Blockchain Papers

Maha Azabou, Omar Khrouf, Ines Ben Messaoud, J. Feki

No abstract is available for this record.

Abdul-rahman AHMAD, Mohammed Bashir Abdullahi, Yahaya YUSUF

The paper examined the effect of quality regulatory framework on perceived prevalence of cryptocurrency-related financial crimes in Nigeria. Employing mixed methods approach with survey data from 385 respondents and thematic analysis of 15 regulatory documents and 8 expert interviews, the study utilized descriptive and Ordinary Least Square (OLS) regression analysis. Diagnostic tests confirmed no serious multicollinearity (VIF < 5), heteroscedasticity (Breusch Pagan p > 0.05), and normality of residuals (Jarque Bera p > 0.05). The results revealed a significant relationship from improved monitoring tools (RUMT, β = 0.0939, p < 0.01) and blockchain compliance mechanisms (BCAC, β = 0.0960, p < 0.01) to the perceived prevalence of financial crimes thereby indicating rise in financial crimes and by exposing previously hidden irregularities. In addition, regulatory guidelines exhibit a weak deterrent effect (RAGC, β = -0.0412, p < 0.10), attributable to weak enforcement by agencies responsible for implementation (e.g., EFCC, NPF), lack of clarity in guidelines issued by the CBN and SEC, and fragmented institutional structures on AML/CFT (despite CBN and SEC being the guideline issuers, enforcement lags due to poor coordination among regulators and law enforcement). Customer Due Diligence Verification (CDDV) was statistically insignificant, indicating that traditional KYC/AML mechanisms are insufficient against sophisticated crypto-related activities involving mixers, decentralized finance platforms, and offshore exchanges. However, Culture of Compliance (CCUL, β = 0.0531, p < 0.05) and geopolitical zone (β = -0.0341, p < 0.05) also significantly shape perceptions. The findings suggest that Nigeria remains in an awareness stage of regulation defined as a phase where detection and monitoring capabilities improve (evidenced by positive coefficients for RUMT and BCAC) but enforcement mechanisms lag behind, allowing crimes to be identified more readily without commensurate reduction. Law enforcement agencies such as EFCC and NPF should prioritize strengthening enforcement capacity and adopting advanced blockchain analytics as the first step; policymakers (National Assembly, CBN, SEC) should ensure regulatory frameworks are clear and comprehensive, thereby reducing ambiguity and institutional fragmentation among CBN, CAC, NFIU, and others.

Souhaila EL ASRI

Abstract. The global transition to renewable energy requires substantial capital mobilization beyond conventional banking channels. This systematic bibliometric investigation examines 1,245 scientific publications addressing innovative financing approaches through blockchain technology, crowdfunding platforms, and green fintech solutions. Using data from the Scopus database covering the period 2018-2025, we systematically assessed publication trends, international collaboration structures, and conceptual frameworks shaping this field. Our results demonstrate remarkable expansion, with publication output increasing sevenfold between 2018 and 2024. Chinese research institutions contribute to approximately 40,2% of global scientific output, while thematic clustering reveals five main research streams. By identifying leading organizations, influential researchers, and developing concepts including asset tokenization and decentralized energy trading, this investigation provides evidence-based guidance for advancing research agendas and informing climate finance policy development.

Anitha Kumari B

The introduction of block chain-supported investment tools like cryptocurrencies, DeFi platforms and tokenized assets has brought new decentralized, clear and exciting choices to the world of finance. As the use of impact investing expands all over the world, learning how investors view these projects matters for their continued success. This study investigates the motivations, risk perceptions, and decision-making processes of investors engaging with blockchain-based financial products. Drawing on behavioral finance theories and existing literature, it explores how psychological biases, technological literacy, and external influences such as social media and regulatory shifts shape investor actions. The research identifies key gaps, including the limited focus on non-cryptocurrency products, underdeveloped behavioral models, and insufficient attention to demographic and longitudinal factors. By addressing these gaps, this study aims to provide actionable insights for policymakers, financial institutions, and technology developers, contributing to a deeper understanding of investor dynamics in the blockchain era.

Xuejian Gong, Mulang Song, Roger J. Jiao, Linda Zhang

No abstract is available for this record.

Kailash Chandra Bandhu, Ratnesh Litoriya, Pradeep Lowanshi, Manav Jindal · 6 authors

This study and implementation focus on the complex healthcare supply chain, encompassing resource procurement, supply management, and service delivery to all the stakeholders without any geographical boundaries. It introduced a novel approach utilising Ethereum blockchain technology to establish a track-and-trace mechanism for healthcare supply chains, bolstered by smart contracts and data immutability. By leveraging smart contracts, contractual obligations are automatically executed, ensuring prompt results without intermediaries or time delays. The proposed solution addresses the prevalent issues of transparency and monitoring within conventional supply chains. The method, rooted in solidity smart contracts, undergoes rigorous testing across various inputs, culminating in an average gas cost evaluation for various functionalities of system. This innovative system meticulously tracks the lineage of goods, with an average gas cost of 18,027 for all accounts. Notably, the process incurs a gas cost of 292,000 for all operations.

