Open Access
ARTICLE
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, DOI:10.32604/cmes.2026.085976
（This article belongs to the Special Issue: Environment Modeling for Applications of Mobile Robots)
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 >
Open Access
REVIEW
Yeog Kim, Changhoon Lee, Kiwook Sohn*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.083669
（This article belongs to the Special Issue: The Evolution of Cybersecurity and AI: Surveys and Tutorials)
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 >
Open Access
ARTICLE
Rana Muhammad Adnan Ikram1, Jing-Cheng Han1,*, Ahmed A. Ewees2, Mo Wang3, Ozgur Kisi4,5,6,*, Salim Heddam7, Mohammad Zounemat-Kermani8
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.083313
（This article belongs to the Special Issue: Explainable AI, Digital Twin, and Hybrid Deep Learning Approaches for Urban–Regional Hydrology, Water Quality, and Risk Modeling under Uncertainty)
Abstract Two-phase optimized machine learning and deep learning models play a key role in enhancing the prediction accuracy of nonlinear time series modeling. This study assesses the performance of a novel two-phase optimized Long Short-Term Memory (LSTM) model with integration of Aquila Optimizer (AO) and Wild Horse Optimizer (WHO) in predicting monthly streamflow in a snow-fed catchment. The two-phase optimized LSTM-WHOAO model is compared with single-phase optimized models such as LSTM-GA (Genetic Algorithm), LSTM-GWO (Grey Wolf Optimizer), LSTM-WOA (Whale Optimization Algorithm), LSTM-AO, and LSTM-WHO. The outcomes acquired from the deep learning models were compared using four… More >
Open Access
EDITORIAL
Sathishkumar Veerappampalayam Easwaramoorthy*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.087043
（This article belongs to the Special Issue: Advances in AI-Driven Computational Modeling for Image Processing)
Abstract This article has no abstract. More >
Open Access
ARTICLE
Aoran Huang1,2,*, Sinuo Zhang1,2, Haoxiang Zhu1,2, Xiaojing Fan1,2, Huachun Zhou1,2,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.083972
（This article belongs to the Special Issue: Advanced Security and Privacy for Future Mobile Internet and Convergence Applications: A Computer Modeling Approach)
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 >
Open Access
ARTICLE
Lei Yang1,2, Yiyun Hu1, Xianglong Yang1,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.083905
（This article belongs to the Special Issue: Computational Advances in Nanofluids: Modelling, Simulations, and Applications)
Abstract Accurate prediction of entropy generation in nanofluid turbulent convection is essential for optimizing thermal system efficiency, yet remains challenging due to complex near-wall phenomena and thermal property variations with temperature. This study applied an elliptic blending turbulence model (SST k-ω-φ-α) to numerically analyze entropy generation in alumina-water nanofluid flow through a uniformly heated circular tube. The model’s performance was validated using both experimental data and established heat transfer and fluid flow correlations at small wall-bulk temperature difference condition, and its superiority was rigorously evaluated against two widely adopted turbulence models (SST k-ω and realizable k-ε).… More >
Open Access
ARTICLE
Emrah Altun1,*, Christophe Chesneau2, Atacan Erdis1
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.083459
（This article belongs to the Special Issue: Computer Modeling in Statistics)
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 >
Open Access
ARTICLE
Ki-Young Shin1, Soonmo Kwon2, Kyudong Park3,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.083410
Abstract Multimodal AI systems often suffer from “over-informing”, where excessive raw visual input introduces noise that distracts from task-relevant decisions. Motivated by selective human attention strategies, we propose ARS-MMT (Attention and Reasoning through Source Sentences for Multimodal Machine Translation), an architecture that operationalizes a “look-and-think” pipeline: a source-language encoder first builds contextualized linguistic representations, a relation reasoning network then produces a query-conditioned visual channel, and a multimodal decoder generates the translation conditioned in parallel on the encoded text and on this visual channel. We quantify the contribution of the visual modality through a controlled ablation: zeroing… More >
Open Access
ARTICLE
Abeer S. Hadad1, Fahman Saeed2, Adeeb A. Ahmed3,*, Jiangbin Zheng1
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.083873
Abstract Loop invariant inference is fundamental to program verification, yet it remains particularly challenging for multipath loops, where different execution paths may exhibit incompatible behaviors across feasible executions. In such settings, invariants that are both sound and sufficiently precise often require disjunctive forms, whose automatic inference remains difficult. This paper presents an efficient, path-sensitive, counterexample-guided framework for automated loop invariant inference. Our approach leverages a Path Dependency Automaton (PDA) to systematically decompose the semantics of multipath loops by modeling feasible execution paths independently. Building on this decomposition, we introduce a localized, path-guided Counterexample-Guided Invariant Refinement (CEGIR) More >
Open Access
ARTICLE
Alireza Norouzpour Shahrbejari1, Nafiseh Pishbin2, Mohammad Reza Maghami3,*, Mazlan Mohamed4,*, Mohammad Golmohammad1
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.083763
