Open Access
ARTICLE
Ying Zhu1,2, Minghui Hu3, Shengxu Wang1,2, Xiaoliang Dong3,*, Xuewen Xiao1,2, Richeng Liu3, Meng Wang1,2, Zheng Yuan3
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.076381
（This article belongs to the Special Issue: Artificial Intelligence and Advanced Numerical Modeling Integration Techniques in Tunnel and Underground Engineering)
Abstract In-situ enlargement of super-large-span tunnels can intensify excavation-induced unloading in the surrounding rock, increasing deformation demand and failure risk during construction. This study combines laboratory model tests with FLAC3D simulations to evaluate the stabilizing role of prestressed anchor cables and to establish an energy-balance framework for support optimization. Comparative model tests of existing and enlarged tunnel sections, with and without anchors, show that reinforcement increases load-carrying capacity, reduces displacement, and confines damage to more localized zones. The numerical simulations reproduce displacement fields, shear-strain localization, and plastic-zone evolution with good agreement against the experimental observations. The energy More >
Open Access
REVIEW
Luiz Tadeu Dias Júnior, Rafaelle Piazzaroli Finotti, Flávio de Souza Barbosa*, Alexandre Abrahão Cury
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.075433
Abstract Structural Health Monitoring (SHM) plays a critical role in ensuring the safety, integrity, longevity and economic efficiency of civil infrastructures. The field has undergone a profound transformation over the last few decades, evolving from traditional methods—often reliant on visual inspections—to data-driven intelligent systems. This review paper analyzes this historical trajectory, beginning with the approaches that relied on modal parameters as primary damage indicators. The advent of advanced sensor technologies and increased computational power brings a significant change, making Machine Learning (ML) a viable and powerful tool for damage assessment. More recently, Deep Learning (DL) has More >
Open Access
ARTICLE
Nagwan Abdel Samee1, Ahmed Fathy2,*, Mohamed A. Mahdy3, Maali Alabdulhafith1, Essam H. Houssein4,5
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.069931
Abstract This research proposes an improved Puma optimization algorithm (IPuma) as a novel dynamic reconfiguration tool for a photovoltaic (PV) array linked in total-cross-tied (TCT). The proposed algorithm utilizes the Newton-Raphson search rule (NRSR) to boost the exploration process, especially in search spaces with more local regions, and boost the exploitation with adaptive parameters alternating with random parameters in the original Puma. The effectiveness of the introduced IPuma is confirmed through comprehensive evaluations on the CEC’20 benchmark problems. It shows superior performance compared to both established and modern metaheuristic algorithms in terms of effectively navigating the… More >
Open Access
ARTICLE
Fazal Ur Rehman1, Yazeed Alkhrijah2, Syed Muhammad Usman3, Muhammad Irfan1,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.076332
（This article belongs to the Special Issue: Machine Learning and Deep Learning-Based Pattern Recognition)
Abstract Automated detection of Motor Imagery (MI) tasks is extremely useful for prosthetic arms and legs of stroke patients for their rehabilitation. Prediction of MI tasks can be performed with the help of Electroencephalogram (EEG) signals recorded by placing electrodes on the scalp of subjects; however, accurate prediction of MI tasks remains a challenge due to noise that is incurred during the EEG signal recording process, the extraction of a feature vector with high interclass variance, and accurate classification. The proposed method consists of preprocessing, feature extraction, and classification. First, EEG signals are denoised using a… More >
Open Access
ARTICLE
H. Ahmed1, Naglaa E. Ghannam2,*, H. Mancy3, Esraa A. Mahareek4
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.075732
（This article belongs to the Special Issue: Artificial Intelligence Models in Healthcare: Challenges, Methods, and Applications)
Abstract Parkinson’s disease remains a major clinical issue in terms of early detection, especially during its prodromal stage when symptoms are not evident or not distinct. To address this problem, we proposed a new deep learning 2-based approach for detecting Parkinson’s disease before any of the overt symptoms develop during their prodromal stage. We used 5 publicly accessible datasets, including UCI Parkinson’s Voice, Spiral Drawings, PaHaW, NewHandPD, and PPMI, and implemented a dual stream CNN–BiLSTM architecture with Fisher-weighted feature merging and SHAP-based explanation. The findings reveal that the model’s performance was superior and achieved 98.2%, a More >
