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检索条件"任意字段=8th IEEE International Conference on Software Engineering and Computer Systems, ICSECS 2023"

共 1533 条记录，以下是871-880 订阅

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Design and Research of the Whole Process Non-blocking Technology in High Concurrency Scenario

Design and Research of the Whole Process Non-blocking Techno...

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international conference on computers, Communications, and systems (ICCCS)

作者： Xuehua Liao Chunqi Luo Peng Lu Pengcheng Xiao Zhousen Zhu Meng Lei College of Computer Science Sichuan Normal University Chengdu China College of Physics and Electronic Engineering Sichuan Normal University Chengdu China

Under high concurrency scenarios, software applications can suffer from low access efficiency and high exception rate due to I/O bottlenecks and database connection number limitations. Without changing the hardware conditions, a fullprocess non-blocking technology is employed to alleviate the performance bottleneck of software applications under high concurrency scenarios from the perspective of optimizing access request reception and data read/write rates. Firstly, to mitigate the huge concurrency pressure on database caused by a large number of non-essential operations, a Bloom filter is selected to filter invalid access requests. then, a concurrent data processing caching strategy is applied to hand with the performance bottleneck problem caused by the limitation of database connections in the high-concurrency scenario. Furthermore, to address the issue that synchronous I/O cannot take advantage of the responsive framework and combine with the new achievements in database technology development, an asynchronous non-blocking database access middleware is designed and developed to achieve a completely asynchronous non-blocking and streaming data processing mode. After testing and application practice, the effectiveness and efficiency of the proposed methods are confirmed, and it also has certain wide applicability.

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A Mechanism for Controller Failover in Distributed software-Defined Networks 8

A Mechanism for Controller Failover in Distributed Software-...

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8th international conference on computer and Communication engineering, ICCCE 2021

作者： Dharam, Poonam Dey, Mithila Saginaw Valley State University Department of Computer Science and Information Systems SaginawMI United States North Dakota State University Department of Computer Science Fargo United States

ISBN: (纸本)9781728110646

In software Defined Networks (SDN), reliability plays a major role in maintaining a consistent end-to-end connectivity and dealing with controller failures. A traditional SDN implementation relies on a central controller that runs the control plane. However, in a large-scale Wide Area Network (WAN) deployment, centralized approach has several limitations related to performance and scalability. To address these issues, distributed SDN where multiple controllers that work cooperatively to control a network are deployed. One key limitation of such a distributed network is failure of one or more controllers may cause severe issues like network outage and reduced Quality of Service (QoS). In this paper, we address the problem of controller failure in distributed SDN by proposing two controller failover techniques, namely (a) RandomWeightLoadBalancing and (b) ProgressiveAssignmentLoadBalancing. Our work focuses on load balancing the orphan switches, connected to a failed controller, among available controllers rather than overloading a single controller with all orphan switches. Simulation results show that our proposed solutions perform a successful reassignment of orphan switches and ensure that the load of each controller is well balanced. © 2021 ieee.

关键词： Controllers

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Detection & Classification of Tuberculosis HIV-Positive Patients using Deep Learning

Detection & Classification of Tuberculosis HIV-Positive Pati...

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international conference for Convergence of Technology (I2CT)

作者： Princy K T M Tripty Singh Vineet Vinayak Prakash Duraisamy Department of Computer Science and Engineering Amrita School of Engineering Bengaluru Amrita Vishwa Vidyapeetham India Department of Computer Science and Engineering Amrita School of Engineering Bengaluru Amrita Vishwa Vidyapeetham Bangalore India Department of Computer Science University of South Alabama South Alabama United States of America

Tuberculosis [TB] recently struck several countries all through the entire globe. According to the World Health Organization [WHO], there have been a calculated 2.8 million TB fatalities in 2022, with an extra 0.3 million deaths mainly due to TB illness in HIV-positive patients. the majority of Deaths can’t be avoided unless the disease is diagnosed early. Conventional diagnostic procedures, such as blood and urine tests or sputum testing, not only are inconvenient, and they also take a lot of time to analyze and cannot compare different drug-resistant phases of TB. In this paper, authors look at how deep learning-based method might be a good alternative to decision forest medical image-categorization systems. these experiments are conducted on chest X Ray of both tuberculosis and normal patient and identify the X-Rays of normal and tuberculosis patient separately. this experiment objective is to create generalized model to overcome the problems of the existing model. In this paper authors are experimenting various models such as normal CNN and Transfer Learning Methods.

