Real estate in smart cities and the metaverse is being reshaped by NFTs, tokenization, blockchain, AI valuation models, digital twins, and cybersecurity. Tokenization enables fractional ownership, access, and liquidity, while NFTs provide immutable rights that reduce fraud and enhance transparency. AI valuation uses machine learning, predictive analytics, and computer vision to improve pricing and integrate with blockchain for auditability. Digital twins link physical and virtual assets, supporting predictive maintenance, energy efficiency, and immersive walkthroughs. Yet adoption faces risks from smart contract exploits, market manipulation, and quantum computing, requiring quantum-resistant cryptography and privacy tools like zero-knowledge proofs. Case studies highlight Dubai's NFT registry, U.S. pilots, Europe's blockchain registries, and Asia's metaverse platforms. Economically, the market is projected to grow from USD 2.33 billion in 2025 to USD 67.40 billion by 2034, underscoring the need for harmonized laws, ethical AI, and sustainable frameworks.
Cross-border trade finance supports liquidity and global supply chain growth, especially for small and medium enterprises (SMEs). Traditional documentation and invoice factoring systems face delays, huge documentation, fraud risks, poor interoperability, and limited transparency which results in unsustainable action. Blockchain-based trade finance improves immutability, decentralized validation, and automation through smart contracts. However, it suffers from scalability limitations, fragmented data management, and regulatory integration issues. Academic literature lacks a unified framework combining blockchain and cloud to address these operational gaps. This study fills the gap by evaluating traditional, blockchain, and hybrid models in trade finance systems. The paper reviews major use cases to understand realworld blockchain implementations in trade finance. These include HSBC and ING's blockchain letter of credit, and Bank of Canada's Project Jasper. UBS's blockchain payment initiative is also analyzed for its real-time settlement and auditability benefits. The study compares traditional and blockchain systems across speed, security, transparency, and integration parameters. It identifies that blockchain alone is insufficient due to its lack of scalability and limited system compatibility. To address this, the paper proposes a Blockchain-Cloud Integrated Trade Finance (TF) Framework. This includes hybrid data storage, Application Programming Interface (API) compliance, digital identity verification, and stakeholder dashboards. Using a design science approach, the framework improves auditability, transparency, and regulatory alignment. Findings benefit regulators, banks, and FinTech's. The framework not only enhances efficiency and compliance but also contributes to sustainable trade finance
Oct 30, 2025·2025 1st IEEE Uttar Pradesh Section Women in Engineering International Conference on Electrical Electronics and Computer Engineering (UPWIECON)
The decentralized structure, lack of regulation, and susceptibility to manipulation of Bitcoin markets result in a high level of volatility, which presents substantial obstacles to the accurate prediction of prices. Traditional statistical models, like ARIMA, frequently fall short in describing the dynamic and nonlinear nature of bitcoin markets. In order to overcome this constraint, this research utilizes sophisticated machine learning and deep learning techniques, including as Convolutional Neural Networks (CNN), Decision Trees, Long Short-Term Memory (LSTM), and Logistic Regression, to predict changes in the price of Bitcoin. Using historical Bitcoin datasets, the suggested models are trained and assessed using performance measures like accuracy and RMSE. In comparison to traditional techniques, experimental results show that deep learning modelsâin particular, LSTMâachieve greater prediction accuracy, offering a more dependable framework for forecasting bitcoin prices.
As healthcare ecosystems shift toward digital-first operations, personal health data faces unprecedented security and privacy risks from increasingly sophisticated cyber threats. This paper examines how the integration of Artificial Intelligence (AI), including Agentic AI, blockchain, and cloud computing, can establish an advanced security framework for resilient healthcare data management. Unlike traditional siloed systems, the proposed model leverages AI-driven anomaly detection, multi-agent orchestration, and explainable AI (XAI) for real-time threat prediction and adaptive defense. Blockchain contributes decentralized trust, tamper-proof auditability, and consent-enforcing smart contracts, while cloud platforms deliver elastic scalability, encrypted storage, and hybrid multi-cloud deployment models. The framework also incorporates federated learning, Model-Chaining Protocols (MCPs), and Zero-Knowledge Proofs (ZKPs) to enhance interoperability, preserve privacy, and enable verifiable compliance. Findings highlight significant improvements in confidentiality, integrity, and availability (CIA) of healthcare data, while simultaneously addressing regulatory obligations such as HIPAA and GDPR through embedded governance and risk orchestration layers. Despite challenges around system complexity and policy harmonization, the paper provides a state-of-the-art synthesis and proposes actionable best practices for healthcare practitioners and policymakers, including adopting continuous AI-powered risk monitoring, blockchain-based patient-centric data ownership, and automated compliance verification mechanisms. Overall, the convergence of AI, blockchain, and cloud technologiesâaugmented by governance-driven orchestrationâoffers a future-proof, cyber-resilient architecture for safeguarding personal health data in digital-first healthcare ecosystems.
