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

共 6956 条记录，以下是791-800 订阅

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Application of integrated energy data processing based on real-time stream processing platform 4

Application of integrated energy data processing based on re...

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4th international conference on Computer Science and Management Technology, ICCSMT 2023

作者： Jiao, Xinyuan Hou, Zheng Zhang, Mimi Feng, Dechao Cai, Zhencong State Grid Information and Telecommunication Group Co. Ltd. Beijing China Beijing Energy Group Co. Ltd. Beijing China

ISBN: (纸本)9798400709517

Integrated energy is an important component of the new power system, and its IoT components need to collect and process energy equipment measurement data in real time. In order to solve the problems of large batches of equipment measurement data, measurement data requiring multiple calculations based on the relationship between equipment, and timely data processing requirements, distributed real-time data synchronization and processing technology is introduced to build a real-time stream processing platform, thereby ensuring stable operation of the system and efficient use of computing resources. First, the key technologies of the real-time stream processing platform are introduced, then the design of the integrated energy data collection and processing architecture is explained, and finally the real-time stream processing platform is built and functionally verified. © 2023 ACM.

关键词： Computer architecture

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Fusing SynerGATE for Improved Anomaly Detection in Cloud data Centers

Fusing SynerGATE for Improved Anomaly Detection in Cloud Dat...

引用

international conference on Computational Science and Computational Intelligence (CSCI)

作者： Althoubi, Asaad Miami Univ Dept Comp Sci & Software Engn Oxford OH 45056 USA

ISBN: (数字)9798350361513

ISBN: (纸本)9798350361513;9798350372304

As cloud computing advances, safeguarding cloud data centers from security threats takes precedence. Enter SynerGATE, a hybrid intrusion detection system. SynerGATE combines LSTM and GRU networks, aiming to predict temporal patterns within network traffic. SynerGATE's core architecture features dual LSTM and GRU layers, trained using the cross-entropy loss function. These algorithms uncover long-term feature dependencies, with extracted features classified by the Adam classifier. This fusion provides a holistic understanding of network behavior, exposing intrusion attempts. Real-world experiments in live cloud data centers evaluated SynerGATE, using metrics like accuracy, precision, recall, and F1-score. Results highlight its ability to predict network patterns, swiftly detect traffic shifts, and identify evolving threats in dynamic conditions. SynerGATE excels in adapting to ever-evolving attack strategies, enhancing security in the cloud.

关键词： Intrusion Detection LSTM GRU Cloud Security Machine Learning

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Poster Abstract: Attentive Multimodal Learning on Sensor data using Hyperdimensional computing 22

Poster Abstract: Attentive Multimodal Learning on Sensor Dat...

引用

22nd ACM/IEEE international conference on Information processing in Sensor Networks, IPSN 2023

作者： Zhao, Quanling Yu, Xiaofan Rosing, Tajana Computer Science and Engineering University of California San Diego La Jolla CA United States

ISBN: (纸本)9798400701184

With the continuing advancement of ubiquitous computing and various sensor technologies, we are observing a massive population of multimodal sensors at the edge which posts significant challenges in fusing the data. In this poster we propose MultimodalHD, a novel Hyperdimensional computing (HD)-based design for learning from multimodal data on edge devices. We use HD to encode raw sensory data to high-dimensional low-precision hypervectors, after which the multimodal hypervectors are fed to an attentive fusion module for learning richer representations via inter-modality attention. Our experiments on multimodal time-series datasets show MultimodalHD to be highly efficient. MultimodalHD achieves 17x and 14x speedup in training time per epoch on HAR and MHEALTH datasets when comparing with state-of-the-art RNNs, while maintaining comparable accuracy performance. © 2023 Owner/Author.

关键词： Ubiquitous computing

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Automatic data Workflow and Disk Management Tool for the ALICE Experiment 19

Automatic Data Workflow and Disk Management Tool for the ALI...

