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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81-90 订阅

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A review: Map-reduce (Hadoop) based data clustering for big data

A review: Map-reduce (Hadoop) based data clustering for big ...

引用

international conference on Artificial Intelligence, Blockchain, computing and Security, ICABCS 2023

作者： Srivastava, Mili Kansal, Hitesh Gautam, Aditi Shivani Ajay Kumar Garg Engineering Ghaziabad India Hi-tech Institute of Engineering College and Technology Ghaziabad India I.A.M.R Duhai Ghaziabad India SRM Institute of Science and Technology NCR Campus India

ISBN: (纸本)9781032669663

Clustering problems are becoming more challenging as the quantity of data and unstructured data handling complexities is increasing. Such big amount and complex data that cannot handle by two days DBMS tools is known as "Big data". processing time for data is directly y proportional to increase in data. To access (Read/write) big data efficiently data clustering is a good solution. Because of it differentiates between dissimilar data. Routine data clustering algorithms are considered as N P-Hard problem. To deal with such situation huge research is going on with respect to parallelization of resources and algorithms. Howes’er, parallelization gives the worst results when data is dependent on each other and dedicated setup is required. Alternative to Parallel architecture is a distributed environment and that can utilize remote system processing capacity y on the fly. Hadoop as a distributed environment and framework runs the same code on partitioned data, and finally gathers the result at one place. Hence, to solve the data clustering problems distributed environment is very helpful. data classification or data distribution in distinct classes has an obligation to reduce the dependency. The availability of similar data at a single point from a big data is known as data Clustering. To solve the data-clustering problem of big data distributing environment has proposed. This paper provides an in-depth info and future research idea about big data-clustering problems using distributed computing architecture. © 2024 The Author(s).

关键词： MapReduce

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Time-Sensitive data processing Strategy for Enhancing the Performance of BFT Consensus Mechanism in IoT Edge computing Environment 23

Time-Sensitive Data Processing Strategy for Enhancing the Pe...

引用

2nd international conference on algorithms, data Mining, and Information Technology, ADMIT 2023

作者： Qian, Cheng Tang, Wenzhong Wang, Yanyang School of Computer Science and Engineering Beihang University Beijing China

ISBN: (纸本)9798400707629

Applying the BFT consensus mechanism in the IoT edge computing environment effectively solves the problem of data consistency and trustworthiness. However, the existing BFT consensus mechanism needs to pay attention to the impact of the data processing flow on consensus performance, and the repetitive data processing operations lead to the waste of computing resources of consensus nodes. Therefore, in this paper, we analyze the data processing flow of the BFT consensus mechanism, propose a time-sensitive data processing strategy from the idea of reducing the number of consensus nodes involved in data processing, and select data processing nodes by taking data processing time as the optimization target to improve the resource utilization of consensus nodes while ensuring consensus reaching. Through simulation experiments, our proposed time-sensitive data processing strategy achieves the best latency and throughput performance in a simulated IoT edge computing environment, demonstrating that the time-sensitive data processing strategy can effectively improve the performance of the BFT consensus mechanism. © 2023 ACM.

关键词： Internet of things

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Privacy-Preserving Detection Method for Transmission Line Based on Edge Collaboration 22

Privacy-Preserving Detection Method for Transmission Line Ba...

引用

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

作者： Shi, Quan Deng, Kaiyuan Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Sichuan Peoples R China

ISBN: (纸本)9798350381993;9798350382006

Unmanned aerial vehicles (UAVs) are commonly used for edge collaborative computing in current transmission line object detection, where computationally intensive tasks generated by user nodes are offloaded to more powerful edge servers for processing. However, performing edge collaborative processing on transmission line image data may result in serious privacy breaches. To address this issue, we propose a secure single-stage detection model called SecYOLOv7 that preserves the privacy of object detecting. Based on secure multi-party computation (MPC), a series of secure computing protocols are designed for the collaborative execution of Secure Feature Contraction, Secure Bounding-Box Prediction and Secure Object Classification by two non-edge servers. Performance evaluation shows that both computational and communication overhead in this framework as well as calculation error significantly outperform existing works.

关键词： transmission line object detection privacy computing edge collaboration data security

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Machine Learning for Flood Susceptibility Mapping of the Chennai Floods of 2023

Machine Learning for Flood Susceptibility Mapping of the Che...

