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Blockchain based electronic educational document management with role-based access control using machine learning

P Chinnasamy1, B Subashini2, Ramesh Kumar Ayyasamy3

  • 1Department of Computer Science and Engineering, School of Computing, Kalasalingam Academy of Research and Education, Srivilliputtur, India.

Scientific Reports
|May 29, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel blockchain-based system using a fuzzy feed-forward convolutional temporal neural network (FCTNN) and remora swarm optimisation (SRSO) to enhance educational credential management and detect malicious users. The B-FCTNN_SRSO system significantly improves security and verification processes in digital education.

Keywords:
BlockchainElectronic educational document managementRemora swarm optimizationRole-based accessTemporal neural network

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Area of Science:

  • Computer Science
  • Information Security
  • Educational Technology

Background:

  • Digital transformation necessitates secure educational credential management.
  • Traditional methods face challenges like forgery, data tampering, and slow verification.
  • Machine learning (ML) and blockchain offer advanced solutions for educational data security.

Purpose of the Study:

  • To propose a novel electronic educational document management system.
  • To enhance security through malicious user detection using advanced AI and blockchain.
  • To improve the efficiency and reliability of credential verification and access control.

Main Methods:

  • Development of a blockchain-based fuzzy feed-forward convolutional temporal neural network (B-FCTNN).
  • Integration of simulated remora swarm optimisation (SRSO) for access control.
  • Utilisation of Natural Language Processing (NLP) for document word weight indexing during training.
  • Logging access requests on the blockchain for authenticated user verification.

Main Results:

  • The B-FCTNN_SRSO system achieved 98% prediction accuracy, 95% mean average precision (MAP), and 97% F1 score.
  • Latency was recorded at 96%, demonstrating efficient performance.
  • Blockchain security analysis showed 97% Quality of Service (QoS), 94% precision, and 96% throughput.

Conclusions:

  • The proposed B-FCTNN_SRSO system effectively manages electronic educational documents and detects malicious users.
  • The integration of blockchain and advanced ML techniques provides a robust and secure solution for educational credential management.
  • The system demonstrates high performance metrics, validating its efficacy in real-world applications.