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检索条件"任意字段=IEEE Conference on Local Computer Networks"

共 18622 条记录，以下是411-420 订阅

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A Key Node Identification Algorithm in Social networks Based on Global Betweenness Entropy 5

A Key Node Identification Algorithm in Social Networks Based...

引用

5th International conference on Big Data and Artificial Intelligence and Software Engineering, ICBASE 2024

作者： Luo, Guilan Wen, Shuzhen Wang, Caikui Wang, Long Zeng, Yongwang Chen, Zijie Dali University School of Mathematics and Computer Science Yunnan Dali China Dali University School of Engineering Yunnan Dali China

ISBN: (纸本)9798331506612

As social networks continue to develop, accurately identifying key nodes has become crucial. Existing centrality algorithms often rely on local network structures and are susceptible to changes in the degree of individual nodes, making it difficult to identify truly important nodes from a global perspective. This paper proposes an algorithm based on an improved Global Betweenness Degree Entropy (GBDE) to identify key nodes in complex networks. The algorithm combines closeness centrality and the eigenvector centrality of neighboring nodes, iteratively selecting and optimizing the node set to ultimately identify nodes that are critical to the network structure and the propagation process. This algorithm not only considers the network position of the nodes but also takes into account their relationships with adjacent nodes, thereby improving the accuracy and effectiveness of key node identification. Experimental results on five real-world networks demonstrate that the GBDE algorithm outperforms BDE, DC, BC, LE, and ME algorithms in the task of identifying key nodes. © 2024 ieee.

关键词： Consensus algorithm

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Design of an Iterative Model Integrating Temporal Convolutional networks and Transfer Learning for Hydrological Analysis 2

Design of an Iterative Model Integrating Temporal Convolutio...

引用

2nd ieee International conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Munganda, Venkata Lakshmi Reeja, S.R. Department of Computer Science and Engineering VIT-AP University Amaravati India

ISBN: (纸本)9798331504960

Advanced artificial intelligence with hydrological analysis promotes accurate Water-Level (WL) forecasting and anomaly detection, which are crucial for disaster management, especially in frequent flood-prone Kerala. This research presents a Seasonal Hybrid Extreme Studentized Deviation Test (SHESD), and transfer learning based Long Short-Term Memory (LSTM) with advanced technique Temporal Convolutional networks (TCN) in modelling temporal patterns. By incorporating the SHESD, seasonal anomalies will be effectively found. The residuals were measured using ESD by classifying timestamp series into seasonal and trend dataset NDWI downloaded from GEE. The method detects abnormal WL occurrences that indicate significant flood consequences for diverse scenarios with 90% high accuracy. WL predictions are accurately measured using the proposed model with 87% R2, 0.095 mean absolute error and 0.03 Mean Squared Error. Fine-tuning these integrated deep learning models using local data enhanced the model prediction accuracy. © 2024 ieee.

关键词： Contrastive Learning

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Application of Attention Mechanism-Based Neural networks in Predicting Chemical Molecular Properties 5

Application of Attention Mechanism-Based Neural Networks in ...

引用

5th International conference on Artificial Intelligence and computer Engineering, ICAICE 2024

作者： Lin, Junhao Jiang, Weifeng He, Shaohong Ma, Junyuan College of Sciences China Jiliang University Hangzhou China

ISBN: (纸本)9798331528911

The prediction of molecular properties is a hot issue in the field of materials chemistry, and the use of neural networks to predict chemical molecular properties is one of the more effective means. In recent years, with the deepening of the research, a variety of neural network prediction algorithms have appeared, such as the neural network combined with particle swarm algorithm and the prediction method based on RBF neural network. However, all these methods face the problems of difficult hyperparameter adjustment and local optimal solution. In this paper, the prediction of chemical molecule properties based on the attention mechanism-based neural network model is realized through the adaptive adjustment of the attention weights, which significantly improves the accuracy of the prediction of chemical molecule properties. Taking the complex property prediction of 100 chemical molecules as an example, taking the same test set, the loss function mean square deviation of the traditional neural network model on the test set is 978.3049079, and the attentional mechanism neural network model reduces this value to 931.909225, which significantly improves the accuracy of the prediction of chemical molecule properties. The study shows that the attention mechanism-based neural network model outperforms the traditional neural network model in molecular property prediction with higher prediction accuracy. © 2024 ieee.

关键词： Prediction models

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A New Age for Agricultural Diagnostics: Federated CNN for Papaya Leaf Diseases 2

A New Age for Agricultural Diagnostics: Federated CNN for Pa...

