Akhter Javed, Huma Gul, Ali Husnain, Rahmat Said · 5 authors
Background: In this study, the increased complexity of today supply chains and explain why conventional forecasting and inventory management techniques are inadequate in today's dynamic and uncertain market conditions. As globalization and data increase, AI has become a gamechanger in delivering better demand forecasting and inventory management, in turn driving a better operation and cost savings. Objectives: This study seeks to assess the performance of AI-based demand forecasting models combined with inventory optimization methods on improving the overall performance of the supply chain. Methods: A quantitative, data-driven methodology was employed, and secondary data were used, including historical demand, inventory levels, and other external data that included seasonality and economic indicators. Demand forecasting models: Advanced machine learning and deep learning models such as Long Short-Term Memory (LSTM), Random Forest and Gradient Boosting were used for demand forecasting. The results of the forecasts were fed into an inventory optimization system using reinforcement learning for dynamic decision-making. Standard deviations like Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) were used to measure the model's performance along with cost-performance analysis. Results: The accuracy of the prediction is significantly higher in AI-based models, especially the LSTM model, than the traditional models, which decreases the errors of the prediction and enhances its responsiveness. AI-powered inventory optimization resulted in significant savings on inventory holding and shortage/cost of order, and improved service levels and inventory stockout rates. The use of external data had yet further improved predictive performance. Conclusion: AI-powered demand forecasting and inventory optimization offer a solid solution to improve the efficiency of the supply chain, make intelligent decisions and minimize operational costs. References Ahn, H. I., Song, Y. C., Olivar, S., Mehta, H., & Tewari, N. (2024). GNN-based probabilistic supply and inventory predictions in supply chain networks. arXiv. Albayrak Ünal, Ö., Erkayman, B., & Usanmaz, B. (2023). Applications of artificial intelligence in inventory management: A systematic review of the literature. Archives of Computational Methods in Engineering. Advance online publication. https://doi.org/10.1007/s11831-023-09977-2 Ayub, M. I., Gharami, A. K., Nitu, F. N., Uddin, M. N., Islam, M. I., Nijhum, A. M., … Yezdani, S. (2025). AI-driven demand forecasting for multi-echelon supply chains: Enhancing forecasting accuracy and operational efficiency through machine learning and deep learning techniques. Emerging Frontiers Library for The American Journal of Management and Economics Innovations, 7(7), 74–85. Cannas, V. G., Ciano, M. P., Saltalamacchia, M., & Secchi, R. (2024). Artificial intelligence in supply chain and operations management: A multiple case study research. International Journal of Production Research. Advance online publication. https://doi.org/10.1080/00207543.2024.2330633 Choi, T. M. (2022). Supply chain analytics and AI-driven forecasting. Annals of Operations Research. https://doi.org/10.1007/s10479-022-04652-6 Dolgui, A., Ivanov, D., & Sokolov, B. (2022). Reconfigurable supply chain systems. International Journal of Production Research, 60(2), 413–440. https://doi.org/10.1080/00207543.2021.1897179 Douaioui, K., Oucheikh, R., Benmoussa, O., & Mabrouki, C. (2024). Machine learning and deep learning models for demand forecasting in supply chain management: A critical review. Applied System Innovation, 7(2), 40. https://doi.org/10.3390/asi7020040 Fatima, A., & Salam, M. A. (2026). A data-driven predictive framework for inventory optimization using context-augmented machine learning models. arXiv. Ghodake, S. P., Malkar, V. R., Santosh, K., Jabasheela, L., Abdufattokhov, S., & Gopi, A. (2024). Enhancing supply chain management efficiency: A data-driven approach using predictive analytics and machine learning algorithms. International Journal of Advanced Computer Science and Applications, 15(4). Islam, M. K., Ahmed, H., Al Bashar, M., & Taher, M. A. (2024). Role of artificial intelligence and machine learning in optimizing inventory management across global industrial manufacturing and supply chain: A multi-country review. International Journal of Management Information Systems and Data Science, 1(2), 1–14. Ivanov, D., & Dolgui, A. (2021). A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0. International Journal of Production Research, 59(18), 5633–5645. https://doi.org/10.1080/00207543.2020.1768450 Jin, Z. L., Maasoumy, M., Liu, Y., Zheng, Z., & Ren, Z. (2025). Stochastic optimization of inventory at large-scale supply chains. arXiv. Judijanto, L., Riandari, F., & Marsoit, P. T. (2024). Leveraging AI for optimization in supply chain decision support. Jurnal Teknik Informatika. Kache, F., & Seuring, S. (2022). Challenges and opportunities of digital information at the intersection of big data analytics and supply chain management. International Journal of Operations & Production Management, 42(1), 1–30. https://doi.org/10.1108/IJOPM-02-2021-0129 Kagalwala, H., Radhakrishnan, G. V., Mohammed, I. A., Kothinti, R. R., & Kulkarni, N. (2025). Predictive analytics in supply chain