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Статті в журналах з теми "Module clustering"

Автор: Grafiati

Опубліковано: 17 січня 2024

Оновлено: 31 липня 2025

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1

Alshareef, Haya, and Mashael Maashi. "Application of Multi-Objective Hyper-Heuristics to Solve the Multi-Objective Software Module Clustering Problem." Applied Sciences 12, no. 11 (2022): 5649. http://dx.doi.org/10.3390/app12115649.

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Анотація:

Software maintenance is an important step in the software lifecycle. Software module clustering is a HHMO_CF_GDA optimization problem involving several targets that require minimization of module coupling and maximization of software cohesion. Moreover, multi-objective software module clustering involves assembling a specific group of modules according to specific cluster criteria. Software module clustering classifies software modules into different clusters to enhance the software maintenance process. A structure with low coupling and high cohesion is considered an excellent software module

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2

Hou, Jie, Xiufen Ye, Chuanlong Li, and Yixing Wang. "K-Module Algorithm: An Additional Step to Improve the Clustering Results of WGCNA Co-Expression Networks." Genes 12, no. 1 (2021): 87. http://dx.doi.org/10.3390/genes12010087.

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Among biological networks, co-expression networks have been widely studied. One of the most commonly used pipelines for the construction of co-expression networks is weighted gene co-expression network analysis (WGCNA), which can identify highly co-expressed clusters of genes (modules). WGCNA identifies gene modules using hierarchical clustering. The major drawback of hierarchical clustering is that once two objects are clustered together, it cannot be reversed; thus, re-adjustment of the unbefitting decision is impossible. In this paper, we calculate the similarity matrix with the distance co

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3

Hu, Hai Yan, and You Qiao Zhang. "The Study and Realization of Energy-Aware Routing Algorithm of Wireless Sensor Networks." Applied Mechanics and Materials 201-202 (October 2012): 767–72. http://dx.doi.org/10.4028/www.scientific.net/amm.201-202.767.

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Based on the analysis of routing algorithm of typical wireless sensor networks, the author puts forward with the objectives of routing algorithm and designs energy-aware routing algorithm to reduce energy consumption and extend life cycle of the whole network. The algorithm constitutes four modules: clustering module, dynamic cluster head election module, dormant state module and inter-cluster routing module. Aiming at effectively using the energy of sensor nodes, the paper makes use of honeycomb-like two-level clustering structure to increase coverage rate of nodes. Also, studies of routing a

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4

Kirve, Shraddha. "Clustering Techniques in Wireless Sensor Networks: A Practical Study." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 536–38. http://dx.doi.org/10.22214/ijraset.2021.34990.

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Our Solution for the Mentioned Problem Statement Comprised of Different Modules such as Alert &Notification Module, Real-Time Data Collection Module from Authenticated Source, Precaution Module to Define and Broadcast Protocol to Disaster Affected Areas, Social Media Message Circulation (SMMC) Module. IENS (Indian Early Notification System) has been designed by our team to Get & Fetch Notification System as soon as Disaster Stuck or Popped-Up (Introduce/Originated) and notifies as well as channelize Related Information via Different Social Media Official Platforms.

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5

Karayiannis, Dimitrios, and Spyros Tragoudas. "Clustering Network Modules with Different Implementations for Delay Minimization." VLSI Design 7, no. 1 (1998): 1–13. http://dx.doi.org/10.1155/1998/69289.

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In recent years there has been an extensive interest in clustering the modules of a network so that the maximum delay from any primary input to any primary output is minimized [8, 7, 6]. Clusters have a maximum capacity and modules may have different implementations. All existing CAD frameworks initially select an implementation of each module, and at a later stage they cluster the modules. We present an approach that clusters the nodes, while considering their alternative implementations, so that we further minimize the maximum delay after the clustering. Our approach is based on optimal algo

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6

Mohammad Shahid, Sunil Gupta, and MS. Sofia Pillai. "Machine Learning-Based False Positive Software Vulnerability Analysis." Global Journal of Innovation and Emerging Technology 1, no. 1 (2022): 29–35. http://dx.doi.org/10.58260/j.iet.2202.0105.

