Follow blockchain research across journals, conferences, and preprint repositories.
1,337 results · page 6 of 56
Inam Ul Haq, Sanna Mehraj Kak, Mashhud Ul Islam
No abstract is available for this record.
V. Prema Kumari, S. Antony Raj
Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.
Anitha Kumari
The remarkable progression of blockchain technology on the edge of the fourth industrial revolution will require new economic and organisational structures to achieve sustainable growth in every sector, especially in banking and finance. Blockchain technology in Industry 4.0 revitalises technological adaptation and illuminates the global banking industry with the high potential value of cryptocurrency, secure protocols, and distributed ledger technology. The research objective of the study is to overview the industrial revolution, blockchain technology in Industry 4.0, and the implementation challenges of the banking and finance industry. This study examines the understanding of technological advancement in industrial penetration that empowers a complete digital transformation of traditional banking services by integrating blockchain. The systematic literature review results highlight the blockchain technology implementation challenges and the benefits of the emerging technologies in Industry 4.0. Understanding the technological revolution would enable bankers to implement industrial-based blockchain applications for transparent, secure, and fast transactions.
V. E. Sathishkumar, S. Neelakandan, Angela Lee Siew Hoong
No abstract is available for this record.
Harsha Gowda R, Sahana M Gowda, Chethan J, Gopika R · 5 authors
The healthcare industry continues to have issues regarding the transparency and trust of the financial transactions, and especially in case of handling of insurance claims and the funding of the patient. Intermediaries and centralization is generally accompanied by inefficiencies, delay and lack of accountability. To eliminate these problems, in this paper, Medicare Chain is proposed as a decentralized blockchain-based fund management system in order to ensure the secure and transparent medical transaction. The system utilizes smart contracts of the Ethereum network to automate the process of transfer of funding between the patients, doctors and donors without the need of centralized authority in the process. Data and transaction logs of nurses is set into the InterPlanetary File System (IPFS) to ensure integrity and prevent any kind of tampering. Django-based web interface allows users authentication, access control and access to the blockchain network. By introducing a framework for auditable, secure and efficient management of medical funds using the concepts of decentralization, the proposed framework shows the possibilities of decentralized systems to create more reliability and trust amongst the healthcare ecosystems.
Uche-Nwachi E, Orogwu C. P, Mikop E, Anoke C.S · 7 authors
The advancement of blockchain technology has revolutionized the field of data security by introducing a decentralized, transparent, and tamper-resistant system originally designed for secure cryptocurrency transaction. Blockchain technology plays a pivotal role in a wide range of data protection applications beyond finance, to sectors like healthcare and supply chain management, where safeguarding sensitive data is essential. This paper explores how blockchain enhances data security by decentralizing data storage, using cryptographic algorithms, and enabling transparency and immutability. Through an analysis of case studies and research literature, we survey the significant impact blockchain has on reducing vulnerabilities, preventing data breaches, and ensuring trust in digital systems. The findings emphasize the potential of blockchain in the revolution of data security while focusing on current limitation in scalability and privacy.
Guanghui Zhang
The introduction of Vehicle-to-Everything (V2X) communications is a fundamental requirement for the evolution of today’s Autonomous Driving, but it leads to a new set of vulnerabilities in network infrastructure. It is important to note that cyber-attacks, including the availability ones, such as DoS, represent a significant threat to the safety of Intelligent Transport Systems (ITS). Traditional signature-based Intrusion Detection Systems (IDS) have a disadvantage in security due to their inability to adapt and manage these new and evolving attacks: they can be blind to new or “zero-day” kinds of attacks. This project is to solve this problem by proposing and validating an unsupervised Intrusion Detection System using a Deep Autoencoder architecture. Unlike typical supervised models, where labelled attack data is needed, this system is trained on normal network traffic patterns only. It tracks anomalies by learning to compress and reconstruct legitimate traffic features, marking large reconstruction errors as malicious intrusions. The model was developed in TensorFlow and tested against the KDD Cup 99 benchmark dataset. Experimental results show the high performance of the system with a total Accuracy of 99.49% and a critical Recall of 99.86%, effectively suppressing almost all availability attacks. In addition, the model is consistent with a Matthews Correlation Coefficient (MCC) of 0.9530, confirming its robustness and reliability even for very asymmetric network traffic. This research establishes solid proof-of-concept for the concept that unsupervised deep learning can work as a powerful new mechanism of security architecture for V2X infrastructure without relying on prior knowledge about specific attack signatures.
