检索结果-内蒙古大学图书馆

20th International Multi-Conference on systems, Signals and Devices, SSD 2023

作者： Hadded, Ahmed Ayed, Mossaad Ben Alshaya, Shaya A. Sfax University Computer and Embedded System Laboratory National Engineering School of Sfax Sfax3038 Tunisia Sousse University Electronic Industrial ENISo Tunisia Sfax University Tunisia Computer and Embedded System Laboratory ENIS Sfax3038 Tunisia Majmaah University Digital Transformation and E-Transactions Faculty of Sciences and Humanities Sciences Prince Sultan University Department of Computer Science Majmaah11952 Saudi Arabia

ISBN: (纸本)9798350332568

In 2020, the world suffered from the COVID-19 pandemic. This situation highlighted the considerable need for systems to detect viruses quickly. Providing an accurate e-health system that detects, monitors, and controls virus spread became necessary. Countries attempt to reduce the virus's spread using basic processes such as quarantine and lockdown. Early virus detection is requested to reduce the threat and decrease the propagation of the pandemic. This paper proposes an attempt to avoid speed-up spreading based on airborne virus detection. The suggested detection based on the SiNW-FET biosensor is not approved as a standardized diagnostic test. To support the airborne detection methodology, a metasurface technology based on Reflector cylindrical antenna with a sensing unit is proposed to detect viruses. The proposal considered the propagation delays and concentration of a virus. Conducted results prove that the receiver's volume is considered a primary key to an accurate detection rather than the virus's concentration. © 2023 IEEE.

关键词： Reflection

来源：评论

学校读者我要写书评

暂无评论

Fractional Order Differential Evolution

引用

IEEE Transactions on Evolutionary Computation 2024年第3期29卷 1-1页

作者： Wang, Kaiyu Gao, Shangce Zhou, MengChu Zhan, Zhi-Hui Cheng, Jiujun Faculty of Engineering University of Toyama Toyama-shi Japan Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA College of Artificial Intelligence Nankai University Tianjin China Ministry of Education Key Laboratory of Embedded System and Service Computing Tongji University Shanghai China

Differential evolution (DE) is a widely recognized method to solve complex optimization problems as shown by many researchers. Yet, non-adaptive versions of DE suffer from insufficient exploration ability and uses no historical information for its performance enhancement. This work proposes Fractional Order Differential Evolution (FODE) to enhance DE performance from two aspects. Firstly, a bi-strategy co-deployment framework is proposed. The population-based and parameter-based strategies are combined to leverage their respective advantages. Secondly, the fractional order calculus is first applied to the differential vector to enhance DE’s exploration ability by using the historical information of populations, and ensures the diversity of population in an evolutionary process. We use the 2017 IEEE Congress on Evolutionary Computation (CEC) test functions, and CEC2011 real-world problems to evaluate FODE’s performance. Its sensitivity to parameter changes is discussed and an ablation study of multi-strategies is systematically performed. Furthermore, the variations of exploration and exploitation in FODE are visualized and analyzed. Experimental results show that FODE is superior to other state-of-the-art DE variants, the winners of CEC competitions, other fractional order calculus-based algorithms, and some powerful variants of classic algorithms. IEEE

关键词： Particle swarm optimization (PSO)

来源：评论

学校读者我要写书评

暂无评论

Intrusion Detection system for IoMT through Blockchain-based Federated Learning 15

Intrusion Detection System for IoMT through Blockchain-based...

引用

15th IEEE International Conference on Security of Information and Networks, SIN 2022

作者： Zaabar, Bessem Cheikhrouhou, Omar Abid, Mohamed University of Sfax Computer Embedded System Laboratory CES-ENIS Tunisia Higher Institute of Computer Science of Mahdia University of Monastir Tunisia

