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  • Open Access

    ARTICLE

    Quick Weighing of Passing Vehicles Using the Transfer-Learning-Enhanced Convolutional Neural Network

    Wangchen Yan1,*, Jinbao Yang1, Xin Luo2
    CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2023.044709
    Abstract Transfer learning could reduce the time and resources required for the training of new models and be therefore important in generalized applications of the trained machine learning algorithms. In this study, a transfer learningenhanced convolutional neural network (CNN) was proposed to identify the gross weight and the axle weight of moving vehicles on the bridge. The proposed transfer learning-enhanced CNN model was expected to weigh different bridges based on a small amount of training datasets and provide high identification accuracy. First of all, a CNN algorithm for bridge weigh-in-motion (B-WIM) technology was proposed to identify the axle weight and the… More >

  • Open Access

    REVIEW

    Exploring the Latest Applications of OpenAI and ChatGPT: An In-Depth Survey

    Hong Zhang1,*, Haijian Shao2
    CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2023.030649
    Abstract OpenAI and ChatGPT, as state-of-the-art language models driven by cutting-edge artificial intelligence technology, have gained widespread adoption across diverse industries. In the realm of computer vision, these models have been employed for intricate tasks including object recognition, image generation, and image processing, leveraging their advanced capabilities to fuel transformative breakthroughs. Within the gaming industry, they have found utility in crafting virtual characters and generating plots and dialogues, thereby enabling immersive and interactive player experiences. Furthermore, these models have been harnessed in the realm of medical diagnosis, providing invaluable insights and support to healthcare professionals in the realm of disease detection.… More >

  • Open Access

    ARTICLE

    An Optimized System of Random Forest Model by Global Harmony Search with Generalized Opposition-Based Learning for Forecasting TBM Advance Rate

    Yingui Qiu1, Shuai Huang1, Danial Jahed Armaghani2, Biswajeet Pradhan3, Annan Zhou4, Jian Zhou1,*
    CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2023.029938
    (This article belongs to this Special Issue: Meta-heuristic Algorithms in Materials Science and Engineering)
    Abstract As massive underground projects have become popular in dense urban cities, a problem has arisen: which model predicts the best for Tunnel Boring Machine (TBM) performance in these tunneling projects? However, performance level of TBMs in complex geological conditions is still a great challenge for practitioners and researchers. On the other hand, a reliable and accurate prediction of TBM performance is essential to planning an applicable tunnel construction schedule. The performance of TBM is very diffcult to estimate due to various geotechnical and geological factors and machine specifications. The previously-proposed intelligent techniques in this field are mostly based on a… More >

  • Open Access

    EDITORIAL

    Introduction to the Special Issue on Computational Intelligent Systems for Solving Complex Engineering Problems: Principles and Applications

    Danial Jahed Armaghani1,*, Ahmed Salih Mohammed2,3, Ramesh Murlidhar Bhatawdekar4, Pouyan Fakharian5, Ashutosh Kainthola6, Wael Imad Mahmood7
    CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2023.031701
    (This article belongs to this Special Issue: Computational Intelligent Systems for Solving Complex Engineering Problems: Principles and Applications)
    Abstract This article has no abstract. More >

  • Open Access

    ARTICLE

    A Novel Accurate Method for Multi-Term Time-Fractional Nonlinear Diffusion Equations in Arbitrary Domains

    Tao Hu1, Cheng Huang2, Sergiy Reutskiy3,*, Jun Lu4, Ji Lin5,*
    CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2023.030449
    (This article belongs to this Special Issue: Advances on Mesh and Dimension Reduction Methods)
    Abstract A novel accurate method is proposed to solve a broad variety of linear and nonlinear (1+1)-dimensional and (2+1)- dimensional multi-term time-fractional partial differential equations with spatial operators of anisotropic diffusivity. For (1+1)-dimensional problems, analytical solutions that satisfy the boundary requirements are derived. Such solutions are numerically calculated using the trigonometric basis approximation for (2+1)-dimensional problems. With the aid of these analytical or numerical approximations, the original problems can be converted into the fractional ordinary differential equations, and solutions to the fractional ordinary differential equations are approximated by modified radial basis functions with time-dependent coefficients. An efficient backward substitution strategy that… More >

  • Open Access

    ARTICLE

    A Differential Privacy Federated Learning Scheme Based on Adaptive Gaussian Noise

    Sanxiu Jiao1, Lecai Cai2,*, Xinjie Wang1, Kui Cheng2, Xiang Gao3
    CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2023.030512
    (This article belongs to this Special Issue: Federated Learning Algorithms, Approaches, and Systems for Internet of Things)
    Abstract As a distributed machine learning method, federated learning (FL) has the advantage of naturally protecting data privacy. It keeps data locally and trains local models through local data to protect the privacy of local data. The federated learning method effectively solves the problem of artificial Smart data islands and privacy protection issues. However, existing research shows that attackers may still steal user information by analyzing the parameters in the federated learning training process and the aggregation parameters on the server side. To solve this problem, differential privacy (DP) techniques are widely used for privacy protection in federated learning. However, adding… More >

  • Open Access

    ARTICLE

    Threshold-Based Software-Defined Networking (SDN) Solution for Healthcare Systems against Intrusion Attacks

    Laila M. Halman, Mohammed J. F. Alenazi*
    CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2023.028077
    (This article belongs to this Special Issue: Smart and Secure Solutions for Medical Industry)
    Abstract The healthcare sector holds valuable and sensitive data. The amount of this data and the need to handle, exchange, and protect it, has been increasing at a fast pace. Due to their nature, software-defined networks (SDNs) are widely used in healthcare systems, as they ensure effective resource utilization, safety, great network management, and monitoring. In this sector, due to the value of the data, SDNs face a major challenge posed by a wide range of attacks, such as distributed denial of service (DDoS) and probe attacks. These attacks reduce network performance, causing the degradation of different key performance indicators (KPIs)… More >