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Measurements and fault data from an older software version were used to build the fault prediction model for the new release. When past fault data isn't available, it's a problem. The software industry's assessment of programme module failure rates without fault labels is a difficult task. Unsupervised learning can be used to build a software fault prediction model when module defect labels are not available. These techniques can help identify programme modules that are more prone to errors. One method is to make use of clustering algorithms. Software module failures can be predicted using uns

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7

Strauch, Martin, Jochen Supper, Christian Spieth, et al. "A Two-Step Clustering for 3-D Gene Expression Data Reveals the Main Features of the Arabidopsis Stress Response." Journal of Integrative Bioinformatics 4, no. 1 (2007): 81–93. http://dx.doi.org/10.1515/jib-2007-54.

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Summary We developed an integrative approach for discovering gene modules, i.e. genes that are tightly correlated under several experimental conditions and applied it to a threedimensional Arabidopsis thaliana microarray dataset. The dataset consists of approximately 23000 genes responding to 9 abiotic stress conditions at 6-9 different points in time. Our approach aims at finding relatively small and dense modules lending themselves to a specific biological interpretation. In order to detect gene modules within this dataset, we employ a two-step clustering process. In the first step, a k-mean

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8

Yu, Limin, Xianjun Shen, Jincai Yang, Kaiping Wei, Duo Zhong, and Ruilong Xiang. "Hypergraph Clustering Based on Game-Theory for Mining Microbial High-Order Interaction Module." Evolutionary Bioinformatics 16 (January 2020): 117693432097057. http://dx.doi.org/10.1177/1176934320970572.

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Microbial community is ubiquitous in nature, which has a great impact on the living environment and human health. All these effects of microbial communities on the environment and their hosts are often referred to as the functions of these communities, which depend largely on the composition of the communities. The study of microbial higher-order module can help us understand the dynamic development and evolution process of microbial community and explore community function. Considering that traditional clustering methods depend on the number of clusters or the influence of data that does not

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9

Alam, M. K., Azrina Abd Aziz, S. A. Latif, and Azlan Awang. "Error-Aware Data Clustering for In-Network Data Reduction in Wireless Sensor Networks." Sensors 20, no. 4 (2020): 1011. http://dx.doi.org/10.3390/s20041011.

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Анотація:

A wireless sensor network (WSN) deploys hundreds or thousands of nodes that may introduce large-scale data over time. Dealing with such an amount of collected data is a real challenge for energy-constraint sensor nodes. Therefore, numerous research works have been carried out to design efficient data clustering techniques in WSNs to eliminate the amount of redundant data before transmitting them to the sink while preserving their fundamental properties. This paper develops a new error-aware data clustering (EDC) technique at the cluster-heads (CHs) for in-network data reduction. The proposed E

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10

Wu, Yong Liang, Bao Quan Mao, Li Xu, Dong Ming Dai, and Yan Chao Liu. "The Evaluation of Module Division Programme Based on Information Entropy." Advanced Materials Research 479-481 (February 2012): 1592–95. http://dx.doi.org/10.4028/www.scientific.net/amr.479-481.1592.

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Firstly, analysis product’s customer demand correlation, function correlation, geometric correlation, structure the corresponding correlation matrix, distribute the respective weighting factor, and then establish an integrated correlation matrix. Application of fuzzy clustering, the establishment of cluster map, the program has been divided into different modules. Based on information entropy theory, select product’s design and manufacturing complexity, cost, maintenance as the optimization objective, establish mathematical evaluation model of module division. Evaluating a number of options ge

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11

Pan, Weijun, Yidi Wang, Yumei Zhang, and Boyuan Han. "ATC-SD Net: Radiotelephone Communications Speaker Diarization Network." Aerospace 11, no. 7 (2024): 599. http://dx.doi.org/10.3390/aerospace11070599.

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This study addresses the challenges that high-noise environments and complex multi-speaker scenarios present in civil aviation radio communications. A novel radiotelephone communications speaker diffraction network is developed specifically for these circumstances. To improve the precision of the speaker diarization network, three core modules are designed: voice activity detection (VAD), end-to-end speaker separation for air–ground communication (EESS), and probabilistic knowledge-based text clustering (PKTC). First, the VAD module uses attention mechanisms to separate silence from irrelevant

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12

Li, Lianwei, Cunjin Xue, Yangfeng Xu, Chengbin Wu, and Chaoran Niu. "PoSDMS: A Mining System for Oceanic Dynamics with Time Series of Raster-Formatted Datasets." Remote Sensing 14, no. 13 (2022): 2991. http://dx.doi.org/10.3390/rs14132991.

