Open Access
ARTICLE
Xuan Kien Pham1, Van Dat Nguyen2,*, Van Thanh Phan3,*, Duc Trien Nguyen4,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.086196
（This article belongs to the Special Issue: Intelligent Scheduling and Optimization in Engineering and Management)
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 >
Open Access
ARTICLE
Yunus Celik*, Burhan Necati Kiziloglu
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.084744
Abstract This study investigates the passive amplification of aerodynamic excitation forces through coordinated bluff-body geometric modifications to quantify the vortex-induced vibration (VIV) energy harvesting potential of stationary cylinders in the laminar regime. Two-dimensional laminar simulations on fixed bodies isolate geometric effects from structural feedback. Circumferential biomimetic grooves are first optimised on a circular baseline at
Open Access
ARTICLE
Yiyan Zhang1,*, Yi Xin2, Qin Li2
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.082841
Abstract Medical data has specificity compared to other fields of data, and the description of medical data characteristics is still in a qualitative stage. This study included 293 sub-datasets of 138 independent datasets. First, data preprocessing was performed using methods such as incomplete data removal, inconsistent data normalization, and data integration. Then, the characteristics of 293 research datasets were quantified using 26 indicators in three categories: simple indicators, statistical indicators, and informational indicators. Furthermore, statistical analysis was performed on the above-mentioned quantitative characteristics, and stepwise regression and decision tree methods were used for modeling learning. The… More >
Open Access
ARTICLE
Mutlu Seçer*, Ali Alper Saylan
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.084228
（This article belongs to the Special Issue: Numerical Modeling in Technical Diagnostics and Predictive Maintenance)
Abstract Climate change modifies environmental exposure conditions and affects the corrosion-driven deterioration of steel bridges, thereby challenging conventional maintenance planning approaches. Thus, more advanced maintenance management strategies are required to address the challenges associated with varying corrosion rate projections. In this study, a novel adaptive maintenance management framework is proposed for steel truss bridges to address climate change-induced corrosion under evolving deterioration conditions. Adaptivity is achieved by updating corrosion rates to consider time-varying deterioration conditions associated with climate change. This enables time-dependent representation of corrosion progression under changing environmental conditions. The framework is demonstrated on a… More >
Open Access
ARTICLE
Shu-Chuan Chu1,2, Libin Fu2, Jeng-Shyang Pan1,2,3,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.084391
（This article belongs to the Special Issue: Advances in Computational Intelligence for Complex Systems)
Abstract The Animated Oat Optimization Algorithm (AOO) is a novel evolutionary algorithm inspired by the behavior of animated oats. This paper proposes a Competitive Parallel Animated Oat Optimization Algorithm (CPAOO) comprising two components. First, a parallel strategy is employed in which inter-subpopulation communication is triggered at predefined iteration thresholds to balance exploration and exploitation. Second, a grouped competition strategy with incentive mechanisms is introduced, enabling the prioritized evolution of superior individuals to enhance the algorithm’s efficiency. Furthermore, building on the Prediction Error Expansion (PEE) algorithm, this paper proposes a Dual-Layer PEE (DLPEE) algorithm for reversible digital More >
Open Access
ARTICLE
Kazutaka Toda1, Yuntao Wang1,*, Hyungrok Jo2, Yang Li1
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.083407
（This article belongs to the Special Issue: Advanced Security and Privacy for Future Mobile Internet and Convergence Applications: A Computer Modeling Approach)
Abstract The rapid evolution of quantum computing poses a fundamental challenge to classical public-key cryptosystems, accelerating the adoption of lattice-based post-quantum cryptography in large-scale digital infrastructures, including Future Mobile Internet Technologies (FMIT) and their convergence applications (FMIT-CA). As lattice-based cryptography is expected to play an important role in such environments, accurate hardness estimation and parameter assessment of underlying lattice problems have become increasingly important. Since the security of these cryptographic schemes is closely related to the computational hardness of the Shortest Vector Problem (SVP), improving practical SVP-solving techniques contributes indirectly to the security evaluation of such… More >
