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  • Open Access

    ARTICLE

    Real-Time Prediction Algorithm for Intelligent Edge Networks with Federated Learning-Based Modeling

    Seungwoo Kang1, Seyha Ros1, Inseok Song1, Prohim Tam1, Sa Math2, Seokhoon Kim1,3,*

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1967-1983, 2023, DOI:10.32604/cmc.2023.045020

    Abstract Intelligent healthcare networks represent a significant component in digital applications, where the requirements hold within quality-of-service (QoS) reliability and safeguarding privacy. This paper addresses these requirements through the integration of enabler paradigms, including federated learning (FL), cloud/edge computing, software-defined/virtualized networking infrastructure, and converged prediction algorithms. The study focuses on achieving reliability and efficiency in real-time prediction models, which depend on the interaction flows and network topology. In response to these challenges, we introduce a modified version of federated logistic regression (FLR) that takes into account convergence latencies and the accuracy of the final FL model within healthcare networks. To establish… More >

  • Open Access

    ARTICLE

    Revolutionizing Tight Reservoir Production: A Novel Dual-Medium Unsteady Seepage Model for Optimizing Volumetrically Fractured Horizontal Wells

    Xinyu Zhao1,2,*, Mofeng Li2, Kai Yan2, Li Yin3

    Energy Engineering, Vol.120, No.12, pp. 2933-2949, 2023, DOI:10.32604/ee.2023.041580

    Abstract This study presents an avant-garde approach for predicting and optimizing production in tight reservoirs, employing a dual-medium unsteady seepage model specifically fashioned for volumetrically fractured horizontal wells. Traditional models often fail to fully capture the complex dynamics associated with these unconventional reservoirs. In a significant departure from these models, our approach incorporates an initiation pressure gradient and a discrete fracture seepage network, providing a more realistic representation of the seepage process. The model also integrates an enhanced fluid-solid interaction, which allows for a more comprehensive understanding of the fluid-structure interactions in the reservoir. This is achieved through the incorporation of… More >

  • Open Access

    ARTICLE

    Two-Way Neural Network Performance Prediction Model Based on Knowledge Evolution and Individual Similarity

    Xinzheng Wang1,2,*, Bing Guo1, Yan Shen3

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1183-1206, 2024, DOI:10.32604/cmes.2023.029552

    Abstract Predicting students’ academic achievements is an essential issue in education, which can benefit many stakeholders, for instance, students, teachers, managers, etc. Compared with online courses such as MOOCs, students’ academic-related data in the face-to-face physical teaching environment is usually sparsity, and the sample size is relatively small. It makes building models to predict students’ performance accurately in such an environment even more challenging. This paper proposes a Two-Way Neural Network (TWNN) model based on the bidirectional recurrent neural network and graph neural network to predict students’ next semester’s course performance using only their previous course achievements. Extensive experiments on a… More > Graphic Abstract

    Two-Way Neural Network Performance Prediction Model Based on Knowledge Evolution and Individual Similarity

  • Open Access

    ARTICLE

    Optical Based Gradient-Weighted Class Activation Mapping and Transfer Learning Integrated Pneumonia Prediction Model

    Chia-Wei Jan1, Yu-Jhih Chiu1, Kuan-Lin Chen2, Ting-Chun Yao3, Ping-Huan Kuo1,4,*

    Computer Systems Science and Engineering, Vol.47, No.3, pp. 2989-3010, 2023, DOI:10.32604/csse.2023.042078

    Abstract Pneumonia is a common lung disease that is more prone to affect the elderly and those with weaker respiratory systems. However, hospital medical resources are limited, and sometimes the workload of physicians is too high, which can affect their judgment. Therefore, a good medical assistance system is of great significance for improving the quality of medical care. This study proposed an integrated system by combining transfer learning and gradient-weighted class activation mapping (Grad-CAM). Pneumonia is a common lung disease that is generally diagnosed using X-rays. However, in areas with limited medical resources, a shortage of medical personnel may result in… More >

  • Open Access

    ARTICLE

    Novel defined N7-methylguanosine modification-related lncRNAs for predicting the prognosis of laryngeal squamous cell carcinoma

