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

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Mammogram Tumor Segmentation With Preserved local Resolution: An Explainable AI System

Mammogram Tumor Segmentation With Preserved Local Resolution...

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ieee conference on Global Communications (ieee GLOBECOM) - Intelligent Communications for Shared Prosperity

作者： Farrag, Aya Gad, Gad Fadlullah, Zubair Md Fouda, Mostafa M. Lakehead Univ Dept Comp Sci Thunder Bay ON Canada Western Univ Dept Comp Sci London ON Canada Idaho State Univ Dept Elect & Comp Engn Pocatello ID USA

ISBN: (纸本)9798350310900

Medical image segmentation is a crucial component of computer-aided diagnosis (CAD) systems, as it aids in identifying important areas in medical images. In order to achieve optimal segmentation results, it is important to preserve the resolution of the input image. The dilated convolution module was introduced to maintain resolution across layers of a deep convolutional neural network by increasing the receptive field exponentially while keeping the parameters increase linearly. However, one drawback of using dilated convolution is that it can result in local spatial resolution loss by increasing the sparsity of the kernel in checkboard patterns. This work proposes a double-dilated convolution module to maintain local spatial resolution in medical image segmentation tasks while having a large receptive field. The module is applied to tumor segmentation in breast cancer mammograms using the state-of-art Deeplabv3+ network. The study also evaluates the developed models with the Gradient weighted Class Activation Map (Grad-CAM) and compares the performance of lesion segmentation networks on mammogram screenings from the INBreast dataset before and after using the proposed dilation module. The results show that the proposed module effectively improves the segmentation performance.

关键词： Medical image analysis tumor segmentation mammograms CAD deep learning CNN explainable AI spatial resolution

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Research on Data Privacy Protection and Heterogeneous Data Fusion of Joint Learning in Intelligent Sensor networks 2

Research on Data Privacy Protection and Heterogeneous Data F...

引用

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

作者： Guo, Wenxin College of Economics and Management Chang'an University Xi'an China

ISBN: (纸本)9798350373646

The traditional information fusion technology between WSN nodes is faced with the weak privacy protection ability of users, and the required communication traffic is large. Under this background, this paper designs and implements a new information security mechanism. The realization process and method of this method are introduced. A fusion algorithm based on segmented encryption is adopted to protect the privacy of users. The method uses local data interaction mechanism to reduce redundant information interaction in the collusive process, and adopts random transmission mechanism to improve the accuracy of fusion results. TOSSIM technology is used to verify the proposed data transmission capability, privacy protection capability and data fusion accuracy. Scalable data fusion technology and fragment encryption technology are adopted to ensure the confidentiality of information. Experimental results show that the proposed method can improve the communication efficiency and fusion effect greatly. The research results of this project can effectively improve the security protection ability of WSN for typical information. © 2024 ieee.

关键词： Differential privacy

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A Deep Learning-Based Bearing Fault Diagnosis Model: IEMDWave-SwinTGAMNet 8

A Deep Learning-Based Bearing Fault Diagnosis Model: IEMDWav...

引用

8th International conference on Electrical, Mechanical and computer Engineering, ICEMCE 2024

作者： Zeng, Yuansan Liu, Jingquan Long, Yang Shen, Zhiyi Liang, Jie Chongqing University College of Mechanical and Vehicle Engineering Chongqing China

ISBN: (纸本)9798331506230

In the realm of mechanical equipment fault diagnosis, the identification of bearing faults using deep learning techniques has garnered significant attention. Many researchers aim to enhance the accuracy of bearing fault detection. This paper proposes an improved IEMDWave-SwinTGAMNet model, which integrates advanced signal noise reduction techniques with deep learning methodologies. The model features a low-noise time-frequency image generation method and employs a deep neural network that utilizes a fusion attention mechanism to extract both local and global features from time-frequency images. The model's effectiveness was validated using a bearing fault dataset from Case Western Reserve University, USA. The results demonstrate that the proposed model achieves high recognition accuracy for bearing faults. © 2024 ieee.

关键词： Deep neural networks

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A Data-Driven Approach to Vehicle Safety: IoT and ML Accident Detection 9

A Data-Driven Approach to Vehicle Safety: IoT and ML Acciden...

