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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201-210 订阅

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Importance of CURE Clustering Algorithm over K-Means Clustering Algorithm for Large data-set 3

Importance of CURE Clustering Algorithm over K-Means Cluster...

引用

3rd international conference on Secure Cyber computing and Communications, ICSCCC 2023

作者： Bharadwaj, Prakhar Gupta, Ragesh Gurjar, Ravi Singh, Anurag National Institute of Technology Delhi Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350300710

The method of finding natural groupings within multidimensional data based on some similarity metrics is known as data clustering. There are many clustering algorithms proposed over time, and many of them have gained considerable popularity due to the ease of use and efficiency of such algorithms. Although most of the algorithms perform well in many use cases, some are limited to specific types of data distributions. This paper aims to demonstrate the importance of Cure algorithm over the K-Means Algorithm with the help of a specified performance metric for large data sets. © 2023 IEEE.

关键词： K-means clustering

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Cloud Cryptography: A Cutting-Edge Method to Protect Your Cloud 6

Cloud Cryptography: A Cutting-Edge Method to Protect Your Cl...

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6th international conference on Contemporary computing and Informatics, IC3I 2023

作者： Aryan, Rishabh Bathla, Ruchika Vats, Satvik Arora, Ginni Amity Institute of Information Technology Amity University Uttar Pradesh Noida India Graphic Era Hill University Uttarakhand Dehradun India Manav Rachna International Institute of Research and Studies Haryana Faridabad India

ISBN: (纸本)9798350304480

Cloud computing has revolutionized the way data is stored, processed, and accessed. However, it also introduces new security challenges, making the protection of sensitive data a top priority for cloud service providers and users. Cryptographic techniques play a crucial role in ensuring data security in cloud computing environments. This abstract presents a study that investigates various cryptographic techniques employed to safeguard data in the cloud. This research aims to analyze different cryptographic techniques used in cloud computing, focusing on their effectiveness, performance, and suitability for different cloud deployment models. The study encompasses both symmetric and asymmetric encryption algorithms. The comparative analysis includes popular symmetric key algorithms such as Advanced Encryption Standard (AES), data Encryption Standard (DES), Triple DES, Twofish and Blowfish, as well as widely adopted asymmetric key algorithms such as RSA. © 2023 IEEE.

关键词： Cloud computing Cloud Cryptography Cloud Security Cryptographic algorithms Cryptography

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Parallel and Distributed data Series processing on Modern and Emerging Hardware 15th

Parallel and Distributed Data Series Processing on Modern an...

引用

15th international conference on Management of Digital EcoSystems (MEDES)

作者： Fatourou, Panagiota Inst Comp Sci Fdn Res & Technol Iraklion Greece Univ Crete Dept Comp Sci Iraklion Greece

ISBN: (纸本)9783031516429;9783031516436

This paper summarizes state-of-the-art results on data series processing with the emphasis on parallel and distributed data series indexes that exploit the computational power of modern computing platforms. The paper comprises a summary of the tutorial the author delivered at the 15th international conference on Management of Digital EcoSystems (MEDES'23).

关键词： data series Time series Indexing Similarity search Query answering Multi-core architectures Parallelization GPU processing Disk-based index In-memory index Distributed processing

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An Online Predicting Method for Product Quality Based on Energy Consumption data Using Random Forest 8

An Online Predicting Method for Product Quality Based on Ene...

引用

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

作者： Wang, Meilin He, Jiaxian Feng, Zhipeng Xiao, Wen Guangdong University of Technology School of Information Engineering Guangzhou China

ISBN: (纸本)9798350302455

An effective real-time product production quality assessment method is of great significance for production manufacturing workshops to increase production capacity and enhance enterprise competitiveness. In this study, a workshop industrial product production quality prediction method based on the DeepAR-Random Forest combined model is proposed based on industrial big data. This method first uses the autoregressive recurrent neural network (DeepAR) algorithm to predict real-time energy consumption of equipment, and then uses multi-source heterogeneous data fusion technology to model the production data of the manufacturing execution system (MES) and the predicted energy consumption data as data vectors. Based on Random Forest, the product quality condition is assessed online. Finally, based on data collected from the automotive engine valve production line, this study compares Random Forest and commonly used classification models like boosted regression trees and artificial neural networks, demonstrating that this design better solves the problem of product quality prediction, overcoming the limitations of human error and statistical lag in manually statistical product quality, and providing new ideas for the engineering application of subsequent artificial intelligence algorithms in production workshops. © 2023 IEEE.

关键词： Big data

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A data-Driven Analytical Framework for ESG-based Stock Investment Analytics using Machine Learning and Natural Language processing 12

A Data-Driven Analytical Framework for ESG-based Stock Inves...