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Many effective and advanced methods have been developed to explore oceanic dynamics using time series of raster-formatted datasets; however, they have generally been designed at a scale suitable for data observation and used independently of each other, despite the potential advantages of combining different modules into an integrated system at a scale suited for dynamic evolution. From raster-formatted datasets to marine knowledge, we developed and integrated several mining algorithms at a dynamic evolutionary scale and combined them into six modules: a module of raster-formatted dataset pret

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13

Ling, Yawen, Jianpeng Chen, Yazhou Ren, et al. "Dual Label-Guided Graph Refinement for Multi-View Graph Clustering." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 7 (2023): 8791–98. http://dx.doi.org/10.1609/aaai.v37i7.26057.

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With the increase of multi-view graph data, multi-view graph clustering (MVGC) that can discover the hidden clusters without label supervision has attracted growing attention from researchers. Existing MVGC methods are often sensitive to the given graphs, especially influenced by the low quality graphs, i.e., they tend to be limited by the homophily assumption. However, the widespread real-world data hardly satisfy the homophily assumption. This gap limits the performance of existing MVGC methods on low homophilous graphs. To mitigate this limitation, our motivation is to extract high-level vi

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14

Chen, F. Y., and G. W. Zhang. "The Module Partition Approach for the Personalized Products and Production." Materials Science Forum 697-698 (September 2011): 650–55. http://dx.doi.org/10.4028/www.scientific.net/msf.697-698.650.

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The rational and effective partition of the module has been the vital significance to the product design and manufacture. In this paper, a deficiency about the existing module partition can’t satisfy customer personalized is pointed out. Therefore, a method is put forward based on the design method of axiomatic, by analyzing the correlation among the parts, combining the dynamical clustering and mathematical statistic calculation to achieve the rough-to-fine division according to the principles of module partition. Finally, the based modules and the personalized modules are determined through

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15

Samrin, Rafath, and Devara Vasumathi. "Hybrid Weighted K-Means Clustering and Artificial Neural Network for an Anomaly-Based Network Intrusion Detection System." Journal of Intelligent Systems 27, no. 2 (2018): 135–47. http://dx.doi.org/10.1515/jisys-2016-0105.

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AbstractDespite the rapid developments in data technology, intruders are among the most revealed threats to security. Network intrusion detection systems are now a typical constituent of network security structures. In this paper, we present a combined weighted K-means clustering algorithm with artificial neural network (WKMC+ANN)-based intrusion identification scheme. This paper comprises two modules: clustering and intrusion detection. The input dataset is gathered into clusters with the usage of WKMC in clustering module. In the intrusion detection module, the clustered information is train

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16

Jeet, Kawal, and Renu Dhir. "Software Module Clustering Using Hybrid SocioEvolutionary Algorithms." International Journal of Information Engineering and Electronic Business 8, no. 4 (2016): 43–53. http://dx.doi.org/10.5815/ijieeb.2016.04.06.

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17

Sun, Jiaze, and Beilei Ling. "Software Module Clustering Algorithm Using Probability Selection." Wuhan University Journal of Natural Sciences 23, no. 2 (2018): 93–102. http://dx.doi.org/10.1007/s11859-018-1299-9.

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18

Lopez, Fabrice, Lionel Spinelli, and Christine Brun. "Clust&See3.0 : clustering, module exploration and annotation." F1000Research 13 (November 14, 2024): 994. http://dx.doi.org/10.12688/f1000research.152711.2.

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Background Cytoscape is an open-source software to visualize and analyze networks. However, large networks, such as protein interaction networks, are still difficult to analyze as a whole. Methods Here, we propose Clust&See3.0, a novel version of a Cytoscape app that has been developed to identify, visualize and manipulate network clusters and modules. It is now enriched with functionalities allowing custom annotations of nodes and computation of their statistical enrichments. Results As the wealth of multi-omics data is growing, such functionalities are highly valuable for a better unders

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19

Lopez, Fabrice, Lionel Spinelli, and Christine Brun. "Clust&See3.0 : clustering, module exploration and annotation." F1000Research 13 (September 2, 2024): 994. http://dx.doi.org/10.12688/f1000research.152711.1.

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Анотація:

Background Cytoscape is an open-source software to visualize and analyze networks. However, large networks, such as protein interaction networks, are still difficult to analyze as a whole. Methods Here, we propose Clust&See3.0, a novel version of a Cytoscape app that has been developed to identify, visualize and manipulate network clusters and modules. It is now enriched with functionalities allowing custom annotations of nodes and computation of their statistical enrichments. Results As the wealth of multi-omics data is growing, such functionalities are highly valuable for a better unders

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20

Hwa, Jimin, Shin Yoo, Yeong-Seok Seo, and Doo-Hwan Bae. "Search-Based Approaches for Software Module Clustering Based on Multiple Relationship Factors." International Journal of Software Engineering and Knowledge Engineering 27, no. 07 (2017): 1033–62. http://dx.doi.org/10.1142/s0218194017500395.

