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检索条件"任意字段=IEEE International Conference on Distributed Computing Systems"

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distributed Computation Offloading in Dynamic Fog computing Networks: A Learning based Matching Approach

Distributed Computation Offloading in Dynamic Fog Computing ...

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2024 international conference on Electronics, Information, and Communication, ICEIC 2024

作者： Tran-Dang, Hoa Kim, Dong-Seong Kumoh National Institute of Technology Dept. of IT Convergence Engineering Gumi-si Korea Republic of

ISBN: (纸本)9798350371888

Matching theory has demonstrated its efficacy in solving the computation offloading in fog computing networks (FCNs) with the presence of selfish and ratio task nodes (TNs) and helper nodes (HN s), Fundamentally, to achieve the stable and fair matching outcomes, the preference relations of two sides of matching game are known. However, in the dynamic of FCNs where the resources of HNs are time varying, the preference relation of TNs to HNs are unpredictable and must be estimated by iterative interaction. To address this challenge, we apply a multi-armed bandit (MAB) learning particularly using Thomson sampling (TS) mechanism to allow TNs to learn and match with their corresponding HNs efficiently. Based on these, this paper proposes an online learning based matching algorithm called OLM for distributed computation offloading. Extensive simulation results demonstrate the potential advantages of the TS-type algorithm over the -greedy and UCB based offloading algorithms. © 2024 ieee.

关键词： Fog computing

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Analysis of Optimization and Improvement of Massive MIMO Signal Detection Algorithm 4

Analysis of Optimization and Improvement of Massive MIMO Sig...

引用

4th international conference on Electronic Information Engineering and Computer Technology, EIECT 2024

作者： Fan, Shuhang Beijing University of Posts and Telecommunications International School Beijing China

ISBN: (纸本)9798331528850

Massive MIMO (Multiple-Input Multiple-Output) technology hugely enhances spectral and energy efficiency in wireless communication systems, but signal detection remains a key challenge due to its high computational complexity. Traditional methods like Maximum Likelihood detection offer optimal performance but are not practical for large-scale systems due to exponential complexity growth. This paper analyzes four types of signal detection algorithms for massive MIMO: iterative optimization algorithms, deep learning-based methods, message passing and iterative soft threshold techniques, and Neumann series expansion. For each category, we explore their strengths and limitations, and propose optimization strategies such as adaptive adjustment mechanisms, hardware acceleration, model compression, and parallel computing to improve performance and computational efficiency. © 2024 ieee.

关键词： Maximum likelihood estimation

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Pancreatic Cancer Prediction Machine Learning using Multi Fusion Model

Pancreatic Cancer Prediction Machine Learning using Multi Fu...

引用

2024 international conference on distributed systems, Computer Networks and Cybersecurity, ICDSCNC 2024

作者： Sathiyapriya, V. Devi, K. Nethra Deepa, S. Marimuthu, M. Manikandan, P. Prabu, S. Knowledge Institute of Technology Department of Computer Science and Engineering Kakapalayam India Gnanamani College of Technology Department of Artificial Intelligence and Data Science Namakkal India Vellore Institute of Technology School of Computer Science and Engineering Chennai India Srm Institute of Science and Technology School of Computing Tiruchirappalli India

ISBN: (纸本)9798350375442

Pancreatic cancer poses a significant challenge in early detection and treatment due to its malignant nature within the digestive tract. Recent studies, such as those conducted by the Pancreatic Cancer Action Network, predict that pancreatic cancer will rank as the world's second leading cause of cancer-related deaths in 2020. Various methods have been explored in the literature to segment pancreatic tumors and detect lesions from CT images, crucial for assessing severity. One such method is the Min-Max segmentation algorithm, which effectively removes the jaundiced sclera region from affected eyes by analysing color gradients, offering more feasible and discriminative solutions. Additionally, the utilization of Whale Optimized feature-based selection aids in extracting optimal features for optimization purposes. Through an analysis of different classification algorithms and the implementation of a suggested technique for achieving optimum solutions and maximum accuracy, the proposed work is structured into two phases. Phase I involves pre-processing and artifact removal, followed by segmentation, while Phase II focuses on feature extraction, feature selection, and classification. The initial step of pre-processing the image facilitates pixel location using intensity measures, crucial for accurate tumor segmentation and minimizing false detection. Ultimately, the proposed approach aims to design and develop an optimal feature selection strategy for classifying pancreatic cancer tumors in PET/CT images, resulting in increased classification efficiency 92% and improved cancer tumor detection rates with compare other existing approaches. © 2024 ieee.

