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检索条件"任意字段=IEEE 34th Conference on Local Computer Networks"

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Hierarchical Visual Concept Interpretation for Medical Image Classification 34

Hierarchical Visual Concept Interpretation for Medical Image...

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34th ieee International Symposium on computer-Based Medical Systems (ieee CBMS)

作者： Khaleel, Mohammed Tavanapong, Wallapak Wong, Johnny Oh, Junghwan de Groen, Piet Iowa State Univ Dept Comp Sci Ames IA 50011 USA Univ North Texas Dept Comp Sci & Engn Denton TX 76203 USA Univ Minnesota Dept Med Box 736 UMHC Minneapolis MN 55455 USA

ISBN: (纸本)9781665441216

Most state-of-the-art local interpretation methods explain the behavior of deep learning classification models by assigning importance scores to image pixels based on how influential each pixel was towards the final decision. these interpretations are unable to provide further details to aid understanding of a complex concept in a domain such as medicine. We propose a novel Hierarchical Visual Concept (HVC) interpretation framework for CNN-based image classification models. As an explanation of the classification decision of a given image, HVC presents a concept hierarchy of most relevant visual concepts at multiple semantic levels. these concepts are automatically learned during training such that the lower-level concepts in the hierarchy support the corresponding higher-level concepts. Our quantitative and qualitative evaluation of the interpretation of VGG16 and ResNet50 classifiers on public and private colonoscopy image datasets shows very promising results.

关键词： Interpretation of deep neural networks Hierarchical visual concept interpretation Convolutional Neural networks

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Multi-Granularity Augmented Graph Learning for Spoofing Transaction Detection 25

Multi-Granularity Augmented Graph Learning for Spoofing Tran...

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34th ACM Web conference, WWW 2025

作者： Liu, Xin Rui, Haojun Cheng, Dawei Han, Li Zhou, Zhongyun Zhao, Guoping School of Computer Science and Technology Tongji University Shanghai China East China Normal University Shanghai China Tongji University Shanghai China China Futures Market Monitoring Center Beijing China

ISBN: (纸本)9798400712746

Spoofing is a deceptive trading strategy where fraudsters place a large number of fake orders to manipulate market prices, severely distorting market fairness and threatening market stability. With the advancement of fraudulent tactics, spoofing patterns span across various levels of interaction, involving not only the local structure of individual spoofing transactions but also spoofing groups and global patterns. Relying solely on local context makes it challenging to capture multi-granularity risk signals, especially for organized and covert spoofing. Additionally, existing methods fail to consider the differences and relative importance between features of varying granularity, leading to feature distortion and noise. therefore, we propose a multi-granularity augmented graph learning method that differentially captures fraud signals at local, group, and global levels. It utilizes multi-hop differential aggregation and community-augmented strategy to capture information from local to global perspectives, adaptively distinguishing the contributions of different granularity. To avoid excessive fusion of multi-granularity information, we combine contrastive loss and cross-entropy loss for joint optimization, preserving key features while enhancing the method’s robustness and accuracy. Extensive experiments on real-world datasets demonstrate the effectiveness of our proposed approach in spoofing detection, providing a robust solution for regulatory agencies. Our work will help financial institutions enhance their regulatory capabilities, protect investors’ interests, and promote the healthy development of financial markets. © 2025 Copyright held by the owner/author(s).

关键词： Graph neural networks

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Domain Adaptation for Holistic Skin Detection 34

Domain Adaptation for Holistic Skin Detection

引用

34th SIBGRAPI conference on Graphics, Patterns and Images (SIBGRAPI)

作者： Dourado, Aloisio Guth, Frederico de Campos, Teofilo Li Weigang Univ Brasilia UnB Dept Ciencia Comp CIC Brasilia DF Brazil

ISBN: (纸本)9781665423540

Human skin detection in images is a widely studied topic of computer Vision for which it is commonly accepted that analysis of pixel color or local patches may suffice. However, we found that the lack of contextual information may hinder the performance of local approaches. In this paper, we present a comprehensive evaluation of holistic and local Convolutional Neural Network (CNN) approaches on in-domain and cross-domain experiments and compare them with state-of-the-art pixel-based approaches. We also propose combining inductive transfer learning and unsupervised domain adaptation methods evaluated on different domains under several amounts of labelled data availability. We show a clear superiority of CNN over pixel-based approaches even without labeled training samples on the target domain and provide experimental support for the superiority of holistic over local approaches for human skin detection.

关键词： Training Graphics computer vision Image color analysis Transfer learning Skin Convolutional neural networks

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Multi-armed Bandit-based Channel Bonding for Off-body Communication in ieee 802.15.6 Cognitive Radio Wireless Body Area networks

Multi-armed Bandit-based Channel Bonding for Off-body Commun...

