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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471-480 订阅

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Smart Facial Attendance Management System Using Machine Learning 5

Smart Facial Attendance Management System Using Machine Lear...

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

作者： Joshi, Harnish Bharatia, Nihar Sakariya, Urmil Garg, Dweepna Goel, Parth Department of Computer Engineering Changa388421 India Department of Computer Science and Engineering Changa388421 India

ISBN: (纸本)9798331530013

Face recognition systems are essential in practically every industry in our digital age. One biometric that is frequently utilized is face recognition. It is helpful for security and has a ton of other advantages, identity, and authentication. Despite being less accurate than fingerprint and iris recognition, it is nonetheless commonly utilized because it is a non-invasive and contact-free technique. Additionally, facial recognition systems can be used to track attendance in workplaces, colleges, and schools, among other places. By solving problems with manual attendance procedures, like time consumption and the possibility of proxy attendance, this system seeks to develop an effective facial recognition-based classroom attendance system. As a result, this mechanism becomes more necessary. There are four components to this system capture face, train model, detect and recognize faces, update attendance record, and face detection. The photos of the children in the classroom are what make the data folder. The Haar-Cascade classifier and the local Binary Pattern Histogram technique are used, respectively, for face detection and recognition. The suggested technique achieves approximately 80% accuracy when three to four students are present. Faces from live streaming video are identified and discovered. As a quick and safe substitute for manual attendance procedures, attendance is tracked in XLS sheets and forwarded to the appropriate faculty members at the conclusion of the session. © 2024 ieee.

关键词： local binary pattern

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Scene Text Detection in Medical Based on Differentiable Binarization 16

Scene Text Detection in Medical Based on Differentiable Bina...

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16th ieee International conference on Computational Intelligence and Communication networks, CICN 2024

作者： Wen, Nuan School of Computer Science Northeast Electric Power University Jilin China

ISBN: (纸本)9798331505264

Scene text detection methods based on segmentation have been extensively employed in the area of scene text detection. However, for tasks involving dense text, multi-directional texts and extreme aspect ratios, existing detection way still struggle to deliver desired results. In this article, we introduce an capable attention mechanism and a better post-processing method. Specifically, the initial image pass through the ResNet backbone network, the resulting feature map is enhanced by an attention module that integrates both global and local features to improve the effectiveness of feature extraction. This enhanced feature map is then fed into the FPN (Feature Pyramid Network) for feature fusion, where multi-scale information is combined to produce the final features used for segmentation. Furthermore, the post-processing module using differentiable binarization, enabling the segmentation network to dynamically adjust the binarization threshold. The effectiveness and feasibility of our method are demonstrated through validation on two datasets. © 2024 ieee.

关键词： Deep learning

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Rotation-Invariant Descriptors Learned with Circulant Convolution Neural networks 35

Rotation-Invariant Descriptors Learned with Circulant Convol...

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35th ieee International conference on Tools with Artificial Intelligence (ICTAI)

作者： Lin, Wenwei Zhong, Chonghao Sun, Xunpei Meng, Haitao Chen, Gang Hu, Biao Gu, Zonghua Sun Yat Sen Univ Guangzhou Peoples R China Tech Univ Munich Munich Germany China Agr Univ Beijing Peoples R China Umea Univ Umea Sweden

ISBN: (纸本)9798350342734

Extracting local features for accurate correspondences between image pairs is an essential basis for various computer vision tasks. Recent works have shown that deep neural networks (DNNs) have demonstrated promising performance in challenging environments. However, these state-of-the-art DNN-based approaches are not well suited for the scenario with geometry rotations due to their intrinsic deficiencies of square kernel structure. That is, square kernel structures in standard DNNs cannot fully identify the essentials of the rotations in geometry. To address this problem, we present RICNN, a novel deep learning framework that encodes invariance against the rotations in geometry explicitly into convolutional neural networks. Rather than using the square-shaped kernel structure, RICNN adopts sector-shaped convolutional kernels to achieve encoding invariance in all rotations. With the explicitness of such rotation encoding, RICNN enables the transfer of perspective DNN models to obtain rotation-invariant descriptions. Furthermore, we propose a novel multi-level hinge triplet loss function to strengthen the matching constraints against geometry rotations. Comprehensive experiments demonstrate the strong generalization ability of the RICNN descriptor on the HPatches dataset. Toward the rotation invariance evaluation, our method shows state-of-the-art results.