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Software remodularization seeks to cluster software modules with high cohesion and low coupling: such a structure can help the comprehension and maintenance of complex systems. The modularization quality is usually captured using either structural, semantic, or history-based factors. All existing techniques apply a single factor to the entire system, which raises the following issues. First, a single factor may fail to capture the quality across the entire project: some modules may form semantic bondings, while others may form more structural ones. Second, the user of the technique has to choo

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21

You, Ying, Zhiqiang Liu, Youqian Liu, et al. "K-Means Module Division Method of FDM3D Printer-Based Function–Behavior–Structure Mapping." Applied Sciences 13, no. 13 (2023): 7453. http://dx.doi.org/10.3390/app13137453.

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Product performance, function, cost, and the level of module generalization are all significantly influenced by product modular design, but different goods require different division indicators and techniques. The purpose of this study is to provide a set of appropriate modular division techniques for FDM 3D printers. This research offers an ecologically friendly module division index and uses module clustering as the module division principle in accordance with the current industrial development trend and the fundamental requirements of FDM 3D printer consumers in the current market. The K-me

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22

Arasteh, Bahman, Peri Gunes, Asgarali Bouyer, Farhad Soleimanian Gharehchopogh, Hamed Alipour Banaei, and Reza Ghanbarzadeh. "A Modified Horse Herd Optimization Algorithm and Its Application in the Program Source Code Clustering." Complexity 2023 (December 27, 2023): 1–16. http://dx.doi.org/10.1155/2023/3988288.

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Maintenance is one of the costliest phases in the software development process. If architectural design models are accessible, software maintenance can be made more straightforward. When the software’s source code is the only available resource, comprehending the program profoundly impacts the costs associated with software maintenance. The primary objective of comprehending the source code is extracting information used during the software maintenance phase. Generating a structural model based on the program source code is an effective way of reducing overall software maintenance costs. Softw

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23

Hu, Jinyu, and Zhiwei Gao. "Modules Identification in Gene Positive Networks of Hepatocellular Carcinoma Using Pearson Agglomerative Method and Pearson Cohesion Coupling Modularity." Journal of Applied Mathematics 2012 (2012): 1–21. http://dx.doi.org/10.1155/2012/248658.

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In this study, a gene positive network is proposed based on a weighted undirected graph, where the weight represents the positive correlation of the genes. A Pearson agglomerative clustering algorithm is employed to build a clustering tree, where dotted lines cut the tree from bottom to top leading to a number of subsets of the modules. In order to achieve better module partitions, the Pearson correlation coefficient modularity is addressed to seek optimal module decomposition by selecting an optimal threshold value. For the liver cancer gene network under study, we obtain a strong threshold v

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24

Ranjan, Goyal, Gnanaprakasam Anuradha, and Pal Singh Paritosh. "Identification of mobile vehicle through multilayer intercommunication." Indonesian Journal of Electrical Engineering and Computer Science (IJEECS) 17, no. 3 (2020): 1390–98. https://doi.org/10.11591/ijeecs.v17.i3.pp1390-1398.

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In the recent times of data transmission, applying the Internet of Things (IoT) device, networking to the various IoT modules is still a great challenge with the deployment stage. Very few localities in the world have the deployed IoT module integrated in its communication, but still most of the locations are in testbed. As a practical application, the IoT modules can be used to collect the information about either the drunken person who is driving the vehicle or the heavily loaded vehicles. The vehicle that we are about to search should be integrated with the IoT module. In this paper, a nove

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25

Pan, Chao, Qide Tan, and Benshuang Qin. "A new method of wind speed prediction based on weighted optimal fuzzy c-means and modular extreme learning machine." Wind Engineering 42, no. 5 (2018): 447–57. http://dx.doi.org/10.1177/0309524x18779337.