引用

12th international conference on Networks, Communication and computing (ICNCC)

作者： Cao, Elliot Sun, Yu Issaquah High Sch Issaquah WA 98027 USA Calif State Polytech Univ Pomona Comp Sci Dept Pomona CA 91768 USA

ISBN: (纸本)9798400709265

This research explores the intricate relationship between sentiment analysis, stock market dynamics, and Environmental, Social, and Governance (ESG) based investment analytics, harnessing sentiment as a predictive tool for stock price movements. Leveraging Twitter data, Natural Language processing (NLP), TextBlob, and the scikit-learn RandomForestRegressor, in combination with machine learning algorithms, the study evaluates public sentiment's impact on stock prices, offering valuable insights to investors and risk managers. Moreover, the findings elucidate the potential to enhance ESG-based investment analytics by incorporating sentiment-derived insights into investment decision-making processes, which is particularly pertinent given the increasing market focus on sustainable investing. Experimental results unveil the potential of sentiment analysis in forecasting stock price changes and augmenting ESG investment strategies, underlining its utility as both a forecasting instrument and a risk management mechanism. However, the research also identifies challenges, including limitations of the Twitter API and the need for data refinement. Strategies to address these challenges are discussed, emphasizing the importance of diversifying data sources and enhancing data quality. This study advances our understanding of sentiment analysis in financial markets and its applicability to ESG-based investment analytics, offering data-driven guidance to navigate the complexities of the stock market landscape. Ultimately, it highlights the promising prospect of integrating social media sentiment analysis with machine learning for more informed stock market predictions, risk management, and sustainable investment strategy formulation.

关键词： Machine Learning Stock Analysis Natural Language processing Twitter API

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FedCime: An Efficient Federated Learning Approach For Clients in Mobile Edge computing 7

FedCime: An Efficient Federated Learning Approach For Client...

引用

7th IEEE international conference on Edge computing and Communications (IEEE EDGE) / IEEE World Congress on Services (SERVICES)

作者： Agbaje, Paul Anjum, Afia Talukder, Zahidur Islam, Mohammad Nwafor, Ebelechukwu Olufowobi, Habeeb Univ Texas Arlington Dept Comp Sci & Engn Arlington TX 76019 USA Villanova Univ Dept Comp Sci Villanova PA 19085 USA

ISBN: (纸本)9798350304831

Federated learning (FL) enables collaborative training of a global model using localized data from multiple devices. However, in resource-constrained mobile edge computing (MEC) environments, non-independent and identically distributed (non-IID) data generated by these devices poses challenges for traditional FL algorithms like Federated Averaging (FedAvg), leading to decreased accuracy of the global model. In addition, dynamic mobile networks with intermittent connectivity, dropouts, and high migration rates hinder the communication of model updates to the central server. To address these challenges, we present FedCime, a novel tier-based FL approach that selects high-utility mobile clients likely to complete training to replace migrating clients during the round of training. Our evaluation shows that FedCime is scalable and significantly improves training performance in terms of accuracy and computational efficiency compared to state-of-the-art FL algorithms.

关键词： Federated learning machine learning mobile edge computing data heterogeneity

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Storage and Retrieval of Water Conservancy Remote Sensing data in Cloud computing Environment

Storage and Retrieval of Water Conservancy Remote Sensing Da...

引用

2023 international conference on Computer Simulation and Modeling, Information Security, CSMIS 2023

作者： Wang, Dan Li, Zichen Liu, Jiangyue Guangdong Polytechnic of Water Resources and Electric Engineering Guangdong Guangzhou510635 China Urumqi Vocational University Training Center Urumqi830002 China

ISBN: (纸本)9798350343724

The traditional storage method of water conservancy remote sensing data usually uses local servers to store remote sensing data, so the storage capacity is limited. For large-scale remote sensing data sets, a large amount of physical storage equipment needs to be invested, and the cost is high. This article proposes the storage and retrieval of cloud computing environment, using experimental evaluation and performance analysis methods to conduct research by building a cloud computing platform and simulating the storage and retrieval process of water conservancy remote sensing data. The research conclusion shows that the storage speed under cloud computing is 0.23-2.9MB/S. The storage solution under cloud computing environment can improve the data storage speed, and the distributed storage and hardware optimization technology make data writing and reading operations more efficient. At the same time, retrieval solutions in a cloud computing environment can significantly improve data retrieval speed through parallel processing and optimization algorithms. Through the combined effect of these improved technologies, the throughput of water conservancy remote sensing data has been greatly improved. © 2023 IEEE.