management: The role of AI and machine learning in demand forecasting. Advances in Consumer Research, 2, 142–149. Kamble, S. S., Gunasekaran, A., & Sharma, R. (2023). Modeling blockchain-enabled traceability in supply chains. International Journal of Information Management, 68, 102509. https://doi.org/10.1016/j.ijinfomgt.2022.102509 Kaul, D., & Khurana, R. (2022). AI-driven optimization models for e-commerce supply chain operations: Demand prediction, inventory management, and delivery time reduction with cost efficiency considerations. International Journal of Social Analytics, 7(12), 59–77. https://doi.org/10.4018/IJSA.315876 Liu, R., & Vakharia, V. (2024). Optimizing supply chain management using hybrid AI models. Journal of Organizational and End User Computing, 36(2), 1–18. https://doi.org/10.4018/JOEUC.347356 Min, H. (2022). Artificial intelligence in supply chain management: Theory and applications. International Journal of Logistics Research and Applications, 25(3), 289–303. https://doi.org/10.1080/13675567.2020.1849508 Mitta, N. R. (2023). AI-driven optimization of supply chain networks in manufacturing: Utilizing machine learning for demand forecasting, inventory management, and logistics efficiency. Los Angeles Journal of Intelligent Systems and Pattern Recognition, 3, 404–446. Nweje, U., & Taiwo, M. (2025). Leveraging artificial intelligence for predictive supply chain management: Focus on how AI-driven tools are revolutionizing demand forecasting and inventory optimization. International Journal of Science and Research Archive, 14(1), 230–250. Pasupuleti, V., Thuraka, B., Kodete, C. S., & Malisetty, S. (2024). Enhancing supply chain agility and sustainability through machine learning: Optimization techniques for logistics and inventory management. Logistics, 8(3), 73. https://doi.org/10.3390/logistics8030073 Patil, D. (2024). Artificial intelligence-driven supply chain optimization: Enhancing demand forecasting and cost reduction (SSRN Working Paper No. 5057408). SSRN. https://doi.org/10.2139/ssrn.5057408 Queiroz, M. M., & Telles, R. (2023). Big data analytics in supply chain management: A review. Transportation Research Part E: Logistics and Transportation Review, 170, 102987. https://doi.org/10.1016/j.tre.2022.102987 Sajja, G. S., Addula, S. R., Meesala, M. K., & Ravipati, P. (2025). Optimizing inventory management through AI-driven demand forecasting for improved supply chain responsiveness and accuracy. In AIP Conference Proceedings (Vol. 3306, No. 1, Article 050003). AIP Publishing. Shahnawaz, M., & Safder, A. (2025). Stochastic learning-optimization model for resilient supply chains. arXiv. Shen, L., & Zang, Z. (2024). Enterprise supply chain network optimization algorithm based on blockchain-distributed technology. Information Discovery and Delivery. Advance online publication. Sodhi, M. S., & Tang, C. S. (2021). Supply chain management for extreme conditions. MIT Sloan Management Review, 62(2), 1–8. Tang, W. (2024). Improvement of inventory management and demand forecasting by big data analytics in supply chain. Applied Mathematics and Nonlinear Sciences, 9(1). Verma, P. (2024). Transforming supply chains through AI: Demand forecasting, inventory management, and dynamic optimization. Integrated Journal of Science and Technology, 1(3). Waller, M. A., & Fawcett, S. E. (2021). Data science, predictive analytics, and big data: A revolution that will transform supply chain design and management. Journal of Business Logistics, 34(2), 77–84. https://doi.org/10.1111/jbl.12010
Yurii Koroliuk, Olha Vdovichena, Анатолій Вдовічен
The purpose of the paper is to analyze the relationships between digital skills of participants in the educational process, their readiness for digital transformation, and the barriers to technology integration in economic education in Ukraine, with a particular focus on the implementation of enterprise resource planning (ERP) systems. The study also aims to examine how awareness of digital technologies and perceptions of their benefits influence educational outcomes and professional preparedness. Methodology. The research employs structural equation modeling (SEM) to investigate the relationships between key constructs, including technology awareness, perceived usefulness, educational readiness, digital skills, barriers to implementation, readiness for change, and students’ preparedness for professional activity. The empirical analysis is based on a dataset of 256 respondents (ID 1–256) collected through an online survey conducted between May and August 2025 among individuals involved in economic education in Ukraine. The questionnaire was designed to assess respondents’ awareness, perceptions, and readiness to integrate modern digital technologies (AI, blockchain, ERP, RPA, and digital educational platforms) into the training of economic specialists, as well as the availability of technological resources at the university level. The survey included 33 Likert-scale items covering demographic characteristics, professional experience, digital competencies, attitudes toward emerging technologies, access to infrastructure, alignment of curricula with labour market needs, and barriers to technology integration. The instrument provided both quantitative and qualitative insights into participants’ experiences. Results. The findings demonstrate that digital competencies