Open Access
ARTICLE
Tan-Nhu Nguyen1,2, Trong-Pham Nguyen-Huu1,2, Tien-Tuan Dao3,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.075386
Abstract Vaginal delivery is a fascinating physiological process, but also a high-risk process. Up to 85%–90% of vaginal deliveries lead to perineal trauma, with nearly 11% of severe perineal tearing. It is a common occurrence, especially for first-time mothers. Computational childbirth plays an essential role in the prediction and prevention of these traumas, but fast personalization of the pelvis and floor muscles is challenging due to their anatomical complexity. This study introduces a novel shape-prediction-based personalization of the pelvis and floor muscles for perineal tearing management and childbirth simulation. 300 subjects were selected from public Computed… More >
Open Access
ARTICLE
Abdul Jabbar Siddiqui1,2,*, Saheed Ademola Bello2, Muhammad Liman Gambo2, Abdul Khader Jilani Saudagar3,*, Mohamad A. Alawad4, Amir Hussain5
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.074283
（This article belongs to the Special Issue: Exploring the Impact of Artificial Intelligence on Healthcare: Insights into Data Management, Integration, and Ethical Considerations)
Abstract Visual diagnosis of skin cancer is challenging due to subtle inter-class similarities, variations in skin texture, the presence of hair, and inconsistent illumination. Deep learning models have shown promise in assisting early detection, yet their performance is often limited by the severe class imbalance present in dermoscopic datasets. This paper proposes CANNSkin, a skin cancer classification framework that integrates a convolutional autoencoder with latent-space oversampling to address this imbalance. The autoencoder is trained to reconstruct lesion images, and its latent embeddings are used as features for classification. To enhance minority-class representation, the Synthetic Minority Oversampling… More >
Open Access
ARTICLE
Silvia Alayón1,*, Carlos Martín1, Jesús Torres1, Manuel Bacallado1, Rosa Aguilar1, Guzmán Savirón2
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.073488
（This article belongs to the Special Issue: Emerging Frontiers and Disruptive Technologies in Computer Science Engineering: Advancements in AI, Machine Learning, and Large Language Models to Shape Intelligent Systems)
Abstract Business Process Modelling (BPM) is essential for analyzing, improving, and automating the flow of information within organizations, but traditional approaches based on manual interpretation are slow, error-prone, and require a high level of expertise. This article proposes an innovative alternative solution that overcomes these limitations by automatically generating comprehensive Business Process Modelling and Notation (BPMN) diagrams solely from verbal descriptions of the processes to be modeled, utilizing Large Language Models (LLMs) and multimodal Artificial Intelligence (AI). Experimental results, based on video recordings of process explanations provided by an expert from an organization (in this case,… More >
Open Access
ARTICLE
Waseem Akhtar1,2, Mahwish Ilyas3, Romana Aziz4,*, Ghadah Aldehim4, Tassawar Iqbal5, Muhammad Ramzan6
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.071669
Abstract Human Activity Recognition (HAR) is a novel area for computer vision. It has a great impact on healthcare, smart environments, and surveillance while is able to automatically detect human behavior. It plays a vital role in many applications, such as smart home, healthcare, human computer interaction, sports analysis, and especially, intelligent surveillance. In this paper, we propose a robust and efficient HAR system by leveraging deep learning paradigms, including pre-trained models, CNN architectures, and their average-weighted fusion. However, due to the diversity of human actions and various environmental influences, as well as a lack of… More >
Open Access
ARTICLE
Victor Leiva1,2,*, Carlos Martin-Barreiro3,*, Viviana Giampaoli4
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.074800
（This article belongs to the Special Issue: Data-Driven Artificial Intelligence and Machine Learning in Computational Modelling for Engineering and Applied Sciences)
