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  • Open Access

    ARTICLE

    Machine Learning Security Defense Algorithms Based on Metadata Correlation Features

    Ruchun Jia, Jianwei Zhang*, Yi Lin

    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2391-2418, 2024, DOI:10.32604/cmc.2024.044149

    Abstract With the popularization of the Internet and the development of technology, cyber threats are increasing day by day. Threats such as malware, hacking, and data breaches have had a serious impact on cybersecurity. The network security environment in the era of big data presents the characteristics of large amounts of data, high diversity, and high real-time requirements. Traditional security defense methods and tools have been unable to cope with the complex and changing network security threats. This paper proposes a machine-learning security defense algorithm based on metadata association features. Emphasize control over unauthorized users through privacy, integrity, and availability. The… More >

  • Open Access

    ARTICLE

    Highly Accurate Golden Section Search Algorithms and Fictitious Time Integration Method for Solving Nonlinear Eigenvalue Problems

    Chein-Shan Liu1, Jian-Hung Shen2, Chung-Lun Kuo1, Yung-Wei Chen2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1317-1335, 2024, DOI:10.32604/cmes.2023.030618

    Abstract This study sets up two new merit functions, which are minimized for the detection of real eigenvalue and complex eigenvalue to address nonlinear eigenvalue problems. For each eigen-parameter the vector variable is solved from a nonhomogeneous linear system obtained by reducing the number of eigen-equation one less, where one of the nonzero components of the eigenvector is normalized to the unit and moves the column containing that component to the right-hand side as a nonzero input vector. 1D and 2D golden section search algorithms are employed to minimize the merit functions to locate real and complex eigenvalues. Simultaneously, the real… More >

  • Open Access

    ARTICLE

    An Adaptive Parallel Feedback-Accelerated Picard Iteration Method for Simulating Orbit Propagation

    Changtao Wang, Honghua Dai*, Wenchuan Yang

    Digital Engineering and Digital Twin, Vol.1, pp. 3-13, 2023, DOI:10.32604/dedt.2023.044210

    Abstract A novel Adaptive Parallel Feedback-Accelerated Picard Iteration (AP-FAPI) method is proposed to meet the requirements of various aerospace missions for fast and accurate orbit propagation. The Parallel Feedback-Accelerated Picard Iteration (P-FAPI) method is an advanced iterative collocation method. With large-step computing and parallel acceleration, the P-FAPI method outperforms the traditional finite-difference-based methods, which require small-step and serial integration to ensure accuracy. Although efficient and accurate, the P-FAPI method suffers extensive trials in tuning method parameters, strongly influencing its performance. To overcome this problem, we propose the AP-FAPI method based on the relationship between the parameters and the convergence speed leveraging… More >

  • Open Access

    ARTICLE

    Integration of Digital Twins and Artificial Intelligence for Classifying Cardiac Ischemia

    Mohamed Ammar1,*, Hamed Al-Raweshidy2,*

    Journal on Artificial Intelligence, Vol.5, pp. 195-218, 2023, DOI:10.32604/jai.2023.045199

    Abstract Despite advances in intelligent medical care, difficulties remain. Due to its complicated governance, designing, planning, improving, and managing the cardiac system remains difficult. Oversight, including intelligent monitoring, feedback systems, and management practises, is unsuccessful. Current platforms cannot deliver lifelong personal health management services. Insufficient accuracy in patient crisis warning programmes. No frequent, direct interaction between healthcare workers and patients is visible. Physical medical systems and intelligent information systems are not integrated. This study introduces the Advanced Cardiac Twin (ACT) model integrated with Artificial Neural Network (ANN) to handle real-time monitoring, decision-making, and crisis prediction. THINGSPEAK is used to create an… More >

  • Open Access

    ARTICLE

    Sanxingdui Cultural Relics Recognition Algorithm Based on Hyperspectral Multi-Network Fusion

    Shi Qiu1, Pengchang Zhang1,*, Xingjia Tang2, Zimu Zeng1, Miao Zhang1, Bingliang Hu1,*

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3783-3800, 2023, DOI:10.32604/cmc.2023.042074

    Abstract Sanxingdui cultural relics are the precious cultural heritage of humanity with high values of history, science, culture, art and research. However, mainstream analytical methods are contacting and detrimental, which is unfavorable to the protection of cultural relics. This paper improves the accuracy of the extraction, location, and analysis of artifacts using hyperspectral methods. To improve the accuracy of cultural relic mining, positioning, and analysis, the segmentation algorithm of Sanxingdui cultural relics based on the spatial spectrum integrated network is proposed with the support of hyperspectral techniques. Firstly, region stitching algorithm based on the relative position of hyper spectrally collected data… More >