The rapid digitization of financial services has resulted in a staggering increase in sophisticated fraud, endangering global economies and damaging public trust. The dynamic nature of current fraud is outpacing classic fraud detection systems, which frequently rely on static, rule-based methods. This study reveals a new hybrid framework that pairs distributed ledger technology for immutable transaction avoidance with Machine Learning (ML) for real-time fraud detection. The fundamental driving force is to address the inherent shortcomings of centralized systems, as well as the lack of an unchangeable audit trail in ML-only solutions. Using a range of classification algorithms, our methodology entails creating separate machine learning pathways for three important financial domains: credit card, UPI, and loan applications. A fraud verdict is subsequently produced using the top-performing model for each domain, which is determined by a thorough analysis of metrics. Through a smart contract, this decision is safely and irrevocably documented on a private blockchain. This study shows how a strong security architecture may be produced by fusing the decentralized trust and immutability of blockchain technology with the predictive performance of machine learning. The findings demonstrate that this integrated approach strengthens the integrity and dependability of digital financial transactions by achieving high performance in fraud detection as well as creating a transparent and impenetrable record.
This study examines the role of blockchain-based smart contracts' influence on financial transparency and effectiveness in the economic activities of the emerging markets. In this study, the researchers utilised a mixed-method approach that includes a systematic literature review, comparative case studies from Africa, Southeast Asia, and Latin America, and expert interviews. The research findings evidence that the adoption of smart contracts can lower transaction costs, eliminate intermediary services, improve trust in financial systems, and serve as alternatives to the current financial systems. The results further demonstrate that smart contracts can improve financial inclusion through low-cost microfinance, insurance, and trade finance solutions, as well as enhance trust and transparency with immutable records and real-time auditing. Nevertheless, weaknesses in infrastructure, digital literacy, and regulatory uncertainty create difficulties for adoption. In addition, the study augments the existing prior research emphasising the impacts of financial technology innovation in emerging markets by offering findings that are beneficial to the market stakeholders including policymakers, financial services institutions, and technology innovators, by effectively positioning blockchain-based solutions implementation as better and viable option that can drive inclusive financial development in the emerging economies.
Abstract In the context of the metaverse and Web3, we have a novel set of immersive, decentralised, and interactive environments for its interaction with customers and audiences by marketers. This work represents how AR, VR, blockchain, and AI affect conventional marketing approaches. It speaks of changes such as e-stores and NFTs, the gamification, all to optimize traditional environments and design lively clientsâ experiences. They recommend consumer behaviour analysis, ethical concerns, and diversity within the Metaverse as the future research agendas for this research. It analyses Decentraland and The Sandbox as immersive platforms, virtual commerce, and âgamificationâ of marketing, and more, as immersive models. Customer Experience 3.0 demonstrates that through advanced technologies, organisations can reimagine customer engagements and generate value by turning clients into fans. This paper also seeks to provide orientation to the marketers in a positive way while unveiling opportunities and threats within the metaverse in which it lays down its foundation as a crucial aspect of the future of marketing and consumer research. Keywords: Metaverse marketing, Web3 technologies, immersive customer experience, NFTs, AR/VR, blockchain marketing.
Purpose: The purpose of this research is to explore how new technologies, such as DLT (distributed ledger technology), ML (machine learning) and AI (artificial intelligence), can support green economic growth and sustainable finance. Need for the study: Awareness of environmental challenges highlights the importance of using technology to support and promote sustainable financial practices. Therefore, this study, among other things, aims to explore this importance by analysing how AI, ML and DLT can contribute to continuously improving innovation and efficiency in various sustainable finance projects. Methodology: This study uses a literature review technique to examine technology use, sustainable finance, and the transition to a sustainable economy. To achieve this goal, qualitative interviews were conducted with professionals in the fields of sustainability, technology and finance to explore different practices and identify different strategies for developing the future of the finance industry. Findings: Based on the findings of previous studies, AI, ML and DLT play an important role in improving risk management, increasing transparency and simplifying procedures that serve to make decisions in the sustainable finance sector. The transformation to a green and sustainable economy can be more straightforward if it relies on the ability of these important technologies to incorporate some ESG (environmental, social and governance) considerations into organisationsâ plans for potential investments. Practical implications: The study's findings will help software developers, financial institutions and policymakers promote and strengthen sustainable development. Furthermore, the use of technology, especially advances in AI, ML and DLT, offers various valuable perspectives for those engaged in the transition to more environmentally friendly financial practices, contributing to creating a more sustainable global economy.