Abstract Mortality prediction in respiratory health is challenging, especially when using large-scale clinical datasets composed primarily of categorical variables. Traditional digital twin (DT) frameworks often rely on longitudinal or sensor-based data, which are not always available in public health contexts. In this article, we propose a novel proto-DT framework for mortality prediction in respiratory health using a large-scale categorical biomedical dataset. This dataset contains 415,711 severe acute respiratory infection cases from the Brazilian Unified Health System, including both COVID-19 and non-COVID-19 patients. Four classification models—extreme gradient boosting (XGBoost), logistic regression, random forest, and a deep neural… More >
Open Access
ARTICLE
Yu Zhou1, Jiawei Tian2, Kyungtae Kang3,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.077373
（This article belongs to the Special Issue: Recent Advances in Signal Processing and Computer Vision)
Abstract Arrhythmias are a frequently occurring phenomenon in clinical practice, but how to accurately distinguish subtle rhythm abnormalities remains an ongoing difficulty faced by the entire research community when conducting ECG-based studies. From a review of existing studies, two main factors appear to contribute to this problem: the uneven distribution of arrhythmia classes and the limited expressiveness of features learned by current models. To overcome these limitations, this study proposes a dual-path multimodal framework, termed DM-EHC (Dual-Path Multimodal ECG Heartbeat Classifier), for ECG-based heartbeat classification. The proposed framework links 1D ECG temporal features with 2D time–frequency More >
Open Access
ARTICLE
Zehao Li1, Xuting Zhang1, Hongqi Lin1, Wu Qin2, Junyu Qi3, Zhuyun Chen1,*, Qiang Liu1,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.075436
（This article belongs to the Special Issue: Intelligent Dynamics Modeling, Predictive Operations & Maintenance, and Control Optimization for Complex Systems)
Abstract To ensure the safe and stable operation of rotating machinery, intelligent fault diagnosis methods hold significant research value. However, existing diagnostic approaches largely rely on manual feature extraction and expert experience, which limits their adaptability under variable operating conditions and strong noise environments, severely affecting the generalization capability of diagnostic models. To address this issue, this study proposes a multimodal fusion fault diagnosis framework based on Mel-spectrograms and automated machine learning (AutoML). The framework first extracts fault-sensitive Mel time–frequency features from acoustic signals and fuses them with statistical features of vibration signals to construct complementary More >
Open Access
REVIEW
Sameera V. Mohd Sagheer1,#,*, U. Nimitha2,#, P. M. Ameer2, Muneer Parayangat3, Mohamed Abbas3, Krishna Prakash Arunachalam4
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.067108
（This article belongs to the Special Issue: Advances in AI-Driven Computational Modeling for Image Processing)
Abstract Over the years, Generative Adversarial Networks (
Open Access
REVIEW
M. Mohsin Raza1,#, Muhammad Umair1,#, Imran Arshad Choudhry1, Muhammad Qasim1, Muhammad Tahir Naseem2,*, Mamoona Naveed Asghar3, Daniel Gavilanes4,5,6,7, Manuel Masias Vergara4,8,9, Imran Ashraf10,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.074164
Abstract Over the past decade, the landscape of cybersecurity has been increasingly shaped by the growing sophistication and frequency of malware attacks. Traditional detection techniques, while still in use, often fall short when confronted with modern threats that use advanced evasion strategies. This systematic review critically examines recent developments in malware detection, with a particular emphasis on the role of artificial intelligence (AI) and machine learning (ML) in enhancing detection capabilities. Drawing on literature published between 2019 and 2025, this study reviews 105 peer-reviewed contributions from prominent digital libraries including IEEE Xplore, SpringerLink, ScienceDirect, and ACM… More >
Open Access
ARTICLE
Jun Zhe Tan1, Rodney H. G. Tan1,*, Nor Ashidi Mat Isa2, Sew Sun Tiang1, Chun Kit Ang1, Kuo-Ping Lin1,3,4, Wei Hong Lim1,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.073821