and perceived usefulness of technologies significantly influence readiness for digital transformation and learning effectiveness. Organizational support and systematic user training are identified as critical success factors for ERP implementation. Furthermore, technology awareness and readiness for change mediate the relationship between digital skills and students’ preparedness. The integration of ERP systems enhances practical competencies in business process management and strengthens analytical thinking. Practical implications. The results provide a foundation for improving economic education through the systematic integration of ERP systems into curricula. The study highlights the importance of investing in digital skills development, academic staff training, and institutional support mechanisms to ensure effective technology adoption and alignment with labour market demands. Value / originality. The paper contributes to the literature by offering an integrated empirical SEM-based model linking digital competencies, ERP adoption, and educational outcomes in the context of Ukraine, providing a scientifically grounded approach to modernising economic education and enhancing graduates’ competitiveness in the global labour market.
John Alexander Taborda, Cesar Enrique Polo Castro, Miguel Martínez
Just energy transitions in the Global South unfold under conditions of institutional fragmentation, fiscal constraints, and high socio-ecological turbulence, making governance capacity a critical bottleneck for effective decarbonization and climate justice. This study proposes the Cybernetic Environmental Hub (CEH) framework, which extends the Viable System Model (VSM) to sustainability governance by integrating AIoT-enabled environmental monitoring, Early Warning Systems, decentralized data governance, and justice-centered institutional design. Methodologically, the article is primarily a conceptual framework paper accompanied by an illustrative single-site qualitative case study designed to probe the plausibility and diagnostic utility of the proposed architecture rather than to generate statistical generalization. The research combines theoretical development with participatory territorial diagnostics in the Caribbean Mining Corridor, where socio-ecological challenges were collected through participatory innovation workshops, thematically coded, and mapped onto the five VSM subsystems to identify systemic “variety gaps.” The analysis indicates that fragmented operational initiatives coexist with weak meta-systemic coordination, limiting adaptive capacity in energy transition processes. The CEH architecture is proposed to address these deficiencies by embedding AIoT sensing, federated learning, blockchain-based coordination, and Early Warning Systems within recursive governance structures and is grounded in a real cyber-physical deployment of around 90 monitoring stations across Albania, La Jagua de Ibirico and Algarrobo. The study also introduces a Territorial Governance Maturity Model (H1–H3) to diagnose systemic learning capacities and transition readiness across technological, institutional, data governance, and justice dimensions. The findings suggest that cybernetic environmental hubs may function as socio-technical infrastructures supporting coordinated, adaptive, and justice-centered energy transitions in the Global South, while comparative empirical evidence remains an agenda for future work.
Smart technology and innovation have become increasingly important in transforming seaweed aquaculture into a more sustainable, efficient, and data-driven industry. This study conducted a global bibliometric and scientometric review to examine research trends, technological developments, collaboration networks, and emerging scientific directions related to smart seaweed aquaculture. Bibliographic data were retrieved from the Scopus database using a structured search string covering technologies such as Internet of Things (IoT), artificial intelligence (AI), automation, remote sensing, blockchain, machine learning, and digital monitoring systems. The collected datasets were analyzed using RStudio through the Bibliometrix package and visualized using VOSviewer for network mapping and thematic analysis. Findings revealed a continuous increase in scientific publications, particularly after 2015, indicating growing global interest in precision aquaculture and sustainable marine resource management. The results identified China, India, Indonesia, and the United States as leading contributors in terms of research productivity and international collaboration. Keyword co-occurrence and thematic evolution analyses demonstrated the increasing integration of AI, IoT, automation, environmental monitoring, and sustainability-focused technologies within seaweed farming systems. Scientometric clustering further highlighted the interdisciplinary nature of the field, combining marine science, environmental studies, biotechnology, and digital innovation. The study also identified research gaps associated with technological accessibility, collaboration disparities, and sustainable implementation in developing regions. Overall, the findings confirm that smart technologies are playing a transformative role in advancing seaweed aquaculture toward more intelligent, climate-resilient, and sustainable production systems globally.
