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ARTICLE

Amel Ksibi^1,*, Manel Ayadi¹, Hela Elmannai², Monia Hamdi², Ala Saleh Alluhaidan¹, Imen Ksibi³
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.078347 - 27 May 2026
（This article belongs to the Special Issue: Advances in AI-Driven Computational Modeling for Image Processing)
Abstract Cerebral palsy is a prevalent neurodevelopmental syndrome that disrupts motor development in children, making early detection vital for effective intervention. Traditional clinical assessments rely on subjective observations, often missing minor motor abnormalities until they become severe, typically after 12 months of age. This article presents a novel deep learning model, TransCP-Net (Transformer-based Cerebral Palsy Network), designed for early detection of infant cerebral palsy through spatiotemporal pose representation learning. The architecture employs hierarchical spatial and temporal attention to analyze complex motion patterns in video sequences, integrating multi-modal data for improved accuracy. TransCP-Net incorporates specialized preprocessing, including More >

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EDITORIAL

Bo Yang^1,*, Chao Liu²
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.083726 - 27 May 2026
（This article belongs to the Special Issue: Recent Advances in Signal Processing and Computer Vision)
Abstract This article has no abstract. More >

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EDITORIAL

Ji Su Park^1,*, Pan Yi², Jong Hyuk (James) Park³
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.083347 - 27 May 2026
（This article belongs to the Special Issue: Machine learning and Blockchain for AIoT: Robustness, Privacy, Trust and Security)
Abstract This article has no abstract. More >

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REVIEW

Mohammed A. El-Shorbagy¹, Anas Bouaouda^2,*, Fatma A. Hashim³
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.077765 - 27 May 2026
（This article belongs to the Special Issue: Swarm and Metaheuristic Optimization for Applied Engineering Application)
Abstract As real-world optimization problems become more complex, the development of sophisticated and robust algorithms has become essential. Consequently, researchers are focusing on advanced optimization methods that efficiently explore the feasible solution space. This involves designing new high-performance algorithms or enhancing existing meta-heuristic methods by integrating advanced evolutionary strategies. Barnacles Mating Optimizer (BMO) is an evolutionary-based meta-heuristic algorithm inspired by the mating behavior of barnacles, incorporating Hardy–Weinberg principles and the sperm-cast mechanism. Introduced in 2020, BMO has attracted significant attention and has been successfully applied across diverse fields due to its simple design, ease of implementation,… More >

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REVIEW

Shuvodeep De^1,*, Agnivo Gosai^2,#, Karun Thankachan^3,#, Ramadan A. ZeinEldin⁴, Abdulaziz T. Almaktoom⁵, Mustafa Bayram⁶, Ali Wagdy Mohamed^7,8,*
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.080601 - 27 May 2026
Abstract Sentiment analysis (SA) has evolved from a niche text-classification task into a central problem in natural language processing, spanning multiple domains, modalities, and languages. This survey provides a comprehensive review of sentiment analysis methods from their origins in lexicon-based approaches through classical machine learning, deep learning architectures, pre-trained transformers, and the current era of large language models (LLMs). We formalize the SA problem across multiple granularity levels (document, sentence, and aspect) and present a taxonomy that encompasses classification, regression, aspect-based sentiment analysis (ABSA), emotion detection, and stance detection tasks across diverse domains including movie reviews,… More >

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REVIEW

Oluwatosin Ahmed Amodu¹, Zurina Mohd Hanapi^1,*, Chedia Jarray², Huda Althumali³, Faten A. Saif ⁴, Raja Azlina Raja Mahmood¹, Mohammed Sani Adam⁵, Nor Fadzilah Abdullah⁵
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.077054 - 27 May 2026
（This article belongs to the Special Issue: Artificial Intelligence for 6G Wireless Networks)
Abstract Non-terrestrial networks (NTNs)—including UAVs, HAPs, and satellite systems—are rapidly becoming key enablers of wide-area, resilient connectivity for large-scale IoT applications. As these platforms integrate with terrestrial networks to form space–air–ground architectures, optimization challenges related to trajectory, resource management, energy efficiency, and security become increasingly complex. Machine learning (ML) has emerged as a central tool for addressing these challenges by enabling adaptive, data-driven decision-making under uncertainty. This survey presents an optimization-centric review of ML-based NTN-assisted IoT systems focusing on aspect-specific datasets. Using a structured methodology involving dataset curation, keyword filtering, metadata analysis, and citation-based paper selection,… More >

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REVIEW

Ghadi Almaktoom, Suliman Aladhadh, Salim El Khediri^*
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.078774 - 27 May 2026
Abstract The rapid growth of phishing attempts in the enterprise could potentially lead to bankruptcy. The primary focus of the research is on detecting phishing attacks, with no interest in how the data is processed. Attackers use fraudulent methods to obtain valuable, confidential information, resulting in billions of dollars in financial losses for enterprises. In our review, we examined the methods used in phishing-detection studies. We concluded that the two main sections, centralized and decentralized methods, were the centralized ones, which aggregate data in a central server and thus violate data protection regulations, such as GDPR.… More >

