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  • Open Access

    ARTICLE

    Task Offloading in Edge Computing Using GNNs and DQN

    Asier Garmendia-Orbegozo1, Jose David Nunez-Gonzalez1,*, Miguel Angel Anton2

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 2649-2671, 2024, DOI:10.32604/cmes.2024.045912

    Abstract In a network environment composed of different types of computing centers that can be divided into different layers (clod, edge layer, and others), the interconnection between them offers the possibility of peer-to-peer task offloading. For many resource-constrained devices, the computation of many types of tasks is not feasible because they cannot support such computations as they do not have enough available memory and processing capacity. In this scenario, it is worth considering transferring these tasks to resource-rich platforms, such as Edge Data Centers or remote cloud servers. For different reasons, it is more exciting and appropriate to download various tasks… More > Graphic Abstract

    Task Offloading in Edge Computing Using GNNs and DQN

  • Open Access

    ARTICLE

    Improving Prediction of Chronic Kidney Disease Using KNN Imputed SMOTE Features and TrioNet Model

    Nazik Alturki1, Abdulaziz Altamimi2, Muhammad Umer3,*, Oumaima Saidani1, Amal Alshardan1, Shtwai Alsubai4, Marwan Omar5, Imran Ashraf6,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3513-3534, 2024, DOI:10.32604/cmes.2023.045868

    Abstract Chronic kidney disease (CKD) is a major health concern today, requiring early and accurate diagnosis. Machine learning has emerged as a powerful tool for disease detection, and medical professionals are increasingly using ML classifier algorithms to identify CKD early. This study explores the application of advanced machine learning techniques on a CKD dataset obtained from the University of California, UC Irvine Machine Learning repository. The research introduces TrioNet, an ensemble model combining extreme gradient boosting, random forest, and extra tree classifier, which excels in providing highly accurate predictions for CKD. Furthermore, K nearest neighbor (KNN) imputer is utilized to deal… More >

  • Open Access

    ARTICLE

    Full-Scale Isogeometric Topology Optimization of Cellular Structures Based on Kirchhoff–Love Shells

    Mingzhe Huang, Mi Xiao*, Liang Gao, Mian Zhou, Wei Sha, Jinhao Zhang

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 2479-2505, 2024, DOI:10.32604/cmes.2023.045735

    Abstract Cellular thin-shell structures are widely applied in ultralightweight designs due to their high bearing capacity and strength-to-weight ratio. In this paper, a full-scale isogeometric topology optimization (ITO) method based on Kirchhoff–Love shells for designing cellular tshin-shell structures with excellent damage tolerance ability is proposed. This method utilizes high-order continuous nonuniform rational B-splines (NURBS) as basis functions for Kirchhoff–Love shell elements. The geometric and analysis models of thin shells are unified by isogeometric analysis (IGA) to avoid geometric approximation error and improve computational accuracy. The topological configurations of thin-shell structures are described by constructing the effective density field on the control… More >

  • Open Access

    REVIEW

    Recent Advances on Deep Learning for Sign Language Recognition

    Yanqiong Zhang, Xianwei Jiang*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 2399-2450, 2024, DOI:10.32604/cmes.2023.045731

    Abstract Sign language, a visual-gestural language used by the deaf and hard-of-hearing community, plays a crucial role in facilitating communication and promoting inclusivity. Sign language recognition (SLR), the process of automatically recognizing and interpreting sign language gestures, has gained significant attention in recent years due to its potential to bridge the communication gap between the hearing impaired and the hearing world. The emergence and continuous development of deep learning techniques have provided inspiration and momentum for advancing SLR. This paper presents a comprehensive and up-to-date analysis of the advancements, challenges, and opportunities in deep learning-based sign language recognition, focusing on the… More >

  • Open Access

    ARTICLE

    Privacy-Preserving Federated Deep Learning Diagnostic Method for Multi-Stage Diseases

    Jinbo Yang1, Hai Huang1, Lailai Yin2, Jiaxing Qu3, Wanjuan Xie4,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3085-3099, 2024, DOI:10.32604/cmes.2023.045417

    Abstract Diagnosing multi-stage diseases typically requires doctors to consider multiple data sources, including clinical symptoms, physical signs, biochemical test results, imaging findings, pathological examination data, and even genetic data. When applying machine learning modeling to predict and diagnose multi-stage diseases, several challenges need to be addressed. Firstly, the model needs to handle multimodal data, as the data used by doctors for diagnosis includes image data, natural language data, and structured data. Secondly, privacy of patients’ data needs to be protected, as these data contain the most sensitive and private information. Lastly, considering the practicality of the model, the computational requirements should… More >

