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Related Concept Videos

Ischemic Stroke l: Introduction01:15

Ischemic Stroke l: Introduction

Ischemic stroke is an acute cerebrovascular condition in which blood flow to a brain region is suddenly interrupted, leading to tissue infarction. Neurons depend on continuous oxygen and glucose supply, so even brief reductions in perfusion cause energy failure, ionic imbalance, and irreversible injury. Ischemic strokes are classified into thrombotic and embolic types based on their underlying mechanisms.Thrombotic MechanismsThrombotic stroke develops when a clot forms within a cerebral artery.
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An ischemic stroke occurs when a cerebral blood vessel becomes obstructed, most often by a thrombus or embolus, interrupting the delivery of oxygen and glucose to brain tissue. Because neurons rely on continuous aerobic metabolism, energy failure begins within minutes of reduced perfusion. The region receiving the least blood flow becomes the infarct core, an area of irreversible cellular death. Surrounding this core lies the penumbra, a zone of hypoperfused but still viable tissue that is...
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Related Experiment Video

Updated: Jul 5, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Quantum-resistant hybrid encryption framework for secure and intelligent Vehicle-to-Vehicle communication using deep

Tawfiq Hasanin1, Zahyah H Alharbi2, Majdy M Eltahir3

  • 1Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.

Scientific Reports
|May 6, 2026
PubMed
Summary
This summary is machine-generated.

A new method enhances vehicular communication security using Quantum-Resistant Hybrid Encryption for Secure Vehicle-to-Vehicle Communication Using Deep Representation Learning (QRHEV2V-DRL). This approach achieves 99.62% accuracy in detecting malicious attacks in vehicle networks.

Keywords:
ClusteringDeep learningKernel Fuzzy C-MeansQuantum-resistant hybrid encryptionVANETVehicle-to-Vehicle

Related Experiment Videos

Last Updated: Jul 5, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Area of Science:

  • Computer Science
  • Cybersecurity
  • Network Engineering

Background:

  • Vehicular communication, particularly vehicle-to-vehicle (V2V), is crucial for modern applications but faces security challenges due to high mobility and network dynamics.
  • Vehicular Ad Hoc Networks (VANETs) are susceptible to malicious attacks like fabrication attacks, leading to jamming and increased network delays.
  • Existing security measures struggle to provide long-term data protection against evolving cyber threats in dynamic V2V environments.

Purpose of the Study:

  • To propose a novel Quantum-Resistant Hybrid Encryption for Secure Vehicle-to-Vehicle Communication Using Deep Representation Learning (QRHEV2V-DRL) method.
  • To enhance the security and efficiency of V2V communication networks against sophisticated cyber threats.
  • To ensure long-term data security and integrity in vehicular communication systems.

Main Methods:

  • Cluster formation using the Kernel Fuzzy C-Means (KFCM) model to segment the network based on communication patterns.
  • Attack detection and classification using min-max normalization and a stacked sparse autoencoder (SSAE) model.
  • Secure data transmission via a quantum-resistant hybrid encryption (QRHE) model to the cloud.

Main Results:

  • The QRHEV2V-DRL method demonstrated superior performance in attack detection and classification.
  • Achieved a high accuracy of 99.62% on the ToN-IoT dataset, outperforming existing models.
  • Successfully implemented a multi-stage approach for robust V2V communication security.

Conclusions:

  • The QRHEV2V-DRL method offers a significant advancement in securing V2V communication networks.
  • The proposed approach provides effective protection against malicious attacks and ensures data confidentiality.
  • This study highlights the potential of deep representation learning and quantum-resistant encryption for future vehicular security.