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2nd ieee International conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Suryavanshi, Ankita Kukreja, Vinay Chamoli, Sushant Mehta, Shiva Garg, Ashish Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India Graphic Era Deemed to Be University Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350360523

The growing problem of papaya leaf diseases has prompted a pressing need for effective remedies. As a reaction, the study utilizes the emerging capabilities of convolutional neural networks (CNN) and federated learning to provide a distinct, decentralized, and all-encompassing diagnostic method. Using five different classes of Papaya leaf diseases, the technique examined and analyzed each local data repository independently for each of the five customers (cy-1 through cy-5). After extensive training and validation, several statistical discoveries were made. The precision values obtained from the local data result analysis were as follows: 80.32% for cy-1-fe-1 and a staggering 90.37% for cy-5-fe-5. The accuracy, recall, F1-score, support, and accuracy values further validated the robustness and flexibility of the federated CNN model. Extending an integrative perspective, the research used Federated Averaging methods (Macro, Micro, and Weighted Averages) to convert local knowledge into a comprehensive worldwide outlook. With figures like 83.10% in Macro, 83.76% in Weighted, and 83.64% in Micro averages, cy-5 demonstrated unmatched numerical dominance, although other clients maintained vital metrics like 80.46% (cy-1 Macro) and 78.93% (cy-4 Micro). Federated Averaging was essential in turning local data into global data at this crucial stage. Client-specific information was combined to create a harmonized, universally applicable model, such as the 80.70% accuracy and 83.39% recall of cy-1. The work provides a new diagnostic tool for papaya leaf diseases. It establishes a standard for using decentralized data in conjunction with CNNs and federated learning to address domain-specific problems. © 2024 ieee.

关键词： Diagnosis

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LG-FAL: locality-customized GSA Federated Active Learning 15

LG-FAL: Locality-customized GSA Federated Active Learning

引用

15th ieee International conference on Knowledge Graph, ICKG 2024

作者： Wang, Shuwen Zhu, Xingquan Portland State University Department of Computer Science Portland United States Florida Atlantic University Dept. of Electrical Engineering & Computer Science Boca Raton United States

ISBN: (纸本)9798331508821

Federated Learning (FL) is a learning paradigm which constructs machine learning models using decentralized datasets, with the goal of training a globally optimized model while preserving data privacy. In practice, annotating training data for FL is expensive and time-consuming. To solve this problem, researchers proposed to integrate active Learning (AL) strategy as Federated Active Learning (FAL) framework. However, existing research on FAL has two main limitations: (1) In the active learning part, more attention is paid to the global model, while ignoring the local information;(2) In the federated learning part, the popular Federated averaging (FedAvg) method relies on the assumption that the corresponding nodes in distributed neural networks share the same importance when averaging. In this paper, in order to tackle the two limitations, we propose a locality-customized GSA federated active learning (LG-FAL) method. Specifically, (i) both local and global information are taken into consideration to evaluate and select informative data samples to annotate for active learning;(ii) we integrate Gravitational Search Algorithm (GSA) to dynamically average local network parameters into the global parameter, by mimicking the principles of gravity and motion in the universe, for effective federated learning. As a result, LG-FAL can select a small subset of informative samples considering both local and global information, and at the same time, provide an improved trade-off between communication cost and learning accuracy. Experimental results show that LG-FAL significantly outperforms the current state-of-the-art baselines in terms of performance and effectiveness. © 2024 ieee.

关键词： Federated learning

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Personalized Federated Learning by Domain-Aware Network Pruning and Re-growth

Personalized Federated Learning by Domain-Aware Network Prun...

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2024 ieee International conference on Big Data, BigData 2024

作者： Suzuki, Yuto Banaei-Kashani, Farnoush University of Colorado Denver Department of Computer Science and Engineering DenverCO United States

ISBN: (纸本)9798350362480

Federated learning (FL) is a machine learning paradigm where multiple clients train their local machine learning models collaboratively (without sharing private data). One of the main challenges in FL is statistical heterogeneity of the data distributions across clients. Personalized FL (PFL) mitigates statistical heterogeneity by collaborative model training across homogeneous clients. In this paper, we propose a novel personalized federated learning by domain-aware network pruning and re-growth, called FedDNPR, that is more accurate as compared to existing PFL methods while maintaining high efficiency. This is achieved by 1) introducing a regularization term capturing heterogeneity of weights in iterative network pruning in order to reduce network sharing among unrelated clients, and 2) iterative network re-growing only from weights of related clients to increase network sharing among related clients. With FedDNPR, model clustering is performed considering the similarity between gradient updates in the last layers of the networks, with cosine as the similarity measure to achieve both accuracy and efficiency in model personalization. With extensive experimental evaluation, we show that FedDNPR significantly outperforms the state-of-the-art PFL approaches, while maintaining comparable efficiency. © 2024 ieee.

关键词： Federated learning

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Hybrid CNN-SSM Network for Efficient and Perceptually Enhanced Super-Resolution 3

Hybrid CNN-SSM Network for Efficient and Perceptually Enhanc...

