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

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Power Engineering Construction Management Information System Based on data Mining Algorithm

Power Engineering Construction Management Information System...

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2023 international conference on Networking, Informatics and computing, ICNETIC 2023

作者： Chunyi, Lu State Grid Jinhua Power Supply Company Jinhua321000 China

ISBN: (纸本)9798350313314

At present, my country's electric power enterprises are moving towards the stage of informatization and intelligent development, but there are still many problems in the process of electric power engineering construction management, such as the large amount of engineering construction business information, untimely processing, and lack of system in the process of electric power engineering construction. Management, etc. are common problems. How to effectively deal with the information of all aspects of the engineering construction project and make each department complete its own project management work is a key problem to be solved urgently for the managers of electric power enterprises. In this regard, this paper designs a power engineering construction management information system, introduces the decision tree algorithm of data mining technology to design the functional modules of the system, and builds a data warehouse on the basis of data mining technology. This paper uses the LoadRunner test tool to test the performance of the system to form a system evaluation and analysis report, and then realize the information management of the power engineering project by the system administrator through the login module. © 2023 IEEE.

关键词： data mining

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Methods for Assessment of Gait Asymmetry Based on data from Depth Sensors 12

Methods for Assessment of Gait Asymmetry Based on Data from ...

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12th IEEE international conference on Intelligent data Acquisition and Advanced computing Systems: Technology and Applications, IDAACS 2023

作者： Wagner, Jakub Warsaw University of Technology Institute of Radioelectronics and Multimedia Technology Faculty of Electronics and Information Technology Warsaw00-665 Poland

ISBN: (纸本)9798350358056

Six quantitative indicators of asymmetry of human gait, whose values can be estimated by processing data acquired using depth sensors, are considered. Procedures for the estimation of these indicators have been developed and tested using measurement data representative of typical gait and two types of asymmetric pathological gait. The results of the completed experiments indicate that among the considered indicators, the one based on the quasi-correlation between the speed of the feet allows for best differentiating among the three considered types of gait. © 2023 IEEE.

关键词： data processing depth sensor gait analysis gait asymmetry healthcare Kinect

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data GENERATION WITH STRUCTURE ENFORCING ADVERSARIAL LEARNING 30

DATA GENERATION WITH STRUCTURE ENFORCING ADVERSARIAL LEARNIN...

引用

30th IEEE international conference on Image processing (ICIP)

作者： Solomon, Indu Kumar, Uttam Jayavelu, Senthilnath Int Inst Informat Technol Bangalore Bangalore Karnataka India ASTAR Inst Infocomm Res I2R Singapore Singapore

ISBN: (纸本)9781728198354

Class imbalance issues are very common among real-world datasets. Traditional oversampling approaches are interpolation based and are not well suited for image datasets. These techniques lead to class overlapping and the generation of visually unappealing minority class images. Lately, Generative Adversarial Network (GAN)-based models are used widely for oversampling of image data;however, the learning bias towards the majority classes lead to generation of majority classes in excess and minority classes in rarity. Most of the existing oversampling techniques work on data space, whereas low-dimensional latent space for oversampling is less explored. To tackle these issues, we propose a novel latent space oversampling framework called Structure Enforcing Adversarial Learning (SEAL). In the proposed architecture, the generator is trained by additionally minimizing the structure loss. This boosts the generation of synthetic samples, which retain the covariance structure of each class. The proposed model is evaluated on four image datasets and is compared with the state-of-the-art methods.

关键词： Class imbalance oversampling generative models latent space structure loss

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Enhancing data Quality in Image Pre-processing: A Case Study on Plant Disease Classification

Enhancing Data Quality in Image Pre-processing: A Case Study...

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2023 international conference for Technological Engineering and its Applications in Sustainable Development, ICTEASD 2023

作者： Ong, Ge Ye Abdullah, Nurzulaikha Ridzuan, Fakhitah Mustafa, Wan Azani Alzubaidi, Laith H. INTI International College Penang School of Engineering and Technology Penang Malaysia Universiti Malaysia Kelantan Faculty of Data Science and Computing Kota Bharu Malaysia Universiti Malaysia Perlis Faculty of Electrical Engineering & Technology Perlis Arau02600 Malaysia The Islamic University Najaf Faculty of Engineering Iraq

ISBN: (纸本)9798350336474

data quality plays a vital role in image pre-processing for machine learning applications. The effectiveness of building accurate and reliable models lies in the high-quality of data. Prioritizing data quality in the initial stages of the image pre-processing pipeline lays a strong foundation for subsequent machine learning stages. Therefore, this research aims to identify the appropriate steps for image pre-processing and compare the performance of machine learning model based on different pre-processing approaches. In this study, we utilized a plant diseases dataset sourced from Kaggle, comprising approximately 87,000 RGB images of both healthy and diseased crop leaves, categorized into 38 distinct classes. To significantly enhance the image quality, we implemented a range of transformation techniques, including resizing, normalization, data augmentation, and cropping. The analysis clearly indicates that implementing comprehensive pre-processing techniques enhances data quality and improves the machine learning model's classification performance. © 2023 IEEE.

