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2023 IEEE International Conference on Electrical, Automation and Computer Engineering, ICEACE 2023

作者： Yao, Yingzhu Tianjin University of Technology Tianjin China

ISBN: (纸本)9798350309614

The report provides an overview of the dataset and the models and algorithms applied in the project. Initially, we present a comprehensive review of the RFM model and clustering algorithms. Utilizing the online transaction data from a British e-retail company, we compile the data, apply the RFM model to extract user segmentation information, devise K-means algorithms, and ultimately leverage user behavior to efficiently meet user demands. This approach facilitates precise user targeting, minimizes marketing expenses, and maximizes overall returns. © 2023 IEEE.

关键词： clustering algorithm K-Means algorithm RFM Model user classification

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Optimal Design of Topological Structure for Mountainous Wind Farm Collection System 8

Optimal Design of Topological Structure for Mountainous Wind...

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8th International Conference on Power and Renewable Energy, ICPRE 2023

作者： Chen, Guoyu Gao, Heng Li, Ming Dalian University of Technology State Key Laboratory of Structural Analysis for Industrial Equipment Dalian China

ISBN: (纸本)9798350328813

In wind farm projects, the investment cost of the collection system is high, and optimizing the topological structure of the collection system can save a significant amount of cost. This paper addresses the optimization problem of the collection system in mountainous wind farms and proposes a feasible optimization method, including methods for acquiring geographical information data and establishing topographic maps, subdomain partitioning considering terrain and cable load flow impact, three-dimensional shortest path search method, and cost-optimal collection cable connection method. Case studies demonstrate that for mountainous wind farm projects with significant terrain influences, this method exhibits good optimization performance. © 2023 IEEE.

关键词： A-star algorithm clustering algorithm collection system minimum cost tree algorithm mountainous wind farm optimized design

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Privacy Preservation on the Cloud Data Multikeyword Search Using Deep Learning

Privacy Preservation on the Cloud Data Multikeyword Search U...

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2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Antony Samy, I. Arockia Mary, M. Safish Manonmaniam Sundaranar University Abishekapatti St. Xavier's College Department of Computer Science Tamil Nadu Tirunelveli India

ISBN: (纸本)9798350319200

The technology was vulnerable to online hackers and assaults on the open and dispersed nature of the cloud. To recognize and stop both internal and external attacks in a cloud ecosystem with high identification accuracy and minimal false alerts, it should be essential to create an anomalous network platform. In this study, researchers proposed an intelligent method of instantaneously creating an effective and hybrid classification method called Network Privacy Preservation based K-Means Recurrent Neural Network (NPK-RNN) predicted using both of normalized K-means segmentation method to RNN & Privacy Preservation that results would be known as NPK. The researchers use three freely accessible data sets to detect negative/offensive comments on social media in multilingual languages to respond to such an in-depth study. The proposed approach is to improve the privacy preservation on searching the data on cloud using multikeyword. As demonstrated empirically by comparison with other classification techniques developed successful invasions. © 2023 IEEE.

关键词： and Privacy Preservation Artificial Intelligence Classification Model Cloud Computing clustering algorithm Recurrent Neural Network

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Identifying Urban Functional Regions by LDA Topic Model with POI Data 1

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10th CCF Big Data Conference (BigData)

作者： Huang, Yuhao Zhang, Lijun Wang, Haijun Wang, Siqi Northwestern Polytech Univ Sch Comp Sci Xian Peoples R China Northwestern Polytech Univ Minist Ind & Informat Technol Key Lab Big Data Storage & Management Xian Peoples R China

ISBN: (数字)9789811983313

ISBN: (纸本)9789811983306;9789811983313

Identifying Urban Functional Regions (UFR) can achieve the rational aggregation of social resource space, realize urban economic and social functions, promote the deployment of urban infrastructure, radiate and drive the development of surrounding regions, so the identification of urban functional regions can promote the efficient development of cities. However, the traditional functional region identification method is mainly based on remote sensing mapping, which relies more on the natural geographical characteristics of the region to describe and identify the region, while the urban functional region is closely related to human activities, and the traditional functional region identification results are not ideal. Social data includes a series of data that reflect people's activities and behaviors, such as trajectory data, social media data, and travel data, thus the analysis of social data can more effectively solve the difficulties of traditional mapping and identification. POI (Point of Interest) data, as a typical type of social data, can be used to identify urban functional regions. We apply the LDA topic model to the POI data, and propose a new urban functional region identification method, which makes full use of the POI data to reflect the activity categories of urban populations to characterize the features of regional functions and achieve a high degree of identification of urban functional regions. Through experimental verification on real data, the experimental results show that the proposed method can more accurately identify urban functions, which proves the method reliable.

