Dealing with privacy and security becomes more complicated nowadays with the emergence of big data era. Privacy, data value, and system efficiency should be managed using multiple solutions in current analytics. In this paper, privacy-preserving techniques were selected and reviewed for big data analysis to reduce threats imposed on healthcare data. Various security solutions, including k-anonymity, differential privacy, homomorphic encryption, and secure multi-party computation (SMPC), were programmed and examined using the Medical Information Mart for Intensive Care III (MIMIC-III) healthcare dataset. Assessments were conducted cautiously for each method of data collection in respect of security, time required, capacity of handling large data sets, usefulness of the data, and compliance with regulations. By using differential privacy, it was possible to maintain a balance between privacy and utility by allocating additional resources to the program. The security of data was facilitated by homomorphic encryption though it was not easy to operate and reduce the speed of computer systems. Moreover, achieving scalability in the SMPC required a significant amount of computing power. Although k-anonymity enhanced data utility, it was vulnerable to certain types of attacks. Protecting privacy in big data would limit the performance of systems; for multiple copies of data, scientists can now utilize analytics, differential privacy, and the SMPC, which is highly effective for analyzing private data. However, such approaches should be further optimized to handle real-time processing in big data applications. Experimental evaluation showed that processing 10,000 patient records using differential privacy took an average of 2.3 seconds per query and retained 92% of data utility, while homomorphic encryption required 15.7 seconds per query with 88% utility retention. The SMPC achieved a high degree of privacy with 12.5 seconds per query but slightly reduced scalability. As recommended in this study, the implementation of privacy-focused solutions in big data could help researchers and companies establish appropriate privacy policies in healthcare and other similar areas.

Osteoarthritis (OA) affects approximately 240 million individuals globally. Knee osteoarthritis, a crippling ailment marked by joint stiffness, discomfort, and functional impairment, is particularly the most widespread kind of arthritis among the elderly. To assess the severity of this disease, physical symptoms, medical history, and further joint screening examinations including radiography, Magnetic Resonance Imaging (MRI), and Computed Tomography (CT) scans have frequently been considered. It is difficult to identify early development of this disease as conventional diagnostic methods could be subjective. Therefore, doctors utilize the Kellgren and Lawrence (KL) scale to evaluate the severity of knee OA with visual images obtained from X-ray or MRI. The detection and prediction of the severity of knee OA indeed requires a novel model that uses deep learning models, including Inception and Xception. Utilizing the KL grading scale, the model, including Xception, ResNet-50, and Inception-ResNet-v2 could determine the degree of knee OA suffered by patients. The experimental results revealed that the Xception network achieved the highest classification accuracy of 67%, surpassing ResNet-50 and Inception-ResNet-v2, demonstrating its superior ability to automatically grade OA severity from radiographic images.

This study introduces a grammar-based, chaotic-oriented programming language, termed ChaosL, to address persistent numerical precision and reproducibility challenges in the computational analysis of chaotic systems. The language, along with its compiler and parser, is designed end-to-end with consideration of chaotic maps. Numerical accuracy is systematically managed through grammar-level precision specification and automated error monitoring mechanisms, enabling exact control over floating-point representations, including single precision, double precision, and arbitrary-precision BigDecimal arithmetic with configurable decimal resolution of up to 100 digits. The proposed grammar natively supports ten widely studied one-dimensional and two-dimensional discrete chaotic maps, which may be composed using newly defined hybrid composition paradigms, namely alternate, blend, cascade, and feedback-driven coupling. To ensure computational reliability, multiple error assessment strategies are integrated, including direct error estimation, shadow computation, and interval arithmetic. In addition, ensemble-based simulation capabilities are incorporated to evaluate trajectory separation and estimate predictability horizons. The automated computation of Lyapunov exponents is embedded at the language level, achieving an accuracy of up to 99.6% while simultaneously enabling code-size reductions of approximately 85–92%. The adaptable architecture of ChaosL establishes a reproducible computational framework for discrete chaos research and facilitates the systematic identification of emergent behaviors in hybrid dynamical systems. Moreover, the design provides a scalable foundation for future extensions toward continuous-time systems, interactive visualization environments, and cloud-based collaborative experimentation, thereby advancing precision-aware computational practices in nonlinear dynamics and chaos theory.