Lucas Monteiro de Oliveira, Olívia Brandão Melo Campelo
O presente artigo tem por objetivo analisar os limites jurídicos da autoexecução nos smart contracts, especialmente quando tais instrumentos, estruturados em tecnologia blockchain, produzem efeitos obrigacionais de difícil ou impossível reversão técnica. Parte-se do problema segundo o qual a programação contratual pode executar automaticamente prestações, transferências patrimoniais ou efeitos negociais sem intervenção humana posterior, ao mesmo tempo em que o ordenamento jurídico brasileiro preserva institutos como arrependimento, anulação, resolução, restituição, responsabilidade civil e vedação ao enriquecimento sem causa. A pesquisa, de natureza bibliográfica e qualitativa, examina a compatibilidade entre a lógica algorítmica dos contratos inteligentes e os fundamentos clássicos do Direito Civil e do Direito do Consumidor. Sustenta-se que a irreversibilidade técnica não pode ser convertida em irreversibilidade jurídica, pois a eficácia automática do código não afasta a incidência das normas relativas à validade do negócio jurídico, à boa-fé objetiva, à função social do contrato, à proteção do consumidor e à reparação de danos. Conclui-se que os smart contracts podem ser admitidos no ordenamento brasileiro, desde que sua arquitetura tecnológica permaneça subordinada à normatividade jurídica, mediante mecanismos de reversão, compensação, suspensão, auditoria, governança e responsabilização.
Huriye Gonca Di̇ler, Münevvere YILDIZ, N. Serap VURUR, Letife Özdemir
In today's world, sustainability strategies play a critical role in the transformation of global economies and industries. Green Economic Growth (GEG), which prioritizes environmental factors, is gaining increasing importance. Financial and green innovation are identified as the main driving forces behind GEG. However, research on the effects of these factors in OECD countries remains limited, and existing findings often show inconsistencies regarding the direction and magnitude of these effects. This study aims to comprehensively examine the impact of financial and green innovation on GEG in OECD countries. Using annual data from 15 OECD countries for the period 1996–2021, panel data techniques are applied. Cointegration tests are conducted to determine the presence of long-run relationships among the variables. Subsequently, long-run coefficients are estimated using the panel quantile regression method. The robustness of the findings is tested through OLS and fixed effects models. Additionally, causality tests are employed to explore the directional relationships between the variables. The results indicate that green innovation has a positive long-run effect on GEG, whereas financial innovation exerts a negative impact. Causality tests reveal bidirectional relationships among all variables. Policy recommendations include the promotion of green bonds and sustainable finance instruments, support for green investments through regulations that take environmental risks into account, and the expansion of access to green projects via technologies such as blockchain-based carbon markets. This research provides valuable insights for policymakers in designing more effective strategies to foster sustainable economic growth.
The rapid advancement of digital technologies has significantly transformed auditing practices, leading to the emergence of audit analytics as an important research domain that integrates accounting, auditing, and data science. This study aims to examine the evolution, intellectual structure, influential contributions, and emerging research trends in audit analytics through a bibliometric analysis approach. Data were collected from the Scopus database using relevant keywords related to audit analytics and analyzed using VOSviewer to perform citation analysis, keyword co-occurrence analysis, density visualization, and collaboration network analysis. The findings indicate that audit analytics research has experienced substantial development, particularly with the increasing adoption of big data analytics, artificial intelligence, machine learning, predictive analytics, blockchain, and automation technologies. Citation analysis identifies key contributions focusing on the role of big data and artificial intelligence in improving audit quality, audit judgment, fraud detection, and decision-making processes. The keyword analysis reveals that recent research trends have shifted from traditional analytical methods toward intelligent and automated audit systems that support continuous auditing and risk-based decision-making. Furthermore, collaboration analysis demonstrates the global nature of audit analytics research, with the United States emerging as the most influential contributor and strong research connections among countries and institutions. This study contributes to the literature by providing a comprehensive understanding of the development trajectory of audit analytics and identifying future research opportunities related to generative artificial intelligence, explainable AI, cybersecurity, and digital audit transformation.
