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共 1358 条记录，以下是331-340 订阅

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A Comprehensive Review on data Partitioning and Sampling Techniques for Processing big data 3

A Comprehensive Review on Data Partitioning and Sampling Tec...

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3rd international conference on Power, Energy, Control and Transmission Systems, ICPECTS 2022

作者： Anupama, C.G. Lakshmi, C. Department of Computational Intelligence School of Computing SRMIST Tamilnadu Kattankulathur India

ISBN: (纸本)9781665462754

big data processing and analysis use shared-nothing computer clusters. Cluster computing relies heavily on data segmentation and sampling to boost the speed and scalability of large data computations. In this study, we provide a thorough review of sampling and data division approaches applicable to large data processing and analysis. Next, we'll go over some of the fundamentals of data partitioning, such as the difference between range partitioning, hash partitioning, and random partitioning. Then, we get into the standard data sampling techniques such stratified sampling, reservoir sampling, and simple random sampling and approaches suitable for clusters. Our proposal is to take both data partitioning and sampling into consideration simultaneously while processing large data sets in parallel environment. © 2022 IEEE.

关键词： Large dataset

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Research on Logistics big data Asset Management and data Mining Based on Particle Swarm Optimization

Research on Logistics Big Data Asset Management and Data Min...

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2022 international conference on Artificial intelligence and Autonomous Robot Systems, AIARS 2022

作者： Guo, Shengqing Yunnan University of Business Management Kunming650104 China

ISBN: (数字)9781665454575

ISBN: (纸本)9781665454575

The increasing progress of mobile Internet provides more opportunities for asset management technology, such as the rise of Internet of things, cloud computing and big data technology. In the field of logistics distribution, with the increasing transaction volume generated by mobile devices, the huge amount of data generated continuously also needs more and more efficient and practical data processing methods. In the field of logistics, logistics big data asset management has also become a new technical means to optimize logistics management and improve distribution efficiency. With the increasing progress of logistics informatization, the scale of logistics data increases geometrically. At present, many logistics information platforms have huge databases, but the data in them are lack of effective analysis and mining, which hides the law and value behind the data. Therefore, it is very necessary to establish a data mining (DM) system based on logistics information platform. According to the characteristics of large amount of cloud data and complex structure, this paper plans and implements a system that can manage the whole life cycle of data from collection to processing, then to cleaning, and finally to production, and capitalize these disordered data. © 2022 IEEE.

关键词： data mining

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A Health-Care Architecture Based on Energy-Efficient Cloud computing 7

A Health-Care Architecture Based on Energy-Efficient Cloud C...

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7th international conference on computing, Communication and Security, ICCCS 2022 and 2022 4th international conference on big data and Computational intelligence, ICBDCI 2022

作者： Ghildiyal, Sunil Joshi, Kapil Kanti, Puneet Memoria, Minakshi Singh, Ajay Gupta, Ashulekha Uttaranchal University Department of CSE UIT Dehradun India Uttaranchal University School of Applied and Life Sciences Dehradun India Department of Management Studies Dehradun India

ISBN: (纸本)9798350309904

With emerging advancements, the medical domain of health monitoring systems has a necessity to combine cloud computing and IoT technology for the current requirements. This study is concerned with designing IoT modules under the supervision of the Cloud computing technology connected with a server. The device is used to coordinate people and health care, people and sensors, gateway, and servers with a database. From the sensor point, health maintenance metrics like sugar level, blood pressure status, the oxygen level of the human body, and other parameters will be collected. The records will be transferred by a gateway to the health care server which is connected using the cloud technology. The corresponding gateway is used to receive instructions from the server by analyzing the healthcare metrics from the users. As the server is connected to the cloud technology, a user can transfer the records or receive instructions from the server either in their residential location or any outdoor area. For the efficiency of the system, a Cloud computing Health care Architecture (CCHA) is designed. Using this health indicating metrics will be transferred through any of the GSM techniques. Through the efficient integration of health care and cloud platform, the interconnection of servers, professional instruction from medical experts, and other value-added services may be added. The CPU usage is 42%, internal usage is 7.55 GHz, and memory utilization is 591.65 Mb. On the whole study of the system, CCHA works efficiently without any restart, crash, and other performancerelated issues. © 2022 IEEE.

关键词： Internet of things

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Visual Monitoring Method of Digital Computer Room Based on Digital Twin 2nd

Visual Monitoring Method of Digital Computer Room Based on D...