关键词： CNN descriptor feature matching rotation invariance

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SAM-Mamba: Mamba Guided SAM Architecture for Generalized Zero-Shot Polyp Segmentation

SAM-Mamba: Mamba Guided SAM Architecture for Generalized Zer...

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2025 ieee/CVF Winter conference on Applications of computer Vision, WACV 2025

作者： Dutta, Tapas Kumar Majhi, Snehashis Nayak, Deepak Ranjan Jha, Debesh University of Surrey United Kingdom Côted' Azur University France Malaviya National Institute of Technology Jaipur India University of South Dakota United States

ISBN: (纸本)9798331510831

Polyp segmentation in colonoscopy is crucial for detecting colorectal cancer. However, it is challenging due to variations in the structure, color, and size of polyps, as well as the lack of clear boundaries with surrounding tissues. Traditional segmentation models based on Convolutional Neural networks (CNNs) struggle to capture detailed patterns and global context, limiting their performance. Vision Transformer (ViT)-based models address some of these issues but have difficulties in capturing local context and lack strong zero-shot generalization. To this end, we propose the Mamba-guided Segment Anything Model (SAM-Mamba††Code, Modes:https://***/TapasKumarDuttal/SAM_Mamba_2025) for efficient polyp segmentation. Our approach introduces a Mamba-Prior module in the encoder to bridge the gap between the general pre-trained representation of SAM and polyp-relevant trivial clues. It injects salient cues of polyp images into the SAM image encoder as a domain prior while capturing global dependencies at various scales, leading to more accurate segmentation results. Extensive experiments on five benchmark datasets show that SAM-Mamba outper-forms traditional CNN, ViT, and Adapter-based models in both quantitative and qualitative measures. Additionally, SAM-Mamba demonstrates excellent adaptability to unseen datasets, making it highly suitable for real-time clinical use. © 2025 ieee.

关键词： Convolutional neural networks

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Spectral Feature Selection Via Low Rank Decomposition and local Preservation 3

Spectral Feature Selection Via Low Rank Decomposition and Lo...

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3rd International conference on Neural networks, Information and Communication Engineering, NNICE 2023

作者： Jiang, Luxi Chen, Xiuhong College of Artificial Intelligence and Computer Science Jiangnan University Wuxi Jiangsu Province China

ISBN: (纸本)9798350335972

Feature selection is an important step in data mining and pattern recognition tasks. In unsupervised cases, feature selection becomes more difficult due to the lack of labels in the samples. Therefore, this paper proposes a spectral feature selection (JLRDLP) algorithm that combines low rank decomposition and local preservation. First, the model predicts pseudo-labels using spectral clustering, which jointly learns the cluster labels and feature selection matrix, enabling the algorithm to select more discriminative features. In order to realize the selection of data features, the feature selection matrix is guaranteed to be sparse by L_2,1 -norm minimization constraint;the projection matrix is decomposed into the product of two low-rank matrices to reveal the global correlation of data features;The idea is to preserve the local geometry of the data when transforming the original data into a low-dimensional space. Finally, the experimental results on multiple real datasets show that the above has better robustness, thus improving the effect of the classification. © 2023 ieee.

关键词： Feature Selection

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Transparent Ensemble Deep Learning for Intrusion Detection in Industrial Internet of Things 1

Transparent Ensemble Deep Learning for Intrusion Detection i...

