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Federated Patient Similarity network for Data-Driven Diagnosis of COVID-19 Patients 18

Federated Patient Similarity Network for Data-Driven Diagnos...

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18th ACS/IEEE International conference on Computer Systems and Applications (AICCSA)

作者： El Kassabi, Hadeel T. Serhani, Mohamed Adel Navaz, Alramzana Nujum Ouhbi, Sofia UAEU Dept Comp Sci & Software Engn Coll Informat Technol Al Ain U Arab Emirates UAEU Dept Informat Syst & Secur Coll Informat Technol Al Ain U Arab Emirates

ISBN: (纸本)9781665409698

Sensitive patient data is generated from a variety of sources and then transferred to a cloud for processing. Therefore, it is exposed to security and privacy and may lead to an increase in communication costs. Edge computing will ease computing pressure through distributed computational capabilities while improving security and privacy. In this paper, we propose a Federated PSN (FPSN) model where the model is moved directly to the edge to minimize computation and communication costs. PSN has been applied as a successful approach in categorizing and diagnosing patients based on similarities against some clinical and non-clinical features. Our proposed model distributes processing at each edge node, then fuses the constructed PSN matrices at the cloud premises, which significantly reduce the model's training and inference time and ensures quick model updates with the local client/nodes. In this paper, we propose: (i) an algorithm to evaluate patient's data similarity at the edge;and (ii) an algorithm to implement the federated similarity network fusion at the Cloud. We conducted a set of experiments to evaluate our FPSN model against other machine learning algorithms using a COVID-19 dataset. The results obtained prove that the FPSN model accuracy is higher than the distributed PSNs at various edges and higher than the accuracies of other classification models.

关键词： Patient Similarity network Precision Medicine neural networks Deep Learning Edge Computing COVID-19 Federated PSN Federated Learning

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RiverMapper: Step-wisely Mapping the Surface Rivers on Optical Remote Sensing Images

RiverMapper: Step-wisely Mapping the Surface Rivers on Optic...

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2022 International conference on neural networks, Information, and Communication Engineering, NNICE 2022

作者： Zhang, Peng Pan, Hengyue Yang, Ke Dou, Yong Niu, Xin National Laboratory for Parallel and Distributed Processing National University of Defense Technology 410073 China Artificial Intelligence Research Center National Innovation Institute of Defense Technology BeiJing100850 China

ISBN: (纸本)9781510655171

Accurately mapping the surface rivers is important in ecological environment monitoring and disaster prevention. The development of remote sensing technology and computer vision greatly improves the efficiency of this task. However, there are few methods that map the rivers from an image directly. The existing automatic river mapping methods usually had two successive stages: waterbody extraction and flow-path extraction, where the latter methods were very dependent on the waterbody masks generated by the former methods. Errors in waterbody masks caused breaks and redundancies in the extracted graphs. This paper proposed RiverMapper, which mapped the rivers step-wisely without dividing into two stages. Following the directions and actions predicted by the convolution neural network, RiverMapper walked along the rivers step by step and cropped the fixed-size image patches at each step for segmentation. Final river graphs were constructed by the waterbody mask patches and those tracks generated by RiverMapper. We applied RiverMapper on optical remote sensing images containing the Changjiang River and the Huanghe River. Without the degradation of the performance on waterbody extraction, RiverMapper outperformed other methods in terms of the local topological and geometrical similarity between the predicted and the ground-truth river graphs. © 2022 SPIE.

关键词： Mapping

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A&D Graph-Based Graph neural network Intrusion Detection for In-Vehicle Controller Area network

A&D Graph-Based Graph Neural Network Intrusion Detection for...

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IEEE International conference on Communications in China (ICCC)

作者： Yaru He Jiaqi Gao Mingrui Fan Daoqi Han Yueming Lu Yaojun Qiao State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350378412

ISBN: (纸本)9798350378429

The rapid advancement of the Internet of Vehicles (IoV) has significantly enhanced the diversity and intelligence of Electronic Control Units (ECUs) within the Controller Area network (CAN) of vehicles. Implementing an effective Intrusion Detection System (IDS) is paramount for ensuring the security of the in-vehicle CAN. Traditional approaches in intrusion detection often rely on single-sample analysis, demanding extensive labeled data. To circumvent this limitation, a graph-based IDS that eliminates the necessity for single-sample labeling becomes imperative. Nonetheless, existing graph-based IDS models encounter the challenge of achieving high accuracy, especially for impersonation attacks. In this paper, we propose Arbitration&Data graph-based Graph neural networks (A&D-GNN) IDS to improve detection accuracy for in-vehicle CAN effectively. The proposed A&D-GNN IDS can significantly improve detection accuracy for impersonation attacks, which includes the Arbitration&Data (A&D) graph construction algorithm, representation learning, and intrusion detection. Based on the CAN message characteristics, the A&D directed attribute graph can extract comprehensive information, including information about both the arbitration field and the data field in the CAN message frame, notably, the data field is associated with the arbitration field. Therefore, the following Graph neural networks (GNN) with powerful representation capabilities can learn more precise graph-level representations. Lastly, a multi-classification layer is applied to perform intrusion detection. Experimental results on the public benchmark OTIDS dataset show that our proposed model is superior to other models with higher accuracy and an impressive accuracy of 99.92% for impersonation attacks.