关键词： Urban functional regions identification POI LDA topic model clustering algorithm

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Research on the deployment scheme of edge servers for vehicle task offloading 29

Research on the deployment scheme of edge servers for vehicl...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Yao, Lu Ren, Liguo Wang, Yan Inner Mongolia University College of Computer Science Hohhot China

ISBN: (纸本)9798350330717

Mobile edge computing can expand the limited computing power of on-board equipment, which is an effective means to assist vehicles to realize complex applications. However, the task offloading process brings a certain delay. How to achieve faster offloading to reduce the delay of vehicle tasks is crucial for delay-sensitive vehicle tasks. The deployment of edge servers is the basis of vehicle computing task offloading, and an efficient deployment method of edge servers can effectively meet the low latency of mobile vehicle access. Therefore, in order to minimize the average access delay between AP points and edge servers, this paper establishes a deployment model of edge servers, and proposes a mobile edge server deployment method based on the K-means algorithm and the genetic algorithm, called KGA, which fully considers the position relationship among vehicles, APs and edge servers and the limitation of wireless transmission bandwidth to determine the deployment location of edge servers and optimize communication delay of all vehicle tasks. Finally, a large number of simulation experiments under different scenarios were completed. The experimental results show that the proposed KGA method can effectively reduce the delay under the premise of the edge server capacity limitation, and its effect is better than several existing representative algorithms. © 2023 IEEE.

关键词： Access Delay clustering algorithm Deployment of Edge Servers Internet of Vehicle Mobile Edge Computing

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A comparison of clustering algorithms for automatic modulation classification

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EXPERT SYSTEMS WITH APPLICATIONS 2020年 151卷

作者： Mouton, Jacques P. Ferreira, Melvin Helberg, Albertus S. J. North West Univ Sch Elect Elect & Comp Engn ZA-2531 Potchefstroom South Africa

In this paper, the k-means, k-medoids, fuzzy c-means, Density-Based Spatial clustering of Applications with Noise (DBSCAN), Ordering Points To Identify the clustering Structure (OPTICS), and hierarchical clustering algorithms (with the addition of the elbow method) are examined for the purpose of Automatic Modulation Classification (AMC). This study compares these algorithms in terms of classification accuracy and execution time for either estimating the modulation order, determining centroid locations, or both. The best performing algorithms are combined to provide a simple AMC method which is then evaluated in an Additive White Gaussian Noise (AWGN) channel with M-Quadrature Amplitude Modulation (QAM) and M-Phase Shift Keying (PSK). Such an AMC method does not rely on any thresholds to be set by a human or machine learning algorithm, resulting in a highly flexible system. The proposed method can be configured to not give false positives, making it suitable for applications such as spectrum monitoring and regulatory enforcement. (C) 2020 Published by Elsevier Ltd.

关键词： Automatic modulation classification Constellation diagram I/Q plane clustering algorithm Centroid estimation

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Survey on clustering in VANET Networks 18

Survey on Clustering in VANET Networks

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18th International Multi-Conference on Systems, Signals & Devices (SSD)

作者： Alsabah, Mays Kareem Jabbar Trabelsi, Hafedh Jerbi, Wassim Sfax Univ CES Lab ENIS Sfax Tunisia Univ Misan Fac Engn Al Amarah City 62001 Misan Province Iraq

ISBN: (纸本)9781665414937

A Vehicular Ad Hoc Network is a dynamic network due to uncertain vehicles presence on the road and data transmission among vehicles and Road Side Unit (RSU). Efficient communication among the vehicles and RSUs is useful for the Intelligent Transportation System (ITS). There are two types of communication Vehicle 2 Vehicle (V2V), and Vehicle 2 Infrastructure (V2I) performs in VANET by mobile ad hoc technology. The communication performance of the VANET depends on the routing data. The routing protocols play an important role in efficient data routing in VANET. VANET routing protocols are reviewed, and clustering in VANET is introduced in this study. An extensive survey is carried out on various benchmark clustering algorithms designed by different researchers for Cluster Head (CH) selection. Various parameters for evaluating the performance in a network are compared and analyzed. Also, we introduce our proposed algorithm which aims to increase the reliability of VANET routing.