关键词： Deep learning Training Learning systems Tuberculosis Transfer learning Lung X-rays

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A Twitter-based software Vulnerability Alert Framework using Natural Language Processing

A Twitter-based Software Vulnerability Alert Framework using...

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international conference on Communication and Electronics systems (ICCES)

作者： Yadhu Krishna M Sneha C K thejaswi A R Sayooj B Kumar Greeshma Sarath Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

With the increasing usage of networks, the frequency of cyber attacks are expected to increase. To prepare against these attacks, modern organizations require effective ways to ensure cyber security. Twitter is one of the most well-known social media platforms in the world. Tweets by users on Twitter cover a wide range of topics, from daily life to politics and breaking news. this social media platform has a sizable and diverse user base, provides great accessibility, and timeliness, resulting in the production of a huge amount of publicly available data, which in turn can be used effectively for a variety of purposes. For detecting security vulnerability events via Twitter, we introduce a Natural Language Processing-based approach. the tool can be used to provide alerts and notifications to users when a security vulnerability is detected.

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Fraud Detection using Recurrent Neural Networks for Digital Wallet Security

Fraud Detection using Recurrent Neural Networks for Digital ...

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international conference on computer Science and engineering (UBMK)

作者： Can Iscan Fatma Patlar Akbulut R&D Center of Formica AI Istanbul Türkiye Department of Software Engineering Istanbul Kültür University Istanbul Türkiye

E-wallet use has exploded, bringing in a new era of convenient and secure digital transactions; nevertheless, it has also led to a surge in fraudulent transactions. Using Recurrent Neural Networks (RNNs), this research provides an experimental approach for detecting fraudulent use of e-wallets. To detect fraudulent behaviors in e-wallet transactions, we have developed Long-Short Term Memory (LSTM) architecture to make use of the sequential nature of transaction data. Using the United Payment dataset for training and testing, the proposed model successfully classifies fraudulent transactions while reducing the number of false positives. the findings underline the utility of the RNN-based strategy in strengthening the safety and reliability of e-wallet systems, lead off for more secure online financial dealings. this study adds to the growing body of literature on detecting fraudulent use of electronic wallets by developing a model that makes use of deep learning to enhance fraud detection effectiveness in the ever-changing domain of digital payments.

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Uniform Resource Clustering for Service Function Chaining in NFV-based Cloud Environment

Uniform Resource Clustering for Service Function Chaining in...

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international conference for Convergence of Technology (I2CT)

作者： Anshul Mishra Amruta Ayachit Shivani Halemani Sparsha Shetty Narayan D G Somashekar Patil School of Computer Science and Engineering KLE Technological University Hubballi India

Network function virtualization (NFV) and software-defined networking (SDN) have had a significant positive influence on the development of technology that mostly resolves the problems with conventional network management systems. Hypervisors enable the virtualization of numerous network functions and services. Virtualized network services such as firewall, load balancer, and network address translator (NAT) along with others can be chained together to form a service chain. A data packet can go through the service chain as per its defined policy and not every available service. Such service function chaining (SFC) policies can be hosted at end devices to conserve a significant amount of energy and time. In this work, we have proposed an approach in which the SFC policies use an adequate amount of network parameters and a technique that uses uniform resource clustering (URC). Two Parameters namely bandwidth and CPU utilization are considered. Our findings indicate that the URC approach saves energy and time significantly. the average network service time with our approach is 9% less than the default approach.

关键词： Cloud computing Virtual machine monitors Service function chaining Firewalls (computing) Bandwidth Network function virtualization Virtualization

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Design of Robust Controller for Tail-Sitter VTOL Aircraft

Design of Robust Controller for Tail-Sitter VTOL Aircraft

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ieee international conference on engineering Technologies and Applied Sciences (ICETAS)

作者： Suha S. Husain Luay T. Rasheed Rawnaq A. Mahmod Ekhlas K. Hamza Noaman M. Noaman Amjad Jaleel Humaidi Department of Construction and Projects University of Technology Baghdad Iraq Control and Systems Eng. Department University of Technology Baghdad Iraq Training & Energy Researches Office Ministry of Electricity Baghdad Iraq Department of Mechatronics Engineering University of Technology Bahrain Control and Systems Eng. Dept. University of Technology Baghdad Iraq

In this study, a robust controller design using an Integral Sliding Mode Control (ISMC) approach for controlling the roll motion during the Vertical Take-off and Landing of Unmanned Air Vehicles (VTOL-UAV) is introduced. First, the mathematical model of the VTOL-UAV system is derived, and then the ISMC design is carried out via the system's states. the proposed controller design for controlling the VTOL-UAV system comprises two parts. the first handled the nominal term of the system, while the second tackled and mitigated the effects perturbations from the first instant. the computer simulation has been carried out to demonstrate the ISMC's robustness and its ability to reduce the chattering effect in the control signal. Furthermore, a performance comparison between the proposed controller and other controllers in the literature has been conducted.

