Discrete chaotic systems based on memristors exhibit excellent dynamical properties and are more straightforward to implement in hardware, making them highly suitable for generating cryptographic keystreams. However, most existing memristor-based chaotic systems rely on a single memristor. This paper introduces a novel discrete chaotic system employing dual memristors, named the 3D memristive cubic map with dual discrete memristors (3D-MCM). The 3D-MCM system demonstrates richer and more intricate dynamical behaviors compared to its single-memristor counterparts, as verified through bifurcation diagrams, Lyapunov exponent spectra, and complexity analyses. Notably, the system exhibits coexisting attractors, substantially enhancing its dynamical complexity. Hardware implementation of the 3D-MCM attractors confirms its feasibility for industrial applications. To illustrate the system’s potential in encryption tasks, this study integrates the quaternary-based permutation and dynamic emanating diffusion (QPDED-IE) scheme for image encryption with the 3D-MCM.Experimental results demonstrate that the QPDED-IE scheme based on the 3D-MCM exhibits strong diffusion and confusion properties, effectively resisting cryptanalytic attacks.

The resistance state of a memristor can be influenced by external stimuli, and these variations can be converted into a pseudorandom sequence through appropriate circuitry and control mechanisms. By leveraging this property, a reliable and complex pseudorandom number generator suitable for encryption can be designed. To enhance the chaotic complexity of memristor-based discrete systems, this paper introduces a three-dimensional hyperchaotic map based on a memristor (3D-HMBM), which integrates a sine-function nonlinearity with a discrete memristor model. Analyzing its dynamical properties via Lyapunov exponents, the 3D-HMBM exhibits evolution from periodicity to chaos and hyperchaos. The complexity of its iterated sequences is verified through metrics such as Spectral Entropy and C0 complexity. Furthermore, the 3D-HMBM displays a unique phenomenon of infinite coexisting attractors. As initial values vary, the system generates attractors at different positions, suggesting that—in theory—an infinite number of attractors exist. Finally, the simulation results are validated via digital-circuit implementation. Building on this foundation, we propose a multi-image encryption algorithm based on the 3D-HMBM, offering a more secure solution for encrypting large volumes of data. Through statistical testing and cryptographic analysis, we confirm the significant potential of the keystream generated by the 3D-HMBM for cryptographic applications.

Currently, discrete memristors are a focal point in the study of chaotic maps. Similar to memristors, memcapacitors-another type of memory circuit component-have not received widespread attention in the design of chaotic maps. In this article, we propose a 4-D memcapacitive hyperchaotic logistic map (4D-MHLM) by integrating memcapacitors with the logistic map. The dynamical behavior of the 4D-MHLM is analyzed using Lyapunov exponent analysis, and the impact of different parameters on system performance is discussed. The complexity of generating pseudo-random sequences with the 4D-MHLM is investigated through complexity analysis, including spectral entropy complexity and C0 complexity. Notably, attractor analysis reveals a unique phenomenon of infinite coexisting attractors within the 4D-MHLM. Finally, the chaotic attractor generated by the 4D-MHLM is successfully implemented on a hardware platform. Theoretical analysis and digital circuit implementation results indicate that the 4D-MHLM exhibits rich dynamical behavior and higher complexity, offering significant value for practical applications.

Psoriasis is a chronic inflammatory skin disease with a high global prevalence. A skin biopsy is still required to diagnose the disease; no non-invasive diagnosis method has been found. It has become a popular approach for physicians as a support system, as it classifies biological data collected without human intervention in various ways with machine learning methods. Numerous studies have been conducted using machine learning methods to increase the accuracy, performance, speed, and reliability of diagnosing various diseases. This study aims to predict whether a group of patients admitted to Hitit University Erol Olçok Training and Research Hospital have psoriasis based on exhaled breath measurements using an electronic nose system which was produced for this study by the authors. In total, 263 clinical records were examined; 120 (45.6%) were obtained from healthy individuals, while 143 (54.4%) belonged to psoriasis patients. In order to distinguish data from those of psoriasis patients and those of healthy individuals, six different machine learning algorithms were used on the breath data set. The best classification result was provided by the ExtraTreesClassifier algorithm, with an accuracy rate of 96.1%, while other algorithms have rates between 66.6% and 94.2%. The most important outcome of this study is that the model determined to distinguish psoriasis patients from healthy ones can also help in the early diagnosis of psoriasis.