检索结果-内蒙古大学图书馆

HHO-ACO hybridized load balancing technique in cloud computing

International Journal of Information technology (Singapore) 2023年第3期15卷 1357-1365页

作者： Sumathi, Muruganandam Vijayaraj, Natarajan Raja, Soosaimarian Peter Rajkamal, Murugesan School of Computing SASTRA Deemed University Tamil Nadu Thanjavur India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala Research and Development Institute of Science and Technology Tamil Nadu Chennai India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India IBM Karnataka Bangalore India

Due to on-demand requirement of computing resources, cloud computing (CC) is the widely used technology in data storage, software and platform. CC provides everything as a service to requester. In an Infrastructure as... 详细信息

Due to on-demand requirement of computing resources, cloud computing (C_C) is the widely used technology in data storage, software and platform. C_C provides everything as a service to requester. In an Infrastructure as a Service (IaaS), virtual machines (VMs) play a vital role to provide infrastructure to the requester. The performance VMs depends on the distribution of work (load balancing) between VMs. The process of distributing a set of tasks or workloads over a set of VM resources is called as Load Balancing (LB). Unbalanced task distribution leads to overloaded or under-loaded issues and performance degradation. Hence, LB is a desirable and essential task in C_C. To improve the LB performance the two different meta-heuristic optimization algorithms, Harries Hawks Optimization (HHO) and Ant Colony Optimization (ACO) are hybridized in the proposed technique. The hybridized load balancing (HLB) algorithm performance is compared to HHO and ACO. The factors that are analyzed in the proposed system are the average waiting time (A_WT), average execution time (A_ET), average response time (A_RT), make-span, throughput analysis, turnaround time and LB time. The HLB mechanism is implemented and simulated in java and Cloudsim respectively. By the allocation of workloads to VMs will shows which one gives the best efficiency in LB among the VM’s. The proposed HLB technique takes 9.29% A_ET, 2.65% of A_RT, 0% of A_WT, less turnaround time and LB time than the basic HHO and ACO algorithms. Thus, the proposed HLB provided better performance than HHO and ACO is proven with different performance metrics. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's institute of computer Applications and Management.

关键词： Ant colony optimization Cloud computing Harries haws optimization Hybridized load balancing Virtual machine

来源：评论

学校读者我要写书评

暂无评论

Intelligent 5G Evolution: AI-Augmented Hybrid Model with Network Slicing and Autonomous Self-Healing for Future-Ready Connectivity 3

Intelligent 5G Evolution: AI-Augmented Hybrid Model with Net...

引用

3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Endla, Purushotham Pure, Ramya S. Soni, Anita Andal, C. Jagadeeswari, D. Devi, K. Nirmala Sr University School of Sciences and Humanities Department of Physics Telangana Warangal506371 India Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India Ies University Cse Department Bhopal India Dr. Mgr Educational and Research Institute Chennai India Sri muthukumaran Institute of Technology Department of Ece India Madanapalle Institute of Technology & Science Deartment of Computer Applications India

ISBN: (纸本)9798331509675

The increasing demand for dynamic, efficient, and resilient network management in 5G systems has driven advancements in AI-enhanced architectures. This research proposes an AI-Augmented Hybrid 5G Model that integrates dynamic network slicing, self-healing capabilities, and AI-driven predictive analytics to optimize network performance and reliability. The hybrid model employs supervised and unsupervised machine learning algorithms, achieving a fault detection accuracy of 95.2% and traffic demand prediction accuracy of 92.8%. Key innovations include dynamic resource allocation for enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC), and massive machine-type communication (mMTC). Self-healing capabilities reduced fault recovery times to an average of 300 ms, significantly improving system uptime to 99.8%. Compared to traditional 5G systems, the model demonstrates a 50% reduction in latency, a 28.6% improvement in resource efficiency, and an 80% faster slice reconfiguration. These results highlight the model's potential to revolutionize 5G network management, ensuring reliability and adaptability for diverse applications. The study concludes by exploring the applicability of the hybrid model in future 6G networks and cross-domain orchestration. © 2025 IEEE.