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2nd EAI international conference on Application of big data, Blockchain, and Internet of Things for Education Informatization, bigIoT-EDU 2022

作者： Chao, Huang Chen, Zhang Rongtao, Liao Liang, Dong Dangdang, Dai Yue, Guo Information and Communication Branch Huber EPC Hubei Wuhan430074 China

ISBN: (纸本)9783031239434

With the rapid development of the Internet of things, big data, artificial intelligence and other new generation information technologies, more and more information technologies are applied in actual production activities. The powerful computing and analysis capabilities of the new generation of information technology have rapidly promoted the digitalization and intelligence of the industry. Visual monitoring is used to monitor the status of the system. It is also called a visual inspection method, because it involves the use of a microscope, magnifying glass or binoculars and other visual tools to observe the system. The main purpose of this method is to obtain an overall view of all components and their functional relationships, so as to identify any fault conditions that may occur on them. Visual monitoring method can be applied to control room, machine room and other different fields, including many types of equipment, such as PLC (programmable logic controller), HMI (human machine interface), SCADA (monitoring and data acquisition) system. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： data acquisition

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Optimization of computing Power Network Routing Strategies based on Multi-Agent Soft Actor-Critic 9

Optimization of Computing Power Network Routing Strategies b...

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9th international conference on Intelligent computing and Signal Processing, ICSP 2024

作者： Chen, Zhongyue Zhang, Shicong Tang, Ying Xu, Mengke Wu, Xiaoqing Ye, Minchao Zhang, Zhaojuan Qi, Yaping Lu, Huijuan Huo, Wanli China Jiliang University Zhejiang Hangzhou China Novel Internet Exchange Zhejiang Hangzhou China Division of Ionizing Radiation Metrology Zhejiang Hangzhou China

ISBN: (纸本)9798350376548

Despite rapid developments in cloud computing, big data, and artificial intelligence, the computing Power Network, as a new type of information infrastructure supporting the digital transformation and upgrading of various industries, faces significant challenges in efficiently scheduling, and managing computing and network resources. This paper introduces an optimization method for the computingRoute algorithm based on the Multi-Agent Soft Actor-Critic algorithm, operating within a Software-Defined Networking (SDN) architecture to address the efficiency issues in data transmission within the computing power network. The method leverages collaboration among multiple intelligent agents and employs deep reinforcement learning to optimize routing paths. Experimental results indicate that the MASAC algorithm, compared to traditional Actor-Critic algorithms, shows a 43.63 % improvement in stable average reward values, demonstrating better learning efficiency and performance. Furthermore, the soft policy updates, entropy regularization, and the use of target networks in the MASAC algorithm enhance its stability, improving training efficiency and performance. In performance comparisons with other computing power network routing strategies, the proposed computingRoute solution minimizes computing power requirements while reducing latency by 39.06% compared to DRL-TE and 21.42% compared to SmartPath. However, computingRoute exhibits a slightly higher packet loss rate than SmartPath. © 2024 IEEE.

关键词： computing power

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The Numbers’ Sign Language Detection Based on Deep Learning 3

The Numbers’ Sign Language Detection Based on Deep Learning

引用

3rd international conference on Signal Image Processing and Communication, ICSIPC 2023

作者： Lian, Li Sheng, Tang School of Big Data and Artificial Intelligence Anhui Xinhua University Hefei China College of Computing and Information Technologies Philippines National University Manila Philippines

ISBN: (纸本)9781510670945

This paper builds an end-to-end custom object detection model that could translate Chinese numbers’ sign language in real time based on deep leaning. The main work of this paper is as follows, collect images for deep learning using OpenCV, label images for sign language detection using LabelImg, setup Tensorflow Object Detection pipeline configuration, and use transfer learning to train a deep learning model. At last, detect sign language in real time using OpenCV. © 2023 SPIE.

关键词： Object detection

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Critical Analysis of big data Applications using Functional Linguistics and Diversified Integration 1

Critical Analysis of Big Data Applications using Functional ...

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1st international conference on Applied Artificial intelligence and computing, ICAAIC 2022

作者： Kumar, M.Jogendra Srinivasa Rao, B. Kumar Reddy, R Vijaya Kumar, G.V.S. Raj Sai, N. Raghavendra Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering AP Vaddeswaram India Lakireddy Bali Reddy College of Engineering Department of It Mylavaram India Gitam Institute of Technology Gitam Deemed to Be University Department of Computer Science and Engineering Andhra Pradesh Vishakapatnam India

