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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261-270 订阅

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A Study of Fusion Strategies for Self-Attention Enhanced Convolutional Neural networks in Short Text Classification 6

A Study of Fusion Strategies for Self-Attention Enhanced Con...

引用

6th International conference on Communications, Information System and computer Engineering, CISCE 2024

作者： Zhu, Jun Cao, Yaqun Electronic Information Engineering College Anhui Water Conservancy Technical College Hefei China Anhui Water Conservancy Technical College Basic Department Hefei China

ISBN: (纸本)9798350353174

In this paper, we investigate the application of self-attention enhanced convolutional neural network in short text classification and introduce a dynamic fusion strategy. Given the sparseness and semantic ambiguity of short text, traditional methods are difficult to handle effectively. In this paper, the global comprehension ability of the model is improved by combining the CNN with the self-attention mechanism, using the CNN to capture local features and highlighting key semantic information through the self-attention mechanism. A dynamic fusion strategy is adopted to dynamically adjust the fusion degree of CNN and self-attention mechanism to further improve the classification performance. Experimental results show that the fusion model outperforms traditional methods in accuracy, recall and F1 score on datasets such as Twitter sentiment analysis, IMDB movie reviews and Yelp restaurant reviews, validating the effectiveness of the dynamic fusion strategy. © 2024 ieee.

关键词： Convolutional neural networks

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Quantum Machine Learning of Bigdata Set Using Randomized Measurements 7

Quantum Machine Learning of Bigdata Set Using Randomized Mea...

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7th International conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Minu, R.I. Nagarajan, G. Jabez, J. Yamini, G. SRM Institute of Science and Technology Department of Computing Technologies Kattankulathur India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India Veltech Institute of Science and Technology Department of English Tamil Nadu India

ISBN: (纸本)9798350372816

Utilizing discriminative deep belief networks (DDBNs) within the framework of semi-supervised learning, this technique leverages a combination of a limited set of labeled samples to enhance intrusion prevention efficacy while reducing recognition errors. By integrating both non-local and local regularization techniques, the method effectively tackles the inherent challenge of high error rates in cyber prevention models. Additionally, this approach is designed for implementation on Quantum Computing platforms, harnessing advanced machine learning algorithms to significantly improve cybersecurity measures. © 2024 ieee.

关键词： Cybersecurity

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Graph-Based Neural networks for Public Transport Speed Prediction in Santiago, Chile 43

Graph-Based Neural Networks for Public Transport Speed Predi...

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43rd International conference of the Chilean computer Science Society, SCCC 2024

作者： Muller, Daniel Peralta, Billy Nicolis, Orietta Universidad Andres Bello Departamento de Ciencias de Ingeniería Chile

ISBN: (纸本)9798331527891

This paper focuses on short-term traffic speed projections, with the goal of estimating vehicle speeds within specific time intervals using advanced machine learning models. The research proposes the use of graph-based neural models for the prediction of short-term speeds of public buses on a segment of Avenida Alameda Libertador Bernardo O'Higgins in Santiago, Chile. This paper describes the methodology employed, including data collection, preprocessing, model training, and performance evaluation. The findings and recommendations derived from the models have the potential to offer valuable information to improve traffic efficiency and urban mobility planning. The study highlights the importance of leveraging technological advances and local data to develop more accurate and efficient traffic forecasting models, ultimately benefiting public and private entities involved in transportation management. © 2024 ieee.

关键词： Graph neural networks

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Multi-Scale Hybrid Attention Integrated with Vision Transformers for Enhanced Image Segmentation 2

Multi-Scale Hybrid Attention Integrated with Vision Transfor...

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2nd International conference on Algorithm, Image Processing and Machine Vision, AIPMV 2024

作者： Gong, Zhaoxuan Chanmean, Meas Gu, Wenwen School of Computer Engineering Shenyang Aerospace University Liaoning Shenyang China School of Computer Science Shenyang Aerospace University Liaoning Shenyang China

ISBN: (纸本)9798350390254

In this paper, we present an advanced approach for image segmentation that enhances Vision Transformers (ViTs) by integrating multi-scale hybrid attention mechanisms. While ViTs use self-attention to capture global dependencies in images, they often miss local details crucial for segmentation tasks. Our method addresses this by incorporating convolutional layers with different kernel sizes to extract local features at multiple scales. These features are then merged with the global self-attention outputs, creating a hybrid attention map that captures both fine details and overall structures. Testing on standard benchmarks shows our approach significantly improves segmentation accuracy and robustness compared to traditional ViTs, demonstrating its potential for handling complex visual scenes. © 2024 ieee.