关键词： Predictive analytics

来源：评论

学校读者我要写书评

暂无评论

Using analytic hierarchy process to evaluate deep learning for infrared target recognition

引用

Multimedia Tools and Applications 2024年第38期83卷 86229-86245页

作者： Xu, Laixiang Cao, Bingxu Wei, Xinhong Li, Bei Su, Jingfeng Ning, Shiyuan Zhao, Junmin School of Computer and Data Science Henan University of Urban Construction Pingdingshan467036 China School of Information Engineering Luohe Vocational Technology College Henan University of Technology Luohe Institute of Technology Luohe462000 China School of Computer and Data Science Innovation Laboratory of Smart Traffic and Big Data Development of Hainan Province Pingdingshan467036 China School of Computer and Data Science Research Center of Smart City and Big Data Engineering of Henan Province Henan University of Urban Construction Pingdingshan467036 China Department of Software Information China Electronics Technology Group Corporation 36-th Research Institute Jiaxing314033 China School of Computer and Data Science Research Center of Smart City and Big Data Engineering of Henan Province Research Center of Intelligent Campus Application Engineering of Henan Province Innovation Laboratory of Smart Traffic and Big Data Development of Hainan Province Henan University of Urban Construction Pingdingshan467036 China

In order to promote the evaluation performance of deep learning infrared automatic target recognition (ATR) algorithms in the complex environment of air-to-air missile research, we proposed an analytic hierarchy process (AHP) evaluation system consisting of the target recognition ability, the generalization recognition ability, the anti-interference ability, the background suppression ability, and the cost metrics ability. According to detailed quantitative analysis and experimental results, we establish probability evaluation models and get their probability values. Compared with traditional evaluation systems, our proposed method implementation is showing high reliability. Similar superior performances are also achieved, such as quick, comprehensive, and scientific. Through the above research, the key technology of deep learning performance evaluation in automatic target recognition has been broken through, which provides some basis and guidance for the analysis of infrared imaging-guided weapon systems. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Infrared imaging

来源：评论

学校读者我要写书评

暂无评论

Multi‐granularity re‐ranking for visible‐infrared person re‐identification

引用

CAAI Transactions on Intelligence technology 2023年第3期8卷 770-779页

作者： Yadi Wang Hongyun Zhang Duoqian Miao Witold Pedrycz Department of Computer Science and Technology Tongji UniversityShanghaiChina Key Laboratory of Embedded System and Service Computing Ministry of EducationShanghaiChina Department of Electrical and Computer Engineering University of AlbertaEdmontonAlbertaCanada System Research Institute Polish Academy of SciencesWarsawPoland

Visible‐infrared person re‐identification(VI‐ReID)is a supplementary task of single‐modality re‐identification,which makes up for the defect of conventional re‐identification under insufficient *** is more challenging than single‐modality ReID because,in addition to difficulties in pedestrian posture,camera shoot-ing angle and background change,there are also difficulties in the cross‐modality *** works only involve coarse‐grained global features in the re‐ranking calculation,which cannot effectively use fine‐grained ***,fine‐grained features are particularly important due to the lack of information in cross‐modality re‐*** this end,the Q‐center Multi‐granularity K‐reciprocal Re‐ranking Algorithm(termed QCMR)is proposed,including a Q‐nearest neighbour centre encoder(termed QNC)and a Multi‐granularity K‐reciprocal Encoder(termed MGK)for a more comprehensive feature *** converts the probe‐corresponding modality features into gallery corresponding modality features through modality transfer to narrow the modality *** takes a coarse‐grained mutual nearest neighbour as the dominant and combines a fine‐grained nearest neighbour as a supplement for similarity *** experiments on two widely used VI‐ReID benchmarks,SYSU‐MM01 and RegDB have shown that our method achieves state‐of‐the‐art ***,the mAP of SYSU‐MM01 is increased by 5.9%in all‐search mode.

关键词： computer vision recognition

来源：评论

学校读者我要写书评

暂无评论

Joint IoT Device Selection and Health-Aware Beamforming Design for MIMO-WPT

引用

IEEE Transactions on Mobile Computing 2024年第9期23卷 8822-8838页

作者： Wang, Chih-Hang Shi, Yishuo Yang, De-Nian Chen, Wei-Yu Chen, Wen-Tsuen Academia Sinica Institute of Information Science Taipei115201 Taiwan Wenzhou University Department of Statistics and Information Science College of Mathematics and Physics Wenzhou325035 China Academia Sinica Institute of Information Science The Research Center for Information Technology Innovation Taipei115201 Taiwan University of Southern California Department of Electrical and Computer Engineering Los AngelesCA90007 United States National Tsing Hua University Department of Computer Science Hsinchu300044 Taiwan

