Rongwei Yu, Xiya Guo^*, Peihao Zhang, Kaijuan Zhang

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2643-2664, 2024, DOI:10.32604/cmc.2024.056165 - 18 November 2024

Abstract Encrypted traffic plays a crucial role in safeguarding network security and user privacy. However, encrypting malicious traffic can lead to numerous security issues, making the effective classification of encrypted traffic essential. Existing methods for detecting encrypted traffic face two significant challenges. First, relying solely on the original byte information for classification fails to leverage the rich temporal relationships within network traffic. Second, machine learning and convolutional neural network methods lack sufficient network expression capabilities, hindering the full exploration of traffic’s potential characteristics. To address these limitations, this study introduces a traffic classification method that utilizes… More >

Yingchao Li^1,*, Jianbin Wang¹, Haibin Wang²

Energy Engineering, Vol.121, No.4, pp. 1049-1065, 2024, DOI:10.32604/ee.2023.045228 - 26 March 2024

Abstract With the increasing demand for electrical services, wind farm layout optimization has been one of the biggest challenges that we have to deal with. Despite the promising performance of the heuristic algorithm on the route network design problem, the expressive capability and search performance of the algorithm on multi-objective problems remain unexplored. In this paper, the wind farm layout optimization problem is defined. Then, a multi-objective algorithm based on Graph Neural Network (GNN) and Variable Neighborhood Search (VNS) algorithm is proposed. GNN provides the basis representations for the following search algorithm so that the expressiveness… More >

Asier Garmendia-Orbegozo¹, Jose David Nunez-Gonzalez^1,*, Miguel Angel Anton²

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 2649-2671, 2024, DOI:10.32604/cmes.2024.045912 - 11 March 2024

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… More > Graphic Abstract

Ming Luo¹, Huili Dou², Ning Zheng^3,*

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 265-282, 2024, DOI:10.32604/cmc.2023.040067 - 30 January 2024

Abstract Traffic prediction already plays a significant role in applications like traffic planning and urban management, but it is still difficult to capture the highly non-linear and complicated spatiotemporal correlations of traffic data. As well as to fulfil both long-term and short-term prediction objectives, a better representation of the temporal dependency and global spatial correlation of traffic data is needed. In order to do this, the Spatiotemporal Graph Neural Network (S-GNN) is proposed in this research as a method for traffic prediction. The S-GNN simultaneously accepts various traffic data as inputs and investigates the non-linear correlations… More >

Wonyong Chung, Jaeuk Moon, Dongjun Kim, Eenjun Hwang^*

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5817-5836, 2023, DOI:10.32604/cmc.2023.036830 - 29 April 2023

Abstract Multivariate time-series forecasting (MTSF) plays an important role in diverse real-world applications. To achieve better accuracy in MTSF, time-series patterns in each variable and interrelationship patterns between variables should be considered together. Recently, graph neural networks (GNNs) has gained much attention as they can learn both patterns using a graph. For accurate forecasting through GNN, a well-defined graph is required. However, existing GNNs have limitations in reflecting the spectral similarity and time delay between nodes, and consider all nodes with the same weight when constructing graph. In this paper, we propose a novel graph construction More >