muddasar naeem, Zaib Ullah, Fadi M. Al Turjman, Abdullah waqas · 6 authors
Abstract Blockchain technology, characterized by its distributed ledger system, has revolutionized currency and the global digital economy. Its potential applications extend beyond finance, captivating academia and industry alike. This article delves into the latest advancements in blockchain technology, exploring its innovative state-of-the-art applications in various futuristic communication domains such as the Internet of Things (IoT), cloud computing, security, privacy, artificial intelligence (AI), wireless network optimization, intelligent grids, smart transportation, and more. The article also provides an overview of blockchain's operational principles, including smart contracts and consensus algorithms, highlighting their contributions to decentralization, security, and transparency. Moreover, through statistical analysis of research articles, we identify current challenges and outline future research directions in the field.
Permissionless Proof-of-Stake (PoS) blockchain networks must demonstrate security and dependability as blockchain technology evolves. This research analyzes the ongoing need for a way to identify and eradicate rogue nodes inside such networks, also to advocate for the real-time rogue node’s identification in PoS blockchain networks by the application of neural network methodologies, particularly random neural networks. Experimental results indicate the ability of the developed model in differentiating legitimate blockchain nodes from malicious ones. The dataset in this paper is divided into two groups- malicious (Permissionless Proof-Of-Stake Blockchains) and non-malicious; this dataset is crucial for anyone interested in blockchain. The dataset includes details of the creation and validation of the PoS blocks. The paper aims to detect malicious nodes, analyze node behavior, and enhance security on PoS permissionless blockchains through data visualization. This paper shows the performance and process of the random neural network to refine and learn and then recognize the permissionless blockchain (malicious nodes) from the dataset of Proof-of-Stake Blockchain. We selected 100 records from the original dataset to examine them with our proposal. After analyzing the results, we found clearly how the proposal algorithm works properly with the proposed dataset to achieve fine accuracy and efficiency in the work to distinguish benign nodes from malicious (permission-less blockchain) nodes. This is the result of refining and learning the dataset using the random neural network for 340 instances, coming from the neural learning of 100 instances and 21 variables: [15 features: BlockHeight, UnixTimestamp, TxnFee (ETH), Block Generation Rate, TxnFee (Binary), Status (Tags), Stake Reward, Txnsize, Coin Days, Coin Age, Coin Stake, Stake Distribution Rate, Block Density, Block Score, Coin Day Weight) + 5 meta (node label, neural network, neural network0, neural network1, fold, selected) + no missing value), by two attributes: (node label, block score), two classes(0 non-malicious nodes,1 malicious nodes).
José-María Oliet-Villalba, Jose-Amelio Medina-Merodio, Mikel Ferrer-Oliva, José-Javier Martínez-Herráiz
The rapid growth of cryptocurrencies and non-fungible tokens (NFTs) has expanded technological opportunities, but it has also increased the exposure surface to cyber threats, creating a need for a more precise understanding of the field’s scientific evolution. This study aims to systematically analyse academic output related to cybersecurity and cyber threats within cryptocurrency and NFT ecosystems, identifying central themes, the most influential authors, and emerging trends. A bibliometric methodology was employed, based on the PRISMA 2020 protocol and scientific mapping tools such as SciMAT (v1.1.06) and VOSviewer (v1.6.20), using a corpus of 337 articles published between 2014 and 2025. The findings indicate sustained growth in the literature, a marked geographical and editorial concentration, and the presence of motor themes such as blockchain, cybersecurity, emerging technologies and illegal mining, alongside emerging areas such as intrusion detection. The results also reveal a progressive integration of artificial intelligence techniques in the detection and prevention of attacks. In conclusion, this study provides a comprehensive overview of the state of the art, identifies critical gaps, and underscores the need for interdisciplinary approaches to strengthen security in decentralised environments.
