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

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Identify Skin Disorder Using Dermoscopic Analysis Through Federated Learning 2

Identify Skin Disorder Using Dermoscopic Analysis Through Fe...

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2nd ieee International conference on Emerging Research in Computational Science, ICERCS 2024

作者： Chaitanya, V. Pavan Madhavi Devi, B. Naveen, Batta Akash, Mulkala Institute of Aeronautical Engineering Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331534950

Skin problems are common health issues around the world, and effective treatment frequently requires an accurate and prompt diagnosis. The objective of this work is to use federated learning to enable dermoscopic analysis for the identification of skin problems. Federated learning reduces the data sharing and privacy violations by decentralizing model training across several collaborating institutions, hence ensuring data security and privacy. To improve data privacy, the weights of the skin condition are kept in both the local client and the global client (federated server). Consequently, the Federated server balances the server for both local clients. The YOLO8 approach is used to extract the features of the skin condition in order to classify it. The HAM10000 dataset is used to train the VGG19 and Convolutional Neural networks (CNN) models. The accuracy rates of the local servers is 93% and the global server is 94%. © 2024 ieee.

关键词： Federated learning

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Optimization of Energy Storage and Charging Coordination in Distribution networks Based on Improved Grey Wolf Algorithm 8

Optimization of Energy Storage and Charging Coordination in ...

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8th International conference on Electrical, Mechanical and computer Engineering, ICEMCE 2024

作者： Jiao, Xiaobo Wang, Yuanyuan Xu, Dan Li, Junjian Wang, Nannan She, Wei State Grid Henan Electric Power Company Henan China Zhengzhou University School of Cyber Science and Engineering Henan China Xj Electric Co. Ltd. Henan China

ISBN: (纸本)9798331506230

In the context of integrating renewable energy and orderly charging of electric vehicles, this paper proposes a distribution network scheduling model that includes energy storage systems and orderly charging, aiming to enhance renewable energy utilization and minimize grid operating costs. The traditional grey wolf algorithm is prone to local optima. In this work, a chaos mapping mechanism is introduced to initialize the population positions, and a nonlinear convergence factor is applied for smooth transitions. Additionally, adaptive parameter adjustments allow the algorithm to flexibly respond to different search stages. Finally, a mutation strategy based on fitness is incorporated. Compared to the traditional grey wolf algorithm, the improved grey wolf algorithm enhances global search capability and convergence speed, resulting in better solution accuracy. © 2024 ieee.

关键词： Power distribution networks

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SVTNet: Dual Branch of Swin Transformer and Vision Transformer for Monocular Depth Estimation

SVTNet: Dual Branch of Swin Transformer and Vision Transform...

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2025 ieee International conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Jia, Shuwen Wang, Yongxiong Chen, Han Huang, Shuai School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai China

ISBN: (纸本)9798350368741

In monocular depth estimation, effective acquisition of global and local information is the key to improving accuracy. We introduce a novel dual branch network called Swin Vision Transformer Net (SVTNet), where the Swin Transformer and Vision Transformer are combined to learn features with global and local information. In Swin Transformer branch, we remove the Feature Pyramid networks which reduces the noise introduced by interpolation and maintains the original characteristics of the hierarchical features extracted from each layer of Swin Transformer. In Vision Transformer Branch, we remove the multi-scale convolutional structure and keep the same-scale feature maps extracted by Vision Transformer for reducing the consumption of memory and extracting more global features. The experimental results on the NYU-Depth v2, KITTI, and CityScapes datasets show that compared with existing advanced methods, the SVTNet has achieved significant performance improvements in multiple evaluation metrics. © 2025 ieee.

关键词： dual branch Feature Pyramid networks monocular depth estimation Same-scale feature Swin Transformer Vision Transformer

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local Overlapping Attentional Selection with VIT for Image Classification in Remote Sensing

Local Overlapping Attentional Selection with VIT for Image C...

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2025 International conference on Electronics, Information, and Communication, ICEIC 2025

作者： Zou, ChangMan Jeon, Wang-Su Rhee, Sang-Yong University of Kyungnam Department of IT Convergence Engineering Changwon51767 Korea Republic of College of Computer Science and Technology Beihua University Jilin132013 China University of Kyungnam Department of Computer Engineering Changwon51767 Korea Republic of

ISBN: (纸本)9798331510756

Remote sensing image classification presents unique challenges due to the complexity of the scenes and the variability in visual features caused by the overhead perspective. Traditional convolutional neural networks (CNNs) struggle with long-range dependencies and extracting sufficient context for accurate classification. To address this, we propose the LSViT model, a novel approach that incorporates a local attention selection module into the Transformer architecture, specifically designed for remote sensing scene classification. LSViT enhances the classification performance by selecting discriminative tokens and removing redundant information. Our model leverages the Transformer's capacity for capturing both global and local features, improving the focus on important image regions. We evaluate LSViT on two benchmark datasets, AID and NWPU-RESISC45, and demonstrate its competitive performance, achieving state-of-the-art accuracy compared to traditional CNN and other Transformer-based methods. Notably, the local attention selection module enhances the model's ability to classify scenes with overlapping objects and complex backgrounds, making it an effective solution for large-scale remote sensing image classification tasks. © 2025 ieee.

