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检索条件"任意字段=5th International Conference on Informational Processing in Sensor Networks"

共 8138 条记录，以下是491-500 订阅

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Empirical Analysis of Classification Approaches for Indian Language processing 5th

Empirical Analysis of Classification Approaches for Indian L...

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5th international conference on Communication and Intelligent Systems (ICCIS)

作者： Suthar, Sanket Dalwadi, Bijal Charotar Univ Sci & Technol CHARUSAT Fac Technol Engn FTE Chandubhai S Patel Inst Technol CSPIT Dept Informat Technol Changa India Birla Vishwakarma Mahavidyalaya Engn Coll Dept Informat Technol Vallabh Vidyanagar Gujarat India

ISBN: (纸本)9789819720811;9789819720828

In the realm of pattern recognition, decoding handwritten characters remains a focal point, drawing enduring interest from researchers across various domains. Optical Character Recognition (OCR) stands as a pivotal technique in image processing, extracting text, and identifying language from digital images. While OCR has made strides in Arabic and Chinese, Indian scripts lack comprehensive exploration. this paper addresses this gap, delving into OCR methodologies for Indian scripts and multiple languages. By scrutinizing each language, it unravels the intricacies and challenges of character recognition. the study offers a comprehensive view of OCR techniques' adaptability and effectiveness in diverse linguistic contexts. this research marks a significant contribution by extensively exploring OCR's application to Indian scripts and their languages, filling a notable void in current understanding within this domain.

关键词： Convolution neural network English characters Typefaces Font recognition

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Research on Intelligent Aging Furniture System Enabled by Information Interaction and Communication Technology 5

Research on Intelligent Aging Furniture System Enabled by In...

引用

5th international conference on Computers, Information processing and Advanced Education, CIPAE 2024

作者： Liu, Huan Zhao, Xiaohan School of Art and Design Guangdong Baiyun University Guangzhou China

ISBN: (纸本)9798331527662

this study focuses on the application of information interaction and communication technology in intelligent furniture system, aiming to develop intelligent furniture solutions. Advanced deep learning techniques, especially the combination of convolutional neural networks (CNN) and recurrent neural networks (RNN), are used in this study to extract efficient features from furniture images in indoor environments. through this method, the multi-label automatic labeling of furniture can be realized, and the key attribute information such as the type, location, color and material of furniture can be accurately identified. the research results show that the proposed method performs well in improving the accuracy and efficiency of furniture identification, and provides strong technical support for the design and implementation of intelligent aging furniture system. In addition, the system can also realize intelligent interaction between furniture and users through information interaction technology, such as the function of controlling furniture by voice or gesture, which greatly improves the living comfort and convenience of the elderly. © 2024 IEEE.

关键词： Convolutional neural networks

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Automatic Gait Gender Classification Using Convolutional Neural networks 23

Automatic Gait Gender Classification Using Convolutional Neu...

引用

5th international conference on Image processing and Machine Vision, IPMV 2023

作者： Srinivasan, Lavanya Royal Holloway University of London United Kingdom

ISBN: (纸本)9781450397926

In this study, automatic gait gender classification using convolutional neural networks includes three phases: i) human gait signature generation, ii) which convolves the gait energy images with filters for feature extraction and iii) classified using feed-forward convolutional neural networks. Analysed performance of Gabor and Log Gabor features using classification accuracy. the Log Gabor filter's accuracy was 92.11% for the Normal vs Normal dataset, 74.14% for the Normal vs Bag dataset, 46.55% for the Normal vs Coat dataset, 72.41% for the Normal vs Case dataset and whiles Gabor filter's accuracy was 75% for the Normal vs Normal dataset, 60.34% for the Normal vs Bag dataset 65.52% for the Normal vs Coat dataset and 55.17% for the Normal vs Case dataset. © 2023 ACM.

关键词： Convolutional neural networks

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Artificial Neural networks Model for Predicting the Strength of FRP-Contained Concrete 5

Artificial Neural Networks Model for Predicting the Strength...

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5th IEEE international conference on Electrical, Computer and Communication Technologies, ICECCT 2023

作者： John, Merrisha PSG College of Technology Department of Civil Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9781665493604

Numerous studies have demonstrated that FRP (Fibre-reinforced Polymer) can significantly increase the strength of concrete columns. Numerous mathematical equations and manual methods are available for predicting the strength of concrete columns composed of FRP, all of which are time-consuming tasks. this present study develops a novel computerized method for determining the axial strain and axial strength of FRP (Fibre-reinforced Polymer)-confined concrete columns utilizing real-time experimental data and artificial neural networks (ANNs). In order to increase prediction accuracy, an ANN model is trained and evaluated using experimental data collected in real-time. Additionally, advanced pre-processing techniques are applied in this study to minimize noise and enhance the prediction accuracy of the suggested ANN model. To demonstrate the efficacy of this proposed strategy, this model is trained and verified using the data set. the experimental outcomes from training and validation have been compared to recent methods. It is evident from the comparison results that the proposed method has reduced MAE, RSME and regression values. © 2023 IEEE.