The study aims to examine how blockchain is used in the multimodal and interdisciplinary metaverse as a nexus of education and training, accounting, banking and finance, entertainment and media, marketing and e-commerce, and retail, healthcare, and wellness. It seeks to evaluate the influence of blockchain combined with artificial intelligence, the Internet of Things, and other emerging technologies in the metaverse, so evaluating the challenges and concerns in the field, new business prospects, and sustainable development paths corresponding to Sustainable Development Goals. Using a Systematic Literature Review (SLR) technique, this article addresses the problem from a commercial viewpoint. The study investigates the topic structure of the literature by means of Biblioshiny for R combined with VOSviewer version 1.6.20. Furthermore, increasing the analytical depth is a bibliographic coupling method used on a dataset of 172 Scopus (2024) items. The study underlines the economic rationality and direct consequences of the characteristics of the blockchainâNFTs, DeFi, cryptocurrencies, transparency, decentralization, and securityâon the relevance of business models in the metaverse. The information provided here is a vital literature study on how the blockchain addresses issues in constructing and metamorphosing the metaverse from several sectors and angles. In the framework of the metaverse, this article offers a thorough theoretical study of the possibilities and possible challenges in blockchain integration.
Abstract: The transition from centralized digital ecosystems to decentralized, trust - driven architectures represents a defining paradigm shift in Customer Experience (CX). This paper presents a strategic blueprint for leveraging block chain technologies to build secure, transparent, and interoperable customer - centric environments between 2025 and 2030. Through a comprehensive review of market forecasts, enterprise case studies, and emerging regulatory frameworks, the study demonstrates how decentralized identity (DID), verifiable credentials, and tokenized loyalty systems fundamentally reshape customer engagement, ownership of personal data, and trust models. Findings indicate that block chain adoption empowers customers with self - sovereign identity control, enhances privacy compliance, and delivers measurable efficiency gains in verification, loyalty management, and supply - chain transparency. Case evidence from leading enterprises â including JPMorgan, AXA, Santander, and Accenture â highlights significant improvements in transaction speed, operational costs, and customer engagement. Despite challenges such as legacy system integration and GDPR - related constraints, hybrid architectures, Layer - Two scalability, and permissioned block chain environments provide viable adoption pathways. This paper concludes that block chain is not a supplementary technology for CX, but a foundational enabler of decentralized trust, competitive differentiation, and customer - driven digital ecosystems. Keywords: Block chain; Customer Experience (CX), Decentralized Identity (DID), Verifiable Credentials, Tokenized Loyalty Programs, Digital Trust, Self - Sovereign Identity, Smart Contracts, Hybrid Data Architecture, GDPR Compliance, Enterprise Digital Transformation, Web3 Customer Strategy
Blockchain technology was first developed to support Bitcoin, but it has come a long way since then and is now used for more than just money. It features a clear and decentralized structure that changed a lot of industries, especially those that deal with money and managing the supply chain. In the supply chain industry, blockchain makes it easier to trace things, cuts down on fraud, and speeds up operations. Blockchain makes it possible for decentralized finance (DeFi) platforms to work with financial institutions. This makes it easier for more people to use financial services and lowers the cost of transactions. This paper investigates the substantial impacts of blockchain technology across several industries, emphasizing adoption challenges like as scalability and regulatory hurdles, while exploring its benefits, challenges, and practical applications.