ISBN: (数字)9781665454650

ISBN: (纸本)9781665454650

Federated Learning (FL) is a feasible technology to collaboratively train a model without sharing private data. This approach differs from traditional machine learning techniques, which aggregate local datasets in a single server. Thus, FL is adopted in the Healthcare sector to preserve the privacy of collected sensitive medical data from heterogenous and resource-constrained Internet of Medical Things (IoMT). However, FL requires aggregating all trained local models in a central server that presents a single point of failure. To address this issue, we propose a novel Blockchain-based Federated Learning architecture, which is applied to detect malicious network traffic in IoMT environments. In this paper, the proposed architecture takes advantage of a Hyperledger Fabric channel coupled with FL to manage efficiently and securely the learning process of an Intrusion Detection system (IDS). The Blockchain channel replaces the commonly used central server with the traditional FL approach. Moreover, the proposed approach benefits from the inherent features of Blockchain and FL. Besides, it secures patient data collection from the IoMT by examining the network traffic for unauthorised behaviour or policy breaches. © 2022 IEEE.

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

An Adaptive Heterogeneous Credit Card Fraud Detection Model Based on Deep Reinforcement Training Subset Selection

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2024年第8期5卷 4026-4041页

作者： Zhu, Kun Zhang, Nana Ding, Weiping Jiang, Changjun the Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai201804 China Collaborative Innovation Center for Financial Network Security Tongji University Shanghai201804 China the School of Computer Science and Technology Donghua University Shanghai201620 China the School of Information Science and Technology Nantong University Nantong226019 China

Heterogeneous fraud detection is an important means of credit card security assurance, which can utilize historical transaction records in a source and target domain to build an effective fraud detection model. Nevertheless, large feature distribution differences between source and target transaction instances and the complex intrinsic structure hidden behind transaction data make it difficult for existing credit card fraud detection (CCFD) models to capture and transfer the most informative feature representations and seriously hinder detection performance. In this work, we propose a novel adaptive heterogeneous CCFD model named adaptive heterogeneous credit card fraud detection model based on deep reinforcement training subset selection (RTAHC) based on deep reinforcement training subset selection, which mainly contains two components: selection distribution generator (SDG) and transaction fraud detector (TFD, including feature extractor with an attention mechanism and classifier). The SDG can generate the selection probability distribution vector via the reinforcement reward mechanism, and then transaction instances in the source domain relevant to the target domain are selected. The feature extractor with an attention mechanism can learn the abstract deep semantic feature representations of selected source transaction instances and the target domain. The joint training of SDG and TFD can provide more real-time and accurate transaction feature representations to reduce the distribution discrepancy between the two domains. We verify the detection performance of RTAHC across a large real-world credit card transaction dataset and four public datasets. Experimental results demonstrate that the RTAHC model can exhibit competitive CCFD performance. Impact Statement—With the rise of artificial intelligence (AI)generated models, credit card fraud has become increasingly rampant, which also causes tens of billions of U.S. dollars in credit card losses worldwide every year

关键词： Hidden Markov models

来源：评论

学校读者我要写书评

暂无评论

Improved Density Peaking Algorithm for Community Detection Based on Graph Representation Learning

引用

computer systems Science & Engineering 2022年第12期43卷 997-1008页

作者： Jiaming Wang Xiaolan Xie Xiaochun Cheng Yuhan Wang Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin541006GuangxiChina School of Information Science and Engineering Guilin University of TechnologyGuilin541006GuangxiChina Department of Computer Science Middlesex UniversityLondon

There is a large amount of information in the network data that we canexploit. It is difficult for classical community detection algorithms to handle network data with sparse topology. Representation learning of network data is usually paired with clustering algorithms to solve the community detection ***, there is always an unpredictable distribution of class clusters outputby graph representation learning. Therefore, we propose an improved densitypeak clustering algorithm (ILDPC) for the community detection problem, whichimproves the local density mechanism in the original algorithm and can betteraccommodate class clusters of different shapes. And we study the communitydetection in network data. The algorithm is paired with the benchmark modelGraph sample and aggregate (GraphSAGE) to show the adaptability of ILDPCfor community detection. The plotted decision diagram shows that the ILDPCalgorithm is more discriminative in selecting density peak points compared tothe original algorithm. Finally, the performance of K-means and other clusteringalgorithms on this benchmark model is compared, and the algorithm is proved tobe more suitable for community detection in sparse networks with the benchmarkmodel on the evaluation criterion F1-score. The sensitivity of the parameters ofthe ILDPC algorithm to the low-dimensional vector set output by the benchmarkmodel GraphSAGE is also analyzed.