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1st International conference on Innovations in Communications, Electrical and computer Engineering, ICICEC 2024

作者： Sneha, M. Prasad, G.R. B.M.S College of Engineering Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350376517

The growing complexity and scale of Industrial Internet of Things (IIoT) networks have made them increasingly vulnerable to cyber-attacks, underscoring the need for reliable and precise Intrusion Detection Systems (IDS). However, many existing IDS solutions face challenges such as high false-positive rates, limited detection accuracy, and a lack of transparency in their decision-making processes, which hinders their practical application. This work presents an advanced IDS based on ensemble deep learning specifically designed for IIoT networks to overcome these limitations. The suggested system integrates a number of deep learning models, such as Artificial Neural networks (ANNs), Convolutional Neural networks (CNNs), and LASSO, to harness their unique advantages and enhance detection capabilities. Moreover, the system integrates explainability methods, such as Random Forest, linear regression, and local Interpretable Model-agnostic Explanations (LIME), to offer clear and interpretable perspectives on the process of making decisions. Intrusion Detection System (IDS) proposed in this study is trained and tested using the ToN-IoT dataset, a comprehensive IIoT dataset that encompasses diverse network traffic data and various attack scenarios. Experimental findings indicate that the ensemble model notably improves detection accuracy while lowering false-positive rates compared to traditional baseline models. The incorporation of Random Forest, linear regression, and LIME enhances the system's transparency by helping to elucidate the role of individual features in the forecasts of the model, thereby making the system more interpretable additionally reliable. This research advances the field by introducing a scalable, precise, and explainable IDS for IIoT networks, with strong potential to enhance cybersecurity in industrial settings. © 2024 ieee.

关键词： Network intrusion

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Towards a Network Aware Model of the Time Uncertainty Bound in Precision Time Protocol

Towards a Network Aware Model of the Time Uncertainty Bound ...

引用

2023 ieee INFOCOM conference on computer Communications Workshops, INFOCOM WKSHPS 2023

作者： Deshpande, Yash Diederich, Philip Kellerer, Wolfgang Technical University of Munich Communication Networks Germany

ISBN: (纸本)9781665494274

Synchronizing the system time between devices by exchanging timestamped messages over the network is a popular method to achieve time-consistency in distributed applications. Accurate time synchronization is essential in applications such as cellular communication, industrial control, and transactional databases. These applications consider the maximum possible time offset or the Time Uncertainty Bound (TUB) in the network while configuring their guard bands and waiting times. Choosing the right value for the TUB poses a fundamental challenge to the system designer - a conservatively high value of the TUB decreases the chances of time-based byzantine faults but increases latency due to larger guard bands and waiting times. The TUB is affected by packet delay variation (PDV) of time synchronization messages due to congestion from background network traffic. In this work, we use Network Calculus (NC) to derive the relation between network traffic and the TUB for a network built with commercial off-the-shelf (COTS) hardware. For centrally deployed and monitored local area networks (LAN)s such as in cellular networks and datacenters this relation could be useful for system designers to plug a better-informed value of the TUB. © 2023 ieee.

关键词： Systems analysis

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An Enhanced Face Spoof Detection using ResNet50 Based Cosine Learning Rate

An Enhanced Face Spoof Detection using ResNet50 Based Cosine...

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2024 International conference on Distributed Systems, computer networks and Cybersecurity, ICDSCNC 2024

作者： Hussein, Layth Sharanya, D. Kumar, Gotte Ranjith Prashanth, V. Ramya, R. The Islamic University Department of Computers Techniques Engineering College of Technical Engineering Najaf Iraq Department of Cse Hyderabad India Sr University Department of Computer Science & Artificial Intelligence Warangal India Nitte Meenakshi Institute of Technology Department of Electrical & Electronics Engineering Bengaluru India Bannari Amman Institute of Technology Department of Computer Science and Engineering Sathyamangalam India