V. Prema Kumari, S. Antony Raj

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

C. M. Nalayini, M. Ahil, A. B. Swetha, P. B. Vaishali Srija

No abstract is available for this record.

Keqing Wang, Wenyin Zhang, Mengru Tu, Weihao Su

With the widespread use of smart contracts in blockchain applications, particularly in the consumer Internet of Things (CIoT), the security of smart contracts has become increasingly critical. CIoT refers to an IoT environment where various consumer devices are intelligently connected and interact via the internet. In this context, smart contracts are frequently used to automate tasks such as device control, data sharing, and transaction verification. However, vulnerabilities in smart contracts can lead to system attacks, thereby compromising the security of the entire IoT network. The Cascade Graph Convolutional Network-based Vulnerability Detection Framework for smart contracts (CGCN-DF) proposed in this study effectively enhances the security assessment of smart contracts in CIoT applications. Devices and systems in CIoT environments typically exhibit high heterogeneity and complex interaction patterns, but a single graph structure cannot fully capture the multidimensional behavioral features and complex structural relationships of smart contracts. To address this issue, this paper introduces the Semantic Contract Graph (SCG), which integrates three graph representations—Abstract Syntax Tree (AST), Control Flow Graph (CFG), and Data Flow Graph (DFG)—into a unified graph structure, comprehensively covering different aspects of the code. Furthermore, as smart contracts in CIoT environments often involve real-time data flows and complex execution paths, the CGCN-DF framework employs a cascading mechanism that performs three-level graph convolutional processing through relational, Meta-path structures, and cyclic structures. This approach extracts rich and complementary information from multi-level features, explicitly models the dynamic interactions among syntax, execution paths, and data flows, and enhances the model’s contextual awareness of vulnerability-triggering conditions. Ultimately, the framework achieves coarse-grained detection at the contract level and fine-grained detection at the line level. Experimental results demonstrate that the proposed method can effectively localize vulnerabilities down to specific code lines. This not only enhances the precision and practicality of smart contract vulnerability detection but, more importantly, contributes an innovative technical framework and theoretical methodology to the CIoT field—particularly in areas such as DSL program analysis and the intersection of IoT and blockchain security. This work thereby helps advance the field toward more refined and context-aware security analysis aligned with the realistic characteristics of complex systems.

Yaoxin Chen, Haiming Zhu, Haibo Li, Yaming Yang · 6 authors

Frequent security issues with smart contract vulnerabilities have become a pressing challenge in the industry. Conventional program analysis methods lack flexibility and extensibility, leading to high false positive rates. Deep learning approaches are emerging as a new trend to address this issue. Compared to other neural networks, graph convolutional networks can better capture the structural and logical information of smart contracts. However, existing methods do not fully consider the scale-free characteristics of smart contracts and fail to leverage their complex hierarchical structures and semantic information. Therefore, we develop an end-to-end vulnerability detection framework using Riemannian Graph Convolutional Networks (RGCNet). We first construct smart contract graphs that are rich in semantic and structural information. Next, we learn features of the smart contract graph in the Riemannian manifold, thereby better reflecting its actual topology. Simultaneously, the word embedding network extracts semantic features, forming an end-to-end network where modules promote one another. Extensive experiments are conducted on three vulnerabilities using real-world smart contracts. The results show that the proposed approach exhibits superior performance over state-of-the-art methodologies in terms of accuracy, precision, and recall.

Ekaterina Abrosimova

No abstract is available for this record.

Omar Bilal

No abstract is available for this record.

MichaelaUrbanová

This work deals with the possibility of using recurrent neural networks of the LSTM and GRU type for predicting daily logarithmic returns of selected cryptocurrencies (BTC, ETH, LTC, BNB). Based on daily OHLCV data from the period 2017–2024, three sets of input variables are constructed: a basic set (transformed price and volume variables and logarithmic returns), a set of technical indicators, and a set of technical indicators together with macroeconomic data. The LSTM and GRU prediction models are calibrated for different memory lengths (10, 20, 50 days), numbers of neurons, and all three sets, and their performance is evaluated using RMSE, MAE, and directional accuracy. The results show that daily returns of cryptocurrencies are difficult to predict from the point of view of a one-time prediction: random walk remains a very strong benchmark in all cases, and the best neural networks only come close to it. Subsequently, simple Long/Short strategies are constructed based on the predictions and compared with the passive Buy&hold strategy. For all four cryptocurrencies, configurations are found whose strategies achieve higher annualized returns and Sharpe ratios than Buy&hold in the test period, especially for more volatile altcoins. However, this outperformance is conditioned by ex post selection of the “best” models and neglect of transaction costs, and therefore it must be interpreted with caution as an illustration of the potential and limits of deep neural networks in short-term prediction of cryptocurrencies.