  • Open Access

    ARTICLE

    Enhancing IoT Data Security with Lightweight Blockchain and Okamoto Uchiyama Homomorphic Encryption

    Mohanad A. Mohammed*, Hala B. Abdul Wahab

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1731-1748, 2024, DOI:10.32604/cmes.2023.030528

    Abstract Blockchain technology has garnered significant attention from global organizations and researchers due to its potential as a solution for centralized system challenges. Concurrently, the Internet of Things (IoT) has revolutionized the Fourth Industrial Revolution by enabling interconnected devices to offer innovative services, ultimately enhancing human lives. This paper presents a new approach utilizing lightweight blockchain technology, effectively reducing the computational burden typically associated with conventional blockchain systems. By integrating this lightweight blockchain with IoT systems, substantial reductions in implementation time and computational complexity can be achieved. Moreover, the paper proposes the utilization of the Okamoto Uchiyama encryption algorithm, renowned for… More >

  • Open Access

    ARTICLE

    Parallel Integrated Model-Driven and Data-Driven Online Transient Stability Assessment Method for Power System

    Ying Zhang1, Xiaoqing Han2, Chao Zhang1, Ying Qu1, Yang Liu1, Gengwu Zhang2,*

    Energy Engineering, Vol.120, No.11, pp. 2585-2609, 2023, DOI:10.32604/ee.2023.026816

    Abstract More and more uncertain factors in power systems and more and more complex operation modes of power systems put forward higher requirements for online transient stability assessment methods. The traditional model-driven methods have clear physical mechanisms and reliable evaluation results but the calculation process is time-consuming, while the data-driven methods have the strong fitting ability and fast calculation speed but the evaluation results lack interpretation. Therefore, it is a future development trend of transient stability assessment methods to combine these two kinds of methods. In this paper, the rate of change of the kinetic energy method is used to calculate… More >

  • Open Access

    ARTICLE

    A Transmission and Transformation Fault Detection Algorithm Based on Improved YOLOv5

    Xinliang Tang1, Xiaotong Ru1, Jingfang Su1,*, Gabriel Adonis2

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2997-3011, 2023, DOI:10.32604/cmc.2023.038923

    Abstract On the transmission line, the invasion of foreign objects such as kites, plastic bags, and balloons and the damage to electronic components are common transmission line faults. Detecting these faults is of great significance for the safe operation of power systems. Therefore, a YOLOv5 target detection method based on a deep convolution neural network is proposed. In this paper, Mobilenetv2 is used to replace Cross Stage Partial (CSP)-Darknet53 as the backbone. The structure uses depth-wise separable convolution toreduce the amount of calculation and parameters; improve the detection rate. At the same time, to compensate for the detection accuracy, the Squeeze-and-Excitation… More >

  • Open Access

    ARTICLE

    Enhanced Electric Power Adaptability Using Hybrid Pumped-Hydro Technology with Wind and Photovoltaic Integration

    Uwem O. Ikitde1, Abayomi A. Adebiyi1,*, Innocent E. Davidson2, Ayodeji S. Akinyemi1

    Energy Engineering, Vol.120, No.9, pp. 1939-1961, 2023, DOI:10.32604/ee.2023.027574

    Abstract The integration of solar and wind energy into the electrical grid has received global research attention due to their unpredictable characteristics. Because wind energy varies across all timescales of utility activity, renewable energy generation should be supplemented and enhanced, from real-time, minute-to-minute variations to annual alterations influencing long-term strategy. Wind energy generation does not only fluctuate but is also challenging to accurately forecast the timeframes of significance to electricity decision makers; day-ahead and long-term making plans of framework sufficiency such as meeting the network peak load annually. A utility that integrates wind and solar energy into its electricity mix would… More >

  • Open Access

    ARTICLE

    Cloud Resource Integrated Prediction Model Based on Variational Modal Decomposition-Permutation Entropy and LSTM

    Xinfei Li2, Xiaolan Xie1,2,*, Yigang Tang2, Qiang Guo1,2

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 2707-2724, 2023, DOI:10.32604/csse.2023.037351

    Abstract Predicting the usage of container cloud resources has always been an important and challenging problem in improving the performance of cloud resource clusters. We proposed an integrated prediction method of stacking container cloud resources based on variational modal decomposition (VMD)-Permutation entropy (PE) and long short-term memory (LSTM) neural network to solve the prediction difficulties caused by the non-stationarity and volatility of resource data. The variational modal decomposition algorithm decomposes the time series data of cloud resources to obtain intrinsic mode function and residual components, which solves the signal decomposition algorithm’s end-effect and modal confusion problems. The permutation entropy is used… More >

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