Machine learning (ML) has become a key ingredient in revolutionizing the physical layer security of next-generation devices across Industry 4.0, healthcare, and communication networks. Many conventional and unconventional communication architectures now incorporate ML algorithms for performance and security enhancement. In this study, we propose an unconventional, high-data-rate, machine-learning-driven, secure random communication system (HDR-MLRCS). Instead of utilizing traditional static methods to encrypt and decrypt alpha-stable (α-stable) noise as a random carrier, we integrated several ML algorithms to convey binary information to the intended receivers covertly. A support vector machine-aided receiver (SVM-R), Naïve Bayes-aided receiver (NB-R), k-Nearest Neighbor-aided receiver (kNN-R), and decision tree-aided receiver (DT-R) were integrated into a single architecture to provide an accelerated data rate with robust security. All intended receivers were pre-trained on a restricted-access dataset (R-
Task decomposition is a fundamental challenge in multi-agent collaborative maintenance systems, where unstructured natural language instructions must be precisely translated into logically coherent, executable sub-task sequences. This paper formulates task decomposition as a cons…
Machine learning technologies are increasingly embedded in financial decision-making processes, yet their influence on investor behavior and market efficiency remains insufficiently understood. This study investigates how machine learning-assisted investment decisions affect inve…
With the rapid advancement of artificial intelligence technologies in the era of big data, time series forecasting has become indispensable in critical fields such as environmental monitoring and financial market analysis. However, the existing forecasting models often encounter…
This paper develops a semi-closed-form pricing framework for vulnerable geometric Asian options under a three-factor stochastic volatility jump-diffusion model with stochastic interest rates. To the best of our knowledge, this is the new framework to simultaneously accommodate co…
In this descriptive scoping review, we assessed how Bayesian network structures are built and learned in applied work by screening 5993 recent papers (2020–2025) whose abstracts mention “Bayesian (belief) network” and deeming 3661 relevant. Among these relevant papers, expert kno…
Combinatorial optimization is a key component in critical decision problems such as routing, scheduling, network design, and graph optimization. Although combinatorial optimization methods, including exact algorithms, approximation methods, constraint programming, mixed integer p…