ISBN: (纸本)9798350375442

In recent times, face biometric systems have recognized widely and got attention in computer vision. Further, the biometric face recognition systems are susceptible for face spoofing attacks, where an attacker uses a fake face image to copy a legitimate user. An effective face spoof detection method can protect user identity, prevent unauthorized access and maintain trust in biometric authentication. The existing face spoof detection methods offer low efficiency in detecting small objects and cause overfitting. Therefore, this paper introduces ResNet50- Cosine Learning Rate (CLR) for face spoof detection which accurately distinguishes between real and fake face images by preventing unauthorized access in biometric systems. The proposed ResNet50-CLR takes input images from Institute of Automation, Chinese Academy of Sciences (CASIA) dataset. These images were preprocessed by background subtraction where features are extracted by local Binary Patterns (LBP);then, the extracted features are classified by Proposed ResNet50-CLR which achieved better results in terms of Equal Error Rate (EER) value as 0.12 (Btu/h per watt) and Half Total Error Rate (HTER) as 0.45%, False Acceptance Rate (FAR) as 0.65% and False Rejection Rate (FRR) as 0.66% when compared with existing Stacked models. © 2024 ieee.

关键词： local binary pattern

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Flood and Loot Attack in Distributed Routing Protocols in PCNs 17

Flood and Loot Attack in Distributed Routing Protocols in PC...

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17th International conference on COMmunication Systems and networks, COMSNETS 2025

作者： Sharma, Neeraj Kapoor, Kalpesh Indian Institute of Technology Dept. of Computer Science & Engg. Guwahati India Indian Institute of Technology Dept. of Mathematics Guwahati India

ISBN: (纸本)9798331531195

Payment Channel networks (PCNs) can significantly enhance the scalability of blockchain transactions without requiring major modifications to the underlying distributed ledger protocol. However, to enable efficient and secure payment routing over multiple channels, PCNs necessitate additional components: an onion routing protocol for anonymity and a locking mechanism to prevent race conditions. Unfortunately, these mechanisms can be exploited by malicious actors to launch flood and loot attacks. We investigate the impact of flood and loot attacks on networks that employ local knowledge-based routing algorithms. In these scenarios, malicious nodes can manipulate hash-locks to unfairly capitalize on the benefits of participating nodes. We specifically examine the effects of these attacks on three popular routing algorithms: Swift, Speedy Murmurs, and Silent Whispers. Our analysis employs metrics like attack gain, attack cost, and transaction ratio to assess the impact of the attacks. Our simulation-based results demonstrate that, even with a relatively small fraction of malicious nodes (between 0.66% and 3.3%), the average attack transaction ratio across all evaluated routing algorithms is a concerning 16.23%. © 2025 ieee.

关键词： Distributed ledger

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TSFC: TEXTURE AND STRUCTURE FEATURES COUPLING FOR IMAGE INPAINTING 30

TSFC: TEXTURE AND STRUCTURE FEATURES COUPLING FOR IMAGE INPA...

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30th ieee International conference on Image Processing (ICIP)

作者： Liu, Lu Wang, Qi Yu, Wenxin Chen, Shiyu Gong, Jun Chen, Peng Southwest Univ Sci & Technol Mianyang Sichuan Peoples R China Southwest Automat Res Inst Mianyang Sichuan Peoples R China Chengdu Hongchengyun Technol Co Ltd Chengdu Peoples R China

ISBN: (纸本)9781728198354

Image inpainting has made significant progress benefiting from the advantages of convolutional neural networks (CNNs). Deep learning-based methods have shown extraordinary performance in this field. In this paper, we propose a novel image inpainting architecture with pure CNN that can jointly reconstruct the structure and texture of the image. Our generative network architecture (TSFC) consists of two parallel stages: structure generation and texture generation. In the structure generation stage, we use the large convolution kernel, which is highly neglected in modern networks, using the effective perceptual field of the large convolution kernel to enhance the perception of overall structural features. In the texture generation stage, we use the small convolution kernel to extract local texture features. Qualitative and quantitative experimental results on CelebA-HQ and Paris Street View datasets demonstrate the effectiveness and superiority of our method.

关键词： Image Inpainting Encoder-decoder Network computer Vision Deep Learning

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