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  • Open Access

    ARTICLE

    The Lightweight Edge-Side Fault Diagnosis Approach Based on Spiking Neural Network

    Jingting Mei, Yang Yang*, Zhipeng Gao, Lanlan Rui, Yijing Lin

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4883-4904, 2024, DOI:10.32604/cmc.2024.051860

    Abstract Network fault diagnosis methods play a vital role in maintaining network service quality and enhancing user experience as an integral component of intelligent network management. Considering the unique characteristics of edge networks, such as limited resources, complex network faults, and the need for high real-time performance, enhancing and optimizing existing network fault diagnosis methods is necessary. Therefore, this paper proposes the lightweight edge-side fault diagnosis approach based on a spiking neural network (LSNN). Firstly, we use the Izhikevich neurons model to replace the Leaky Integrate and Fire (LIF) neurons model in the LSNN model. Izhikevich… More >

  • Open Access

    ARTICLE

    GCAGA: A Gini Coefficient-Based Optimization Strategy for Computation Offloading in Multi-User-Multi-Edge MEC System

    Junqing Bai1, Qiuchao Dai1,*, Yingying Li2

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5083-5103, 2024, DOI:10.32604/cmc.2024.050921

    Abstract To support the explosive growth of Information and Communications Technology (ICT), Mobile Edge Computing (MEC) provides users with low latency and high bandwidth service by offloading computational tasks to the network’s edge. However, resource-constrained mobile devices still suffer from a capacity mismatch when faced with latency-sensitive and compute-intensive emerging applications. To address the difficulty of running computationally intensive applications on resource-constrained clients, a model of the computation offloading problem in a network consisting of multiple mobile users and edge cloud servers is studied in this paper. Then a user benefit function EoU (Experience of Users) is… More >

  • Open Access

    ARTICLE

    Empowering Diagnosis: Cutting-Edge Segmentation and Classification in Lung Cancer Analysis

    Iftikhar Naseer1,2, Tehreem Masood1,2, Sheeraz Akram3,*, Zulfiqar Ali4, Awais Ahmad3, Shafiq Ur Rehman3, Arfan Jaffar1,2

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4963-4977, 2024, DOI:10.32604/cmc.2024.050204

    Abstract Lung cancer is a leading cause of global mortality rates. Early detection of pulmonary tumors can significantly enhance the survival rate of patients. Recently, various Computer-Aided Diagnostic (CAD) methods have been developed to enhance the detection of pulmonary nodules with high accuracy. Nevertheless, the existing methodologies cannot obtain a high level of specificity and sensitivity. The present study introduces a novel model for Lung Cancer Segmentation and Classification (LCSC), which incorporates two improved architectures, namely the improved U-Net architecture and the improved AlexNet architecture. The LCSC model comprises two distinct stages. The first stage involves… More >

  • Open Access

    ARTICLE

    EG-STC: An Efficient Secure Two-Party Computation Scheme Based on Embedded GPU for Artificial Intelligence Systems

    Zhenjiang Dong1, Xin Ge1, Yuehua Huang1, Jiankuo Dong1, Jiang Xu2,*

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4021-4044, 2024, DOI:10.32604/cmc.2024.049233

    Abstract This paper presents a comprehensive exploration into the integration of Internet of Things (IoT), big data analysis, cloud computing, and Artificial Intelligence (AI), which has led to an unprecedented era of connectivity. We delve into the emerging trend of machine learning on embedded devices, enabling tasks in resource-limited environments. However, the widespread adoption of machine learning raises significant privacy concerns, necessitating the development of privacy-preserving techniques. One such technique, secure multi-party computation (MPC), allows collaborative computations without exposing private inputs. Despite its potential, complex protocols and communication interactions hinder performance, especially on resource-constrained devices. Efforts… More >

  • Open Access

    ARTICLE

    LKPNR: Large Language Models and Knowledge Graph for Personalized News Recommendation Framework

    Hao Chen#, Runfeng Xie#, Xiangyang Cui, Zhou Yan, Xin Wang, Zhanwei Xuan*, Kai Zhang*

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4283-4296, 2024, DOI:10.32604/cmc.2024.049129

    Abstract Accurately recommending candidate news to users is a basic challenge of personalized news recommendation systems. Traditional methods are usually difficult to learn and acquire complex semantic information in news texts, resulting in unsatisfactory recommendation results. Besides, these traditional methods are more friendly to active users with rich historical behaviors. However, they can not effectively solve the long tail problem of inactive users. To address these issues, this research presents a novel general framework that combines Large Language Models (LLM) and Knowledge Graphs (KG) into traditional methods. To learn the contextual information of news text, we… More >