关键词： Computerized tomography

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Discrepancy and Thematic Bibliometric Analyses of the Remaining Limitations in Artificial Intelligence

Discrepancy and Thematic Bibliometric Analyses of the Remain...

引用

2024 ieee international conference on computing, ICOCO 2024

作者： Mansor, Mahayaudin M. Ibrahim, Nurain Radi, Noor Fadhilah Ahmad Rahim, Nadirah Abdul Yahaya, Syarul Heiry Bakar, Mohd Aftar Abu Zakaria, Roslinazairimah School of Mathematical Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Malaysia Institute of Big Data Analytics and Artificial Intelligence Universiti Teknologi MARA Malaysia Department of Electrical and Computer Engineering Kuliyyah of Engineering International Islamic University Malaysia Department of Science Computer and Mathematics Universiti Teknologi MARA Penang Campus Malaysia Department of Mathematical Sciences Faculty of Science & Technology Universiti Kebangsaan Malaysia Centre for Mathematical Sciences Universiti Malaysia Pahang Al-Sultan Abdullah Malaysia

ISBN: (纸本)9798331530303

As artificial intelligence (AI) advances, it is essential to continuously comprehend its limitations to optimise the integration of AI into autonomous systems that empower humans. The first objective of this study is to highlight the key capabilities of both humans and AI, focusing on the differences and identifying limitations of AI from the literature. Through discrepancy analysis, this study uses a Venn diagram to visualise the remaining limitations of AI into five key domains: (i) emotional intelligence, (ii) consciousness and awareness, (iii) creative imagination, (iv) communication, and (v) ethical decision-making. Furthermore, this study employs thematic bibliometric analysis to provide a more detailed examination of each AI limitation as the second objective. This study has identified underdeveloped and emerging research themes with potential for future development, such as emotion recognition, human-computer interaction, digital health, and situational awareness, which may require further research. Additionally, this study commends the ongoing efforts to harness AI’s computational power and algorithmic innovations to enhance AI’s overall performance and applicability. ©2024 ieee.

关键词： Emotion Recognition

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CADET: Control-Aware Dynamic Edge computing for Real-Time Target Tracking in UAV systems 48

CADET: Control-Aware Dynamic Edge Computing for Real-Time Ta...

引用

48th ieee international conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Florenzan Reyes, Luis Felipe Smarra, Francesco D'Innocenzo, Alessandro Levorato, Marco University of l'Aquila Department of Information Engineering Computer Science and Mathematics AQ 67100 Italy University of California Department of Computer Science IrvineCA92697 United States

ISBN: (纸本)9781728163277

The autonomous operations of unmanned aerial vehicles (UAV) necessitate the real-time analysis of information-rich signals, such as camera and LiDAR feeds, where the analysis algorithms often take the form of extremely complex deep neural networks (DNN). The continuous execution of such models onboard the UAV imposes a considerable resource consumption (e.g., energy), while offloading the execution of the models to edge servers requires the transmission of the input signals over capacity constrained, time-varying, wireless channels. In this paper, we propose an innovative approach - CADET - to control where sensor signals are processed in the system. In addition to traditional features and measures, such as channel state, energy consumption and channel usage, CADET makes dynamic task routing decisions - local computing vs edge computing - based on the state of the flight controller. The proposed methodology is based on Markov jump switched linear systems, where an embedded filter predicts and controls the state of the joint motion/computing dynamics. © 2023 ieee.

