Sathishkumar Veerappampalayam Easwaramoorthy^*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.087043 - 27 July 2026

Abstract This article has no abstract. More >

Xuan Kien Pham¹, Van Dat Nguyen^2,*, Van Thanh Phan^3,*, Duc Trien Nguyen^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.086196 - 27 July 2026

Abstract Accurate energy consumption forecasting faces two major challenges: limited historical data and complex consumption patterns. To address these challenges, this study proposes a new hybrid framework named the Decomposition-based Grey-Neuro-Fuzzy Architecture (DeGNA). The model first uses the Denton method to convert limited annual records into high-frequency monthly data. Next, it applies STL decomposition to separate the data into trend, seasonal and residuals components. A rolling-window GM(1,1) model is then used to predict the main growth trend, while a GWO-optimized ANFIS model uses economic indicators (IIP and FDI) to forecast complex seasonal changes. This study evaluates… More >

Adrián Bañuls-Arias, Cipriano Galindo, Ana Cruz-Martín, Manuel Castellano-Quero, Juan M. Gandarias, Juan-Antonio Fernández-Madrigal, Vicente Arévalo-Espejo^*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.085976 - 27 July 2026

Abstract The basic operation of a mobile robot is navigating to some target, avoiding collisions and possibly minimizing other criteria. A diversity of methods have been developed since the past century, and the research is still active, but there is one aspect that is often neglected: the duration of the steps in which computational devices divide the navigation process. Usually, it is set heuristically to a small, constant value for sampling observations frequently enough to ensure safety; however, each robot and environment has particularities that can make such a fixed timestep sub-optimal under some criteria. This… More >

Aoran Huang^1,2,*, Sinuo Zhang^1,2, Haoxiang Zhu^1,2, Xiaojing Fan^1,2, Huachun Zhou^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.083972 - 27 July 2026

Abstract In Future Mobile Internet and convergence application scenarios, existing network attack traffic detection methods are insufficient in characterizing cross-flow correlations and structural dependencies during the attack process, and therefore still have limited generalization ability in complex scenarios and unknown attack identification tasks. To address this issue, this paper proposes a cross-flow modeling large language model framework, which extends the traditional detection paradigm based on single-flow features to joint modeling oriented toward cross-flow context and relational structure. Specifically, this paper constructs cross-flow context through flow sorting, grouping, and cross-group sampling, and combines an inter-flow relation matrix… More >

Yi Chen, Xiaodan Ren^*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.083966 - 27 July 2026

Abstract Concrete exhibits significant stochasticity and nonlinearity, making the calibration of nonlinear damage models challenging for high-precision structural analysis. To address the high computational cost of finite element model calibration and the influence of model bias, this study proposes a machine learning surrogate framework for Bayesian calibration of nonlinear concrete damage models. The framework integrates a bi-scalar damage constitutive model, support vector regression based surrogate modeling, response-level finite element model bias representation, and adaptive Markov Chain Monte Carlo sampling within a unified probabilistic setting. The surrogate models are constructed to approximate the nonlinear mapping from constitutive… More >

Hyunghoon Kim¹, Wonsuk Choi², Kyungho Joo^3,*, Hyojin Jo^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.083948 - 27 July 2026

Abstract As mobile networks evolve toward next-generation architectures in which cellular and IP-based voice services are increasingly converged, SIMBox-based call routing has emerged as an important issue in modern telecommunication networks. By converting Voice over IP (VoIP) traffic into local cellular calls, SIMBox appliances allow IP-originated calls to appear as domestic cellular calls. Although SIMBox usage is not inherently fraudulent, detecting SIMBox-routed calls is important for identifying abnormal call-routing behavior and supporting network-side and client-side security applications. In this paper, we propose a client-side framework for SIMBox-routed call detection. Calls routed through SIMBox infrastructure are identified… More > Graphic Abstract

Li-Woei Chen¹, Kun-Lin Tsai^2,*, Fang-Yie Leu³, Chao-Tung Yang^3,4,5, Wei-Zong Liang²

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.083880 - 27 July 2026

Abstract Unmanned aerial vehicle (UAV) swarm networks are increasingly deployed in surveillance, disaster response, and intelligent transportation systems, where secure and efficient communication is critical under resource-constrained environments. However, conventional public-key-based security mechanisms introduce excessive computational overhead, while standalone intrusion detection systems are insufficient to defend against dynamic and multi-vector attacks in swarm networks. To address these challenges, in this paper, a lightweight time-indexed secure communication framework with intrusion detection modeling (TSCID) is proposed for resource-constrained UAV swarm networks. The proposed TSCID integrates a time-indexed session key derivation mechanism with lightweight authenticated encryption to ensure confidentiality,… More >

Yeog Kim, Changhoon Lee, Kiwook Sohn^*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.083669 - 27 July 2026

Abstract With the widespread adoption of encryption protocols, payload-based traffic analysis has become increasingly infeasible, posing significant challenges for intrusion detection systems (IDS). Consequently, AI-based approaches for encrypted traffic analysis have gained substantial attention. However, existing studies are often evaluated using inconsistent criteria, including heterogeneous attack labels, behavioral representations, and model architectures, making systematic comparison difficult. To address this limitation, this paper proposes a three-level analytical taxonomy for encrypted traffic analysis, structured around attack objectives (Level 1), observable network behaviors (Level 2), and detection models (Level 3). The proposed framework provides a structured perspective for analyzing… More >

Emrah Altun^1,*, Christophe Chesneau², Atacan Erdis¹

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.083459 - 27 July 2026

Abstract Modeling bounded response variables is an important problem in computational statistics, especially in applications involving skewed, heavy-tailed data. In such cases, the modal regression is a robust alternative to traditional mean-based modeling approaches. In this study, a new bounded distribution, called the extended Bradford distribution, is proposed as a flexible extension of the classical Bradford distribution. By incorporating an additional shape parameter, the corresponding model can capture various shape structures, such as left and right skewness, increasing, and bathtub hazard shapes. The new distribution provides an explicit expression for the mode, making it suitable for More >

Guglielmo D’Amico^1,*, Edoardo Lui², Filippo Petroni¹

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.082608 - 27 July 2026

Abstract This paper develops an operational framework for short-horizon risk management in multistate stochastic systems, with application to wind farm performance. We focus on instantaneous mobility-based indicators derived from finite-state continuous-time Markov chains, which capture the local propensity of a system to transition between states. Unlike classical reliability and availability measures, these indicators provide a dynamic description of system behavior. The indicators are interpreted as policy signals to support decision-making under budget constraints. We introduce a state-conditional expected short-horizon loss, representing non-production risk, and use it to evaluate ranking-based intervention strategies. The framework is applied to More >