset tokenization simulated transactions, transaction switching and routing operations, tailored peer-to-peer lending, and proactive credit line management. Collaborative unstructured enterprise data processing, infrastructure, and governance can develop on AI decision and behavior automation technology, retrieval augmented generation and development management systems, and real-time data descriptive and predictive analytics, driving productivity surges and competitive advantage in digital twin industrial metaverse. Methods: Reference and review management tools, together with evidence synthesis screening software, harnessed were Abstrackr, AMSTAR, ASReview Lab, CASP, Catchii, Citationchaser, DistillerSR, JBI SUMARI, Litstream, PICO Portal, and Rayyan. Findings & value added: The current state of the art is improved for theory on organizational issues and for policy making as deep learning-based generative AI tools and workplace monitoring systems can augment performance and productivity, gauge employee effectiveness, build resilient, satisfied, and engaged workforce, assess human capital, skill, and career development, drive employee and productivity expectations in relation to flexibility and stability, and shape turnover, retention, and loyalty. Cloud and account servicing technologies can be deployed in generative AI fintechs for embedded cryptocurrency trading, transaction monitoring and processing, digital asset transfers, payment screening, corporate and retail banking operations, and fraud prevention. Generative AI technologies can reshape jobs and reimagine meaningful work, involving creativity and innovation and adaptable and resilient sustained performance, providing valuable constructive feedback, optimizing workplace flexibility and psychological safety, and measuring and supporting autonomy and flexibility-based efficiency, performance, and productivity, while configuring demanding, engaging, and rewarding experiences by cloud and edge computing devices in digital twin industrial metaverse.