The US Department of Transportation (USDOT) delivers a wide range of infrastructure projects, backed by a fiscal year 2023 budget exceeding $100 billion. These projects face mounting pressures to meet performance, accountability, and delivery standards, driven by their dependence on public funding and their operational complexity. Transportation infrastructure presents sector-specific challengesâsuch as time-sensitive user disruptions, multiparty coordination, and asset intersection risksâthat demand more robust, automated, and transparent project delivery mechanisms. Blockchain-enabled smart contracts have emerged as a promising solution to address these operational pain points through real-time automation, immutable data records, and decentralized transaction processing. However, the practical realities of the transportation sectorâits fragmented systems, regulatory layers, and diverse stakeholder interfacesâcreate unique integration challenges that remain underexamined. To address this, this study investigates how smart contracts can be effectively integrated into transportation infrastructure by identifying the context-specific needs, requirements, capabilities, and challenges that govern their adoption. A three-phase research design was employed. First, a literature review was conducted to extract generalized integration factors for smart contract use in the broader construction domain. Secondly, these factors were evaluated and ranked by qualified transportation experts to reflect their relevance in sector-specific contexts. Thirdly, structural equation modeling (SEM) was used to analyze expert survey responses and isolate the most influential integration drivers. The results indicate that, unlike general construction projects, the top integration priorities in transportation include (1) compliance checking for quality management (needs); (2) integration with existing cloud repositories or enterprise platforms (requirements); (3) the ability to maintain immutable records (capabilities); and (4) uncertainty regarding usability (challenges). These findings provide a targeted knowledge base for practitioners and policymakers, outlining the critical considerations required for effective and sector-sensitive implementation of smart contracts in transportation infrastructure.
The current research introduces the Decentralized Autonomous MetaUniversity (DAMU), a novel framework for redefining academic governance through Blockchain, Machine Learning, and IoT. Unlike prior blockchain-in-education efforts which are limited to certificate verification, DAMU supports the complete academic lifecycle right from university creation and instructor assignment to student enrollment, assessment recording and credit redemption. The framework contributes a role-based DAO governance model, tokenized academic credits and NFT-based learning passports integrated with IoT-verified activity tracking to name a few. Performance validation across Goerli, Polygon PoS, and zkSync Era demonstrates up to 62% gas cost reduction, 48.6% faster DApp responsiveness and improved scalability thereby making DAMU a pioneering step toward Education 5.0 in a Web3 ecosystem.
The global carbon credit trading market faces significant challenges including lack of real-time verification, double-spending issues, and insufficient transparency in emission measurements. This paper presents a novel blockchain-enabled framework integrating Internet of Things (IoT) sensors for automated carbon credit generation and trading. Our proposed system combines tamper-proof IoT sensor networks with smart contract automation to address current limitations in carbon credit systems. The methodology employs distributed sensor nodes equipped with CO2, temperature, and humidity sensors connected to an Ethereum-based blockchain network. Through extensive simulation and real-world testing, our system demonstrates 99.2% accuracy in emission measurement and real-time carbon credit generation. The framework reduces verification time by 87% compared to traditional manual verification processes while ensuring immutable transaction records. Key contributions include: (1) a decentralized IoT-blockchain architecture for carbon monitoring, (2) smart contract protocols for automated credit generation, (3) a novel consensus mechanism for sensor data validation, and (4) comprehensive security analysis demonstrating resistance to common blockchain attacks. Results indicate significant potential for transforming carbon credit markets through enhanced transparency, reduced fraud, and improved environmental monitoring accuracy.
The article examines the economic and organizational efficiency of implementing smart contracts based on blockchain technology in the public procurement system of the construction sector of the Russian Federation and St. Petersburg. Relevance research conditioned by the need to increase transparency, reduce transaction and administrative costs, and speed up procurement procedures in the context of large-scale public investment and limited budget resources. The paper develops a methodology for quantitatively assessing the economic effect of using smart contracts, including an analysis of direct savings in budget funds, reduced procurement processing time, and increased capital turnover. Based on official statistics and economic and mathematical modeling, it is shown that the introduction of smart contracts can reduce costs by 10% of the total volume of purchases, which is equivalent to savings of about 550 billion rubles for the Russian Federation and 68.2 billion rubles for St. Petersburg. Additional savings are achieved by reducing the average procurement processing time from 15 to 10 days, which leads to a decrease in administrative costs by 8.15 billion rubles and 1.13 billion rubles, respectively. A comprehensive assessment of the total economic effect confirms the high feasibility of digitalizing procurement procedures using smart contracts, which can become the basis for further transformation of the public finance management system and increasing the efficiency of using budget funds in the construction industry.