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According to the characteristics of randomness, volatility, and unpredictability of wind speed, this article provides a new wind speed prediction method which includes three modules that are attribute weighting module, intelligent optimization clustering module, and wind speed prediction module based on extreme learning machine. First, the Pearson coefficient values of the attribute matrix elements are calculated and weighted considering the fluctuation characteristics of time series and influences of different weather attributes on the wind speed. Then the fuzzy c-means clustering method opti

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26

Wang, Li Jing, Tao Xi, Yin Feng Zhou, and Run Hua Tan. "Product Module Partition Method for Product Lifecycle Based on LSSVC." Advanced Materials Research 139-141 (October 2010): 1540–44. http://dx.doi.org/10.4028/www.scientific.net/amr.139-141.1540.

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Most of present product module partition methods are based on product function partition and use fuzzy clustering algorithm, but these methods are not only complex in implementation but also difficult to meet the requirements of product development oriented to product lifecycle. By analyzing interactive effects of product components in product lifecycle, a new method for product module partition is put forward. Firstly, LSSVC which has fast calculation speed and high accuracy is used to illustrate the generating process of modules, so several module partition schemes are obtained. Secondly, mo

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27

Hu, Shizhe, Binyan Tian, Weibo Liu, and Yangdong Ye. "Self-supervised Trusted Contrastive Multi-view Clustering with Uncertainty Refined." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 16 (2025): 17305–13. https://doi.org/10.1609/aaai.v39i16.33902.

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Multi-view clustering (MVC), especially contrastive MVC, has demonstrated promising potential in many fields and practical scenarios. However, existing contrastive MVC methods still ignore the reliability of clustering results and the impact of false negative pairs, which limits the application of methods in critical security areas. To solve the above challenges, we propose a Self-supervised Trusted Contrastive Multi-view Clustering with Uncertainty Refined (STCMC-UR) method, which integrates clustering results and uncertainty learning to guide the self-supervised contrastive learning (CL). Fi

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28

Lange, Danillo, Marc Ribalta, Lluís Echeverria, and Joshua Pocock. "Profiling urban water consumption using autoencoders and time-series clustering techniques." IOP Conference Series: Earth and Environmental Science 1136, no. 1 (2023): 012005. http://dx.doi.org/10.1088/1755-1315/1136/1/012005.

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Abstract Nowadays, water utilities face the rising challenge of ensuring water availability amidst a rapidly growing society and a shifting climate. Our research aims to develop a household clustering solution based on water consumption behaviour in Southwest England, to enable utilities to identify different profiles and enhance customized control of household consumption, resulting in improved resource management. The solution is composed of three modules. The first one is based on a K-Means clustering model, designed to group domestic water use behaviours. This module uses the Dynamic Time

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29

Chen, Jianguo, Kenli Li, Keqin Li, Philip S. Yu, and Zeng Zeng. "Dynamic Planning of Bicycle Stations in Dockless Public Bicycle-sharing System Using Gated Graph Neural Network." ACM Transactions on Intelligent Systems and Technology 12, no. 2 (2021): 1–22. http://dx.doi.org/10.1145/3446342.

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Benefiting from convenient cycling and flexible parking locations, the Dockless Public Bicycle-sharing (DL-PBS) network becomes increasingly popular in many countries. However, redundant and low-utility stations waste public urban space and maintenance costs of DL-PBS vendors. In this article, we propose a Bicycle Station Dynamic Planning (BSDP) system to dynamically provide the optimal bicycle station layout for the DL-PBS network. The BSDP system contains four modules: bicycle drop-off location clustering, bicycle-station graph modeling, bicycle-station location prediction, and bicycle-stati

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30

Srivastava, Vivek, Bipin K. Tripathi, and Vinay K. Pathak. "Hybrid Computation Model for Intelligent System Design by Synergism of Modified EFC with Neural Network." International Journal of Information Technology & Decision Making 14, no. 01 (2015): 17–41. http://dx.doi.org/10.1142/s0219622014500813.

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In recent past, it has been seen in many applications that synergism of computational intelligence techniques outperforms over an individual technique. This paper proposes a new hybrid computation model which is a novel synergism of modified evolutionary fuzzy clustering with associated neural networks. It consists of two modules: fuzzy distribution and neural classifier. In first module, mean patterns are distributed into the number of clusters based on the modified evolutionary fuzzy clustering, which leads the basis for network structure selection and learning in associated neural classifie

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31

Puspitasari, Yenni, Imas Sukaesih Sitanggang, and Rina Trisminingsih. "VISUALIZATION MODULE OF DENSITY-BASED CLUSTERING FOR HOTSPOT DISTRIBUTION IN INDONESIA USING MAPSERVER." Journal of Tropical Silviculture 7, no. 3 (2016): S58—S60. http://dx.doi.org/10.29244/j-siltrop.7.3.s58-s60.

