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检索条件"任意字段=2023 International Conference on Algorithms, Computing and Data Processing, ACDP 2023"

共 6952 条记录，以下是141-150 订阅

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23rd international conference on algorithms and Architectures for Parallel processing, ICA3PP 2023

23rd International Conference on Algorithms and Architecture...

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23rd international conference on algorithms and Architectures for Parallel processing, ICA3PP 2023

ISBN: (纸本)9789819708109

The proceedings contain 165 papers. The special focus in this conference is on algorithms and Architectures for Parallel processing. The topics include: MDCF: Multiple Dynamic Cuckoo Filters for LSM-Tree;a Game Theory Based Task Offloading Scheme for Maximizing Social Welfare in Edge computing;research on Dos Attack Simulation and Detection in Low-Orbit Satellite Network;malware Detection Method Based on Visualization;TOC: Joint Task Offloading and Computation Reuse in Vehicular Edge computing;Distributed Task Offloading for IoAV Using DDP-DQN;a Task Offloading and Resource Allocation Optimization Method in End-Edge-Cloud Orchestrated computing;multi-agent Cooperative Intrusion Detection Based on Generative data Augmentation;an Uncertainty-Aware Auction Mechanism for Federated Learning;Long Short-Term Deterministic Policy Gradient for Joint Optimization of Computational Offloading and Resource Allocation in MEC;query Optimization Mechanism for Blockchain-Based Efficient data Traceability;research on the Evolution Path of Network Hotspot Events Based on the Event Evolutionary Graph;Task Offloading in UAV-Assisted Vehicular Edge computing Networks;path Planning of Coastal Ships Based on Improved Hybrid A-Star;data Augmentation Method Based on Partial Noise Diffusion Strategy for One-Class Defect Detection Task;K Asynchronous Federated Learning with Cosine Similarity Based Aggregation on Non-IID data;MPQUIC Transmission Control Strategy for SDN-Based Satellite Network;an Energy Prediction Method for Energy Harvesting Wireless Sensor with Dynamically Adjusting Weight Factor;Bayesian Optimization for Auto-tuning Convolution Neural Network on GPU;persistent Sketch: A Memory-Efficient and Robust Algorithm for Finding Top-k Persistent Flows;faCa: Fast Aware and Competition-Avoided Balancing for data Center Network;Optimizing GNN Inference processing on Very Long Vector Processor;GDTM: Gaussian Differential Trust Mechanism for Optimal Recommender System;privacy-Enhanced Dyna

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23rd international conference on algorithms and Architectures for Parallel processing, ICA3PP 2023

23rd International Conference on Algorithms and Architecture...

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23rd international conference on algorithms and Architectures for Parallel processing, ICA3PP 2023

ISBN: (纸本)9789819708581

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SSF-EDZL Scheduling Algorithm on Heterogeneous Multiprocessors 22

SSF-EDZL Scheduling Algorithm on Heterogeneous Multiprocesso...

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IEEE 22nd international conference on Trust, Security and Privacy in computing and Communications (TrustCom) / BigdataSE conference / CSE conference / EUC conference / ISCI conference

作者： Wu, Peng Han, Chengzhuo Yan, Tao Chen, Lu Guo, Tianhao Li, Zhi Shanxi Univ Inst Big Data Sci & Ind Taiyuan 030006 Peoples R China Shanxi Univ Sch Comp & Informat Technol Taiyuan 030006 Peoples R China Shanxi Univ Coll Phys & Elect Engn Taiyuan 030006 Peoples R China Shanxi Univ Sch Comp & Informat Technol Taiyuan 030006 Peoples R China

ISBN: (纸本)9798350381993;9798350382006

With the increasing demand for high performance and low power consumption in embedded real-time systems, performance asymmetric multiprocessor architecture is beginning to be applied to embedded real-time systems, because this heterogeneous architecture allows the system to allocate computational resources as needed to satisfy the demands of each application and dynamic workloads. Most of the previous studies on EDZL scheduling algorithms have focused on task prioritization, and they simply assign the highest priority tasks to the fastest processors. In this paper, we choose to assign the highest priority tasks on the slowest processor and propose SSF-EDZL (slowest speed fit for earliest deadline first until zerolaxity) scheduling algorithm. We compare SSF-EDZL scheduling algorithm with other scheduling algorithms and the experimental results show that SSF-EDZL is a better scheduling algorithm which has higher processor utilization and better scheduling performance.

