检索结果-内蒙古大学图书馆

Reliable Task Offloading for 6G-Based IoT Applications

computers, Materials & Continua 2025年第2期82卷 2255-2274页

作者： Usman Mahmood Malik Muhammad Awais Javed Ahmad Naseem Alvi Mohammed Alkhathami Department of Computer Software Engineering National University of Science and Technology(NUST)Islamabad44000Pakistan Department of Electrical and Computer Engineering COMSATS University IslamabadIslamabad45550Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia

Fog computing is a key enabling technology of 6G systems as it provides quick and reliable computing,and data storage services which are required for several 6G *** Intelligence(AI)algorithms will be an integral part of 6G systems and efficient task offloading techniques using fog computing will improve their performance and *** this paper,the focus is on the scenario of Partial Offloading of a Task to Multiple Helpers(POMH)in which larger tasks are divided into smaller subtasks and processed in parallel,hence expediting task ***,using POMH presents challenges such as breaking tasks into subtasks and scaling these subtasks based on many interdependent factors to ensure that all subtasks of a task finish simultaneously,preventing resource ***,applying matching theory to POMH scenarios results in dynamic preference profiles of helping devices due to changing subtask sizes,resulting in a difficult-to-solve,externalities *** paper introduces a novel many-to-one matching-based algorithm,designed to address the externalities problem and optimize resource allocation within POMH ***,we propose a new time-efficient preference profiling technique that further enhances time optimization in POMH *** performance of the proposed technique is thoroughly evaluated in comparison to alternate baseline schemes,revealing many advantages of the proposed *** simulation findings indisputably show that the proposed matching-based offloading technique outperforms existing methodologies in the literature,yielding a remarkable 52 reduction in task latency,particularly under high workloads.

关键词： 6G IoT task offloading fog computing

来源：评论

学校读者我要写书评

暂无评论

A Global-Local Probsparse Self-Attention Transformer for LEO Satellite Orbit Prediction 23

A Global-Local Probsparse Self-Attention Transformer for LEO...

引用

23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Huang, Guan Shu, Tao Auburn University Department of Computer Science and Software Engineering Auburn United States

ISBN: (纸本)9798350374889

In recent years, the proliferation of LEO (Low-Earth Orbit) satellites and the accumulation of space debris have made Near-Earth space more and more crowded, and hence significantly increased the risk of collisions in this space. As a result, precise orbit prediction becomes essential for LEO satellites to avoid collision, maintain the right constellation, and perform normal space operations. Although machine learning(ML)-based satellite orbit prediction methods have been extensively explored, most existing methods are trained on simulated/synthetic orbit data, which essentially assumes a stationary orbit process and cannot reflect the non-stationary dynamic orbit changes in real-world LEO satellite constellations that are caused by satellite flight status adjustment (e.g., for the purpose of collision avoidance). In this study, we propose a novel multi-range (global-local) self-attention transformer-based ML model, the GloLoSAT (Global-Local Probsparse Self-Attention Transformer), and train the model over real-world LEO satellite orbit data crawled from *** to give more precise orbit prediction in case of a series of orbit adjustments. Theoretical analysis demonstrates our Global-Local Probsparse Self-Attention mechanism can achieve O(Llog(L)) computational complexity with respect to the input sequence length. Extensive experiments conducted on real satellite orbit tracking datasets demonstrate the efficacy of GloLoSAT in achieving consistent performance improvements across various prediction scenarios compared to their counterparts. © 2024 IEEE.

关键词： Space debris

来源：评论

学校读者我要写书评

暂无评论

COVID-19 Prediction Model Empowered with Fused Computational Intelligence Technique

引用

IEIE Transactions on Smart Processing and Computing 2025年第1期14卷 109-117页

作者： Khan, Muhammad Adnan Naseer, Iftikhar Ali, Muhammad Nadeem Kim, Byung-Seo Department of Software Faculty of Artificial Intelligence & Software Gachon University Seongnam13120 Korea Republic of Faculty of Computer Science & Information Technology The Superior University Lahore54600 Pakistan Department of Software and Communications Engineering Hongik University Sejong Campus Korea Republic of

