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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191-200 订阅

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Securing data using Cryptography 1

Securing Data using Cryptography

引用

1st IEEE international conference on Contemporary computing and Communications, InC4 2023

作者： Pemmaiah, U.T. Devi, A. REVA University School of Computer Science and Applications Bengaluru India

ISBN: (纸本)9798350335774

In the information world, every piece of information travels through unidentified channels. Today, there are numerous simple techniques to hack data transferred to a destination. To keep the data protected from unknown third parties, it is encrypted and decrypted using the cryptography principle to keep it secure. There are many different algorithms that have been developed in cryptography, and numerous attacks render them vulnerable. The RSA algorithm is one of the best-known asymmetric cryptography algorithms. The different data security algorithms and data security in the area of blockchain applications are discussed here. Encryption time, decryption time, and other various data security characteristics have also been examined in a number of articles and discussed. © 2023 IEEE.

关键词： Cryptography

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Demo: GALOISim - Simulating On -The -Edge processing of Distributed Stream Queries 18

Demo: GALOISim - Simulating On -The -Edge Processing of Dist...

引用

18th international conference on Distributed and Event-Based Systems (DEBS)

作者： Woehner, Felix Tirpitz, Liam May, Friedrich Geisler, Sandra Rhein Westfal TH Aachen Aachen Germany

ISBN: (纸本)9798400704437

With the increasing prevalence of IoT devices, efficiently using these resources to process data has become a topic of great relevance and the interest in systems for distributed data stream processing on heterogeneous edge hardware is rising. A core challenge in developing and evaluating those systems lies in the dynamic and heterogeneous environments that such systems need to operate in. With GALOISim, we present a simulation software to develop and test algorithms for operator placement and clustering in hierarchical, distributed environments. The application enables users to submit algorithms and simulate their behavior against user-defined topologies and queries and interactively explore the results.

关键词： data streams distributed systems edge processing clustering operator placement

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An Evaluation of AI-Driven data Mining Techniques for Large-Scale data processing 3

An Evaluation of AI-Driven Data Mining Techniques for Large-...

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3rd international conference on Smart Generation computing, Communication and Networking, SMART GENCON 2023

作者： Choudhary, Sheetal Khatoon, Naghma Singh, K. Ranjith Divya, K. Kumar, Santosh D. Gupta, Abhishek Kumar Himalayan University Department of Computer Science Itanagar India Usha Martin University Department of Coputing & Information Technology Ranchi India Karpagam Academy of Higher Education Department of Computer Science Coimbatore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Mangalayatan University Department of Computer Science Aligarh India

ISBN: (纸本)9798350319125

Information mining and synthetic intelligence (AI) have gained a lot interest in recent years in the discipline of large-scale statistics processing. AI-driven statistics mining strategies are especially effective for identifying beneficial styles and functions in huge datasets. This paper presents an assessment of several typically used AI-pushed information mining techniques for processing big datasets and compares their performance in phrases of pace, accuracy, and scalability. Not unusual algorithms including precept component evaluation and clustering analysis are evaluated and in comparison to recent advances in AI-driven records mining which includes deep mastering and associative statistics mining. The overall performance of every technique is measured throughout 3 categories: speed, accuracy, and scalability. The effects of the evaluation show that deep learning and associative information mining outperform the conventional strategies in terms of accuracy and scalability whilst they're fairly slower than the other techniques. Therefore, these algorithms can be more suitable for positive types of massive-scale statistics processing tasks that require excessive accuracy and scalability. © 2023 IEEE.

关键词： component evaluation learning processing scalability

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13th international conference on Frontier computing, FC 2023

13th International Conference on Frontier Computing, FC 2023

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13th international conference on Frontier computing, FC 2023

ISBN: (纸本)9789819994151

The proceedings contain 180 papers. The special focus in this conference is on Frontier computing. The topics include: Optimization of English Machine Translation Model Based on Neural Network;exploration on Text Detection Optimization Algorithm Based on Neural Network Technology;CO2 Emission Prediction of Vehicle Fuel Consumption Based on EMD-LSTM;application of data Fusion algorithms in the data processing of Intelligent Greenhouses;intelligent Film and Television Communication Optical Technology Based on Network New Media;design of Heterogeneous database Encryption and Decryption System Based on data Mining;research and Implementation of Music Recommendation System Based on Particle Swarm Algorithm;design of High-Speed Multi-channel data Transmission System Based on Single-Chip Microcomputer;application of Distributed database System in Financial Management;Research on the Development of Japanese MOOC System Based on BP Neural Algorithm;research on Default Risk Prediction of Listed Companies’ Green Credit Based on Deep Learning Algorithm;improvement of Cloud Platform Utilization Based on Evaluation and Optimization of computing Resource Runtime Surplus Capacity;design and Implementation of Operation and Maintenance Monitoring Platform Based on data Mining Algorithm;civil Aviation Logistics Transportation Route Optimization Algorithm Based on A+ Algorithm;design of Interactive Japanese Translation System Based on Feature Extraction Algorithm;a Preliminary Study on Low-Carbon City Planning Methods Supported by the Cluster Optimization Algorithm;object Detection algorithms in Embedded Systems and their Applications;Application of Computer VR Technology in Digital Media System Design;discuss the Technical Application of Cloud data Center and Cloud Management Platform;Design of ARM-Based Program Automatic Shelling data Acquisition System.