AI-powered predictive systems for decision support are revolutionizing the way that smart enterprises and industrial organizations are analysing data, predicting future conditions, and making operational and strategic decisions. The systems include machine learning, deep learning, predictive analytics, prescriptive analytics, real-time monitoring, and intelligent recommendation systems to enhance decision-making accuracy, efficiency, and responsiveness. They are used in business forecasting, customer and financial analytics, supply-chain and inventory management, predictive maintenance, production optimization, quality control, energy management, workplace safety and asset monitoring. The addition of new technologies like the Internet of Things, Industrial Internet of Things, digital twins, cloud and edge computing, robotics, blockchain and next generation networks further improve system connectivity, scalability and real-time performance. The successful implementation of these steps needs a structured framework for problem identification, data collection, preprocessing, feature engineering, model selection, training, validation, system integration, deployment, and continual monitoring. Despite these progressions, data quality, interoperability, scalability, algorithmic bias, explainability, privacy, cybersecurity, organizational readiness, and regulatory compliance are all important challenges that still need to be addressed. There is still a need for human oversight, especially when dealing with safety-critical and high-impact decisions. It includes the technological foundations, system architecture, implementation processes, enterprise and industrial applications, performance evaluation, governance requirements, and future directions of AI-supported predictive decision support systems. It concludes that the systems that are trustworthy, secure, transparent, sustainable and intelligent are enterprise and industrial operations.
Recent advancements in technology, along with the availability of large volumes of healthcare data, offer an opportunity to adopt innovative technologies such as artificial intelligence (AI), machine learning, and big data in healthcare for better health service delivery. The use of innovative technologies such as artificial intelligence, machine learning, and big data enables efficient decision-making, disease detection, and personalized treatment. This paper reviews machine learning and big data in personalized medicine, presenting details about various tools that can be utilized within the context of healthcare, such as predictive modeling, data mining, and healthcare analytics. Furthermore, emerging technologies in personalized medicine have been discussed, including federated learning, blockchain technology, and real-world data. In addition, the paper also discusses existing developments in intelligent healthcare systems, such as patient monitoring, adaptive learning models, and using healthcare analytics for decision-making processes. Additionally, the paper highlights existing key challenges related to applying machine learning and big data for personalized healthcare, including data heterogeneity, lack of high-quality training data, algorithmic biases, difficulty in model interpretation, issues with security and data privacy, and technical barriers. Finally, the paper highlights the research gaps, examines the existing ways of addressing the problem, and provides recommendations regarding the future of personalized medicine using AI technology.
Open access
2 source records
Artificial Intelligence in Healthcare
Machine Learning in Healthcare
Artificial Intelligence in Healthcare and Education
Shahreza Shauqi Ismail, Nura Abubakar Allumi, Yousif Munadhil Ibrahim
Purpose: The aim of this study is to analyze the evolution of research trends in green supply chain management (GSCM) in agriculture, constructing an intellectual framework to improve the efficiency and environmental sustainability of the supply chain with smart agricultural technologies. Design/methodology/approach: The review used the bibliometric approach with two science mapping approaches (i.e., co-citation and co-word analysis) were perfomed to analyze 381 articles published in the Web of Science (WoS) database to investigate past and future research direction in GSCM using the VOSviewer software. Findings: Previous studies mainly focused on CE integration, sustainable agri-food supply chain design, and blockchain in supply chain management, whereas future research is expected to emphasize green logistics, low-carbon supply chains, smart circular systems, and digital innovation in green agri-SCM. Limitations and Research implications: This study is limited to the WoS database and two bibliometric techniques. Future research should validate the identified themes through additional bibliometric and empirical studies. Practical Implications: This study provides practical insights for managers, policymakers, and researchers to support the development of sustainable agricultural supply chains through circular economy, green logistics, and digital technologies. Originality/value: This study provides a comprehensive knowledge map of GSCM in agriculture by identifying past research themes and future research directions through bibliometric analysis
Since announcing the implementation of a single electronic health record for all South Africans, the government has not yet informed healthcare facilities of how this would be accomplished. The siloed South African healthcare system would have to be redesigned to accommodate a single electronic health record. A systematic literature review conducted across three databases returned 9 790 results. By applying ten filters, 22 documents were eventually retrieved for analysis. The analysis showed that existing research focuses on healthcare architectures from a theoretical perspective. Therefore, the literature review revealed a practically based research deficiency and a lack of theoretical studies merged with practical cases. Seeking to enhance the understanding of designing a single electronic health record, the documents were analysed using a qualitative inductive content analysis technique, revealing that a single electronic health record cannot be formulated using a fully centralised architecture as this is not practical. A fully decentralised architecture, such as blockchain, is equally infeasible because this requires significant changes to the existing systems and infrastructure and would require re-skilling system builders. Since the South African healthcare architecture is already decentralised, hybrid architecture incorporating edge computing with clusters of systems and information that connect using middleware should be considered.