Okan Dardağan
This thesis presents a comprehensive predictive maintenance system and application interface that integrates deep learning and blockchain technologies in order to enhance maintenance strategies in industrial systems. Traditional predictive maintenance systems have significant issues regarding data security and decentralization. This study aims to address these limitations by leveraging blockchain technology, with a specific focus on improving the reliability and verifiability of predictive maintenance processes. In this study, an LSTM-CNN hybrid model was developed to evaluate complex patterns in both time and features, thereby enabling high-accuracy fault prediction. The proposed model is designed to perform binary classification for fault prediction in industrial equipment. During the implementation phase of the study, an open-source dataset was used to train and test the developed model. The Randomized Search method was used in the hyperparameter optimization process to increase the prediction success of the proposed model. The hybrid model was trained with 5-fold cross-validation, and class weighting and threshold value optimization methods were applied to eliminate the class imbalance problem. In the threshold optimization phase, F1-score-based methods are applied to maximize recall at three predefined minimum precision levels (0.05, 0.2, and 0.85), while identifying the most balanced trade-off between precision and recall. In the proposed system, sensor data are stored in a database (SQLite3), and cryptographic proofs generated using zero-knowledge techniques are transmitted to the Ethereum network. The Poseidon hash function is used to ensure data integrity, and the Groth16 protocol is used for Zk-Snark proof generation. This approach enables secure verification of data validity without publicly disclosing sensor data and simultaneously addresses scalability concerns. The system architecture is designed to include manager, operator, and engineer nodes, and all smart contracts are implemented using Solidity. In addition, a graphical user interface is developed using the Tkinter library in Python. The experimental results demonstrate that the proposed LSTM–CNN hybrid model produces successful outcomes in terms of fault prediction performance. According to scenario where the decision threshold is optimized based on the F1-score, the model achieves an accuracy of 0.987, an AUC value of 0.979, and an F1-score of 0.794. In future studies, the proposed system is planned to be implemented on the Ethereum mainnet instead of a test network, with a comprehensive evaluation of on-chain operational costs. However, instead of Zk-Snark proofs, which have a centralized structure, the use of Zk-Stark proofs, which are transparent and do not violate the principle of decentralization, is planned.
Yulun Miao
No abstract is available for this record.
Puneet Bakshi, Saurabh Shinde, Sunita Dhavale
AI agents now coordinate cryptographic tasks such as key management, protocol negotiation, zero-knowledge verification, and anomaly response across heterogeneous systems. Despite rapid progress, practical deployments still face gaps in protocol interoperability, verifiable privacy, and post-quantum readiness that hinder trustworthy adoption. This survey systematizes the field across cryptanalysis, cryptographic design, and secure multi-agent coordination, structuring the discussion around protocol families (MCP, A2A, ACP, ANP) and core primitives (MPC, HE, ZKP, PQC). Contributions include a two-dimensional taxonomy, a transparent survey methodology, a state-of-the-art comparison using shared criteria (interpretability, robustness, scalability, PQC readiness, proof overhead), an explicit limitations analysis, and practice-oriented guidance for engineering and governance. The synthesis clarifies trade-offs and provides a deployment roadmap for agentic cryptography in IoT, finance, and identity systems.
M. Kavitha Margret, V. Ranjith, M. Sakthivel, P. Yuven Krishna
No abstract is available for this record.