引用

2024 IEEE international conference on Signal processing, Informatics, Communication and Energy Systems, SPICES 2024

作者： Shah, Aditya Goutam Nakhuda, Mohammed Sahil Kumar, Spandan Vaibhav Vipin, M. Vincent, Shweta Kumar, Om Prakash Manipal Academy of Higher Education Manipal Institute of Technology Department of Information and Communication Technology Manipal576104 India Manipal Academy of Higher Education Manipal Institute of Technology Department of Mechatronics Manipal576104 India Manipal Academy of Higher Education Manipal Institute of Technology Department of Electronics and Communication Engineering Manipal576104 India

ISBN: (纸本)9798350376135

Rapid and precise urban flood forecasting is crucial for promptly implementing preventive actions. This article presents a review of the literature focusing on soft computing techniques and algorithms for predicting urban floods. We also present two famous machine learning algorithms viz., Random Forest (RF) and XGBoost to create a Flood Susceptibility Map (FSM) for the city of Chennai in the state of Tamil Nadu, India based on the historical data of the 2023 floods of Chennai. A decent classification accuracy of 65% and 70% has been achieved using our approach for both RF and XGBoost algorithms respectively. © 2024 IEEE.

关键词： Random forests

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Optimization of Natural Language processing algorithms Based on Computer Deep Learning in Big data Environment

Optimization of Natural Language Processing Algorithms Based...

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2023 international conference on Applied Physics and computing, ICAPC 2023

作者： Yao, Yuwei Guangdong Business and Technology University Guangdong Zhaoqing526020 China

ISBN: (纸本)9798350359244

This paper proposes an intelligent monitoring method based on Web semantics from three levels: data source, data acquisition layer, data processing layer and application layer. In addition, this project will also study the intelligent semantic monitoring method based on multidimensional attention mechanism. In this paper, the method of Internet based searcher is adopted to build a semantic-based database. This project takes semantic Web, deep network, dependency analysis and other existing semantic element networks as the research object. A new semantic analysis technique for semantic network based on CBOW word vector is studied. The network is composed of four parts: semantic unit, internal relation, external relation and feature property, and adopts the hierarchical cycle method to realize the complete semantic space expression from phrase to sentence to text. This method is combined with HMM, PCFG and other mathematical models to analyze the maximum likelihood estimation of EM method. The model can deal with semantic dependency and human-computer interaction in complex systems. © 2023 IEEE.

关键词： Big data

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Research and Application on Anomaly Detection of Dynamic Baseline Performance Time Series data Based on Machine Learning

Research and Application on Anomaly Detection of Dynamic Bas...

引用

2023 IEEE international conference on Image processing and Computer Applications, ICIPCA 2023

作者： Song, Ningxi Li, Wencui Liu, Boyu Zhang, Jing Niu, Binbin Jia, Jingli Henan Zhengzhou China State Grid Henan Electric Power Company Henan Zhengzhou China

ISBN: (纸本)9798350314670

Anomaly detection of dynamic baseline performance time series data is an important problem in data center and cloud computing. In order to solve this problem, an anomaly detection method based on machine learning for the time series data of dynamic baseline performance was proposed. Firstly, sliding window is used to extract data features, and a variety of machine learning algorithms are used for anomaly detection. At the same time, a method based on dynamic baseline is proposed, which can adjust the baseline adaptively and detect anomalies. Experimental results show that the proposed method can achieve good anomaly detection results in data center and cloud computing. © 2023 IEEE.

关键词： Anomaly detection

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A Deep Learning Approach for Bengali News Headline Categorization 14

A Deep Learning Approach for Bengali News Headline Categoriz...

引用

14th international conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Bandan, Sheikh Sadi Ahmed Sunve, Sabid Romel, Shaklian Mostak Daffodil International University Dept. of Cse Dhaka Bangladesh

ISBN: (纸本)9798350335095

A huge amount of information and data is now available in seconds through the internet. Due to the availability of internet in the world, as the amount of online news is increasing on the one hand, people are interested in reading news from online news portals i.e., Facebook, Twitter, WhatsApp, Telegram, Instagram, blogs etc. Along with the increase in the amount of digital data in news portals, the number of readers is also increasing, thus the need for data classification for digital data is also increasing day by day. There are various methods of data classification, such as machine learning, deep learning, etc., as well as various data mining algorithms. data is classified using these algorithms, so that people can make sense of the news just by reading the news headlines. Natural language processing methods are used to classify data in any language for such problems. In this paper, Bangla news is classified into 6 categories using deep learning algorithm. The categories are international, National, Sports, Entertainment, Politics and IT. In deep learning, BiLSTM and GRU algorithms have been used to classify. BiLSTM has an accuracy of 83.42% and GRU has an accuracy of 80.01%. BiLSTM has been found to have the highest accuracy among deep learning algorithms. © 2023 IEEE.