关键词： VANET MANET V2V Routing Protocols clustering algorithm CH Performance Metrics

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clustering-Based Reinforcement Learning Routing Protocol with Low Complexity for Underwater Acoustic Sensor Networks

Clustering-Based Reinforcement Learning Routing Protocol wit...

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IEEE/CIC International Conference on Communications in China (ICCC)

作者： Liu, Weixing Chen, Yougan Huang, Weidi Xiong, Changjing Li, Shiyu Zhang, Wenxiang Xu, Xiaomei Xiamen Univ Shenzhen Res Inst Shenzhen 518000 Peoples R China Minist Educ Key Lab Underwater Acoust Commun & Marine Informa Xiamen 361005 Peoples R China Xiamen Univ Coll Ocean & Earth Sci Xiamen 361102 Peoples R China

ISBN: (纸本)9781665439442

With the increase in the number of relay nodes in large-scale underwater acoustic sensor networks (UWA-SNs), the learning costs of routing protocols based on reinforcement learning algorithms continue to increase. In this paper, we take the Q-learning (QL) algorithm as the example of the reinforcement learning algorithm, combined with the clustering algorithm based on genetic simulated annealing algorithm (SAGAFCM), and propose an improved QL routing protocol (IQLR) aimed at reducing the time complexity of QL-based routing algorithm. The core idea is to preprocess the underwater relay nodes before applying the traditional QL algorithm for routing optimization. The simulation results show that compared with the un-preprocessed/traditional QL routing algorithm (UQLR), the proposed IQLR can greatly reduce the time cost of the QL-based routing algorithm while ensuring that the transmission quality does not change much.

关键词： Underwater Acoustic Sensor Networks clustering algorithm reinforcement learning routing protocol

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Differential Privacy Preservation in Adaptive K-Nets clustering 20

Differential Privacy Preservation in Adaptive K-Nets Cluster...

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20th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom)

作者： Liu, Xiaohong Cai, Hanbo Li, De Li, Xianxian Wang, Jinyan Guangxi Normal Univ Guangxi Key Lab Multisource Informat Min & Secur Guilin Peoples R China Guangxi Normal Univ Sch Comp Sci & Engn Guilin Peoples R China

ISBN: (纸本)9781665416580

K-Nets is a deterministic clustering algorithm based on the network structure. It can automatically detect the symmetric structure in the data and can be used to process clusters of different sizes, shapes or a specific number. However, K-Nets has the following shortcomings: (1) the clustering result is more sensitive to the manually input parameter K, so the accuracy will be affected;(2) the algorithm only considers the average distance of K-nearest neighbors, which may lead to some wrong distribution center points in the dataset with large density difference or the same score values during calculation;(3) it does not consider the privacy leakage during the clustering process. To solve the above problems, we propose a differential privacy protection method in adaptive K-Nets clustering, called ADP-K-Nets. Firstly, for reducing the influence of the parameters on the result, the natural eigenvalues are adaptively obtained through the characteristic of the natural neighbors and used as parameter values to find data points. Then we define a new method for calculating the score, which can solve the problem of incorrectly selecting cluster centers when there are large density differences or conflicts in the calculation process. Also, the Laplace noise is added in calculating the local density of every data point to protect data privacy. Experimental results show that our method ensures the performance of clustering compared with some existing algorithms.

关键词： clustering algorithm Privacy preservation Differential privacy K-Nets

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Study on Optimization of Data-Driven Anomaly Detection

Study on Optimization of Data-Driven Anomaly Detection

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International Conference on Data Science and Its Applications (ICoDSA)

作者： Zhou, Yiqing Liao, Rui Chen, Yongjia Nanfang Coll Guangzhou Peoples R China

ISBN: (数字)9781665486651

ISBN: (纸本)9781665486651

In the paper, according to the original data and the value of the sensor at different moments, the box diagram method is used to process the data, and divides the normal value and outliers. The two types of outliers were distinguished based on the persistence of the outliers in the longitudinal time of the data and the linkage of the lateral sensors, and the clustering algorithm was used to reclassify the data. Then, persistence and linkage were calculated within each class, dividing the sum of persistence and linkage by the result of the maximum number of possible anomalies as the risk coefficient, and then defining a threshold to distinguish between risk-specific and non-risk anomalies. Later, a comprehensive evaluation model of anomaly degree was established through quantitative score, principal component analysis and 0,1 planning. Finally, this quantitative evaluation method is evaluated objectively.

关键词： box graph clustering algorithm principal component analysis method 0,1 planning data exception

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