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A web-based Geographic Information System (GIS) has been built by previous researchers to visualize hotspots data in Indonesia. That GIS still has not contained a hotspot analysis module. Data mining method can be used to analyze hotspot data. This research aims to develop and to integrate a clustering module of hotspot in GIS which has been developed in the previous research. The clustering module for grouping hotspot data was built using the DBSCAN algorithm with PHP programming language. Clustering hotspot data was performed based on year, month, and province. Clustering parameters are epsi

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32

Arasteh, Bahman, Amir Seyyedabbasi, Jawad Rasheed, and Adnan M. Abu-Mahfouz. "Program Source-Code Re-Modularization Using a Discretized and Modified Sand Cat Swarm Optimization Algorithm." Symmetry 15, no. 2 (2023): 401. http://dx.doi.org/10.3390/sym15020401.

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Анотація:

One of expensive stages of the software lifecycle is its maintenance. Software maintenance will be much simpler if its structural models are available. Software module clustering is thought to be a practical reverse engineering method for building software structural models from source code. The most crucial goals in software module clustering are to minimize connections between created clusters, maximize internal connections within clusters, and maximize clustering quality. It is thought that finding the best software clustering model is an NP-complete task. The key shortcomings of the earlie

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33

Donets, Volodymyr, Dmytro Shevchenko, Maksym Holikov, Viktoriia Strilets, and Serhiy Shmatkov. "Application of a data stratification approach in computer medical monitoring systems." Information and controlling system 2, no. 9 (128) (2024): 6–16. http://dx.doi.org/10.15587/1729-4061.2024.298805.

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The research object is the processes occurring in the data stratification subsystem in the medical monitoring computer system, which is part of the decision support system. Such a subsystem aims to solve data analysis and processing problems in the medical monitoring system. Among them, the problems of anomaly detection, data marking, state determination, selection of the most informative variables, and justification of decision-making are selected for solving. The paper proposes the structure and implementation of the data stratification subsystem in the decision support system. The subsystem

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34

Li, Huichao, and Dan Li. "The Application of Big Data in the Management of Ideological and Political Education in the Development of Education Network." International Journal of Web-Based Learning and Teaching Technologies 19, no. 1 (2024): 1–17. http://dx.doi.org/10.4018/ijwltt.350223.

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Based on a brief analysis of the current situation of university education management and research on intelligent algorithms, this article constructs a university education management system based on big data. For the clustering and prediction modules in higher education management, corresponding algorithms are used for optimization design. A fuzzy clustering algorithm based on entropy weight is proposed to address the shortcomings of the C-means clustering algorithm. This algorithm adds weighting coefficients on the basis of improvement and continuously updates the clustering centers. The pre

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35

Li, Xiao Ling, Huai Min Wang, Chang Guo Guo, Bo Ding, and Xiao Yong Li. "A Resource Finding Mechanism for Network Virtualization Environment." Advanced Materials Research 433-440 (January 2012): 5078–86. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.5078.

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There are large numbers of infrastructure resources in network virtualization environment (NVE), how to quickly and accurately find the resources that virtual network required is a challenging problem. Pointing to this problem, a resource finding mechanism for network virtualization environment (NVERFM) is proposed. NVERFM is mainly comprised of three modules, virtual resources publishing module (VRPM), virtual resources clustering framework (VRCF), and virtual resources finding module (VRFM). VRPM is responsible for publishing the infrastructure resources to VRCF; and the published informatio

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36

Zhang, Meng, Guo Xi Li, Wei Li, and Jing Zhong Gong. "Uneven Granular Module Clustering and Intelligent Optimization for Customizable Products." Applied Mechanics and Materials 215-216 (November 2012): 426–32. http://dx.doi.org/10.4028/www.scientific.net/amm.215-216.426.

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To solve the problems of the traditional approach to uniform granular module clustering, a new method for module clustering based on uneven granularity and intelligent optimization oriented to the customizable product design was proposed. Considering the impacts of requirements, functions and structures, the integrated fuzzy similarity matrix of parts was built using the correlativity analysis, and then the hierarchical structure was generated through a fuzzy clustering algorithm. All the universes of the granular layers in the hierarchical structure were gathered and the uneven granular modul

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37

Sikka, Geeta, and Harleen Kaur. "Enriching Module Dependency Graphs for Improved Software Clustering." International Journal of System of Systems Engineering 12, no. 1 (2022): 1. http://dx.doi.org/10.1504/ijsse.2022.10038005.