The rapid proliferation of IoT devices in metropolitan environments poses critical challenges for heterogeneous device management under minimal centralized control. This paper presents DCRO, a Distributed Coalition-based Resource Orchestration framework enabling IoT devices to self-organize into dynamic coalitions for cooperative resource management. Unlike traditional hierarchical approaches that suffer from scalability bottlenecks, DCRO integrates three core components: a Self-Organizing Device Clustering Algorithm (SODCA) that adapts to topology changes without global coordination; a Game-Theoretic Coalition Formation Mechanism (GT-CFM) that drives fair resource allocation through Shapley value-based negotiation; and a Lightweight Hierarchical Consensus Protocol (LHCP) coupled with a Merkle-DAG security architecture that ensures tamper-resistant coordination without blockchain overhead. Experiments across three metropolitan testbeds demonstrate 26.2% latency reduction and 31.4% energy savings over centralized baselines, only 11.3% throughput degradation under continuous fault injection, and stable coalition convergence at 5,000 devices within 15 iterations.
The rapid growth of Real World Asset (RWA) tokenization faces a critical vulnerability: the "Physical Oracle Problem." While blockchain ensures digital immutability, it remains blind to the physical state of the underlying asset (e.g., structural degradation in real estate or hidden damage in naval vessels). This document introduces the Prop Trust Verified Standard (PTVS), a comprehensive forensic methodology designed to bridge this gap. Developed by Aurema Group, PTVS establishes a rigorous protocol for physical asset auditing, combining certified judicial expertise (Perito Judicial) with cryptographic anchoring. The methodology ensures that physical inspections, material verifications, and compliance checks are immutably recorded and linked to smart contracts (e.g., ERC-3643), providing institutional-grade trust for Family Offices, tokenization platforms, and regulatory bodies under frameworks like eIDAS (EU 910/2014). This report outlines the core principles, verification workflows, and case study applications of PTVS in real estate and maritime sectors. Español: El rápido crecimiento de la tokenización de Activos del Mundo Real (RWA) enfrenta una vulnerabilidad crítica: el "Problema del Oráculo Físico". Mientras que la blockchain garantiza la inmutabilidad digital, permanece ciega al estado físico del activo subyacente (ej. degradación estructural en inmuebles o daños ocultos en embarcaciones). Este documento presenta el Estándar Prop Trust Verified (PTVS), una metodología forense integral diseñada para resolver esta brecha. Desarrollado por Aurema Group, PTVS establece un protocolo riguroso de auditoría física de activos, combinando la pericia judicial certificada con el anclaje criptográfico. La metodología garantiza que las inspecciones físicas, verificaciones de materiales y controles de cumplimiento se registren de forma inmutable y se vinculen a contratos inteligentes (ej. ERC-3643), proporcionando confianza de grado institucional para Family Offices, plataformas de tokenización y organismos reguladores bajo marcos como eIDAS (UE 910/2014). Este informe detalla los principios fundamentales, flujos de trabajo de verificación y aplicaciones prácticas de PTVS en los sectores inmobiliario y naval.
Financial Technology (FinTech) is reshaping the worldwide financial industry by introducing innovations like digital transactions, artificial intelligence (AI), blockchain, mobile banking, data analysis, and integrated finance. These advancements are improving the effectiveness, openness, and availability of financial services, fostering financial inclusion, and decreasing reliance on traditional banking systems. This research investigates how FinTech plays a crucial role in stimulating innovation, inclusivity, and digital change in the financial landscape. It also delves into the opportunities arising from digital financial services and the obstacles related to cybersecurity, data protection, adhering to regulations, and ethical considerations. The research is grounded in an examination of recent literature, industry studies, and policy papers to grasp present trends and future advancements in FinTech. The results indicate that FinTech has emerged as a vital facilitator of sustainable financial expansion and economic progress. The research offers valuable perspectives for scholars, decision-makers, financial organizations, and industry professionals to comprehend the direction of digital finance.
The construction industry is undergoing a significant transformation with the adoption of decentralized models, which leverage distributed decision-making, collaborative networks, and advanced technologies such as blockchain, digital twins, and artificial intelligence (AI). These innovations promise enhanced transparency, efficiency, and stakeholder engagement in high- rise building projects. However, decentralization introduces unique risks—spanning technical, social, economic, legal, and environmental domains—that challenge traditional risk management frameworks. This research systematically identifies and categorizes these risks, emphasizing their implications for decentralized high-rise construction. Key technical risks include design clashes and quality inconsistencies due to fragmented workflows, while social risks encompass labor disputes and community opposition. Economic risks arise from budget fragmentation and supply chain volatility, legal risks stem from contractual ambiguities and regulatory non- compliance, and environmental risks involve waste mismanagement and increased carbon footprints. To address these challenges, the study proposes a comprehensive risk management framework integrating emerging technologies. For instance, Building Information Modeling (BIM) and digital twins enable real-time clash detection and quality assurance, blockchain ensures transparent and automated contract execution, and AI-driven analytics predict safety hazards and cost overruns. The framework is validated through a case study of Skyline Towers in Dubai, where decentralized strategies reduced design errors by 45% and payment delays by 80%. The research employs a mixed-methods approach, combining a systematic literature review with empirical analysis of real-world projects. Findings highlight the critical role of stakeholder alignment, hybrid governance models, and sustainable practices in mitigating risks. The study concludes with actionable recommendations for policymakers and industry practitioners, advocating for standardized digital protocols, adaptive risk governance, and proactive environmental controls. By bridging the gap between technological innovation and risk management, this research contributes a forward-looking framework to enhance resilience and efficiency in decentralized high-rise construction, ensuring sustainable urban development in an increasingly complex industry landscape.