关键词： Edge computing

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MACEdge: Real-Time Video Analytics Based on Multi-Access Collaborative Edge computing

MACEdge: Real-Time Video Analytics Based on Multi-Access Col...

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2023 ieee international conference on systems, Man, and Cybernetics, SMC 2023

作者： Zhong, Dian Zhu, Minghua Huang, Binbin East China Normal University Moe Engineering Research Center for Software/Hardware Co-Design Technology and Application Shanghai China

ISBN: (纸本)9798350337020

Video analysis typically requires a significant amount of computing resources and energy. Traditional cloud-based video analysis relies on concentrating computing resources in the cloud, which puts a tremendous load on network bandwidth and introduces poor user experience due to latency. Edge computing offers a solution by offloading a portion of the video analysis tasks to the edge, significantly reducing the pressure on bandwidth and latency. However, the fixed video configurations (resolution, frame rate, etc.) uploaded to the edge may not be optimal for specific situations. Moreover, using a low video configuration makes it difficult for edge video analysis systems to recognize environmental changes, while using a high video configuration poses challenges to latency and bandwidth. Therefore, in this paper, we propose the MACEdge-an edge video analysis framework by leveraging the multi-access capability of the edge gateway to enhance edge perception by accessing heterogeneous sensors. We offer an algorithm based on Q Learning for dynamic adaptive adjustment of sensor threshold and video configurations. In experiments using real-world data, our collaborative solution improved accuracy and reduced high bandwidth costs compared to other video configuration adjustment benchmarks. © 2023 ieee.

关键词： Edge Computation Multi-access Collaboration Video analysis

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Evaporation Parameter-Ant Colony Optimization (EP-ACO) Algorithm With Elam Neural Network (ENN) for Sentiment Analysis 2

Evaporation Parameter-Ant Colony Optimization (EP-ACO) Algor...

引用

2nd international conference on distributed computing and Optimization Techniques, ICDCOT 2024

作者： Malliga, L. Malathy, V. Ramappa, Srikanth Alsalami, Zaid Bala Dhandayuthapani, V. Electronics and Communication Engineering Department Telangana Hyderabad India Sr University Department of Ece Warangal India o9 Solutions Bengaluru India The Islamic University Najaf Iraq College of Computing and Information Sciences University of Technology and Applied Sciences Department of It Shinas Branch Oman

ISBN: (纸本)9798350382952

In recent times, online shopping is major stream that refers to users for purchasing and also consume along development of internet technology. The user satisfaction is maximized efficiently through performing Sentiment Analysis (SA) of huge amount of user reviews on e-commerce platforms. That is still difficult for predicting a correct sentiment polarities of user reviews due to modifications in sequence length, order of textual with difficult logic. In this research, proposed an Evaporation Parameter-Ant Colony Optimization (EP-ACO) algorithm with Elam Neural Network (ENN) for sentiment analysis of amazon product reviews. The reviews from amazon platform are taken for sentiment analysis and then the pre-processing and word embedding process are performed. The features are selected by using EP-ACO algorithm which selects the relevant features for sentiment analysis. Then the sentiment analysis id performed by ENN network, that classifies the reviews as good or bad. Proposed technique obtained 98.26% accuracy, 97.73% precision, 97.03% recall and 97.41% f1-score that is better than previous methods like Convolutional Neural Network (CNN) - Long Short-Term Memory (LSTM). © 2024 ieee.