Qinnan Hu, Yuntao Wang, Su Zhou, Tom H. Luan · 6 authors
Due to the immutable nature of smart contracts, online contract diagnosis is the only viable approach for revealing vulnerabilities in deployed contracts. Existing online approaches face significant challenges in terms of efficiency, adaptability, and reliance on vulnerability labels. This paper proposes ConWatcher+, a new adaptive and label-efficient online contract diagnosis framework from the diffusion perspective, which is capable to detect yet unknown attacks under evolving tactics without reliance on vulnerability labels. ConWatcher+ simulates the Advanced Persistent Threat (APT) tactics commonly used in yet unknown attacks by continuously applying minor perturbations to legitimate interaction behaviors. It then reversely learns the denoising process, guided by potential logic vulnerabilities (i.e., functionality dependencies), to adaptively identify stealthy anomalies and detect yet unknown attacks without needing vulnerability labels. ConWatcher+ proceeds in five steps. First,real-time data extraction. We design a cost-effective contract runtime information collector, incorporating on-demand data retrieval and event-driven data update mechanisms to reduce communication overhead in online contract diagnosis. Second,interaction behavior modeling. Via bytecode-level, account-level, revenue-level modeling, and side-channel level behavior modeling, we propose behavior-aware multivariate time series model to accurately represent long-term contract interactions with multi-faceted behaviors. Third,APT-like noise adding. We leverage the forward diffusion model to produce minor and stochastic APT-like noises with efficiency. Fourth,reverse denoising learning. To effectively guide reverse denoising using functionality dependencies, we devise an adaptive contract-level analysis engine equipped with heterogeneous control flow graph modeling and heterogeneous message passing mechanisms to extract function-level and bytecode-level functionality dependencies. Last,contract anomaly detection. We establish a label-efficient attack detector based on reconstruction error for contract anomaly detection. It combines complex dependency analysis and deterministic inference to ensure high-quality data reconstruction and low detection latency. Extensive empirical validations on a manually constructed dataset, covering both mainstream and novel vulnerabilities, demonstrate ConWatcher+âs effectiveness, adaptability, and label efficiency, with an average F1-score of 0.92 across all types of attacks without prior knowledge of corresponding vulnerabilities.
Smart contracts are integral to blockchain technology, enabling decentralized and automated transactions. This study examines 1,000 smart contracts by analyzing metrics such as total transactions, unique users, total value transferred (ETH), gas consumption, and call frequency. Total transactions range from 1 to 18,902, with unique users spanning 1 to 14,839. The average total value transferred is 3,245.87 ETH, peaking at 7,850.16 ETH, while gas consumption averages 25,486,392 units with a maximum of 58,471,065 units. Strong correlations were identified between transaction volume (r = 0.78), user engagement, and gas consumption. Clustering analysis categorizes contracts into low, moderate, and high-activity groups, while anomaly detection highlights 32 contracts with unusual behaviors, indicating inefficiencies or vulnerabilities. These findings emphasize the importance of optimizing smart contract designs to improve efficiency, security, and scalability. The study provides actionable insights into operational patterns and proposes future research directions, including design optimization, real-time monitoring, cross-platform analysis, and machine learning applications for predictive modeling. By addressing these aspects, this research contributes to the ongoing development of robust and efficient decentralized systems.
Effective data governance is crucial in modern digital ecosystems, ensuring secure, transparent, and efficient data sharing. Traditional centralized governance models often suffer from trust issues, inefficiencies, and security vulnerabilities. Blockchain technology offers a decentralized and tamper-resistant solution to address these challenges. This paper proposes a blockchain-based data governance architecture that enhances data sharing mechanisms and optimizes smart contract execution. The framework leverages a permissioned blockchain to ensure controlled data access while maintaining data integrity and security. To further improve performance, an optimized smart contract mechanism is introduced using gas-efficient transaction designs and layer-2 scaling solutions. Experimental evaluations demonstrate that the proposed model improves transaction efficiency, reduces computational overhead, and enhances security compared to conventional blockchain-based governance systems. The results highlight the potential of blockchain in establishing a decentralized, efficient, and transparent data governance framework for secure and scalable data exchange.
The increasing complexity of computational problems across many scientific and technological domains often challenges traditional centralized computing resources. As the internet evolves toward Web 3.0, blockchain technology is emerging as a foundational infrastructure for decentralization, transparency, and distributed collaboration. Applications like the metaverse, which demand real-time responsiveness and high computational throughput, further underscore the need for scalable and resilient computing frameworks. In this regard, we propose a novel framework for crowdsourcing computationally intensive tasks using blockchain and smart contracts. Operating atop existing blockchain networks, a Master (or Requester) node defines a computational problem, decomposes it into subtasks, and deploys a smart contract to manage task distribution. Worker nodes perform the computations off-chain using local resources and submit their results via on-chain transactions. The smart contract aggregates these results and finally, the Master validates them and automatically distributes rewards in cryptocurrency. A proof-of-concept simulation using Ganache, Solidity, and off-chain scripts demonstrates the feasibility of this approach, showcasing key features such as task decomposition, off-chain computation, and automated result handling. These findings underscore blockchainâs potential to enable transparent, automated, and scalable coordination of distributed computing tasks.