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38

Chen, Yujie, Wenhui Wu, Le Ou-Yang, Ran Wang, and Debby D. Wang. "GeCC: Generalized Contrastive Clustering with Domain Shifts Modeling." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 15 (2025): 15966–74. https://doi.org/10.1609/aaai.v39i15.33753.

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Contrastive clustering performs clustering and data representation in a unified model, where instance- and cluster-level constrastive learning are conducted simultaneously. However, commonly-used data augmentation methods make contrastive mechanism effect but may cause representation learning getting stuck in domain-specific information, which further deteriorates clustering performance and limits generalization ability. To this end, we propose a new framework, named Generalized Contrastive Clustering with domain shifts modeling (GeCC), which can integrate diverse domain knowledge to improve t

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Gao, Shu Ying, and Li Li. "Research of Generalized Structure Module Modeling Based on Similar Feature Clustering." Applied Mechanics and Materials 151 (January 2012): 61–65. http://dx.doi.org/10.4028/www.scientific.net/amm.151.61.

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he creation of generalized structure module model is studied in this paper. Firstly, the component of generalized structure module is analyzed by the form of sets, then the modeling process of generalized structure module is explained, also the method of similar feature clustering-based for modeling is presented. Finally, an example of the drafting module in flyer frame is given.

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40

Xu, Yi Qiao. "Massive Data Analysis Based MapReduce Structure on Hadoop System." Advanced Materials Research 981 (July 2014): 262–66. http://dx.doi.org/10.4028/www.scientific.net/amr.981.262.

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Massive Data analysis is becoming increasingly prominent in a variety of application fields ranging from scientific studies to business researches. In this paper, we demonstrate the necessity and possibility of using MapReduce [1] module on Hadoop System [2]. Furthermore, we conducted MapReduce module to implement Clustering Algorithms [3] on our Hadoop System [4] and improved the efficiency of the Clustering Algorithms sharply. We showed how to design parallel clustering algorithms based on Hadoop System. Experiments by different size of data demonstrate that our purposed clustering algorithm

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41

Altameem, Arwa A., and Alaaeldin M. Hafez. "Behavior Analysis Using Enhanced Fuzzy Clustering and Deep Learning." Electronics 11, no. 19 (2022): 3172. http://dx.doi.org/10.3390/electronics11193172.

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Companies aim to offer customized treatments, intelligent care, and a seamless experience to their customers. Interactions between a company and its customers largely depend on the company’s ability to learn, understand, and predict customer behaviors. Customer behavior prediction is a pivotal factor in improving a company’s quality of services and thus its growth. Different machine learning techniques have been applied to gather customer data to predict behavioral patterns. Traditional methods are unable to discover hidden patterns in ideal situations and need to be improved to produce more a

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42

Jha, Monica, and Swarup Roy. "Extracting Functional Modules from RNASeq Counts Using Ensemble of Clustering Based Module Detection Methods." Journal of Computational and Theoretical Nanoscience 15, no. 6 (2018): 2359–63. http://dx.doi.org/10.1166/jctn.2018.7469.

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43

Shu, Zhenqiu, Teng Sun, Yunwei Luo, and Zhengtao Yu. "Ambiguous Instance-Aware Contrastive Network with Multi-Level Matching for Multi-View Document Clustering." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 19 (2025): 20479–87. https://doi.org/10.1609/aaai.v39i19.34256.

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Multi-view document clustering (MvDC) aims to improve the accuracy and robustness of clustering by fully considering the complementarity of different views. However, in real-world clustering applications, most existing works suffer from the following challenges: 1) They primarily align multi-view data based on a single perspective, such as features and classes, thus ignoring the diversity and comprehensiveness of representations. 2) They treat each instance equally in cross-view contrastive learning without considering ambiguous ones, which weakens the model's discriminative ability. To addres

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44

Deev, M. V. "The architecture of the system of intelligent analysis of competencies for updating university educational programs." Informatics and education 39, no. 3 (2024): 29–43. http://dx.doi.org/10.32517/0234-0453-2024-39-3-29-43.

