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

共 8123 条记录，以下是281-290 订阅

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Enhancing Road Safety and User Experience through Intelligent Tollgate Systems and Vehicular Communication networks 5

Enhancing Road Safety and User Experience through Intelligen...

引用

5th international conference on Image processing and Capsule networks, ICIPCN 2024

作者： Jebaraj, Bharath Singh Nasreen, M.Ayeesha Sushmitha, S. Azarudeen, A. Subha, T.D. Vivekrabinson, V. Kalasalingam Academy of Research and Educatuion Department of Computer Science and Engineeing Sriviliputhur India R.M.D. Engineering College Department of Electronics and Communication Engineering Thiruvallur India Rajalakshmi Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India R.M.K. Engineering College Thiruvallur Department of Electronics and Communication Engineering Thiruvallur India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu India

ISBN: (纸本)9798350367171

the automotive industry's growth, fueled by technology and job opportunities, has heightened global automobile sales. Despite this progress, road safety remains a concern, with numerous lives lost in car accidents annually. To address this, Vehicle-to-Vehicle (V2V) communication emerges, enhancing road safety through real-time information exchange. Roadside Communication Units (RCUs) provide vital services, offering traffic updates, weather notifications, and immediate alerts about incidents. Intelligent tollgate systems in vehicles streamline transactions, and speed control systems ensure safer driving. Leveraging Zigbee technology enables seamless communication with toll plazas, improving traffic flow. this study aims to enhance road safety through intelligent tollgate systems, V2V communication, and innovative technologies, emphasizing their potential to optimize traffic flow and provide a more comfortable driving experience. Identified are future research directions and development opportunities in this dynamic field. © 2024 IEEE.

关键词： Zigbee

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DDoS Attack Detection and Classification Based on Hybrid Deep Learning Model in SDN 5

DDoS Attack Detection and Classification Based on Hybrid Dee...

引用

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

作者： Jin, Lisha Zhang, Jianhua Li, Zhongrui Liao, Yanlin Southwest Minzu University School of Computer Science and Engineering Chengdu China

ISBN: (纸本)9798331506612

the Industrial Internet of things (IIoT) involves numerous industrial systems and equipment. Due to challenges in node data processing, a software-defined network (SDN) can improve IIoT control and management. However, SDN's centralized control can be susceptible to distributed denial of service (DDoS) attacks. Existing deep learning detection models are often complex and computationally demanding when handling high-dimensional datasets. this paper presents a solution for DDoS detection in SDN-enabled IIOT by selecting optimal features with a random forest and classifying them using convolutional neural network (CNN) and long short-term memory network (LSTM). To improve accuracy, model training is refined with the gray wolf algorithm. Evaluations with the InSDN dataset show this approach achieves 99.43% accuracy with lower complexity and minimal time cost. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

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Style Transfer: From Stitching to Neural networks 5

Style Transfer: From Stitching to Neural Networks

引用

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

作者： Xu, Xinhe Wang, Zhuoer Zhang, Yihan Liu, Yizhou Wang, Zhaoyue Xu, Zhihao Zhao, Muhan Luo, Huaiying University of Illinois Urbana-Champaign Urbana United States Washington University in St. Louis St. Louis United States Georgia Institute of Technology Atlanta United States Carnegie Mellon University Pittsburgh United States Cornell University Ithaca United States

ISBN: (纸本)9798331506612

this article compares two style transfer methods in image processing: the traditional method, which synthesizes new images by stitching together small patches from existing pattern images, and a modern machine learning-based approach that uses a segmentation network to isolate foreground objects and apply style transfer solely to the background. the traditional method excels in creating artistic abstractions but can struggle with seamlessness, whereas the machine learning method preserves the integrity of foreground elements while enhancing the background, offering improved aesthetic quality and computational efficiency. Our study indicates that machine learning-based methods are more suited for real-world applications where detail preservation in foreground elements is essential. © 2024 IEEE.

关键词： Adversarial machine learning

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DGT: Dynamic Graph Transformer for Enhanced processing of Dynamic Joint Sequences in 2D human pose estimation 5

DGT: Dynamic Graph Transformer for Enhanced Processing of Dy...

引用

5th international conference on Computer Vision, Image and Deep Learning, CVIDL 2024

作者： Zhou, Xianwei Li, Zhenfeng Yu, Songsen South China Normal University Scnu School of Software Foshan China

ISBN: (纸本)9798350373820

In video-based 2D human pose estimation, existing Transformer models mainly focus on image features, neglecting the implicit information in skeletal connections. this oversight leads to inaccuracies in dynamic scenes and joint localization challenges in conditions like motion blur or occlusion. To tackle this, our Dynamic Graph Transformer integrates graph convolutional multi-head attention layers with a dynamic joint constraint module, leveraging Graph Neural networks, extracts dynamic joint structures following the multi-head attention mechanism's outcomes, improving the analysis of skeletal connections' structural invariance. Our experiments on the Sub-JHMDB dataset reveal that our Dynamic Graph Transformer model not only outperforms traditional Transformer models in accuracy and robustness but also marks a significant 1% improvement in the PCK@0.05 metric. © 2024 IEEE.

