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  • Open Access

    ARTICLE

    Predicting the Construction Quality of Projects by Using Hybrid Soft Computing Techniques

    Ching-Lung Fan*

    CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.2, pp. 1995-2017, 2025, DOI:10.32604/cmes.2025.059414 - 27 January 2025

    Abstract The construction phase of a project is a critical factor that significantly impacts its overall success. The construction environment is characterized by uncertainty and dynamism, involving nonlinear relationships among various factors that affect construction quality. This study utilized 987 construction inspection records from 1993 to 2022, obtained from the Taiwanese Public Construction Management Information System (PCMIS), to determine the relationships between construction factors and quality. First, fuzzy logic was applied to calculate the weights of 499 defects, and 25 critical construction factors were selected based on these weight values. Next, a deep neural network was… More >

  • Open Access

    REVIEW

    Enhancing Evapotranspiration Estimation: A Bibliometric and Systematic Review of Hybrid Neural Networks in Water Resource Management

    Moein Tosan1, Mohammad Reza Gharib2,*, Nasrin Fathollahzadeh Attar3, Ali Maroosi4

    CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.2, pp. 1109-1154, 2025, DOI:10.32604/cmes.2025.058595 - 27 January 2025

    Abstract Accurate estimation of evapotranspiration (ET) is crucial for efficient water resource management, particularly in the face of climate change and increasing water scarcity. This study performs a bibliometric analysis of 352 articles and a systematic review of 35 peer-reviewed papers, selected according to PRISMA guidelines, to evaluate the performance of Hybrid Artificial Neural Networks (HANNs) in ET estimation. The findings demonstrate that HANNs, particularly those combining Multilayer Perceptrons (MLPs), Recurrent Neural Networks (RNNs), and Convolutional Neural Networks (CNNs), are highly effective in capturing the complex nonlinear relationships and temporal dependencies characteristic of hydrological processes. These… More >

  • Open Access

    ARTICLE

    A Novel Model for Describing Rail Weld Irregularities and Predicting Wheel-Rail Forces Using a Machine Learning Approach

    Linlin Sun1,2, Zihui Wang3, Shukun Cui1,2, Ziquan Yan1,2,*, Weiping Hu3, Qingchun Meng3

    CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 555-577, 2025, DOI:10.32604/cmes.2024.056023 - 17 December 2024

    Abstract Rail weld irregularities are one of the primary excitation sources for vehicle-track interaction dynamics in modern high-speed railways. They can cause significant wheel-rail dynamic interactions, leading to wheel-rail noise, component damage, and deterioration. Few researchers have employed the vehicle-track interaction dynamic model to study the dynamic interactions between wheel and rail induced by rail weld geometry irregularities. However, the cosine wave model used to simulate rail weld irregularities mainly focuses on the maximum value and neglects the geometric shape. In this study, novel theoretical models were developed for three categories of rail weld irregularities, based… More >

  • Open Access

    ARTICLE

    Machine Learning Techniques in Predicting Hot Deformation Behavior of Metallic Materials

    Petr Opěla1,*, Josef Walek1,*, Jaromír Kopeček2

    CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 713-732, 2025, DOI:10.32604/cmes.2024.055219 - 17 December 2024

    Abstract In engineering practice, it is often necessary to determine functional relationships between dependent and independent variables. These relationships can be highly nonlinear, and classical regression approaches cannot always provide sufficiently reliable solutions. Nevertheless, Machine Learning (ML) techniques, which offer advanced regression tools to address complicated engineering issues, have been developed and widely explored. This study investigates the selected ML techniques to evaluate their suitability for application in the hot deformation behavior of metallic materials. The ML-based regression methods of Artificial Neural Networks (ANNs), Support Vector Machine (SVM), Decision Tree Regression (DTR), and Gaussian Process Regression More >

  • Open Access

    PROCEEDINGS

    Inductive and Deductive Scale-Bridging In Hierarchical Multiscale Models for Dislocation Pattern Formation in Metal Fatigue

    Yoshitaka Umeno1,*, Atsushi Kubo2, Emi Kawai1

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.32, No.1, pp. 1-2, 2024, DOI:10.32604/icces.2024.012708

    Abstract Fatigue fracture accounts for a substantial fraction of failure cases in industrial products, especially in metal materials. While the mechanism of fatigue crack propagation can be understood in the mechanical point of view considering the effect of microstructures and crystal orientations on crack growth, there is still much room for investigations of the mechanism of fatigue crack formation under cyclic loading. It is widely understood that the fatigue crack formation in macroscopic metal materials originates in the persistent slip band (PSB) formed as a result of self-organization of dislocation structures [1]. Nevertheless, the PSB formation… More >