关键词： heterogeneous multiprocessor SSF-EDZL scheduling algorithm processor utilization

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Optimizing international Talent database Management Using Artificial Intelligence algorithms

Optimizing International Talent Database Management Using Ar...

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2023 international conference on Internet of Things, Robotics and Distributed computing, ICIRDC 2023

作者： Wei, Wei Nanjing Institute of Tourism and Hospitality Jiangsu Nanjing211100 China

ISBN: (纸本)9798350375275

The international talent database plays a crucial role in modern globalized society, but its management faces many challenges, including issues such as large data volume, data quality, data security, and query efficiency. This article provides a detailed introduction to the AI (Artificial Intelligence) technology used, including machine learning, data mining, and automated query optimization. These technologies are applied to solve various problems in database management, such as data cleaning, anomaly detection, automated security, and performance optimization. Through the application of machine learning algorithms, we can automatically identify and correct data quality issues, improving the accuracy and completeness of data. In the experimental and results section, we described the design of the experiment and the analysis of the results. We validated the effectiveness of the proposed method through practical cases and simulated datasets. The experimental results show that AI algorithms can significantly improve the efficiency and effectiveness of international talent database management, reduce data errors, improve data security, and accelerate query speed. © 2023 IEEE.

关键词： database systems

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Application Analysis and Development Strategy of Cloud computing Technology in Computer data processing 2

Application Analysis and Development Strategy of Cloud Compu...

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2nd IEEE international conference on Distributed computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Lina, Liu Changchun Humanities and Sciences College China

ISBN: (纸本)9798350347456

In the past, data processing was completed on a computer or a server. This is because the resources available at that time were limited. However, with the progress of technology, it is possible to use multiple computers and servers to process large amounts of data. This is where cloud computing comes into play. Cloud computing allows users to access data from anywhere at any time using an Internet connection. It also reduces the cost of enterprises because they do not have to buy additional hardware or software for each user. Aiming at the problems of single scale of traditional computer data processing and poor degree of adaptive parallel computing, this paper proposes a multi-source information accelerated data processing method of cloud computing technology, analyzes the application of cloud computing technology in computer data processing, and adopts the dynamic optimization technology of cloud computing speed to build a dynamic adjustment model of multi-source information data flow application of cloud computing, so as to reduce the energy cost of cloud computing, Combined with the experience of data processing users, the model can support multi-user computing entity sharing in the cloud to improve the running efficiency of data flow applications and reduce the energy cost of computer data processing. This article first outlines the basic meaning of cloud computing technology, starting with providing conditions for data transmission and sharing, and highlighting the application value of cloud computing technology. It analyzes the trend of cloud computing technology, and combines the modeling of cloud computing technology in computer data processing to propose application countermeasures for cloud computing technology in computer data processing. © 2023 IEEE.

关键词： Cloud computing

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Classification of Indicators Based on Morbidity and Mortality

Classification of Indicators Based on Morbidity and Mortalit...

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2023 international conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Deepak Kumar, A. Jayakumar, Shruti Balamurali, A. Mithun, P. Janagiraman, S. Rajesh Kanna, R. St. Joseph's Institute of Technology Department of Computer Science and Engineering Chennai India SRM Institute of Science and Technology Department of Computer Science and Engineering Chennai Ramapuram India

ISBN: (纸本)9798350319200

In our evolving world, human vulnerability to diseases remains a critical concern, with an overwhelming volume of health data generated by applications. Managing this massive and complex big data requires advanced processing methods. This is particularly evident in medicine, where predicting outcomes is vital for patient care [15]. The intricate and variable nature of healthcare data presents challenges, necessitating the development of sophisticated algorithms and tools. These advancements have the potential to revolutionize disease prevention, personalized treatment, and healthcare [17] system optimization. Enhancing analytical capabilities is urgent, enabling healthcare professionals to uncover valuable patterns that improve patient care [8]. Efforts are underway to efficiently analyze medical big data [11], promising a brighter future for healthcare. Ultimately, of all the algorithms used, Decision Tree analysis proves to be the most effective approach after enhancing using bagging classifier. © 2023 IEEE.