Wireless power transfer (WPT) has emerged to enhance the robustness of the energy harvesting Internet of Things (EH-IoT), whereas beamforming has been leveraged to significantly boost the efficiency of far-field WPT. Nevertheless, potential negative impacts due to high electromagnetic fields (EMF) exposure for radiation-susceptible users have not been thoroughly considered in the design of WPT for EH-IoT with IoT application-level requirements (e.g., coverage). In this article, we explore the health-aware beamforming and IoT selection problem under the EH and human safety constraints. First, we formulate a new optimization problem Health-Aware Beamforming and IoT Selection (HABIS) and prove the NP-hardness. Second, we design an approximation algorithm, named Minimum Radiation Exposure and Maximum IoT Coverage (MREMIC), to exploit the EH-health dependency (EHHD) graph for properly addressing the trade-off between EH efficiency and potential EMF radiation exposure to human bodies. We also discover the optimal health-aware beamforming to minimize the total radiation energy absorption of humans. Simulation results show that MREMIC can effectively charge IoT devices and significantly outperforms existing EH approaches by more than 200% regarding human safety. © 2002-2012 IEEE.

关键词： Energy harvesting

来源：评论

学校读者我要写书评

暂无评论

Providing Robust and Low-Cost Edge Computing in Smart Grid:An Energy Harvesting Based Task Scheduling and Resource Management Framework

引用

China Communications 2025年第2期22卷 226-240页

作者： Xie Zhigang Song Xin Xu Siyang Cao Jing School of Information Science and Engineering Yanshan UniversityQinhuangdao 066004China Engineering Optimization and Smart Antenna Research Institute Northeastern UniversityQinhuangdao 066004China College of Computer Science and Engineering Northeastern UniversityShenyang 110819China School of Mathematics and Information Science&Technology Hebei Normal University of Science and TechnologyQinhuangdao 066004China

Recently,one of the main challenges facing the smart grid is insufficient computing resources and intermittent energy supply for various distributed components(such as monitoring systems for renewable energy power stations).To solve the problem,we propose an energy harvesting based task scheduling and resource management framework to provide robust and low-cost edge computing services for smart ***,we formulate an energy consumption minimization problem with regard to task offloading,time switching,and resource allocation for mobile devices,which can be decoupled and transformed into a typical knapsack ***,solutions are derived by two different ***,we deploy renewable energy and energy storage units at edge servers to tackle intermittency and instability ***,we design an energy management algorithm based on sampling average approximation for edge computing servers to derive the optimal charging/discharging strategies,number of energy storage units,and renewable energy *** simulation results show the efficiency and superiority of our proposed framework.

关键词： edge computing energy harvesting energy storage unit renewable energy sampling average approximation task scheduling

来源：评论

学校读者我要写书评

暂无评论

A Review on DeepLungNet: CNN-Based Lung Cancer Detection Techniques Using CT Images

A Review on DeepLungNet: CNN-Based Lung Cancer Detection Tec...

引用

2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Nair, Surabhi S. John, Asha Susan Raju, Juby Musaliar College Of Engineering And Technology Department Of Electronics And Communication Engineering Pathanamthitta India Kalasalingam Academy Of Research And Education Department Of Electronics And Communication Engineering Virudhanagar Tamil Nadu Krishnan kovil626 126 India Hindustan Institute Of Technology And Science Department Of Computer Science Engineering TamilNadu India

ISBN: (纸本)9798350364828

The disease known as lung cancer, which is common and frequently deadly, starts in the cells of the lungs and causes symptoms like exhaustion, chest pain, and persistent coughing. Since small cell lung cancer (SCLC) is less frequent but more dangerous than non-small cell lung cancer (NSCLC), knowledge of its causes, including smoking, pollution, and genetic factors, is essential. Survival rates are greatly increased by early identification by about 20%. This study comprehensively analyzes deep learning and machine learning models-based lung cancer prediction methods from 2016 to 2023. The review emphasizes how well these models work to achieve greater accuracy. Expanding on this, a new method is suggested that combines data augmentation and denoising preprocessing methods with a CNN in a ResNet architecture for classification. The goal of this hybrid model is to maintain and improve accuracy levels in the identification of benign and malignant lung pictures. This methodology offers promising promises for more accurate and reliable lung cancer prediction by utilizing advances in both preprocessing and classification techniques. © 2024 IEEE.