The increasing adoption of cryptocurrencies has created new opportunities for digital financial innovation while simultaneously exposing individuals and institutions to sophisticated forms of financial fraud. Conventional rule-based fraud detection systems have become inadequate in addressing the dynamic and complex nature of blockchain-enabled financial crimes, leading to growing interest in the application of artificial intelligence (AI). This study systematically reviews the literature on artificial intelligence techniques for cryptocurrency fraud detection, with particular emphasis on their relevance to the Kenyan digital financial ecosystem. The review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 framework. Peer-reviewed studies published between 2020 and 2026 were identified from Scopus, Web of Science, IEEE Xplore, ScienceDirect, SpringerLink, and Google Scholar. Following the screening and eligibility assessment, 19 studies were included in the final qualitative synthesis. The findings reveal that machine learning, deep learning, hybrid AI models, and blockchain analytics significantly enhance cryptocurrency fraud detection by improving anomaly detection, transaction monitoring, predictive accuracy, and anti-money laundering compliance. Compared with traditional rule-based approaches, AI techniques provide faster, more adaptive, and scalable solutions capable of detecting evolving fraud patterns in decentralized financial systems. However, the review also identifies challenges relating to limited high-quality datasets, algorithmic bias, lack of explainability, cybersecurity risks, privacy concerns, and inadequate regulatory frameworks, particularly within developing economies. Furthermore, the review highlights a scarcity of empirical research focusing on cryptocurrency fraud detection in Kenya and identifies opportunities for developing localized datasets, explainable AI models, and context-specific regulatory frameworks. The study concludes that artificial intelligence has considerable potential to strengthen cryptocurrency fraud detection and financial security in Kenya, provided that technological, ethical, and regulatory challenges are adequately addressed. The findings provide valuable insights for researchers, financial institutions, technology developers, and policymakers seeking to enhance AI-driven fraud prevention within the country's evolving digital financial ecosystem.
E-Healthcare Systems (EHS) are transforming medical service delivery by enabling real-time data sharing, remote diagnostics, and integrated care via IoT and cloud infrastructures.However, the increasing volume of sensitive medical data being transmitted over distributed systems creates serious privacy and security concerns.This article reviews several papers on the EHS Data Privacy Framework, addressing critical issues such as illegal data access, identity exposure, and data integrity breaches.This review examines the existing data privacy frameworks used in EHS, focusing on four domains: traditional EHS, cloud-based EHS, IoT-based EHS and blockchain-based EHS with an emphasis on author, year, objective, and limitation.This framework provides a scalable and interoperable approach to protecting privacy for future healthcare systems.
As the world is witnessing the emergence of Metaverse, which is an immersive decentralised digital environment, there has been a sudden rise in unprecedented cross border economic and social activities which has facilitated transactions through the medium of virtual goods, NFTs, digital avatars and user generated content. This shift deviates from the conventional definition of Intellectual property and hence presents a notable threat in the territorial and national legal systems that is built on the roots of these grundnorm leading to substantial jurisdictional and enforcement gaps. This paper adopts a systemic literature review method by blending academic research, legal precedents, and policy documents to put forward how the core concepts of metaverse like user anonymity, decentralized blockchain structures, instantaneous duplication of digital assets, and borderless virtual economies unsettle the framework of traditional frameworks of intellectual property. With the help of comparative analysis of landmark case like Hermès International v. Rothschild, Nike v. Stock X, and Juventus F.C. v. Blockeras, the study puts forward the different challenges that the modern-day courts are facing in tackling with emerging virtual disputes while implementing the copyright and trademark doctrines. The paper further delves into the efficiency of international agreements like TRIPS and the Berne Convention in underlining the dispersion in global regulatory services. Alongside problem identification, this paper also proposes a hybrid framework that would bring together blockchain verification, cryptographic rights management, AI-based monitoring, legal harmonization with the help of model treaties and statutory reforms accompanying decentralised arbitration mechanisms. The study concludes that effective, equitable, and sustainable IP enforcement in the metaverse requires coordinated international cooperation, collaborative multi-stakeholder governance models balancing robust IP pr...