Open Access
ARTICLE
Jiawang Zou, Annan Jiang*, Xinzhi Wang, Hao Huang
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.082647
（This article belongs to the Special Issue: Artificial Intelligence and Advanced Numerical Modeling Integration Techniques in Tunnel and Underground Engineering)
Abstract Accurate forecasting of settlement in buildings adjacent to shield tunnels remains a critical challenge in underground engineering due to complex spatiotemporal interactions and nonlinear relationships among multi-source monitoring data and construction parameters. To address this issue, a Sparrow Search Algorithm (SSA)-optimized Convolutional Neural Network–Long Short-Term Memory–Transformer (CNN-LSTM-Transformer) hybrid framework is proposed, explicitly incorporating the relative spatial relationship between the shield excavation face and adjacent structures. In this framework, the Convolutional Neural Network (CNN) module extracts spatial features from monitoring data and tunneling parameters, capturing interdependencies among different construction indicators and reflecting local spatial heterogeneity of… More >
Graphic Abstract
Open Access
ARTICLE
Zhongjian Ling, Yongou Zhang*, Yifan Li, Xianzhong Wang
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.082363
（This article belongs to the Special Issue: Recent Developments in Nonlocal Meshfree Particle Methods for Solids and Fluids )
Abstract Due to the complex and dynamic nature of multi-phase interfaces, accurately capturing interface evolution remains one of the key challenges in multi-phase flow simulations, particularly in modeling bubble rising. In this study, a fully Lagrangian method is developed by using the Generalized Finite Difference (GFD) scheme, which we refer to as Finite Difference Particle Method (FDPM), and the Continuum Surface Force (CSF) model to simulate bubble dynamics. In this framework, the fluid is represented by particles, and all partial differential terms in the Navier–Stokes equations are discretized into symmetric linear systems using the GFD scheme.… More >
Graphic Abstract
Open Access
ARTICLE
Safa Alsafari1, Ayman Yafoz2,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.084403
Abstract Bearing fault diagnosis in industrial deployment must contend with two simultaneous distributional shifts: fault severity increases as damage progresses, and motors operate at loads unseen during training. We define this compound setting as the double domain shift and present a rigorous few-shot benchmark on the Case Western Reserve University (CWRU) and Paderborn University (PU) bearing datasets. Six architectures spanning distinct learning paradigms—a multilayer perceptron (MLP), a capsule network (CapsNet), a residual capsule network (ResCaps), a prototypical network (ProtoNet), a modified residual convolutional network (MRCN), and Deep Correlation Alignment (Deep CORAL)—are evaluated under a strict three-way split… More >
Open Access
ARTICLE
José González-Cabrero1,2, Carmelo Gómez1,2, Manuel Paredes3, Francisco Cavas1,2,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.082643
（This article belongs to the Special Issue: Advances in Modeling and Optimization of Biological and Bio-Inspired Systems)
Abstract The development of accurate digital twin models of the human cornea is a key factor for planning and monitoring eye treatments and clinical supervision. Corneal tissue can be simulated with the implementation of hyperelastic models based on strain energy density functions. However, the number of hyperelastic models and the parameters’ variation that define these models hinder comparison across different studies. Furthermore, parameter calculations based on a single test are an ill-posed problem. In this research, a novel sequential methodology based on collagen fibril crimping strain threshold has been implemented to calculate the corneal material’s parameters. More >
Graphic Abstract
Open Access
ARTICLE
Sachin Tom, Eva Gutheil*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.084886
（This article belongs to the Special Issue: Modeling and Applications of Bubble and Droplet in Engineering and Sciences)
Abstract A single droplet heating, vaporization, and detailed combustion model is developed for pure
Open Access
ARTICLE
Sahil Tripathi1, Manaswi Kulahara2, Abdul Khader Jilani Saudagar3, Hatoon S. AlSagri3,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.083755
（This article belongs to the Special Issue: Applied NLP with Large Language Models: AI Applications Across Domains)
