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Survival Tree01:19

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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Deep Neural Networks for Image-Based Dietary Assessment
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Optimization of a Deep Learning Algorithm for Security Protection of Big Data from Video Images.

Qiang Geng1,2, Huifeng Yan3, Xingru Lu1

  • 1School of Big Data & Software Engineering, Chongqing College of Mobile Communication, Chongqing 401520, China.

Computational Intelligence and Neuroscience
|March 18, 2022
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Summary
This summary is machine-generated.

This study introduces a deep learning-based encryption algorithm to secure image and video data in the cloud. The novel approach enhances data security against neural cryptography attacks, improving privacy protection.

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Area of Science:

  • Computer Science
  • Cryptography
  • Deep Learning

Background:

  • Digital technology proliferation increases data security risks.
  • Cloud platforms require robust protection for image and video data.
  • Existing encryption methods face challenges from advanced cyber threats.

Purpose of the Study:

  • To develop a deep learning-based encryption algorithm for secure cloud storage and sharing of image and video data.
  • To enhance privacy protection against neural cryptography and sophisticated attacks.
  • To improve the security of existing neural network encryption algorithms.

Main Methods:

  • Image saliency detection to identify important regions in video images.
  • Adaptive division and reorganization of important and non-important image regions for encryption.
  • Development of an improved encryption algorithm based on selective ciphertext attack analysis.
  • Comparative analysis of algorithm security capabilities.

Main Results:

  • The proposed algorithm reduces decryption error rates over time.
  • Attacker's (Eve) classification error rate increases, with accuracy not exceeding random prediction when secure networks are learned.
  • Chosen ciphertext attack-advantageous neural cryptography (CCA-ANC) demonstrates efficient performance (14s encryption, 69mb/s speed).

Conclusions:

  • The self-learning secure encryption algorithm significantly enhances password security and data protection for video images.
  • The method provides a robust defense against advanced cyber threats in cloud environments.
  • The algorithm offers a practical and efficient solution for securing sensitive visual data.