引用

Irish Signals and Systems conference (ISSC)

作者： Shagufta Henna Kevin Meehan Mallikharjuna Rao Sakhamuri Department of Computing Atlantic Technical University Letterkenny Ireland

Off-body communication in Cognitive Radio Wireless Body Area networks (CRWBANs) must cope with different types of interference and dynamic traffic loads. Existing static channel assignment schemes cannot handle dynamic traffic loads with higher data rate requirements. One of the major problems experienced by CRWBANs is contention across wireless body area networks, thereby affecting the network throughput. Channel bonding has been successfully applied to different networks, such as Wireless local Area networks (WLANs), cognitive radio sensor networks, and wireless sensor networks. However, its use for the off-body communication in ieee 802.15.6-based CRWBANs has not been investigated. this article proposes a multi-armed bandit-based bonded channel algorithm (MAB-BCA) with Upper Confidence Bound (UCB) for channel bonding to improve the off-body network capacity. the MAB-BCA based on UCB algorithm maximizes the capacity of off-body communication in CRWBANs. It demonstrates significant performance improvement over Static Channel Assignment (SCA) and Reinforcement Learning—Channel Assignment Algorithm (RL-CAA) in terms of average throughput and comparable bit error rate and dissatisfaction probability.

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Intrusion Detection for IoT networks Using SVM Algorithm

Intrusion Detection for IoT Networks Using SVM Algorithm

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International conference on computer theory and Applications (ICCTA)

作者： Norhan Elnady Aya Adel Wael Badawy Department of Artificial Intelligence School of Artificial Intelligence Egyptian Russian University Cairo Egypt Department of Data Science School of Artificial Intelligence Egyptian Russian University Cairo Egypt

ISBN: (数字)9798331529680

ISBN: (纸本)9798331529697

In this paper, we investigate intrusion detection within IoT networks. We employ the Support Vector Machine (SVM) algorithm and utilize the BoTNeTIoT-L01 dataset as a training dataset. It comprises traffic data from nine IoT devices. the traffic were monitored in a local network during botnet attacks, carried out by Mirai and Gafgyt. We extracted twenty-three features from the packet capture (PCAP) files and applied statistical methods to enhance the detection performance of the SVM model. Our simulation results shows that SVM algorithm effectively identifies malicious traffic with an overall accuracy of 99.17%.

关键词： Support vector machines Training Performance evaluation Statistical analysis computer hacking Simulation Intrusion detection Feature extraction Internet of things Monitoring

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Latency Minimization in Wireless-Powered Federated Learning networks with NOMA

Latency Minimization in Wireless-Powered Federated Learning ...

引用

ieee International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Mohammad Hossein Alishahi Paul Fortier Ming Zeng Fang Fang Aohan Li Department of Electric and Computer Engineering Laval University Quebec City Canada Department of Electrical and Computer Engineering Western University London Canada Graduate School of Informatics and Engineering The University of Electro-Communications Tokyo Japan

Federated learning (FL) has been envisioned as a promising distributed learning framework for next-generation wireless communication systems. FL introduces new challenges in system design, since users need to consider the local processing optimization in addition to traditional communication resources allocation. In this paper, we aim to address this challenge by considering a wireless-powered FL network with multiple users, where non-orthogonal multiple access (NOMA) is employed for uplink transmission. A latency minimization problem is formulated, requiring to jointly optimize the power and time allocation for all FL phases together with the local processing computation frequency at each user. An one-dimensional search algorithm (ODSA) is proposed to obtain the optimal solution for the formulated non-convex problem. Presented numerical results demonstrate that the proposed scheme outperforms its orthogonal counterpart.

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DisCrossFormer: A Deep Light-Weight Image Super Resolution Network Using Disentangled Visual Signal Processing and Correlated Cross-Attention Transformer Operation

DisCrossFormer: A Deep Light-Weight Image Super Resolution N...

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ieee Workshop on Machine Learning for Signal Processing

作者： Alireza Esmaeilzehi Mohammad Javad Rajabi Hossein Zaredar M. Omair Ahmad The Edward S. Rogers Sr. Department of Electrical and Computer Engineering University of Toronto Toronto Ontario Canada Department of Computer Engineering Amirkabir University of Technology Tehran Iran Department of Electrical and Computer Engineering Concordia University Montreal Quebec Canada

ISBN: (数字)9798350372250

ISBN: (纸本)9798350372267

Deep neural networks that employ transformer operations have provided state-of-the-art performances for the task of image super resolution (SR). However, processing the non-local information in the visual signals by the transformers often involves increasing the network complexity. In order to develop a light-weight SR network that can process non-local information for providing superior performance, in this paper, we propose the correlated cross-attention operation. Further, we design a novel overall architecture for our SR network, which processes the disentangled information of the low-resolution images based on the presence of various objects in the visual signals. Disentangling the information of the input low-resolution images facilitates learning by paying more attention to processing a certain number of objects (and not all of them) in the visual signals at a time. the results of various experiments show the effectiveness of both ideas in generating super-resolved images with higher qualities.