Abstract Spatial reasoning, defined as the ability to infer and compose relations among entities—is fundamental to geoinformatics applications such as spatial querying and map understanding. However, existing works such as Bidirectional Encoder Representations from Transformers (BERT)-based spatial Question Answering (QA) models and neuro-symbolic models rely on dataset-specific patterns, leading to shortcut learning, where reliance on superficial lexical cues rather than true relational understanding. Recent Large Language Models (LLMs)-based works, including fine-tuning and Chain-of-Thought (CoT) prompting, partially alleviate shortcut learning but remain limited by non-causal reasoning, where predictions depend on spurious correlations rather than stable relational structure. To address these… More >
Open Access
ARTICLE
Md. Fahmid-Ul-Alam Juboraj1, Fahmid Al Farid2,3, Mahe Zabin4, Jia Uddin5, Muhammad Iqbal Hossain1,*, Sarina Mansor2,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.081254
Abstract The integration of Internet of Things (IoT) technologies in agriculture enables precision farming but introduces significant cybersecurity vulnerabilities. This paper presents FICNet (Feature Integrated Convolutional Network), a lightweight deep learning architecture for intrusion detection in agricultural IoT environments. Evaluated on the Farm-Flow AG-IoT security dataset, FICNet achieves 100% binary classification accuracy and 81.25% multiclass accuracy (macro F1: 80.43%, precision: 91.26%, ROC-AUC: 96.78%) across 8 traffic categories. A multi-dimensional component analysis confirms the contribution of each architectural component: multi-scale convolutions provide 5.3% noise robustness advantage, squeeze-and-excitation attention controls per-class detection trade-offs, and the full architecture achieves More >
Open Access
REVIEW
Ismail Hossain1, Mridul Banik2, Fahmid Al Farid3,4, Jia Uddin5,*, Hezerul bin Abdul Karim4,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.075507
Abstract In recent years, the rapid advancement of Large Language Models (LLMs) has significantly transformed natural language processing (NLP), enabling impressive performance across a wide range of tasks. However, these developments have largely benefited high-resource languages, leaving many low-resource and underrepresented languages at risk of further digital marginalization. Addressing this imbalance is crucial to building more inclusive and culturally sustainable AI systems, which is motivating growing research interest in adapting LLMs for linguistically diverse and resource-scarce communities. This systematic review examines recent progress (2020–2025) in the pretraining and adaptation of LLMs for Low-Resource Languages (LRLs). Analysed… More >
Open Access
ARTICLE
Ali Raza1,*, Umar Shafique1, Marek Lampart1, Dumitru Baleanu2, Emad Fadhal3, Hadil Alhazmi4
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.084939
Abstract Kawasaki disease (KD) is an acute, self-limited pediatric vasculitis of unknown etiology and is one of the leading causes of acquired coronary artery complications in children. Endothelial dysfunction, vascular endothelial growth factor (VEGF) activity, adhesion molecule/chemokine activation, and inflammatory cytokine responses play important roles in its pathogenesis. This paper presents a delay differential equation model with stochastic perturbations to study lesion-level inflammatory mechanisms involved in Kawasaki disease pathogenesis. The model describes interactions among healthy endothelial cells, vascular endothelial growth factor (VEGF), adhesion molecules/chemokines, and inflammatory cytokine activity. Mathematically, endothelial-cell injury promotes VEGF production, VEGF contributes… More >
Open Access
ARTICLE
Changwei Song1, Jiaqi Qiang2, Hongjun Liu1, Jianqiang Li1, Hui Pan2, Qing Zhao1,*, Jiuzuo Huang3, Shi Chen3
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.083525
（This article belongs to the Special Issue: Advances in Computational Intelligence for Complex Systems)
Abstract Cushing’s syndrome (CS) is a rare endocrine disorder characterized by chronic hypercortisolism, and facial image-based intelligent diagnosis has emerged as a promising non-invasive approach. However, existing diagnostic models suffer from two core bottlenecks: inefficient fusion of deep semantic features and clinical prior features, and insufficient multi-view facial feature disentanglement without CS-specific pathophysiological constraints. To address these limitations, we propose a novel Multi-View Facial Feature Disentanglement Network (MVFFD-Net) for high-precision automatic CS diagnosis. The network takes five standard facial views (frontal, bilateral
Open Access
ARTICLE
