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Force Classification01:22

Force Classification

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Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
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Transformers in Distribution System01:27

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Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
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Aggregates Classification01:29

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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
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Related Experiment Videos

Malicious user classification in cognitive 5G networks using novel improved bidirectional encoder representations

Saranya S1, N Malligeswari2, F Twinkle Graf3

  • 1Department of Computer Science and Engineering, Dr. N.G.P. Institute of Technology, Coimbatore, 641048, India. ssaranya065@gmail.com.

Scientific Reports
|December 9, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an intelligent machine learning method for identifying malicious users in cognitive 5G networks. The novel IBERT-ROA model achieves high accuracy in detecting various attacks, enhancing network security.

Keywords:
Cognitive 5G networksImproved bidirectional encoder representations from transformersMalicious user classificationNormalization and scalingRevolution optimization algorithmSelf-Attention recurrent neural Network-Autoencoder

Related Experiment Videos

Area of Science:

  • Cybersecurity
  • Wireless Communication
  • Machine Learning

Background:

  • Identifying malicious users is crucial for securing cognitive 5G networks against dynamic spectrum access attacks.
  • Challenges include network complexity, limited labeled data, and evolving attack vectors, necessitating adaptable detection methods.

Purpose of the Study:

  • To develop and evaluate a novel intelligent machine learning methodology for Malicious User Classification in Cognitive 5G Networks (MUC-C5GN).
  • To enhance real-time detection capabilities, reduce false alarms, and improve generalization in dynamic 5G environments.

Main Methods:

  • Utilized the 5G Network Intrusion Detection Dataset (5G-NIDD) for data collection.
  • Employed Self-Attention Recurrent Neural Network-Autoencoder (RNN-AE) for feature extraction.
  • Implemented an Improved Bidirectional Encoder Representations from Transformers (IBERT) model optimized by the Revolution Optimization Algorithm (ROA) for classification.

Main Results:

  • The proposed IBERT-ROA model achieved superior performance with 99.74% accuracy, 98.48% sensitivity, and 98.91% precision.
  • Demonstrated significant improvements over existing methods, including up to 5.99% in sensitivity and 2.74% in accuracy.
  • Effectively classified seven types of attacks and benign traffic.

Conclusions:

  • The IBERT-ROA methodology offers an effective, scalable, and suitable solution for real-time malicious user detection in cognitive 5G networks.
  • The approach provides dependable effectiveness and confidence in cognitive radio-enabled 5G communication frameworks.