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The problem of updating educational programs and educational content is closely related to the need to adapt them to the changing conditions of regional labor markets. The solution to the problem is achieved through the collection and analysis of data on the competence requirements of employers for the qualifications of specialists (the data itself can be extracted from job advertisements on open resources on the Internet). The article presents a method of using search robots and text data processing modules to obtain formalized descriptions of competence requirements and compare them by degre

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45

Lin, Di, Yansu Pang, Shenyuan Chen, Jun Huang, and Haoqi Xian. "An RF Fingerprinting Blind Identification Method Based on Deep Clustering for IoMT Security." Electronics 14, no. 8 (2025): 1504. https://doi.org/10.3390/electronics14081504.

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To tackle the issue of unknown spoofing attacks in the Internet of Medical Things (IoMT), we put forward an iterative deep clustering model for blind RF fingerprint recognition. This model seamlessly combines a representation learning module and a clustering module, facilitating end—to—end training and optimization. Its parameters are updated according to an innovative loss function. Moreover, this model incorporates a noise—canceling self—encoder module to reduce noise and extract the noise—independent intrinsic fingerprints of devices. In comparison with other algorithms, the proposed model

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46

Liu, Yuxiang, Xinzhong Xia, Jingyang Zhang, et al. "Hybrid Machine Learning-Driven Automated Quality Prediction and Classification of Silicon Solar Modules in Production Lines." Computation 13, no. 5 (2025): 125. https://doi.org/10.3390/computation13050125.

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This research introduces a novel hybrid machine learning framework for automated quality prediction and classification of silicon solar modules in production lines. Unlike conventional approaches that rely solely on encapsulation loss rate (ELR) for performance evaluation—a method limited to assessing encapsulation-related power loss—our framework integrates unsupervised clustering and supervised classification to achieve a comprehensive analysis. By leveraging six critical performance parameters (open circuit voltage (VOC), short circuit current (ISC), maximum output power (Pmax), voltage at

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47

Modi, M., N. G. Jadeja, and K. Zala. "FMFinder: A Functional Module Detector for PPI Networks." Engineering, Technology & Applied Science Research 7, no. 5 (2017): 2022–25. http://dx.doi.org/10.48084/etasr.1347.

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Bioinformatics is an integrated area of data mining, statistics and computational biology. Protein-Protein Interaction (PPI) network is the most important biological process in living beings. In this network a protein module interacts with another module and so on, forming a large network of proteins. The same set of proteins which takes part in the organic courses of biological actions is detected through the Function Module Detection method. Clustering process when applied in PPI networks is made of proteins which are part of a larger communication network. As a result of this, we can define

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48

Modi, M., N. G. Jadeja, and K. Zala. "FMFinder: A Functional Module Detector for PPI Networks." Engineering, Technology & Applied Science Research 7, no. 5 (2017): 2022–25. https://doi.org/10.5281/zenodo.1037222.

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Анотація:

Bioinformatics is an integrated area of data mining, statistics and computational biology. Protein-Protein Interaction (PPI) network is the most important biological process in living beings. In this network a protein module interacts with another module and so on, forming a large network of proteins. The same set of proteins which takes part in the organic courses of biological actions is detected through the Function Module Detection method. Clustering process when applied in PPI networks is made of proteins which are part of a larger communication network. As a result of this, we can define

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49

Wang, Shuai, Yi-Fei Song, Guang-Yu Zou, and Jia-Xiang Man. "Module Partition of Mechatronic Products Based on Core Part Hierarchical Clustering and Non-Core Part Association Analysis." Applied Sciences 15, no. 5 (2025): 2322. https://doi.org/10.3390/app15052322.

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Production using modular architecture can not only shorten the product development cycle and improve the efficiency of product development, but also facilitate the upgrading of a product’s main functions and the recycling of materials. However, mechatronic products are plagued by various problems, such as greater difficulty in development and longer product development cycles due to their large numbers of parts with intricate internal relationships. However, the existing modular design method still faces problems when dealing with the modular design of mechatronic products. The structure of me

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50

Gao, Zhenghao, and Dan Li. "Blockchain-Based Neural Network Model for Agricultural Product Cold Chain Coordination." Computational Intelligence and Neuroscience 2022 (May 31, 2022): 1–12. http://dx.doi.org/10.1155/2022/1760937.

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This paper adopts a blockchain fusion neural network algorithm to conduct an in-depth study on the model of agricultural cold chain coordination. We aim to enable HDFS to meet the demand of storing many small files of various types generated by various stages of agricultural cold chain coordination and then propose an improved balanced merging and index caching strategy based on file types and size grouping. The main three modules are the file preprocessing module, file balanced merging module, and index caching module. The experimental results show that this method can significantly improve t

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