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REVIEW

Yenjou Wang¹, Chihtan Cheng², Jia-Wei Chang^3,*
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.080888 - 27 May 2026
Abstract As large language models (LLMs) become increasingly integrated into enterprise decision-making processes, structural pressures such as version drift, cross-source evidence integration, and regulatory accountability have shifted the primary challenge from isolated generative performance to system-level consistency, traceability, and governability. This paper systematically reviews key technological developments relevant to enterprise requirements, including document perception, retrieval-augmented generation (RAG), hybrid RAG-KG architectures, fine-grained attribution evaluation, and multi-agent coordination. The analysis demonstrates that the main obstacle to enterprise LLM adoption is not model capability, but rather the structural gap between fragmented technical modules and the need for high-reliability decision-making. More >

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ARTICLE

Jiaoyan Li¹, James D. Lee^2,*
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.077471 - 27 May 2026
Abstract The key points of micromorphic theory, including the balance laws and entropy principle, are briefly introduced. Maxwell’s equations and the Lorentz Transformation of E and B fields in both relativistic and non-relativistic electromagnetic theory are discussed. The link between the thermomechanical part and the electromagnetic part of the micromorphic electromagnetic theory is established through the body force, body moment, and energy source. The constitutive theory for thermo-visco-elastic-plastic-electromagnetic (TVEP-EM) materials is formulated. Then the constitutive relations are reduced to the materially linear constitutive equations. Onsager’s postulate is utilized for the derivation of viscosity. Return-Mapping-Algorithm is invoked for plasticity.… More >

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ARTICLE

Nasser Firouzi^*
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.075681 - 27 May 2026
Abstract This study investigates finite transient deformations in hyperelastic beam structures based on the Gent material model. To enable its application within beam formulations, the Gent model is expressed in a linearized form. A five-parameter beam element, incorporating two displacement variables, two difference parameters, and one thickness parameter, is adopted. The nonlinear dynamic response is solved using the implicit Newmark method, allowing efficient analysis of beams subjected to complex loading and boundary conditions. The results show that the proposed approach accurately captures the response of geometrically nonlinear beams and reproduces the behavior of neo-Hookean hyperelastic beams More >

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ARTICLE

Md Al Rifat Anan¹, Donghyeon Ryu², Yu-Lin Shen^1,*
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.080371 - 27 May 2026
（This article belongs to the Special Issue: Advances in Numerical Modeling of Composite Structures and Repairs)
Abstract The development of surface wrinkles on thin films bonded to compliant substrates is recognized as a form of mechanical instability. While this wrinkling behavior is widely studied when the thin film is under direct compression, much less attention has been devoted to the prediction of wrinkle formation caused by tension and cyclic compression-tension deformations. This work focuses on compression vs. tension-induced wrinkles using a relatively stiff polymeric film on an elastomeric substrate as the model system. Experimental observations show that parallel wrinkles formed during unidirectional compression gradually disappear under reverse loading. When the thin film… More >

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ARTICLE

Tan N. Nguyen^1,*, Mohamed-Ouejdi Belarbi², Tan Khoa Nguyen^3,4,*, Canh V. Le⁵, Aman Garg^6,7,*
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.079126 - 27 May 2026
（This article belongs to the Special Issue: Advances in Numerical Modeling of Composite Structures and Repairs)
Abstract Imperfections can significantly reduce the load-carrying capacity of structures, especially in thin shells. Such imperfections can stem from inaccurate fabrication and erection and they should be taken into account in the analysis and design. For the first time, post-buckling behavior of functionally graded triply periodic minimal surface (FG-TPMS) shells under axial compression is investigated in this paper. The proposed formulation considers both geometric imperfection and porosity which can be considered as material imperfection. The two types of porosity in this study are the even and uneven porosity distributions. The nonlinear responses of FG-TPMS shells with… More >

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ARTICLE

Bahareh Ramzikhalesi^1,*, Ali Rakhsh-Mahpour¹, Josep Claramunt-Blanes², Ernest Bernat-Maso³
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.080138 - 27 May 2026
（This article belongs to the Special Issue: Advances in Numerical Modeling of Composite Structures and Repairs)
Abstract The mechanical behavior of nonwoven fabrics as reinforcement in cementitious composites remains insufficiently explored, particularly from a numerical modeling perspective, despite their growing interest as sustainable alternatives to conventional textiles. This study presents a simplified, engineering-oriented numerical modeling framework for reproducing the flexural mechanical response of cementitious composites reinforced with flax nonwoven fabric. Four-point bending (flexural) behavior of nonwoven fabric–reinforced cementitious composites was numerically simulated using ANSYS software. The model is developed using Finite Element Analysis (FEA) and incorporates a Representative Volume Element (RVE) approach to account for the heterogeneous fiber–matrix interaction. The required material… More >

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ARTICLE

Huilan Wu, Yijun Liu^*
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.081675 - 27 May 2026
（This article belongs to the Special Issue: AI-Enhanced Computational Mechanics and Structural Optimization Methods)
Abstract Metasurface design often requires solving field distributions across varying structural parameters and frequencies, where neural operators offer a promising avenue for fast prediction. However, conventional neural operators have problems with degradation of the accuracy in multi-scale structural analysis. In this work, we propose a Generative Residual Enhanced Neural Operator (GRE-NO) framework that introduces a generative residual network to model the systematic bias of the main predictor. The core model retains the DeepONet architecture with both branch and trunk networks implemented using Fourier Neural Operators, combining strong generalization and efficient global representation. To handle the complexity More >