关键词： Convolutional neural networks

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Cross-Regional Friendship Inference via Category-Aware Multi-Bipartite Graph Embedding 47

Cross-Regional Friendship Inference via Category-Aware Multi...

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47th ieee conference on local computer networks (LCN)

作者： Ren, Linfei Hu, Ruimin Li, Dengshi Wu, Junhang Zang, Yilong Hu, Wenyi Wuhan Univ Natl Engn Res Ctr Multimedia Software Sch Comp Sci Wuhan 430072 Peoples R China Wuhan Univ Hubei Key Lab Multimedia & Network Commun Engn Wuhan Peoples R China Jianghan Univ Sch Artificial Intelligence Wuhan 430056 Peoples R China

ISBN: (数字)9781665480017

ISBN: (纸本)9781665480017

This paper proposes a novel problem of cross-regional friendship inference to solve the geographically restricted friends recommendation. Traditional approaches rely on a fundamental assumption that friends tend to be co-location, which is unrealistic for inferring friendship across regions. By reviewing a large-scale Location-based Social networks (LBSNs) dataset, we spot that cross-regional users are more likely to form a friendship when their mobility neighbors are of high similarity. To this end, we propose Category-Aware Multi-Bipartite Graph Embedding (CMGE for short) for cross-regional friendship inference. We first utilize multi-bipartite graph embedding to capture users' Point of Interest (POI) neighbor similarity and activity category similarity simultaneously, then the contributions of each POI and category are learned by a category-aware heterogeneous graph attention network. Experiments on the real-world LBSNs datasets demonstrate that CMGE outperforms state-of-the-art baselines.

关键词： Knowledge engineering Social networking (online) conferences computer networks History

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ClusterNet: Routing Congestion Prediction and Optimization using Netlist Clustering and Graph Neural networks 42

ClusterNet: Routing Congestion Prediction and Optimization u...

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42nd ieee/ACM International conference on computer-Aided Design (ICCAD)

作者： Min, Kyungjun Kwon, Seongbin Lee, Sung-Yun Kim, Dohun Park, Sunghye Kang, Seokhyeong Pohang Univ Sci & Technol Dept Elect Engn Pohang South Korea

ISBN: (纸本)9798350322255

Accurately predicting routing congestion caused by netlist topology is essential as circuit designs become increasingly complex. To correctly predict routing congestion, the use of graph neural networks (GNNs) has gained great attention. However, existing GNN-based methods have limitations in capturing crucial netlist information and effectively representing complex topologies. In this work, we propose a novel approach, ClusterNet, to predict routing congestion caused by netlist topology. Our approach leverages netlist clustering to overcome these limitations. We first divide the netlist into highly connected clusters using the Leiden algorithm, enabling an analysis of the local netlist topology. We then predict routing congestion by exploiting GNNs to generate cluster embeddings that capture the detailed netlist topology. In addition, we introduce a cluster padding method that utilizes the trained model to mitigate routing congestion. By applying the proposed ClusterNet, we can accurately predict and optimize routing congestion from specific cluster topologies. Our experimental results demonstrated improved prediction performance, with a mean absolute error of 0.056 and an R2 score of 0.669. Furthermore, routing congestion optimization significantly improved the total negative slack and reduced the number of failing endpoints by 14.5% and 9.9%, respectively.

关键词： Routing congestion prediction netlist clustering Leiden algorithm graph neural networks cluster padding post-synthesis optimization

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Application of TTSSA-CNN Algorithm in Fault Diagnosis of Pumping Unit 2

Application of TTSSA-CNN Algorithm in Fault Diagnosis of Pum...

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2nd ieee International conference on Sensors, Electronics and computer Engineering, ICSECE 2024

作者： Bing, Longda Liu, Bao Miao, Wei Wang, Fengran Qingdao China

ISBN: (纸本)9798350373646

In the current oil extraction process, frequent failures of beam pumping units seriously affect the economic benefits of the oilfield. This paper proposes a Convolutional Neural Network condition diagnosis algorithm based on an improved Sparrow Search Algorithm (TTSSA-CNN). Firstly, considering the good distribution, randomness, and regularity of Tent chaotic sequences, the initialization population diversity of the algorithm is improved, and the number of sparrow swarm optimization iterations is used as a degree-of-freedom parameter of the t-distribution to enhance the ability of SSA to jump out of the local search space and to improve the local optimization capability and accuracy of SSA. Finally, TTSSA is employed to optimize various hyperparameters of the CNN pumping unit fault diagnosis model, thereby improving training speed and accuracy. Experimental results indicate that the working condition diagnosis rate of the TTSSA-CNN algorithm is 98.7%, which represents a significant increase compared to three other diagnosis algorithms (BP, CNN, LSTM) by 13.7%, 4%, and 5.2%, respectively. It exhibits higher adaptability and diagnostic accuracy, holding practical and theoretical significance for pumping unit fault diagnosis. © 2024 ieee.