Daniel Martín-Pérez1, Francesc Rodríguez-Díaz1, David Gutiérrez-Avilés2, Alicia Troncoso1, Francisco Martínez-Álvarez1,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.082712
（This article belongs to the Special Issue: Quantum Machine Learning: Methods and Engineering Applications)
Abstract Hybrid classical-quantum architectures have emerged as a practical response to the data and compute demands of modern deep learning, since a pretrained classical backbone can carry the feature extraction while a compact quantum head provides the trainable component. Quantum transfer learning is the most active instance of this idea. However, existing quantum transfer learning pipelines have been evaluated in isolation, typically on a single software framework and without a structured treatment of noise or statistical significance, which makes it difficult to assess how this paradigm contributes over fair classical baselines. A methodological benchmark for quantum… More >
Open Access
ARTICLE
Tanzila Saba1, Muhammad Mujahid1, Faten S. Alamri2,*, Roaa Khalil Mohamed Ali Abed3
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.084816
（This article belongs to the Special Issue: Machine Learning and Deep Learning-Based Pattern Recognition, 2nd Edition)
Abstract In the era of artificial intelligence, pattern recognition techniques have become fundamental in advancing medical image processing, diagnosis, and automated disease classification systems. Among various clinical challenges, breast cancer is the second most dangerous leading cause of death in women worldwide. Early and accurate detection of breast cancer is crucial to develop advanced diagnostic methods to control further loss or reduce mortality rates. This study proposes a dynamic graph multi-scale network for breast cancer diagnosis, integrated with multi-scale convolutional feature extraction, a squeeze-and-excitation block, and a graph convolutional network to jointly model local spatial features… More >
Graphic Abstract
Open Access
REVIEW
Imran Ahsan1, Muhammad Waseem Anwar2, JungYoon Kim3, Mucheol Kim1,4,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.080382
（This article belongs to the Special Issue: Emerging Artificial Intelligence Technologies and Applications-II)
Abstract Graph neural networks (GNNs) have demonstrated promising results in enhancing machine learning and artificial intelligence techniques by addressing graph-related problems, including graph categorization, edge prediction, and node prediction. Despite the rapid growth of the field, a structured synthesis of recent application-oriented GNN research is necessary. This systematic literature review analyzes 363 peer-reviewed, application-oriented GNN studies published from 2019 through February 2026 from four scientific repositories. These studies are organized by GNN model variant, (including single and multimodel approaches), and four classification task categories: graph, link, node, and node and edge combined. In addition, the review… More >
Open Access
ARTICLE
Awais Qasim1, Aniqa Iftikhar1, Hanaa Nafea2, Nay Chi Moe Oo3, Byung-Seo Kim4,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.084691
Abstract Optimal task assignment in holonic multi-agent systems has emerged as a pivotal problem in modern distributed systems. Despite substantial gains in agent coordination, many large-scale systems still suffer from poor job allocation, resulting in performance bottlenecks and resource waste. Effective task assignment is critical for these systems since it influences individual agent performance and overall adaptability. A significant challenge within holonic multi-agent systems is ensuring optimal task assignment while resolving performative inconsistencies, such as role conflicts and coordination failures among agents. This research proposes a novel optimization framework to address these inconsistencies, enabling more efficient More >
Open Access
ARTICLE
Yi Chen, Xiaodan Ren*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.083966
（This article belongs to the Special Issue: AI-Enhanced Computational Methods in Engineering and Physical Science)
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 >
Open Access
REVIEW
Weiqiang Jin1,2,#, Yang Liu2,#, Yang Gao1,#, Shixiang Tang2, Yanghao Zhou3, Jinhu Qi4, Wentao Zhang4, Junli Wang5, Jing Gao2, Yue Ma4, Ziwei Zhang1,*, Biao Zhao2,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.083586
（This article belongs to the Special Issue: Emerging Artificial Intelligence Technologies and Applications-II)