关键词： Representation learning Accuracy Intrusion detection Impersonation attacks Data processing Graph neural networks Security

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Container Anomaly Detection Using neural networks Analyzing System Calls 28

Container Anomaly Detection Using Neural Networks Analyzing ...

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28th Euromicro International conference on Parallel, distributed and network-Based processing (PDP)

作者： Gantikow, Holger Zoehner, Tom Reich, Christoph Furtwangen Univ Appl Sci Inst Data Sci Cloud Comp & IT Secur Furtwangen Germany

ISBN: (纸本)9781728165820

Container environments permeate all areas of computing, such as HPC, since they are lightweight, efficient, and ease the deployment of software. However, due to the shared host kernel, their isolation is considered to be weak, so additional protection mechanisms are needed. This paper shows that neural networks can be used to do anomaly detection by observing the behavior of containers through system call data. In more detail the detection of anomalies in file and directory paths used by system calls is evaluated to show their advantages and drawbacks.

关键词： Container Security Anomaly Detection neural networks

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distributed optimization of Uplink Cell-Free Massive MIMO networks

Distributed optimization of Uplink Cell-Free Massive MIMO Ne...

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IEEE conference on Vehicular Technology (VTC)

作者： Rui Wang Yi Jiang Dept. of Communication Science and Engineering School of Information Science and Technology Fudan University Shanghai China

ISBN: (纸本)9781665454698

This paper studies distributed optimization of an uplink cell-free Massive MIMO (CF-mMIMO) network. By observing some interesting analogies between the CF network and an artificial neural network (ANN), we propose to relate the uplink CF network to a so-called quasi-neural network. Borrowing the idea of the back-propagation (BP) algorithm, we propose a novel scheme to optimize the central processing unit (CPU) and the access points (APs) of the network. The proposed scheme can achieve multi-AP cooperation using only the pilot sequences, but without the channel state information (CSI). To reduce the required throughput of the fronthaul, we let each AP beamform the received vector signals into scalar ones before passing them to the CPU. The effectiveness of the proposed scheme is verified by the simulations.

关键词： Vehicular and wireless technologies Simulation Massive MIMO Artificial neural networks Throughput Linear programming Central processing Unit

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Integrated Delta-Sigma Modulator with Low Pass Filter for Acoustical Applications

Integrated Delta-Sigma Modulator with Low Pass Filter for Ac...

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International Microsystems, Packaging, Assembly and Circuits Technology (IMPACT)

作者： Wen-Cheng Lai Hsi-Jen Liu Cheng-Kai Peng Yu-Xiang Xu Ming Chi University of Technology New Taipei City Taiwan R.O.C.

ISBN: (数字)9798331532246

ISBN: (纸本)9798331532253

The proposed integrated low pass delta-sigma $(\Sigma\Delta)$ modulator with low pass filter has been presented and fabricated in CMOS 0.18um processing. Cascaded integrators approach with feedback as well as distributed input coupling (CIFB) topology erase a summation amplifier and achieve low power dissipation. Bilinear implementation technique is designed by switching capacitors with neural network (NN) and applied to the 5 th -order elliptic switched-capacitor (SC) loop filter. Experimental results of integrated delta-sigma $(\Sigma\Delta)$ modulator with low pass filter exhibit performances of acoustics with the proposed earphone and microphone at anechoic chamber room.

关键词： Headphones Anechoic chambers Capacitors Modulation Low-pass filters Switches Artificial neural networks Topology Standards Microphones

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A Moving Target Defense Approach for the distributed Dynamic network

A Moving Target Defense Approach for the Distributed Dynamic...

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Lin Zhang Yunchuan Guo Siyuan Leng Zifu Li Fenghua Li Liang Fang Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China

The distributed dynamic network is vulnerable to scanning attacks due to the openness of wireless channels. Traditional defense systems tend to be passive and exhibit delayed responses. A moving target defense approach, namely distributed network Address Shuffling (DNAS), is proposed to thwart attackers’ network scanning through the shuffling of network addresses. To resolve address conflicts resulting from this shuffling, DNAS employs a dynamic diffusion method of allocated addresses before the shuffling process to reduce the probability of conflict generation, and utilizes a passive detection based conflict elimination algorithm after the shuffling process to eliminate any generated conflicts. To select low-risk addresses, DNAS leverages an artificial feature selection based Fully Connected neural network (FCNN) to recognize the attacker’s scanning policy, and identifies low-risk addresses based on the scanning range of the policy. Empirical experiments and theoretical analysis indicate that DNAS significantly reduces the probability of address conflict generation at a minimal cost. It effectively eliminates all generated address conflicts within an average conflict resolution time of less than 500ms. Furthermore, DNAS exhibits an accuracy of 99.45% in recognizing scanning policies, surpassing pseudorandom address hopping in diminishing the success rate of sequential, local random, and mixed scanning.