  • Open Access

    ARTICLE

    Maximizing Resource Efficiency in Cloud Data Centers through Knowledge-Based Flower Pollination Algorithm (KB-FPA)

    Nidhika Chauhan1, Navneet Kaur2, Kamaljit Singh Saini3, Sahil Verma3, Kavita3, Ruba Abu Khurma4,5, Pedro A. Castillo6,*

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3757-3782, 2024, DOI:10.32604/cmc.2024.046516

    Abstract Cloud computing is a dynamic and rapidly evolving field, where the demand for resources fluctuates continuously. This paper delves into the imperative need for adaptability in the allocation of resources to applications and services within cloud computing environments. The motivation stems from the pressing issue of accommodating fluctuating levels of user demand efficiently. By adhering to the proposed resource allocation method, we aim to achieve a substantial reduction in energy consumption. This reduction hinges on the precise and efficient allocation of resources to the tasks that require those most, aligning with the broader goal of… More >

  • Open Access

    ARTICLE

    Arrhythmia Detection by Using Chaos Theory with Machine Learning Algorithms

    Maie Aboghazalah1,*, Passent El-kafrawy2, Abdelmoty M. Ahmed3, Rasha Elnemr5, Belgacem Bouallegue3, Ayman El-sayed4

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3855-3875, 2024, DOI:10.32604/cmc.2023.039936

    Abstract Heart monitoring improves life quality. Electrocardiograms (ECGs or EKGs) detect heart irregularities. Machine learning algorithms can create a few ECG diagnosis processing methods. The first method uses raw ECG and time-series data. The second method classifies the ECG by patient experience. The third technique translates ECG impulses into Q waves, R waves and S waves (QRS) features using richer information. Because ECG signals vary naturally between humans and activities, we will combine the three feature selection methods to improve classification accuracy and diagnosis. Classifications using all three approaches have not been examined till now. Several More >

  • Open Access

    REVIEW

    Knowledge Mapping of Hybrid Solar PV and Wind Energy Standalone Systems: A Bibliometric Analysis

    Quan Zhou*, Haiyang Li

    Energy Engineering, Vol.121, No.7, pp. 1781-1803, 2024, DOI:10.32604/ee.2024.049387

    Abstract Renewable energy is becoming more attractive as traditional fossil fuels are rapidly depleted and expensive, and their use would release pollutants. Power systems that use both wind and solar energy are more reliable and efficient than those that utilize only one energy. Hybrid renewable energy systems (HRES) are viable for remote areas operating in standalone mode. This paper aims to present the state-of-the-art research on off-grid solar-wind hybrid energy systems over the last two decades. More than 1500 published articles extracted from the Web of Science are analyzed by bibliometric methods and processed by CiteSpace… More >

  • Open Access

    REVIEW

    Research Progress on Economic Forest Water Stress Based on Bibliometrics and Knowledge Graph

    Xin Yin1,#, Shuai Wang1,#, Chunguang Wang1, Haichao Wang2, Zheying Zong1,3,*, Zeyu Ban1

    Phyton-International Journal of Experimental Botany, Vol.93, No.5, pp. 843-858, 2024, DOI:10.32604/phyton.2024.049114

    Abstract This study employed the bibliometric software CiteSpace 6.1.R6 to analyze the correlation between thermal infrared, spectral remote sensing technology, and the estimation of economic forest water stress. It aimed to review the development and current status of this field, as well as to identify future research trends. A search was conducted on the China National Knowledge Infrastructure (CNKI) database using the keyword “water stress” for relevant studies from 2003 to 2023. The visual analysis function of CNKI was used to generate the distribution of annual publication volume, and CiteSpace 6.1.R6 was utilized to create network More >

  • Open Access

    ARTICLE

    Direct Pointwise Comparison of FE Predictions to StereoDIC Measurements: Developments and Validation Using Double Edge-Notched Tensile Specimen

    Troy Myers1, Michael A. Sutton1,*, Hubert Schreier2, Alistair Tofts2, Sreehari Rajan Kattil1

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1263-1298, 2024, DOI:10.32604/cmes.2024.048743

    Abstract To compare finite element analysis (FEA) predictions and stereovision digital image correlation (StereoDIC) strain measurements at the same spatial positions throughout a region of interest, a field comparison procedure is developed. The procedure includes (a) conversion of the finite element data into a triangular mesh, (b) selection of a common coordinate system, (c) determination of the rigid body transformation to place both measurements and FEA data in the same system and (d) interpolation of the FEA nodal information to the same spatial locations as the StereoDIC measurements using barycentric coordinates. For an aluminum Al-6061 double edge More >

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