Abstract Large Vision-Language Models (LVLMs) have achieved strong performance in multimodal perception, understanding, and generation, but their ability to perform complex logical reasoning remains insufficiently understood. In particular, it is still unclear whether current LVLMs can reliably conduct explicit logical operations, multi-step inference, abstract relational reasoning, and cross-modal evidence integration. Reasoning abilities such as deductive, inductive, abductive, multi-hop, and causal inference are fundamental to robust decision making, trustworthy interaction, and real-world deployment, yet they have not been systematically examined in the LVLM literature. Existing surveys mainly discuss mathematical reasoning, general multimodal intelligence, or benchmark progress, but… More >
Open Access
ARTICLE
Mohammed Ansar Mohammed Manaz1,*, Shang Ping Hong2, Tzung-Lin Lee1
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.083270
（This article belongs to the Special Issue: Stochastic Modeling and Reliability Assessment in Industrial Engineering Systems)
Abstract Silicon Carbide Metal Oxide Semiconductor Field Effect Transistors (SiC MOSFETs) have superior characteristics compared to traditional Silicon-based switching devices. SiC devices can support fast switching speeds and high blocking voltages. Due to limited historical data and rapid technological improvements, there is not enough field data to correctly evaluate the reliability of the state-of-the-art SiC MOSFETs. An accurate model of their reliability and aging characteristics is needed to expedite their rapid commercial adoption in mission-critical applications, such as offshore wind farms and electric vehicles. Classical handbook-based methods produce large errors due to their inability to correctly… More >
Open Access
ARTICLE
Mahander Kumar1, Lal Khan2,*, Mohammad Zubair Khan3,*, Ibrahim Aljubayri4
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.081699
（This article belongs to the Special Issue: Applied NLP with Large Language Models: AI Applications Across Domains)
Abstract Aspect-Based Sentiment Analysis (ABSA) is a fundamental Natural Language Processing (NLP) task that aims to determine fine-grained sentiment polarity toward specific aspects mentioned in text. With the emergence of Large Language Models (LLMs) and transformer-based architectures, significant improvements have been achieved in contextual representation learning for sentiment analysis. However, existing LLM-inspired and transformer-based ABSA frameworks often suffer from inadequate aspect-context alignment, redundant feature integration, limited interpretability, and insufficient coordination between contextual and sequential modeling components. To address these challenges, this paper proposes two hybrid architectures, namely HABSA and AAC-HABSA, centered on a novel Adaptive Aspect… More >
Graphic Abstract
Open Access
ARTICLE
Waqas Ahmad1, Shahzad Anwar2, Abid Iqbal3,*, Abuzar Khan4, Saad Arif5, Ali S. Alzahrani3, Mohammed Al-Naeem6, Fatimah Alhayan7, Syed Hashim Raza Bukhari3, Ghassan Husnain4,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.083950
（This article belongs to the Special Issue: Machine Learning and Data Fusion for Autonomous Control and Surveillance Systems)
Abstract Accurate vehicle localization is essential for safety-critical vehicular ad hoc networks (VANETs), including emergency response, navigation, traffic monitoring, and cooperative driving. However, conventional GPS/GNSS positioning systems have often shown degradation in tunnels, dense urban corridors, and non-line-of-sight (NLOS) environments, where satellite visibility and signal reliability are limited. This paper proposes a calibrated Link Quality Indicator (LQI)-based closed-form localization framework for partially connected Roadside Unit (RSU)-assisted VANETs. The proposed framework first calibrates the LQI-to-range relationship using numerical regression parameters and then converts accepted LQI observations into distance estimates. The distances are processed through a variance-aware weighted… More >
Open Access
ARTICLE
Junho Lee, Sanghyun Park, Younghun Lee, Namsu Kim*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.083185
Abstract Monitoring the conditions of electric motors in industrial applications is an essential step for ensuring safety and reducing maintenance costs. This paper deals with one of the most frequent winding failures—the inter-turn short fault of an interior permanent magnet synchronous motor. A novel high-fidelity co-simulation framework toward a digital twin-based approach combining Maxwell simulation in finite element method (FEM) for the motor and control system in system software for the inverter is presented. An analysis of the motor based on a 2D FEM model is performed considering the motor topology and non-linear properties, and inductance… More >
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 >
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