关键词： Dynamics

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Multi-sensor Data Fusion and Augmentation for Imbalanced Fault Diagnosis of Bearings 5

Multi-Sensor Data Fusion and Augmentation for Imbalanced Fau...

引用

5th international conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2024

作者： Fan, Zhongding Liu, Xianzeng Cao, Zheng Wang, Hang Zhou, Yuanyuan Liu, Yongbin School of Electrical Engineering and Automation Anhui University Hefei China

ISBN: (纸本)9798331529192

Stable operation of bearing is required, while on the other side, it can lead to insufficient fault data collection and imbalanced data, which further deteriorates the performance of deep learning(DL) methods in the fault diagnosis of bearings. In this paper, a novel method combining multi-sensor data fusion and augmentation for bearing imbalanced fault diagnosis is proposed. First, the limited real fault samples are fed into the one-dimensional Wasserstein generative adversarial network with gradient penalty (1D-WGAN-GP) to generate the fake samples for augmenting the fault data. then, the features of augmented data from different sensors are extracted and fused by using the proposed multi-branch one-dimensional convolutional neural network (1D-MCNN). Finally, imbalanced fault diagnosis of bearings is achieved based on the fused features. the performance of the proposed method is verified experimentally. the results show that, compared to existing methods the proposed method can be used to fuse fault features from multiple sensors, and effectively enhance the fault data, thereby achieving excellent accuracy and stability under the imbalanced data of bearing. © 2024 IEEE.

关键词： Generative adversarial networks

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Software for Building Human Behavioral Assessment Based on Analysis of Text Tones from Social networks 1

引用

5th international Russian Automation conference, RusAutoCon 2023

作者： Martyshkin, A.I. Pashchenko, D.V. Trokoz, D.A. 1a/11 Baydukov Proyezd / Gagarin Street Penza440039 Russia

ISBN: (数字)9783031511271

ISBN: (纸本)9783031511264

the article is devoted to the development of software for building a behavioral assessment of a person based on the analysis of tones of texts from social networks. this software is aimed at collecting, storing and processing data of users of the social network "VKontakte". the paper deals with natural language processing using deep machine learning techniques. In the paper, we need to analyze the tone of the text. One of our goals is to solve the problem of analyzing posts in social networks. the intonation analysis of the text allows us to understand what intonation coloring a particular post in social networks has - positive or negative. Based on the results of the work, we have proposed a working prototype of a web application for building a behavioral assessment of a person based on the analysis of tones of texts from social networks. the results of the research will be useful to organizations when hiring employees, when it is possible to get data on a particular person from a social network and preview them. Based on the conducted research with trained models, the most accurately trained model with the highest accuracy equal to 75.10% was identified. It was obtained while training the neural networks on a group consisting of 4 examples. this accuracy was achieved at the 5th epoch of training. the main results of this research are given in conclusion. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Recurrent neural networks

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Experimental Analysis of Radioscopy Images based Pneumonia Disease Detection using Improved Neural Network Methodology 5

Experimental Analysis of Radioscopy Images based Pneumonia D...

引用

5th international conference on Image processing and Capsule networks, ICIPCN 2024

作者： Suneel, Sajja Rani, B.M.S. Begum, M. Amina thatipudi, Jayendra Gopal Yadav, Ajay Singh Muthukumaran, D. Telangana Hyderabad India Vignan's Nirula Institute of technology and science for women Department of ECE India Oxford College of Engineering Department of ECE Tamilnadu Venmani India University of North Texas Dept. of Computer Science United States SRM Institute of Science and Technology Delhi-NCR Campus Department of Mathematics U.P Ghaziabad India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology India

ISBN: (纸本)9798350367171

the bacterium Streptococcus pneumonia is the most prevalent causative agent of pneumonia, a severe and potentially fatal lung infection in humans. According to the World Health Organization, pneumonia accounts for one in three deaths in India. Assessing chest radiographs to detect pneumonia typically requires skilled radiologists, making an automatic detection method crucial for rapid treatment, especially in rural areas. this study introduces an Improved Neural Network (INN) learning method, cross-validated against existing Convolutional Neural networks (CNNs), reflecting the success of deep-learning algorithms in medical image analysis. the research highlights the utility of these methods for disease classification. the features learned by INN and CNN models on large datasets are particularly valuable for image classification tasks. By using feature extractors programmed on trained INN and CNN frameworks, this study compares the performance of various classifiers in identifying normal and abnormal chest radiographs. Statistical results indicate that pre-trained INN models combined with supervised classifier approaches significantly enhance the evaluation of chest radiographs, particularly in detecting pneumonia. this advancement is pivotal for improving diagnosis and treatment outcomes in resource-limited environment. © 2024 IEEE.