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Privacy-aware Data Fusion and Prediction for Smart City Services in Edge Computing Environment 15

Privacy-aware Data Fusion and Prediction for Smart City Serv...

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2022 ieee Congress on Cybermatics: 15th ieee international conferences on Internet of things, ithings 2022, 18th ieee international conferences on Green Computing and Communications, GreenCom 2022, 2022 ieee international conference on Cyber, Physical and Social Computing, CPSCom 2022 and 8th ieee international conference on Smart Data, SmartData 2022

作者： Qi, Lianyong Chi, Xiaoxiao Zhou, Xiaokang Liu, Qi Dai, Fei Xu, Xiaolong Zhang, Xuyun School of Computer Science Qufu Normal University China Faculty of Data Science Shiga University Japan Riken Center for Advanced Intelligence Project Japan School of Computer and Software Nanjing University of Information Science and Technology China School of Big Data and Intelligence Engineering Southwest Forestry University China Department of Computing Macquarie University Australia

ISBN: (数字)9781665454179

ISBN: (纸本)9781665454179

With fast development of Cyber Physical System, the variety and volume of data generated from different edge servers are fairly considerable. Mining and exploiting the data would definitely bring huge advantages. However, there are two major issues exposed in the process. First, utilizing the data with high efficiency is challenging in edge computing environment. Second, data from multiple edge servers often contain sensitive information such as user preferences, which will give rise to privacy breach risks when using the data. Locality-Sensitive Hashing and amplified technology are utilized for dealing with two problems. In this paper, we propose a novel privacy-protection approach named SRchain-LSH in edge computing environment which is based on amplified Locality-Sensitive Hashing technique. In order to verify high accuracy and efficiency of our proposal, large sets of experiments are conducted. According to experimental results, our proposal shows better performance than those of comparative methods in terms of MAE , RMSE and time costs. For our method, the best value of MAE is about 0.41 and time cost is about 0.1 seconds, which are more accurate and efficient than comparative methods. © 2022 ieee.

关键词： Smart city

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Flexible Autoencoder for Orthogonal Chirp Division Multiplexing Communications

Flexible Autoencoder for Orthogonal Chirp Division Multiplex...

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ieee international conference on Signal and Image Processing (ICSIP)

作者： Min-Sung Koh Department of Computer Science and Electrical Engineering Eastern Washington University Cheney WA USA

this paper introduces a flexible autoencoder (AE) designed for orthogonal chirp division multiplexing (OCDM). the AE is based on a deep neural network (DNN) and a flexible structure of OCDM, which can accommodate various structures for, such as orthogonal frequency division multiplexing, chirp spread spectrum, affine frequency division multiplexing etc. the AE is designed by jointly training a mapper and a demapper made up of a DNN in the flexible structure. Comparing with traditional M-ary quadrature amplitude modulation (MQAM) systems, the trained AE shows different shapes of constellation for additive white Gaussian noise channels and outperforms the traditional MQAMs in bit error rates.

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A Performance Prediction Method Based on Multi-task Spatio-Temporal Convolution Network for SDN Heterogeneous Network 1

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2024

作者： Zhou, Zongping Du, Dajun Chen, Zheyi Yang, Junlin Zhang, Yi Shanghai Key Laboratory of Power Station Automation Technology School of Mechanical Engineering and Automation Shanghai University Shanghai200072 China

ISBN: (数字)9789819603138

ISBN: (纸本)9789819603121

As a large number of terminal devices are deployed in heterogeneous network based on software-defined networking (SDN), the complexity of topology and dynamic change of performance parameters (e.g., latency, load, jitter, etc.) increase importantly, leading to the difficulties in network performance prediction. To address these issues, a performance prediction method for SDN-enabled heterogeneous network is proposed. First, according to the acquired real-time measurement data from SDN-enabled heterogeneous network, a graph neural networks (GNN) feature extraction model based on temporal gated networks is constructed to obtain feature vectors for network performance prediction. Second, considering the coupling relationship of network performance parameters, a multi-task learning method based on multi-expert gated networks is proposed to accurately achieve the performance prediction of network. Finally, experimental results confirm the feasibility and effectiveness of the proposed prediction method. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Prediction models

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