关键词： Machine learning

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Research on dynamic network security detection technology based on SDN 4

Research on dynamic network security detection technology ba...

引用

4th international conference on Network Communication and Information Security, ICNCIS 2024

作者： Zhao, Kai Beijing Polytechnic Beijing100176 China

ISBN: (数字)9781510688261

ISBN: (纸本)9781510688254

Traditional network architecture can no longer meet the development needs of technologies such as cloud computing and big data. SDN network architecture has the characteristics of high openness and programmability, which can quickly respond to changes in business requirements. SDN decouples the control management function of the network from the data forwarding function, simplifies network configuration work based on logic centralized controllers and open programming interfaces, thereby achieving a flat management mode and flexible data forwarding function. The centralized control and programmability of SDN networks also pose significant security threats to SDN. Based on the CSE-CIC-IDS2018 dataset, this article tests the performance of various intelligent learning algorithms, analyzes data flows, distinguishes normal data from abnormal data, improves detection accuracy, and has practical significance for the security defense of SDN networks. © 2025 SPIE.

关键词： Network security Computer security Network architectures Control systems Machine learning data transmission Defense and security Detection and tracking algorithms Information security Security technologies

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Research on shipping statistics method based on AIS big data mining analysis 4

Research on shipping statistics method based on AIS big data...

引用

4th international conference on Geology, Mapping, and Remote Sensing, ICGMRS 2023

作者： Zhao, Longfei Jiang, Xiaoyi Lu, Wenhu Kang, Linchong Wang, Yi Technology Innovation Center of Marine Information MNR Tianjin China National Marine Data and Information Service Tianjin China Key Laboratory of Urban Land Resources Monitoring and Simulation Ministry of Natural Resources Shenzhen China

ISBN: (纸本)9781510672741

In order to solve the problems of traditional shipping statistics, this paper puts forward the method of shipping statistics based on AIS big data, and gives complete technical process and technical scheme including big data platform construction, data access, data cleaning, data warehouse construction, navigation event analysis, voyage number mining and generation of statistical index and systematically applies the above processes and schemes. Firstly, based on the distributed storage and computing framework, the four-layer system architecture of data acquisition and processing layer, data storage layer, data analysis layer and service encapsulation layer is used to put forward the construction technology method of AIS big data platform, which meets the requirements of real-time AIS stream data acquisition, historical AIS file extraction, data cleaning, data storage management, data mining analysis, statistical analysis, visual display and application service;Secondly, AIS big data mining analysis model including electronic fence analysis, berth supplement, berth commodity identification, navigation event analysis, voyage number mining, voyage cargo capacity calculation and other model algorithms is constructed and integrated into the big data platform to meet the needs of AIS big data mining analysis and statistical analysis;Finally, taking the application of AIS data of global bulk cargo ships from March to May of 2019 as an example, it shows that this method can provide statistical indicators of shipping big data in three aspects including port, sea passage and bulk cargo, and provides a new idea for realizing real-time, accurate and refined statistical analysis and display of shipping. © 2024 COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Big data

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Big data Real-Time processing Architecture Based on Hadoop

Big Data Real-Time Processing Architecture Based on Hadoop

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2022 international conference on Artificial Intelligence, Virtual Reality, and Visualization, AIVRV 2022

作者： Cao, Yong Chongqing City Vocational College Chongqing Yongchuan402160 China

ISBN: (纸本)9781510662988

The data generated in the Internet age is increasing exponentially. Sometimes such a huge amount of data cannot be processed in time, and people cannot dig out useful information from it. In order to realize the efficient processing of massive data, this paper develops a big data real-time processing architecture based on the Hadoop platform, uses HBase as the database, and combines the C# programming language and the MapReduce programming mode to design a big data processing system, so that users can view and upload data through mobile devices. The data processing results of the cloud computing center. The performance test of MapReduce and various functional modules of the big data processing architecture is carried out. The test results show that MapReduce has certain advantages in processing big data, and the data processing time of each functional module increases with the increase of data volume. © 2023 SPIE. All rights reserved.

关键词： data handling

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Levenberg Marquardt Method for Optimized Clustering of data Mining 15th

Levenberg Marquardt Method for Optimized Clustering of Data ...