关键词： Convolutional neural networks

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Generative Adversarial networks for Speech Enhancement 7

Generative Adversarial Networks for Speech Enhancement

引用

7th International conference on Signal Processing and Information Security, ICSPIS 2024

作者： Hamdan, Mohammad Punjabi, Prateek Yarmouk University Faculty of IT Department of Computer Science Jordan Heriot Watt University School of Mathematical and Computer Sciences Dubai United Arab Emirates

ISBN: (纸本)9798350368673

Speech is the fundamental form of communication among humans. Speech Enhancement is a technique that helps in improving the quality of speech. The most common method used for enhancing speech is the removal of background noise. However, speech is now being used as an input for text-based applications (such as Speech-to-Text), conferencing systems (such as Microsoft Teams), etc. A baseline GAN model is constructed to find out the inefficiencies that are present in the current GAN based approaches for speech enhancement. We propose an improved lightweight novel GAN model for speech enhancement that uses the self-attention layer adapted from non-local attention coupled with the convolutional layers in the discriminator network to improve its performance. The proposed model is evaluated on an independent and unseen test set. The audio generated by the model confirm the viability of the model. © 2024 ieee.

关键词： Generative adversarial networks

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Optimization Strategy of PSO Algorithm in computer Programming and Mathematical Model Construction 3

Optimization Strategy of PSO Algorithm in Computer Programmi...

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3rd ieee International conference on Mobile networks and Wireless Communications, ICMNWC 2023

作者： Wen, Xuejuan Yin, Dongmei Changchun Guanghua University Basic Teaching and Research Department Jilin Changchun China

ISBN: (纸本)9798350317022

Traditional mathematical modeling and programming rely on the quality and accuracy of input data. If the data is incorrect or uncertain, it can have a significant impact on the results of the model. PSO (Particle Swarm Optimization) algorithm can effectively solve these problems. This study used case analysis and experimental testing methods to analyze the optimization strategies of PSO algorithm in computer programming and mathematical model construction. By summarizing existing research and cases, the improved mathematical model performance of the PSO algorithm was tested. After research, it was found that the mean square error of the improved mathematical model ranged from 0.01 to 0.05. The optimization strategy of PSO algorithm in computer programming and mathematical model construction can significantly improve accuracy, stability of the optimal solution, and mean square error indicators. Parameter tuning is an important strategy to improve the performance of PSO algorithms. By optimizing key parameters, global and local search capabilities can be adjusted. Multiple runs and averaging can reduce randomness and noise effects, improve the stability and accuracy of the optimal solution and maintain population diversity to avoid local optima. © 2023 ieee.

关键词： computer Programming Mathematical Model Optimization Strategy PSO Algorithm

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Speech Emotion Recognition Using Mel-Frequency Cepstral Coefficients & Convolutional Neural networks 2

Speech Emotion Recognition Using Mel-Frequency Cepstral Coef...

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2nd International conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Kadam, Shubhan Jani, Jay Kudtarkar, Aniket Koshy, Reeta Sardar Patel Institute of Technology Department of Computer Engineering Mumbai India

ISBN: (纸本)9798350327533

Speech emotion recognition (SER) plays a key role in human-computer interaction, affective computing, mental health diagnosis, and natural language processing (NLP). This paper presents a groundbreaking approach to SER utilizing Mel-Frequency Cepstral Coefficients (MFCC) for feature extraction and a hybrid architecture combining Convolutional Neural networks (CNN) and transformers. While early methods relied on handcrafted features and traditional machine learning algorithms, recent advancements introduced recurrent neural networks (RNNs). However, limitations in capturing long-term dependencies prompted the exploration of alternative architectures. The proposed method integrates CNNs to capture spectral features and transformers to model long -range dependencies, mitigating existing shortcomings. Evaluation on a publicly available dataset showcases improved accuracy and reduced computational complexity. Comparative analysis against conventional RNN-based models validates the efficacy of the hybrid architecture. The study significantly enhances SER systems, enabling precise emotion analysis in diverse NLP applications. Utilizing CNN for spatial feature representation and the Transformer for sequence encoding, an 80.44% accuracy is achieved on a held-out test set derived from the RAVDESS dataset. This hybrid approach is capable of capturing both local and global speech features, holds promise for real-time emotion prediction in applications like speech therapy, human-robot interaction, and customer service sentiment analysis. © 2024 ieee.