引用

3rd International conference on Cloud Computing, Big Data Application and Software Engineering, CBASE 2024

作者： Zheng, Yiyang School of Computer Science and Technology Xidian University Xi'an China

ISBN: (纸本)9798331506582

This paper introduces HybSR, a novel image super- resolution method that leverages the strengths of both Convolutional Neural networks (CNNs) and State Space Models (SSMs) within a hybrid architecture. CNNs effectively capture local image details, while SSMs offer linear scalability and excel at modeling long-range dependencies. By combining these two paradigms, HybSR achieves a compelling balance between efficiency and performance. Trained with a hybrid loss function encompassing pixel-wise and perceptual components, the model ensures both accurate reconstruction and high visual fidelity. Furthermore, knowledge distillation from a larger teacher network to a smaller student network enhances the model's efficiency. Ex- tensive evaluations on benchmark datasets demonstrate HybSR's superior performance in terms of reconstruction quality, model size, and computational efficiency compared to existing efficient super-resolution methods. © 2024 ieee.

关键词： Convolutional neural networks

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Study of Spatial Reuse in ieee 802.11ax networks over Propagation Models 14

Study of Spatial Reuse in IEEE 802.11ax Networks over Propag...

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14th International conference on Ambient Systems, networks and Technologies networks, ANT 2023 and The 6th International conference on Emerging Data and Industry 4.0, EDI40 2023

作者： Peng, Jun Miller, Paola Electrical and Computer Engineering Department University of Texas Rio Grande Valley EdinburgTX78539 United States

The paper studies the spatial reuse in ieee 802.11ax networks over propagation models in ns3. The propagation models used in the study include the Friis model, the Nakagami model, and the combination of the two models. The results show that not all access points in an area benefit from the spatial reuse, and some even lose throughput. These results introduce the question of how to justify the spatial reuse for those access points who lose throughput, although the total throughput of the access points in the area benefits from the spatial reuse in general. The results also show that the total throughput gains heavily depend on the physical environment of the networks as the environment determines the signal propagation in the networks. The results show that an environment of significant attenuation plus multipath fading offers significant throughput gains for the access points as a whole. Our main contributions in the paper are the results of the ns3 simulations which were not found in the literature. © 2023 Elsevier B.V.. All rights reserved.

关键词： Wireless local area networks (WLAN)

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Intercommunication Method between local Edge Computing Devices Using QUIC-Based CYPHONIC

Intercommunication Method between Local Edge Computing Devic...

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2024 ieee International conference on Consumer Electronics, ICCE 2024

作者： Horisaki, Shota Matama, Kazushige Goto, Ren Naito, Katsuhiro Suzuki, Hidekazu Meijo University Graduate School of Science and Technology Nagoya Japan Aichi Institute of Technology Graduate School of Business Administration and Computer Science Toyota Japan Aichi Institute of Technology Faculty of Information Science Toyota Japan Meijo University Faculty of Information Engineering Nagoya Japan

ISBN: (纸本)9798350324136

Internet of Things (IoT) devices are installed in various locations to realize smart cities, smart factories, smart homes, etc., and are connected to the Internet for data communication. Most current IoT systems are built on a client-server model using servers built on the cloud. On the other hand, edge computing services are increasing and there are still demands to manage sensitive data in a local environment, and a local edge computing is emerging as a new concept to address these demands. Next-generation IoT services are expected to increase the importance of interoperability between different IoT systems, and connectivity to the local edge computing environment will be required. The authors have proposed QUIC-based CYPHONIC (CYber PHysical Overlay Network over Internet Communication) as a communication architecture that simultaneously achieves communication connectivity and mobility transparency in a mixed IPv4/IPv6 environment. This paper shows that QUIC-based CYPHONIC can be used to realize end-To-end encrypted communication between local edge devices located in different local networks over the actual Internet environment with practically acceptable performance. © 2024 ieee.

关键词： End-To-End Connectivity local Edge Computing Overlay Network QUIC

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Mobile Device Security in Wireless networks: Challenges, issues and Solutions 15

Mobile Device Security in Wireless Networks: Challenges, iss...

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15th International conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Gopalakrishna, K. Sahoo, Laxman Arunadevi, R. Wasatkar, Namrata N. Sahoo, Girija Shankar Singh, Dhiraj Faculty of Engineering and Technology Department of Electronics and Communication Engineering Karnataka Ramnagar District562112 India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India Vishwakarma Institute of Technology Department of Computer Engineering Pune India Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India Chitkara University Institute of Engineering and technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350370249

Mobile device protection has emerged as an increasingly important difficulty as more and more customers depend upon wireless networks for everyday conversation and other duties. Wi-Fi networks offer convenience and versatility for users;however, they also come with safety worries. This paper presents an overview of the safety-demanding situations related to Wi -Fi networks, discusses safety issues related to cellular devices, and offers more than a few answers for creating cozy cell surroundings. It focuses on defensive person identification, privacy, statistics, and defending against possible malicious activities. Primarily, the paper explores security solutions, encryption, authentication, community access control, and tool control. Moreover, it appears on the role of service carriers in cell tool protection and shows an aggregate of quality practices and generation to sell secure usage. Finally, the paper recommends persevering with studies to identify new threats and answers to decorate mobile safety similarly. © 2024 ieee.

关键词： Wireless local area networks (WLAN)

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