关键词： Remote sensing

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Privacy Preservation and Secured data Storage on Cloud Using Encryption algorithms

Privacy Preservation and Secured Data Storage on Cloud Using...

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2023 international conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2023

作者： Kaur, Puninder Kaur, Rajwinder Kaur, Amandeep Sharma, Vidhu Kiran Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Ch Devil Al State Institute of Engineering and Technology Paniwala Mota Sirsa India

ISBN: (纸本)9798350344387

In the recent time, most of the crucial data are stored on the cloud due to ease of access and extra storing capacity. Cloud computing is an emerging technology that has a power of on-demand availability of computer resources, elasticity of digital data in terms of storage and easy access as per the user's requirement. Cloud computing creates an interaction between client and server by using cloud service model and deployment model. Therefore, security of such important data is mandatory. In this research articles the major issues while storing and accessing data on or from the cloud are discussed and the encryption algorithms such as DES, AES, RSA, Blowfish, and Homographic are discussed along with its security features. The Cryptographic algorithms are discussed as it provides more security to the user by using security keys. © 2023 IEEE.

关键词： Cloud computing Cloud Security Could Services Decryption Encryption Public key

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Benchmarking Onboard Science data Retrieval algorithms on the Snapdragon Platform

Benchmarking Onboard Science Data Retrieval Algorithms on th...

引用

IEEE Aerospace conference

作者： Lightholder, Jack Donitz, Benjamin Castillo-Rogez, Julie Sheldon, Douglas CALTECH Jet Prop Lab 4800 Oak Grove Dr Pasadena CA 91109 USA

ISBN: (纸本)9781665490320

The coming decade of planetary missions will have stringent data requirements. Optimizing limited bandwidth is a major motivation for the use of onboard science data analysis algorithms for deep space missions. Under the traditional mission operations paradigm, all data is sent to Earth before follow-on activities are planned by ground operators. This operational model creates challenges for time and bandwidth constrained mission events. The Near Earth Asteroid Scout (NEAScout) mission is a prime example of a mission with these challenges. NEAScout is an interplanetary CubeSat manifested on Artemis-1. The mission will use a solar sail to navigate to a Near Earth asteroid and perform flyby reconnaissance imaging. Vehicle size, configuration, trajectory and distance from Earth all impose bandwidth constraints. To minimize these limitations, the Project decided to move image processing capabilities onboard the spacecraft. This onboard science software enables image calibration, image coaddition, image subtraction, data compression, downsampling and cropping all onboard the spacecraft. The goal is to reduce the data downlink, without compromising science contents. NEAScout utilizes the SPHINX computing platform, whose processing performance is akin to the RAD750, but tailored for CubeSat resources. Although sufficient to enable these sorts of algorithms, the SPHINX takes multiple minutes per image, which diminishes the prospect of using image content for rapid response reasoning. This effort ported, characterized and benchmarked the performance of the NEAScout science software on the Snapdragon platform, a next-generation higher performing processor. Between 10x and 200x processing time speedup was achieved, depending on the image processing algorithm, with a 50x speedup for a notional image processing pipeline. This work highlights new performance baselines for onboard science data processing algorithms which missions could expect to achieve with modern computing hardw

关键词： data handling

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Deep Reinforcement Learning algorithms for Low Latency Edge computing Systems 3

Deep Reinforcement Learning algorithms for Low Latency Edge ...

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3rd international conference on Artificial Intelligence and Signal processing, AISP 2023

作者： Kumaran, K. Sasikala, E. SRM Institute of Science and Technology Chennai India Department of Information Technology Easwari Engineering College Chennai India SRM Institute of Science and Technology Kattankulathur Department of Data Science & Business Systems Chennai India

ISBN: (纸本)9798350320749

Nowadays, due to the increase of technological development in smart devices, more computational capabilities are needed with better performance. The maximization of the cloud computing resources used in mobile networks and Internet of Things makes good results. The data processing along with delay in the communication network will be easily handled by edge computing technology. The edge server brought as much as close to the end devices to avoid the frequent cloud access. In edge servers, the processing of the computational task is time consuming, which causes latency. The inference of deep learning along with reinforcement models makes the better resource allocation and task scheduling for complex systems. In this work, deep reinforcement models like Q learning, Double Q learning, Deep Q network, Double Deep Q learning has been compared. The developed Deep Reinforcement learning model has been deployed in the edge-based system. As a result, it is inferred that Double Deep Q learning is having the ability to make better decisions in resource allocation by maximizing the gains and rewards. The simulation result shows that Double Deep Q learning performs better with decreased latency and increases the performance of the edge systems. © 2023 IEEE.

关键词： Reinforcement learning

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