关键词： Ant colony optimization

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Blockchain Simulators: A Systematic Mapping Study

Blockchain Simulators: A Systematic Mapping Study

引用

ieee international conference on Services computing (ieee SCC)

作者： Albshri, Adel Alzubaidi, Ali Awaji, Bakri Solaiman, Ellis Newcastle Univ Sch Comp Newcastle Upon Tyne Tyne & Wear England Univ Jeddah Jeddah Saudi Arabia Umm Al Qura Univ Jeddah Saudi Arabia Najran Univ Najran Saudi Arabia

ISBN: (数字)9781665481465

ISBN: (纸本)9781665481465

Recently, distributed ledger technologies like blockchain have been proliferating and have attracted interest from the academic community, government, and industry. A wide range of blockchain solutions has been introduced, such as Bitcoin, Ethereum, and Hyperledger technologies in the literature. However, tools for evaluating these solutions and their applications are still lacking, limiting the exploration of their potentiality and associated challenges/limitations. That is, experimenting with real blockchain networks usually requires a solid budget;and thus, sophisticated blockchain simulators can facilitate designing and evaluating solutions before the actual implementation stage. The quality of such simulators depends on several factors such as usability, reliability, provided capabilities, and supported features. This paper aims to provide a systemic mapping review of blockchain simulators focusing on these quality factors. This paper also sheds light on the configuration parameters (inputs) and produced metrics (outputs) supported by each simulator. Furthermore, it investigates which metrics supported by each simulator are scientifically validated/evaluated. Moreover, code quality comparison is carried out to assess the source code of the covered simulators. The results reveal that no simulator fully covers the wide operational range of features and capabilities of existing blockchain technologies. However, several promising efforts exist in the domain of blockchain simulation with interesting and useful features. Finally, we discuss the subject of blockchain simulation and provide our insight into the matter.

关键词： Blockchain distributed Ledger Technology Simulation Performance Systematic Review

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FedWNS: Data Distribution-Wise Node Selection in Federated Learning via Reinforcement Learning 26

FedWNS: Data Distribution-Wise Node Selection in Federated L...

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26th international conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Tu, Chengwu Zhao, Shengjie Deng, Hao Tongji University School of Software Engineering Shanghai201800 China Key Laboratory of Embedded System and Service Computing Shanghai201800 China Engineering Research Center of Key Software Technologies for Smart City Perception and Planning Ministry of Education Shanghai201800 China

ISBN: (纸本)9798350331684

To deal with the discrepancy between global and local objectives in the federated learning invoked by the non-independent, identically distributed (non-IID) data and mitigate the impact of catastrophic forgetting in the training phase, we propose a federated learning framework with data distribution-wise reinforcement learning to perform node selection to accelerate the convergence process and alleviate the accuracy degradation. In this framework, the agent on the central server observes the number of samples every node owns, the derived distribution information of every dataset, and the current local and global accuracy. Then infer the selected node-set to participate in the current federated learning round through policy network in reinforcement learning. Finally, we conduct simulations with publicly data sets. Simulation results indicate that our FedWNS outperforms the existing FedAvg and CSFedAvg on the testing accuracy and the communication rounds to reach target accuracy under different settings. © 2023 ieee.

关键词： Reinforcement learning

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Brain Tumor Detection and Classification using Vision Transformer (ViT) 3

Brain Tumor Detection and Classification using Vision Transf...

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3rd international conference on Automation, computing and Renewable systems, ICACRS 2024

作者： Sundar, G. Naveen Narmadha, D. Jerry, Nivin Abraham Thangavel, Senthil Kumar Shanmugam, Selvanayaki Kolandapalayam Ajibesin, Adeyemi Abel Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Computer Science and Engineering Amrita School of Computing Coimbatore India Department of Mathematics and Computer Science Ashland University United States Department of Information Technology Cape Peninsula University of Technology South Africa

ISBN: (纸本)9798331532420

TThis study uses the Vision Transformer (ViT) architecture to present a sophisticated approach for brain tumor identification and classification. ViT models are assessed based on their capacity to extract global contextual information from MRI images. With an average accuracy of 97%, the refined ViT models surpass traditional CNN-based models on a dataset of brain tumor MRI images classified into glioma, meningioma, pituitary, and no-tumor classes. This study shows how ViT models can increase diagnostic precision, which makes them a good substitute for actual medical imaging applications. © 2024 ieee.

关键词： Brain mapping

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