Influence Maximization in Social Networks Using Improved Genetic Algorithm by Ali Chodari Khosroshahi, Saeid Taghavi Afshord, Bagher Zarei, Bahman Arasteh

“…Influence maximization is one of the important problems in network science, data mining, and social media analysis. …”
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The Intelligent Sizing Method for Renewable Energy Integrated Distribution Networks by Zhichun Yang, Fan Yang, Yu Liu, Huaidong Min, Zhiqiang Zhou, Bin Zhou, Yang Lei, Wei Hu

“…To address the problem of balancing investment costs and reliability benefits, as well as to establish the target network structure, firstly, the investment cost of the distribution network is calculated based on the determined number of network structure units. …”
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Online Learning to Cache and Recommend in the Next Generation Cellular Networks by Krishnendu S. Tharakan, B. N. Bharath, Vimal Bhatia

“…Motivated by this, in this paper, we consider the problem of joint caching and recommendation in a 5G and beyond heterogeneous network. …”
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A novel software-defined networking controlled vehicular named-data networking for trustworthy emergency data dissemination and content retrieval assisted by evolved interest packe... by Mazen Alowish, Yoshiaki Shiraishi, Yasuhiro Takano, Masami Mohri, Masakatu Morii

“…However, involvement of vast number of vehicles in Internet of vehicles limits the performance of vehicle ad hoc network. To tackle this problem, a novel vehicle ad hoc network architecture with two different technologies such as software-defined networking and named-data networking is proposed in this article. …”
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Custom Network Quantization Method for Lightweight CNN Acceleration on FPGAs by Lingjie Yi, Xianzhong Xie, Yi Wan, Bo Jiang, Junfan Chen

“…Moreover, some operators cannot be accelerated heterogeneously on FPGAs, resulting in frequent switching between the Advanced RISC Machine (ARM) and FPGA environments for computation tasks. To address these problems, this paper proposes a custom network quantization approach. …”
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Encrypted traffic classification method based on convolutional neural network by Rongna XIE, Zhuhong MA, Zongyu LI, Ye TIAN

“…Aiming at the problems of low accuracy, weak generality, and easy privacy violation of traditional encrypted network traffic classification methods, an encrypted traffic classification method based on convolutional neural network was proposed, which avoided relying on original traffic data and prevented overfitting of specific byte structure of the application.According to the data packet size and arrival time information of network traffic, a method to convert the original traffic into a two-dimensional picture was designed.Each cell in the histogram represented the number of packets with corresponding size that arrive at the corresponding time interval, avoiding reliance on packet payloads and privacy violations.The LeNet-5 convolutional neural network model was optimized to improve the classification accuracy.The inception module was embedded for multi-dimensional feature extraction and feature fusion.And the 1*1 convolution was used to control the feature dimension of the output.Besides, the average pooling layer and the convolutional layer were used to replace the fully connected layer to increase the calculation speed and avoid overfitting.The sliding window method was used in the object detection task, and each network unidirectional flow was divided into equal-sized blocks, ensuring that the blocks in the training set and the blocks in the test set in a single session do not overlap and expanding the dataset samples.The classification experiment results on the ISCX dataset show that for the application traffic classification task, the average accuracy rate reaches more than 95%.The comparative experimental results show that the traditional classification method has a significant decrease in accuracy or even fails when the types of training set and test set are different.However, the accuracy rate of the proposed method still reaches 89.2%, which proves that the method is universally suitable for encrypted traffic and non-encrypted traffic.All experiments are based on imbalanced datasets, and the experimental results may be further improved if balanced processing is performed.…”
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Improving deep convolutional neural networks with mixed maxout units by Hui-zhen ZHAO, Fu-xian LIU, Long-yue LI, Chang LUO

“…The maxout units have the problem of not delivering non-max features， resulting in the insufficient of pooling operation over a subspace that is composed of several linear feature mappings,when they are applied in deep convolutional neural networks.The mixed maxout (mixout) units were proposed to deal with this constrain.Firstly,the exponential probability of the feature mappings getting from different linear transformations was computed.Then,the averaging of a subspace of different feature mappings by the exponential probability was computed.Finally,the output was randomly sampled from the max feature and the mean value by the Bernoulli distribution,leading to the better utilizing of model averaging ability of dropout.The simple models and network in network models was built to evaluate the performance of mixout units.The results show that mixout units based models have better performance.…”
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Research on reconfigurable service carrying network active protection algorithms by Ning QI, Bin-qiang WANG, Zhi-ming WANG

“…To construct reconfigurable service carrying network (RSCN) in reconfigurable flexible network (RFNet) infrastructure could provide traffic specific communication services for different end users.This could effectively solve the puzzle faced by traditional Internet infrastructure.Because of the blight of network failure,active protection problems of RSCN were discussed.Mathematics model of RSCN active protection issues were established.To avoid enormous influence because of important resource failure,a resource stress factor (RSF) awareness main link construction algorithm named RSF-aware MLCA was proposed.To improve success running ratio and reduce network link failure loss of RSCN,a RSCN protection link construction algorithm named RPLCA was implemented.Considering of the two sub-algorithms,RSCN active protection algorithm named RAPA was designed.The efficiency of algorithms was evaluated by emulation experiments according to RSCN success running ratio and main link utilization ratio and average network link failure loss under several scenarios.…”
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Research on reconfigurable service carrying network active protection algorithms by Ning QI, Bin-qiang WANG, Zhi-ming WANG