The novel Coronavirus (COVID-19) spread rapidly around the world and caused overwhelming effects on the health and economy of the world. It first appeared in Wuhan city of China and was declared a pandemic by the World Health Organization (WHO). Many researchers, as well as experts in clinical and artificial intelligence experts, are working together to control the rapid spread of COVID-19 with early detection. This study focused on intelligent prediction for coronavirus using computational intelligence approaches (IPC-FCIA) like convolutional neural networks, support vector machines, and fuzzy logic techniques. The proposed IPC-FCIA model is based on two sections namely the training section and the validation section. Features fusion and decision-level fusion are used in this study to enhance the performance of the recommended IPC-FCIA model. The proposed model predicts the early detection of COVID-19 in two types COVID-negative and COVID-positive. The benchmark results of the model show an accuracy of 97.66 % on decision-level fusion in the detection of COVID-19. The proposed model can be helpful for medical experts as well as COVID-19-affected patients. © 2025 Institute of Electronics Engineers of Korea. All rights reserved.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

Aspect Extraction Approach for Sentiment Analysis Using Keywords

引用

computers, Materials & Continua 2023年第3期74卷 6879-6892页

作者： Nafees Ayub Muhammad Ramzan Talib Muhammad Kashif Hanif Muhammad Awais Department of Computer Science Government College UniversityFaisalabad38000Pakistan Department of Software Engineering Government College UniversityFaisalabad38000Pakistan

Sentiment Analysis deals with consumer reviews available on blogs,discussion forums,E-commerce websites,andApp *** online reviews about products are also becoming essential for consumers and companies as *** rely on these reviews to make their decisions about products and companies are also very interested in these reviews to judge their products and *** reviews are also a very precious source of information for requirement *** companies and consumers are not very satisfied with the overall sentiment;they like fine-grained knowledge about consumer *** to this,many researchers have developed approaches for aspect-based sentiment *** existing approaches concentrate on explicit aspects to analyze the sentiment,and only a few studies rely on capturing implicit *** paper proposes a Keywords-Based Aspect Extraction method,which captures both explicit and implicit *** also captures opinion words and classifies the sentiment about each *** applied semantic similarity-basedWordNet and SentiWordNet lexicon to improve aspect *** used different collections of customer reviews for experiment purposes,consisting of eight datasets over seven *** compared our approach with other state-of-the-art approaches,including Rule Selection using Greedy Algorithm(RSG),Conditional Random Fields(CRF),Rule-based Extraction(RubE),and Double Propagation(DP).Our results have shown better performance than all of these approaches.

关键词： Sentiment analysis aspect extraction keywords-based machine learning

来源：评论

学校读者我要写书评

暂无评论

Location Enhancement and Multi-template Fusion for Fast Long-Term Tracking 5

Location Enhancement and Multi-template Fusion for Fast Long...

引用

5th International Conference on Artificial Intelligence and Electromechanical Automation, AIEA 2024

作者： Chen, Shixin Software Engineering Institute of Guangzhou Department of Computer Science Guangzhou510980 China

ISBN: (纸本)9798350366174

Owing to the challenge of target occlusion leading to tracking failure during the target tracking process, achieving efficient and robust tracking of targets under occlusion scenarios has become a focal point of research. In this paper, we addresses the issue of tracking failure caused by the reduction or disappearance of target appearance information in occlusion scenarios. It proposes a study on target tracking under occlusion scenes based on deep learning methodologies. A foundation tracker is constructed that leverages location enhancement and multi-template fusion. Built upon this, graph attention mechanisms and target loss detection mechanisms are introduced. In cases where the target is lost due to occlusion, a re-detection strategy is employed to resume tracking once the target reappears. Experiments conducted on datasets demonstrate that the proposed algorithm can effectively determine whether a target is undergoing occlusion and significantly enhances tracking performance under such conditions. © 2024 IEEE.

关键词： Target tracking

来源：评论

学校读者我要写书评

暂无评论

A Traffic-Aware and Cluster-Based Energy Efficient Routing Protocol for IoT-Assisted WSNs

引用

computers, Materials & Continua 2024年第8期80卷 1831-1850页

作者： Hina Gul Sana Ullah Ki-Il Kim Farman Ali Department of Computer and Software Technology University of SwatMingora19130Pakistan Department of Computer Science and Engineering Chungnam National UniversityDaejeon34134Republic of Korea Department of Applied AI Sungkyunkwan UniversitySeoul03063Republic of Korea