Manaf Ahmed, Mohammed Adnan, Ali Matar, Faez Hlail Srayyih · 7 authors
Predicting cryptocurrency price is challenging owing to high volatility, less historical data, and the impact of external parameters like news, public sentiment, and regulatory announcements. This challenge is tackled in this research by employing models of deep learning like Recurrent Neural Network (RNN), Bidirectional Long Short-Term Memory (BiLSTM) and Gated Recurrent Unit (GRU)—to predict Bitcoin's OHLC prices daily. Based on historical time-series data of Coin Codex, the research uses an autoencoder-based feature extraction method with five-day sliding window method for sequence generation. Hyperband optimization is used to tune hyperparameter of each model. The result shows that BiLSTM performs better than all the other models with minimum Mean Squared Error (MSE = 0.001183), Mean Absolute Error (MAE = 0.026090), and maximum R² score (0.980596) after optimization. The results emphasize the significance of deep learning in capturing nonlinear dynamics in time series of financial applications and bear testimony to the effectiveness of hyperparameter tuning in enhancing model accuracy. The study enhances the development of prediction tools for digital asset markets and enables more informed investment decisions.
K. Ilangovan, Dhilipan C, John Yesudas Valluri, S. Chitradevi
Federated learning based on blockchain-powered smart contracts transforms supply chain finance, with decentralized risk analysis, data privacy, and automatic transaction approval. The presented framework offers the operations of five strategic steps including data preprocessing, federated model training, risk forecasting, smart contract deployment, and system integration. In Federated Averaging (FedAvg), supply chain actors train models with their local information not being disclosed globally and use it to tweak global models, which in turn are trained by them. Blockchain provides verifiability which is not tamperable resulting into fraud and financial transparency. The classification of risks based on the patterns of the transactions and the operational parameters applies safe decision making in the decentralized networks. The platform enhances compliance, efficiency, and trust and provides an affordable system of autonomous financial validation at scale. The metrics like Transaction Throughput, Latency, Auditability Score, Trust Score demonstrate the performance of the framework in the real-life environment of the supply chain finance.
This chapter will focus on the basic concepts of blockchain: the decentralized nature of the network, cryptographic security, and an immutable ledger. This section shall discuss, from the literature review, how blockchain can disruptively promote transparency, efficiency, and security in business processes. Particular attention will be given to main areas of blockchain applications: financial, supply chain, healthcare, and verification of digital identity. In finance, blockchain is pointed out as allowing secure, low-cost transactions and decentralized finance (DeFi) platforms, while in supply chain management, it increases traceability and accountability. Later, this chapter looks at combining blockchain and other new technologies, like the internet of things (IoT) and artificial intelligence (AI), to make business processes even better. It also closely examines problems in adopting blockchain, including unclear regulations, scalability issues, and high energy use in some blockchain networks. Ethical concerns with blockchain use – like its potential to allow anonymous and untraceable actions – are also closely reviewed.
India holds a crucial place in the worldwide leadership of sustainable development since it is the largest democracy in the world and has one of the nations with the greatest economic growth. With innovation, inclusivity, and sustainability at its core, Viksit Bharat @2047 symbolizes India's ambition to become a fully developed country by the century of its independence. Emerging technologies are increasing productivity, boosting global competitiveness, and spurring innovation in various industries. By providing tailored financial assistance & investment suggestions, artificial intelligence-powered chatbots & robo-advisors are democratizing the provision of financial planning services. Decentralized finance (DeFi) systems and other blockchain-based solutions are simplifying trade finance procedures, lowering operating costs, and facilitating safe and transparent cross-border transactions. This chapter examines how innovation and technology are essential to achieving this lofty goal. It provides a thorough examination of India's contemporary digital infrastructure, the country's ascent in international innovation rankings, & the use of cutting-edge technologies including biotechnology, renewable energy, artificial intelligence, and space research. The story highlights government programs such as Start-up India, Digital India, and the National AI & Green Hydrogen Missions. Furthermore, the story underscores the importance of inclusive growth, which encompasses youth empowerment, women-led innovation, and rural digitization. Alongside strategic advice, issues like cybersecurity concerns, low investment in research and growth, and the digital divide are also discussed. India is positioned to emerge as a worldwide leader in technology, not simply a consumer, by cultivating a strong innovation ecosystem and utilizing partnerships between university, industry, and the private sector. This chapter provides a comprehensive plan for a tech-powered, inclusive, and sustainable Viksit Bharat before 2047. Higher education is one of the areas that must use developing technology, especially artificial intelligence (AI), to achieve Viksit Bharat 2047 (the Developed India 2047). Outside of higher education, artificial intelligence influences technology and economic progress. Young minds will realize this transformative vision as soon as they actively interact with AI. AI literacy empowers students in higher education to investigate, produce, and innovate. Students may do research, find solutions to real-world issues, and alter the course of history as they learn AI.