Niral Parmar, Hetal Parmar, Krushi Savani, Fakhruddin Kamdar
Everybody throws around the term "blockchain" these days, like it's some secret sauce. But smart contracts are where things actually start to get interesting. Forget endless forms and relying on someone's handshake; smart contracts handle things automatically. They're just coded agreements that trigger themselves no middlemen, no second-guessing if someone's being honest. You know what you're getting. This review looks at how smart contracts are changing the game in two touchy areas: healthcare and voting, where trust and privacy can't be taken lightly. Dealing with healthcare is usually a hassle. People lose records, insurance companies bounce you around, and privacy feels flimsy. With smart contracts, you're in charge of your data, claims happen faster, and private info stays private. Doctors can share what they need to, without breaking the rules. Voting? It's had trust issues forever people aren't sure their votes count for anything. Smart contracts clean things up. They make voting more transparent, help stop fraud, and lock down the results. You can check your ballot and know nobody's changing numbers behind the scenes. Of course, it's not all smooth sailing blockchain slows down when things get big, laws haven't caught up, some of the interfaces are confusing, and big organizations don't like change. This paper covers what works, what needs help, and where things could go next.
Bahar Jalali Farahani, Farshad Firouzi, Avid Alizadeh, Mahmoud Daneshmand
No abstract is available for this record.
Jolhfred Alania-Chalan, Ivar Rodrigo Farfán-Muñoz, Angel F. Navarro-Raymundo, Jorge Nolasco-Valenzuela · 8 authors
No abstract is available for this record.
Tanuj Meshram, Mou Dasgupta
No abstract is available for this record.
Asmaa Alkholy, Mariam Essam
Bitcoin price prediction is a popular topic in finance and technology circles. Developing an accurate bitcoin price prediction algorithm is crucial for the cryptocurrency market's growth and development. The development of bitcoin price prediction algorithms is challenging because bitcoin prices fluctuate heavily. Many researchers have attempted to predict the future price of Bitcoin using a variety of methods. This paper presents a web-based application for Bitcoin price prediction using a Hybrid LSTM-GRU model. The experimental results show the results of the Hybrid LSTM-GRU model compared to other models, such as LSTM and GRU. The models were evaluated using various metrics such as mean absolute error, root mean squared error, and mean squared error. The findings indicated that our model outperformed other deep learning models with RMSE, MSE, and MAE values of 0.136, 0.018, and 0.105, respectively. A web application was built using the Streamlit library.
Naveen Meka, Praveen Tripathy
India’s carbon free power is on an exponential rise, and has recently surpassed 50 percent of installed capacity five years ahead of scheduled target. Growing penetration of renewables accounts to 184.62 GW which is 38 percent of the overall energy mix. By 2030, contribution of wind and solar energy is likely to cross the mark of 44 percent. Pradhan Mantri Suryoday Yojana (PMSY) gives major impetus to Residential based Roof Top Solar (RTS) scheme which alone is a significant component. Eventually, growing number of solar based Distributed Energy Resources (DERs) will result into availability of sufficient power in the households. Potential to trade excess power in the neighborhood will soon emerge and be a new norm. Conventionally, Power Purchase Agreements (PPAs) are executed between power producers and consumers forming a legal binding among the entities. Growing number of DERs will mandate resilient, secured, concurrent and faster contracting mechanisms. While, conventional PPAs are often associated with potential vulnerabilities of being tampered, thefts, inflicted destructions, foisted litigations, non-compliance issues, non-availability to all stakeholders etc. Seizing this problem, blockchain will serve as an effective solution. All requisites of contract being resilient, auto-executable, immutable and scalable will be well achieved using blockchain technology. Chaincode based PPA smart contract can ensure secured, transparent and accelerated contracting mechanism. The paper evolves client based solution in developing a decentralized application (dApp) for carrying out energy trading using Hyperledger fabric.