Abstract This study presents a weighted-sum multi-criteria optimization framework using PSO for the optimal siting, sizing, and scenario-based operation of energy storage systems (ESSs) in renewable-integrated distribution networks. The proposed model concurrently addresses technical, economic, and reliability objectives—minimizing active power losses (PL), voltage deviation (VD), expected energy not supplied (EENS), and short-circuit level (SCL), while maximizing voltage sensitivity index (VSI) and power-loss sensitivity factor (PLSF). A Particle Swarm Optimization (PSO) algorithm with weighted-sum scalarization is employed to solve this complex, nonlinear optimization problem and effectively balance the conflicting operational goals. The framework is validated using IEEE… More >
Open Access
ARTICLE
Kiran Asma, Muhammad Asif Zahoor Raja*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.083532
（This article belongs to the Special Issue: Emerging Technologies in Information Security: Modeling, Algorithms, and Applications)
Abstract The accelerated digital transformation of critical infrastructure has yielded unprecedented system interconnectivity, enhancing operational efficiency, simultaneously expanding the epidemiological propagation surface in heterogeneous networks. A novel machine learning-driven neuroarchitecture is designed in the present study, leveraging multilayer autoregressive exogenous neural networks (ARXNNs) iteratively trained with the Levenberg Marquardt (LM) algorithm, i.e., ARXNNs-LM, to address the intricate temporal dynamics of nonlinear fractional epidemiological computer virus propagation in the networks. The proposed ARXNNs-LM methodology effectively models the dynamic state transitions between susceptible, infected, and recovered systems. The dataset is synthesized through the application of the Grünwald–Letnikov (GL)… More >
Open Access
ARTICLE
Marta Leal, Verónica Abad-Alcaraz, María del Mar Castilla, José Domingo Álvarez*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.080261
（This article belongs to the Special Issue: Intelligent Control and Machine Learning for Renewable Energy Systems and Industries)
Abstract The effective control of parabolic trough collectors (PTCs) remains a significant challenge due to the inherent non-linearities of the system and the continuous impact of environmental disturbances. Although PTCs are a key technology for industrial process heat and large-scale electricity generation, classical control strategies often struggle to maintain optimal performance under fluctuating conditions. To address these limitations, this paper presents a novel reinforcement learning (RL)-based controller, designed specifically for solar thermal systems. The proposed RL agent is designed to learn directly from operational data, enabling it to adapt its control policy in real time to More >
Open Access
ARTICLE
Teng Long1, Leyu Wang2,*, James D. Lee3, Cing-Dao Kan2
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.080866
Abstract Titanium alloy Ti-6Al-4V has been widely applied in many industries, for example, aerospace, marine, automotive, and biomedical engineering systems, where accurate characterization of plastic deformation is important for evaluating material performance and potential failure under severe loading conditions. This material shows nonlinear plasticity and tension–compression asymmetry, which makes the strain hardening characterization important for computational failure analysis and crashworthiness-related simulations. However, conventional strain hardening models and parameter identification methods often rely on linear or extrapolation-based assumptions and are sensitive to initial guesses due to the non-convex nature of the optimization problem. In this study, a More >
Open Access
ARTICLE
AL-Wesabi Ibrahim1, Hassan M. Hussein Farh2,*, Jiazhu Xu1,*, Mohamad A. Alawad2, Ahmed Alqurashi3, Abdullrahman A. Al-Shamma'a2
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.081371
（This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Methods Applied to Energy Systems, 2nd Edition)
Abstract Load frequency control (LFC) in interconnected power systems has always been a challenging task in the
presence of uncertainty and variability in the power systems arising primarily due to the integration of renewable energy
sources and the impact of electric vehicles on the power system. Although various PI/PID and other advanced control
strategies have been employed for LFC in power systems, the existing methods have shown some limitations in terms of
dynamic flexibility and robustness in the presence of nonlinearities and couplings in the power systems. Moreover, the
optimization methods employed for the tuning of… More >
Open Access
ARTICLE
Manal Ghannam1, Bilgen Kaymakamzade1,2, Muhammad Farman1,3,4, Kottakkaran Sooppy Nisar5,*, Mohammed Altaf Ahmed6
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.081075
（This article belongs to the Special Issue: Mathematical Aspects of Computational Biology and Bioinformatics-III)
Abstract In Vitro Fertilization (IVF) has been a major medical advancement in the field of fertility treatment.
It has helped millions of individuals and couples overcome infertility by providing a workable option. It involves
removing eggs from the ovaries of a female, fertilizing those eggs with male sperm in a monitored lab condition. In
this work, we developed a new model to show the success of In Vitro Fertilization rates in women through a fractional-
order compartmental modeling framework by using real data. The developed model is analyzed statistically, and the
biological feasibility of the model. The Lipschitz… More >
Login
Register
Submit a Paper
Propose a Special lssue