Fractional-order (FO) chaotic systems exhibit richer and more complex dynamic behaviors compared to integer-order ones. This inherent richness and complexity enhance the security of FO chaotic systems against various attacks in image cryptosystems. In the present study, a comprehensive examination of the dynamical characteristics of the fractional-order Arneodo (FOAR) system with cubic nonlinearity is conducted. This investigation involves the analysis of phase planes, bifurcation diagrams, Lyapunov exponential spectra, and spectral entropy. Numerical studies show that the Arneodo chaotic system exhibits chaotic behavior when the lowest fractional-order (FO) value is set to 0.55. In this context, the aim is to securely encrypt biomedical images based on the Arneodo chaotic system with the lowest FO value using the Nvidia Jetson Nano development board. However, though the lowest FO system offers enhanced security in biomedical image encryption due to its richer dynamic behaviors, it necessitates careful consideration of the trade-off between high memory requirements and increasing complexity in encryption algorithms. Within the scope of the study, a novel random number generator (RNG) is designed using the FOAR chaotic system. The randomness of the random numbers is proven by using internationally accepted NIST 800-22 and ENT test suites. A biomedical image encryption application is developed using pseudo-random numbers. The images obtained as a result of the application are evaluated with tests such as histogram, correlation, differential attack, and entropy analyses. As a result of the study, it has been shown that encryption and decryption of biomedical images can be successfully performed on a mobile Nvidia Jetson Nano development card in a secure and fast manner.

Digital implementation of chaotic systems has many advantages. Chaotic systems realized on digital platforms offer higher flexibility and computational accuracy compared to traditional analog systems. This flexibility and accuracy are of great importance for engineering applications. Furthermore, digital solutions are usually more cost-effective, which is a great economic advantage. In this study, Nvidia Jetson AGX Orin board and 16-bit converter board are used for digital realization of chaotic systems. The Nvidia Jetson AGX Orin is a platform that stands out with its high processing power and energy efficiency. The 16-bit converter card stands out with its sensitive data processing capacity. Thanks to this technological infrastructure, chaotic system equations can be successfully solved in a digital environment and more advanced, flexible, reliable and cost-effective solutions are obtained. These solutions provide significant advantages in various applications in engineering. In particular, the use of digital chaotic systems in areas such as modeling, simulation and control of complex systems offers more efficient and effective results than traditional methods. Digital platforms also facilitate parametric investigations and optimization processes of chaotic systems, enabling more in-depth analysis.

The growing prevalence of digital communication and interconnected devices has amplified the need for robust data security measures. True random number generators (TRNG) play a pivotal role in protecting information by generating unpredictable and irreproducible sequences required for encryption, secure authentication, and cryptographic key generation. This research presents a TRNG model based on the fractional-order Sprott B chaotic system. The chaotic properties of the system were confirmed through Lyapunov exponent calculations, bifurcation diagrams, and phase space analyses. The fractional-order dynamics enhance the complexity and unpredictability of the generated entropy source, making it suitable for secure applications. The performance of the generated random numbers was assessed using the NIST 800-22 statistical test suite, successfully passing all tests and meeting the randomness requirements. This study introduces a unique approach by leveraging the fractional-order Sprott B chaotic system for TRNG design, demonstrating its effectiveness in cryptographic systems and secure communication frameworks.

Digital data is increasing rapidly in the world day by day. Information security is important during data exchange over the Internet. The way to securely transmit images over the network is through the image encryption technique. In the proposed cryptography system, the hybridization of Lorenz-Rossler chaotic systems is used, and a random number sequence is generated. The security analyses such as histogram, correlation, differential attack, information entropy, and duration analysis of the study are performed. It is seen that the proposed system performs well, especially in terms of correlation. Additionally, the performance of the developed embedded system platforms is compared after testing on Nvidia Jetson Nano and Xilinx PYNQ Z1 boards. The Nvidia Jetson Nano board is more performant than the Xilinx PYNQ Z1 board. The safety and feasibility of the proposed system have been demonstrated.

Titanium alloys are widely preferred in the healthcare sector as biocompatible materials due to their superior properties such as low density and exceptional mechanical strength. Their low density provides lightweight solutions, and their density is closer to that of human bone compared to other metallic alloys with similar strength. This similarity facilitates a balanced load distribution between the bone and the implant, enhancing biomechanical compatibility. This study investigates the effects of alloying elements on the density of titanium-based biomedical materials using a computational materials science approach. A total of 72 different compositions of Ti-Al-V alloys were modeled using JMatPro software, and their densities were simulated at room temperature (25°C). The simulation produced a comprehensive dataset, which was utilized to train an explainable artificial intelligence (XAI) model. Advanced interpretability techniques, including SHAP (SHapley Additive exPlanations), LIME (Local Interpretable Model-agnostic Explanations), and Partial Dependence Plots (PDP), were employed to elucidate the influence of each alloying element on the density. The dataset was analyzed using an XAI-based regression model implemented with the Artificial Neural Network (ANN) algorithm. The interpretability graphs provided insights into the individual contributions of the alloying elements, revealing their positive or negative effects on the density. The findings offer a deeper understanding of the role of alloying elements in optimizing the performance of titanium-based biomedical materials, particularly in achieving lightweight designs. This study highlights the potential of integrating computational material modeling with explainable AI to advance the design and development of high-performance lightweight materials for biomedical applications.

Artificial intelligence-driven prediction models have emerged as powerful tools for estimating material properties with high accuracy, yet the preparation of training datasets often demands labor-intensive and time-consuming experimental procedures. Leveraging the Computational Materials Science (CMS) approach, this study utilizes phase transformation calculations and thermodynamic data to simulate the mechanical properties of Ni-Cr-Fe alloys. Using JMatPro software, mechanical properties (0.2% Proof Stress, Fracture Stress, and Young's Modulus) of 50 Ni-Cr-Fe alloy compositions were simulated across a temperature range of 540–920°C, generating a dataset of 1000 rows. This dataset was used to train an Artificial Neural Network (ANN) model, with 80% allocated for training and 20% for validation and testing. The trained AI model demonstrated robust predictive capabilities, achieving a 96,61% accuracy rate in forecasting material compositions with the desired thermo-physical properties at specific temperatures. To validate the model's reliability, predicted alloy compositions were re-simulated under identical conditions in JMatPro, confirming the high fidelity of the model's predictions. The results underscore the efficacy of Computational Materials Science (CMS)-generated datasets as a scalable and reliable source for training AI models in materials science. This study highlights the potential of integrating Computational Materials Science (CMS) and Machine Learning approaches to accelerate material design and development processes, delivering significant improvements in prediction speed and accuracy.