Ranjeeth Kashetty
Over one billion people worldwide lack a recognised legal identity. Existing identity systems — built around passwords, static biometrics, and centralised authorities — are fragile, exclusionary, and increasingly vulnerable to breach, coercion, and state failure. This paper introduces the Blockchain-Based Identity Management System (BIMS), a decentralised identity framework that replaces static credentials with continuous, behaviour-informed validation. Rather than asking "what do you know or carry?", BIMS asks "does this person's pattern of behaviour, movement, and context match who they say they are?" — mirroring how humans naturally recognise one another. BIMS integrates IoT-derived behavioural signals, privacy-preserving cryptography (zero-knowledge proofs and homomorphic encryption), Trusted Execution Environments (TEEs), and a leaderless Byzantine Fault Tolerant consensus network. Raw personal data never leaves the user's device. Validators receive only mathematical confidence scores. The result is identity verification that is private by architecture, not by policy. Critically, BIMS embeds humanitarian protection at the protocol level. A dedicated governance layer — including NGO and neutral-nation validators with weighted oversight powers — ensures that refugees, stateless persons, and people in crisis can establish and maintain digital identity even when institutions have failed them. The system is designed to be interoperable with existing standards (W3C DIDs/VCs, OAuth/OIDC), scalable via Layer 2 zero-knowledge rollups, and energy-efficient through edge inference. BIMS proposes that privacy, security, and inclusion are not competing priorities — they are mutually reinforcing design goals.
Ujjwal Mishra, Rajesh Sasane, Sachin Lad, Pallavi Bhujbal · 6 authors
No abstract is available for this record.
Esmot Ara Tuli, Mohtasin Golam, Dong-Seong Kim
No abstract is available for this record.
Sandeepan Banerjee
The use of blockchain technology in smart contracts is changing the way businesses are run in the modern world, with the ability to perform automated, transparent and additional contractual terms without being tampered with. This paper examines how smart contracts can be utilised with real-time business performance analytics to improve the efficiency of operations, accountability and strategic decision-making. Smart contracts can be utilized to validate transactions in a decentralized system by including a set of rules that ensure that the transactions are automatically validated immediately, minimize the reliance on intermediaries and minimize human error. Through the integration of the real-time analytics tools, the organizations will be able to track the key performance indicators (KPI), financial operations, supply chain operations and compliance indicators more precisely and promptly. The integration of these technologies promotes integrity of data, reinforcement of audit trails and predictive insights due to the continuous flow of data stream. Moreover, the study points to its use in finance, supply chain management, healthcare and digital services, noting that it has better transparency, cost reduction in operations and the trust of stakeholders. In spite of the benefits, there are still issues like scalability, interoperability, regulatory unpredictability and data privacy. The paper comes to a conclusion that a combination of smart contracts and real-time analytics offers a strategic platform to data-driven companies, which make agility decisions and sustainable competitive edges in dynamic digital markets.
Kunal Kumar Mandal, Abhoy Chand Mondal
No abstract is available for this record.
Jiazheng Lin, Yingying Wang
ABSTRACT The increasing complexity and interdependence of global supply chains necessitate innovative solutions to enhance collaboration while safeguarding data privacy. This paper presents a lightweight multi‐blockchain framework designed to address the challenges of privacy‐preserving collaboration in Internet of Things (IoT)‐driven supply chains. By integrating multiple blockchain networks, the proposed framework ensures data confidentiality, integrity, and traceability across various stakeholders. The system employs advanced cryptographic techniques, including zero‐knowledge proofs and differential privacy, to protect sensitive information during data exchange and processing. Additionally, the framework incorporates lightweight consensus mechanisms to accommodate the resource constraints of IoT devices. Experimental evaluations demonstrate the effectiveness of the proposed framework in improving data privacy and system scalability compared to existing solutions.
C. R. Reshma, B. R. Arun Kumar
No abstract is available for this record.