关键词： Compressive strength

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Surrogate-Assisted Hybrid Metaheuristic for Mixed-Variable 3-D Deployment Optimization of Directional sensor networks 5

Surrogate-Assisted Hybrid Metaheuristic for Mixed-Variable 3...

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5th international conference on Data-Driven Optimization of Complex Systems, DOCS 2023

作者： Zhang, Yuntian Chen, Chen Wu, Tongyu Miao, Changhao Ding, Shuxin School of Automation Beijing Institute of Technology Beijing100081 China National Key Lab of Autonomous Intelligent Unmanned Systems Beijing100081 China Signal and Communication Research Institute China Academy of Railway Sciences Corporation Limited Beijing100081 China Natl. Engineering Research Center of System Technology for High-Speed Railway and Urban Rail Transit Traffic Management Laboratory for High-Speed Railway Beijing100081 China

ISBN: (纸本)9798350393521

A major concern in designing sensor networks is the deployment problem. However, establishing an efficient algorithm for the real-world deployment problem is challenging due to three issues, which are 1) the realistic mixed-integer nonlinear programming problem (MINLP) with mixed-variable;2) the combinatorial subset selection problem;and 3) the expensive computational cost for fitness evaluation in the 3-D coverage problem. therefore, this paper addresses these challenges and proposes a surrogate-assisted hybrid metaheuristic for mixed-variable 3-D deployment optimization of directional sensor networks (DSNs). First, an MINLP with flexible coordinate transformation technique and an efficient mixed-variable encoding scheme are introduced to model and represent the problem. We propose hybrid metaheuristic which applies two reproduction methods respectively for discrete and continuous variables. Second, we design sparse population-based incremental learning (s-PBIL) to handle inherent subset selection problem. s-PBIL could accurately learn the required information, and automatically learn a sparse distribution. third, a mixed-variable surrogate with unifying space under Bayesian model management is incorporated to reduce the expensive computational cost. Experiment results on real-world deployment scenarios scaling from small-size to large-size show the effectiveness of the proposed algorithm. © 2023 IEEE.

关键词： sensor networks

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5th international conference on Artificial Intelligence in China, AIC 2023

5th International Conference on Artificial Intelligence in C...

引用

5th international conference on Artificial Intelligence in China, AIC 2023

ISBN: (纸本)9789819975440

the proceedings contain 64 papers. the special focus in this conference is on Artificial Intelligence in China. the topics include: Research Review on Application of Genetic Algorithms for Multi-Objective Optimal Management of Building Construction;agsNet: An Attention-Guided Lightweight Segmentation Network;A TDOA-Based Optimization Method for Staircase Area Positioning;design and Implementation of Intelligent Classified Garbage Bin;knowledge-Enhanced Recommendation System Design for Mechatronics IoT Cloud Platform;aspect-Level Sentiment Analysis by Fusing Local Information with Graph Attention networks;image Segmentation Algorithm Based on Improved U-Net for Mineral Froth Flotation Process;automatic Summarization Research for Long Texts Targeting think Tanks;research on Semantic Segmentation Algorithm for Autonomous Driving Based on Improved DeepLabv3+;joint Security Protection and Transmission Delay Scheduling Scheme of Voice Switching Network;subway Tunnel Segment Disease Detection System Based on Machine Vision Technology;device-Free Cross-Environment Human Action Recognition Using Wi-Fi Signals;the Performance Analysis of Double Intelligent Reflecting Surface-Assisted Cognitive Radio Network;a Novel Point Cloud Completion Method Based on Height Map and Dual-Mask Images;deep Learning for Skin Lesion Segmentation: A Review and Outlook;research on Revenue Prediction of Automobile Manufacturing Enterprises;Coal Mine Risk Classification Prediction Model Based on BERT;research on Construction and Application of Knowledge Graph for Coal Mine Safety Production Specifications;A TMFV-Based Adaptive E-Learning System for Generating and Allocating Questions;MedFPNet: A Medical Image Segmentation Network Based on Fourier Transform;image Classification Algorithm Based on Improved Soft thresholding and Residual Network;graph Similarity Based on Optimal Transmission for Optimal Deployment of Intelligent Wireless sensor networks.

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Advanced Financial Fraud Detection Using GNN-CL Model 5

Advanced Financial Fraud Detection Using GNN-CL Model

引用

5th international conference on Computers, Information processing and Advanced Education, CIPAE 2024

作者： Cheng, Yu Guo, Junjie Long, Shiqing Wu, You Sun, Mengfang Zhang, Rong Columbia University New York United States Rutgers University New Brunswick United States Independent Researcher Beijing China College of William & Mary Williamsburg United States Stevens Institute of Technological Hoboken United States University of California Davis United States

ISBN: (纸本)9798331527662

the innovative GNN-CL model proposed in this paper marks a breakthrough in the field of financial fraud detection by synergistically combining the advantages of graph neural networks (GNN), convolutional neural networks (CNN) and long short-term memory (LSTM) networks. this convergence enables multifaceted analysis of complex transaction patterns, improving detection accuracy and resilience against complex fraudulent activities. A key novelty of this paper is the use of multilayer perceptrons (MLPS) to estimate node similarity, effectively filtering out neighborhood noise that can lead to false positives. this intelligent purification mechanism ensures that only the most relevant information is considered, thereby improving the model's understanding of the network structure. Feature weakening often plagues graph-based models due to the dilution of key signals. In order to further address the challenge of feature weakening, GNN-CL adopts reinforcement learning strategies. By dynamically adjusting the weights assigned to central nodes, it reinforces the importance of these influential entities to retain important clues of fraud even in less informative data. Experimental evaluations on Yelp datasets show that the results highlight the superior performance of GNN-CL compared to existing methods. © 2024 IEEE.