关键词： Learning algorithms

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Analysis of Artificial Intelligence Methods and algorithms for processing data as a Series of Signals 26

Analysis of Artificial Intelligence Methods and Algorithms f...

引用

26th international conference on Soft computing and Measurements, SCM 2023

作者： Belyaev, Pavel Y. Sheinman, Elena L. Kim, Iuliia V. Saint Petersburg Electrotechnical University LETI Saint Petersburg Russia

ISBN: (纸本)9798350322484

The paper deals with the issues of improving the accuracy when performing the task of classifying several signals, where the main task is to determine the class of an object based on the data of the time series of this object. Examples of such signals are ECG signals, sounds, vibrations, and others. To successfully solve the classification problem, it is important to select the appropriate method correctly and qualitatively prepare the data for model training, including pre-processing of data and selection of model parameters. This study includes a review of artificial intelligence methods in the field of data analysis based on several signals, including machine learning algorithms. The datasets used for the study are Sonar, Doppler, and Winnipeg. Based on the comparison of the studied methods, SVM, Random Forest, AdaBoost, KNN showed the highest accuracy. The average accuracy of the classification was 0.9. © 2023 IEEE.

关键词： Support vector machines

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Adaptive data Sampling with Dual-Scale Prediction in Deterministic Edge Network 16

Adaptive Data Sampling with Dual-Scale Prediction in Determi...

引用

international conferences on Internet of Things (iThings) / Green computing and Communications (GreenCom) / Cyber, Physical and Social computing (CPSCom) / Smart data, Smartdata and Congress on Cybermatics (Cybermatics)

作者： Li, Lei Zheng, Yiming Wang, Jinyi State Grid Hubei Elect Power Informat & Commun Co Wuhan Peoples R China Energy Internet Lab Wuhan Peoples R China

ISBN: (纸本)9798350309461

With the rapid increase of data traffic and real-time requirements for control-based services in the Electricity-Energy Internet, the information processing technologies of deterministic edge network has broad application prospects for real-time and high reliable transmission. However, the high dimensionality of sensing data, complex processing algorithms, and difficulty in learning in deterministic edge networks pose high challenges to sampling detection. Therefore, a sampling period adaptive adjustment algorithm with dual-scale prediction is proposed in this paper. Dual-scale prediction is utilized, to depict the short term trend of power equipment's operation data and the long term fluctuation of them. The proposed algorithm realizes data reconstruction for the missing time points, which can greatly reduce the amount of transmitted data, thus satisfying high-quality transmission. Extensive simulation results validate that this proposed algorithm can effectively reduce the data reconstruction error through edge sensing.

关键词： Electricity-Energy Internet data sampling dule-scale

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Pure Orientation Passive Localization Technique for UAV Based on Greedy Algorithm

Pure Orientation Passive Localization Technique for UAV Base...

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algorithms, computing and data processing (acdp), international conference on

作者： Mingrui Lin Junhuai Zhang Jiaxian Chen Xinhai Wu Maynooth International Engineering College Fuzhou University Fujian China College of Computer and Data Science Fuzhou University Fujian China

In recent years, UA V technology has been developing rapidly, the pure orientation passive positioning of UAVs in formation flight is a hot research topic. Based on the circular formation formatted by 10 UAVs, a “UAV three-point positioning” model is given to find the position coordinates of deviating UAVs using angle information; research found that 3 UAVs can achieve fuzzy positioning of UAVs when the UAV index is unknown, and at least 4 UAVs are needed to achieve accurate positioning. Based on the greedy algorithm, a model is established to find the optimal solution by multiple iterations using the “UA V three-point positioning” model, the MATLAB simulation result show that the error value is 10– 25 and basically remains constant after 7 iterations, which can be considered as optimal state, and the optimal adjustment solution is given. The theory that “if a convex polyhedron (convex polygon) formed by all known points has no symmetry plane (axis), the desired position can be uniquely determined by these points” is proved, base on this theory, general UA V location and position adjustment principle of any formation is given.

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