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Novel Tabu learning neuron model with variable activation gradient and its application to secure healthcare

Author

Listed:
  • Jiang, Donghua
  • Njitacke, Zeric Tabekoueng
  • Long, Guoqiang
  • Awrejcewicz, Jan
  • Zheng, Mingwen
  • Cai, Lei

Abstract

Currently, the latest advances in artificial neural networks have deeply affected various aspects of the general public. To this end, a new Tabu Learning Neuron (TLN) model with variable activation gradients is proposed in this paper. Specifically, its kinetic behaviors and intrinsic properties are investigated by means of a two-parameter Lyapunov exponential spectrum, a bifurcation and an equilibrium point analysis. Moreover, its electronic circuit built in the PSpice environment agrees with the numerical results. Besides, in respect of its engineering applications, a novel data compression-encryption scheme based on the new TLN model, matrix factorization theory and compressive sensing technology is introduced for providing a secure data exchange environment in the healthcare community. Finally, performance evaluation indicates that the proposed cryptography scheme has remarkable advantages in terms of reconstruction quality and security.

Suggested Citation

  • Jiang, Donghua & Njitacke, Zeric Tabekoueng & Long, Guoqiang & Awrejcewicz, Jan & Zheng, Mingwen & Cai, Lei, 2024. "Novel Tabu learning neuron model with variable activation gradient and its application to secure healthcare," Chaos, Solitons & Fractals, Elsevier, vol. 189(P1).
    • Handle: RePEc:eee:chsofr:v:189:y:2024:i:p1:s0960077924011846
    DOI: 10.1016/j.chaos.2024.115632
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    References listed on IDEAS

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