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Research on the application of computer deep learning technology in fault diagnosis model of smart photovoltaic power station

Research on the application of computer deep learning techno...

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Electronic Technology, Communication and Information (ICETCI), IEEE International conference on

作者： Wang Yong Chen Guang Ye Weixing Lan Bin Fu Jiangque Yi Le'An Jiang Qiao Li Hongming China Energy Jiangxi New Energy Industry Co. Ltd Nanchang China Central Southern China Electric Power Design Institute Co. Ltd. of China Power Engineering Consulting Group Wuhan China

ISBN: (数字)9798350361643

ISBN: (纸本)9798350361650

This project intends to study the fault identification method of distributed photovoltaic power generation system based on deep residual neural network. By analyzing and processing the working time series of each component, the type of system fault can be quickly and accurately determined. This project proposes a time sequence information recognition method for convolutional neural networks. The multi-layer convolutional structure is used to improve the performance of disease recognition. At the same time, residuals are used to overcome the gradient loss caused by depth growth, thus speeding up the learning speed. It is proved that the recognition accuracy of this method is better than that of other common intelligent models. The research results of this project will greatly reduce the operation and maintenance cost of photovoltaic power generation system, and will also greatly improve the operation performance of photovoltaic power generation system. The research results of this paper provide reference for the future operation and maintenance work.

关键词： Photovoltaic systems Fault diagnosis Time series analysis Prediction methods Maintenance Timing Convolutional neural networks

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16th International conference on Multi-disciplinary Trends in Artificial Intelligence, MIWAI 2023

16th International Conference on Multi-disciplinary Trends i...

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16th International conference on Multi-disciplinary Trends in Artificial Intelligence, MIWAI 2023

ISBN: (纸本)9783031364013

The proceedings contain 72 papers. The special focus in this conference is on Multi-disciplinary Trends in Artificial Intelligence. The topics include: Hybrid Model Using Interacted-ARIMA and ANN Models for Efficient Forecasting;demand and Price Forecasting Using Deep Learning Algorithms;conversational Artificial Intelligence in Digital Healthcare: A Bibliometric Analysis;Statistical Analysis of the Monthly Costs of OPEC Crude Oil Using Machine Learning Models;Redefining the World of Medical Image processing with AI – Automatic Clinical Report Generation to Support Doctors;sign Language Interpretation Using Deep Learning;interpretable Chronic Kidney Disease Risk Prediction from Clinical Data Using Machine Learning;traffic Prediction in Indian Cities from Twitter Data Using Deep Learning and Word Embedding Models;iAOI: An Eye Movement Based Deep Learning Model to Identify Areas of Interest;an Ensemble Technique to Detect Stress in Young Professional;automatic Differentiation Using Dual Numbers - Use Case;a Hybrid Intelligent Cryptography Algorithm for distributed Big Data Storage in Cloud Computing Security;Low Light Image Illumination Adjustment Using Fusion of MIRNet and Deep Illumination Curves;Multi-dimensional STAQR Indexing Algorithm for Drone Applications;eduKrishnaa: A Career Guidance Web Application Based on Multi-intelligence Using Multiclass Classification Algorithm;iSTIMULI: Prescriptive Stimulus Design for Eye Movement Analysis of Patients with Parkinson’s Disease;AI Based Employee Attrition Prediction Tool;rescheduling Exams Within the Announced Tenure Using Reinforcement Learning;a Yolo-Based Deep Learning Approach for Vehicle Class Classification;pixel Value Prediction Task: Performance Comparison of Multi-Layer Perceptron and Radial Basis Function neural network;sentiment Analysis of Twitter Data on ‘The Agnipath Yojana’;how Good are Transformers in Reordering?;LPCD: Incremental Approach for Dynamic networks.

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Power Line Partial Discharge Detection Using Multi-Scale 1D Convolutional neural networks

Power Line Partial Discharge Detection Using Multi-Scale 1D ...

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International conference on Power and Energy Technology (ICPET)

作者： Yuliang Jiang Zhenhuan Xu Dan Fang Guoliang Zhang Bihen Zhi Bing Wang State Grid Shanghai Electric Power Corporation Training Center Shanghai China Nanjing Haoqing Information Technology Co. Ltd Nanjing China

In recent years, due to the widespread access of distributed power supply, the topology of electrical network becomes more and more complex such that it is difficult to analyze the fault causes of transmission lines, and partial discharge (PD) is a type of failure that often occurs. An automated transmission lines PD detection system requires automated feature extraction and online identification of PD. Firstly, A PD detection method based on multi-scale one dimensional convolutional neural network (MS-Conv1D) is proposed in this paper, which takes an arbitrary-long time series of fault signals as input. Furthermore, the single-phase and three-phase electrical signals can be dealt with an unified structure of neural network. Secondly, a complete flow of training MS-Conv1D and data processing is given. Finally, simulations on a real PD dataset shows that the detection accuracy of MS-Conv1D reaches 90% with 20MHz sampling rate, and the receiver operating characteristic (ROC) points indicate the robustness of MS-Conv1D at different frequencies.

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