关键词： Representation learning data mining low-dimensional embedding community detection density peaking algorithm

来源：评论

学校读者我要写书评

暂无评论

Improving Power Consumption Efficiency and Cost Reduction Using Firefly Algorithm in Smart Home: A Multi-scenario Analysis 4th

Improving Power Consumption Efficiency and Cost Reduction Us...

引用

4th International Conference on Electronic Engineering and Renewable Energy systems, ICEERE 2024

作者： Dhriyyef, Mohammed El Mehdi, Abdelmalek Benzaouia, Mohammed Hebal, Sara Smart ICT Laboratory National School of Applied Sciences Mohamed First University Oujda Morocco Renewable Energy Embedded System and Data Processing Laboratory National School of Applied Sciences Mohamed First University Oujda Morocco LRSD Laboratory Computer Science Department Ferhat ABBAS Sétif 1 Sétif Algeria

ISBN: (纸本)9789819799749

This paper proposes a multi-scenario analysis based on the Firefly Algorithm (FA) to optimize power consumption efficiency and electricity cost reduction in smart homes. It integrates demand side management (DSM), in particular demand response (DR), to encourage consumers to adjust their energy consumption during peak hours. A home energy management system (HEMS) is developed, optimizing appliance scheduling and air-conditioning system control while minimizing electricity costs and ensuring thermal comfort. Moreover, appliance comfort. Based on three scenarios reflecting different user priorities, significant reductions in electricity costs are achieved, ranging from 6.42 to 21%, while maintaining user-defined comfort levels. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Smart homes

来源：评论

学校读者我要写书评

暂无评论

Information Security Evaluation by Information Flow Analysis Based on Stochastic Petri Nets

Information Security Evaluation by Information Flow Analysis...

引用

2024 IEEE International Conference on systems, Man, and Cybernetics, SMC 2024

作者： Tu, Hanqian Xiang, Dongming Lin, Wang Liu, Guanjun Zhejiang Sci-Tech University Department of Computer Science and Technology Hangzhou310018 China Shanghai Electronic Transactions and Information Service Collaborative Innovation Center Tongji University Key Laboratory of Embedded System and Service Computing Ministry of Education Department of Computer Science Shanghai201804 China

ISBN: (纸本)9781665410205

The Petri-net-based information flow analysis offers an effective approach for detecting information leakage by the concept of non-interference. Although the related studies propose efficient solutions, they lack quantitative evaluation on information leakage. In this paper, we propose a novel method for quantitative evaluation of information security based on stochastic labeled Petri nets (SLPNs) and information flow analysis. Specifically, we introduce four different levels of security metrics, and provide a methodology for evaluating the information security. Furthermore, a case study is presented to show the feasibility of our method. © 2024 IEEE.

关键词： Information leakage

来源：评论

学校读者我要写书评

暂无评论

Performances Comparison of PV/T Solar Plants with Roll-Bond and Sheet-and-Tube Absorbers 3rd

Performances Comparison of PV/T Solar Plants with Roll-Bond ...

引用

3rd International Conference on Electronic Engineering and Renewable Energy, ICEERE 2022

作者： El Fouas, Chaimae Hajji, Mohamed El Manssouri, Oussama Bekkay, Hajji Gagliano, Antonio Tina, Giuseppe Marco Laboratory of Renewable Energy Embedded System and Information Processing National School of Applied Sciences Mohammed First University Oujda Morocco Department of Electric Electronics and Computer Engineering University of Catania Catania Italy

