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

Donghua Jiang, Zeric Tabekoueng Njitacke, Guoqiang Long, Jan Awrejcewicz, Mingwen Zheng and Lei Cai

Chaos, Solitons & Fractals, 2024, vol. 189, issue P1

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.

Keywords: Tabu learning neuron; Compressive sensing; Matrix factorization; Medical cryptosystem; Dynamic analysis (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960077924011846
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:189:y:2024:i:p1:s0960077924011846

DOI: 10.1016/j.chaos.2024.115632

Access Statistics for this article

Chaos, Solitons & Fractals is currently edited by Stefano Boccaletti and Stelios Bekiros

More articles in Chaos, Solitons & Fractals from Elsevier
Bibliographic data for series maintained by Thayer, Thomas R. ().