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9th ieee International conference for Convergence in Technology, I2CT 2024

作者： Kocharla, Laxmaiah Tiwari, Rashika Kumari, E.N. Vijaya Shankar, D. Al-Salman, Ghafar Ahmed Sai Kumar, K. Telangana Aushapur501301 India Geetanjali Olympiad School Vartur Hobli Bengaluru560037 India Mlr Institute of Technology Department of Computer Science And Information Technology Telangana Hyderabad India Vishnu Institute of Technology Department of Cse Bhimavaram Andhra Pradesh Vishnupur534202 India Al-Ayen University Scientific Research Center Thi-Qar64001 Iraq Koneru Lakshmaiah Education Foundation Computer Science Green Fields Andhra Pradesh Vaddeswaram522302 India

ISBN: (纸本)9798350394474

Accidents prevent injured people from communicating in emergencies. They may use IoT to send accident data to an emergency unit as soon as possible, depending on severity. IoT networks use standard models to categorize situation severity and report it to the control unit. These obsolete models operate poorly. Logistic regression was presented to overcome embedded, cloud, and server model restrictions. The suggested approach is applied to a previous accident database and provided to the local emergency unit for quick testing. Unlike a human review, the suggested technology instantly discovers accident data and delivers it to the local cloud database. A nearby guardian receives accident data from the user's Sensors. IoT networking & emergency alerting to local guardians benefit from SVM with Logistic regression (LR). The suggested application got 96.74% accuracy, 98.67% sensitivity, 96.86% recall, 96.54% throughput, and 95.87% F1 score, which was good. © 2024 ieee.

关键词： Accidents

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Enhancing DTN Routing Strategies with Deep Reinforcement Learning in Disaster Recovery networks 6

Enhancing DTN Routing Strategies with Deep Reinforcement Lea...

引用

6th International conference on Frontier Technologies of Information and computer, ICFTIC 2024

作者： Wen, Xin Tan, Long Heilongjiang University Department of Computer Science and Technology Harbin China

ISBN: (纸本)9798331541750

The application of Delay Tolerance Network (DTN) technology in emergency situations has gained significant attention. This paper proposes a routing protocol designed to facilitate communication and coordination among various rescue departments during emergencies, such as post-disaster scenarios, to enable rapid search, rescue, and transportation of trapped individuals and essential supplies. The network's mobile nodes are classified into two categories, and specific post-disaster routing strategies are developed for the messages carried by these nodes. The paper employs deep reinforcement learning methods in a multi-agent setting, allowing each agent to autonomously select suitable nodes for information transmission in local environments. Additionally, a CVR voting mechanism is used to evaluate the quality of information collected by multiple nodes. Finally, simulations conducted using the ONE simulator validate the feasibility of the proposed method. Results indicate that the proposed scheme performs well in terms of network load, message delivery rate, and transmission delay over time. © 2024 ieee.

关键词： Deep reinforcement learning

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DAGNN: Deep Autoencoder-Based Graph Neural Network for local Anomaly Detection 6

DAGNN: Deep Autoencoder-Based Graph Neural Network for Local...

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

作者： Cai, Hongwei Wang, Yan Liu, Lijuan Zhu, Shunzhi Chen, Manjin Xiamen University of Technology Computer and Information Engineering Xiamen China Longyan Tobacco Industry Co. Ltd. Longyan China

ISBN: (纸本)9798350353174

Nowadays, traditional anomaly detection methods, which are known as local outlier methods, such as LOF and DBSCN, are always based on measuring distances between one sample with its neighborhoods to judge whether it is an outlier or not. Although their principles are straightforward, their performance are always good for feature-based datasets. However, there exist many such cases that a group of neighboring samples are anomalies, which are unsuitable for these methods. In this paper, a novel local anomaly detection model DAGNN is proposed, which incorporates a graph neural network to better aggregate neighbors' distance information of each sample for forming its latent representation vector, and a deep autoencoder to reconstruct this latent representation vector for eliminating noises collected from neighbors. According to results of extensive experiments implemented on multiple publicly available datasets, our model DAGNN outperforms existing baseline methods in terms of detection performance and execution speed. Additionally, these experiment results also indicate that DAGNN is more robust, particularly when the number of neighbors changes. © 2024 ieee.

关键词： Graph neural networks

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QVal: A Novel Routing Algorithm for Dragonfly networks 25

QVal: A Novel Routing Algorithm for Dragonfly Networks

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25th ieee International conferences on High Performance Computing and Communications, 9th International conference on Data Science and Systems, 21st ieee International conference on Smart City and 9th ieee International conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Navaridas, Javier Pascual, Jose A. Dept. of Computer Architecture and Technology Paseo Manuel de Lardizabal 1 Donostia-San Sebastián20018 Spain

ISBN: (纸本)9798350330014

Dragonfly is becoming one of the networks of choice for high-performance computer systems as it offers a sweet spot in terms of cost, simplicity, performance, fault-tolerance and power. In a Dragonfly topology, compute nodes are connected to routers forming fully-connected groups, which are then connected to every other group. This provides very high connectivity with very low diameter which are desired characteristics of high performance interconnection networks. In this work, we propose Quick Valiant, a new routing algorithms for dragonfly networks which leverages Valiant routing with novel proxy selection criteria which reduce the utilization of network resources and eases congestion under pathological loads. These criteria restrict the possible proxies to these that are adjacent to either the local or the remote router, codenamed QVL and QVR, respectively. We compare our proposals with canonical routing functions and observe reductions of average path length of up to 27% and throughput improvements of up to 46% when compared with Valiant routing. Out of these criteria, restricting to local proxies only, QVL, obtains the best results in all scenarios. © 2023 ieee.