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ARTICLE

Chaoshuai Duan, Yin Wang, Guohua Zhu^*, Xiaotian Zhang, Jiale Wang, Zhen Wang
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.081676 - 27 May 2026
（This article belongs to the Special Issue: Modeling Strategy and “Material-Structure-Function” Integrated Design for Composite Components)
Abstract Hybrid fiber reinforced plastic (HFRP) composites, especially intra-layer carbon/glass hybrids, offer a promising balance of specific strength, impact resistance, and cost efficiency for thin-walled energy-absorbing structures. This study investigates the low-velocity impact response and energy absorption of intra-layer carbon/glass hybrid hat-shaped beams. Tensile and impact tests evaluated the effects of hybrid ratio and fiber orientation. A multiscale damage model based on micromechanical damage and failure criteria was established via Abaqus/VUMAT, integrating stress amplification factors to bridge micro-meso-macro scales. Experimental results show that carbon fibers aligned with the loading direction yield hybrid composites with superior tensile… More >

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ARTICLE

Salamat Ullah^1,2,*, Muhammad Zahid³, Khaled Aati⁴, Abdulrahman Abbadi⁴, Haroon Ijaz⁵, Ali Qabur⁴
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.080484 - 27 May 2026
（This article belongs to the Special Issue: Emerging Artificial Intelligence & Data-Driven Modeling in Civil Engineering)
Abstract Designing thin-walled plate structures is challenging due to their susceptibility to various forms of structural instability. In addition, the substantial computational cost of finite element analyses, especially in optimization scenarios, underscores the need for efficient and reliable surrogate models. To address this challenge, the present study employs machine learning (ML) techniques to predict the buckling response of thin plates under complex boundary conditions. Four ML models, including XGBoost, CatBoost, Light GBM, and Random Forest, are developed to predict the buckling coefficient based on input features, including aspect ratio, boundary condition, and compressive loading pattern. The… More >

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ARTICLE

Chuanjiang Chen¹, Junyong Zhou^1,*, Binbin Du¹, Miaosi Dong^2,*, Liwen Zhang¹, Bitang Zhu³
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.081546 - 27 May 2026
（This article belongs to the Special Issue: Emerging Artificial Intelligence & Data-Driven Modeling in Civil Engineering)
Abstract Computer vision has been widely adopted in intelligent construction monitoring; however, existing studies primarily focus on identifying individual construction elements or isolated activities, with limited capability for integrated monitoring of complete construction workflows. Such workflow-level automation is a prerequisite for intelligent construction and unmanned job sites. To address the challenge of reliable visual recognition in drill-and-blast tunnel environments characterized by uneven illumination, localized glare, and dust interference, this study proposes a methodological framework for construction workflow recognition at the tunnel face using computer vision and context reasoning. The framework consists of three components: (1) a… More >

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ARTICLE

Hamed Saber¹, Antonio Zippo^1,2, Farhad S. Samani³, Francesco Pellicano^1,2,*
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.079313 - 27 May 2026
（This article belongs to the Special Issue: Advances in Modeling and Analysis of Complex Dynamics in Nonlinear Systems)
Abstract Lightweight bridges are increasingly used in modern infrastructure due to their structural efficiency; however, their relatively low stiffness and damping lead to a high sensitivity to vibration excitation induced by moving loads such as pedestrians and vehicles. Conventional vibration mitigation strategies are often insufficient to suppress low-frequency responses, which has caused the development of advanced nonlinear isolation mechanisms. This paper investigates the effectiveness of nonlinear quasi-zero stiffness supports (QZSS) in suppressing vertical vibrations of lightweight bridges. Such structures are highly susceptible to vibrations induced by moving loads because of low stiffness and dissipation, with consequent… More >

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Angel Prado^1,*, Alejandro Alañón², Ricardo Castedo³, Anastasio Pedro Santos³, Lina María López³, María Chiquito³
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.079804 - 27 May 2026
（This article belongs to the Special Issue: Modeling and Simulation of Explosive Effects on Structural Elements and Materials)
Abstract This study presents a numerical investigation into the influence of reinforcement layout on the blast response of a reinforced concrete (RC) slab subjected to a close-in explosion. The reference scenario is based on a blast test from the SEGTRANS project using a 15 kg TNT equivalent charge. A validated LS-DYNA model was used, applying the Load Blast Enhanced method and the Continuous Surface Cap Model for concrete behaviour. Forty-nine reinforcement configurations were assessed, all with constant steel mass but varying numbers of longitudinal bars and stirrups. Damage metrics such as eroded elements and internal energy… More >

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ARTICLE

Quynh-Anh Thi Bui^1,*, Son Hoang Trinh¹, Maryam Sayadi², Reza Khanali³
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.081794 - 27 May 2026
Abstract Geopolymer concrete has attracted increasing attention as a lower-carbon alternative to ordinary Portland cement concrete because it can utilize aluminosilicate-rich industrial by-products while still achieving satisfactory mechanical performance. However, the 28-day compressive strength of steel fiber-reinforced geopolymer concrete (SFGPC) is governed by multiple interacting mixture variables, which makes reliable prediction difficult, especially for medium-sized experimental datasets. This study developed an interpretable deep-learning framework to predict the 28-day compressive strength (CS28) of SFGPC using an original experimental dataset of 189 mixtures produced under a consistent laboratory protocol in Vietnam. The dataset covered nine mixture variables, including… More >