关键词： Convolutional neural networks

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Predicting the Onset of Alzheimer's Disease using Machine Learning and Deep Learning Techniques 5

Predicting the Onset of Alzheimer's Disease using Machine Le...

引用

5th international conference on Image processing and Capsule networks, ICIPCN 2024

作者： Nair, Rohan Mahajan, Nishant Badwaik, Prathamesh Pote, Utkarsh Tupe, Umakant JSPM RSCOE Department on Information Technology Tathawade Pune India

ISBN: (纸本)9798350367171

Alzheimer's Disease (AD) represents a crucial challenge in healthcare, necessitating effective diagnostic tools and therapeutic interventions. In an effort to promote early identification and intervention, this research describes a unique method for predicting the onset of AD using machine learning and deep learning approaches. Leveraging advanced computational methods, the proposed model analyses MRI data to identify individuals at risk of developing AD. through data preprocessing, feature extraction, and model development, the proposed model demonstrates the efficacy of Convolutional Neural networks (CNNs), Random Forest classifiers, Support Vector Machines (SVMs), Gradient Boosting Machines (GBMs), and ensemble learning methods in accurately detecting AD from MRI scans. the deployment of user-friendly web applications enables seamless interaction between healthcare professionals and predictive models, empowering proactive interventions in diverse clinical settings. this research contributes to bridging the gap between cutting-edge technology and clinical practice, offering a transformative approach to AD diagnosis and management. © 2024 IEEE.

关键词： Convolutional neural networks

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M2FU: Multi-Modal Fusion for Urban Autonomous Driving 5

M2FU: Multi-Modal Fusion for Urban Autonomous Driving

引用

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

作者： You, Daiqi Feng, Gang South China Normal University School of Computer Guangzhou China

ISBN: (纸本)9798331506612

Existing sensor fusion approaches are poor, this paper proposes a novel end-to-end autonomous driving model called M2FU. this model solves the problem of past end-to-end models that have many collisions, are heavily blocked, and are unable to effectively detect traffic lights. the perception module of the M2FU improves on the transformer-based detection model by using a dual-attention mechanism for its intermediate feature fusion and a recurrent neural network with gated recurrent units for path planning. In addition, it includes two controllers for managing the vehicle's speed and steering angle. the model was trained using an imitation learning approach and subsequently evaluated on the CARLA autonomous driving benchmark. the results demonstrate that the end-to-end model based on the dual attention transformer outperforms traditional classifier-based models in avoiding dynamic obstacles. We validate the effectiveness of our method through experiments in urban environments with complex scenes, utilizing the CARLA urban driving simulator. Our driving scores improved by 17% compared to geometry-based fusion. © 2024 IEEE.

关键词： Recurrent neural networks

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Deep Learning for Sentiment Analysis with Adaptive Neural networks for Emotion Classification 5

Deep Learning for Sentiment Analysis with Adaptive Neural Ne...

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5th international conference on Sustainable Communication networks and Application, ICSCNA 2024

作者： Jayalakshmi, V. thilakavathy, P. Manikandan, G. Deepa, R. Surendran, R. SRM Institute of Science and Technology Department of Computer Science and Applications Tamilnadu Chennai India Computer Science and Engineering Vels Institute of Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India St. Joseph's College of Engineering Department of Artificial Intelligence and Machine Learning Chennai India School of Computing SRM institute of Science and Technology Department of Computing Technologies Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530013

In understanding human emotions, sentiment analysis has been invaluable in social media monitoring, consumer comment analysis, and psychological evaluations. Customer service, human-computer interface, and public mood comprehension depend on appropriate emotion categorisation. Despite its potential, sentiment analysis struggles with sarcasm, multimodal data integration, and emotion processing. Implementing real-time applications requires improving deep learning model computational complexity. Multimodal Optimised Emotion Analysis using Deep Learning (MOEA-DL) uses CNNs for image processing, RNNs for text., and LSTM networks for sequential audio data. this improves emotion recognition by combining modality strengths. Marketing sentiment, mental health monitoring, social media sentiment, and consumer feedback use MOEA-DL. Multimodal solutions are better than text-based ones for communicating emotion. Customer contacts, film reviews, and social media platforms are used to evaluate the proposed strategy. According to simulations, MOEA-DL categorises emotions more accurately and robustly than previous sentiment analysis methods. Its adaptive neural network lets the system learn and perform well in many cultural and language environments. © 2024 IEEE.

关键词： Emotion Recognition

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