  • Open Access

    ARTICLE

    KSKV: Key-Strategy for Key-Value Data Collection with Local Differential Privacy

    Dan Zhao1, Yang You2, Chuanwen Luo3,*, Ting Chen4,*, Yang Liu5

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3063-3083, 2024, DOI:10.32604/cmes.2023.045400

    Abstract In recent years, the research field of data collection under local differential privacy (LDP) has expanded its focus from elementary data types to include more complex structural data, such as set-value and graph data. However, our comprehensive review of existing literature reveals that there needs to be more studies that engage with key-value data collection. Such studies would simultaneously collect the frequencies of keys and the mean of values associated with each key. Additionally, the allocation of the privacy budget between the frequencies of keys and the means of values for each key does not yield an optimal utility tradeoff.… More >

  • Open Access

    ARTICLE

    Synergistic Swarm Optimization Algorithm

    Sharaf Alzoubi1, Laith Abualigah2,3,4,5,6,7,8,*, Mohamed Sharaf9, Mohammad Sh. Daoud10, Nima Khodadadi11, Heming Jia12

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 2557-2604, 2024, DOI:10.32604/cmes.2023.045170

    Abstract This research paper presents a novel optimization method called the Synergistic Swarm Optimization Algorithm (SSOA). The SSOA combines the principles of swarm intelligence and synergistic cooperation to search for optimal solutions efficiently. A synergistic cooperation mechanism is employed, where particles exchange information and learn from each other to improve their search behaviors. This cooperation enhances the exploitation of promising regions in the search space while maintaining exploration capabilities. Furthermore, adaptive mechanisms, such as dynamic parameter adjustment and diversification strategies, are incorporated to balance exploration and exploitation. By leveraging the collaborative nature of swarm intelligence and integrating synergistic cooperation, the SSOA… More >

  • Open Access

    ARTICLE

    Sub-Homogeneous Peridynamic Model for Fracture and Failure Analysis of Roadway Surrounding Rock

    Shijun Zhao1, Qing Zhang2, Yusong Miao1, Weizhao Zhang3, Xinbo Zhao1, Wei Xu1,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3167-3187, 2024, DOI:10.32604/cmes.2023.045015

    Abstract The surrounding rock of roadways exhibits intricate characteristics of discontinuity and heterogeneity. To address these complexities, this study employs non-local Peridynamics (PD) theory and reconstructs the kernel function to represent accurately the spatial decline of long-range force. Additionally, modifications to the traditional bond-based PD model are made. By considering the micro-structure of coal-rock materials within a uniform discrete model, heterogeneity characterized by bond random pre-breaking is introduced. This approach facilitates the proposal of a novel model capable of handling the random distribution characteristics of material heterogeneity, rendering the PD model suitable for analyzing the deformation and failure of heterogeneous layered… More >

  • Open Access

    ARTICLE

    An Optimization Approach of IoD Deployment for Optimal Coverage Based on Radio Frequency Model

    Tarek Sheltami1,*, Gamil Ahmed1, Ansar Yasar2

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 2627-2647, 2024, DOI:10.32604/cmes.2023.044973

    Abstract Recently, Internet of Drones (IoD) has garnered significant attention due to its widespread applications. However, deploying IoD for area coverage poses numerous limitations and challenges. These include interference between neighboring drones, the need for directional antennas, and altitude restrictions for drones. These challenges necessitate the development of efficient solutions. This research paper presents a cooperative decision-making approach for an efficient IoD deployment to address these challenges effectively. The primary objective of this study is to achieve an efficient IoD deployment strategy that maximizes the coverage region while minimizing interference between neighboring drones. In deployment problem, the interference increases as the… More >

  • Open Access

    ARTICLE

    Nonlinear Study on the Mechanical Performance of Built-Up Cold-Formed Steel Concrete-Filled Columns under Compression

    Oulfa Harrat1,*, Yazid Hadidane1, S. M. Anas2,*, Nadhim Hamah Sor3,4, Ahmed Farouk Deifalla5, Paul O. Awoyera6, Nadia Gouider1

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3435-3465, 2024, DOI:10.32604/cmes.2023.044950

    Abstract Given their numerous functional and architectural benefits, such as improved bearing capacity and increased resistance to elastic instability modes, cold-formed steel (CFS) built-up sections have become increasingly developed and used in recent years, particularly in the construction industry. This paper presents an analytical and numerical study of assembled CFS two single channel-shaped columns with different slenderness and configurations (back-to-back, face-to-face, and box). These columns were joined by double-row rivets for the back-to-back and box configurations, whereas they were welded together for the face-to-face design. The built-up columns were filled with ordinary concrete of good strength. Finite element models were applied,… More > Graphic Abstract

    Nonlinear Study on the Mechanical Performance of Built-Up Cold-Formed Steel Concrete-Filled Columns under Compression

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