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ARTICLE

Arslan Qayyum Khan¹, Muhammad Dawood Rasheed², Muhammad Huzaifa Naveed², Amorn Pimanmas^3,*
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.083654 - 27 May 2026
（This article belongs to the Special Issue: Machine Learning for Reinforced Concrete Structures: Modeling, Optimization, and Monitoring)
Abstract The construction industry’s substantial carbon footprint, primarily attributed to the production of Ordinary Portland Cement, necessitates a transition toward more sustainable alternatives. Geopolymer concrete (GPC), an innovative binder synthesized from industrial by-products like fly ash (FA), offers a promising low-carbon solution but is hindered by performance variability and a lack of standardized design protocols. This research addresses this critical barrier by developing robust predictive models for the compressive strength of FA-based GPC. Six machine learning algorithms, including Bagging, Categorical Boosting (CatBoost), K-Nearest Neighbors (KNN), LightGBM, Random Forest Regressor (RFR), and eXtreme Gradient Boosting (XGBoost), were… More >

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ARTICLE

Irfan Saif Ud Din¹, Imran Siddique^2,3,4,5, Zohaib Zahid¹, Muhammad Nadeem⁶, Ibrahim Alraddadi^2,*, Taha Radwan^7,*
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.081726 - 27 May 2026
（This article belongs to the Special Issue: Computational Advances in Nanofluids: Modelling, Simulations, and Applications)
Abstract This study examines the variable thermal conductivity and electroosmotic performance of Sutterby hybrid nanofluid (SBHNF) thin film flow over a stretched inclined sheet using an artificial neural network (ANN)-based on NARX (Multilayer Nonlinear Autoregressive Networks with Exogenous Inputs) multiple-layer backpropagation simulation with the Levenberg-Marquardt algorithm (LMA). AA7075 and AA7072 nanoparticles suspended in sodium alginate (SA) base fluid make up the hybrid nanofluid (HNF), which was selected due to its improved heat transfer properties and superior thermal conductivity. The model’s practical applicability is enhanced by melting heat, nonlinear thermal radiation, boundary slip, and Newtonian heating effects,… More >

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Basma Souayeh^*
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.081163 - 27 May 2026
（This article belongs to the Special Issue: Computational Advances in Nanofluids: Modelling, Simulations, and Applications)
Abstract This study conducts a comprehensive numerical investigation of magnetohydrodynamic (MHD) mixed convection and entropy generation in a two-dimensional square cavity filled with a ternary hybrid nanofluid. The working fluid consists of Multi-Walled Carbon Nanotubes (MWCNT), Copper (Cu), and Ferric Oxide (Fe₃O₄) nanoparticles dispersed in water, selected for their superior thermal properties. Two vertically aligned, saw-tooth-shaped cooling structures are embedded along the left and right walls of the cavity, with four distinct configurations considered based on their vertical positioning. An externally imposed uniform magnetic field is applied to assess its influence on fluid flow, heat transfer, and… More >

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H. Kamlesh¹, E. P. Siva^1,*, P. Bathmanaban², O. D. Makinde³, Dharmendra Tripathi⁴
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.081147 - 27 May 2026
（This article belongs to the Special Issue: Computational Advances in Nanofluids: Modelling, Simulations, and Applications)
Abstract The current study comprehensively investigates Williamson nanofluid flow and transport in an asymmetric porous tapered channel under varying slip conditions, using both analytical and supervised machine learning approaches. This mathematical model integrates thermophoresis, Brownian motion, the Soret and Dufour effects, thermal radiation, and a transverse magnetic field to accurately describe thermosoluble transport phenomena relevant to biomedical contexts. The non-Newtonian Williamson formulation is used to explain how fluids, such as blood, dilute when sheared. Darcy resistance is used to describe porous structures in tissue scaffolds, capillary networks, and dialysis membranes. A perturbation method is used to… More >

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Sarna Soren¹, Samrat Hansda^1,*, Umair Khan^2,3, Anuar Ishak⁴, Ahmed Kadhim Hussein⁵, Md Mamun Molla^6,7
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.079635 - 27 May 2026
（This article belongs to the Special Issue: Computational Methods in Mono/hybrid nanofluids: Innovative Applications and Future Trends)
Abstract This study presents a numerical investigation of thermosolutal convection within a baffled porous cavity filled with a radiative Casson-based ternary aqueous nanofluid. The ternary hybrid nanofluid is formulated by dispersing three distinct nanoparticles into a water-based solution, aiming to enhance the thermal and solute transport characteristics. The cavity includes internal baffles that modulate convective flow and facilitate improved energy transport. The governing equations for momentum, energy, species concentration, and entropy generation are discretized and solved using a higher-order compact (HOC) finite difference scheme, ensuring superior numerical precision. The novelty of the present study lies in… More >