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19th IEEE international conference on Intelligent Computer Communication and processing conference, ICCP 2023

作者： Suiu, Alice Florenta Grigoras, Costin Tapus, Nicolae Betev, Latchezar Politehnica University of Bucharest Bucharest Romania CERN Geneva Switzerland

ISBN: (纸本)9798350370355

CERN is an European research facility that operates the largest and most powerful particle accelerator in the world, called the Large Hadron Collider (LHC). The LHC hosts four large experiments studying proton-proton and heavy-ion collisions at high energies. One of the four experiments is ALICE, specifically designed to study the physics of heavy-ion collisions. The experiments collect hundreds of PB of data each year and require complex mechanisms to maintain and distribute these data over many different storage elements around the world on the Worldwide LHC computing Grid. This paper describes a data management system used by ALICE, which maintains the data workflow from the experimental apparatus to the primary 150 PB disk storage. The system operates in real time and fulfills stringent requirements for transfer speed, transfer control, and monitoring. It is also completely automatic and autonomous, with sophisticated fault detection along the entire data transfer and registration process. © 2023 IEEE.

关键词： data transfer

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A Case Study of Big data processing in the Cloud Environments with Insights into Advantages, Tools, and Techniques 14

A Case Study of Big Data Processing in the Cloud Environment...

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14th international conference on Cloud computing, data Science and Engineering, Confluence 2024

作者： Chowdary, Pullela Harish Thapar, Puneet Alyas, Arsalan Ahmed Singh, Urvashi Lovely Professional University Department of Computer Science and Engineering Punjab Phagwara India Information System Technologies Wilmington University United States

ISBN: (纸本)9798350344837

Every single day, users are increasing as the world has explored technology where data is producing more and more big data and cloud technologies are the reason behind the scene because big data and cloud computing are the booming pieces of knowledge currently with AI helpful for users and for other professional and practical purposes. Nowadays integration of technologies also been an emerging topic it helps in the reduction of disadvantages of particular technology to provide better solutions. This paper talks about what, and why big data and cloud technology in daily lives and where big data in cloud technology is used. This paper examines recently proposed frameworks/models where big data and cloud technologies are utilized together and reviewed in a detailed way from the year 2020 to 2023. What Advantages can be there in utilizing both big data and cloud together is also discussed. This study explains the most used big data analytical tools and widely used cloud platforms and performed a simple case study on two datasets (world data and disease symptoms dataset) to draw some insights into which Rapid Miner and Power BI are used where RF and Gradient Boosted Trees got more accuracy than other classification algorithms with 0.7 accuracy while using disease symptoms dataset and some visualizations are drawn with use of world data. © 2024 IEEE.

关键词： Trees (mathematics)

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Edge Intelligent-based data processing Method for High-frequency Information Acquisition in Low-voltage Substation Area 8

Edge Intelligent-based Data Processing Method for High-frequ...

引用

8th international conference on Energy System, Electricity, and Power, ESEP 2023

作者： Ma, Jian He, Junwen Lu, Tao Song, Peng Li, Qian Zhao, Wen Shang, Bing Guangdong Power Grid Co. Ltd. Metrology Center Guangdong China

ISBN: (数字)9781510679801

ISBN: (纸本)9781510679795

Aiming at the problem of large data Transmission delay and computation delay in data processing of High-frequency information acquisition in low-voltage substations area, the model of edge intelligent high-frequency information acquisition system is constructed, and a joint optimization method of task unloading strategy and transmission strategy based on mobile edge computing is proposed. With the goal of minimizing system delay, the task unloading and transmission strategy is optimized to achieve two-way rapid detection of multiple power lines, effectively improving system performance. © 2024 SPIE.

关键词： data handling

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Comparative Performance Analysis using Machine Learning for Churn Prediction in E-commerce 2

Comparative Performance Analysis using Machine Learning for ...

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2nd international conference on Automation, computing and Renewable Systems, ICACRS 2023

作者： Alexander, R. Nancy, Maria A. Aswini, E. Sarao, Parwaz Singh Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Chennai603203 India

ISBN: (纸本)9798350340235

New clients cost a business significantly more money in e-commerce than keeping its existing clients. Companies can boost consumer retention, which will result in more revenue and faster growth, by anticipating which customers will quit. There are several products and solutions in today's competitive industry. Because of this, most clients are accustomed to quickly switching from one brand to another and from one supplier to another in their search for the best possible product or item to fulfill their requirements. This problem, known as "client churn," affects e-commerce enterprises. Due to their ability to process large volumes of data and recognize complex patterns, machine learning algorithms have emerged as a powerful tool for predicting client churn in recent years. Using a publicly accessible dataset, the proposed model examines various machine learning methods for predicting customer churn in this study. Also, by using performance metrics, the proposed model compares how well different algorithms perform. © 2023 IEEE.