Open access
Innovation and Socioeconomic Development
Innovations and Analysis in Business and Education
Blockchain technology has emerged as a pivotal and transformative force, establishing transparent, secure, and decentralized frameworks for transaction management. Its core strengths include immutability, data decentralization, and consensus validation, alongside the automation provided by self-executing smart contracts. This review examines its foundational technologies, diverse applications, and associated challenges. Blockchain demonstrates profound potential across sectors like finance (e.g., Anti-Money Laundering and fraud reduction), education (credential verification), healthcare (secure record management), and the Metaverse (verifiable digital asset ownership via non-fungible tokens). However, adoption is significantly hindered by critical issues, including scalability bottlenecks, the energy inefficiency of protocols like Proof of Work, and security risks stemming from smart contract flaws, with case-based testing revealing up to 40% of public contracts have exploitable vulnerabilities. Recent advancements in high-throughput rollups and formal verification mitigate these risks. This coincides with a 2025 shift toward structured legal mandates, such as the EU’s MiCA, India’s VDA policy, and the U.S. GENIUS and CLARITY Acts. Therefore, future research must prioritize enhancing smart contract verification, developing energy-efficient consensus mechanisms, cross-chain interoperability, and fostering the continued alignment of supportive legal and regulatory frameworks.
B Sreedevi, J Thirunavukkarasu, Sridhar A, Sabareesh CS · 5 authors
In the realm of today's healthcare systems, data security leads to inefficiency & counterfeits drug supply chains have big issues. Counterfeit drugs is a threat to patient safety & public society. Existing healthcare systems don't connect all of these medical tasks well this leads to vulnerabilities in the supply chain management, drug misuse & issues with tracking and verifying pharmaceuticals. This paper proposes a solution using blockchain by integrating smart contracts, we can build a secure transparent & efficient drug supply chain management system. This project plan will connect all parts of the pharmaceutical supply chain - from research and development to delivery to patients - on one platform. It ensures that the drugs can be verified for authenticity at every stage in Supply chain management with the blockchain's decentralized & im-mutable ledger the application will promote transparency & traceability in the pharmaceutical supply chain which will increase trust & safety while trying to speed up & efficiency of healthcare services.
This study examines scientific articles on the transformation of blockchain technology in various industries, including banking, agriculture, finance, and transportation, identifying trends, emerging areas, and research challenges. This study explores the potential of blockchain technology for decentralizing the internet, addressing concerns related to privacy, security, and censorship, while also exploring its trends and associated issues. This study explores the potential of blockchain technology to revolutionize internet elements including data storage, content delivery, and identity management, while examining current acceptance patterns for decentralization. Smart contracts enhance transaction efficiency and transparency, while interoperability standards enable seamless communication across blockchain networks, despite substantial obstacles in decentralising the internet. Scalability, regulatory uncertainty, interoperability, and environmental concerns all pose challenges to blockchain networks, hindering widespread adoption and compromising the interoperability and energy usage of internet infrastructure.