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T. I. Eldho^1,*, Sanjukta Das¹, Aatish Anshuman², Tinesh Pathania³
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.078705 - 27 May 2026
（This article belongs to the Special Issue: Meshless Methods and Its Applications in Porous Media Problems)
Abstract In recent years, meshless methods have been increasingly applied to the simulation of various engineering problems due to their inherent advantages over traditional mesh-based approaches, including greater flexibility, independence from predefined meshing, simpler adaptive analysis, improved automation, and suitability for complex problems. Several meshless methods have been used for porous media simulation, and are broadly categorized into collocation, global weak form and local weak form methods. In this study, a comprehensive comparison of the applicability of these three categories of meshless methods for simulating coupled flow and transport problems in porous media is presented. The… More >

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Jisun Kim, Jung Jin Kim^*
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.079665 - 27 May 2026
（This article belongs to the Special Issue: Advances in Mathematical Modeling: Numerical Approaches and Simulation for Computational Biology)
Abstract Synthetic proximal femur models avoid the ethical and technical limitations of human specimens and thus serve as an effective alternative for studying the proximal femur. This structure is directly connected to the hip joint, endures complex multi-directional loads, and exhibits region-specific structural adaptations due to its unique triangular geometry. However, most previous studies have examined only global load distributions or restricted regions, limiting the understanding of regional structural optimality. Therefore, this study aims to quantitatively evaluate the load adaptability and structural optimality of the proximal femur across individual regions of interest (ROIs). Three types of… More >

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Roshana Mukhtar¹, Chuan-Yu Chang², Muhammad Asif Zahoor Raja^1,*
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.079390 - 27 May 2026
（This article belongs to the Special Issue: Recent Developments on Computational Biology-II)
Abstract Parkinson’s disease (PD) is a complex neurodegenerative disease associated with the accumulation of α-synuclein, which is linked to the dysfunctional ubiquitin–proteasome system. Fractional calculus has emerged as a powerful tool for modeling complex disease dynamics due to its promising features that inherently capture memory and hereditary effects. This paper presents a fractional-order Proteasome-Fibril interaction model (F-PFIM) for the dynamics of PD, represented by three fractional differential classes, showing concentrations of fibrils (F), proteasomes (P), and proteasome fibril complex (C). The three classes of the F-PFIM collectively make a controlling system that works for the clearance… More >

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Zhenhua Yang^1,2, Yifan Li^1,2, Aimin Yang^1,2,*, Jie Li^2,3, Tao Xue^1,2
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.081754 - 27 May 2026
Abstract Sinter quality prediction in iron ore sintering is a challenging computational modeling problem because of highly nonlinear process behavior, strong cross-variable interactions, and disturbances caused by changing operating conditions. This study develops a data-driven multi-index soft-sensing framework for sinter quality prediction by combining feature selection and hierarchical model optimization. An improved binary Greylag Goose Optimization algorithm is first employed to identify a compact subset of informative variables, reducing redundancy and multicollinearity in the original process data. A hierarchical two-stage Greylag Goose Optimization strategy is then designed to optimize the hyperparameters of a support vector regression… More >

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Zafer Serin^1,*, Cihan Karakuzu², Uğur Yüzgeç²
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.079254 - 27 May 2026
（This article belongs to the Special Issue: AI-Enhanced Computational Methods in Engineering and Physical Science)
Abstract This study proposes SWAGE-3D (Spectral Wasserstein Attention Generative Ensemble), an enhanced 3D-VAE-GAN framework for single-view 3D object reconstruction using voxel-based representations. The proposed model integrates RGB-D encoding, Wasserstein adversarial learning with hybrid Lipschitz regularization, and a self-attention–augmented generator to improve structural coherence and training stability. By combining variational latent modeling with stabilized Wasserstein optimization, the framework aims to address common challenges in 3D generative modeling, including mode collapse, unstable convergence, and insufficient global consistency. The encoder employs a depth-aware feature extraction strategy, while the discriminator utilizes a hybrid spectral normalization and gradient penalty mechanism to More >

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Amal H. Alharbi¹, Marwa M. Eid^2,*, Nima Khodadadi³, Ebrahim A. Mattar⁴, Sayed Elkenawy^5,6
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.080044 - 27 May 2026
Abstract Seismic time series forecasting remains challenging due to the nonlinearity, non-stationarity, and noise of earthquake data, and because deep learning models are sensitive to preprocessing and hyperparameter settings. Although recent studies have improved neural architectures and optimization techniques, preprocessing is often treated as a fixed or manually designed stage, with limited integration into model optimization. To address this, this paper proposes an integrated, data-driven modelling framework that combines guided preprocessing with systematic hyperparameter optimization for seismic prediction, specifically forecasting earthquake magnitude from seismic catalog time-series data, with experiments conducted on Canadian seismic records. The method… More >

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Qilong Wang¹, Ning Wang¹, Shuhan Luo¹, Xiang Gao², Yuqian Lu³, Min He^4,*
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.080372 - 27 May 2026
Abstract Tunnel environments often suffer from GPS denial, uneven illumination, and structural uniformity, which lead to feature degradation, loop closure failure, and long-distance drift in SLAM systems. To solve these problems, this study aims to propose a high-precision SLAM method suitable for tunnel structural health monitoring. Firstly, an ABA-CLAHE image enhancement algorithm is proposed, which adopts cascaded processing of nonlinear brightness adjustment in HSV space and CLAHE local contrast optimization to improve low-light image quality and enhance feature stability. Then, SURF feature matching combined with the RANSAC algorithm is used to ensure feature matching accuracy. Finally, More >