关键词： Visualization conferences Superresolution Machine learning Artificial neural networks Benchmark testing Transformers Complexity theory Signal resolution

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A Comparative Analysis for Anomaly Detection in Blockchain networks Using Machine Learning Techniques

A Comparative Analysis for Anomaly Detection in Blockchain N...

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International conference on computer theory and Applications (ICCTA)

作者： Noha E. El-Attar Marwa H. Salama Mohamed Abdelfattah Sanaa Taha Information Systems Dep. Faculty of Computers and Artificial Intellegence Benha University Benha Egypt Information Systems Dep. Faculty of Computers and Informatics Tanta University Tanta Egypt Information Technology Dep. Faculty of Computers and Artificial Intellegence Cairo University Cairo Egypt

ISBN: (数字)9798331529680

ISBN: (纸本)9798331529697

Cryptocurrency-related crimes peaked in 2021, and current trends indicate the enduring presence of blockchain technology. With transaction volumes projected to rise, manual investigations into criminal activities will soon become impractical. Consequently, anomaly detection systems are essential for upholding the financial integrity of blockchain networks. Despite the security measures implemented through consensus mechanisms, blockchain platforms remain vulnerable to illicit behavior. Leveraging machine learning presents a promising strategy for mitigating these risks. this study undertakes a thorough comparison of various machine-learning algorithms aimed at detecting suspicious transactions within blockchain networks. the evaluated techniques encompass Support Vector Machine, Decision Tree, Logistic Regression, K-means, Random Forest, XGBoost, local Outlier Factor, Light Gradient, and Isolation Forest.

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Transfer Learning in Deep Neural Network for Land Cover Classification 25

Transfer Learning in Deep Neural Network for Land Cover Clas...

引用

25th International conference on computer and Information Technology, ICCIT 2022

作者： Sadia, Amina Uddin, S. M. Shihab Islam, Rafiqul University of Chittagong Institute of Forestry and Environmental Sciences Bangladesh Islamic University of Technology Dept. of Cse Bangladesh Dhaka University of Engineering and Technology Dept. of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350346022

Land cover classification research contributes significantly to sustainable development by enhancing the baseline for urban planning, natural resource management, and environmental monitoring at the local, regional, national, and global levels. In practice, only the RGB and near-infrared (NIR) image bands are obtainable for identifying land cover since high-resolution satellite remote sensing data may not even be accessible due to cost, storage systems, or bandwidth restrictions. this work presents Transfer Learning in Deep Neural networks (DNN) for land cover classification, where ResNet101 is considered as a DNN model. Numerical experiments are conducted using the EuroSAT dataset, a multispectral dataset based on Sentinel-2 satellite images of 34 European cities covering 13 spectral bands in visible, near-infrared, and short-wave infrared parts. Several experiments were conducted, and the proposed ResNet101-based technique effectively classifies the land cover classes with more than 99% accuracy. © 2022 ieee.

关键词： Deep neural networks

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I-CovidVis - A Visual Analytics Tool for Interoperable Healthcare Databases using Graphs 34

I-CovidVis - A Visual Analytics Tool for Interoperable Healt...

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34th ieee International Symposium on computer-Based Medical Systems (ieee CBMS)

作者： Linhares, Claudio D. G. Lima, Daniel M. Bones, Christian C. Rebelo, Marina F. S. Gutierrez, Marco A. Traina Jr, Caetano Traina, Agma J. M. Univ Sao Paulo Inst Ciencias Matemat & Comp ICMC Sao Carlos SP Brazil Univ Sao Paulo Inst Coracao InCor HC FMUSP Sao Paulo SP Brazil

ISBN: (纸本)9781665441216

the current COVID-19 pandemic has promoted the periodic release of several health databases aimed at discovering relationships in the data, detecting similar problems in patients, and studying the evolution of the disease. A way to exploit the data is to use visualization techniques, which can lead to the discovery of insights and patterns, as well as to guide analysis procedures to understand the data. In this paper, we present I-CovidVis, a visualization tool to explore data from interoperable healthcare systems, able to compare and navigate in both global and local perspectives. Our approach is to model data as a graph and explore its structural and temporal views. Our proposal facilitates the perception of patterns, trends, periodicity, and anomalies, resulting in faster decision making.

关键词： information visualization visual analytics data visualization complex networks graph visualization healthcare systems data mining

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