  • Open Access

    ARTICLE

    Improving Multiple Sclerosis Disease Prediction Using Hybrid Deep Learning Model

    Stephen Ojo1, Moez Krichen2,3,*, Meznah A. Alamro4, Alaeddine Mihoub5, Gabriel Avelino Sampedro6, Jaroslava Kniezova7,*

    CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 643-661, 2024, DOI:10.32604/cmc.2024.052147 - 15 October 2024

    Abstract Myelin damage and a wide range of symptoms are caused by the immune system targeting the central nervous system in Multiple Sclerosis (MS), a chronic autoimmune neurological condition. It disrupts signals between the brain and body, causing symptoms including tiredness, muscle weakness, and difficulty with memory and balance. Traditional methods for detecting MS are less precise and time-consuming, which is a major gap in addressing this problem. This gap has motivated the investigation of new methods to improve MS detection consistency and accuracy. This paper proposed a novel approach named FAD consisting of Deep Neural Network… More >

  • Open Access

    REVIEW

    Review of Artificial Neural Networks for Wind Turbine Fatigue Prediction

    Husam AlShannaq, Aly Mousaad Aly*

    Structural Durability & Health Monitoring, Vol.18, No.6, pp. 707-737, 2024, DOI:10.32604/sdhm.2024.054731 - 20 September 2024

    Abstract Wind turbines have emerged as a prominent renewable energy source globally. Efficient monitoring and detection methods are crucial to enhance their operational effectiveness, particularly in identifying fatigue-related issues. This review focuses on leveraging artificial neural networks (ANNs) for wind turbine monitoring and fatigue detection, aiming to provide a valuable reference for researchers in this domain and related areas. Employing various ANN techniques, including General Regression Neural Network (GRNN), Support Vector Machine (SVM), Cuckoo Search Neural Network (CSNN), Backpropagation Neural Network (BPNN), Particle Swarm Optimization Artificial Neural Network (PSO-ANN), Convolutional Neural Network (CNN), and nonlinear autoregressive… More >

  • Open Access

    ARTICLE

    Artificial Intelligence Prediction of One-Part Geopolymer Compressive Strength for Sustainable Concrete

    Mohamed Abdel-Mongy1, Mudassir Iqbal2, M. Farag3, Ahmed. M. Yosri1,*, Fahad Alsharari1, Saif Eldeen A. S. Yousef4

    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 525-543, 2024, DOI:10.32604/cmes.2024.052505 - 20 August 2024

    Abstract Alkali-activated materials/geopolymer (AAMs), due to their low carbon emission content, have been the focus of recent studies on ecological concrete. In terms of performance, fly ash and slag are preferred materials for precursors for developing a one-part geopolymer. However, determining the optimum content of the input parameters to obtain adequate performance is quite challenging and scarcely reported. Therefore, in this study, machine learning methods such as artificial neural networks (ANN) and gene expression programming (GEP) models were developed using MATLAB and GeneXprotools, respectively, for the prediction of compressive strength under variable input materials and content… More >

  • Open Access

    ARTICLE

    Classified VPN Network Traffic Flow Using Time Related to Artificial Neural Network

    Saad Abdalla Agaili Mohamed*, Sefer Kurnaz

    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 819-841, 2024, DOI:10.32604/cmc.2024.050474 - 18 July 2024

    Abstract VPNs are vital for safeguarding communication routes in the continually changing cybersecurity world. However, increasing network attack complexity and variety require increasingly advanced algorithms to recognize and categorize VPN network data. We present a novel VPN network traffic flow classification method utilizing Artificial Neural Networks (ANN). This paper aims to provide a reliable system that can identify a virtual private network (VPN) traffic from intrusion attempts, data exfiltration, and denial-of-service assaults. We compile a broad dataset of labeled VPN traffic flows from various apps and usage patterns. Next, we create an ANN architecture that can… More >

  • Open Access

    ARTICLE

    Microarray Gene Expression Classification: An Efficient Feature Selection Using Hybrid Swarm Intelligence Algorithm

    Punam Gulande*, R. N. Awale

    Computer Systems Science and Engineering, Vol.48, No.4, pp. 937-952, 2024, DOI:10.32604/csse.2024.046123 - 17 July 2024

    Abstract The study of gene expression has emerged as a vital tool for cancer diagnosis and prognosis, particularly with the advent of microarray technology that enables the measurement of thousands of genes in a single sample. While this wealth of data offers invaluable insights for disease management, the high dimensionality poses a challenge for multiclass classification. In this context, selecting relevant features becomes essential to enhance classification model performance. Swarm Intelligence algorithms have proven effective in addressing this challenge, owing to their ability to navigate intricate, non-linear feature-class relationships. This paper introduces a novel hybrid swarm More >

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