The increasing importance of privacy and secure communication in distributed environments has fueled research into innovative solutions that combine data concealment and tamper-resistant recordkeeping. This article presents a logically structured architectural framework for covert steganographic communication, utilizing the Microsoft Azure web3 ecosystem as its foundation. The motivation behind this research stems from the limitations of traditional steganography and blockchain technologies when used independently, particularly in addressing the challenges of operational transparency, scalability, and robust data protection. To bridge these gaps, the proposed system integrates Azure Blockchain Development Kit with other Azure native services to provide a unified architecture. This research article introduces a pioneering architectural framework designed to facilitate covert steganographic communication through blockchain technologies, with a focus on leveraging the Microsoft Azure web3 ecosystem. By integrating Azure Blockchain Development Kit (BDK), Azure Confidential Ledger, Azure Blockchain Services, and Azure Blockchain Workbench with Open Steganography solutions deployed on Azure Virtual Machines (VM), the proposed system aims to achieve secure, confidential, and unobtrusive data exchange. The research methodology encompasses a comprehensive literature review, system design, implementation, and rigorous security analysis, followed by experimental evaluation on cloud infrastructure. By leveraging the strengths of Azure’s blockchain and confidential ledger capabilities alongside advanced steganographic techniques, this study demonstrates a practical approach to achieving secure, confidential, and unobtrusive data exchange. The findings confirm the feasibility and effectiveness of the proposed solution, highlighting its potential to facilitate adaptive, scalable, and privacy-preserving covert communication networks. In conclusion, this work charts new directions for integrating blockchain and steganography within cloud-native platforms, offering enhanced privacy and security for sensitive communications in distributed settings.
Muhammad Zeeshan Ullah Khan, Syed Imran Zaman, Sharfuddin Ahmed Khan
This chapter explores how blockchain-enabled smart contracts can revolutionize supply chain finance (SCF) by reducing operational costs, mitigating fraud, and expediting settlements. Although SCF is recognized for its capacity to improve liquidity and foster trust, it grapples with fragmented documentation, manual oversight, and information asymmetries, particularly in complex industries like agriculture. Blockchain as a decentralized and tamper-proof ledger, addresses these pain points by creating a single, shared version of transactional data across stakeholders. When applied to SCF, blockchain’s transparency and permanence can encourage financial institutions to offer more favo rable loan terms, while its security features deter fraud and unauthorized data manipulation. A central element of blockchain’s promise in SCF is the smart contract, self-executing code that performs contractual obligations once specific triggers are met. By automating tasks such as invoice verification, payment releases, and milestone tracking, smart contracts eliminate the need for third-party enforcement and accelerate the cash conversion cycle. This automation proves especially beneficial in multilateral trade finance, reverse factoring, and agricultural financing, where timely and accurate exchanges of data underpin credit decisions. The upshot is lower transactional friction, improved traceability, and an enhanced ability to manage credit risk, all of which can unlock new opportunities for smaller suppliers and underserved markets.
This study reviews the advancements in AI-driven methods for predicting stock prices, tracing their evolution from traditional approaches to modern finance. The role of AI in the market extends beyond predictive systems to encompass the intersection of financial markets with emerging technologies, such as blockchain, and the potential influence of quantum computing on economic modeling. A decentralized finance system examines the application of Reinforcement Learning in financial market prediction, highlighting its potential for continuous learning from dynamic market conditions. The study discusses the development of hybrid prediction models, stock market machine learning systems, and AI-driven investment portfolio management. The potential of quantum computing enhances portfolio analysis, fraud detection, optimization, and asset valuation for complex market predictions, as well as the impact of blockchain technologies on transparency, security, and efficiency. Machine learning techniques can significantly automate data collection and purification. Financial decision-making and the application of time-series analysis techniques can be readily learned through deep reinforcement learning for stock price prediction. Deep Neural Networks and Strategic Asset Allocation can be managed by evaluating performance and portfolio using real-time market insights from AI models. Although there are numerous ethical, sentimental, regulatory, and data quality issues in market prediction, the future job market is heavily dependent on these criteria, particularly through effective risk management and fraud detection.
In this work, we utilize the blockchain transactions and financial instruments to pre-dict the Bitcoin price using machine learning. We use three models: Light Gradient Boosting Machine (LightGBM), Decision Tree Regressor and Random Forest Regressor applied on a feature set which includes lagged close prices, 14-day Simple Moving Av-erage (SMA), Relative Strength Index (RSI) and daily confirmed Bitcoin transactions. The data is temporally aligned and pre-processed to maintain temporal coherence, as well as for conversational fluency. Through the results assessment by means of RMSE MAE, MAPE and R², we can found that Random Forest model has results closer to best performance with values of: 264.81 (RMSE); 175.41(MAE); for MAPE is 0.27% and; R² equals to 0.9958. Our findings also lend strong support for the effectiveness of simul-taneously considering not only blockchain-specific market variables but also tradi-tional financial predictors towards improved model performance and generalization. Our findings underscore the importance of raw blockchain transaction data for pre-dicting cryptocurrency prices, and present a new tool for data-based decision making in decentralized finance.