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15th international conference on Soft computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired computing, NaBIC 2023

作者： Baig, Maughal Ahmed Ali Madhusudhan, Mudimela Rasheeduddin, Sayyad Kumar, V. Sravani Sivaiah, Borra Venkateswara Rao, K. CMR Technical Campus Telangana Hyderabad India CMR Engineering College Telangana Hyderabad India CMR College of Engineering and Technology Telangana Hyderabad India

ISBN: (纸本)9783031810824

Rich data and inadequate knowledge can be effectively addressed with data mining technology. An essential component of data mining is cluster analysis, which includes concepts based on partition, hierarchy, density, grid, and model analysis. One common model-based clustering technique is the Levenberg-Marquardt method. It is an organic fusion of data mining and brain cognitive science. It is theoretically well grounded in our understanding of how the brain processes information. In order to optimize the network clustering data mining algorithm, this study employs the Levenberg Marquardt algorithm. Additionally, experiments are designed to validate the suggested Levenberg Marquardt method data mining clustering optimization algorithm. According to the experimental study findings presented in this paper, the Levenberg Marquardt data mining clustering optimization methods have mean values of 78.8 and 89.92 on four datasets, respectively, which are higher than the values of the other algorithms. The technique is also used in this research to examine the distribution of leftover oil. The cluster analysis of the flooding degree shows clear results that the algorithm has produced. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Clustering algorithms

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Application of Gaussian distribution-based resampling technique on imbalanced food dataset

Application of Gaussian distribution-based resampling techni...

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2023 international conference on algorithms, High Performance computing, and Artificial Intelligence, AHPCAI 2023

作者： Li, Tianle Sun, Lei Zuo, Enguang Chen, Chen College of Information Science and Engineering Xinjiang University Xinjiang Urumqi830046 China Xinjiang Uygur Autonomous Region Product Quality Supervision and Inspection Research Institute Urumqi China Xinjiang Key Laboratory of Cloud Computing Application Karamay China Xinjiang Aiqi Sider Testing Technology Company Limited Urumqi China

ISBN: (纸本)9781510671881

The imbalanced food dataset is one of the various imbalanced datasets that are commonly found in the real world. For imbalanced datasets, we usually want algorithms to identify minority class samples in the imbalanced datasets, but traditional classification algorithms often suffer from performance degradation, overfitting and other problems. To address these problems, we propose a method based on Gaussian distribution resampling combined with a machine learning classifier. The method generates new samples in the form of Gaussian distribution by metering minority class samples in the unbalanced dataset, and after selecting the appropriate anchor samples, a balanced dataset is obtained. Finally, the balanced dataset is classified by a classifier. By conducting experiments on two real imbalanced food datasets, our method outperformed the classification performance of mainstream resampling methods on both datasets, achieving the best AUC and G-mean values. The experimental results demonstrate that our method can effectively identify minority class samples in the imbalanced food dataset and has some application value in the food field. © 2023 SPIE. All rights reserved.

关键词： data mining

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30th international Symposium on String processing and Information Retrieval, SPIRE 2023

30th International Symposium on String Processing and Inform...

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30th international Symposium on String processing and Information Retrieval, SPIRE 2023

ISBN: (纸本)9783031439797

The proceedings contain 31 papers. The special focus in this conference is on international Symposium on String processing and Information Retrieval. The topics include: CAGE: Cache-Aware Graphlet Enumeration;Space-Time Trade-Offs for the LCP Array of Wheeler DFAs;computing All-vs-All MEMs in Grammar-Compressed Text;Sublinear Time Lempel-Ziv (LZ77) Factorization;new Advances in Rightmost Lempel-Ziv;engineering a Textbook Approach to Index Massive String Dictionaries;count-Min Sketch with Variable Number of Hash Functions: An Experimental Study;dynamic Compact Planar Embeddings;a Simple Grammar-Based Index for Finding Approximately Longest Common Substrings;on Suffix Tree Detection;On the Number of Factors in the LZ-End Factorization;Non-overlapping Indexing in BWT-Runs Bounded Space;efficient Parameterized Pattern Matching in Sublinear Space;largest Repetition Factorization of Fibonacci Words;string Covers of a Tree Revisited;compacting Massive Public Transport data;constant Time and Space Updates for the Sigma-Tau Problem;linear-Time Computation of Generalized Minimal Absent Words for Multiple Strings;frequency-Constrained Substring Complexity;chaining of Maximal Exact Matches in Graphs;optimally computing Compressed Indexing Arrays Based on the Compact Directed Acyclic Word Graph;algorithms and Hardness for the Longest Common Subsequence of Three Strings and Related Problems;binary Mixed-Digit data Compression Codes;evaluating Regular Path Queries on Compressed Adjacency Matrices;approximate Cartesian Tree Matching: An Approach Using Swaps;optimal Wheeler Language Recognition;approximation and Fixed Parameter algorithms for the Approximate Cover Problem;data Structures for SMEM-Finding in the PBWT;compressibility Measures for Two-Dimensional data.

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