Blockchain based electronic educational document management with role-based access control using machine learning model Article Swipe
P. Chinnasamy
,
B. Subashini
,
Ramesh Kumar Ayyasamy
,
Ajmeera Kiran
,
Binay Kumar Pandey
,
Digvijay Pandey
,
Mesfin Esayas Lelisho
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-025-99683-5
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-025-99683-5
The emergence of digital technology has led to a significant increase in the importance of educational credential storage, exchange, and verification for organisations, enterprises, and universities. Academic record forgery, record misuse, credential data tampering, time-consuming verification procedures, ownership and control difficulties, and other problems plague the education sector. Machine learning (ML) and blockchain, two of the most disruptive methods, have replaced traditional techniques in the education sector with highly technological and efficient ways. Our study aims to propose a novel electronic educational document management technique using a blockchain-based fuzzy feed-forward convolutional temporal neural network that detects malicious users. Here, the training is carried out based on NLP analysis in document word weight indexing. This document management access control is based on role-based access with simulated remora swarm optimisation. In order to identify malicious users, this suggested system logs access requests on the blockchain and authenticated users. The findings demonstrate that this suggested architecture performs as intended in every case. The experimental analysis is based on a malicious user detection dataset regarding Prediction accuracy, Mean average precision, F-measure, Latency, QoS, Contract execution time, and Throughput. Based on dataset feature analysis, the proposed B-FCTNN_SRSO achieved a prediction accuracy of 98%, a mean average precision (MAP) of 95%, and an F1 score of 97%, with a latency of 96%. Additionally, based on blockchain security analysis, the B-FCTNN_SRSO attained a QoS of 97%, a precision of 94%, and a throughput of 96%.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1038/s41598-025-99683-5
- https://www.nature.com/articles/s41598-025-99683-5.pdf
- OA Status
- gold
- Cited By
- 3
- References
- 51
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4410869199
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4410869199Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1038/s41598-025-99683-5Digital Object Identifier
- Title
-
Blockchain based electronic educational document management with role-based access control using machine learning modelWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-05-29Full publication date if available
- Authors
-
P. Chinnasamy, B. Subashini, Ramesh Kumar Ayyasamy, Ajmeera Kiran, Binay Kumar Pandey, Digvijay Pandey, Mesfin Esayas LelishoList of authors in order
- Landing page
-
https://doi.org/10.1038/s41598-025-99683-5Publisher landing page
- PDF URL
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https://www.nature.com/articles/s41598-025-99683-5.pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.nature.com/articles/s41598-025-99683-5.pdfDirect OA link when available
- Concepts
-
Computer science, Credential, Artificial intelligence, Machine learning, Access control, Quality of service, Convolutional neural network, Blockchain, Data mining, Computer security, Computer networkTop concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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