Abstract Accurate estimation of photovoltaic (PV) parameters is essential for optimizing solar module performance and enhancing resource efficiency in renewable energy systems. This study presents a process innovation by introducing, for the first time, the Triangulation Topology Aggregation Optimizer (TTAO) integrated with parallel computing to address PV parameter estimation challenges. The effectiveness and robustness of TTAO are rigorously evaluated using two standard benchmark datasets (KC200GT and R.T.C. France solar cells) and a real-world dataset (Poly70W solar module) under single-, double-, and triple-diode configurations. Results show that TTAO consistently achieves superior accuracy by producing the lowest RMSE More >
Open Access
ARTICLE
Adnan Ashique1, Nehad Ali Shah1, Usman Afzal1, Yazen Alawaideh2, Sohaib Abdal3, Jae Dong Chung1,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.073292
（This article belongs to the Special Issue: Applied Artificial Intelligence: Advanced Solutions for Engineering Real-World Challenges)
Abstract There is a need for accurate prediction of heat and mass transfer in aerodynamically designed, non-Newtonian nanofluids across aerodynamically designed, high-flux biomedical micro-devices for thermal management and reactive coating processes, but existing work is not uncharacteristically remiss regarding viscoelasticity, radiative heating, viscous dissipation, and homogeneous–heterogeneous reactions within a single scheme that is calibrated. This research investigates the flow of Williamson nanofluid across a dynamically wedged surface under conditions that include viscous dissipation, thermal radiation, and homogeneous-heterogeneous reactions. The paper develops a detailed mathematical approach that utilizes boundary layers to transform partial differential equations into ordinary… More >
Open Access
ARTICLE
Borja Bordel Sánchez*, Ramón Alcarria, Tomás Robles
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.072603
（This article belongs to the Special Issue: Cutting-Edge Security and Privacy Solutions for Next-Generation Intelligent Mobile Internet Technologies and Applications)
Abstract In this paper, we propose a new privacy-aware transmission scheduling algorithm for 6G ad hoc networks. This system enables end nodes to select the optimum time and scheme to transmit private data safely. In 6G dynamic heterogeneous infrastructures, unstable links and non-uniform hardware capabilities create critical issues regarding security and privacy. Traditional protocols are often too computationally heavy to allow 6G services to achieve their expected Quality-of-Service (QoS). As the transport network is built of ad hoc nodes, there is no guarantee about their trustworthiness or behavior, and transversal functionalities are delegated to the extreme nodes. However, More >
Open Access
ARTICLE
Junmin Lyu1, Guangyu Xu2, Feng Bao3,*, Yu Zhou4, Yuxin Liu5, Siyu Lu5,*, Wenfeng Zheng5
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.075239
（This article belongs to the Special Issue: Emerging Artificial Intelligence Technologies and Applications-II)
Abstract Graph neural networks (GNN) have shown strong performance in node classification tasks, yet most existing models rely on uniform or shared weight aggregation, lacking flexibility in modeling the varying strength of relationships among nodes. This paper proposes a novel graph coupling convolutional model that introduces an adaptive weighting mechanism to assign distinct importance to neighboring nodes based on their similarity to the central node. Unlike traditional methods, the proposed coupling strategy enhances the interpretability of node interactions while maintaining competitive classification performance. The model operates in the spatial domain, utilizing adjacency list structures for efficient… More >
Open Access
ARTICLE
Junmin Lyu1, Feng Bao2,*, Guangyu Xu3, Siyu Lu4,*, Bo Yang5, Yuxin Liu5, Wenfeng Zheng5
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.075178
Abstract Robust teleoperation in image-guided interventions faces critical challenges from latency, deformation, and the quasi-periodic nature of physiological motion. This paper presents a fully integrated, latency-aware visual servoing system leveraging stereo vision, hand–eye calibration, and learning-based prediction for motion-compensated teleoperation. The system combines a calibrated binocular camera setup, dual robotic arms, and a predictive control loop incorporating Long Short-Term Memory (LSTM) and Temporal Convolutional Network (TCN) models. Through experiments using both in vivo and phantom datasets, we quantitatively assess the prediction accuracy and motion-compensation performance of both models. Results show that TCNs deliver more stable and precise More >
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