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A Literature Review on AWS-Based Cloud computing: A Case in South Korea

A Literature Review on AWS-Based Cloud Computing: A Case in ...

引用

IEEE international conference on Big data and Smart computing (BigComp)

作者： Lee, Byeongcheon Oh, Jinyeong Shon, Woojin Moon, Jihoon Soonchunhyang Univ Dept AI & Big Data Asan South Korea

ISBN: (纸本)9781665475785

A collection of information technology (IT) services known as "cloud computing" is offered to customers across a network on a subscription basis with the flexibility to scale up or down based on their service needs. The emergence of cloud computing technology, which has tremendous advantages, is one of the major advancements in recent times. Many computers and servers are specifically devoted to meeting the demands of businesses in a cloud computing system for internal communications. Users can access their services via an internet connection. Registered users have remote access to both hardware and software, thanks to the cloud service, which has made essential adjustments to how information is stored and made accessible. This paper investigates the use of Amazon Web Services (AWS) for big data processing and analytics in South Korea. We collected several domestic journal and conference papers that studied local cloud services based on AWS to introduce distributed systems and cloud computing technologies. This study can provide researchers with a compact version of the extensive AWS-based data processing literature and potential future insights. It can also provide stakeholders tailored services, information about cutting-edge solutions that can influence academics, and details about current research needs.

关键词： Amazon Web Services (AWS) big data processing cloud computing distributed system

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13th international conference on Frontier computing, FC 2023

13th International Conference on Frontier Computing, FC 2023

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13th international conference on Frontier computing, FC 2023

ISBN: (纸本)9789819995370

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13th international conference on Frontier computing, FC 2023

13th International Conference on Frontier Computing, FC 2023

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13th international conference on Frontier computing, FC 2023

ISBN: (纸本)9789819993413

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Convolutional Neural Networks For Eye Detection Trained With Manually And Automatically Generated Ground Truth data 25

Convolutional Neural Networks For Eye Detection Trained With...

引用

25th international Symposium on Symbolic and Numeric algorithms for Scientific computing (SYNASC)

作者： Valcan, Sorin Gaianu, Mihail West Univ Timisoara Dept Comp Sci Timisoara Romania Continental Automot Romania User Experience UX Timisoara Romania

ISBN: (纸本)9798350394139;9798350394122

Eye detection represents an essential facial feature to be detected in driver monitoring systems representing the basis for further processing for attention or drowsiness detection. The machine learning approach for infrared image vision problems comes with multiple obstacles like creating a recordings data set with drivers and labeling it to be able to train a model. In very strict areas like automotive or medical imaging machine learning approaches are still a big debate especially because of the black box that is represented by the model, meaning that a wrong detection is impossible to be predicted or explained. That's why the entire focus is shifted to control the data set and the labeling process with very hard manual effort. This paper presents the experimental results of training convolutional neural networks for eye detection in automotive industry using ground truth data obtained from an automatic labeling module. These results are compared with neural networks that were trained using the same data set but with labels created by manual human effort. This experiment shows that similar accuracy of convolutional neural network can be obtained in image vision problems without manual work for image labeling.

关键词： labeling automation infrared camera driver monitoring eye detection convolutional neural networks

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An Efficient Pre-Clusters Assessment Technique for Efficient data Partitions 2

An Efficient Pre-Clusters Assessment Technique for Efficient...

引用

2nd international conference on Edge computing and Applications, ICECAA 2023

作者： Prasad, K. Rajendra Sireesha, Vedururu Mohammed, Moulana Jeevitha, Kypa. Institute of Aeronautical Engineering Department of Cse Telangana Hyderabad India Gandhi Institute of Technology and Management Department of Cse Telangana Hyderabad India Koneru Lakshmaiah Education Foundation Department of Cse Andhra Pradesh Guntur India Malla Reddy College of Engineering for Women Department of It Telangana Hyderabad India

ISBN: (纸本)9798350347579

Massive information is being created over smartphones, IoT devices, and social media. Clusters are necessary for managing such unlabeled massive data for organizing the vast data into manageable data partitions. The survey is conducted on visual clustering methods for determining the number of clusters (also known as cluster tendency). These algorithms visually displayed the number of clusters as square-shaped dark blocks along the diagonal. Cluster tendency is depicted as a series of square, black blocks. The techniques, as mentioned earlier, perform admirably on small datasets. However, the clustering accuracy diminishes on large datasets and processing time increases. This paper proposed a system that improves upon the current state of the art in visual approaches to address this issue. Instead of using the traditional Euclidean distance to evaluate clustering, this work proposes a cosine metric for their visual algorithms. Experiments illustrating and contrasting the efficacy of proposed and existing visual clustering algorithms are carried out on benchmarked large datasets. © 2023 IEEE.

关键词： Large dataset

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13th international conference on Frontier computing, FC 2023

13th International Conference on Frontier Computing, FC 2023

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13th international conference on Frontier computing, FC 2023

ISBN: (纸本)9789819992980

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