ISBN: (纸本)9781665497107

In recent times, the top-down use of big information data analysis and the mechanical manipulation of man-made intellectual abilities has provided the center with specialized means to advance the practical coordination of semantic structure. Potentially related to the great information research of artificial reasoning (AI), the various embodiments of utilitarian phonetics have routinely experienced problems in applications. As a result, it created critical difficulties for the far-reaching improvement of useful etymology. In this unique situation, ideas related to the improvement of useful phonetics and artificial reasoning are explained. Starting from this premise, the useful etymologies of innovative union and internal incorporation are analyzed with respect to the scenario of large-scale information AI research. In addition, in order to stimulate the useful advancement of phonetics inside and outside, this study makes some proposals, among which are the rationalization of a dispersed group climate, the promotion of a practical phase of information about the language, the assumption of models, modalities of equality of information and the transmission of an equitable preparation of the brain network. These ideas constitute an initial phase of hypothetical etymological analyzes and capacity for improvement. In addition, some ideas were made to advance the internal and external improvement of the utilitarian etymology, such as structuring a useful linguistic information phase, updating an adequate group climate, preparing the circulating brain network, assuming equal information patterns and modalities to provide a reference to skills development and hypothetical phonetic exploration. © 2022 IEEE.

关键词： big data

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Approximate Supplement-Based Neighborhood Rough Set Model in Incomplete Hybrid Information Systems 20th

Approximate Supplement-Based Neighborhood Rough Set Model in...

引用

20th Pacific Rim international conference on Artificial intelligence, PRICAI 2023

作者： Meng, Xiong Yang, Jilin Wu, Die Liu, Tang Department of Computer Science Sichuan Normal University Chengdu China Visual Computing and Virtual Reality Key Laboratory of Sichuan Province Sichuan Normal University Chengdu China

ISBN: (纸本)9789819970247

Incomplete hybrid information systems (IHISs) contain hybrid data (e.g., categorical data, numerical data) and incomplete data. With the development of big data, IHISs widely exist in various practical applications. Due to the heterogeneity of hybrid data and the complex semantics of incomplete data, effectively processing the IHIS has become a significant challenge. The established indiscernibility relations of the existing studies for dealing with IHIS over-amplify the uncertainty of missing values, which may achieve unsatisfactory results. In this paper, we propose an approximate supplement-based neighborhood rough set model (AS-NRSM) to deal with the data of IHISs. Specifically, we propose a method to approximate supplement missing values with known values or constructed interval values, and the original IHIS is becoming the constructed IHIS*. Then, we formulate a novel similarity function to construct the improved neighborhood tolerance relation and the corresponding neighborhood tolerance classes. Finally, we design two experiments on 5 UCI data sets by introducing three performance metrics. Experimental results illustrate that the proposed AS-NRSM has higher classification performance than the two representative models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Semantics

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Exploration of classroom teaching reform in higher vocational colleges from the perspective of metaverse

Exploration of classroom teaching reform in higher vocationa...

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2022 international conference on Cloud computing, big data and Internet of Things, 3CBIT 2022

作者： Fu, Wanying Pan, Jie Shanghai Publishing and Printing College Shanghai China

ISBN: (纸本)9781665492256

Based on the application review in the field of meta-universe teaching at home and abroad, this paper provides a new perspective for the reform of higher vocational classroom teaching based on the perspective of meta-universe, which is helpful to deepen the theoretical research of higher vocational classroom teaching reform and broaden the spatial dimension of traditional vocational teaching. It is proposed that the hybrid teaching mode empowered by the metaverse and multi-scenario integration will be the future development trend of classroom teaching in vocational colleges, innovate the talent training model, build diversified learning activities, and realize the digital transformation and intelligent upgrading of future vocational teaching. © 2022 IEEE.

关键词： artificial intelligence metaverse teaching

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In-Memory computing Architectures for big data and Machine Learning Applications 9th

In-Memory Computing Architectures for Big Data and Machine L...

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9th international conference, FDSE

作者： Snasel, Vaclav Tran Khanh Dang Pham, Phuong N. H. Kueng, Josef Kong, Lingping VSB Tech Univ Ostrava Fac Elect Engn & Comp Sci Ostrava Czech Republic Ho Chi Minh City Univ Food Ind HUFI Fac Informat Technol Ho Chi Minh City Vietnam Johannes Kepler Univ Linz Linz Austria

ISBN: (纸本)9789811980688;9789811980695

Traditional computing hardware is working to meet the extensive computational load presented by the rapidly growing Machine Learning (ML) and Artificial intelligence algorithms such as Deep Neural Networks and big data. In order to get hardware solutions to meet the low-latency and high-throughput computational needs of these algorithms, Non-Von Neumann computing architectures such as In-memory computing (IMC) have been extensively researched and experimented with over the last five years. This study analyses and reviews works designed to accelerate Machine Learning task. We investigate different architectural aspects and directions and provide our comparative evaluations. We further discuss IMC research's challenges and limitations and present possible directions.

关键词： In-memory computing Machining learning Deep neural network In-memory accelerator

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