The seamless integration of intelligent Internet of Things devices with conventional wireless sensor networks has revolutionized data communication for different applications,such as remote health monitoring,industrial monitoring,transportation,and smart *** and reliable data routing is one of the major challenges in the Internet of Things network due to the heterogeneity of *** paper presents a traffic-aware,cluster-based,and energy-efficient routing protocol that employs traffic-aware and cluster-based techniques to improve the data delivery in such *** proposed protocol divides the network into clusters where optimal cluster heads are selected among super and normal nodes based on their residual *** protocol considers multi-criteria attributes,i.e.,energy,traffic load,and distance parameters to select the next hop for data delivery towards the base *** performance of the proposed protocol is evaluated through the network simulator *** different traffic rates,number of nodes,and different packet sizes,the proposed protocol outperformed LoRaWAN in terms of end-to-end packet delivery ratio,energy consumption,end-to-end delay,and network *** 100 nodes,the proposed protocol achieved a 13%improvement in packet delivery ratio,10 ms improvement in delay,and 10 mJ improvement in average energy consumption over LoRaWAN.

关键词： Internet of Things wireless sensor networks traffic load clustering routing energy efficiency

来源：评论

学校读者我要写书评

暂无评论

Enhancing Security and Privacy in Distributed Face Recognition Systems through Blockchain and GAN Technologies

引用

computers, Materials & Continua 2024年第5期79卷 2609-2623页

作者： Muhammad Ahmad Nawaz Ul Ghani Kun She Muhammad Arslan Rauf Shumaila Khan Javed Ali Khan Eman Abdullah Aldakheel Doaa Sami Khafaga School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu611731China Department of Computer Science University of Science&TechnologyBannu28100Pakistan Department of Computer Science University of HertfordshireHatfieldAL109ABUK Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

The use of privacy-enhanced facial recognition has increased in response to growing concerns about data securityand privacy in the digital age. This trend is spurred by rising demand for face recognition technology in a varietyof industries, including access control, law enforcement, surveillance, and internet communication. However,the growing usage of face recognition technology has created serious concerns about data monitoring and userprivacy preferences, especially in context-aware systems. In response to these problems, this study provides a novelframework that integrates sophisticated approaches such as Generative Adversarial Networks (GANs), Blockchain,and distributed computing to solve privacy concerns while maintaining exact face recognition. The framework’spainstaking design and execution strive to strike a compromise between precise face recognition and protectingpersonal data integrity in an increasingly interconnected environment. Using cutting-edge tools like Dlib for faceanalysis,Ray Cluster for distributed computing, and Blockchain for decentralized identity verification, the proposedsystem provides scalable and secure facial analysis while protecting user privacy. The study’s contributions includethe creation of a sustainable and scalable solution for privacy-aware face recognition, the implementation of flexibleprivacy computing approaches based on Blockchain networks, and the demonstration of higher performanceover previous methods. Specifically, the proposed StyleGAN model has an outstanding accuracy rate of 93.84%while processing high-resolution images from the CelebA-HQ dataset, beating other evaluated models such asProgressive GAN 90.27%, CycleGAN 89.80%, and MGAN 80.80%. With improvements in accuracy, speed, andprivacy protection, the framework has great promise for practical use in a variety of fields that need face recognitiontechnology. This study paves the way for future research in privacy-enhanced face recognition systems, emphasizingt

关键词： Facial recognition privacy protection blockchain GAN distributed systems

来源：评论

学校读者我要写书评

暂无评论

Artificial Intelligence Enabled Future Wireless Electric Vehicles with Multi-Model Learning and Decision Making Models

引用

Tsinghua science and Technology 2024年第6期29卷 1776-1784页

作者： Gajula Ramesh Anil Kumar Budati Shayla Islam Louai A.Maghrabi Abdullah Al-Atwai Department of Computer Science and Engineering Gokaraju Rangaraju Institute of Engineering&TechnologyHyderabad 500090India Institute of Computer Science and Digital Innovation(ICSDI) UCSI UniversityKuala Lumpur 56000Malaysia also with Department of ECE KoneruLakshmaiah Education FoundationHyderabad 500090India ICSDI UCSI UniversityKuala Lumpur 56000Malaysia Department of Software Engineering College of EngineeringUniversity of Business and TechnologyJeddah 21448Kingdom of Saudi Arabia Department of Computer Science Applied CollegeUniversity of TabukTabuk 47512Kingdom of Saudi Arabia

In the contemporary era,driverless vehicles are a reality due to the proliferation of distributed technologies,sensing technologies,and Machine to Machine(M2M)***,the emergence of deep learning techniques provides more scope in controlling and making such vehicles energy *** existing methods,it is understood that there have been many approaches found to automate safe driving in autonomous and electric vehicles and also their energy ***,the models focus on different aspects *** is need for a comprehensive framework that exploits multiple deep learning models in order to have better control using Artificial Intelligence(AI)on autonomous driving and energy *** this end,we propose an AI-based framework for autonomous electric vehicles with multi-model learning and decision *** focuses on both safe driving in highway scenarios and energy *** deep learning based framework is realized with many models used for localization,path planning at high level,path planning at low level,reinforcement learning,transfer learning,power control,and speed *** reinforcement learning,state-action-feedback play important role in decision *** simulation implementation reveals that the efficiency of the AI-based approach towards safe driving of autonomous electric vehicle gives better performance than that of the normal electric vehicles.