关键词： Convolutional Neural networks (CNN) Deep Learning Human-computer Interaction Mel-frequency Cepstral Coefficients (MFCC) Natural Language Processing Speech Emotion Recognition Transformers

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Meta-ATMoS+: A Meta-Reinforcement Learning Framework for Threat Mitigation in Software-Defined networks 48

Meta-ATMoS+: A Meta-Reinforcement Learning Framework for Thr...

引用

48th ieee conference on local computer networks , LCN 2023

作者： Tsang, Hauton Salahuddin, Mohammad A. Limam, Noura Boutaba, Raouf David R. Cheriton School of Computer Science University of Waterloo Waterloo Canada

ISBN: (纸本)9798350300734

As cyber threats become increasingly common, automated threat mitigation solutions are more necessary than ever. Conventional threat mitigation frameworks are difficult to tune for different network environments, but frameworks utilizing deep reinforcement learning (RL) have been proven to be an effective approach that can adapt to different networks automatically. Existing RL-based frameworks have shown to be generalizable to different network sizes and threats, and robust to false positives. However, training RL agents for these frameworks can be challenging in a production environment as the training process is time-consuming and disruptive to the production network. Hence, a staging environment is required to effectively train them. In this paper, we propose Meta-ATMoS+, a meta-RL framework for threat mitigation in software-defined networks. We leverage Model-Agnostic Meta-Learning (MAML) to find an initialization for the RL agent that generalizes to a variety of different network configurations. We show that the RL agent with MAML-learned initialization can accomplish few-shot learning on a target network with comparable performance to training on a staging environment. Few-shot learning not only allows the model to be trainable directly in the production environment but also enables human-in-the-loop RL for the mitigation of threats that do not have an easily-definable reward function. © 2023 ieee.

关键词： Reinforcement learning

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ieee INFOCOM 2024 - ieee conference on computer Communications

IEEE INFOCOM 2024 - IEEE Conference on Computer Communicatio...

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43rd ieee conference on computer Communications, INFOCOM 2024

ISBN: (纸本)9798350383508

The proceedings contain 256 papers. The topics discussed include: AdaStreamer: machine-centric high-accuracy multi-video analytics with adaptive neural codecs;fast algorithms for loop-free network updates using linear programming and local search;Diff-ADF: differential adjacent-dual-frame radio frequency fingerprinting for LoRa devices;BCC: Re-architecting congestion control in DCNs;your mega-constellations can be slim: a cost-effective approach for constructing survivable and performant LEO satellite networks;BREAK: a holistic approach for efficient container deployment among edge clouds;transparent broadband VPN gateway: achieving 0.39 Tbps per tunnel with bump-in-the-wire;OTAS: an elastic transformer serving system via token adaptation;and memory-efficient and secure DNN inference on TrustZone-enabled consumer IoT devices.

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Neighbor Load Rank Based Load Balancing Routing in LEO Satellite networks

Neighbor Load Rank Based Load Balancing Routing in LEO Satel...

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2024 ieee Global Communications conference, GLOBECOM 2024

作者： Lu, Xueyu Wei, Wenting Wang, Kun Fu, Liying Li, Chenxi Wu, Celimuge Xidian University State Key Laboratory of Integrated Service Networks Xi'an China National Key Laboratory of Advanced Communication Networks Shijiazhuang China Xidian University School of Computer Science and Technology Xi'an China University of Electro-Communications Graduate School of Informatics and Engineering Tokyo Japan

ISBN: (纸本)9798350351255

The emerging space-terrestrial integrated networks (STIN) are envisioned to provide seamless connectivity for global coverage. As a promising key component for STIN, the Low Earth Orbiting (LEO) satellite network still faces significant technological challenges in routing for ensuring both reachability and efficiency, due to frequent topology changes and uneven load distribution. Load-balancing routing is a feasible solution to improve efficiency by alleviating regional overload, however, it may encounter either slow convergences or local perceptions. In this paper, we propose a Neighbor Load Rank (NLR) based load-balancing routing for LEO satellite networks, where potential congestion at the queue buffer of a satellite node is characterized by load scores of its neighboring satellites, so as to reduce the perspective limitation of local load-balancing routing. To accelerate routing convergence and reduce computational complexity, we design region delineation, boundary penalty and directional incentive strategies to obtain the approximate minimum hop count path. Meanwhile, we employ the topology-stabilizing model (TSM) to convert the frequent satellite-ground interconnection changes into traffic fluctuations. Simulations demonstrate that NLR can maintain low-latency capacity with lower transmission overhead and effectively balance the overloaded traffic. © 2024 ieee.

关键词： Satellite communication systems

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