关键词： Bagging Classifier Big data Decision Tree Machine Learning

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Towards a Benchmark for Fog data processing 11

Towards a Benchmark for Fog Data Processing

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11th IEEE international conference on Cloud Engineering (IC2E)

作者： Pfandzelter, Tobias Bermbach, David Tech Univ Berlin Berlin Germany Einstein Ctr Digital Future Mobile Cloud Comp Res Grp Berlin Germany

ISBN: (纸本)9798350343946

Fog data processing systems provide key abstractions to manage data and event processing in the geo-distributed and heterogeneous fog environment. The lack of standardized benchmarks for such systems, however, hinders their development and deployment, as different approaches cannot be compared quantitatively. Existing cloud data benchmarks are inadequate for fog computing, as their focus on workload specification ignores the tight integration of application and infrastructure inherent in fog computing. In this paper, we outline an approach to a fog-native data processing benchmark that combines workload specifications with infrastructure specifications. This holistic approach allows researchers and engineers to quantify how a software approach performs for a given workload on given infrastructure. Further, by basing our benchmark in a realistic IoT sensor network scenario, we can combine paradigms such as low-latency event processing, machine learning inference, and offline data analytics, and analyze the performance impact of their interplay in a fog data processing system.

关键词： fog computing internet of things performance benchmarking

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Topological data Analysis in Natural Language processing – A Tutorial 36th

Topological Data Analysis in Natural Language Processing – ...

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36th international Florida Artificial Intelligence Research Society conference, FLAIRS-36 2023

作者： Zadrozny, Wlodek College of Computing University of North Carolina Charlotte United States School of Data Science University of North Carolina Charlotte United States

Topological data Analysis (TDA) introduces methods that capture the underlying structure of shapes in data. Within the last two decades, TDA has been mostly examined in unsupervised machine learning tasks. TDA has been often considered an alternative to the conventional algorithms due to its capability to deal with highdimensional data. in different tasks including but not limited to clustering, This tutorial will focus on applications of topological data analysis to text data. © 2023 by the authors. All rights reserved.

关键词： Natural language processing systems

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Image Encryption Based On Chaotic Scrambling And DNA Sequence Multi-rule Coding 8

Image Encryption Based On Chaotic Scrambling And DNA Sequenc...

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8th international conference on Intelligent computing and Signal processing, ICSP 2023

作者： Chen, Yunyu Zhang, Yuan China Telecom Research Institute College of Big Data and Artificial Intelligence Shanghai China Zhejiang University College of Information Science and Electronic Engineering China China Telecom Research Institute College of Big Data and Artificial Intelligence Hangzhou China

ISBN: (纸本)9798350302455

An image encryption algorithm based on chaotic scrambling and DNA sequence multi-rule coding is proposed. Through the research of related algorithms, the original image and the chaotic sequence generated by Logistic chaos are encoded, fused and decoded by using the multi-rule coding principle of DNA sequence, and the pixel value is changed to encrypt. Then, the PWLCM algorithm is used to generate new chaos to scramble the pixel blocks of the image, so as to obtain the encrypted image. The experimental results show that the algorithm not only improves the encryption efficiency of the traditional encryption algorithm, increases the key space, but also resists the exhaustive and statistical attacks. © 2023 IEEE.

关键词： DNA sequences

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Use of Big data Technologies for Credit Card Fraud Prediction 2

Use of Big Data Technologies for Credit Card Fraud Predictio...

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2nd international conference on Sustainable computing and data Communication Systems, ICSCDS 2023

作者： Krishna, K. Gayathri Kulkarni, Prasanna Natraj, N.A. Lavale Pune India

ISBN: (纸本)9781665491990

Globalization has led to an increased usage of credit cards as the mode of payment for various kinds of transactions, both online as well as offline. Just as a coin has two sides, Globalization also has another side to it - increased affinity of the Banking industry to fall prey to fraud. The main objective of this paper is to study the best methodology to predict credit card fraud accurately. By utilizing big data technologies and Machine learning algorithms (Logistic Regression, Decision Tree, Random Forest, etc.), fraudulent transactions in credit card operations can be predicted in advance to mitigate further risk. This paper outlines the best pre-processing practices for accurate results of credit card fraud prediction. The secondary mode of Qualitative research is chosen for this research paper. This study would benefit the banking industry, credit card companies, and academicians by helping them understand how algorithms can be refined to detect and mitigate fraudulent credit card practices. © 2023 IEEE.

关键词： data Analytics

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