关键词： wireless vehicles deep learning multi-model learning reinforcement learning Artificial Intelligence(Al)

来源：评论

学校读者我要写书评

暂无评论

DualGuard: Obfuscated Federated Learning with Two-Party Secure Robust Aggregation

引用

IEEE Internet of Things Journal 2025年第11期12卷 16595-16609页

作者： Pan, Hao Bao, Haiyong Guan, Menghong Li, Zhiqiang Huang, Cheng Dai, Hong-Ning East China Normal University Software Engineering Institute Shanghai200062 China Fudan University School of Computer Science Shanghai200438 China Hong Kong Baptist University Department of Computer Science Hong Kong

Federated Learning (FL) is a promising privacy-preserving distributed machine learning paradigm. However, data privacy leakage and Byzantine clients are common challenges in the FL aggregation phase. While extensive research has been conducted to explore defenses for these risks independently, there is a notable lack of scholarly work on integrated defense strategies to address both challenges simultaneously. To bridge this gap, we propose a novel Two-Party Secure Robust Aggregation (TPSRA) framework. The critical insight of TPSRA is to couple client-side gradient obfuscation with server-side secure two-party computation to achieve robust and private FL aggregation. Specifically, clients obfuscate and split local gradients using matrix theory, while servers utilize a novel secure multi-party computation protocol based on mutually orthogonal matrices to preserve the privacy of local gradients. Additionally, TPSRA designs and integrates state-of-the-art robust aggregation algorithms into compatible sub-protocols, enabling efficient parallel computation. This establishes a highly efficient and versatile secure robust aggregation framework for FL. Experiments demonstrate that our TPSRA framework not only effectively resists gradient leakage attacks and detects malicious gradients, but also exhibits superior computational and communication efficiency. We also prove theoretically that TPSRA is secure under the semi-honest adversary model. © 2014 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Resource Based Automatic Calibration System (RBACS) Using Kubernetes Framework

引用

Intelligent Automation & Soft Computing 2023年第1期35卷 1165-1179页

作者： Tahir Alyas Nadia Tabassum Muhammad Waseem Iqbal Abdullah S.Alshahrani Ahmed Alghamdi Syed Khuram Shahzad Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Department of Computer Science&IT Virtual University of PakistanLahore54000Pakistan Department of Software Engineering The Superior UniversityLahore54000Pakistan Department of Computer Science&Artificial Intelligence College of Computer Science&EngineeringUniversity of Jeddah21493Saudi Arabia Department of Software Engineering College of Computer Science and EngineeringUniversity of Jeddah21493Saudi Arabia Department of Informatics&Systems University of Management&TechnologyLahore54000Pakistan

Kubernetes,a container orchestrator for cloud-deployed applications,allows the application provider to scale automatically to match thefluctuating intensity of processing *** cluster technology is used to encapsulate,isolate,and deploy applications,addressing the issue of low system reliability due to interlocking ***-based platforms usually entail users define application resource supplies for eco container *** is a constant problem of over-service in data centers for cloud service *** operating costs and incompetent resource utilization can occur in a waste of *** revolutionized the orchestration of the container in the cloud-native *** can adaptively manage resources and schedule containers,which provide real-time status of the cluster at runtime without the user’s *** clusters face unpredictable traffic,and the cluster performs manual expansion configuration by the *** to operational delays,the system will become unstable,and the service will be *** work proposed an RBACS that vigorously amended the distribution of containers operating in the entire Kubernetes *** allocation pattern is analyzed with the Kubernetes *** estimate the overall cost of RBACS,we use several scientific benchmarks comparing the accomplishment of container to remote node migration and on-site *** experiments ran on the simulations to show the method’s effectiveness yielded high precision in the real-time deployment of resources in eco *** to the default baseline,Kubernetes results in much fewer dropped requests with only slightly more supplied resources.

关键词： Docker container virtualization cloud resource kubernetes

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：