关键词： Diseases

来源：评论

学校读者我要写书评

暂无评论

A SiC-Based 243 kW 60 kW/L Electric Vehicle Traction Inverter

A SiC-Based 243 kW 60 kW/L Electric Vehicle Traction Inverte...

引用

2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Wu, Chih-Chiang Rout, Uma Sankar Chou, Hsin-Ping Hu, Jwu-Sheng Industrial Technology Research Institute Mechanical and Mechatronics Systems Research Laboratories Hsinchu Taiwan National Yang Ming Chiao Tung University Department of Electrical Engineering & Computer Science Hsinchu Taiwan

ISBN: (纸本)9798350376067

This paper investigates the evolving landscape of EV traction inverters, addressing key technical goals outlined by US Drive and analyzing parameters such as voltage, power density, and efficiency. Through innovative methodologies, a high-efficiency SiC-based traction inverter is developed to overcome challenges like spatial constraints. Leveraging international benchmarks, a cutting-edge SiC-based 800V 243 kW traction inverter with a power density of 60 kW/L is presented, poised to surpass US Drive's 2025 targets. This study emphasizes SiC technology's role in advancing EV propulsion systems towards sustainable and efficient transportation solutions. © 2024 IEEE.

关键词： power density silicon carbide high-efficiency sustainable transportation traction inverter

来源：评论

学校读者我要写书评

暂无评论

research on the Application of Agile Testing in Satellite Ground Control System 24

Research on the Application of Agile Testing in Satellite Gr...

引用

2024 International Conference on Machine Learning, Pattern Recognition and Automation engineering, MLPRAE 2024

作者： Chen, Zhanhua Li, Guoyong Cao, Yanun Du, Chenxin The 54th Research Institute of China Electronics Technology Group Corporation China College of Computer Internet of Things Engineering Nanjing University of Information Science and Technology Binjiang College China

ISBN: (纸本)9798400709876

Traditional software testing has gradually exposed many drawbacks in multi-project development, through a multi-dimensional comparative analysis of the limitations of traditional testing and the characteristics of agile testing, based on the characteristics of the satellite ground control system and the development mode, a targeted and operable agile software testing model is designed to increase the cyclic iteration of the test, test-driven development and exploratory testing are introduced into the testing methodology, and the test implementation enhances and clarifies interface automation testing and accurate regression testing based on impact factors, utilizes integration testing based on micro-service groups of business domains and script testing of system operation scenarios, and provides a feasible method for solving the testing of large-scale information systems. The test results show that the test method alleviates the pressure of the centralized outbreak of traditional testing, digs out the hidden dangers of the system early and effectively, improves the testing efficiency, ensures the product quality, and enhances the users' confidence in the software products. © 2024 ACM.

关键词： Integration testing

来源：评论

学校读者我要写书评

暂无评论

Digital Integrity: Enhancing Image Forensics with Error Level Analysis and Deep Learning 2

Digital Integrity: Enhancing Image Forensics with Error Leve...

引用

2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Arumugam, M. Arun, G. Jayanthi, C. Nelson Samuvel, A. Pg Kongu Engineering College Department of Computer Technology Perundurai Tamilnadu Erode638060 India Vel Tech Rangarajan Dr.Sangunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Pg& Research Department of Computer Science Tamilnadu Karur639005 India Kongu Engineering College Perundurai Tamilnadu Erode638 060 India

ISBN: (数字)9798350368413

ISBN: (纸本)9798350368413

The widespread use of digitally altered images highlight the need for trustworthy methods to differentiate between real and altered content. This study introduces ELA as a more effective and economical method for detecting changes in digital images, diverging from the original method described in the base work, which employed Fourier transform of residual images. This study offers a reliable method for differentiating between authentic and altered photos using error level analysis (ELA) in combination with a lightweight convolutional neural network (CNN), which is MobileNet. The proposed methodology uses ELA to detect faint aberrations suggestive of picture alteration, from which MobileNet extracts significant features in an effective manner. An SVM is then used to classify the collected features, thereby drawing a line between genuine and artificial intelligence (AI)-generated images. The results of the experiments show that the suggested design performs robustly in picture classification tasks, achieving a high accuracy of 94.18%. This research adds to the field of image forensics by improving the dependability of fake image recognition and providing a scalable solution that can be used in a variety of real-world circumstances, such as cybersecurity and content verification. This integration of ELA with SVM and MobileNet is a major step forward in the ongoing actions taken to maintain the integrity of digital media and fight false information. © 2024 IEEE.

关键词： Convolutional neural networks

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：