In this work, we analyze a fractional model describing the transmission dynamics of community‐acquired and hospital‐acquired methicillin‐resistant Staphylococcus aureus (CA‐MRSA and HA‐MRSA) in a hospital setting. The model replaces the classical time derivative by a Caputo fractional derivative to represent memory effects in the transmission process. We establish existence, uniqueness, and Hyers–Ulam stability under explicit sufficient conditions using fixed‐point arguments. We then apply the Laplace residual power series method (LRPSM) to construct approximate solutions and assess their accuracy against RK4 and Adams–Bashforth–Moulton (ABM) reference solutions. For the baseline parameter set, the 30th‐order LRPS solution has error below 3 × 10 −6 for η ≥ 0.75 and does not exceed 2.1 × 10 −3 over the reported test interval; for the second parameter set, the corresponding error is at most 1 × 10 −5 at the tabulated times. Long‐time simulations further show that decreasing the fractional order slows the decay toward the disease‐free state, reflecting the memory effect. These results provide a quantitative assessment of the LRPSM approximation and clarify the range in which the analytical fixed‐point results apply. For η < 1 the reference is itself a numerical ABM solution with a resolution of about 2 × 10 −5 , so errors of that size or smaller measure agreement with the reference.
Security has become a critical concern for the networks of companies and institutions today, with numerous intrusion attempts aiming to successfully access their data and web services. The researchers proposed a number of techniques such as (firewall, encryption) to prevent such penetration and protect the infrastructure of computers and networks from possible threats, intrusion detection systems (IDS) has taken much of the attention of researchers, IDS monitors the resources computer and sends reports on the activities of any anomaly or strange patterns. The goal of IDS is to monitor network assets to detect anomalous behavior and misuse in network to reduce the risk. An IDS is a software tool or device designed to monitor a system or network for any policy violations or malicious activities, generating reports for the management system. The primary role of IDS is to identify potential incidents, log relevant information, and report any attempts. IDS has become a vital part of the security infrastructure in most organizations. Various techniques can be employed to detect intrusions, with each being tailored to a specific detection method. The main objective of an IDS is to efficiently detect attacks, with early‐stage detection being crucial to minimize the impact of potential threats. Anomaly‐based intrusion detection systems (AIDS) rely on statistical models to detect deviations from typical network traffic volume (NTV) and CPU usage. Two prominent statistical approaches include the use of mean and standard deviation for anomaly detection, and outliers for refining the detection process. This article compares these two approaches, focusing on their ability to detect anomalies in network traffic while minimizing the false positive rate and computational power. Our analysis reveals that although both models offer significant insights, the outliers model demonstrates superior performance in reducing false positives and computational power. The experimental results demonstrated that the proposed outlier‐based method achieved lower false positive rates and better computational efficiency compared with the traditional mean and standard deviation approach, while maintaining effective anomaly detection performance in different traffic conditions. Unlike traditional statistical techniques that depend strongly on distribution assumptions, the proposed outlier‐based approach focuses directly on identifying rare and abnormal traffic behavior. This allows the proposed framework to improve anomaly detection performance while reducing unnecessary false alarms and maintaining low computational complexity.
This study proposes a machine learning framework for analyzing blight diseases in rice, cassava, sugarcane, and corn, which are the primary economic crops of Thailand. To better reflect real-world conditions, the framework performs two related tasks within a single model: crop classification and ordinal disease-severity classification. Hamming loss and subset accuracy are employed as key evaluation metrics for multilabel prediction performance. The framework is designed to improve diagnostic accuracy and support practical applications in precision agriculture, contributing to more effective disease monitoring and management.
In this paper, the dynamic behavior of an incommensurate fractional-order single-machine infinite-bus (IFOSMIB) power system is investigated through numerical simulations. Different dynamical phenomena are observed in the IFOSMIB system, including periodic oscillations, period-doubling bifurcations, and chaotic attractors. Furthermore, the complexity of the system dynamics is quantified using the complexity measure C0. In addition, the positivity of the largest Lyapunov exponent (LLE) and the results of the 0–1 test for chaos, a robust indicator for distinguishing regular dynamics from chaotic dynamics, confirm the existence of chaos in the system. Moreover, an effective control scheme is proposed to suppress the chaotic oscillations of the IFOSMIB power system, and its effectiveness is verified through numerical simulations. Furthermore, the proposed fractional-order chaotic system is investigated for information security applications. Its complex and highly sensitive dynamics are exploited in the development of a chaos-based encryption scheme, in which the generated chaotic sequences are employed for secure data encryption. The obtained results demonstrate the potential of the proposed system for cryptographic and secure communication applications.
The concept of trimmed tree structures for multilevel fast multipole algorithm (MLFMA), referred to as trimmed-MLFMA (T-MLFMA), is proposed for the solution of volume integral equations for the fast analysis of scattering from large, inhomogeneous objects, where the conventional MLFMA suffers from high number of iterations and matrix vector multiplication (MVM) of large matrices at each iteration. In T-MLFMA, thresholding and machine learning techniques are used to eliminate the redundant interactions as the iterations proceed. In particular, the converged basis function coefficients are estimated with a fully connected neural network and, together with the thresholding, the MLFMA tree structure is systematically pruned, and the resulting far-interaction matrix becomes sparser. As a result, both the number of iterations and the MVM time per iteration are dramatically reduced. Using only a group of small homogeneous dielectric spheres with different permittivity values at the training stage, we are able to show that scattering from large, highly inhomogeneous and fairly complex objects are solved accurately and significantly faster than the conventional MLFMA solution (up to 10 times).