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Thao Nguyen-Trang^1,2,*, Hiep Ha-Hoang³
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.080808 - 27 May 2026
Abstract Solving differential equations (DEs), including ordinary differential equations (ODEs) and partial differential equations (PDEs), is fundamental to scientific computing and engineering. The development of deep learning has led to Physics-Informed Neural Networks (PINNs), in which physical laws are embedded directly into the loss function. However, PINNs inherit the intrinsic instability of deep neural networks (DNNs) and lack an effective mechanism for Uncertainty Quantification (UQ). This paper proposes a stochastic ensemble framework to address these limitations. The proposed method is a double-stochastic ensemble framework that combines bagging (via bootstrap resampling and randomized collocation points) with Monte… More >

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Menahil Rahman¹, Waqas Ishtiaq², Amerah Alabrah^3,*, Arif Mehmood⁴, Rana Faraz Ahmed⁴, Iqra Khalid⁵, Farhan Amin^6,*
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.078549 - 27 May 2026
（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 Access to safe drinking water is a fundamental determinant of global health. The presence of contaminated water affects the citizens’ health. Per- and polyfluoroalkyl substances (PFAS) are often referred to as forever chemicals. They pose a persistent and growing threat to drinking water. In the literature, machine learning methods are used to identify the forever chemicals in water. However, traditional methods are not efficient and scalable. Thus, to solve this issue. This study develops a large-scale machine-learning framework for PFAS risk screening in US public water systems. The proposed framework incorporates data ingestion, preprocessing, and More >

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Natalia Prieto-Fernández¹, Martín Bayón-Gutiérrez^1,*, Sergio Fernández-Blanco¹, Álvaro Fernández-Blanco¹, Francisco Carro-De-Lorenzo², José Alberto Benítez-Andrades¹
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.080540 - 27 May 2026
（This article belongs to the Special Issue: Environment Modeling for Applications of Mobile Robots)
Abstract Feature-based Simultaneous Localization and Mapping (SLAM) using 2D Light Detection and Ranging (LiDAR) in structured indoor environments commonly relies on the extraction of straight segments and corners from raw scan data. The quality of these landmarks depends not only on the fitting algorithm, but also on how uncertainty is modeled and propagated from line estimates to derived corner features. Although the magnitude of LiDAR uncertainty has been widely studied, the influence of line parameterization and geometric conditioning on uncertainty propagation has received less attention. In particular, the scale ambiguity inherent to implicit line representations can… More >

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Sergio Sánchez de la Fuente^*, Luis Prieto-López, Miguel Á González-Santamarta, Vicente Matellán-Olivera, Ángel Manuel Guerrero-Higueras
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.080739 - 27 May 2026
（This article belongs to the Special Issue: Environment Modeling for Applications of Mobile Robots)
Abstract The development and testing of autonomous agricultural robots requires realistic simulation environments that accurately represent field conditions and terrain features. Traditional manual scenario creation is time-consuming, expensive and limits the diversity of testing conditions. This paper presents an integrated two-stage system for semi-automated generation of realistic 3D simulation scenarios. The first stage transforms publicly available geospatial data into high-fidelity 3D terrain models, supporting 23 discrete levels of detail (LoD), from 0 to 22, and generating simulation-ready models compatible with the Gazebo robotics simulator. The second stage provides a web-based tool that enables users to populate… More >

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Yousra Abudaqqa^*, Zulaiha Ali Othman, Azuraliza Abu Bakar
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.079034 - 27 May 2026
Abstract Signal categorization is a critical component of the Dendritic Cell Algorithm (DCA), as it directly influences its anomaly detection capability. Conventional DCA implementations typically rely on heuristic or optimization-based approaches, such as Grouping Particle Swarm Optimization (GPSO), Grouping Genetic Algorithms (GGA), Principal Component Analysis (PCA), and Support Vector Machines (SVM), to determine mappings between input features and the three immunological signal categories: Pathogen-Associated Molecular Patterns (PAMP), Danger Signals (DS), and Safe Signals (SS). These approaches depend heavily on domain expertise and predefined rules, making the resulting signal mappings static and often dataset specific. Consequently, the… More >

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Kalim Sattar¹, Malik Muhammad Saad Missen², Syeda Zoupash Zahra^1,3, Najia Saher⁴, Rab Nawaz Bashir^3,5,6,*, Oumaima Saidani⁷, Shahid Kamal⁵, Muhammad I. Khan⁶
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.078569 - 27 May 2026
（This article belongs to the Special Issue: Applied Machine Learning for FAIR and Responsible Modelling)
Abstract Tropical Cyclone (TC) genesis forecasting is an important aspect of early warning systems, as it allows the adoption of early warnings and mitigation plans. However, existing methods often rely on binary classification or fail to capture the complex spatio-temporal dependencies that govern TC formation. To address this limitation, this study introduces STAG-Net, a novel Spatio-Temporal Attention-Gated Network designed to directly predict the geographical coordinates of TC genesis. The model uses multivariate variables of meteorological factors such as u-wind, v-wind, relative humidity, temperature, and large-scale dynamic features using a Convolutional Neural Network (CNN), Gated Recurrent Units… More >

