The rapid growth in Android device usage has resulted in a significant increase in malware targeting this platform, posing serious threats to user security and privacy. This research tackles the challenge of Android malware detection by leveraging advanced machine learning techniques, with a particular emphasis on the random forest (RF) algorithm. Our primary objective is to accurately identify and classify malicious applications to enhance the security of Android devices. In this study, we employed the RF algorithm to analyze a comprehensive dataset of Android applications, where the classification of each application as either malware or benign is known. The method was rigorously tested, yielding impressive results: an average accuracy of 98.47%, a sensitivity of 98.60%, and an F-score of 98.60%. These metrics underscore the effectiveness of our approach. Moreover, we conducted a comparative analysis of the RF algorithm against other malware detection methods. The results demonstrate that the RF algorithm outperforms these alternative methods, offering superior detection capabilities and contributing to more robust Android security measures.

This paper presents a surrogate-based uncertainty quantification (UQ) framework for coupled structural–acoustic systems subject to material and geometric variability. The proposed methodology integrates the Finite Element Method (FEM) with two metamodeling techniques—the Quadratic Response Surface (QRS) and Kriging—and Monte Carlo Simulations (MCS), to efficiently characterize the probabilistic behavior of the acoustic response. Two accuracy metrics (cross-validation error and prediction error) are used to validate the surrogate models. Numerical experiments demonstrate that the Kriging metamodel trained with 30 Latin Hypercube Sampling (LHS) points achieves superior predictive accuracy, with a Relative Maximum Error of 4.125 × 10⁻⁷. Monte Carlo Simulations conducted via the Kriging surrogate reduce the computational cost by more than six orders of magnitude compared to direct FEM-based MCS, while maintaining high accuracy. The proposed framework is validated on a rectangular cavity coupled with two flexible aluminum plates, and provides an efficient and accurate tool for vibro-acoustic UQ in complex engineering systems.