    ZHAOXU YAO*, HAIBIN MA, LIN LIU, QIAN ZHAO, LONGCHAO QIN, XUEYAN REN, CHUANJUN WU, KAILI SUN

    BIOCELL, Vol.47, No.9, pp. 1965-1975, 2023, DOI:10.32604/biocell.2023.030796

    Abstract Objective: Through integrated bioinformatics analysis, the goal of this work was to find new, characterised N7-methylguanosine modification-related long non-coding RNAs (m7G-lncRNAs) that might be used to predict the prognosis of laryngeal squamous cell carcinoma (LSCC). Methods: The clinical data and LSCC gene expression data for the current investigation were initially retrieved from the TCGA database & sanitised. Then, using co-expression analysis of m7G-associated mRNAs & lncRNAs & differential expression analysis (DEA) among LSCC & normal sample categories, we discovered lncRNAs that were connected to m7G. The prognosis prediction model was built for the training category using univariate & multivariate COX… More >

  • Open Access

    ARTICLE

    Rockburst Intensity Grade Prediction Model Based on Batch Gradient Descent and Multi-Scale Residual Deep Neural Network

    Yu Zhang1,2,3, Mingkui Zhang1,2,*, Jitao Li1,2, Guangshu Chen1,2

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 1987-2006, 2023, DOI:10.32604/csse.2023.040381

    Abstract Rockburst is a phenomenon in which free surfaces are formed during excavation, which subsequently causes the sudden release of energy in the construction of mines and tunnels. Light rockburst only peels off rock slices without ejection, while severe rockburst causes casualties and property loss. The frequency and degree of rockburst damage increases with the excavation depth. Moreover, rockburst is the leading engineering geological hazard in the excavation process, and thus the prediction of its intensity grade is of great significance to the development of geotechnical engineering. Therefore, the prediction of rockburst intensity grade is one problem that needs to be… 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 >

  • Open Access

    ARTICLE

    Development of Wet Shotcrete with Solid Waste as Aggregate: Strength Optimization and Mix Proportion Design

    Yafei Hu1,2, Keqing Li1,2, Bo Zhang1,2, Bin Han1,2,*

    Journal of Renewable Materials, Vol.11, No.9, pp. 3463-3484, 2023, DOI:10.32604/jrm.2023.027532

    Abstract The super-fine particle size of tailings is its drawback as a recycled resource, which is reflected in the low strength of the new construction and industrial materials formed when it is mixed with cement and other cementitious materials. Therefore, it is crucial to study the effect of tailings particle size and cementitious material on the strength of tailings wet shotcrete (TWSC) and to investigate the optimal mix proportion. In this paper, a multivariate nonlinear response model was constructed by conducting central composite experiments to investigate the effect of different factors on the strength of TWSC. The strength prediction and mix… More >

  • Open Access

    ARTICLE

    Effect of Freeze-Thaw Cycles on Chloride Transportation in Concrete: Prediction Model and Experiment

    Yongdong Yan*, Youdong Si, Chunhua Lu, Keke Wu

    Structural Durability & Health Monitoring, Vol.17, No.3, pp. 225-238, 2023, DOI:10.32604/sdhm.2022.022629

    Abstract This research aims to investigate the effect of frost damage on chloride transportation mechanism in ordinary and fiber concrete with both theoretical and experimental methods. The proposed theoretical model takes into account the varying damage levels caused by concrete cover depth and freeze-thaw cycles, which are the two primary parameters affecting the expression of the chloride diffusion coefficient. In the experiment, three types of concrete were prepared: ordinary Portland concrete (OPC), polypropylene fiber concrete (PFC), and steel fiber concrete (SFC). These were then immersed in NaCl solution for 120 days after undergoing 10, 25, and 50 freeze-thaw cycles. The damage… More >

  • Open Access

    ARTICLE

    Forecasting Energy Consumption Using a Novel Hybrid Dipper Throated Optimization and Stochastic Fractal Search Algorithm

    Doaa Sami Khafaga1, El-Sayed M. El-kenawy2, Amel Ali Alhussan1,*, Marwa M. Eid3

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2117-2132, 2023, DOI:10.32604/iasc.2023.038811

    Abstract The accurate prediction of energy consumption has effective role in decision making and risk management for individuals and governments. Meanwhile, the accurate prediction can be realized using the recent advances in machine learning and predictive models. This research proposes a novel approach for energy consumption forecasting based on a new optimization algorithm and a new forecasting model consisting of a set of long short-term memory (LSTM) units. The proposed optimization algorithm is used to optimize the parameters of the LSTM-based model to boost its forecasting accuracy. This optimization algorithm is based on the recently emerged dipper-throated optimization (DTO) and stochastic… More >

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