Sukanta Chakraborty, Abhishek Majumder

ABSTRACT Over the years, numerous efforts have been undertaken to accurately forecast traffic conditions and thereby preventing additional congestion. However, existing crowd management techniques focus on recognizing and counting the crowd while leaving the security of crowd information. A typical crowd management system is centralized and faces challenges, such as contributor selection reliability, fair payment evaluation, privacy concerns and high deployment costs. This study investigates security concerns in crowd management and evaluates the potential of blockchain technology to improve crowd management security. Combining the power of blockchain (decentralization and security) and smart contracts, this work proposes a secure crowd management architecture named . The framework operates on blockchain, utilizes cryptographic algorithms, and incorporates reputation management along with credit distribution through smart contracts. effectively safeguards crowd data while its revenue structure entices users to actively contribute to the system. has been simulated on GoQuorum's Ethereum private blockchain, using elliptic curve signatures for secure and efficient processing. Its performance was tested with RAFT, PoA and IBFT consensus mechanisms where RAFT led in throughput, IBFT lagged and PoA offered a middle ground. PoA stands out for balancing scalability and security, supporting network growth while preserving identity‐based validation and data integrity.

Mohsen Minaei, Ranjit Kumaresan, Andrew Beams, Pedro Moreno-Sánchez · 9 authors

Blockchain auction plays an important role in the price discovery of digital assets (e.g.NFTs).However, despite their importance, implementing auctions directly on blockchains such as Ethereum incurs scalability issues.In particular, the on-chain transactions scale poorly with the number of bidders, leading to network congestion, increased transaction fees, and slower transaction confirmation time.This lack of scalability significantly hampers the ability of the system to handle largescale, high-speed auctions that are common in today's economy.In this work, we build a protocol where an auctioneer can conduct sealed bid auctions that run entirely off-chain when parties behave honestly, and in the event that k bidders deviate (e.g., do not open their sealed bid) from an n-party auction protocol, then the on-chain complexity is only O(k).This improves over existing solutions that require O(n) on-chain complexity, even if a single bidder deviates from the protocol.In the event of a malicious auctioneer, our protocol still guarantees that the auction will successfully terminate.We implement our protocol and show that it offers significant efficiency improvements compared to existing on-chain solutions.Our use of zkSnark to achieve scalability also ensures that the on-chain contract and other participants do not learn anything about the bidders' identities and their respective bids, except for the winner and the winning bid amount.

Asghar Ehteshami, Mohammad Sattari

Background: Blockchain has many applications in healthcare and can improve mobile health applications, monitoring devices, electronic media record sharing and storage, clinical trial data, and insurance information storage. In this study, the aim was to investigate the application areas of blockchain and its impact in telemedicine. Methods: This study considers articles use blockchain for telemedicine. PubMed, Science direct, and Web of Science databases are considered as searchable databases. Information on authors' names, year of publication, country, application, privacy mechanism, blockchain platform, and encryption techniques are used. 249 studies were retrieved after the initial search. Finally, 16 cases had the necessary criteria to enter this study. The JBI checklist was applied to all 16 studies. Results: China with 5 studies and Italy with 3 studies are the most important countries about blockchain in telemedicine that electronic health records are more used than others. Blockchain platforms are Ethereum, Internet of thing, cloud-service provider, and GPS. Encryption techniques are Attribute-based encryption: Decentralized identity: Order-preserving encryption- hashcode- Double blockchain. Conclusions: Blockchain plays an important role in creating security for telemedicine technology. In the future, the use of technology will have a significant and important leap, which will attract the attention of many researchers.

Klaus Jaffé

No abstract is available for this record.

Farah Syahfira, Irhamah

No abstract is available for this record.

David Krause

No abstract is available for this record.

Natalia G. Lashkova

The article is devoted to the study of the transformation of the innovation process management paradigm under the influence of the accelerating digitalization of the economy and society. The relevance of the work is due to the need to rethink traditional models of innovation management in the context of the emergence of new digital platforms, data analysis tools and network forms of the organization. The purpose of the study is to identify the key challenges and systemic opportunities that digital transformation creates for technological entrepreneurship as a driver of the generation and commercialization of radical innovations. The paper analyzes how end-to–end digital technologies (big data, artificial intelligence, the Internet of Things, distributed ledgers) not only optimize existing business processes, but also qualitatively change the logic of innovation cycle management – from idea generation to product launch. Special attention is paid to such challenges as the need to manage high-speed iterations, work in conditions of hypercompetition and volatility of digital markets, the problem of cybersecurity of intellectual property, as well as an acute shortage of personnel with competencies at the intersection of technology and management.

Salvatore Furnari, R Lener

No abstract is available for this record.

Ming Sen Thong

No abstract is available for this record.

Kirti Jainani, Pankaj Jainani

No abstract is available for this record.

Anass Mohamed Amallah, Noreddine Abghour, Zouhair Chiba

No abstract is available for this record.