The term “Fintech” (Financial Technology) refers to software and other spearheading technologies adopted by different organizations to automate and enhance financial services. It refers to the technology that improves the backend system at traditional financial institutions. In FY22, $8.53 billion was invested in India's Fintech industry. It has been anticipated that the FinTech industry will generate around $200 billion in revenue by the year 2030 and overall throughput will be $1 trillion. Fintech is expanding quickly, yet there are several problems in the current fintech market including interacting with legacy systems like banks, data and payment security, compliance, lack of end-user awareness, retaining users, and user experience. Due to the development of fintech, more data is now accessible in digital formats, which facilitates analysis and the generation of insights but also increases the risk of security breaches. Blockchain is disruptive technology using which one can securely move money from one account to another without using a bank or any financial organization The term “distributed ledger technology” is often used interchangeably with “blockchain technology” in the financial services corporation. Each transaction has a trustworthy record, thus there is no chance of changing to earlier ones. In essence, blockchain technology can completely ensure the accuracy of every transaction. In this study, the problems facing India's fintech industry are described in detail, and possible solutions employing blockchain distributed ledger technology are suggested. Additionally, it finds blockchain technology has the ability to enhance the security and competence of financial operations in the Indian fintech sector, there are challenges such as regulatory uncertainty and scalability that require to be addressed. The paper concludes with recommendations for the upcoming development and adoption of blockchain technology in the Indian fintech sector.
Internet of Things and AI
Blockchain Technology Applications and Security
Innovations and Analysis in Business and Education
Guma Ali, Otim Emmanuel, Maad M. Mijwil, Bosco Apparatus Buruga · 6 authors
The rapid digitalization of financial services has given rise to smart finance ecosystems that integrate FinTech platforms, Internet of Things (IoT) devices, cloud infrastructures, and decentralized applications. While these systems enhance automation, operational efficiency, and financial inclusion, their highly distributed, data-intensive architectures introduce critical security, privacy, and trust challenges. In this context, artificial intelligence (AI) and blockchain have emerged as complementary technologies capable of addressing these challenges through intelligent decision-making, advanced threat detection, data integrity, and transparent operations. This survey provides a comprehensive review of recent research on securing smart finance systems using AI- and blockchain-based approaches. The survey comprehensively analyzed research published between 2023 and 2026 using the Scopus database, focusing on the keywords “AI,” “blockchain,” and “smart finance.” The analysis reveals extensive use of AI-driven security mechanisms, including credit scoring and risk assessment, transaction monitoring and fraud detection, anti-money laundering (AML) and know-your-customer compliance, identity verification, cyber threat detection, smart contract security analysis, behavioral biometrics, insurance fraud detection, and market risk prediction. In parallel, the survey examines blockchain-enabled security solutions, including secure payment and settlement systems, cross-border remittances, AML and counter-terrorism financing frameworks, digital identity management, smart contracts, asset tokenization, decentralized finance, auditability, and secure interbank communication. The integration of AI and blockchain offers significant advantages, including improved fraud detection accuracy, enhanced transparency and traceability, stronger data integrity, automated compliance, real-time threat response, and increased system resilience. Despite these benefits, key challenges persist, particularly in scalability, privacy preservation, interoperability, regulatory and ethical compliance, energy efficiency, explainability, and post-quantum security. The survey concludes by outlining future research directions and design guidelines for developing secure, scalable, and trustworthy smart finance systems that effectively leverage the integration between AI and blockchain.