关键词： Customer churn prediction Customer Conduct data Frame E-Commerce Machine Learning

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data sharing in Cloud-Assisted IoT 8

Data sharing in Cloud-Assisted IoT

引用

8th IEEE international conference on Science, Technology, Engineering and Mathematics, ICONSTEM 2023

作者： Primya, T. Swetha, M. Ramya, V. Taanusri, S.R. Ridhanya, G. Sekar, Rajasekaran Arun KPR Institute of Engineering and Technology Department of Cse Coimbatore India

ISBN: (纸本)9798350347791

Smart gadgets can now communicate from close to a long distance with one another and with the Internet or cloud. Internet of things (IOT) brings a paradigm shift of employing low resource IOT smart system with cloud computing. However, by employing cloud computing, resource-constrained IoT smart devices can gain a number of advantages, Excluding the weight of data processing and storing the data on the network cloud. By implementing it on network edge offers more merits instead of using network cloud in contra to internet of things (IOT) applications which needs high data rates, mobility, and latency-sensitive real-time data processing. In this paper mainly focused on data transfers to cloud and IOT devices form smart data transfer. Here a suggestion that is authenticated search method to look for required information among one's personal or shared data on storage. At last, by evaluating processing time performance of the suggested scheme, outcomes that discussed in the paper, show that our strategy has a chance of working well in IoT applications. © 2023 IEEE.

关键词： Internet of things

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Big data Analysis in Astronomy by the Lemur Software 6

Big Data Analysis in Astronomy by the Lemur Software

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6th IEEE international conference on Information and Telecommunication Technologies and Radio Electronics, UkrMiCo 2023

作者： Khlamov, Sergii Savanevych, Vadym Briukhovetskyi, Olexander Trunova, Tetiana Kharkiv National University of Radio Electronics Department of Media Systems and Technologies Kharkiv Ukraine Kharkiv National University of Radio Electronics Department of Systems Engineering Kharkiv Ukraine Western Radio Technical Surveillance Center State Space Agency of Ukraine Mukachevo Ukraine

ISBN: (纸本)9798350348484

A very quick progress in information technologies as well as the telescope creation technologies produce a big volume of astronomical big data as well as high dimensional information. All such big data are needed to be analyzed and reviewed in time. But in astronomy such big data can be received in both in the real-time/online mode and from the historical archives. In this paper we have presented the several aspects of the analysis process of such big astronomical data. We have reviewed them and provided some global approaches for the big astronomical data analysis. Also, we have presented examples of sources for big data, different astronomical instruments, big telescopes, different data processing methods and algorithms that are used for the big data analysis process in astronomy. The paper describes an applying of the different data processing methods and algorithms adopted to the various types of astronomical information in scope of the Collection Light Technology (CoLiTec) project. During this research the Lemur software was developed for the online processing and analysis of the big volume of the astronomical big data. The especial OnLine data Analysis System (OLDAS) mode was created for resolving the different tasks during astronomical big data analysis and processing, like preparation, extracting, classification, preprocessing, clustering, data mining, transforming, image processing, etc. © 2023 IEEE.

关键词： data mining

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Relation of Energy Consumption in Green Cloud computing with Big data 15

Relation of Energy Consumption in Green Cloud Computing with...

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15th Annual IEEE Green Technologies conference (GreenTech)

作者： Faria dos Reis, Thiago Nelson Teixeira, Mario Meireles Soares Neto, Carlos de Salles Univ Fed Maranhao UFPI UFMA DCCMAPI Sao Luis Maranhao Brazil

ISBN: (纸本)9781665492874

Cloud computing is already a part of the daily lives of both people and companies and as such, there is a concern about how it impacts the environment. The union of these two realities provides an opportunity for the emergence of Green Cloud computing with new proposals, approaches, and metrics to make data centers more efficient, mainly in terms of energy, as well as to reduce CO2 emissions and its environmental impact. Knowing the metrics that can be used to measure the energy cost and environmental impact of data centers are fundamental in Green computing, this article studies the relationship between energy consumption and execution times, architecture, and costs of a cloud environment. Using simulators it was possible to see an improvement in energy efficiency above 44%, as well as to get a cost reduction of at least 17%, and a reduction of of 55% in the environmental impact from the emission of CO2. It is important to note that there was no impact on processing times, by using the adoption of algorithms for complex problems, in the scheduling of Virtual Machines.

关键词： Green Cloud computing Cloud Simulation Energy Efficiency

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