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Shuai Ma¹, Xiang Fang^1,2,*, Liya Han¹
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.078073 - 27 May 2026
（This article belongs to the Special Issue: Data-Driven and Physics-Informed Machine Learning for Digital Twin, Surrogate Modeling, and Model Discovery, with An Emphasis on Industrial Applications)
Abstract Automated feature recognition (AFR) plays an important role in automated measurement path planning and metrological data processing in the manufacturing industry. Existing AFR methods face critical limitations, such as the loss of geometric-topological fidelity during Computer-aided design (CAD) model conversion and inadequate instance segmentation for dimensional metrology. To address these challenges, we propose an integrated multi-attention and multi-strategy network (MMNet) for feature recognition, which mainly includes the multi-attention geometric and attribute fusion module (MGAM) and the multi-strategy semantic and instance segmentation module (MSIM). Specifically, MGAM employs multi-attention mechanisms to synergize local geometric features with global More >

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Marco Roccetti^*
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.082258 - 27 May 2026
Abstract Geospatial health risk signals, characterized by associations with high magnitude statistical significance, may frequently originate from circumscribed observational data streams. When these signals are fueled by massive N-size datasets, the large dimensional scale of the sample can induce a misleading interpretation of local evidence as a statistically significant risk inflation. The objective of this study is to verify whether such health risk configurations constitute geospatial structural artifacts: namely, stochastic distortions generated by the spatial information of local health repositories that, despite their massive scale, may remain fundamentally distant from broader contextual realities. To this aim,… More >

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Trong-Thua Huynh^1,*, De-Thu Huynh², Cong-Sang Duong¹, Hong-Son Nguyen¹, Quoc H. Nguyen³, Lam-Thanh Tu⁴
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.080616 - 27 May 2026
Abstract Deep iris recognition models are often trained on Cartesian grids, whereas iris texture follows a concentric structure with angular periodicity. This representational mismatch can weaken rotation robustness and limit pupil-to-limbus context modeling, while many pipelines still rely on accurate segmentation masks. We propose RadialFormer, an efficient mask-free iris recognition framework that performs representation learning directly in the polar domain. The pipeline first estimates pupil/iris parameters (cx,cy,rin,rout) using a percentile radial-gradient operator with anatomical constraints, and then applies a crop-based polar transform to obtain a compact 64×512 unwrapped iris map. To better match polar… More >

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Munthir Qasaimeh¹, Mostafa Ali¹, Qasem Abu Al-Haija^2,*
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.080864 - 27 May 2026
（This article belongs to the Special Issue: Advanced Computational Intelligence Techniques, Uncertain Knowledge Processing and Multi-Attribute Group Decision-Making Methods Applied in Modeling of Medical Diagnosis and Prognosis)
Abstract Vision Transformers (ViTs) have recently achieved high performance in retinal Optical Coherence Tomography (OCT) classification studies. However, ViT models continue to face significant challenges, including high computational cost, vulnerability to adversarial attacks, and pronounced sensitivity to preprocessing techniques. This study introduces GreenShield, a unified framework designed to produce an efficient and robust ViT model, referred to as GreenShield-ViT, which outperforms existing lightweight ViT variants in terms of adversarial robustness for retinal OCT classification. The framework integrates a gradient-based block-importance pruning strategy to compress the ViT/B-16 architecture, and adversarial training with proper ImageNet normalization and anti-saturation… More >

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Ezz El-Din Hemdan¹, Haitham Elwahsh^2,3, Samah Alshathri^4,*, Amged Sayed^5,6,*
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.079305 - 27 May 2026
（This article belongs to the Special Issue: Advanced Computational Intelligence Techniques, Uncertain Knowledge Processing and Multi-Attribute Group Decision-Making Methods Applied in Modeling of Medical Diagnosis and Prognosis)
Abstract Ensuring robust methods for maintaining high levels of medical data security is crucial in the Medical Internet of Things (IoT) for the protection of sensitive patient data during real-time transmission and analysis. Electroencephalography (EEG) signals in medical IoT systems are transmitted through cloud and edge networks, which create risks of cyber threats, unauthorized access, and data breaches. Consequently, there is an urgent need for efficient encryption methods to ensure the confidentiality of EEG signals during classification and prediction processes, as several state-of-the-art models either neglect security during classification or suffer from increased computational overhead that… More >

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Manal Mohammed AL-Tamimi^1,2,*, Siti Norul Huda Sheikh Abdullah^1,*, Mohammad Khatim Hasan¹, Mohammed Azmi Al-Betar^3,4, Maw Shin Sim⁵, Abdulrahman Mohammed AL-Tamimi¹
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.079891 - 27 May 2026
（This article belongs to the Special Issue: Artificial Intelligence Models in Healthcare: Challenges, Methods, and Applications)
Abstract Pan-cancer survival prediction remains a major challenge in personalized oncology due to profound tumor heterogeneity and the complexity of high-dimensional molecular data. Diverse risk profiles across cancer types and noisy, sparse features hinder deep learning models from capturing robust prognostic patterns. Prior pan-cancer studies predominantly focus on multimodal integration or unimodal gene expression analysis, leaving other informative modalities such as Copy Number Variation (CNV) and miRNA expression underexplored. We introduce a new formulation of mixture-of-experts (MoE) survival modeling that recasts expert assignment as a clinically interpretable risk-space decomposition problem. The proposed framework, CoxGuided-SE, constructs an… More >

