Open Access
ARTICLE
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
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
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
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
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
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
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 >
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