“…To construct reconfigurable service carrying network (RSCN) in reconfigurable flexible network (RFNet) infrastructure could provide traffic specific communication services for different end users.This could effectively solve the puzzle faced by traditional Internet infrastructure.Because of the blight of network failure,active protection problems of RSCN were discussed.Mathematics model of RSCN active protection issues were established.To avoid enormous influence because of important resource failure,a resource stress factor (RSF) awareness main link construction algorithm named RSF-aware MLCA was proposed.To improve success running ratio and reduce network link failure loss of RSCN,a RSCN protection link construction algorithm named RPLCA was implemented.Considering of the two sub-algorithms,RSCN active protection algorithm named RAPA was designed.The efficiency of algorithms was evaluated by emulation experiments according to RSCN success running ratio and main link utilization ratio and average network link failure loss under several scenarios.…”
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Pre-Routing Slack Prediction Based on Graph Attention Network by Jinke Li, Jiahui Hu, Yue Wu, Xiaoyan Yang

“…In recent years, there has been growing research on pre-routing timing prediction using Graph Neural Networks (GNNs). However, existing approaches struggle with scalability on large graphs and lack generalizability to new designs, limiting their applicability to large-scale, complex circuit problems. …”
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Robust neural network filtering in the tasks of building intelligent interfaces by A. V. Vasiliev, A. O. Melnikov, S. A. Lesko

“…The achieved average 5% reduction in individual noise will help to avoid retraining of the network when classifying EMG signals, as well as improving the accuracy of gesture classification for new users.…”
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Neural Network Based on Dynamic Collaboration of Flows for Temporal Downscaling by Junkai Wang, Lianlei Lin, Yu Zhang, Zongwei Zhang, Sheng Gao, Hanqing Zhao

“…Traditional methods often face the problems of high computing cost and poor generalization ability. …”
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Application of Optimized Convolution Neural Network Model in Mural Segmentation by Zhiqiang Chen, Leelavathi Rajamanickam, Xiaodong Tian, Jianfang Cao

“…To address the problems of blurred target boundaries and inefficient image segmentation in ancient mural image segmentation, a multi-classification image segmentation model MC-DM (Multi-class DeeplabV3+ MobileNetV2) that fuses lightweight convolutional neural networks is proposed. …”
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Caching deployment based on energy efficiency in device-to-device cooperative networks by Weiguang Wang, Hui Li, Yang Liu, Wei Cheng, Haoyang Qin

“…This article investigates the caching deployment problem from the energy efficiency in the cache-enabled device-to-device networks. …”
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VNE-AFS:virtual network embedding based on artificial fish swarm by Qiang ZHU, Hui-qiang WANG, Hong-wu LV, Zhen-dong WANG

“…Recently virtual network embedding problem had been proposed as a research challenge in the cloud computing environment.In order to reduce the costs,a virtual network embedding algorithms based on artificial fish swarm(VNE-AFS)was proposed.A binary combinatorial optimization model was built according to the constraints on nodes and links between virtual network and substrate network,and the artificial fish swarm algorithm was used to achieve the approximate optimal mapping.The simulation results indicate that the costs of substrate network and computation time are reduced and the success rate,average revenue of embedding and average usage of links are increased compared with the existing virtual network embedding algorithms.…”
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Age of Information Minimization in Multicarrier-Based Wireless Powered Sensor Networks by Juan Sun, Jingjie Xia, Shubin Zhang, Xinjie Yu

“…To tackle this intricate problem, we propose a novel approach that leverages Lyapunov optimization to transform the complex original problem into a sequence of per-time-bock deterministic problems. …”
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Distributed computations for large-scale networked systems using belief propagation by Qianqian Cai, Zhaorong Zhang, Minyue Fu

“…These algorithms are suitable for a class of computational problems in large-scale networked systems, ranging from average consensus, sensor fusion, distributed estimation, distributed optimisation, distributed control, and distributed learning. …”
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Startup Drift Compensation of MEMS INS Based on PSO–GRNN Network by Songlai Han, Jingyi Xie, Jing Dong

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Collaborative Beamforming with DQN for Interference Mitigation in 5G and Beyond Networks by Alaelddin F. Y. Mohammed, Salman Md Sultan, Sakshi Patni

“…This paper addresses the problem of side lobe interference in 5G networks by proposing a unique collaborative beamforming strategy based on Deep Q-Network (DQN) reinforcement learning. …”
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Two-branch Shape Complement Network for Feature Missing Splicing Mode by SUNJin, MAHaotian, LEIZhenting, LIANGLi

“…Aiming at the problem of low fidelity of reverse geometric reconstruction of bowl-shaped cultural relics model due to the missing fragments , a 3D point cloud shape completion network based on double discrimination decoder is proposed. …”
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