Open access
2 source records
Law, AI, and Intellectual Property
Dispute Resolution and Class Actions
Legal, Health, Environmental and COVID-19 Challenges
Cryptocurrency has emerged as one of the most significant developments to accompany the digitization of global finance, and its footprint in India has expanded rapidly despite an unsettled regulatory environment. This paper examines how Indian investors perceive the opportunities and risks associated with cryptocurrency and blockchain technology, and evaluates whether their level of awareness shapes that perception. A structured questionnaire survey was administered to 158 respondents drawn from different age groups, educational backgrounds, occupations, and income levels in Karnataka, and the resulting data were analyzed using percentage analysis, frequency distribution, and the Chi-square test of independence. The findings indicate that a large majority of respondents, particularly those aged 21-30, view cryptocurrency and blockchain as tools capable of improving transparency, financial inclusion, and entrepreneurship, while simultaneously expressing concern over price volatility, cybersecurity threats, and unclear taxation rules. The Chi-square test confirmed a statistically significant association between investor awareness and perception of cryptocurrency (calculated value 19.41 against a critical value of 9.488 at 4 degrees of freedom and the 5 percent level of significance), leading to rejection of the null hypothesis. The study concludes that a clear, balanced regulatory framework combined with investor-education initiatives would allow India to capture the innovation potential of digital assets while containing the risks associated with their adoption.
Alternative finance platforms, including crowdfunding, peer-to-peer lending, equity-based platforms, and token-based fundraising mechanisms, have become important channels for financing entrepreneurial, social, and investment-oriented initiatives. Yet their reliance on digital intermediation, dispersed participation, and information asymmetry creates opportunities for fraud, undermining trust, investor protection, and platform sustainability. This study provides a systematic review of fraud detection and prevention in alternative finance, with crowdfunding emerging as the most extensively represented empirical domain. Methodologically, the paper combines a PRISMA-guided systematic literature review with a hybrid topic-modeling strategy that integrates neural topic modeling and probabilistic refinement, thereby supporting both transparent corpus selection and data-driven thematic synthesis. The findings show that Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), and blockchain-based mechanisms are recurrently discussed as promising tools for detecting, preventing, or mitigating fraud. AI and ML approaches are mainly used to identify anomalies, suspicious textual patterns, behavioral signals, and transaction irregularities, while blockchain-based approaches are associated with transparency, traceability, smart contracts, and conditional fund release. The review also shows that fraud differs across alternative finance models, ranging from campaign misrepresentation and intentional and premeditated non-delivery in crowdfunding to borrower or platform misreporting in lending-based models and misleading disclosures or white-paper manipulation in ICO/STO contexts. A central challenge across the literature is the scarcity of labeled fraud data, which limits the use and benchmarking of supervised ML models. Overall, this study contributes by linking a reproducible hybrid SLR methodology to a structured synthesis of fraud types, platform-specific vulnerabilities, and AI-, ML-, and blockchain-based detection strategies in alternative finance.
Ravindra Janardan Lawande, Sudhir Bapurao Lande, Manisha Lande
Internet of Vehicle (IoV) uses heterogeneous access technologies to link automobiles and their surroundings. Effective methods are essential for safeguarding data confidentiality and privacy during communication among the roadside unit (RSU), the control room, and vehicles. Many vehicle-to-infrastructure authentication-based approaches have been developed to secure the IoV environment. However, efficiency and security are challenged by instability, decentralization, and transaction-tracking features. To resolve this, a secure, lightweight, and scalable communication protocol was developed for a 5G-enabled SDN-IoV environment. Efficient block verification is achieved through the Joint-Graph Delegated Practical Byzantine Fault Tolerance (JtGr-DPBFT) mechanism, in which validators create subgraphs to reduce communication overhead. JtGr-DPBFT is combined with an Improved Gossip Algorithm (IGA) to minimize message redundancy and optimize bandwidth utilization. Moreover, a lightweight hierarchical authentication mechanism, assisted by a Merkle Tree with Boneh-Lynn-Shacham (HAMT-BLS) signatures, enables compact block verification and minimizes computational and communication costs. The proposed model achieves tamper-proof, efficient, and scalable block verification by incorporating hierarchical authentication with consensus optimization. This approach is simulated in the NS3 tool, and performance is evaluated in terms of propagation delay, transaction confirmation latency, throughput, communication cost, and network delay. Thus, secure and tamper-proof communication is developed to ensure integrity, trust, and dependability in the SDN-enabled IoV environment.