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Kiran Deep Singh¹, Prabh Deep Singh², Narinder Kaur³, Jawad Khan^4,*, Dildar Hussain⁵, Yeong Hyeon Gu^5,*
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.080915 - 27 May 2026
（This article belongs to the Special Issue: Artificial Intelligence Models in Healthcare: Challenges, Methods, and Applications)
Abstract In smart healthcare systems, Image data of critical patients is essential in controlling and diagnosing the disease development. To acquire the medical images, traditional methods encountered the difficulty of generating cost-effective data. This research work introduces a novel and innovative approach to collect high-quality image data from individuals with atypical clinical presentations. Initially, a new Internet of Medical Things (IoMT) image collection architecture is introduced. This design uses edge intelligence and motion-static synergy to make it easier to record both coarse-grained and fine-grained patient images. This study introduces an image acquisition technique that leverages edge… More >

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Abdulrahman Dira Khalaf^1,2,*, Hazlina Hamdan^1,*, Alfian Abdul Halin¹, Noridayu Manshor¹
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.075537 - 27 May 2026
（This article belongs to the Special Issue: Artificial Intelligence Models in Healthcare: Challenges, Methods, and Applications)
Abstract Current automated lesion segmentation methods have limited success, particularly for segmenting small, irregular, or heterogeneous lesions. Moreover, such models require significant computational power, which restricts their scalability and clinical application. To overcome these limitations, a lightweight LANET, which is a layer-attention network based on an encoder–decoder deep-learning architecture, has the explicit goal of increasing the segmentation performance and computational efficiency. The LANET is coupled with three new modules: (i) an attention module that includes a depthwise separable convolution operator to reduce the number of parameters, (ii) a custom attention mechanism, and (iii) an atrous spatial… More >

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Ronak Patel¹, Miral Patel², Deep Kothadiya³, Noor A. Khan⁴, Shaha Al-Otaibi^5,*, Roaa Khalil Mohamed Ali Abed⁶, Tanzila Saba⁷
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.080819 - 27 May 2026
（This article belongs to the Special Issue: Advanced Image Segmentation and Object Detection: Innovations, Challenges, and Applications)
Abstract Magnetic resonance imaging (MRI) is widely utilized for brain tumor segmentation, yet significant challenges persist due to intensity variations, irregular boundaries, and substantial morphological heterogeneity. Current state-of-the-art deep learning methods often struggle to capture long-range spatial dependencies, delineate fine boundary details, and efficiently process 3D volumetric data. This study introduces a novel hybrid framework that integrates state-space models with frequency-domain learning to address these limitations. The proposed model offers four primary contributions: (1) incorporation of a morphological attention block in the encoder to enhance boundary localization via dilation-erosion gradient modeling; (2) a dual-domain bottleneck module… More >

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Amjad Rehman¹, Tariq Mahmood^1,2, Faten S. Alamri^3,*, Muhammad I. Khan¹
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.076683 - 27 May 2026
Abstract The rapid deployment of Wireless Sensor Networks (WSNs) faces critical challenges due to sensor nodes’ limited energy and communication capabilities, which restrict network lifetime and data transmission efficiency. Traditional clustering and routing protocols often lead to unbalanced energy consumption and uneven load distribution, whereas intelligent optimization approaches are hindered by high computational costs and slow convergence. This research formulates the clustering and routing problems in WSNs as an optimization challenge under resource and energy constraints, aiming to improve stability, energy efficiency, and throughput. This research proposed three quantum optimization-based solutions to address complex issues. First,… More >

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Amjad Hussain^1,*, Ayesha Saadia^2,*, Chihhsiong Shih³, Nazish Nawaz², Amir H. Gandomi^4,*, Khursheed Aurangzeb⁵
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.078451 - 27 May 2026
Abstract Malware has evolved from the early Creeper virus into highly sophisticated and organized cyber threats. Over time, it grew in sophistication, adopting advanced techniques, stealth tactics, and autonomous propagation. Modern malware leverages encryption, obfuscation, zero-day exploits, and AI-assisted techniques to conduct stealthy and persistent attacks. Classification of its exact family is the end goal to defend and mitigate the latest attacks. Researchers have contributed significantly and introduced many techniques to tackle malware threats. Binary detection is performed at a large scale, but very little in multi-class classification. In this research, a hybrid technique is proposed… More >

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Tanveer Ahmad^1,*, Tahani Alsubait², Amina Salhi³, Amani Ibraheem⁴, Muhammad Asim Saleem⁵
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.080285 - 27 May 2026
（This article belongs to the Special Issue: Emerging Technologies in Information Security: Modeling, Algorithms, and Applications)
Abstract The rapid adoption of Edge-AI in smart edge-IoT environments has dramatically led to an augmented vulnerability to cyber risks arising from distributed learning, data heterogeneity, and adversarial manipulation. This paper proposes a new risk-aware adaptive learning model that federated Edge-AI systems explicitly simulates cyber risk in the process of local training and global aggregation. The proposed solution combines stochastic optimization and adversarial risk bounding with adaptive gradient correction to develop strong learning in non-IID data distributions and malicious client behavior. Convergence guarantees are defined by the theoretical analysis in the case of limited adversarial perturbations.… More >

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