关键词： Topology

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Traffic-driven Spectrum and Power Allocation via Scalable Multi-Agent Reinforcement Learning 60

Traffic-driven Spectrum and Power Allocation via Scalable Mu...

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60th Annual Allerton conference on Communication, Control, and Computing, Allerton 2024

作者： Zhang, Yiming Guo, Dongning Northwestern University Department of Electrical and Computer Engineering EvanstonIL60208 United States

ISBN: (纸本)9798331541033

This paper introduces a novel traffic-driven approach to radio resource allocation in cellular networks by leveraging a fully scalable multi-agent reinforcement learning (MARL) framework. The objective is to minimize packets delay of links under stochastic arrivals, where access points (APs) make spectrum and power allocation decisions based on limited local information. Formulated as a distributed learning problem, we implement multi-agent proximal policy optimization (MAPPO) algorithm with recurrent neural networks and queueing dynamics to train flexible policies that map dynamic traffic and channel state information (CSI) to allocation decisions. The proposed MARL-based solution enables decentralized training and execution, ensuring scalability to large networks. Extensive simulations demonstrate that the proposed methods achieve comparable packet delay performance to genie-aided centralized algorithms while using only local information and reducing execution time. The trained policies also show scalability and robustness across various network sizes and traffic conditions. © 2024 ieee.

关键词： Markov processes

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Using dynamically changing step sizes to increase the success rate of adversarial attacks 5

Using dynamically changing step sizes to increase the succes...

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5th International conference on computer Vision, Image and Deep Learning, CVIDL 2024

作者： Huang, Qidong Lu, Leiji Chen, Jun Bao, Lei Army Academy of Artillery and Air Defense Department of Information Engineering Hefei China

ISBN: (纸本)9798350373820

In recent years, it has been widely believed that deep neural networks are vulnerability to adversarial sample attacks, especially under the white-box attack mode, where the attack effects are quite evident. However, experiments have shown that adversarial samples generated in white-box mode can easily fall into local optima quickly, and their attack transferability is relatively poor. Momentum-based methods can effectively skip local optima by accumulating past gradients, thereby enhancing the transferability of adversarial samples. In the process of studying adversarial attack patterns, we noticed that the transferability of adversarial samples generated by the Iterative Fast Gradient Sign Method (I-FGSM) decreases with the increase of iteration times. Based on this phenomenon, we believe that the adversarial perturbation information close to the clean sample, especially its directional information, is more effective in improving the transferability of adversarial samples. Therefore, we propose a dynamically changing step size strategy, which fully utilize the adversarial information of the original sample under constraint conditions. The strategy proposed in this paper can be easily combined with existing adversarial attack methods to improve the transferability of adversarial samples. Experiments show that the method proposed in this paper can be combined with data augmentation methods and other methods to effectively improve the attack transferability of adversarial samples from the original method. © 2024 ieee.

关键词： Deep neural networks

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Hybrid Deep Learning Models with Genetic Algorithm Optimization for Enhanced Kidney Tumor Detection 5

Hybrid Deep Learning Models with Genetic Algorithm Optimizat...

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5th International conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Rajagopal, K. Kumari, V. Sheeja Saraswathy, S. Kumar, V. Suresh Ponmaniraj, S. Deepa, A. Saveetha Institute of Medical and Technical Sciences Simats University Computer Science and Engineering Chennai Thandalam India Saveetha Institute of Medical and Technical Sciences Simats University Department of Computer Science and Engineering Chennai Thandalam India Sri Sairam Engineering College Department of Computer Science and Business Systems Chennai India Institute of Ece Saveetha Institute of Medical and Technical Sciences Simats University Department of Computational Intelligence Chennai Thandalam India Saveetha Engineering College Department of Computer Science and Engineering Chennai Thandalam India

ISBN: (纸本)9798350379945

Accurate and timely segmentation of kidney tumors from medical images is crucial for effective treatment and prognosis. This research study proposes a novel hybrid deep learning framework incorporating Convolutional Neural networks (CNNs) and Recurrent Neural networks (RNNs) for enhanced kidney tumor segmentation. To optimize model performance, Genetic Algorithms (GA) are employed to fine-tune hyperparameters and network architecture. The proposed method effectively captures both local and global image features, leading to improved segmentation accuracy compared to existing techniques. Experimental results on a publicly available dataset demonstrate the superiority of our approach in terms of recall, F1-score, and precision. This research contributes to the advancement of computer-aided diagnosis for kidney cancer. © 2024 ieee.

关键词： Convolutional neural networks

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