关键词： Convolutional neural networks

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Research on Performance Evaluation of Lagrange Mathematical Model Based on Deep Learning in High-Speed Communication networks 5

Research on Performance Evaluation of Lagrange Mathematical ...

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5th international conference on Computers, Information processing and Advanced Education, CIPAE 2024

作者： Zhang, Li Beijing Polytechnic College Beijing China

ISBN: (纸本)9798331527662

In this paper, a novel algorithm model is proposed, which combines the efficient data processing capability of deep learning with the precise optimization characteristics of Lagrange mathematical model, in order to provide more accurate performance prediction in the complex and changeable network environment. the research begins with the construction of a deep learning framework, which is able to extract key features from a large amount of network communication data and learn and predict them through neural network models. then the Lagrange mathematical model is integrated into the deep learning framework to form a comprehensive performance evaluation model. this model can evaluate the performance of communication network comprehensively while considering the multi-dimensional factors such as bandwidth, delay and packet loss rate. In order to verify the validity of the model, the corresponding system simulation platform is developed, and extensive simulation experiments are carried out in various high-speed communication network scenarios. the simulation results show that compared with traditional evaluation methods, the Lagrangian mathematical model based on deep learning has a significant improvement in prediction accuracy and response speed. © 2024 IEEE.

关键词： Lagrange multipliers

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Information-Seeking in Localization and Mission Planning of Multi-Agent Systems 5

Information-Seeking in Localization and Mission Planning of ...

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IEEE 5th international conference on Multimedia Information processing and Retrieval (MIPR)

作者： Lite, Kyriakos Rinner, Bernhard Univ Klagenfurt Inst Networked & Embedded Syst Klagenfurt Austria Univ Klagenfurt Ubiquitous Sensing Syst Lab Silicon Austria Labs Klagenfurt Austria

ISBN: (数字)9781665495486

ISBN: (纸本)9781665495486

Real-time and accurate position estimation is critical for various multi-robot applications and serves as a prerequisite for location-based multi-sensor data analysis. However, it is often impeded by energy, sensing, and processing limitations. In this work, we study the problem of information-seeking in localization and navigation in multi-agent systems, which aims to navigate mobile agents while reducing position errors. We formalize information-seeking as reducing spatial uncertainty and introduce an efficient motion controller based on artificial potential fields superimposing attractive, repulsive, and information-seeking forces. We evaluate the effect of information-seeking on localization and mission planning in a simulation study with non-collaborative and collaborative localization approaches.

关键词： Location awareness Uncertainty Navigation Multimedia systems Mobile agents Information processing Real-time systems

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Cross-Session EEG Motor Imagery Classification Method Based on Transfer Learning 5

Cross-Session EEG Motor Imagery Classification Method Based ...

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5th IEEE international conference on Power, Electronics and Computer Applications, ICPECA 2025

作者： Xiong, Chaolin Li, Hualiang Wang, Yalong Lin, Ziyue Yang, Wenhan Fan, Shengping Wang, Yifan Electric Power Research Institute of Guangdong Power Grid Co. Ltd. Guangzhou Guangdong510080 China Guangdong Power Grid Co. Ltd. Guangzhou Guangdong510080 China

ISBN: (纸本)9798331533694

Motor imagery (MI) electroencephalogram (EEG) signals exhibit cross-session variability, which hinders traditional EEG classification models from achieving satisfactory performance in cross-session EEG data classification. therefore, further adjustments and optimizations are needed. To resolve this problem, the research develops a transfer learning-based approach for cross-session motor imagery EEG classification. To comprehensively analyze EEG data, this study introduces an attention-optimized separable CNN framework for multi-domain feature extraction (spectral, spatial, and temporal). In parallel, supervised learning is applied to reduce the classification error of source domain data. Meanwhile, transfer learning is leveraged to enhance the alignment of feature representations between source and target domains, enabling the training of a neural network capable of effectively processing cross-session EEG datasets. Finally, the proposed algorithm is compared with several selected advanced algorithms and evaluated on the publicly available 2008 BCI Competition IV 2a dataset. the results show that without any manual feature extraction, the proposed cross-session EEG motor imagery classification method based on transfer learning achieves an average accuracy of 73.1% and a Kappa value of 64.8% for the four-class classification dataset. © 2025 IEEE.

关键词： Convolutional neural networks

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