关键词： Convolutional neural networks

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DeepCut: Unsupervised Segmentation using Graph Neural networks Clustering

DeepCut: Unsupervised Segmentation using Graph Neural Networ...

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ieee/CVF International conference on computer Vision (ICCV)

作者： Aflalo, Amit Bagon, Shai Kashti, Tamar Eldar, Yonina Weizmann Inst Sci Fac Math & Comp Sci Rehovot Israel

ISBN: (纸本)9798350307443

Image segmentation is a fundamental task in computer vision. Data annotation for training supervised methods can be labor-intensive, motivating unsupervised methods. Current approaches often rely on extracting deep features from pre-trained networks to construct a graph, and classical clustering methods like k-means and normalized-cuts are then applied as a post-processing step. However, this approach reduces the high-dimensional information encoded in the features to pair-wise scalar affinities. To address this limitation, this study introduces a lightweight Graph Neural Network (GNN) to replace classical clustering methods while optimizing for the same clustering objective function. Unlike existing methods, our GNN takes both the pair-wise affinities between local image features and the raw features as input. This direct connection between the raw features and the clustering objective enables us to implicitly perform classification of the clusters between different graphs, resulting in part semantic segmentation without the need for additional post-processing steps. We demonstrate how classical clustering objectives can be formulated as self-supervised loss functions for training an image segmentation GNN. Furthermore, we employ the Correlation-Clustering (CC) objective to perform clustering without defining the number of clusters, allowing for k-less clustering. We apply the proposed method for object localization, segmentation, and semantic part segmentation tasks, surpassing state-of-the-art performance on multiple benchmarks(1).

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Optimizing Short-Term Load Forecasting for Energy Management Systems: Integrative Time Series Analysis and Advanced Neural Network Methodologies 10

Optimizing Short-Term Load Forecasting for Energy Management...

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10th International conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Bhatt, Chandradeep Barthwal, Ashish Shoyab, Rao Khamboj, Pratham Singh, Teekam Graphic Era Hill University Cse Department Dehradun India Graphic Era Hill University Computer Science Department Uttarakhand Dehradun India Computer Science and Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (纸本)9798350384369

This report investigates the integration of neural networks like GRU and CNN with RNN and how it enhances the predictive analysis performance of said RNN model. Prediction of household energy consumption plays an essential role in optimizing its consumption. This study compares two models: a traditional RNN model and an advance ensemble model comprising of Recurrent Neural networks, Gated Recurrent Unit networks, and Convolutional Neural networks. The LSTM and GRU models are used to capture temporal dependencies in the data, while the CNN model extracts local sequence features. Using this, the model is able to increase the R-squared score from 0.66 in the traditional RNN model to an impressive 0.89 in the ensemble model. © 2024 ieee.

关键词： Neural network models

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Improving the Quality of Transactional Management of Electric Vehicle Charging in local Electricity networks Based on Blockchain 4

Improving the Quality of Transactional Management of Electri...

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4th International conference on Electrical, computer, Communications and Mechatronics Engineering, ICECCME 2024

作者： Khilenko, Volodymyr Denysiuk, Serhii Bielokha, Halyna Slovak University of Technology in Bratislava Faculty of Informatics and Information Technologies Bratislava Slovakia Igor Sikorsky Kyiv Polytechnic Institute Power Supply Department Kyiv Ukraine

ISBN: (纸本)9798350391183

The presence of mobile consumers increases the number of participants in the electricity market and requires new approaches to managing electricity consumption and production. Blockchain technology is an effective tool for solving the emerging problems of improving energy management. The article develops an energy management system for a transactional energy system with electric vehicle charging, which, including through the use of blockchain, improves overall energy efficiency and potentially increases income by reducing electricity costs and selling excess solar energy. Without loss of generality, it is assumed that a public blockchain will be used when performing transactions accompanying technological operations. As an example, the functioning of technological algorithms for charging electric vehicles from an energy storage system is considered. © 2024 ieee.

关键词： blockchain control systems electrical vehicle (EV) charging energy storage systems (ESS) local electricity system transactive energy

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