Abstract The relentless growth of connected devices is transforming industrial, urban and domestic environments, yet it also expands the attack surface for distributed denial of service (DDoS), unauthorized access and data manipulation. Centralized security architectures struggle to cope with the scale and heterogeneity of the Internet of Things, creating single points of failure and privacy risks. This review takes a close look at how blockchain and artificial intelligence (AI) can work together to solve these problems. Blockchain plays an important role in decentralizing trust, maintaining data integrity, and enabling transparent audit trails. AI subfields such as machine learning (ML), deep learning (DL), reinforcement learning (RL), and multi-agent systems (MAS) enhance these benefits. They enable real-time anomaly detection, predictive analytics, and adaptive policy control. A seven axis Blockchain–AI Security Integration Schema (BASIS) is proposed to classify solutions by security objectives, intelligence modalities, trust primitives, deployment choices, scalability techniques, privacy controls and interoperability mechanisms. In this study also review Layer-2 consensus protocols, federated learning and lightweight deep learning models that address energy and computational constraints. Case studies from supply chains, healthcare and smart grids illustrate the benefits and limitations of current deployments. The evidence suggests that while AI improves the accuracy and responsiveness of threat detection, blockchain offers tamper-proof data provenance. However, there are still issues in achieving scalability, reducing computational overhead, and striking a balance between auditability and privacy. Hybrid on-chain/off-chain architectures, quantum-safe cryptography, and standardized frameworks to guarantee adoption and interoperability are some future research avenues.
Nandhini S, Hrithik M, Kamalesh S, Aswin C · 6 authors
ABSTRACT: Centralized digital marketplaces dominate today’s online commerce but suffer from inherent limitations such as single points of failure, lack of transparency, data monopolization, and trust dependency on intermediaries. To address these challenges, this paper presents the design and implementation of a decentralized marketplace built on blockchain technology. The proposed system enables peer-to-peer trading without the involvement of centralized authorities, ensuring transparency, security, and fairness among participants. Smart contracts are employed to automate transactions, enforce business rules, and eliminate the need for trusted third parties. Distributed ledger technology ensures immutability of records, while cryptographic mechanisms provide secure identity management and transaction validation. The marketplace supports secure listings, decentralized payments, dispute resistance, and trustless execution, thereby reducing operational costs and increasing user autonomy. Experimental analysis demonstrates improved reliability, resistance to tampering, and enhanced trust compared to traditional centralized platforms. The proposed decentralized marketplace framework highlights the potential of blockchain-based systems in redefining digital commerce by promoting transparency, decentralization, and user empowerment. Keywords: Decentralized Marketplace, Blockchain Technology, Smart Contracts, Peer-to-Peer Trading, Distributed Ledger, Trustless Transactions, Cryptographic Security, Transparency, Digital Commerce, Disintermediation.
This study systematically examines the transformative role of Artificial Intelligence (AI) in addressing the persistent challenges of blockchain technology across protocols, smart contracts, and distributed ledger management. Although blockchain offers decentralization, immutability, and transparency, its broader adoption remains constrained by scalability limitations, security vulnerabilities, inefficient consensus mechanisms, and the complexity of contract design and auditing. The findings of this review demonstrate that AI provides promising solutions to these barriers. Reinforcement learning (RL) applied to Proof-of-Stake reduced consensus latency by 30-50%, while NLP-based smart contracts lowered vulnerabilities by up to 40%, though both approaches introduced new concerns related to energy overheads and auditability. In addition, intelligent algorithms enhance ledger efficiency and data analytics, supporting more scalable and secure transaction processing. Drawing on 28 peer-reviewed studies published between 2018 and 2024, and guided by the PRISMA 2020 framework, this paper synthesizes state-of-the-art research, maps sector-specific applications in finance, healthcare, and supply chain management, and highlights unresolved gaps in ethics, reproducibility, and regulatory compliance. Notably, only 12% of the reviewed studies validated their approaches on live networks underscoring the gap between simulation-driven research and real-world deployment. The discussion culminates in the AI–Blockchain Interaction Model (AIBIM), a conceptual framework that systematizes synergies across consensus, contract, and application layers. By integrating empirical insights with critical evaluation, this work emphasizes the interdisciplinary nature of AI–blockchain research and provides actionable directions for advancing decentralized, scalable, and ethically aligned systems. This synthesis provides actionable insights for developers, regulators, and researchers in deploying AI-blockchain systems across finance, healthcare, and supply chains.