ISBN: (纸本)9789811962226

Photovoltaic/thermal unit (PV/T) is an innovative technology that allows the renewable energy production by cogenerating electricity and heat simultaneously. This technology is seemed promising for the energy supply in many applications, especially for building envelope. In the present work, the performances of two different PV/T solar plants with a Roll-bond and PV/T sheet-and-tube absorbers are compared under the operating conditions of Catane, Italy. The performance assessment of theses PV/T solar plant is ensured by the dynamic modeling of two PV/T in MATLAB software. Moreover, the PV/T energy yields for suppling the energy need of a simple building are evaluated. From results, the comparison of the two PV/T systems proves that the "Roll-bond" configuration provides a higher thermal performance with a maximum fluid outlet temperature of 42.13 °C with respect to the standard one of 40 °C "Sheet-and-tube" configuration). In terms of the PV/T energy yields used for the energy supply of a standard building, it’s observed that PV/T" Roll-bond" system is very promising. It can fully cover the energy needs of the building with a total coverage factor of 76.79%. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Buildings

来源：评论

学校读者我要写书评

暂无评论

A Better Cardinality Estimator with Fewer Bits, Constant Update Time, and Mergeability 42

A Better Cardinality Estimator with Fewer Bits, Constant Upd...

引用

42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Du, Yang Huang, He Sun, Yu-E Li, Kejian Zhang, Boyu Gao, Guoju Soochow University School of Computer Science and Technology Suzhou China Soochow University School of Rail Transportation Suzhou China Ministry of Education Key Laboratory of Embedded System and Service Computing Shanghai China

ISBN: (纸本)9798350334142

Cardinality estimation is a fundamental problem with diverse practical applications. HyperLogLog (HLL) has become a standard in practice because it offers good memory efficiency, constant update time, and mergeability. Some recent work achieved better memory efficiency, but typically at the cost of impractical update time or losing mergeability, making them incompatible with applications like network-wide traffic measurement. This work presents SpikeSketch, a better cardinality estimator that reduces memory usage of HLL by 37% without sacrificing other crucial metrics. We adopt a bucket-based data structure to promise constant update time, design a smoothed log4 ranking and a spike coding scheme to compress cardinality observables into buckets, and propose a lightweight mergeable lossy compression to balance memory usage, information loss, and mergeability. Then we derive an unbiased estimator for recovering cardinality from the lossy-compressed sketch. Theoretical and empirical results show that SpikeSketch can work as a drop-in replacement for HLL because it achieves a near-optimal MVP (memory-variance-product) of 4.08 (37% smaller than HLL) with constant update time and mergeability. Its memory efficiency even defeats ACPC and HLLL, the state-of-the-art lossless-compressed sketches using linear-time compression to reduce memory usage. © 2023 IEEE.

关键词： Efficiency

来源：评论

学校读者我要写书评

暂无评论

Meta-Heuristic Based Multipath Joint Routing and Scheduling of Time-Triggered Traffic for Time-Sensitive Networking in IIoT 2

Meta-Heuristic Based Multipath Joint Routing and Scheduling ...

引用

2nd International Conference on Green Communication, Network, and Internet of Things, CNIoT 2022

作者： Chen, Chen Li, Zhong The College of Information Science and Technology Donghua University Shanghai201620 China The Key Laboratory of the Ministry of Education for Embedded System and Service Computing Department of Computer Science Tongji University Shanghai201804 China

ISBN: (纸本)9781510662940

Time-Sensitive networking (TSN) meets the needs of industrial internet of things (IIoT). It solves the challenges of deterministic transmission and reliable communication of time sensitive data streams. Traffic scheduling is the core mechanism of time-sensitive networks. Many excellent researches have explored and optimized the method of scheduling time-triggered streams in TSN. However, the existing time-triggered streams scheduling in TSN mostly separates routing and scheduling, which limits the scalability of scheduling. Many researches are based on fixed routing to schedule streams. Due to the interaction between scheduling and routing, the quality of fixed route solution is inferior to that of joint routing and scheduling. In this paper, we propose a meta-heuristic based multipath joint routing and scheduling method for time-triggered traffic in TSN, named MMRS. We set path selection as a variable and consider multipath routing for fault tolerance. At the same time, we establish the integer linear programming formulation and use meta-heuristic to obtain high-quality solutions. The evaluations show that compared with other excellent routing and scheduling methods, the runtime of 60 streams of MMRS method is reduced by 65.6%, and the schedulability is improved by 21% on average. The experiments verify that our proposed scheduling method can obtain high-quality solutions in an acceptable solution time. © 2023 SPIE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：